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arXiv 2019年

作者： Liu, Hanxiao Mo, Yilin Yan, Jiaqi Xie, Lihua Johansson, Karl H. School of Electrical and Electronic Engineering Nanyang Technological University Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Automation and BNRist Tsinghua University China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper considers the problem of designing physical watermark signals in order to optimally detect possible replay attack in a linear time-invariant system, under the assumption that the system parameters are unknown and need to be identified online. We first provide a replay attack model, where an adversary replays the previous sensor data in order to fool the system. A physical watermarking scheme, which leverages a random control input as a watermark to detect the replay attack, is then introduced. The optimal watermark signal design problem is cast as an optimization problem, which aims to achieve the optimal trade-off between control performance and intrusion detection. An online watermarking design and system identification algorithm is provided to deal with systems with unknown parameters. We prove that the proposed algorithm converges to the optimal one and characterize the almost sure convergence rate. A numerical example and an industrial process example are provided to illustrate the effectiveness of the proposed strategy. Copyright © 2019, The Authors. All rights reserved.

关键词： Intrusion detection

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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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...

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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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Path planning of mobile robot based on improved DDQN

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Journal of Physics: Conference Series 2021年第1期2024卷

作者： Yang yunxiao Wangjun Zhang Hualiang Dai shilong School of Computer Science Shenyang University of Chemical Technology Shenyang 110142 China. Industrial Control Network and System Laboratory Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China. Institudes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110169 China.

Aiming at the problem of overestimation and sparse rewards of deep Q network algorithm in mobile robot path planning in reinforcement learning, an improved algorithm HERDDQN is proposed. Through the deep convolutional neural network model, the original RGB image is used as input, and it is trained through an end-to-end method. The improved deep reinforcement learning algorithm and the deep Q network algorithm are simulated in the same two-dimensional environment. The experimental results show that the HERDDQN algorithm solves the problem of overestimation and sparse reward better than the DQN algorithm in terms of success rate and reward convergence speed, Which shows that the improved algorithm finds a better strategy than the DQN algorithm.

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An exploratory study on machine learning to couple numerical solutions of partial differential equations

arXiv

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arXiv 2020年

作者： Tang, Hansong Li, L. Grossberg, M. Liu, Y.J. Jia, Y.M. Li, S.S. Dong, W.B. Civil Engineering Department City College of New York City University of New York New York10031 United States Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Computer Science City College of New York City University of New York New York10031 United States School of Mathematics Georgia Institute of Technology GA30332 United States School of Automation Science and Electrical Engineering Beihang University Beijing10083 China School of Hydraulic and Hydroelectric Engineering Xi'an University of Technology Shaanxi710048 China

As further progress in the accurate and efficient computation of coupled partial differential equations (PDEs) becomes increasingly difficult, it has become highly desired to develop new methods for such computation. In deviation from conventional approaches, this short communication paper explores a computational paradigm that couples numerical solutions of PDEs via machine-learning (ML) based methods, together with a preliminary study on the paradigm. Particularly, it solves PDEs in subdomains as in a conventional approach but develops and trains artificial neural networks (ANN) to couple the PDEs' solutions at their interfaces, leading to solutions to the PDEs in the whole domains. The concepts and algorithms for the ML coupling are discussed using coupled Poisson equations and coupled advection-diffusion equations. Preliminary numerical examples illustrate the feasibility and performance of the ML coupling. Although preliminary, the results of this exploratory study indicate that the ML paradigm is promising and deserves further research. Copyright © 2020, The Authors. All rights reserved.

关键词： Neural networks

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Damping a pendulum's swing by string length adjustment - design and comparison of various control methods

Damping a pendulum's swing by string length adjustment - des...

