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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Liu, Yiwen Shang, Xiuqin Shen, Zhen Hu, Bin Wang, Zhihai Xiong, Gang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing University of Technology Faculty of Information Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China Beijing University of Technology Faculty of Information Beijing China

ISBN: (纸本)9781728128535

Recently, with the development of both 3D sensors and 3D virtual network that bring the needs of interaction with the real world, many 3D applications burst out. However, it is difficult to understanding these three-dimensional scenes with a fixed program. Then, a data-driven method is required to process these 3D data, which brings a strong demand of 3D Deep Learning in 3D data. Towards this goal, with an end-to-end deep learning, the experiment is based on PointNet++, a well proposed method for feature extraction. The experiment optimizes the network structure and parameters to improve the classification results. Finally, the network is applied to tooth model for classification and identification so that the dental model can be found from different perspectives. © 2019 IEEE.

关键词： 3D printers

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Ant Colony Optimization Algorithm for Load Frequency control of Interconnected Power System

Ant Colony Optimization Algorithm for Load Frequency Control...

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第30届中国过程控制会议（CPCC 2019）

作者： Zhijun Li Jia Cao Gonggui Chen Key Laboratory of Network Control & Intelligent Instrument Chongqing University of Posts and Telecommunications Ministry of Education Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications

A widely used linear model of a two-area non-reheat thermal system equipped with proportional-integral-derivative(PID) controllers is considered for the design and analysis purpose in this *** simulation model of Load Frequency control(LFC) has been developed under MATLAB/Simulink *** controller parameters are optimized using the Artificial Intelligence technique based Ant Colony Optimization(ACO) *** comparison with an optimized PID controller based on hBFOA-PSO, PSO and GA, simulation results emphasis on the better performance of the optimized PID controller based on ACO over a wide range of operating conditions and system parameters variations.

关键词： Load Frequency control PID Ant Colony Optimization Sensitivity analysis

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A Blockchain-based Framework for Central Bank Digital Currency

A Blockchain-based Framework for Central Bank Digital Curren...

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Han, Xuan Yuan, Yong Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Research Center of Military Computational Experiments and Parallel System National University of Defense Technology Changsha China

ISBN: (纸本)9781728128535

Cryptocurrency and blockchain technologies have developed in parallel in recent years, with technological breakthroughs in currency issuance, payment methods, and currency storage. However, the existing cryptocurrencies cannot replace fiat money. There is a huge gap between decentralized cryptocurrency and central bank digital currency, namely CBDC, in terms of monetary governance and circulation. In this paper, we propose the function and security requirements of CBDC, through a comprehensive analysis of the existing typical cryptocurrency and the prototype of the CBDC scheme. On this basis, we present a blockchain-based framework for CBDC with three layers, including supervisory layer, network layer and user layer, and describe the key business processes of the CBDC's entire lifecycle of issuance-circulation-withdrawal in detail. Finally, we take cross-border payment as an example to explain the transaction process of CBDC. We aim to provide theoretical guidance for CBDC design. © 2019 IEEE.

关键词： Blockchain

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Large-scale hyperspectral image clustering using contrastive learning

arXiv

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arXiv 2021年

作者： Cai, Yaoming Zhang, Zijia Liu, Yan Ghamisi, Pedram Li, Kun Liu, Xiaobo Cai, Zhihua School of Computer Science China University of Geosciences Wuhan430074 China Helmholtz Institute Freiberg for Resource Technology Freiberg09599 Germany Vienna1030 Austria College of Electronic Science and Technology National University of Defense Technology Changsha410073 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China

