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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Heat distribution in the deep drawing device components working by high temperatures

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IOP Conference Series: Materials science and Engineering 2018年第1期393卷

作者： M Tropp F Brumerčík J Šteininger P Weis A Glowacz Univerzity of Žilina Faculty of Mechanical Engineering Department of Design and Mechanical Elements Univerzitná 1 Žilina 010 26 Slovakia AGHUniversity of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics Al. A. Mickiewicza 30 30-059 Kraków Poland

The subject of the article is a simulation of heat affected components of a device for deep drawing by extreme conditions in vacuum by high temperatures required for forming of crystallization containers made from thin molybdenum sheets. The simulation is focused on the distribution of heat in particular components in relation with their shape. There are presented the design of working components and the boundary conditions of transient thermal simulations with the results.

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Phase chimera states on non-local hyperrings

arXiv

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arXiv 2023年

作者： Muolo, Riccardo Njougouo, Thierry Gambuzza, Lucia Valentina Carletti, Timoteo Frasca, Mattia Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Department of Mathematics University of Namur NamurB5000 Belgium naXys Namur Institute for Complex Systems University of Namur NamurB5000 Belgium Faculty of Computer Science University of Namur NamurB5000 Belgium Department of Electrical and Electronic Engineering University of Buea P.O. Box 63 Buea Cameroon Department of Electrical Electronics and Computer Science Engineering University of Catania Catania95125 Italy Istituto di Analisi dei Sistemi ed Informatica "A. Ruberti" IASI-CNR Roma00185 Italy

Chimera states are dynamical states where regions of synchronous trajectories coexist with incoherent ones. A significant amount of research has been devoted to study chimera states in systems of identical oscillators, non-locally coupled through pairwise interactions. Nevertheless, there is an increasing evidence, also supported by available data, that complex systems are composed by multiple units experiencing many-body interactions, that can be modeled by using higher-order structures beyond the paradigm of classic pairwise networks. In this work we investigate whether phase chimera states appear in this framework, by focusing on a novel topology solely involving many-body, non-local and non-regular interactions, hereby named non-local d-hyperring, being (d + 1) the order of the interactions. We present the theory by using the paradigmatic Stuart-Landau oscillators as node dynamics, and show that phase chimera states emerge in a variety of structures and with different coupling functions. For comparison, we show that, when higher-order interactions are"flattened" to pairwise ones, the chimera behavior is weaker and more elusive. © 2023, CC BY.

关键词： Dynamics

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Preface

Advances in Intelligent Systems and Computing

引用

Advances in Intelligent Systems and Computing 2018年 620卷 v-vi页

作者： Omatu, Sigeru Rodríguez, Sara Villarrubia, Gabriel Faria, Pedro Sitek, Pawel Prieto, Javier Faculty of Engineering Department of Electronics Information and Communication Engineering Osaka Institute of Technology OsakaOsaka Japan Faculty of Science Department of Computer Science and Automation Control University of Salamanca Salamanca Spain GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Instituto Superior de Engenharia do Porto Porto Portugal Faculty of Electrical Engineering and Computer Science Department of Control and Management Systems Department of Information Systems Kielce University of Technology Kielce Poland

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SI-YOLO: A Deep Learning Algorithm Suitable for Surgical Instrument Detection

SSRN

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SSRN 2022年

作者： Liu, Zhenzhong Zhou, Yifan Sun, Qiyuan Wang, Yan Polat, Kemal Nour, Majid Alenezi, Fayadh Alhudhaif, Adi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu14280 Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University 72238 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj 11942 Saudi Arabia

Surgical instrument detection and tracking is one of the core technologies of surgical robots, which can assist clinicians in completing operations. However, existing methods need further improvement in terms of real-time performance and accuracy. We propose a deep learning algorithm, SI-YOLO, for detection of surgical instruments. SimAM attention mechanism is used to improve the feature extraction ability of YOLOv5. Replacing PANet with BiFPN in the neck network improves the feature fusion strategy. The α-IoU loss function is used to improve the regression accuracy of detection boxes. The mAP and FPS of the SI-YOLO reach 99.2% and 120 frames/sec, respectively. Aiming at the problem of ID transformation in the disappearance and occlusion of surgical instruments, the Inception-ResNet-v2 structure is added on the basis of the appearance feature extraction network Cosine of the DeepSort tracking algorithm. Combining SI-YOLO with the improved DeepSort implements the Tracking by Detection (TBD) algorithm. In the case of device disappearance or occlusion, the frequency of ID transitions is reduced by approximately 30%. Finally, we built a simulated surgery experimental platform, established our own data set, and realized the preliminary application of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Object detection

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Designing LUT-based mealy FSMs with transformation of state codes

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AIP Conference Proceedings 2017年第1期1906卷

作者： Kamil Mielcarek Alexander Barkalov Larisa Titarenko 1Institute of Metrology Electronics and Computer Science Faculty of Computer Electrical and Control Engineering University of Zielona Góra ul. prof. Z. Szafrana 2 65-516 Zielona Góra Poland

A design method is proposed for LUT-based Mealy FSMs. The method is based on transformation of state codes into outputs of FSMs. Example of design and results of investigations are given. The method allows obtaining F...

