�ݺ�ߣshows by User: demesos

�ݺ�ߣshows by User: demesos / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: demesos / Sun, 10 Oct 2021 14:29:45 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: demesos Introduction to Inform Game Engine and PunyInform Library /slideshow/introduction-to-inform-game-engine-and-punyinform-library/250411611 punyinform-intro-211010142945
A short introduction to the Inform Game Engine and the PunyInform Library]]>
A short introduction to the Inform Game Engine and the PunyInform Library]]> Sun, 10 Oct 2021 14:29:45 GMT /slideshow/introduction-to-inform-game-engine-and-punyinform-library/250411611 demesos@slideshare.net(demesos) Introduction to Inform Game Engine and PunyInform Library demesos A short introduction to the Inform Game Engine and the PunyInform Library <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/punyinform-intro-211010142945-thumbnail.jpg?width=120&height=120&fit=bounds" /> A short introduction to the Inform Game Engine and the PunyInform Library

Introduction to Inform Game Engine and PunyInform Library from Wilfried Elmenreich

]]> 100 0 https://cdn.slidesharecdn.com/ss_thumbnails/punyinform-intro-211010142945-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Elmenreich Interoperability between smart and legacy devices in energy management systems /slideshow/elmenreich-interoperability-between-smart-and-legacy-devices-in-energy-management-systems/53275780 workshopenergieinformatik-interoperabilitybetweensmartandlegacydevicesinenergymanagementsystems-150928125851-lva1-app6891
Energy management systems can help to decrease energy consumption by giving user feedback or by directly controlling devices. Smart appliances create a network of devices that can be addressed and controlled via a defined network interface. However, legacy devices will establish a significant portion of a system’s power consumption and, therefore, need to be included into the management system. We propose an open architecture to integrate smart and non-smart devices by using smart plugs and non-intrusive load monitoring methods. Devices are connected either as (i) smart appliances via a fieldbus or wireless network, (ii) legacy devices connected to a smart plug, or (iii) other legacy devices being detected from a time sequence of power consumption values, which are disaggregated into the power draws of different devices. At a service layer, device properties are presented in a unified way including a machine-readable description of their features and properties. The data layer provides an abstract representation of data and functionalities. It connects to the application layer where different applications can access the data. The system supports mechanism for service discovery, service coordination, and service and resource description.]]>
Energy management systems can help to decrease energy consumption by giving user feedback or by directly controlling devices. Smart appliances create a network of devices that can be addressed and controlled via a defined network interface. However, legacy devices will establish a significant portion of a system’s power consumption and, therefore, need to be included into the management system. We propose an open architecture to integrate smart and non-smart devices by using smart plugs and non-intrusive load monitoring methods. Devices are connected either as (i) smart appliances via a fieldbus or wireless network, (ii) legacy devices connected to a smart plug, or (iii) other legacy devices being detected from a time sequence of power consumption values, which are disaggregated into the power draws of different devices. At a service layer, device properties are presented in a unified way including a machine-readable description of their features and properties. The data layer provides an abstract representation of data and functionalities. It connects to the application layer where different applications can access the data. The system supports mechanism for service discovery, service coordination, and service and resource description.]]> Mon, 28 Sep 2015 12:58:51 GMT /slideshow/elmenreich-interoperability-between-smart-and-legacy-devices-in-energy-management-systems/53275780 demesos@slideshare.net(demesos) Elmenreich Interoperability between smart and legacy devices in energy management systems demesos Energy management systems can help to decrease energy consumption by giving user feedback or by directly controlling devices. Smart appliances create a network of devices that can be addressed and controlled via a defined network interface. However, legacy devices will establish a significant portion of a system’s power consumption and, therefore, need to be included into the management system. We propose an open architecture to integrate smart and non-smart devices by using smart plugs and non-intrusive load monitoring methods. Devices are connected either as (i) smart appliances via a fieldbus or wireless network, (ii) legacy devices connected to a smart plug, or (iii) other legacy devices being detected from a time sequence of power consumption values, which are disaggregated into the power draws of different devices. At a service layer, device properties are presented in a unified way including a machine-readable description of their features and properties. The data layer provides an abstract representation of data and functionalities. It connects to the application layer where different applications can access the data. The system supports mechanism for service discovery, service coordination, and service and resource description. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/workshopenergieinformatik-interoperabilitybetweensmartandlegacydevicesinenergymanagementsystems-150928125851-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Energy management systems can help to decrease energy consumption by giving user feedback or by directly controlling devices. Smart appliances create a network of devices that can be addressed and controlled via a defined network interface. However, legacy devices will establish a significant portion of a system’s power consumption and, therefore, need to be included into the management system. We propose an open architecture to integrate smart and non-smart devices by using smart plugs and non-intrusive load monitoring methods. Devices are connected either as (i) smart appliances via a fieldbus or wireless network, (ii) legacy devices connected to a smart plug, or (iii) other legacy devices being detected from a time sequence of power consumption values, which are disaggregated into the power draws of different devices. At a service layer, device properties are presented in a unified way including a machine-readable description of their features and properties. The data layer provides an abstract representation of data and functionalities. It connects to the application layer where different applications can access the data. The system supports mechanism for service discovery, service coordination, and service and resource description.

