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Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference

作者： Yuepeng Chen Jingxin Zhang Shenpeng Li Li Chai School of Automation Wuhan University of Technology Department of Electrical and Computer Systems Engineering Monash University Clayton School of Information Science and Engineering Wuhan University of Science and Technology

This paper investigates optimal signal predictive quantization in the presence of source model *** uses polytopic system model to represent the uncertainties in source signal and uses the recent results on robust filtering [8] to obtain a robust H optimal design of signal *** of computational study is presented to demonstrate the advantages of the obtained design.

关键词： Robust predictive quantization source model uncertainties H ∞ optimization polytopic system model

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Social role clustering with topic model

Social role clustering with topic model

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Jie Bai Linjing Li Daniel Zeng Junjie Lin The State Key Laboratory of Management and Control for Complex Systems Institute of Automation China School of Computer and Control Engineering University of Chinese Academy of Sciences China Department of Management of Information Systems The University of Arizona USA

ISBN: (纸本)9781509038664

In this paper, we propose a new role analyzing paradigm for social networks enlightened by topic modeling, which can be adopted as a primitive building block in various security related tasks, such as hidden community finding, important person recognizing and so on. We first present the social network under analyzing as a heterogeneous network constructed by both the users and the subjects discussed among them. We then view this network in a Bag-of-Users schema, which mimics its classical Bag-of-Words counterpart. In this schema, the subjects discussed are treated as “documents” while the users are treated as “words” which construct the “documents”. Based on this novel presentation, we finally apply topic modeling technology to perform the social role clustering. Experiments on a practical security-related social network dataset prove the effectiveness of our approach.

关键词： Social network services Heterogeneous networks Analytical models Computational modeling Finance Security Data models

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Domain-adaptive Person Re-identification without Cross-camera Paired Samples

arXiv

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arXiv 2023年

作者： Li, Huafeng Mao, Yanmei Zhang, Yafei Qi, Guanqiu Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Information Systems Department State University of New York at Buffalo State BuffaloNY14222 United States

Existing person re-identification (re-ID) research mainly focuses on pedestrian identity matching across cameras in adjacent areas. However, in reality, it is inevitable to face the problem of pedestrian identity matching across long-distance scenes. The cross-camera pedestrian samples collected from long-distance scenes often have no positive samples. It is extremely challenging to use cross-camera negative samples to achieve cross-region pedestrian identity matching. Therefore, a novel domain-adaptive person re-ID method that focuses on cross-camera consistent discriminative feature learning under the supervision of unpaired samples is proposed. This method mainly includes category synergy co-promotion module (CSCM) and cross-camera consistent feature learning module (CCFLM). In CSCM, a task-specific feature recombination (FRT) mechanism is proposed. This mechanism first groups features according to their contributions to specific tasks. Then an interactive promotion learning (IPL) scheme between feature groups is developed and embedded in this mechanism to enhance feature discriminability. Since the control parameters of the specific task model are reduced after division by task, the generalization ability of the model is improved. In CCFLM, instance-level feature distribution alignment and cross-camera identity consistent learning methods are constructed. Therefore, the supervised model training is achieved under the style supervision of the target domain by exchanging styles between source-domain samples and target-domain samples, and the challenges caused by the lack of cross-camera paired samples are solved by utilizing cross-camera similar samples. In experiments, three challenging datasets are used as target domains, and the effectiveness of the proposed method is demonstrated through four experimental settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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Optical tracking System for automatic guided vehicles using cellular neural networks 2

Optical tracking System for automatic guided vehicles using ...

