Project On Hard End Fibre ( PSF ) MELT SPINNING
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I'm done my project work on poly staple fibre (PSF) process, in industry Done at Indorama synthetic (I) limited buttibori,Nagpur
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8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
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.
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Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
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- 1. PROJECT ON HARD END FIBRE TITLE: To avoid hard end fibre & plastic in final product DESCRIPTION: ➢ This Loose End may generate during postional threading of Schedule/Drip wiping at Spinning section. In existing system there is a loose end detector photocell which detects the loose end and same material being auto segregated with help of timer. ACTION TAKEN: ➢ 2 additional photo sensors installed for Top & middle band for detecting all loose end ends & its segregation. One flasher with hooter installed and operated against bypassing or malfunctioning of Loose end detector sensor ➢ The trend of loose end detectors in DCS for analysis of Loose end detector actuation against actual wiping in spinning to avoid over segregation. AVOID: ➢ Risk of passing loose end without detection almost negligible. ➢ Chances of sensor bypassing & malfunctioning also