Efficient Plant Diseases Recognition based on Modified Residual Neural Network and Transfer Learning
Deep Learning Based Defect Detection for Solder Joints on Industrial X-Ray Circuit Board Images
Qianru Zhang is a Ph. D. student at National ASIC Center in School of Electronic Science and Engineering, Southeast University, China. She obtained her M.S. degree in Electrical and Computer Engineering from University of California, Irvine in 2016. Her research interests include digital signal processing, big data analysis and deep learning techniques
BA-IKG: BiLSTM Embedded ALBERT for Industrial Knowledge Graph Generation and Reuse
Bin Zhou was born in 1994. He received the M.S degree in Anhui Polytechnic University, Wuhu, China. He is currently pursuing the Ph.D. degree in mechanical engineering from the College of Mechanical Engineering, Donghua University, Shanghai, China. His main research interests include industrial knowledge graph, natural language processing, industrial data and semantic processing.
Maximum Power Point Tracking of Photovoltaic Systems Using Deep Q-networks
Kangshi Wang received the M.Sc. degree in electrical engineering with excellence from the University of New South Wales, Australia, in 2019. He is currently pursuing the Ph.D. degree in computer science and software engineering with the University of Liverpool, Liverpool, U.K. He is a Research Assistant with Xi’an Jiaotong-Liverpool University, Suzhou, China, for the period of 2019–2020. His research interests include optimization, machine learning, and FPGA implementations of intelligent control systems. (Based on document published on 15 April 2020).
Video Object Detection Method Using Single-Frame Detection and Motion Vector Tracking
Name: Masato Nohara
Title : Video Object Detection Method Using Single-Frame Detection and Motion Vector Tracking
School : Graduate School Science and Technology Keio University
E-mail : firstname.lastname@example.org
Phone number : 8180-8696-7535
Address : Ribiangurasu Motosumiyosi 201, KIduki, Nakahara-ku, Kawasaki-shi, Kanagawa-ken, Japan
Object Shape Error Response using Bayesian 3D Convolutional Neural Networks for Assembly Systems with Compliant Parts
Sumit Sinha is a doctoral student in the Digital Lifecycle Management (DLM) research group at WMG, University of Warwick (U.K). His doctoral research focuses on the development and application of deep learning models such as Bayesian 3D Convolutional Neural Networks (CNN) for Root Cause Analysis (RCA) and the application of deep reinforcement learning models such as Deep Deterministic Policy Gradient (DDPG) for Corrective and Preventive Action (CAPA) in multi-station assembly systems. He obtained his Bachelor’s Degree in Industrial and Systems Engineering from the Indian Institute of Technology (IIT) Kharagpur. He has worked as a Data Scientist at ZS Associates where he used Machine Learning models to solve problems in the pharmaceutical sales and marketing domain. Previously he worked as a Research Assistant at the University of Hong Kong to develop a deep learning-based time-series model for risk minimization in the retail industry.
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