�ݺ�ߣshows by User: udaywankar

�ݺ�ߣshows by User: udaywankar / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: udaywankar / Tue, 17 Oct 2017 09:50:21 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: udaywankar TEACHING AND LEARNING BASED OPTIMISATION /slideshow/teaching-and-learning-based-optimisation/80892011 tlbo-171017095021
Teaching�CLearning-Based Optimization (TLBO) seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances. It is reported that it outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. Such a breakthrough has steered us towards investigating the secrets of TLBO��s dominance. This report��s findings on TLBO qualitatively and quantitatively through code-reviews and experiments, respectively.]]>
Teaching�CLearning-Based Optimization (TLBO) seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances. It is reported that it outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. Such a breakthrough has steered us towards investigating the secrets of TLBO��s dominance. This report��s findings on TLBO qualitatively and quantitatively through code-reviews and experiments, respectively.]]> Tue, 17 Oct 2017 09:50:21 GMT /slideshow/teaching-and-learning-based-optimisation/80892011 udaywankar@slideshare.net(udaywankar) TEACHING AND LEARNING BASED OPTIMISATION udaywankar Teaching�CLearning-Based Optimization (TLBO) seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances. It is reported that it outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. Such a breakthrough has steered us towards investigating the secrets of TLBO��s dominance. This report��s findings on TLBO qualitatively and quantitatively through code-reviews and experiments, respectively. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tlbo-171017095021-thumbnail.jpg?width=120&height=120&fit=bounds" /> Teaching�CLearning-Based Optimization (TLBO) seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances. It is reported that it outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. Such a breakthrough has steered us towards investigating the secrets of TLBO��s dominance. This report��s findings on TLBO qualitatively and quantitatively through code-reviews and experiments, respectively.

TEACHING AND LEARNING BASED OPTIMISATION from Uday Wankar

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0 Optimization Simulated Annealing /slideshow/optimization-simulated-annealing-80891498/80891498 optimization-simulatedannealing-171017093531
It is a selection of best element (with regard to some criteria) from some set of available alternatives. In the simplest case, an optimization problem consist of maximizing or minimizing a real function by choosing input values from within an allowed set and computing the value of function. The classical optimization techniques are useful in finding the optimum solution or unconstrained maxima or minima of continuous and differentiable functions. These are analytical methods and make use of differential calculus in locating the optimum solution. The classical methods have limited scope in practical applications as some of them involve objective functions which are not continuous and un-differentiable. Yet, the study of these classical techniques of optimization form a basis for developing most of the numerical techniques that have evolved into advanced techniques more suitable to today��s practical problems.]]>
It is a selection of best element (with regard to some criteria) from some set of available alternatives. In the simplest case, an optimization problem consist of maximizing or minimizing a real function by choosing input values from within an allowed set and computing the value of function. The classical optimization techniques are useful in finding the optimum solution or unconstrained maxima or minima of continuous and differentiable functions. These are analytical methods and make use of differential calculus in locating the optimum solution. The classical methods have limited scope in practical applications as some of them involve objective functions which are not continuous and un-differentiable. Yet, the study of these classical techniques of optimization form a basis for developing most of the numerical techniques that have evolved into advanced techniques more suitable to today��s practical problems.]]> Tue, 17 Oct 2017 09:35:31 GMT /slideshow/optimization-simulated-annealing-80891498/80891498 udaywankar@slideshare.net(udaywankar) Optimization Simulated Annealing udaywankar It is a selection of best element (with regard to some criteria) from some set of available alternatives. In the simplest case, an optimization problem consist of maximizing or minimizing a real function by choosing input values from within an allowed set and computing the value of function. The classical optimization techniques are useful in finding the optimum solution or unconstrained maxima or minima of continuous and differentiable functions. These are analytical methods and make use of differential calculus in locating the optimum solution. The classical methods have limited scope in practical applications as some of them involve objective functions which are not continuous and un-differentiable. Yet, the study of these classical techniques of optimization form a basis for developing most of the numerical techniques that have evolved into advanced techniques more suitable to today��s practical problems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/optimization-simulatedannealing-171017093531-thumbnail.jpg?width=120&height=120&fit=bounds" /> It is a selection of best element (with regard to some criteria) from some set of available alternatives. In the simplest case, an optimization problem consist of maximizing or minimizing a real function by choosing input values from within an allowed set and computing the value of function. The classical optimization techniques are useful in finding the optimum solution or unconstrained maxima or minima of continuous and differentiable functions. These are analytical methods and make use of differential calculus in locating the optimum solution. The classical methods have limited scope in practical applications as some of them involve objective functions which are not continuous and un-differentiable. Yet, the study of these classical techniques of optimization form a basis for developing most of the numerical techniques that have evolved into advanced techniques more suitable to today��s practical problems.

Optimization Simulated Annealing from Uday Wankar

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0 Optimization Shuffled Frog Leaping Algorithm /udaywankar/optimization-shuffled-frog-leaping-algorithm-80891429 optimization-shuffledfrogleapingalgorithm-171017093334
The difficulties associated with using mathematical optimization on large-scale engineering problems have contributed to the development of alternative solutions. Linear programming and dynamic programming techniques, for example, often fail (or reach local optimum) in solving NP-hard problems with large number of variables and non-linear objective functions. To overcome these problems, researchers have proposed evolutionary-based algorithms for searching near-optimum solutions to problems. Evolutionary algorithms (EAs) are stochastic search methods that mimic the metaphor of natural biological evolution and/or the social behaviour of species. Examples include how ants find the shortest route to a source of food and how birds find their destination during migration. The behaviour of such species is guided by learning, adaptation, and evolution. To mimic the efficient behaviour of these species, various researchers have developed computational systems that seek fast and robust solutions to complex optimization problems. The first evolutionary-based technique introduced in the literature was the genetic algorithms (Gas). GAs were developed based on the Darwinian principle of the ��survival of the fittest�� and the natural process of evolution through reproduction. Based on its demonstrated ability to reach near-optimum solutions to large problems, the GAs technique has been used in many applicationsin science and engineering. Despite their benefits, GAs may require long processing time for a near optimum solution to evolve. Also, not all problems lend themselves well to a solution with GAs.]]>
The difficulties associated with using mathematical optimization on large-scale engineering problems have contributed to the development of alternative solutions. Linear programming and dynamic programming techniques, for example, often fail (or reach local optimum) in solving NP-hard problems with large number of variables and non-linear objective functions. To overcome these problems, researchers have proposed evolutionary-based algorithms for searching near-optimum solutions to problems. Evolutionary algorithms (EAs) are stochastic search methods that mimic the metaphor of natural biological evolution and/or the social behaviour of species. Examples include how ants find the shortest route to a source of food and how birds find their destination during migration. The behaviour of such species is guided by learning, adaptation, and evolution. To mimic the efficient behaviour of these species, various researchers have developed computational systems that seek fast and robust solutions to complex optimization problems. The first evolutionary-based technique introduced in the literature was the genetic algorithms (Gas). GAs were developed based on the Darwinian principle of the ��survival of the fittest�� and the natural process of evolution through reproduction. Based on its demonstrated ability to reach near-optimum solutions to large problems, the GAs technique has been used in many applicationsin science and engineering. Despite their benefits, GAs may require long processing time for a near optimum solution to evolve. Also, not all problems lend themselves well to a solution with GAs.]]> Tue, 17 Oct 2017 09:33:34 GMT /udaywankar/optimization-shuffled-frog-leaping-algorithm-80891429 udaywankar@slideshare.net(udaywankar) Optimization Shuffled Frog Leaping Algorithm udaywankar The difficulties associated with using mathematical optimization on large-scale engineering problems have contributed to the development of alternative solutions. Linear programming and dynamic programming techniques, for example, often fail (or reach local optimum) in solving NP-hard problems with large number of variables and non-linear objective functions. To overcome these problems, researchers have proposed evolutionary-based algorithms for searching near-optimum solutions to problems. Evolutionary algorithms (EAs) are stochastic search methods that mimic the metaphor of natural biological evolution and/or the social behaviour of species. Examples include how ants find the shortest route to a source of food and how birds find their destination during migration. The behaviour of such species is guided by learning, adaptation, and evolution. To mimic the efficient behaviour of these species, various researchers have developed computational systems that seek fast and robust solutions to complex optimization problems. The first evolutionary-based technique introduced in the literature was the genetic algorithms (Gas). GAs were developed based on the Darwinian principle of the ��survival of the fittest�� and the natural process of evolution through reproduction. Based on its demonstrated ability to reach near-optimum solutions to large problems, the GAs technique has been used in many applicationsin science and engineering. Despite their benefits, GAs may require long processing time for a near optimum solution to evolve. Also, not all problems lend themselves well to a solution with GAs. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/optimization-shuffledfrogleapingalgorithm-171017093334-thumbnail.jpg?width=120&height=120&fit=bounds" /> The difficulties associated with using mathematical optimization on large-scale engineering problems have contributed to the development of alternative solutions. Linear programming and dynamic programming techniques, for example, often fail (or reach local optimum) in solving NP-hard problems with large number of variables and non-linear objective functions. To overcome these problems, researchers have proposed evolutionary-based algorithms for searching near-optimum solutions to problems. Evolutionary algorithms (EAs) are stochastic search methods that mimic the metaphor of natural biological evolution and/or the social behaviour of species. Examples include how ants find the shortest route to a source of food and how birds find their destination during migration. The behaviour of such species is guided by learning, adaptation, and evolution. To mimic the efficient behaviour of these species, various researchers have developed computational systems that seek fast and robust solutions to complex optimization problems. The first evolutionary-based technique introduced in the literature was the genetic algorithms (Gas). GAs were developed based on the Darwinian principle of the ��survival of the fittest�� and the natural process of evolution through reproduction. Based on its demonstrated ability to reach near-optimum solutions to large problems, the GAs technique has been used in many applicationsin science and engineering. Despite their benefits, GAs may require long processing time for a near optimum solution to evolve. Also, not all problems lend themselves well to a solution with GAs.