Clustering of hyperspectral images is a fundamental but challenging task. The recent development of hyperspectral image clustering has evolved from shallow models to deep and achieved promising results in many benchmark datasets. However, their poor scalability, robustness, and generalization ability, mainly resulting from their offline clustering scenarios, greatly limit their application to large-scale hyperspectral data. To circumvent these problems, we present a scalable deep online clustering model, named Spectral-Spatial Contrastive Clustering (SSCC), based on self-supervised learning. Specifically, we exploit a symmetric twin neural network comprised of a projection head with a dimensionality of the cluster number to conduct dual contrastive learning from a spectral-spatial augmentation pool. We define the objective function by implicitly encouraging withincluster similarity and reducing between-cluster redundancy. The resulting approach is trained in an end-to-end fashion by batch-wise optimization, making it robust in large-scale data and resulting in good generalization ability for unseen data. Extensive experiments on three hyperspectral image benchmarks demonstrate the effectiveness of our approach and show that we advance the state-of-the-art approaches by large margins. Copyright © 2021, The Authors. All rights reserved.

关键词： Spectroscopy

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ℋ∞ Consensus control of Multi-agent systems with Round-robin Protocol and Missing Measurements: The Finite-horizon Case

ℋ∞ Consensus Control of Multi-agent Systems with Round-rob...

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Chinese control and Decision Conference, CCDC

作者： Mengmeng Sun Dongyan Chen Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology Harbin China

The ℋ ∞ consensus control problem is handled for a class of discrete time-varying multi-agent systems with round-robin protocol and missing measurements. The round-robin protocol is considered, which allows only one agent can send its measurement data at each transmission step in order to prevent the data collisions. And the missing measurements phenomenon is described by a sequence of Bernoulli distributed random variables with known probabilities. We focus on designing the controller parameters to guarantee that the closed-loop multi-agent systems with missing measurements satisfy the ℋ ∞ consensus performance. On this basis, some sufficient and necessary conditions are obtained by solving coupled backward recursive Riccati difference equations, where the existence and feasibility of the control scheme can be obtained with the help of the completing squares method and the stochastic analysis technique. At last, a numerical example is given to illustrate the effectiveness of the developed controller design scheme.

关键词： Protocols Multi-agent systems Time-varying systems Difference equations Random variables Output feedback

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A novel online learning RECOC-KFDA method for lithologic identification in drilling process*

A novel online learning RECOC-KFDA method for lithologic ide...

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Asian control Conference

作者： Xi Chen Weihua Cao Chao Gan Min Wu School of Automation China University of Geosciences and Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781728102634

Aiming to achieve a safe and efficient drilling, this paper is concerned with identification of formation lithology, which provides critical information for drilling control. Notice that it is hard to make accurate geological prediction using conventional identification approaches, due to the data characteristics of imbalanced, multi-classification and low value density, a novel reduction error correcting output code kernel fisher discriminant analysis algorithm(RECOC-KFDA) method is developed in an online manner. It consider design optimal error correcting output code(ECOC) matrix based on a reduction algorithm, and it proposed an online method to reduce the computation complexity required for updating the kernel fisher discriminant analysis(KFDA) classifiers rather than recalibrating them. Proposed method has been applied to lithologic identification in drilling site. Simulations and comparisons demonstrate that our method is superior to the existing ones for both offline training and online prediction model.

关键词： Encoding Computational modeling Kernel Geology Drilling machines Data models Diversity reception

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Deep-learning based optimal PMU placement and fault classification for power system

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Expert systems with Applications 2025年 292卷

作者： Xin Lei Zhen Li Huaiguang Jiang Samson S. Yu Yu Chen Bin Liu Peng Shi School of Future Technology South China University of Technology Panyu District Guangdong China School of Automation & Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing 100081 China School of Engineering Deakin University 75 Pigdon Rd Waurn Ponds Victoria 3216 Australia College of Information Science and Engineering China University of Petroleum (Beijing) Beijing China School of Electrical and Electronic Engineering University of Adelaide 5005 SA Australia

