Build the best
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It was mostly based on my experience. Whenever we have started with the approach of building the best and not failing, we have succeeded with having very durable solution(product). And all the experiments we have started with the fail fast approach, have either failed or been created again multiple times. Lot of great ideas don't fail because of opportunity loss. They fail because solution(product) was not good enough. There is always a place for great solution(product) in the world. Time can help in success, but it is not the answer to success.
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原版一模一样【微信：741003700 】【美国密歇根州立大学毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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22CYT12-Unit-V-E Waste and its Management.ppt
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Data Driven Maintenance | UReason Webinar
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This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
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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.
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Build the best
- 1. Fail fast, Learn faster
- 2. The philosophy has worked for several people in the past. Fail fast, learn faster
- 3. Seen people following it very casually. Fail fast, learn faster
- 4. To learn something, Why do you have to fail ? Fail fast, learn faster
- 5. The only thing it is teaching to people. How to fail? And be, Okay about it. Fail fast, learn faster
- 6. Failing! Should look like this. Fail fast, learn faster
- 7. Because: Y ou have just wasted the resources that you got from someone else’s money. Fail fast, learn faster
- 8. Because: You have failed the people working with you on your vision. Fail fast, learn faster
- 9. Perhaps, You failed, because you have started with the option of failing. Fail fast, learn faster
- 10. Instead, Build only the best. And don’t fail with pride. Fail fast, learn faster
- 11. Don’t fail , Learn to build the best ! fast faster Fail fast, learn faster