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2nd International Workshop on Cellular Neural Networks and their Applications, CNNA 1992

作者： Eröss, Gy. Boros, T. Kiss, Á. Radványi, A. Roska, T. Bitó, J. Vass, J. Tungsram TH Co. Ltd Centre of Robotics and Automation Václ ut 77 BudapestH-1340 Hungary Dual and Neural Computing Systems Laboratory Computer and Automation Institute of the Hungarian Academy of Sciences P.O.B.63 BudapestH-1502 Hungary Dep. of Information Technology Faculty of Engineering University Veszprém Egyetem u. 10 VeszprémH-8201 Hungary

ISBN: (纸本)0780308751

computer Integrated Manufacturing (CIM) systems having determining role in the modern industry. These systems contain essentially two different equipments for transporting materials between workstations: - conveyors can be used if the path of transportation is fixed and known in advance, or - if the path of the transportation may change or there are various tasks to be solved along the path, the use of Automatic Guided Vehicles (AGV) is strongly recommended. The path control of an AGV in CIM systems is generally solved either by following an inductive wire or by determining its position and orientation from signals provided by transmitters mounted at some characteristic points of the workshop. Both of these basic methods have their advantages and drawbacks. © 1992 IEEE.

关键词： Automatic guided vehicles

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Modified Iterative-Learning-Control-Based Ramp Metering Strategies for Freeway Traffic Control With Iteration-Dependent Factors

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IEEE Transactions on Intelligent Transportation systems 2012年第2期13卷 606-618页

作者： Zhongsheng Hou Jingwen Yan Jian-Xin Xu Zhenjiang Li Advanced Control Systems Laboratory of School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing China

For a freeway traffic system with strict repeatable pattern, iterative learning control (ILC) has been successfully applied to local ramp metering for a macroscopic freeway environment by formulating the original ramp metering problem as an output tracking, disturbance rejection, and error compensation problem. In this paper, we address the freeway traffic ramp-metering system under a nonstrict repeatable pattern. ILC-based ramp metering and ILC add-on to ALINEA strategies are modified to deal with the presence of iteration-dependent parameters, iteration-dependent desired trajectory, and input constraints. Theoretical analysis and extensive simulations are used to verify the effectiveness of the proposed approaches.

关键词： Traffic control Trajectory Mathematical model Vehicles Equations Feedback control

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Supervised tensor learning

Supervised tensor learning

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IEEE International Conference on Data Mining (ICDM)

作者： Dacheng Tao Xuelong Li Weiming Hu S. Maybank Xindong Wu School of Computer Science and Information Systems Birkbeck College University of London UK National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences China Department of Computer Science University of Vermont USA

This paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines (SVM) and minimax probability machines (MPM). Within the STL framework, many conventional learning machines can be generalized to take n/sup th/-order tensors as inputs. We also study the applications of tensors to learning machine design and feature extraction by linear discriminant analysis (LDA). Our method for tensor based feature extraction is named the tenor rank-one discriminant analysis (TR1DA). These generalized algorithms have several advantages: 1) reduce the curse of dimension problem in machine learning and data mining; 2) avoid the failure to converge; and 3) achieve better separation between the different categories of samples. As an example, we generalize MPM to its STL version, which is named the tensor MPM (TMPM). TMPM learns a series of tensor projections iteratively. It is then evaluated against the original MPM. Our experiments on a binary classification problem show that TMPM significantly outperforms the original MPM.

关键词： Tensile stress Machine learning Support vector machines Supervised learning Data mining Support vector machine classification computer science Minimax techniques Feature extraction Linear discriminant analysis

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Novel Multivariate Calibration Methods for X-Ray Analyzer

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IFAC Proceedings Volumes 2003年第24期36卷 221-226页

作者： Marko Mattila Kari Koskinen Kari Saloheimo Information and Computer Systems in Automation Helsinki University of Technology P.O.Box 5400 FIN-02015 HUT Finland Outokumpu Mintec Oy P.O.Box 84 FIN-02201 Espoo Finland

The key measurement instruments in the control of the base metal flotation are the on-line elemental analyzers like the Courier X-ray fluorescence analyzer. The calibration of the analyser is one of the key factors in the operation of the X-ray fluorescence analyser. The quality of the analyses is limited by the quality of the calibration of the assay calculation model. The co-linearity of the measurement information is problematic for the multi-linear regression used in the assay calculation and calibration. In this study two new regression methods are introduced for the assay calculation and calibration: Principal Component Regression and Partial Least Squares regression. These methods utilize the idea of internal structure of data - latent structure that handles the co-linearity internally.