Optimization Shuffled Frog Leaping Algorithm from Uday Wankar

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0 Optimization technique genetic algorithm /slideshow/optimization-technique-genetic-algorithm-80891212/80891212 optimizationtechnique-geneticalgorithm-171017092756
For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods. The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function��s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity.]]>
For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods. The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function��s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity.]]> Tue, 17 Oct 2017 09:27:56 GMT /slideshow/optimization-technique-genetic-algorithm-80891212/80891212 udaywankar@slideshare.net(udaywankar) Optimization technique genetic algorithm udaywankar For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods. The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function��s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/optimizationtechnique-geneticalgorithm-171017092756-thumbnail.jpg?width=120&height=120&fit=bounds" /> For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods. The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function��s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity.

Optimization technique genetic algorithm from Uday Wankar

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0 Optimization Technique Harmony Search /slideshow/optimization-technique-harmony-search-80891131/80891131 optimizationtechniqueharmonysearch-171017092530
Finding an alternative with the most cost effective or highest achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. It also mean that it make best use of a situation or resource. In comparison, maximization means trying to attain the highest or maximum result or outcome without regard to cost or expense. Practice of optimization is restricted by the lack of full information, and the lack of time to evaluate what information is available (see bounded reality for details). In computer simulation (modeling) of business problems, optimization is achieved usually by using linear programming techniques of operations research.]]>
Finding an alternative with the most cost effective or highest achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. It also mean that it make best use of a situation or resource. In comparison, maximization means trying to attain the highest or maximum result or outcome without regard to cost or expense. Practice of optimization is restricted by the lack of full information, and the lack of time to evaluate what information is available (see bounded reality for details). In computer simulation (modeling) of business problems, optimization is achieved usually by using linear programming techniques of operations research.]]> Tue, 17 Oct 2017 09:25:30 GMT /slideshow/optimization-technique-harmony-search-80891131/80891131 udaywankar@slideshare.net(udaywankar) Optimization Technique Harmony Search udaywankar Finding an alternative with the most cost effective or highest achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. It also mean that it make best use of a situation or resource. In comparison, maximization means trying to attain the highest or maximum result or outcome without regard to cost or expense. Practice of optimization is restricted by the lack of full information, and the lack of time to evaluate what information is available (see bounded reality for details). In computer simulation (modeling) of business problems, optimization is achieved usually by using linear programming techniques of operations research. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/optimizationtechniqueharmonysearch-171017092530-thumbnail.jpg?width=120&height=120&fit=bounds" /> Finding an alternative with the most cost effective or highest achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. It also mean that it make best use of a situation or resource. In comparison, maximization means trying to attain the highest or maximum result or outcome without regard to cost or expense. Practice of optimization is restricted by the lack of full information, and the lack of time to evaluate what information is available (see bounded reality for details). In computer simulation (modeling) of business problems, optimization is achieved usually by using linear programming techniques of operations research.

Optimization Technique Harmony Search from Uday Wankar

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0 Optimization by Ant Colony Method /slideshow/optimization-by-ant-colony-method-80890960/80890960 optimizationbyantcolonymethod-171017092048
The first ant colony optimization (ACO) called ant system was inspired through studying of the behaviour of ants in 1991 by Macro Dorigo and co-workers. An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony. Optimization problems can be solved through simulating ant��s behaviours. Since the first ant system algorithm was proposed, there is a lot of development in ACO. In ant colony system algorithm, local pheromone is used for ants to search optimum result. However, high magnitude of computing is its deficiency and sometimes it is inefficient. Thomas St��tzle etal. Introduced MAX-MIN Ant System (MMAS) in 2000. It is one of the best algorithms of ACO. It limits total pheromone in every trip or sub-union to avoid local convergence. However, the limitation of pheromone slows down convergence rate in MMAS.]]>
The first ant colony optimization (ACO) called ant system was inspired through studying of the behaviour of ants in 1991 by Macro Dorigo and co-workers. An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony. Optimization problems can be solved through simulating ant��s behaviours. Since the first ant system algorithm was proposed, there is a lot of development in ACO. In ant colony system algorithm, local pheromone is used for ants to search optimum result. However, high magnitude of computing is its deficiency and sometimes it is inefficient. Thomas St��tzle etal. Introduced MAX-MIN Ant System (MMAS) in 2000. It is one of the best algorithms of ACO. It limits total pheromone in every trip or sub-union to avoid local convergence. However, the limitation of pheromone slows down convergence rate in MMAS.]]> Tue, 17 Oct 2017 09:20:48 GMT /slideshow/optimization-by-ant-colony-method-80890960/80890960 udaywankar@slideshare.net(udaywankar) Optimization by Ant Colony Method udaywankar The first ant colony optimization (ACO) called ant system was inspired through studying of the behaviour of ants in 1991 by Macro Dorigo and co-workers. An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony. Optimization problems can be solved through simulating ant��s behaviours. Since the first ant system algorithm was proposed, there is a lot of development in ACO. In ant colony system algorithm, local pheromone is used for ants to search optimum result. However, high magnitude of computing is its deficiency and sometimes it is inefficient. Thomas St��tzle etal. Introduced MAX-MIN Ant System (MMAS) in 2000. It is one of the best algorithms of ACO. It limits total pheromone in every trip or sub-union to avoid local convergence. However, the limitation of pheromone slows down convergence rate in MMAS. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/optimizationbyantcolonymethod-171017092048-thumbnail.jpg?width=120&height=120&fit=bounds" /> The first ant colony optimization (ACO) called ant system was inspired through studying of the behaviour of ants in 1991 by Macro Dorigo and co-workers. An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony. Optimization problems can be solved through simulating ant��s behaviours. Since the first ant system algorithm was proposed, there is a lot of development in ACO. In ant colony system algorithm, local pheromone is used for ants to search optimum result. However, high magnitude of computing is its deficiency and sometimes it is inefficient. Thomas St��tzle etal. Introduced MAX-MIN Ant System (MMAS) in 2000. It is one of the best algorithms of ACO. It limits total pheromone in every trip or sub-union to avoid local convergence. However, the limitation of pheromone slows down convergence rate in MMAS.