Elmenreich Interoperability between smart and legacy devices in energy management systems from Wilfried Elmenreich

]]> 3728 9 https://cdn.slidesharecdn.com/ss_thumbnails/workshopenergieinformatik-interoperabilitybetweensmartandlegacydevicesinenergymanagementsystems-150928125851-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Machine Learning Techniques for the Smart Grid – Modeling of Solar Energy using AI /slideshow/elmenreich-khatibmachine-learning-techniques-for-the-smart-grid-modeling-of-solar-energy-using-ai/34986545 elmenreich-khatib-machinelearningtechniquesforthesmartgridmodelingofsolarenergyusingai-140522012456-phpapp02
This talk covers the application of machine learning techniques for energy applications, in particular for modeling solar radiation. The first part explores meta-heuristic search algorithms and envisioned their application for designing distributed, self-organizing control systems using evolutionary algorithms. The second part gives an introduction to solar radiation modeling and shows how neural networks can be used to artificial neural networks to learn the correlation of input parameters such as latitude, longitude, temperature, humidity, month, day, hour to predict global and diffuse solar radiation.]]>
This talk covers the application of machine learning techniques for energy applications, in particular for modeling solar radiation. The first part explores meta-heuristic search algorithms and envisioned their application for designing distributed, self-organizing control systems using evolutionary algorithms. The second part gives an introduction to solar radiation modeling and shows how neural networks can be used to artificial neural networks to learn the correlation of input parameters such as latitude, longitude, temperature, humidity, month, day, hour to predict global and diffuse solar radiation.]]> Thu, 22 May 2014 01:24:56 GMT /slideshow/elmenreich-khatibmachine-learning-techniques-for-the-smart-grid-modeling-of-solar-energy-using-ai/34986545 demesos@slideshare.net(demesos) Machine Learning Techniques for the Smart Grid – Modeling of Solar Energy using AI demesos This talk covers the application of machine learning techniques for energy applications, in particular for modeling solar radiation. The first part explores meta-heuristic search algorithms and envisioned their application for designing distributed, self-organizing control systems using evolutionary algorithms. The second part gives an introduction to solar radiation modeling and shows how neural networks can be used to artificial neural networks to learn the correlation of input parameters such as latitude, longitude, temperature, humidity, month, day, hour to predict global and diffuse solar radiation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/elmenreich-khatib-machinelearningtechniquesforthesmartgridmodelingofsolarenergyusingai-140522012456-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This talk covers the application of machine learning techniques for energy applications, in particular for modeling solar radiation. The first part explores meta-heuristic search algorithms and envisioned their application for designing distributed, self-organizing control systems using evolutionary algorithms. The second part gives an introduction to solar radiation modeling and shows how neural networks can be used to artificial neural networks to learn the correlation of input parameters such as latitude, longitude, temperature, humidity, month, day, hour to predict global and diffuse solar radiation.

