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Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Journal of Dynamics, Monitoring and Diagnostics 2022年第3期1卷 176-187页

作者： Debasish Mishra Ashok Priyadarshi Sarthak M Das sristi shree Abhinav Gupta Surjya K Pal Debashish Chakravarty Advanced Technology Development Centre Indian Institute of Technology KharagpurKharagpurWest Bengal 721302India Mathematics and Computing Birla Institute of Technology MesraRanchiJharkhand 835215India Information Technology Birla Institute of Technology MesraRanchiJharkhand 835215India Computer Science and Engineering Birla Institute of Technology MesraRanchiJharkhand 835215India Electronics and Communication Engineering Birla Institute of Technology MesraRanchiJharkhand 835215India Department of Mechanical Engineering Indian Institute of Technology KharagpurKharagpurWest Bengal 721302India Department of Mining Engineering Indian Institute of Technology KharagpurKharagpurWest Bengal 721302India

Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing assets.This article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing systems.The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)process.It consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real time.The edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real time.It addresses time-critical manufacturing decisions.Cloud receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine.Several experiments are conducted for training the neural networks.The framework successfully tracks process quality and improves the welding by controlling it in real time.The system enables faster monitoring and control achieved in less than 1 s.The framework is validated through several experiments.

关键词： cloud edge deep neural networks friction stir welding Industry 4.0 internet of things machine learning manufacturing process control process monitoring signal processing

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尼泊尔和中国服务业企业员工招聘和选拔过程的对比研究

尼泊尔和中国服务业企业员工招聘和选拔过程的对比研究

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作者： Shakya（Newa）,sristi（倪瑞思）吉林大学

学位级别：硕士

Background Telecommunication is an important part of life which will connect people fromdifferent parts of the world very easily and in time. It helps to update many thingswhich are important to life and for the development of the country as well as inemergencies. There are lots of telecommunication companies in Nepal and China.Recruitment and Selection process play a pivotally important role for the company tobe successful. Recruitment and selection forms a core part of the central activitiesunderlying human resource management: namely, the acquisition, development andreward of workers. It is very important for organizations to acquire workers whoalready possess relevant knowledge, skills and abilities and are also able to make anaccurate prediction regarding their future abilities. Purpose The purpose of this study is to make comparison between two telecommunicationcompanies such as Nepal Telecom of Nepal and LT Company of China on the basis ofRecruitment and Selection process. Its purpose is to study the effectiveness,evaluation steps, various factors influencing in recruitment, selection criteria,predictors, job performance and best practices in general and in company wiserespectively. Materials and Methods This study depends upon the information collected in secondary data which isbasically from the books, referred journals and websites. In addition, necessaryinformation will be collected from the concerned telecom companies of Nepal andChina from the Internet. The data will be collected for the purpose of study. We didcase study in Nepal and China for the two telecommunication companies as NepalTelecom in Nepal and LT Company in China. We collected data as secondary sourceof data from the internet related to these two companies. We performed study on thebasis of three main things;1] Background of the Company;2] Human resource of thecompany;and3] Recruitment and selection process of the company. The secondary data are collected under these headings for both Nepal Telecom and LT Company.Then we

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腮腺良、恶性肿瘤的CT与MRI诊断

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临床医学工程 2014年第8期21卷 960-961,964页

作者： sristi Singh 刘珍银中山大学肿瘤防治中心广东广州510060

目的探讨腮腺良、恶性肿瘤的CT与MR诊断与鉴别诊断,以提高诊断准确率。方法回顾性分析我院经手术与病理证实的28例腮腺良、恶性肿瘤患者的临床资料与CT、MR资料。结果 28例患者中良性腮腺肿瘤18例,CT与MR主要表现为:呈类圆形等密度、MR... 详细信息

目的探讨腮腺良、恶性肿瘤的CT与MR诊断与鉴别诊断,以提高诊断准确率。方法回顾性分析我院经手术与病理证实的28例腮腺良、恶性肿瘤患者的临床资料与CT、MR资料。结果 28例患者中良性腮腺肿瘤18例,CT与MR主要表现为:呈类圆形等密度、MR等信号结节影,边界较清,轻度或中度均匀强化。恶性肿块10例,CT与MR主要表现为:类圆形或不规则形密度不均肿块,边界欠清,MR信号不均匀,增强后不均匀强化,伴侵犯周围组织。结论腮腺良、恶性肿瘤的CT与MR表现具有一定特征性,若肿块密度不均、形态不规则、边界不清、不均匀强化,常提示恶性腮腺肿瘤可能性大。

关键词：腮腺肿瘤良尧恶性计算机体层摄影磁共振成像

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