Optimization by Ant Colony Method from Uday Wankar

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0 Gas turbine engine /slideshow/gas-turbine-engine-80808080/80808080 gasturbineengine-171014143004
The gas turbine is an internal combustion engine that uses air as the working fluid. The engine extracts chemical energy from fuel and converts it to mechanical energy using the gaseous energy of the working fluid (air) to drive the engine and propeller, which, in turn, propel the aeroplane.]]>
The gas turbine is an internal combustion engine that uses air as the working fluid. The engine extracts chemical energy from fuel and converts it to mechanical energy using the gaseous energy of the working fluid (air) to drive the engine and propeller, which, in turn, propel the aeroplane.]]> Sat, 14 Oct 2017 14:30:04 GMT /slideshow/gas-turbine-engine-80808080/80808080 udaywankar@slideshare.net(udaywankar) Gas turbine engine udaywankar The gas turbine is an internal combustion engine that uses air as the working fluid. The engine extracts chemical energy from fuel and converts it to mechanical energy using the gaseous energy of the working fluid (air) to drive the engine and propeller, which, in turn, propel the aeroplane. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gasturbineengine-171014143004-thumbnail.jpg?width=120&height=120&fit=bounds" /> The gas turbine is an internal combustion engine that uses air as the working fluid. The engine extracts chemical energy from fuel and converts it to mechanical energy using the gaseous energy of the working fluid (air) to drive the engine and propeller, which, in turn, propel the aeroplane.

Gas turbine engine from Uday Wankar

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0 Gas turbine engine /slideshow/gas-turbine-engine-80807941/80807941 gasturbineengine-171014142254
The gas turbine is an internal combustion engine that uses air as the working fluid. The engine extracts chemical energy from fuel and converts it to mechanical energy using the gaseous energy of the working fluid (air) to drive the engine and propeller, which, in turn, propel the airplane.]]>
The gas turbine is an internal combustion engine that uses air as the working fluid. The engine extracts chemical energy from fuel and converts it to mechanical energy using the gaseous energy of the working fluid (air) to drive the engine and propeller, which, in turn, propel the airplane.]]> Sat, 14 Oct 2017 14:22:54 GMT /slideshow/gas-turbine-engine-80807941/80807941 udaywankar@slideshare.net(udaywankar) Gas turbine engine udaywankar The gas turbine is an internal combustion engine that uses air as the working fluid. The engine extracts chemical energy from fuel and converts it to mechanical energy using the gaseous energy of the working fluid (air) to drive the engine and propeller, which, in turn, propel the airplane. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gasturbineengine-171014142254-thumbnail.jpg?width=120&height=120&fit=bounds" /> The gas turbine is an internal combustion engine that uses air as the working fluid. The engine extracts chemical energy from fuel and converts it to mechanical energy using the gaseous energy of the working fluid (air) to drive the engine and propeller, which, in turn, propel the airplane.

Gas turbine engine from Uday Wankar

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0 Rewinding a brushless motor /slideshow/rewinding-a-brushless-motor/78532383 newmicrosoftworddocument-170803105150
This ppt show the steps to rewind the Brushless motor(BLDC) If you fly brushless you've probably cooked a motor or two. You also probably know there are many different types of motors. Similar motors when wound differently performs very differently. Whether you've burned the motor up, or just want to alter performance, rewinding is a cheap solution for a patient modeller. For this tutorial, I will be using Dynam E-Razor 450 Brushless Motor 60P-DYM-0011 (2750Kv). It is a Delta wound 8T (It means 8 turns ) quad wind. The winding pattern described in this tutorial (called an ABC wind - ABCABCABC as you go around the stator) works for any brushless motor with 9 stator teeth and 6 magnets. ]]>
This ppt show the steps to rewind the Brushless motor(BLDC) If you fly brushless you've probably cooked a motor or two. You also probably know there are many different types of motors. Similar motors when wound differently performs very differently. Whether you've burned the motor up, or just want to alter performance, rewinding is a cheap solution for a patient modeller. For this tutorial, I will be using Dynam E-Razor 450 Brushless Motor 60P-DYM-0011 (2750Kv). It is a Delta wound 8T (It means 8 turns ) quad wind. The winding pattern described in this tutorial (called an ABC wind - ABCABCABC as you go around the stator) works for any brushless motor with 9 stator teeth and 6 magnets. ]]> Thu, 03 Aug 2017 10:51:50 GMT /slideshow/rewinding-a-brushless-motor/78532383 udaywankar@slideshare.net(udaywankar) Rewinding a brushless motor udaywankar This ppt show the steps to rewind the Brushless motor(BLDC) If you fly brushless you've probably cooked a motor or two. You also probably know there are many different types of motors. Similar motors when wound differently performs very differently. Whether you've burned the motor up, or just want to alter performance, rewinding is a cheap solution for a patient modeller. For this tutorial, I will be using Dynam E-Razor 450 Brushless Motor 60P-DYM-0011 (2750Kv). It is a Delta wound 8T (It means 8 turns ) quad wind. The winding pattern described in this tutorial (called an ABC wind - ABCABCABC as you go around the stator) works for any brushless motor with 9 stator teeth and 6 magnets. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/newmicrosoftworddocument-170803105150-thumbnail.jpg?width=120&height=120&fit=bounds" /> This ppt show the steps to rewind the Brushless motor(BLDC) If you fly brushless you've probably cooked a motor or two. You also probably know there are many different types of motors. Similar motors when wound differently performs very differently. Whether you've burned the motor up, or just want to alter performance, rewinding is a cheap solution for a patient modeller. For this tutorial, I will be using Dynam E-Razor 450 Brushless Motor 60P-DYM-0011 (2750Kv). It is a Delta wound 8T (It means 8 turns ) quad wind. The winding pattern described in this tutorial (called an ABC wind - ABCABCABC as you go around the stator) works for any brushless motor with 9 stator teeth and 6 magnets.

