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ChemSystemsChem 2025年第3期7卷

作者： Prof. Dr. Shogo Hamada Dr. Hironori Sugiyama Prof. Dr. Yiting Zhang Prof. Dr. Shoji Iwabuchi Soichiro Hiroi Toshiki Maruyama Yuktesh Balaji Sota Kumagai Prof. Dr. Satoshi Murata Prof. Dr. Taro Toyota Department of Computer Science School of Computing & Department of Systems and Control Engineering School of Engineering Institute of Science Tokyo 4259 Nagatsuta-cho Midori-ku Yokohama-city Kanagawa 226-8501 Japan These authors contributed equally. Department of Applied Chemistry Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Chemistry College of Science Rikkyo University 3-34-1 Nishi-Ikebukuro Toshima-ku Tokyo 171-8501 Japan Department of Biotechnology and Life Science Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Koganei-shi Tokyo 185-8588 Japan Department of Basic Science Graduate School of Arts and Sciences The University of Tokyo 3-8-1 Komaba Meguro-ku Tokyo 153-8902 Japan Department of Robotics Graduate School of Engineering Tohoku University 6-6-01 Aramaki-Aza Aoba Aoba-ku Sendai Miyagi 980-8579 Japan Universal Biology Institute The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan

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Finite-sample analysis of decentralized temporal-difference learning with linear function Approximation

arXiv

引用

arXiv 2019年

作者： Sun, Jun Wang, Gang Giannakis, Georgios B. Yang, Qinmin Yang, Zaiyue College of Control Science and Engineering State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China

Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice. The goal of a group of agents is to collaboratively learn the value function of a given policy from locally private rewards observed in a shared environment, through exchanging local estimates with neighbors. Despite their simplicity and widespread use, our theoretical understanding of such decentralized TD learning algorithms remains limited. Existing results were obtained based on i.i.d. data samples, or by imposing an ‘additional’ projection step to control the ‘gradient’ bias incurred by the Markovian observations. In this paper, we provide a finite-sample analysis of the fully decentralized TD(0) learning under both i.i.d. as well as Markovian samples, and prove that all local estimates converge linearly to a small neighborhood of the optimum. The resultant error bounds are the first of its type—in the sense that they hold under the most practical assumptions —which is made possible by means of a novel multi-step Lyapunov analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Sampling

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Detecting Glaucoma in the Ocular Hypertension Study Using Deep Learning

TechRxiv

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TechRxiv 2021年

作者： Fan, Rui Bowd, Christopher Christopher, Mark Brye, Nicole Proudfoot, James A. Rezapour, Jasmin Belghith, Akram Goldbaum, Michael H. Chuter, Benton Girkin, Christopher A. Fazio, Massimo A. Liebmann, Jeffrey M. Weinreb, Robert N. Gordon, Mae O. Kass, Michael A. Kriegman, David Zangwill, Linda M. Hamilton Glaucoma Center Viterbi Family Department of Ophthalmology Shiley Eye Institute University of California La Jolla San DiegoCA United States Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Department of Ophthalmology Universitätsmedizin der Johannes Gutenberg-Universität Mainz Rheinland-Pfalz Mainz Germany Department of Ophthalmology School of Medicine The University of Alabama at Birmingham BirminghamAL United States Department of Biomedical Engineering School of Engineering The University of Alabama at Birmingham BirminghamAL United States Bernard and Shirlee Brown Glaucoma Research Laboratory Edward S. Harkness Eye Institute Columbia University Medical Center New YorkNY United States Department of Ophthalmology and Visual Sciences Washington University School of Medicine Washington University St. LouisMO United States

Importance: Automated deep learning (DL) analyses of fundus photographs potentially can reduce the cost and improve the efficiency of reading center assessment of endpoints in clinical trials. Objective: To investigate the diagnostic accuracy of DL algorithms trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG). Study Design: Diagnostic/prognostic Study Setting and Participants: 1,636 OHTS participants from 22 sites with a mean follow-up (range) of 10.7 (0.0, 14.3) years. 66,715 photographs from 3,272 eyes were used to train and test a ResNet-50 model to detect the OHTS Endpoint Committee POAG determination based on optic disc (n=287 eyes, 3,502 photographs) and/or visual field (n=198 eyes, 2,300 visual fields) changes. Three independent test sets were used to evaluate the generalizability of the model. Main Outcome and Measures: Areas under the receiver operating characteristic curve (AUROC) and sensitivities at fixed specificities were calculated to compare model performance. Evaluation of false-positive rates was used to determine whether the DL model detected POAG before the Endpoint Committee POAG determination. Results: The DL model achieved an AUROC (95% CI) of 0.88 (0.82, 0.92) for the overall OHTS POAG endpoint. For the OHTS endpoints based on optic disc changes or visual field changes, AUROCs were 0.91 (0.88, 0.94) and 0.86 (0.76, 0.93), respectively. False-positive rates (at 90% specificity) were higher in photographs of eyes that later developed POAG by disc or visual field (19.1%) compared to eyes that did not develop POAG (7.3%) during their follow-up. The diagnostic accuracy of the DL model developed on the optic disc endpoint applied to 3 independent datasets was lower with AUROC ranging from 0.74 to 0.79. Conclusion and Relevance: The model’s high diagnostic accuracy using OHTS photographs suggests that DL has the potential to standardize and automate POAG determination for cl