Machine Learning Techniques for the Smart Grid – Modeling of Solar Energy using AI from Wilfried Elmenreich

]]> 2862 9 https://cdn.slidesharecdn.com/ss_thumbnails/elmenreich-khatib-machinelearningtechniquesforthesmartgridmodelingofsolarenergyusingai-140522012456-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 AI Techniques for Smart Grids /slideshow/ai-techniques-for-smart-grids/34986104 elmenreichai-techniques-for-smart-grids-140522010405-phpapp02
These are the slides for my keynote lecture "AI Techniques for Smart Grids" at the 2014 IEEE Innovative Smart Grid Technologies - Asia conference where I discussed the role and potential of self-organization in the smart grid. ]]>
These are the slides for my keynote lecture "AI Techniques for Smart Grids" at the 2014 IEEE Innovative Smart Grid Technologies - Asia conference where I discussed the role and potential of self-organization in the smart grid. ]]> Thu, 22 May 2014 01:04:05 GMT /slideshow/ai-techniques-for-smart-grids/34986104 demesos@slideshare.net(demesos) AI Techniques for Smart Grids demesos These are the slides for my keynote lecture "AI Techniques for Smart Grids" at the 2014 IEEE Innovative Smart Grid Technologies - Asia conference where I discussed the role and potential of self-organization in the smart grid. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/elmenreichai-techniques-for-smart-grids-140522010405-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> These are the slides for my keynote lecture "AI Techniques for Smart Grids" at the 2014 IEEE Innovative Smart Grid Technologies - Asia conference where I discussed the role and potential of self-organization in the smart grid.

AI Techniques for Smart Grids from Wilfried Elmenreich

]]> 5211 5 https://cdn.slidesharecdn.com/ss_thumbnails/elmenreichai-techniques-for-smart-grids-140522010405-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Smart Microgrids: Overview and Outlook /slideshow/smart-microgrids-overview-and-outlook/14345462 smartmicrogridspresentation-120919092520-phpapp02
Citation: Anita Sobe, Wilfried Elmenreich, "Smart Microgrids: Overview and Outlook", ITG INFORMATIK 2012, Workshop on Smart Grids, September, 2012 Corresponding paper: http://mobile.aau.at/publications/sobe-2012-smartmicrogrids-overview_and_outlook.pdf ]]>
Citation: Anita Sobe, Wilfried Elmenreich, "Smart Microgrids: Overview and Outlook", ITG INFORMATIK 2012, Workshop on Smart Grids, September, 2012 Corresponding paper: http://mobile.aau.at/publications/sobe-2012-smartmicrogrids-overview_and_outlook.pdf ]]> Wed, 19 Sep 2012 09:25:19 GMT /slideshow/smart-microgrids-overview-and-outlook/14345462 demesos@slideshare.net(demesos) Smart Microgrids: Overview and Outlook demesos Citation: Anita Sobe, Wilfried Elmenreich, "Smart Microgrids: Overview and Outlook", ITG INFORMATIK 2012, Workshop on Smart Grids, September, 2012 Corresponding paper: http://mobile.aau.at/publications/sobe-2012-smartmicrogrids-overview_and_outlook.pdf <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/smartmicrogridspresentation-120919092520-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Citation: Anita Sobe, Wilfried Elmenreich, "Smart Microgrids: Overview and Outlook", ITG INFORMATIK 2012, Workshop on Smart Grids, September, 2012 Corresponding paper: http://mobile.aau.at/publications/sobe-2012-smartmicrogrids-overview_and_outlook.pdf

Smart Microgrids: Overview and Outlook from Wilfried Elmenreich

]]> 6700 12 https://cdn.slidesharecdn.com/ss_thumbnails/smartmicrogridspresentation-120919092520-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation �±�ß http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Evolving a Team of Self-organizing UAVs to Address Spatial Coverage Problems /slideshow/evolving-a-team-of-selforganizing-uavs-to-address-spatial-coverage-problems/12497468 elmenreichemcsr12drones-120411043148-phpapp02
Talk given at EMCSR 2012 conference in Vienna.]]>
Talk given at EMCSR 2012 conference in Vienna.]]> Wed, 11 Apr 2012 04:31:46 GMT /slideshow/evolving-a-team-of-selforganizing-uavs-to-address-spatial-coverage-problems/12497468 demesos@slideshare.net(demesos) Evolving a Team of Self-organizing UAVs to Address Spatial Coverage Problems demesos Talk given at EMCSR 2012 conference in Vienna. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/elmenreichemcsr12drones-120411043148-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Talk given at EMCSR 2012 conference in Vienna.