Rewinding a brushless motor from Uday Wankar

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0 Rewinding a bldc motor /udaywankar/rewinding-a-bldc-motor rewindingabldcmotor-170802184056
This ppt show the steps to rewind the Brushless motor(BLDC) If you fly brushless you've probably cooked a motor or two. You also probably know there are many different types of motors. Similar motors when wound differently performs very differently. Whether you've burned the motor up, or just want to alter performance, rewinding is a cheap solution for a patient modeller. For this tutorial, I will be using Dynam E-Razor 450 Brushless Motor 60P-DYM-0011 (2750Kv). It is a Delta wound 8T (It means 8 turns ) quad wind. The winding pattern described in this tutorial (called an ABC wind - ABCABCABC as you go around the stator) works for any brushless motor with 9 stator teeth and 6 magnets. ]]>
This ppt show the steps to rewind the Brushless motor(BLDC) If you fly brushless you've probably cooked a motor or two. You also probably know there are many different types of motors. Similar motors when wound differently performs very differently. Whether you've burned the motor up, or just want to alter performance, rewinding is a cheap solution for a patient modeller. For this tutorial, I will be using Dynam E-Razor 450 Brushless Motor 60P-DYM-0011 (2750Kv). It is a Delta wound 8T (It means 8 turns ) quad wind. The winding pattern described in this tutorial (called an ABC wind - ABCABCABC as you go around the stator) works for any brushless motor with 9 stator teeth and 6 magnets. ]]> Wed, 02 Aug 2017 18:40:56 GMT /udaywankar/rewinding-a-bldc-motor udaywankar@slideshare.net(udaywankar) Rewinding a bldc motor udaywankar This ppt show the steps to rewind the Brushless motor(BLDC) If you fly brushless you've probably cooked a motor or two. You also probably know there are many different types of motors. Similar motors when wound differently performs very differently. Whether you've burned the motor up, or just want to alter performance, rewinding is a cheap solution for a patient modeller. For this tutorial, I will be using Dynam E-Razor 450 Brushless Motor 60P-DYM-0011 (2750Kv). It is a Delta wound 8T (It means 8 turns ) quad wind. The winding pattern described in this tutorial (called an ABC wind - ABCABCABC as you go around the stator) works for any brushless motor with 9 stator teeth and 6 magnets. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rewindingabldcmotor-170802184056-thumbnail.jpg?width=120&height=120&fit=bounds" /> This ppt show the steps to rewind the Brushless motor(BLDC) If you fly brushless you've probably cooked a motor or two. You also probably know there are many different types of motors. Similar motors when wound differently performs very differently. Whether you've burned the motor up, or just want to alter performance, rewinding is a cheap solution for a patient modeller. For this tutorial, I will be using Dynam E-Razor 450 Brushless Motor 60P-DYM-0011 (2750Kv). It is a Delta wound 8T (It means 8 turns ) quad wind. The winding pattern described in this tutorial (called an ABC wind - ABCABCABC as you go around the stator) works for any brushless motor with 9 stator teeth and 6 magnets.

Rewinding a bldc motor from Uday Wankar

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0 Persistence of Vision Display /slideshow/persistence-of-vision-display/59187710 povdisplayreportakhil-160307090140
Our project is a persistence of vision display (POV) that spins 360 degrees horizontally. The purpose of our POV display project is to create a small apparatus that will create a visual using only a small number of LEDs as it spins in a circle. When the LEDs rotate several times around a point in less than a second, the human eye reaches its limit of motion perception and creates an illusion of a continuous image. Therefore, our POV display demonstrates this phenomenon by creating a visual as the LEDs spin rapidly in a circle and the person watching will see one continuous image. ]]>
Our project is a persistence of vision display (POV) that spins 360 degrees horizontally. The purpose of our POV display project is to create a small apparatus that will create a visual using only a small number of LEDs as it spins in a circle. When the LEDs rotate several times around a point in less than a second, the human eye reaches its limit of motion perception and creates an illusion of a continuous image. Therefore, our POV display demonstrates this phenomenon by creating a visual as the LEDs spin rapidly in a circle and the person watching will see one continuous image. ]]> Mon, 07 Mar 2016 09:01:40 GMT /slideshow/persistence-of-vision-display/59187710 udaywankar@slideshare.net(udaywankar) Persistence of Vision Display udaywankar Our project is a persistence of vision display (POV) that spins 360 degrees horizontally. The purpose of our POV display project is to create a small apparatus that will create a visual using only a small number of LEDs as it spins in a circle. When the LEDs rotate several times around a point in less than a second, the human eye reaches its limit of motion perception and creates an illusion of a continuous image. Therefore, our POV display demonstrates this phenomenon by creating a visual as the LEDs spin rapidly in a circle and the person watching will see one continuous image. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/povdisplayreportakhil-160307090140-thumbnail.jpg?width=120&height=120&fit=bounds" /> Our project is a persistence of vision display (POV) that spins 360 degrees horizontally. The purpose of our POV display project is to create a small apparatus that will create a visual using only a small number of LEDs as it spins in a circle. When the LEDs rotate several times around a point in less than a second, the human eye reaches its limit of motion perception and creates an illusion of a continuous image. Therefore, our POV display demonstrates this phenomenon by creating a visual as the LEDs spin rapidly in a circle and the person watching will see one continuous image.

Persistence of Vision Display from Uday Wankar

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0 Arm cortex (lpc 2148) based motor speed /slideshow/arm-cortex-lpc-2148-based-motor-speed/58493703 armcortexlpc2148basedmotorspeed-160220101434
The project is designed to control the speed of a DC and AC motor using an ARM7 LPC2148 processor. The speed of motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulses applied to it, popularly known as PWM control. The project uses input button interfaced to the processor, which are used to control the speed of motor. Pulse Width Modulation is generated at the output by the microcontroller as per the program. The program is written in Embedded C. The average voltage given or the average current flowing through the motor will change depending on the duty cycle, ON and OFF time of the pulses, so the speed of the motor will change. A motor driver IC is interfaced to the ARM7 LPC2148 processor board for receiving PWM signals and delivering desired output for speed control. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors.]]>
The project is designed to control the speed of a DC and AC motor using an ARM7 LPC2148 processor. The speed of motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulses applied to it, popularly known as PWM control. The project uses input button interfaced to the processor, which are used to control the speed of motor. Pulse Width Modulation is generated at the output by the microcontroller as per the program. The program is written in Embedded C. The average voltage given or the average current flowing through the motor will change depending on the duty cycle, ON and OFF time of the pulses, so the speed of the motor will change. A motor driver IC is interfaced to the ARM7 LPC2148 processor board for receiving PWM signals and delivering desired output for speed control. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors.]]> Sat, 20 Feb 2016 10:14:34 GMT /slideshow/arm-cortex-lpc-2148-based-motor-speed/58493703 udaywankar@slideshare.net(udaywankar) Arm cortex (lpc 2148) based motor speed udaywankar The project is designed to control the speed of a DC and AC motor using an ARM7 LPC2148 processor. The speed of motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulses applied to it, popularly known as PWM control. The project uses input button interfaced to the processor, which are used to control the speed of motor. Pulse Width Modulation is generated at the output by the microcontroller as per the program. The program is written in Embedded C. The average voltage given or the average current flowing through the motor will change depending on the duty cycle, ON and OFF time of the pulses, so the speed of the motor will change. A motor driver IC is interfaced to the ARM7 LPC2148 processor board for receiving PWM signals and delivering desired output for speed control. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/armcortexlpc2148basedmotorspeed-160220101434-thumbnail.jpg?width=120&height=120&fit=bounds" /> The project is designed to control the speed of a DC and AC motor using an ARM7 LPC2148 processor. The speed of motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulses applied to it, popularly known as PWM control. The project uses input button interfaced to the processor, which are used to control the speed of motor. Pulse Width Modulation is generated at the output by the microcontroller as per the program. The program is written in Embedded C. The average voltage given or the average current flowing through the motor will change depending on the duty cycle, ON and OFF time of the pulses, so the speed of the motor will change. A motor driver IC is interfaced to the ARM7 LPC2148 processor board for receiving PWM signals and delivering desired output for speed control. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors.

