02 led-16 w-cylinder
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This document provides specifications for an LED light bulb, including available sockets like GU10, E27, and E14 that can accommodate voltages from 90 to 260VAC. It lists key specifications such as a working temperature range of -20 to 40 degrees Celsius, a life expectancy of around 50,000 hours, and dimensions of 120mm by 31.5mm diameter. The bulb puts out around 1200 lumens in warm white and 1300 lumens in cool white, saves approximately 80% energy, and is RoHS compliant, mercury-free, and CE certified.
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This document provides specifications for an LED light bulb, including available sockets like GU10, E27, and E14 that can accommodate voltages from 90 to 260VAC. It lists technical details such as a working temperature range of -20 to 40 degrees Celsius, a life expectancy of around 50,000 hours, and dimensions of 120mm by 50mm diameter. Additionally, it notes the bulb has a 360 degree beam angle and can save around 80% in energy while providing luminosity of 1050 lumens for a 3000K bulb or 1100 lumens for a 6000K bulb.
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This document provides specifications for LED light bulbs, including available sockets like GU10, E27, and E14 that operate from 90-260VAC. The bulbs have a working temperature range of -20 to 40 degrees Celsius, last around 50,000 hours, and measure 97.5mm x 31.5mm. They provide 360-degree light distribution and energy savings of around 80% compared to incandescent bulbs. Lumens output is listed as 600 for warm white 3000K and 650 for cool white 6000K. The bulbs are RoHS compliant, mercury-free, and CE certified.
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We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
A review on techniques and modelling methodologies used for checking electrom...
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Understanding Inductive Bias in Machine Learning
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
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Generative AI leverages algorithms to create various forms of content
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Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
New techniques for characterising damage in rock slopes.pdf
New techniques for characterising damage in rock slopes.pdf
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Embedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoring
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BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
A review on techniques and modelling methodologies used for checking electrom...
A review on techniques and modelling methodologies used for checking electrom...
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Manufacturing Process of molasses based distillery ppt.pptx
2. Operations Strategy in a Global Environment.ppt
2. Operations Strategy in a Global Environment.ppt
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024)
02 led-16 w-cylinder
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