关键词： Ophthalmology

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Recent Advances in Image Dehazing

引用

IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 410-436页

作者： Wencheng Wang Xiaohui Yuan College of Information and Control Engineering Weifang University IEEE Department of Computer Science and Engineering University of North Texas

Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles,which reduces the contrast,changes the color,and makes the object features difficult to identify by human vision and by some outdoor computer vision *** image dehazing is an important issue and has been widely researched in the field of computer *** role of image dehazing is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to *** paper reviews the main techniques of image dehazing that have been developed over the past ***,we innovatively divide a number of approaches into three categories:image enhancement based methods,image fusion based methods and image restoration based *** methods are analyzed and corresponding sub-categories are introduced according to principles and *** quality evaluation methods are then described,sorted and discussed in ***,research progress is summarized and future research directions are suggested.

关键词： Atmospheric scattering model image dehazing image enhancement quality assessment

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Nonlinear Virtual Inertia control of WTGs for Enhancing Primary Frequency Response and Suppressing Drive-Train Torsional Oscillations

arXiv

引用

arXiv 2020年

作者： Liu, Bi Huang, Qi Zhao, Junbo Milano, Federico Zhang, Yingchen Sichuan Provincial Key Lab of Power System Wide-area Measurement and Control University of Electronic Science and Technology of China Chengdu Sichuan611731 China Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States School of Electrical and Electronic Engineering University College Dublin Dublin Ireland National Renewable Energy Laboratory GoldenCO80401 United States

Virtual inertia controllers (VICs) for wind turbine generators (WTGs) have been recently developed to compensate the reduction of inertia in power systems. However, VICs can induce low frequency torsional oscillations of the drive train of WTGs. This paper addresses this issue and develops a new nonlinear VIC based on objective holographic feedbacks theory. This approach allows transforming the original nonlinear control into a completely controllable system of Brunovsky’s type. Simulation results under various scenarios demonstrate that the proposed method outperforms existing VICs in terms of suppression of low frequency drive-train torsional oscillations, enhancement of system frequency nadir as well as fast and smooth recovery of WTG rotor speed to the original MPP point before the disturbance. The proposed method is also able to coordinate multiple WTGs. Copyright © 2020, The Authors. All rights reserved.

关键词： Frequency response

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Class specific or shared? A hybrid dictionary learning network for image classification

arXiv

引用

arXiv 2019年

作者： Shao, Shuai Wang, Yan-Jiang Liu, Bao-Di Xu, Rui Li, Ye College of Information and Control Engineering China University of Petroleum Qingdao266580 China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center Qilu University of Technology Jinan250000 China

Dictionary learning methods can be split into two categories: i) class specific dictionary learning ii) class shared dictionary learning. The difference between the two categories is how to use the discriminative information. With the first category, samples of different classes are mapped to different subspaces which leads to some redundancy in the base vectors. For the second category, the samples in each specific class can not be described well. Moreover, most class shared dictionary learning methods use the 0-norm regularization term as the sparse constraint. In this paper, we first propose a novel class shared dictionary learning method named label embedded dictionary learning (LEDL) by introducing the 1-norm sparse constraint to replace the conventional 0-norm regularization term in LC-KSVD method. Then we propose a novel network named hybrid dictionary learning network (HDLN) to combine the class specific dictionary learning with class shared dictionary learning together to fully describe the feature to boost the performance of classification. Extensive experimental results on six benchmark datasets illustrate that our methods are capable of achieving superior performance compared to several conventional classification algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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A study for Motion-Planning Method Resident-tracking Robot based on Reinforcement Learning 30

A study for Motion-Planning Method Resident-tracking Robot b...

引用

30th International Symposium on Micro-NanoMechatronics and Human science, MHS 2019

作者： Sugimoto, Masashi Yoshioka, Takashi Ishii, Kohei Nonaka, Shogo Deguchi, Mikio Tsuzuki, Shinji Hiran, Masatsugu Electrical and Electronic Engineering Course Ehime University Division of Electrical and Electronic Engineering and Computer Science 3 Bunkyo-cho Matsuyama City Ehime Pref.790-8577 Japan National Institute of Technology Kagawa College Department of Electrical and Computer Engineering 355 Chokushi-cho Takamatsu City Kagawa Pref.761-8058 Japan National Institute of Technology Kagawa College. Department of Electro-Mechanical Systems Engineering 355 Chokushi-cho Takamatsu City Kagawa Pref.761-8058 Japan National Institute of Technology Tsuyama College Department of Integrated Science and Technology 624-1 Numa Tsuyama City Okayama Pref.790-8577 Japan National Institute of Technology Niihama College Department of Electronics and Control Engineering 7-1 Yakumo-cho Niihama City Ehime Pref.790-8580 Japan National Institute of Technology Niihama College Department of Electrical Engineering and Information Science 7-1 Yakumo-cho Niihama City Ehime Pref.792-8580 Japan