Arm cortex (lpc 2148) based motor speed from Uday Wankar

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0 Arm Processor Based Speed Control Of BLDC Motor /slideshow/arm-processor-based-speed-control-of-bldc-motor/58098526 et20-160210123154
The project is designed to control the speed of a DC motor using an ARM series processor. The speed of DC motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulse applied to it (popularly known as PWM control). The project uses input button interfaced to the processor, which are used to control the speed of motor. PWM (Pulse Width Modulation) is generated at the output by the microcontroller as per the program. The program is written in Embedded C. The average voltage given or the average current flowing through the motor will change depending on the duty cycle (ON and OFF time of the pulses), so the speed of the motor will change. A motor driver IC is interfaced to the STM32 board for receiving PWM signals and delivering desired output for speed control of a small DC motor. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors.]]>
The project is designed to control the speed of a DC motor using an ARM series processor. The speed of DC motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulse applied to it (popularly known as PWM control). The project uses input button interfaced to the processor, which are used to control the speed of motor. PWM (Pulse Width Modulation) is generated at the output by the microcontroller as per the program. The program is written in Embedded C. The average voltage given or the average current flowing through the motor will change depending on the duty cycle (ON and OFF time of the pulses), so the speed of the motor will change. A motor driver IC is interfaced to the STM32 board for receiving PWM signals and delivering desired output for speed control of a small DC motor. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors.]]> Wed, 10 Feb 2016 12:31:54 GMT /slideshow/arm-processor-based-speed-control-of-bldc-motor/58098526 udaywankar@slideshare.net(udaywankar) Arm Processor Based Speed Control Of BLDC Motor udaywankar The project is designed to control the speed of a DC motor using an ARM series processor. The speed of DC motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulse applied to it (popularly known as PWM control). The project uses input button interfaced to the processor, which are used to control the speed of motor. PWM (Pulse Width Modulation) is generated at the output by the microcontroller as per the program. The program is written in Embedded C. The average voltage given or the average current flowing through the motor will change depending on the duty cycle (ON and OFF time of the pulses), so the speed of the motor will change. A motor driver IC is interfaced to the STM32 board for receiving PWM signals and delivering desired output for speed control of a small DC motor. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/et20-160210123154-thumbnail.jpg?width=120&height=120&fit=bounds" /> The project is designed to control the speed of a DC motor using an ARM series processor. The speed of DC motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulse applied to it (popularly known as PWM control). The project uses input button interfaced to the processor, which are used to control the speed of motor. PWM (Pulse Width Modulation) is generated at the output by the microcontroller as per the program. The program is written in Embedded C. The average voltage given or the average current flowing through the motor will change depending on the duty cycle (ON and OFF time of the pulses), so the speed of the motor will change. A motor driver IC is interfaced to the STM32 board for receiving PWM signals and delivering desired output for speed control of a small DC motor. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors.

Arm Processor Based Speed Control Of BLDC Motor from Uday Wankar

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0 Arm cortex ( lpc 2148 ) based motor speed control /slideshow/arm-cortex-lpc-2148-based-motor-speed-control-47019331/47019331 armcortexlpc2148basedmotorspeedcontrol-150415052831-conversion-gate01
The project is designed to control the speed of a DC and AC motor using an ARM7 LPC2148 processor. The speed of motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulses applied to it, popularly known as PWM control. The project uses input button interfaced to the processor, which are used to control the speed of motor. Pulse Width Modulation is generated at the output by the microcontroller as per the program. The program is written in Embedded C. The average voltage given or the average current flowing through the motor will change depending on the duty cycle, ON and OFF time of the pulses, so the speed of the motor will change. A motor driver IC is interfaced to the ARM7 LPC2148 processor board for receiving PWM signals and delivering desired output for speed control. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors.]]>
The project is designed to control the speed of a DC and AC motor using an ARM7 LPC2148 processor. The speed of motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulses applied to it, popularly known as PWM control. The project uses input button interfaced to the processor, which are used to control the speed of motor. Pulse Width Modulation is generated at the output by the microcontroller as per the program. The program is written in Embedded C. The average voltage given or the average current flowing through the motor will change depending on the duty cycle, ON and OFF time of the pulses, so the speed of the motor will change. A motor driver IC is interfaced to the ARM7 LPC2148 processor board for receiving PWM signals and delivering desired output for speed control. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors.]]> Wed, 15 Apr 2015 05:28:31 GMT /slideshow/arm-cortex-lpc-2148-based-motor-speed-control-47019331/47019331 udaywankar@slideshare.net(udaywankar) Arm cortex ( lpc 2148 ) based motor speed control udaywankar The project is designed to control the speed of a DC and AC motor using an ARM7 LPC2148 processor. The speed of motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulses applied to it, popularly known as PWM control. The project uses input button interfaced to the processor, which are used to control the speed of motor. Pulse Width Modulation is generated at the output by the microcontroller as per the program. The program is written in Embedded C. The average voltage given or the average current flowing through the motor will change depending on the duty cycle, ON and OFF time of the pulses, so the speed of the motor will change. A motor driver IC is interfaced to the ARM7 LPC2148 processor board for receiving PWM signals and delivering desired output for speed control. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/armcortexlpc2148basedmotorspeedcontrol-150415052831-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> The project is designed to control the speed of a DC and AC motor using an ARM7 LPC2148 processor. The speed of motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulses applied to it, popularly known as PWM control. The project uses input button interfaced to the processor, which are used to control the speed of motor. Pulse Width Modulation is generated at the output by the microcontroller as per the program. The program is written in Embedded C. The average voltage given or the average current flowing through the motor will change depending on the duty cycle, ON and OFF time of the pulses, so the speed of the motor will change. A motor driver IC is interfaced to the ARM7 LPC2148 processor board for receiving PWM signals and delivering desired output for speed control. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors.

Arm cortex ( lpc 2148 ) based motor speed control from Uday Wankar

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0 Arm cortex ( lpc 2148 ) based motor speed control /slideshow/arm-cortex-lpc-2148-based-motor-speed-control/47019295 armcortexlpc2148basedmotorspeedcontrol-150415052712-conversion-gate01
]]>
]]> Wed, 15 Apr 2015 05:27:12 GMT /slideshow/arm-cortex-lpc-2148-based-motor-speed-control/47019295 udaywankar@slideshare.net(udaywankar) Arm cortex ( lpc 2148 ) based motor speed control udaywankar <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/armcortexlpc2148basedmotorspeedcontrol-150415052712-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Arm cortex ( lpc 2148 ) based motor speed control from Uday Wankar

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0 POWER QUALITY IMPROVEMENT /slideshow/ravi-ppt-47019198/47019198 ravippt-150415052446-conversion-gate01
Power Quality is a combination of Voltage profile, Frequency profile, Harmonics contain and reliability of power supply. The Power Quality is defined as the degree to which the power supply approaches the ideal case of stable, uninterrupted, zero distortion and disturbance free supply.]]>
Power Quality is a combination of Voltage profile, Frequency profile, Harmonics contain and reliability of power supply. The Power Quality is defined as the degree to which the power supply approaches the ideal case of stable, uninterrupted, zero distortion and disturbance free supply.]]> Wed, 15 Apr 2015 05:24:46 GMT /slideshow/ravi-ppt-47019198/47019198 udaywankar@slideshare.net(udaywankar) POWER QUALITY IMPROVEMENT udaywankar Power Quality is a combination of Voltage profile, Frequency profile, Harmonics contain and reliability of power supply. The Power Quality is defined as the degree to which the power supply approaches the ideal case of stable, uninterrupted, zero distortion and disturbance free supply. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ravippt-150415052446-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Power Quality is a combination of Voltage profile, Frequency profile, Harmonics contain and reliability of power supply. The Power Quality is defined as the degree to which the power supply approaches the ideal case of stable, uninterrupted, zero distortion and disturbance free supply.