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Differential Diagnostic Value of Machine Learning–Based Models for Embolic Stroke

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Clinical and Applied Thrombosis/Hemostasis 2023年 29卷 10760296231203663-10760296231203663页

作者： Kuo, HsunYu Liu, Tsai-Wei Huang, Yo-Ping Chin, Shy-Chyi Ro, Long-Sun Kuo, Hung-Chou Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Neurology Chang Gung Memorial Hospital at Linkou Medical Center Chang Gung University College of Medicine Taoyuan Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu Taiwan Institute of Electrical and Electronics Engineers Taipei Taiwan Institution of Engineering and Technology Taipei Taiwan Fellow of Chinese Automatic Control Society Taipei Taiwan Medical Imaging and Intervention Chang Gung Memorial Hospital at Linkou Medical Center Chang Gung University College of Medicine Taoyuan Taiwan

Cancer-associated thrombosis (CAT) and atrial fibrillation (AF)-related stroke are two subtypes of acute embolic stroke with distinct lesion patterns on diffusion weighted imaging (DWI). This pilot study aimed to evaluate the feasibility and performance of DWI-based machine learning models for differentiating between CAT and AF-related stroke. Patients with CAT and AF-related stroke were enrolled. In this pilot study with a small sample size, DWI images were augmented by flipping and/or contrast shifting to build convolutional neural network (CNN) predicative models. DWI images from 29 patients, including 9 patients with CAT and 20 with AF-related stroke, were analyzed. Training and testing accuracies of the DWI-based CNN model were 87.1% and 78.6%, respectively. Training and testing accuracies were 95.2% and 85.7%, respectively, for the second CNN model that combined DWI images with demographic/clinical characteristics. There were no significant differences in sensitivity, specificity, accuracy, and AUC between two CNN models (all P = n.s.). The DWI-based CNN model using data augmentation may be useful for differentiating CAT from AF-related stroke. © The Author(s) 2023.

关键词： atrial fibrillation cancer-associated thrombosis data augmentation differential diagnosis diffusion-weighted imaging machine learning

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A parallel analog intelligent vision sensor with a variable receptive field

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Systems and computers in Japan 1999年第1期30卷 60-69页

作者： Yagi, Tetsuya Kameda, Seiji Iizuka, Kunihiko Faculty of Computer Science and System Engineering Kyushu Institute of Technology Iizuka 820-8502 Japan Advanced Technology Research Laboratories Sharp Tenri 632-0004 Japan Institute of Physiological Science Okazaki Japan Nagoya Institute of Technology Japan Institute of Electronics Information and Communication Engineers Japan Society of Instrument and Control Engineers Japan Japanese Neural Network Society Japan Association for Research in Vision and Opthalmology Japan Kyushu Institute of Technology Japan Advanced Technology Research Laboratories Tenri Japan

The retina is an intelligent vision sensor indispensable in real-time image information processing of animals. The parallel image processing function of the retina was realized in an analog CMOS integrated circuit (vision chip). The chip was implemented with a one-dimensional Laplacian-Gaussian receptive field the size of which can be controlled by external signals. Active pixel sensors were used in the sensor part of the chip where the light sensitivity can be controlled by varying the accumulation time. Circuits compensating for noise due to the mismatch of transistor characteristics were incorporated, and the experimental results verified that sufficiently accurate outputs can be obtained under natural illumination. The chip can effectively extract the spatial frequency bandwidth of the signal in the presence of noise. © 1999 Scripta Technica.

关键词： Analog VLSI Laplacian-Gaussian Offset compensation Parallel processing Variable receptive field Vision chip

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Preface

Lecture Notes in Control and Information Sciences

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Lecture Notes in control and Information sciences 2021年 485卷 v-viii页

作者： Faulwasser, Timm Müller, Matthias A. Worthmann, Karl Department of Electrical Engineering and Information Technology Institute of Energy Systems Energy Efficiency and Energy Economics TU Dortmund University Dortmund Germany Faculty of Electrical Engineering and Computer Science Institute of Automatic Control Leibniz University Hannover Hannover Germany Faculty of Mathematics and Natural Sciences Institute for Mathematics Optimization-based Control Technische Universität Ilmenau Ilmenau Germany

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Guest Editorial

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IEEE Transactions on Industrial electronics 2011年第4期58卷 1074-1080页

作者： Marco Liserre Josep Balcells Thomas Basso Jan T. Bialasiewicz Carlo Cecati Sudipta Chakraborty Josep M. Guerrero Mehrdad Kazerani Friederich Kupzog Adel Nasiri Peter Palensky José Rodriguez Pedro Rodríguez Thilo Sauter Remus Teodorescu Polytechnic University of Bari Bari Italy Universitat Politéchnica de Catalunya Barcelona Spain National Renewable Energy Laboratory (NREL) Golden CO USA Electrical Engineering Department University of Colorado Denver CO USA Department of Electrical and Information Engineering University of L'Aquila L'Aquila Italy National Renewwable Energy Laboratory (NREL) Golden CO USA Department of Automatic Control Systems and Computer Engineering Technical University of Catalonia Barcelona Spain Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Institue of Computer Technology Vienna Technical University Vienna Austria Department of Electrical Engineering and Computer Science University of Wisconsin Milwaukee WI USA Sustainable Building Technologies Austrian Institue of Technology (AIT) Vienna Austria Department of Electronics Engineering Universidad Técnica Federico Santa María Valparaiso Chile Research Group on Renewable Electrical Energy Systems Department of Electrical Engineering Universitat Politécnica de Catalunya (UPC) Barcelona Spain Austrian Academy of Sciences Institute of Integraded Sensor Systems Wiener Neustadt Austria

The 20 papers in this second part of this special section are classified by energy sources (wind, photovoltaic, hydro, etc.), grid-related issues (islanding, synchronization, microgrid, and smart grid), and converters... 详细信息

关键词： Special issues and sections Smart grids Renewable energy resources Photovoltaic systems Power conversion Wind energy Hydroelectric power generation Power grids

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