关键词： PCR PLS MLR X-ray Analyzer

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Situation detection based on knowledge fusion patterns 2

Situation detection based on knowledge fusion patterns

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2nd International Workshop on Ontologies and information systems, WOIS 2014

作者： Levashova, Tatiana Sandkuhl, Kurt Shilov, Nikolay Smirnov, Alexander University of Rostock Institute of Computer Science Department Business Information Systems Albert-Einstein-Str. 22 Rostock18059 Germany St.Petersburg Institute for Informatics and Automation Russian Academy of Sciences 39 14 Line St-Petersburg199178 Russia ITMO University 49 Kronverkskiy pr. St-Petersburg197101 Russia

With the increasing use of sensors and actuators in technical systems and knowledge-intensive services the need for processing the information captured by these sensors and "making sense" out of it increases. Knowledge fusion is supposed to contribute to this field since it aims at integrating knowledge from different sources. Development of knowledge fusion solutions is a complex task which can be compared to systems and software development. As in other development areas there is a need for efficient development processes which can be supported by reusing solution parts, such as patterns or components. The paper brings together experiences from knowledge fusion subsystem development and from design of knowledge fusion patterns. The main contributions of this paper are (1) a real-world application scenario presenting typical requirements to knowledge fusion systems, (2) application of knowledge fusion patterns from context-based decision support to situation recognition, (3) recommendations from this application case.

关键词： Software design

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Composite systems control by combined math-analytical and FS/NN computing approach 3

Composite systems control by combined math-analytical and FS...

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3rd IFAC Workshop on Automatic systems for Building the Infrastructure in Developing Countries, DECOM-TT 2003

作者： Dimirovski, Georgi M. Jing, Yuan-Wei Zhao, Jun Zhang, Si-Ying Dogus University Department of Computer Engineering Faculty of Engineering Acibadem Kadiköy IstanbulTR-34722 Turkey SS Cyril and Methodius University Institute of Automation and Systems Eng Faculty of Electrical Engineering SkopjeMK-1000 Macedonia Northeastern University Institute of Control Science School of Information Science and Eng. Shenyang Liaoning China

A control systems engineering approach, employing a two-level overall system architecture and different but compatible formalisms for system representation on the upper and lower levels, has been investigated in detail. One design alternative is based on employing fuzzy-system approximators and solving for the adaptive tracking of the given, arbitrary, desired system outputs. The other alternative is based on state equations of composite systems and the use of neural-network approximators to deal with uncertainties and control adaptation. In both alternatives similarity property of subsystems has been exploited. Both designs can be implemented within the standard computer process control technology, and are therefore believed to be promising in applied systems engineering. Copyright © 2003 IFAC.

关键词： Fuzzy systems

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ICSGD-Momentum: SGD Momentum based on Inter-gradient Collision 22

ICSGD-Momentum: SGD Momentum based on Inter-gradient Collisi...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zou, Weidong Cao, Weipeng Xia, Yuanqing Zhong, Bineng Li, Dachuan School of Automation Beijing Institute of Technology Beijing China Shenzhen China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798331527471

Deep neural networks (DNNs) are widely used in fields like computer vision and natural language processing. A key component of DNN training is the optimizer. SGD-Momentum is popular in many DNN methodologies, such as ResNet and DenseNet, due to its simplicity and effectiveness. However, its slow convergence rate limits its use. To overcome this, we introduce inter-gradient collision into SGD-Momentum, inspired by the elastic collision model in physics. This new method, called ICSGD-Momentum, aims to improve convergence. We provide theoretical proof of convergence and establish a regret bound for ICSGD-Momentum. Experiments on benchmarks including function optimization, CIFAR-100, ImageNet, Penn Treebank, COCO, and YCB-Video show that ICSGD-Momentum accelerates training and enhances the generalization performance of DNNs compared to optimizers like SGD-Momentum, Adam, RAdam, Adabound, and AdaBelief. © 2024 IEEE.

关键词： Deep neural networks

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