POWER QUALITY IMPROVEMENT from Uday Wankar

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0 CSTPS training REPORT /slideshow/cstps-training-report/47019167 mycstpscdtraining-150415052402-conversion-gate01
MSEB was set up in 1960 to generate, transmit and distribute power to all consumers in Maharashtra excluding Mumbai. MSEB was the largest SEB in the country. The generation capacity of MSEB has grown from 760 MW in 1960-61 to 9771 MW in 2001-02. The customer base has grown from 1,07,833 in 1960-61 to 1,40,09,089 in 2001-02. C.S.T.P.S in contribution much in field of production of electricity. It is not only number one thermal power station in Asia but also has occupied specific position on the international map. The first set was commission on August 1983 & was dedicated to nation by then PM (late) Mrs. Indira Gandhi & second set commission on July 1984. The third & fourth units of CSTPS under stage 2 were commissioned on the 3rd May 1985 & 8th March 1986 respectively. The units 5 & 6 were commissioned on the 22nd March 1991 & 11th March 1992 respectively one more units of 500MW was added to the CSTPS on making its generation to 2340 MW & making ��C.S.T.P.S.�� as the giant in Power Generation of CSTPS.]]>
MSEB was set up in 1960 to generate, transmit and distribute power to all consumers in Maharashtra excluding Mumbai. MSEB was the largest SEB in the country. The generation capacity of MSEB has grown from 760 MW in 1960-61 to 9771 MW in 2001-02. The customer base has grown from 1,07,833 in 1960-61 to 1,40,09,089 in 2001-02. C.S.T.P.S in contribution much in field of production of electricity. It is not only number one thermal power station in Asia but also has occupied specific position on the international map. The first set was commission on August 1983 & was dedicated to nation by then PM (late) Mrs. Indira Gandhi & second set commission on July 1984. The third & fourth units of CSTPS under stage 2 were commissioned on the 3rd May 1985 & 8th March 1986 respectively. The units 5 & 6 were commissioned on the 22nd March 1991 & 11th March 1992 respectively one more units of 500MW was added to the CSTPS on making its generation to 2340 MW & making ��C.S.T.P.S.�� as the giant in Power Generation of CSTPS.]]> Wed, 15 Apr 2015 05:24:02 GMT /slideshow/cstps-training-report/47019167 udaywankar@slideshare.net(udaywankar) CSTPS training REPORT udaywankar MSEB was set up in 1960 to generate, transmit and distribute power to all consumers in Maharashtra excluding Mumbai. MSEB was the largest SEB in the country. The generation capacity of MSEB has grown from 760 MW in 1960-61 to 9771 MW in 2001-02. The customer base has grown from 1,07,833 in 1960-61 to 1,40,09,089 in 2001-02. C.S.T.P.S in contribution much in field of production of electricity. It is not only number one thermal power station in Asia but also has occupied specific position on the international map. The first set was commission on August 1983 & was dedicated to nation by then PM (late) Mrs. Indira Gandhi & second set commission on July 1984. The third & fourth units of CSTPS under stage 2 were commissioned on the 3rd May 1985 & 8th March 1986 respectively. The units 5 & 6 were commissioned on the 22nd March 1991 & 11th March 1992 respectively one more units of 500MW was added to the CSTPS on making its generation to 2340 MW & making ��C.S.T.P.S.�� as the giant in Power Generation of CSTPS. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mycstpscdtraining-150415052402-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> MSEB was set up in 1960 to generate, transmit and distribute power to all consumers in Maharashtra excluding Mumbai. MSEB was the largest SEB in the country. The generation capacity of MSEB has grown from 760 MW in 1960-61 to 9771 MW in 2001-02. The customer base has grown from 1,07,833 in 1960-61 to 1,40,09,089 in 2001-02. C.S.T.P.S in contribution much in field of production of electricity. It is not only number one thermal power station in Asia but also has occupied specific position on the international map. The first set was commission on August 1983 & was dedicated to nation by then PM (late) Mrs. Indira Gandhi & second set commission on July 1984. The third & fourth units of CSTPS under stage 2 were commissioned on the 3rd May 1985 & 8th March 1986 respectively. The units 5 & 6 were commissioned on the 22nd March 1991 & 11th March 1992 respectively one more units of 500MW was added to the CSTPS on making its generation to 2340 MW & making ��C.S.T.P.S.�� as the giant in Power Generation of CSTPS.