ISBN: (纸本)9781728126746

Among the advanced countries, especially in Japan, we are facing on the decreasing birthrate and aging population ahead of the world. In recent, population of single-living elder, and elderly people have been taking care by same-aged person, have been actualized. Unfortunately, the population of caregivers is decreasing according to year. In this situation, the Elderly Person Watching System has been focused on. Especially, Robotics, Internet of Things, and Artificial Intelligence technology have been having high affinity with this system. In this study, firstly, the person watching robot system will be developed. In detail, in this paper, the flexible-analyze system will be focused on. In the algorithm, a resident-tracking task and an energy-saving task are holding. The whole tasks are working based on Reinforcement Learning. Thus, the behavior algorithm of the person watching robot system will be developed. In detail, in this paper, the flexible-analyze system will be focused on. In the algorithm, a resident-tracking task and an energy-saving task are holding. From the verification experiment, the simulation results showed the proposed method has been acquired actions to switch the desired task. © 2019 IEEE.

关键词： Reinforcement learning

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End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images

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Computational intelligence and neuroscience 2023年第1期2023卷 3018320页

作者： Kun Zhang Pengcheng Lin Jing Pan Peixia Xu Xuechen Qiu Danny Crookes Liang Hua Lin Wang School of Electrical Engineering Nantong University Nantong Jiangsu 226001 China. Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong Jiangsu 226001 China. Nantong Key Laboratory of Intelligent Medicine Innovation and Transformation Nantong Jiangsu 226001 China. Department of Radiology Affiliated Hospital 2 of Nantong University Nantong Jiangsu 226001 China. College of Mechanical Engineering Donghua University Shanghai 201620 China. School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast BT7 1NN UK.

Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energyX-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.

关键词：

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Density Peak-based Pre-clustering Support Vector Machine for Multi-class Imbalanced Classification

Density Peak-based Pre-clustering Support Vector Machine for...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Zonglin Di Qi Kang Daogang Peng Mengchu Zhou Department of Control Science and Engineering Tongji University Shanghai China College of Automation Engineering Shanghai University of Electric Power Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Imbalanced classification using a support vector machine (SVM) is a normal but crucial problem in machine learning. Compared with binary classification, multiclass classification is much more complicated. Most existing studies on imbalanced classification using SVM focus on binary imbalanced classification; while only few of them look into imbalanced classification with multiple classes. Pre-clustering is a useful technique to prepare proper data from an imbalanced dataset for a classifier. It can be used to extract the feature of a dataset first and improve classification performance. Density peak based on Euclidean distance proves its effectiveness and generality in clustering. Motivated by this and the fact that the number of clusters is known in multi-class classification using a one-vs-rest strategy, we combine density peak clustering and SVM to propose a new pre-clustering method to perform effective imbalanced classification with multiple classes. Specifically, we transform a multi-class classification problem into several binary classification tasks. The results on 5 public datasets in terms of F-measure, G-mean and Area Under Curve (AUC) show its superiority over the original SVM and SVM with other methods including random under-sampling, Synthetic Minority Oversampling Technique, pre-clustering using K-Means and EasyEnsemble methods using either a one-vs-rest or one-vs-one strategy.

关键词： Support vector machines Feature extraction Nickel Testing Task analysis Euclidean distance Kernel

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A MILP-based MPC Method for Traffic Signal control of Urban Road Networks *

A MILP-based MPC Method for Traffic Signal Control of Urban ...

引用

Chinese Automation Congress (CAC)

作者： Bao-Lin Ye Huimin Gao Lingxi Li Keyu Ruan Weimin Wu Tehuan Chen College of Mechanical and Electrical Engineering Jiaxing University Jiaxing China Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis Indianapolis USA College of Control Science and Engineering Zhejiang University Hangzhou China Faculty of Mechanical Engineering and Mechanics Ningbo University Ningbo China

In this paper, a traffic signal control method via MILP-based model predictive control (MPC) was proposed. We use a macroscopic traffic flow model to describe traffic flows of urban road networks. In the model, 0-1 binary variables were used to describe the switch process of green lights and red lights at each intersection. Under the framework of MPC, aiming at reducing the computational complexity, an original MPC-based traffic signal control problem was reformulated as a mixed integer linear programming (MILP) problem which is conveniently calculated with efficient MILP solvers. Finally, numerical experiments based on a test network were implemented to study the validity of the reported method.

关键词： Roads Computational modeling Computational complexity Mathematical model Vehicle dynamics Predictive control Mixed integer linear programming

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