Accuracy-and-precision.ppt
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John's measurement is closer to the accepted value of 2.7 g/ml. John's measurement: 2.649 g/ml Difference from accepted value: 2.7 - 2.649 = 0.051 g/ml Percentage accuracy = (Difference/Accepted value) x 100% = (0.051/2.7) x 100% = 1.89% Sally's measurement: 2.731 g/ml Difference from accepted value: 2.7 - 2.731 = -0.031 g/ml Percentage accuracy = (Difference/Accepted value) x 100% = (-0.031/2.7) x 100% = 1.15% Therefore
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Liquefaction occurs when saturated, non-cohesive soil loses strength. This phenomenon occurs as the water pressure in the pores rises and the effective stress drops because of dynamic loading. Liquefaction potential is a ratio for the factor of safety used to figure out if the soil can be liquefied, and liquefaction-induced settlements happen when the ground loses its ability to support construction due to liquefaction. Traditionally, empirical and semi-empirical methods have been used to predict liquefaction potential and settlements that are based on historical data. In this study, MATLAB's Fuzzy Tool Adaptive Neuro-Fuzzy Inference System (ANFIS) (sub-clustering) was used to predict liquefaction potential and liquefaction-induced settlements. Using Cone Penetration Test (CPT) data, two ANFIS models were made: one to predict liquefaction potential (LP-ANFIS) and the other to predict liquefaction-induced settlements (LIS-ANFIS). The RMSE correlation for the LP-ANFIS model (input parameters: Depth, Cone penetration, Sleeve Resistance, and Effective stress; output parameters: Liquefaction Potential) and the LIS-ANFIS model (input parameters: Depth, Cone penetration, Sleeve Resistance, and Effective stress; output parameters: Settlements) was 0.0140764 and 0.00393882 respectively. The Coefficient of Determination (R2) for both the models was 0.9892 and 0.9997 respectively. Using the ANFIS 3D-Surface Diagrams were plotted to show the correlation between the CPT test parameters, the liquefaction potential, and the liquefaction-induced settlements. The ANFIS model results displayed that the considered soft computing techniques have good capabilities to determine liquefaction potential and liquefaction-induced settlements using CPT data.Ceramic Multichannel Membrane Structure with Tunable Properties by Sol-Gel Me...
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Cosmetic standards in manufacturing play a crucial role in ensuring the visual quality of products meets customer expectations while maintaining functional integrity. In industries such as electronics, automotive, and consumer goods, cosmetic defects¡ªthough often non-functional¡ªcan impact brand perception, product desirability, and customer satisfaction.
### **Introduction to Cosmetic Standards in Manufacturing**
Cosmetic standards refer to the guidelines set by manufacturers to evaluate the appearance of a product. These guidelines define acceptable and unacceptable visual defects, ensuring products present a clean, professional look. While minor imperfections may be permissible, consistent and visible defects can lead to customer complaints or reduced marketability.
### **Key Cosmetic Defects in Manufacturing**
Manufacturing processes can introduce various cosmetic defects, including:
- **Scratches and Scuffs**: Surface-level marks that occur during handling, assembly, or packaging.
- **Dents and Deformations**: Physical damage to materials due to improper handling or tooling issues.
- **Color Variations**: Differences in shading or texture due to material inconsistencies or environmental factors during production.
- **Molding Defects**: Injection molding processes can introduce flow lines, sink marks, or flash, affecting the visual quality of plastic components.
- **Print and Label Imperfections**: Misaligned text, smudging, or incomplete printing can impact branding and identification.
- **Paint or Coating Defects**: Issues such as peeling, chipping, or uneven application affecting surface finish.
- **Contaminations and Foreign Material**: Dust, hair, or other particles embedded in the product can be perceived as poor workmanship.
### **Defining Cosmetic Acceptance Criteria**
Manufacturers typically establish cosmetic acceptance criteria based on industry standards, customer expectations, and internal quality requirements. These criteria specify:
- **Defect Classification**: Minor, major, or critical defects based on impact on functionality and aesthetics.
- **Inspection Methods**: Visual inspection under controlled lighting conditions and specific angles.
- **Measurement Tools**: Rulers, calipers, or digital inspection systems for consistency in defect evaluation.
- **Pass/Fail Guidelines**: Clear thresholds for acceptable and non-acceptable defects.
### **Inspection and Quality Control Methods**
To enforce cosmetic standards, manufacturers implement stringent inspection processes, including:
- **Automated Vision Systems**: Using AI-powered cameras to detect surface irregularities.