CSTPS training REPORT from Uday Wankar

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0 Hybrid power generation by solar �Cwind /slideshow/hybrid-power-generation-by-solar-wind/47019114 hybridpowergenerationbysolarwind-150415052233-conversion-gate01
With the development of industry and agriculture, a great amount of energy such as coal, oil and gas has been consumed in the world. Extensive use of these fossil energies deteriorates a series of problems like energy crisis, environmental pollution and so on. Everybody knows that the fossil energy reserves are finite, some day it will be exhausted. It is possible that the world will face a global energy crisis due to a decline in the availability of cheap oil and recommendations to a decreasing dependency on fossil fuel. This has led to increasing interest in alternate power/fuel research such as fuel cell technology, hydrogen fuel, biodiesel, Karrick process, solar energy, geothermal energy, tidal energy and wind. Today, solar energy and wind energy have significantly alternated fossil fuel with big ecological problems. With the development of the science and technology, power generation using solar energy and wind power is gradually known by more and more people. And it is widespread used in many developed countries. The merits of the solar and wind power generation are very obvious-infinite and nonpolluting. The raw materials of the solar and wind power generation derived from nature, and wind power generation can work twenty-four hours a day, solar power generation only works by daylight. In addition, this kind of power generation has no exhaust emission and there is no influence to the nature. But it also has some shortcomings. Because of the imperfect of the technology, equipment of the solar and wind power generation is very expensive. By far, it cannot be widely used. In addition, solar and wind power generation system affected by the changing of the weather very much, so it has obvious defects in reliability compared with fossil fuel, and it is difficult to make it fit for practical use the lack of economical efficiency .Because of these problems it needs to increase the reliability of energy supply by developing a system which interacts Solar and wind energy. This kind of system is usually called windsolar hybrid power generation system significantly]]>
With the development of industry and agriculture, a great amount of energy such as coal, oil and gas has been consumed in the world. Extensive use of these fossil energies deteriorates a series of problems like energy crisis, environmental pollution and so on. Everybody knows that the fossil energy reserves are finite, some day it will be exhausted. It is possible that the world will face a global energy crisis due to a decline in the availability of cheap oil and recommendations to a decreasing dependency on fossil fuel. This has led to increasing interest in alternate power/fuel research such as fuel cell technology, hydrogen fuel, biodiesel, Karrick process, solar energy, geothermal energy, tidal energy and wind. Today, solar energy and wind energy have significantly alternated fossil fuel with big ecological problems. With the development of the science and technology, power generation using solar energy and wind power is gradually known by more and more people. And it is widespread used in many developed countries. The merits of the solar and wind power generation are very obvious-infinite and nonpolluting. The raw materials of the solar and wind power generation derived from nature, and wind power generation can work twenty-four hours a day, solar power generation only works by daylight. In addition, this kind of power generation has no exhaust emission and there is no influence to the nature. But it also has some shortcomings. Because of the imperfect of the technology, equipment of the solar and wind power generation is very expensive. By far, it cannot be widely used. In addition, solar and wind power generation system affected by the changing of the weather very much, so it has obvious defects in reliability compared with fossil fuel, and it is difficult to make it fit for practical use the lack of economical efficiency .Because of these problems it needs to increase the reliability of energy supply by developing a system which interacts Solar and wind energy. This kind of system is usually called windsolar hybrid power generation system significantly]]> Wed, 15 Apr 2015 05:22:33 GMT /slideshow/hybrid-power-generation-by-solar-wind/47019114 udaywankar@slideshare.net(udaywankar) Hybrid power generation by solar �Cwind udaywankar With the development of industry and agriculture, a great amount of energy such as coal, oil and gas has been consumed in the world. Extensive use of these fossil energies deteriorates a series of problems like energy crisis, environmental pollution and so on. Everybody knows that the fossil energy reserves are finite, some day it will be exhausted. It is possible that the world will face a global energy crisis due to a decline in the availability of cheap oil and recommendations to a decreasing dependency on fossil fuel. This has led to increasing interest in alternate power/fuel research such as fuel cell technology, hydrogen fuel, biodiesel, Karrick process, solar energy, geothermal energy, tidal energy and wind. Today, solar energy and wind energy have significantly alternated fossil fuel with big ecological problems. With the development of the science and technology, power generation using solar energy and wind power is gradually known by more and more people. And it is widespread used in many developed countries. The merits of the solar and wind power generation are very obvious-infinite and nonpolluting. The raw materials of the solar and wind power generation derived from nature, and wind power generation can work twenty-four hours a day, solar power generation only works by daylight. In addition, this kind of power generation has no exhaust emission and there is no influence to the nature. But it also has some shortcomings. Because of the imperfect of the technology, equipment of the solar and wind power generation is very expensive. By far, it cannot be widely used. In addition, solar and wind power generation system affected by the changing of the weather very much, so it has obvious defects in reliability compared with fossil fuel, and it is difficult to make it fit for practical use the lack of economical efficiency .Because of these problems it needs to increase the reliability of energy supply by developing a system which interacts Solar and wind energy. This kind of system is usually called windsolar hybrid power generation system significantly <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hybridpowergenerationbysolarwind-150415052233-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> With the development of industry and agriculture, a great amount of energy such as coal, oil and gas has been consumed in the world. Extensive use of these fossil energies deteriorates a series of problems like energy crisis, environmental pollution and so on. Everybody knows that the fossil energy reserves are finite, some day it will be exhausted. It is possible that the world will face a global energy crisis due to a decline in the availability of cheap oil and recommendations to a decreasing dependency on fossil fuel. This has led to increasing interest in alternate power/fuel research such as fuel cell technology, hydrogen fuel, biodiesel, Karrick process, solar energy, geothermal energy, tidal energy and wind. Today, solar energy and wind energy have significantly alternated fossil fuel with big ecological problems. With the development of the science and technology, power generation using solar energy and wind power is gradually known by more and more people. And it is widespread used in many developed countries. The merits of the solar and wind power generation are very obvious-infinite and nonpolluting. The raw materials of the solar and wind power generation derived from nature, and wind power generation can work twenty-four hours a day, solar power generation only works by daylight. In addition, this kind of power generation has no exhaust emission and there is no influence to the nature. But it also has some shortcomings. Because of the imperfect of the technology, equipment of the solar and wind power generation is very expensive. By far, it cannot be widely used. In addition, solar and wind power generation system affected by the changing of the weather very much, so it has obvious defects in reliability compared with fossil fuel, and it is difficult to make it fit for practical use the lack of economical efficiency .Because of these problems it needs to increase the reliability of energy supply by developing a system which interacts Solar and wind energy. This kind of system is usually called windsolar hybrid power generation system significantly

Hybrid power generation by solar �MŬind from Uday Wankar

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0 Hybrid power generation by and solar �Cwind /slideshow/hybrid-power-generation-by-and-solar-wind/47019083 hybridpowergenerationbyandsolarwind-150415052152-conversion-gate01
With the development of industry and agriculture, a great amount of energy such as coal, oil and gas has been consumed in the world. Extensive use of these fossil energies deteriorates a series of problems like energy crisis, environmental pollution and so on. Everybody knows that the fossil energy reserves are finite, some day it will be exhausted. It is possible that the world will face a global energy crisis due to a decline in the availability of cheap oil and recommendations to a decreasing dependency on fossil fuel. This has led to increasing interest in alternate power/fuel research such as fuel cell technology, hydrogen fuel, biodiesel, Karrick process, solar energy, geothermal energy, tidal energy and wind. Today, solar energy and wind energy have significantly alternated fossil fuel with big ecological problems. With the development of the science and technology, power generation using solar energy and wind power is gradually known by more and more people. And it is widespread used in many developed countries. The merits of the solar and wind power generation are very obvious-infinite and nonpolluting. The raw materials of the solar and wind power generation derived from nature, and wind power generation can work twenty-four hours a day, solar power generation only works by daylight. In addition, this kind of power generation has no exhaust emission and there is no influence to the nature. But it also has some shortcomings. Because of the imperfect of the technology, equipment of the solar and wind power generation is very expensive. By far, it cannot be widely used. In addition, solar and wind power generation system affected by the changing of the weather very much, so it has obvious defects in reliability compared with fossil fuel, and it is difficult to make it fit for practical use the lack of economical efficiency .Because of these problems it needs to increase the reliability of energy supply by developing a system which interacts Solar and wind energy. This kind of system is usually called windsolar hybrid power generation system significantly]]>
With the development of industry and agriculture, a great amount of energy such as coal, oil and gas has been consumed in the world. Extensive use of these fossil energies deteriorates a series of problems like energy crisis, environmental pollution and so on. Everybody knows that the fossil energy reserves are finite, some day it will be exhausted. It is possible that the world will face a global energy crisis due to a decline in the availability of cheap oil and recommendations to a decreasing dependency on fossil fuel. This has led to increasing interest in alternate power/fuel research such as fuel cell technology, hydrogen fuel, biodiesel, Karrick process, solar energy, geothermal energy, tidal energy and wind. Today, solar energy and wind energy have significantly alternated fossil fuel with big ecological problems. With the development of the science and technology, power generation using solar energy and wind power is gradually known by more and more people. And it is widespread used in many developed countries. The merits of the solar and wind power generation are very obvious-infinite and nonpolluting. The raw materials of the solar and wind power generation derived from nature, and wind power generation can work twenty-four hours a day, solar power generation only works by daylight. In addition, this kind of power generation has no exhaust emission and there is no influence to the nature. But it also has some shortcomings. Because of the imperfect of the technology, equipment of the solar and wind power generation is very expensive. By far, it cannot be widely used. In addition, solar and wind power generation system affected by the changing of the weather very much, so it has obvious defects in reliability compared with fossil fuel, and it is difficult to make it fit for practical use the lack of economical efficiency .Because of these problems it needs to increase the reliability of energy supply by developing a system which interacts Solar and wind energy. This kind of system is usually called windsolar hybrid power generation system significantly]]> Wed, 15 Apr 2015 05:21:52 GMT /slideshow/hybrid-power-generation-by-and-solar-wind/47019083 udaywankar@slideshare.net(udaywankar) Hybrid power generation by and solar �Cwind udaywankar With the development of industry and agriculture, a great amount of energy such as coal, oil and gas has been consumed in the world. Extensive use of these fossil energies deteriorates a series of problems like energy crisis, environmental pollution and so on. Everybody knows that the fossil energy reserves are finite, some day it will be exhausted. It is possible that the world will face a global energy crisis due to a decline in the availability of cheap oil and recommendations to a decreasing dependency on fossil fuel. This has led to increasing interest in alternate power/fuel research such as fuel cell technology, hydrogen fuel, biodiesel, Karrick process, solar energy, geothermal energy, tidal energy and wind. Today, solar energy and wind energy have significantly alternated fossil fuel with big ecological problems. With the development of the science and technology, power generation using solar energy and wind power is gradually known by more and more people. And it is widespread used in many developed countries. The merits of the solar and wind power generation are very obvious-infinite and nonpolluting. The raw materials of the solar and wind power generation derived from nature, and wind power generation can work twenty-four hours a day, solar power generation only works by daylight. In addition, this kind of power generation has no exhaust emission and there is no influence to the nature. But it also has some shortcomings. Because of the imperfect of the technology, equipment of the solar and wind power generation is very expensive. By far, it cannot be widely used. In addition, solar and wind power generation system affected by the changing of the weather very much, so it has obvious defects in reliability compared with fossil fuel, and it is difficult to make it fit for practical use the lack of economical efficiency .Because of these problems it needs to increase the reliability of energy supply by developing a system which interacts Solar and wind energy. This kind of system is usually called windsolar hybrid power generation system significantly <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hybridpowergenerationbyandsolarwind-150415052152-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> With the development of industry and agriculture, a great amount of energy such as coal, oil and gas has been consumed in the world. Extensive use of these fossil energies deteriorates a series of problems like energy crisis, environmental pollution and so on. Everybody knows that the fossil energy reserves are finite, some day it will be exhausted. It is possible that the world will face a global energy crisis due to a decline in the availability of cheap oil and recommendations to a decreasing dependency on fossil fuel. This has led to increasing interest in alternate power/fuel research such as fuel cell technology, hydrogen fuel, biodiesel, Karrick process, solar energy, geothermal energy, tidal energy and wind. Today, solar energy and wind energy have significantly alternated fossil fuel with big ecological problems. With the development of the science and technology, power generation using solar energy and wind power is gradually known by more and more people. And it is widespread used in many developed countries. The merits of the solar and wind power generation are very obvious-infinite and nonpolluting. The raw materials of the solar and wind power generation derived from nature, and wind power generation can work twenty-four hours a day, solar power generation only works by daylight. In addition, this kind of power generation has no exhaust emission and there is no influence to the nature. But it also has some shortcomings. Because of the imperfect of the technology, equipment of the solar and wind power generation is very expensive. By far, it cannot be widely used. In addition, solar and wind power generation system affected by the changing of the weather very much, so it has obvious defects in reliability compared with fossil fuel, and it is difficult to make it fit for practical use the lack of economical efficiency .Because of these problems it needs to increase the reliability of energy supply by developing a system which interacts Solar and wind energy. This kind of system is usually called windsolar hybrid power generation system significantly