Phasor measurement units (PMUs) are vital for power grid monitoring, yet their high cost restricts widespread adoption. PMU measurement data is also crucial for fault analysis in power systems. However, existing research seldom explores the interplay between optimal PMU placement (OPP) and fault analysis, impeding advancements in grid economy and security. This study introduces a perception-driven, deep learning-based optimization approach that integrates OPP, multi-task learning, and fault data augmentation. First, deep reinforcement learning optimizes PMU placement, balancing cost-effectiveness with observability requirements. Next, multi-task learning, enhanced by Bayesian optimization, improves fault classification efficiency using PMU data. Finally, pre-trained models paired with k -means clustering augment fault data, boosting classification accuracy. Extensive simulations across four IEEE standard test systems validate the proposed method’s effectiveness.

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A Fast Quantity and Position Detection Method Based on Monocular Vision for a Workpieces Counting and Sorting System

A Fast Quantity and Position Detection Method Based on Monoc...

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第三十八届中国控制会议

作者： Xin Wang Baokui Li Hongbin Ma Man Luo School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

With the rapid development of machine vision, many technologies have been applied to the robots for improving the efficiency in the industrial field. This paper concerns the industrial sorting and counting technology problems in a workpieces counting and sorting system, and puts forward a solution using monocular vision. The main process consists of three parts. The rough positioning is accomplished first by using the pixel intensity comparison-based object detection(PICO). Then, image preprocessing and extracting geometric features are established, composing of binarization, morphological operation, optimizing the foreground,finding inner as well as outer contours, and calculating areas. Finally, the center coordinates and categories of workpieces are obtained. We choose nuts and gears as experimental objects, and complete the fast detection. The results of counting nuts and locating gears illustrate that the proposal solution not only has high speed, but also can ensure a high accuracy.

关键词： Monocular Vision the PICO Detector Workpieces Counting Fast Positioning

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An Adaptive Disturbance Rejection controller for Artificial Pancreas

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IFAC-PapersOnLine 2020年第2期53卷 16372-16379页

作者： Deheng Cai Wei Liu Eyal Dassau Francis J. Doyle Iii Xiaoling Cai Junzheng Wang Linong Ji Dawei Shi State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing 100081 China Department of Endocrine and Metabolism Peking University People’s Hospital Beijing China. Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA.

Artificial pancreas (AP) systems are designed to automate glucose management for patients with type 1 diabetes. In this work, we propose an adaptive disturbance rejection control approach for AP systems to achieve safe and effective glucose regulation. The controller is built within the framework of active disturbance rejection control, but incorporates safety operation constraints, and glucose- and velocity-dependent parameter adaptation modules for the key control parameters. In silico performance comparison between the proposed controller and an adaptive zone model predictive controller (MPC) (Shi, Dassau, and Doyle III, 2019a) is conducted on the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator. For both announced and unannounced meals, the controller achieves comparable glucose regulation performance in terms of mean glucose (134.9 mg/dL vs. 135.4 mg/dL, p < 0.001; 149.7 mg/dL vs. 151.7 mg/dL, p < 0.001, respectively) and percentage time in [70, 180] mg/dL (93.8% vs. 92.4%, p < 0.001; 76.0% vs. 72.4%, p < 0.001, respectively) without increasing the risk of hypoglycemia. The results indicate the feasibility of achieving comparable glucose regulation performance through a non-optimization control law for AP systems.

关键词： Artificial pancreas Active disturbance rejection control Adaptive control Glucose regulation

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Preface——Special Issue on New Directions in Nonlinear and Distributed control

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Journal of systems Science & complexity 2018年第1期31卷 1-3页

作者： LIU Tengfei XIN Bin JIANG Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing 100081 China Tandon School of Engineering New York University Brooklyn NY 11201 USA

Nonlinearity is ubiquitous in engineering and natural *** development of nonlinear control can be traced back to decades *** date,the research has reached the stage that emphasizes developing methodologies that can ha... 详细信息

关键词：控制问题非线性分布式工程应用程序电子物理自然系统生产线相互作用

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