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American control Conference (ACC)

作者： Milan Anderle Wim Michiels Sergej Čelikovský Tomáš Vyhlídal The Czech Academy of Sciences Institute of Information Theory and Automation (UTIA AV ČR) Prague 8 Czech Republic Department of Computer Science Heverlee Belgium Department of Instrumentation and Control Engineering Czech Technical University in Prague Czech Republic

A novel nonlinear control theory based feedback controller is proposed to damp the oscillations of the suspended load (pendulum) using the active modification of the length of the suspension string. This setting is a highly nonlinear one since the approximate linearization around the equilibrium working point is neither controllable, nor asymptotically stabilizable. The nonlinear design of the control law is therefore based on the conveniently selected control Lyapunov function. The resulting control law is then compared to the previously developed time-delay feedback control law, both in simulations and using the laboratory experimental realization of the suspended load system. Despite the fact that in the simulations the time-delay feedback control law suppresses the oscillations better than the nonlinear control law, in the experiments the performance of the time-delay feedback and of the nonlinear control law are rather similar. Moreover, the former keeps the pendulum string length oscillating, the latter stabilizes the nominal string length as well. Finally, the numerical optimization shows that the ideal damping would be provided by the impulsive-like control producing piece-wise constant string length dynamics.

关键词： Oscillators Mathematical model Damping Aerospace electronics Adaptive control Lyapunov methods Force

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

arXiv

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Fusion of deep shallow features and models for speaker recognition

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Chinese Journal of Acoustics 2018年第4期37卷 477-490页

作者： ZHONG Weifeng FANG Xiang FAN Cunhang WEN Zhengqi TAO Jianhua School of Automation Harbin University of Science and Technology Harbin 150080 National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 CAS Center for Excellence in Brain Science and Intelligence Technology Beijiug 100190 School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100190

In order to further improve the performance of speaker recognition, features fusion and models fusion are proposed. The features fusion method is to fuse deep and shallow features. The fused feature describes speaker characteristics more comprehensively than a single feature because of the complementarity between different levels of features. The models fusion method is to fuse i-vectors extracted from different speaker recognition systems. The fused model can combine advantages of different speaker recognition systems. Experimental results show the effectiveness of the proposed methods. Compared with the state-of-the-art system on CASIA North and South dialect corpus,the proposed features fusion system and models fusion system achieved about 54.8% and 69.5% relative improvement on the equal error rate（EER）,respectively.

关键词： DBF EER DNN

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A New Approach for Face Anti-Spoofing Using Handcrafted and Deep Network Features

A New Approach for Face Anti-Spoofing Using Handcrafted and ...

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

作者： Polash Kumar Das Bin Hu Chang Liu Kaixin Cui Prabhat Ranjan Gang Xiong School of Electronic and Information Engineering South China University of Technology Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Cloud Computing Center Chinese Academy of Science Dongguan China

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature.

关键词： Face Videos Feature extraction Databases Face recognition Password

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Critical technologies in the cluster of virtual and augmented reality

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IOP Conference Series: Materials science and Engineering 2021年第1期1019卷

作者： V Zhmud A Liapidevskiy V Avrmachuk V Sayapin J N Sedinkin W Hardt Department of Automation Novosibirsk State Technical University Novosibirsk Russia General Director of Novosibirsk Institute of Program Systems Novosibirsk Russia Department of Complex Information Security of Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Director of R&D Center of Novosibirsk Institute of Program Systems Novosibirsk Russia Vice-Director on Development of Novosibirsk Institute of Program Systems Novosibirsk Russia Department of Computer Science Technical University of Chemnitz Chemnitz Germany

The program 'Digital Economy of the Russian Federation', launched in 2019, contains virtual and augmented reality technologies in its list. Each such technology consists of many assistive technologies called sub-technologies. These assistive technologies make up a large list, while failure to obtain the required results in at least one of these technologies may lead to the fact that the integral technology will not be created. This article discusses a list of only critical assistive technologies, that is, precisely those technologies whose creation is a critical factor for creating a generalized (integral) technology. It is on these technologies that the main emphasis should be placed not only in financing their development, but also in the thorough planning of goals and objectives, in the selection of performers and in the acceptance of research results.

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