Hybrid power generation by and solar �MŬind from Uday Wankar

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0 Grid solving robot /slideshow/grid-solving-robot/47019051 gridsolvingrobot-150415052053-conversion-gate01
This paper presents Grid Solver Bot which is a self-driven vehicle capable of localizing itself in a grid and planning a path between two nodes. It can avoid particular nodes and plan path between two allowed nodes. Breadth-first search & Dijkstra's Algorithm have been used for finding the path between two allowed nodes. The searching of a block over grid is easier when the rows and columns i.e. m* n of a grid is fixed. But when the grid is dynamic or changes over time than in such situation we require a generalized algorithm for traversing over a grid. In these paper we develop an approach for searching an object and also able to avoid an obstacle which was placed in a junction (meeting point of row and column). Here, we use different algorithms like Dijkistra��s, Best first search and A star algorithms. We develop an approach to find the block with minimum shortest path with the help of priority based algorithm. The vehicle is also capable of transferring blocks from one node to another. In fact, this vehicle is a prototype of a self-driven vehicle capable of transporting passengers and it can also be used in industries to transfer different items from one place to another.]]>
This paper presents Grid Solver Bot which is a self-driven vehicle capable of localizing itself in a grid and planning a path between two nodes. It can avoid particular nodes and plan path between two allowed nodes. Breadth-first search & Dijkstra's Algorithm have been used for finding the path between two allowed nodes. The searching of a block over grid is easier when the rows and columns i.e. m* n of a grid is fixed. But when the grid is dynamic or changes over time than in such situation we require a generalized algorithm for traversing over a grid. In these paper we develop an approach for searching an object and also able to avoid an obstacle which was placed in a junction (meeting point of row and column). Here, we use different algorithms like Dijkistra��s, Best first search and A star algorithms. We develop an approach to find the block with minimum shortest path with the help of priority based algorithm. The vehicle is also capable of transferring blocks from one node to another. In fact, this vehicle is a prototype of a self-driven vehicle capable of transporting passengers and it can also be used in industries to transfer different items from one place to another.]]> Wed, 15 Apr 2015 05:20:53 GMT /slideshow/grid-solving-robot/47019051 udaywankar@slideshare.net(udaywankar) Grid solving robot udaywankar This paper presents Grid Solver Bot which is a self-driven vehicle capable of localizing itself in a grid and planning a path between two nodes. It can avoid particular nodes and plan path between two allowed nodes. Breadth-first search & Dijkstra's Algorithm have been used for finding the path between two allowed nodes. The searching of a block over grid is easier when the rows and columns i.e. m* n of a grid is fixed. But when the grid is dynamic or changes over time than in such situation we require a generalized algorithm for traversing over a grid. In these paper we develop an approach for searching an object and also able to avoid an obstacle which was placed in a junction (meeting point of row and column). Here, we use different algorithms like Dijkistra��s, Best first search and A star algorithms. We develop an approach to find the block with minimum shortest path with the help of priority based algorithm. The vehicle is also capable of transferring blocks from one node to another. In fact, this vehicle is a prototype of a self-driven vehicle capable of transporting passengers and it can also be used in industries to transfer different items from one place to another. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gridsolvingrobot-150415052053-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This paper presents Grid Solver Bot which is a self-driven vehicle capable of localizing itself in a grid and planning a path between two nodes. It can avoid particular nodes and plan path between two allowed nodes. Breadth-first search & Dijkstra's Algorithm have been used for finding the path between two allowed nodes. The searching of a block over grid is easier when the rows and columns i.e. m* n of a grid is fixed. But when the grid is dynamic or changes over time than in such situation we require a generalized algorithm for traversing over a grid. In these paper we develop an approach for searching an object and also able to avoid an obstacle which was placed in a junction (meeting point of row and column). Here, we use different algorithms like Dijkistra��s, Best first search and A star algorithms. We develop an approach to find the block with minimum shortest path with the help of priority based algorithm. The vehicle is also capable of transferring blocks from one node to another. In fact, this vehicle is a prototype of a self-driven vehicle capable of transporting passengers and it can also be used in industries to transfer different items from one place to another.

Grid solving robot from Uday Wankar

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0 https://cdn.slidesharecdn.com/profile-photo-udaywankar-48x48.jpg?cb=1697013593 I am an Electrical Engineer. My interests are in Electronics, project making, computer applications, RC Toys making and controlling. I used to make Engineering Projects and Drones for commercial use www.linkedin.com/in/udaywankar/ https://cdn.slidesharecdn.com/ss_thumbnails/tlbo-171017095021-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/teaching-and-learning-based-optimisation/80892011 TEACHING AND LEARNING ... https://cdn.slidesharecdn.com/ss_thumbnails/optimization-simulatedannealing-171017093531-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/optimization-simulated-annealing-80891498/80891498 Optimization Simulated... https://cdn.slidesharecdn.com/ss_thumbnails/optimization-shuffledfrogleapingalgorithm-171017093334-thumbnail.jpg?width=320&height=320&fit=bounds udaywankar/optimization-shuffled-frog-leaping-algorithm-80891429 Optimization Shuffled ...