Browsing by Author "Pardeshi, Mayuresh Sunil"

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IDS-DLA: Sheet Metal Part Identification System for Process Automation Using Deep Learning Algorithms
(2020-01-01)
Sheu, Ruey-Kai
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Lin, Yuan-Cheng
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Huang, Chin-Yin
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Chen, Lun-Chi
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Pardeshi, Mayuresh Sunil
Sheet metal-based manufacturing industries operate on several varieties of sheet metal parts. Previously QR codes stickers were put on sheet metal parts for identification by manual workers as per their respective shape and size features, thus ensuring synchronized raw material flow for the manufacturing process. However, identifying a particular type of sheet metal part based on its different features is still a challenge for a trained manual operator. Currently, in the market, there exist some automation solutions for solving such kind of problem but are incapable of achieving better performance and possess accuracy issues. So our goal is to provide process automation by overcoming manual work-based dependency and limitations. Therefore, a system is required that can take input a high definition camera captured sheet metal part image and provide an accurately identified type as output by utilizing a deep learning classification model in computer vision. The automation of sheet metal part identification by using ERP, CAD files and scheduling among them would make a smooth workflow by IDS-DLA. This paper aims to solve the identification problem by using the design and implementation of a sheet metal part identification, given as sheet metal part IDentification System for process automation using Deep Learning Algorithms (IDS-DLA). Considering the sheet metal parts there exists a large volume of types but fewer quantities. IDS-DLA performs high accuracy sheet metal part identification from the CAD model database by using the Geometric and CNN triplet filter. The IDS-DLA also evaluates the Hu moment ranking to choose the top 5 rank predictions as final ranking results. The applications can be given as manufacturing process automation for industry, 2D CAD search, 2D measuring solution, closet formation, etc. Ultimately, from the experiments, it can be observed that better accuracy is obtained as compared with the previous benchmarks. This multi-filtering approach using a deep feature extraction algorithm concludes to be the better approach and achieves higher performance.
SMAP Fog/Edge: A Secure Mutual Authentication Protocol for Fog/Edge
(2019-01-01)
Pardeshi, Mayuresh Sunil
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Yuan, Shyan-Ming
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交大名義發表
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資訊工程學系
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National Chiao Tung University
Security is a crucial factor for the appropriate functioning of fog/edge computing. Secured mutual authentication in networks has become key demand as per the current security standard. Several applications are in its requirements like wireless sensor network (WSN), Distributed Systems, Micro-Cloud, Smart City, Smart Industry 4.0. Problem statement is "Design and implementation of Fog servers and edge devices to dynamically interconnect with each other using secured mutual authentication", which is an NP complete problem. Implementation of secured mutual authentication protocol (SMAP) using techniques of pseudo-random number generator, time-stamps and hash functions can only be considered to evaluate the best performance for connecting large number of smart devices. Our protocol avoids storing master secret keys and repetition of session keys, which makes it more secure and carries no overhead. The experimental results show that the secured mutual authentication system is efficient in comparison to recent benchmarks.