- **Manual Inspection**: Trained personnel evaluating each unit based on predefined standards.
- **Sampling Plans**: Statistical methods such as AQL (Acceptable Quality Limit) to ensure representative evaluation.
- **Defect Tagging and Sorting**: Classifying defective units for rework, scrapping, or customer review.
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Managing tech debt in large legacy codebases isn¡¯t just a challenge¡ªit¡¯s an ongoing battle that can drain developer productivity and morale. In this talk, I¡¯ll introduce a Python-powered Tech Debt Framework bar-raiser designed to help teams tackle even the most daunting tech debt problems with 100,000+ violations. This open-source framework empowers developers and engineering leaders by: - Tracking Progress: Measure and visualize the state of tech debt and trends over time. - Recognizing Contributions: Celebrate developer efforts and foster accountability with contribution leaderboards and automated shoutouts. - Automating Fixes: Save countless hours with codemods that address repetitive debt patterns, allowing developers to focus on higher-priority work.
Through real-world case studies, I¡¯ll showcase how we: - Reduced 70,000+ pyright-ignore annotations to boost type-checking coverage from 60% to 99.5%. - Converted a monolithic sync codebase to async, addressing blocking IO issues and adopting asyncio effectively.
Attendees will gain actionable strategies for scaling Python automation, fostering team buy-in, and systematically reducing tech debt across massive codebases. Whether you¡¯re dealing with type errors, legacy dependencies, or async transitions, this talk provides a roadmap for creating cleaner, more maintainable code at scale.introduction to Rapid Tooling and Additive Manufacturing Applications
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Ãû¹ÅÎÝ´óѧ ÇéˆóѧÑо¿¿Æ ÖªÄÜ¥·¥¹¥Æ¥àѧŒŸ¹¥ ‘õÌïÑо¿ÊÒDomain1_Security_Principles --(My_Notes)
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Evaluating Adaptive Neuro-Fuzzy Inference System (ANFIS) To Assess Liquefacti...
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Accuracy-and-precision.ppt
- 1. 1
- 2. Target ? Differentiate accuracy from precision ? Differentiate random errors from systemic error. ? Estimate errors from multiple measurement of physical quantity.
- 3. 3
- 4. 4 Accuracy: how close a measurement is to the accepted value. Precision: how close a series of measurements are to one another or how far out a measurement is taken.
- 5. Interpretation 1 2 3 4 Accuracy Poor Precision Poor
- 7. 7 Accuracy and Precision in Measurements Accuracy: how close a measurement is to the accepted value. Precision: how close a series of measurements are to one another or how far out a measurement is taken.
- 8. FACTORS WHY ERROR OCCUR IN MEASUREMENT 1. The kind of measuring device use. ? Worn out instrument. For instance, a measuring plastic tape becomes slightly stretched over time, resulting in measurements that is slightly longer. Spring balance that is rusty, may result in poor result in your measurement. ? Improper calibrated tools, like weighing scale that does not read zero, may result to inaccurate measurement.
- 9. FACTORS WHY ERROR OCCUR IN MEASUREMENT 2. Methods in getting the measurement A person with a good and proper strategy in his measurements more accurate/precise compare to a person that does not employ proper method of measuring.
- 10. FACTORS WHY ERROR OCCUR IN MEASUREMENT 3. Condition under which the measurement is made. ? If the person taking the measurement is trembling or shaky hands due to sickness or having a bad condition may not be able to determine the proper dimension of the quantity, he is measuring. ? Temperature, pressure, lighting and the like, are also factors in poor results of measurement
- 11. Types of Error 1. Random Error or unsystematic error Random error has no pattern, it is inconsistent. Example, in your first reading, you taught it might be too small, then the next reading might be too large. 2. Systematic Error Systematic error is consistent and repeatable error due to the kind of measuring device used as mentioned above. It is also due to flawed experimental design.
- 12. Note: To minimize errors in measurement, more trials must be made. The mean or average value of these trials will be taken to represent the entire set of data. From this, the degree of accuracy and precision ca be determined.
- 13. Accuracy is the closeness or nearness of measurement to the accepted value.
- 16. Precision is express in terms of deviation or percentage of deviation. The Formula below will help you determine the precision of one¡¯s measurement.
- 19. Do this! 1. John and Sally performed an experiment to measure the density of aluminum ( 2.7 g/ml). John¡¯s measurement is 2.649 g/ml while Sally¡¯s measurement is 2.731g/ml. Who is more accurate between the two. Compute for the percentage of accuracy of both measurement. Show complete solution.