Evolving a Team of Self-organizing UAVs to Address Spatial Coverage Problems from Wilfried Elmenreich

]]> 1044 2 https://cdn.slidesharecdn.com/ss_thumbnails/elmenreichemcsr12drones-120411043148-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Evolution as a Tool for Understanding and Designing Collaborative Systems /slideshow/evolution-as-a-tool-for-understanding-and-designing-collaborative-systems/9721560 elmenreichpro-v-talk-111016114628-phpapp01
Keynote talk by Wilfried Elmenreich at PRO-VE 2011: Self-organizing phenomena can be found in many social systems, either forcing collaboration or destroying it. Typically, these properties have not been designed by a central ruler but evolved over time. While it is straightforward to find examples in many social systems, finding the appropriate interaction rules to design such systems from scratch is difficult due to the unpredictable or counterintuitive nature of such emergent and complex systems. Therefore, we propose evolutionary models to examine and extrapolate the effect of particular collaboration rules. Evolution, in this context, does not replace the work of analyzing complex social systems, but complements existing techniques of simulation, modeling, and game theory in order to lead for a new understanding of interrelations in collaborative systems.]]>
Keynote talk by Wilfried Elmenreich at PRO-VE 2011: Self-organizing phenomena can be found in many social systems, either forcing collaboration or destroying it. Typically, these properties have not been designed by a central ruler but evolved over time. While it is straightforward to find examples in many social systems, finding the appropriate interaction rules to design such systems from scratch is difficult due to the unpredictable or counterintuitive nature of such emergent and complex systems. Therefore, we propose evolutionary models to examine and extrapolate the effect of particular collaboration rules. Evolution, in this context, does not replace the work of analyzing complex social systems, but complements existing techniques of simulation, modeling, and game theory in order to lead for a new understanding of interrelations in collaborative systems.]]> Sun, 16 Oct 2011 11:46:24 GMT /slideshow/evolution-as-a-tool-for-understanding-and-designing-collaborative-systems/9721560 demesos@slideshare.net(demesos) Evolution as a Tool for Understanding and Designing Collaborative Systems demesos Keynote talk by Wilfried Elmenreich at PRO-VE 2011: Self-organizing phenomena can be found in many social systems, either forcing collaboration or destroying it. Typically, these properties have not been designed by a central ruler but evolved over time. While it is straightforward to find examples in many social systems, finding the appropriate interaction rules to design such systems from scratch is difficult due to the unpredictable or counterintuitive nature of such emergent and complex systems. Therefore, we propose evolutionary models to examine and extrapolate the effect of particular collaboration rules. Evolution, in this context, does not replace the work of analyzing complex social systems, but complements existing techniques of simulation, modeling, and game theory in order to lead for a new understanding of interrelations in collaborative systems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/elmenreichpro-v-talk-111016114628-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Keynote talk by Wilfried Elmenreich at PRO-VE 2011: Self-organizing phenomena can be found in many social systems, either forcing collaboration or destroying it. Typically, these properties have not been designed by a central ruler but evolved over time. While it is straightforward to find examples in many social systems, finding the appropriate interaction rules to design such systems from scratch is difficult due to the unpredictable or counterintuitive nature of such emergent and complex systems. Therefore, we propose evolutionary models to examine and extrapolate the effect of particular collaboration rules. Evolution, in this context, does not replace the work of analyzing complex social systems, but complements existing techniques of simulation, modeling, and game theory in order to lead for a new understanding of interrelations in collaborative systems.

Evolution as a Tool for Understanding and Designing Collaborative Systems from Wilfried Elmenreich

]]> 1199 8 https://cdn.slidesharecdn.com/ss_thumbnails/elmenreichpro-v-talk-111016114628-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/punyinform-intro-211010142945-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/introduction-to-inform-game-engine-and-punyinform-library/250411611 Introduction to Inform... https://cdn.slidesharecdn.com/ss_thumbnails/workshopenergieinformatik-interoperabilitybetweensmartandlegacydevicesinenergymanagementsystems-150928125851-lva1-app6891-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/elmenreich-interoperability-between-smart-and-legacy-devices-in-energy-management-systems/53275780 Elmenreich Interoperab... https://cdn.slidesharecdn.com/ss_thumbnails/elmenreich-khatib-machinelearningtechniquesforthesmartgridmodelingofsolarenergyusingai-140522012456-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/elmenreich-khatibmachine-learning-techniques-for-the-smart-grid-modeling-of-solar-energy-using-ai/34986545 Machine Learning Techn...