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arXiv 2021年

作者： Guo, Jinhua Chen, Hao Zhang, Jingxin Chen, Sheng Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic Systems Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou350002 China Department of Automation Tsinghua University Beijing100084 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

In this paper, sparsification techniques aided online prediction algorithms in a reproducing kernel Hilbert space are studied for nonstationary time series. The online prediction algorithms as usual consist of the selection of kernel structure parameters and the kernel weight vector updating. For structure parameters, the kernel dictionary is selected by some sparsification techniques with online selective modeling criteria, and moreover the kernel covariance matrix is intermittently optimized in the light of the covariance matrix adaptation evolution strategy (CMA-ES). Optimizing the real symmetric covariance matrix can not only improve the kernel structure's flexibility by the cross relatedness of the input variables, but also partly alleviate the prediction uncertainty caused by the kernel dictionary selection for nonstationary time series. In order to sufficiently capture the underlying dynamic characteristics in prediction-error time series, a generalized optimization strategy is designed to construct the kernel dictionary sequentially in multiple kernel connection modes. The generalized optimization strategy provides a more self-contained way to construct the entire kernel connections, which enhances the ability to adaptively track the changing dynamic characteristics. Numerical simulations have demonstrated that the proposed approach has superior prediction performance for nonstationary time series. Copyright © 2021, The Authors. All rights reserved.

关键词： Radial basis function networks

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Modeling and Analysis of Pumping Cell of Nox Sensor - Part Ii: Nox Pumping Cell

SSRN

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SSRN 2022年

作者： Wang, Zhen Deng, Zhong-Hua Wang, Jie Lin, Wei-Xun Fu, Xiao-Wei Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Imaging Processing and Intelligent Control Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen518055 China Lambda Company Jiangsu Changzhou213100 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China

To improve the performance of the NOx sensor, a combined model based on the electrochemical model and the diffusion model is developed for the NOx pumping cell of the NOx sensor. Considering that a small amount of oxygen enters the third chamber and part of NO is decomposed in the second chamber, the electrochemical model simultaneously considers the reactions of NO and oxygen at cathode, and the diffusion model additionally considers the diffusion of oxygen and the decomposition of NO. To validate the model at different gas concentrations, the sensor is mounted on the designed test rig. Then, the simulation results are compared with experimental data from the test rig and good agreement is obtained. In addition, three key parameters of the model of the NOx pumping cell are analyzed. The values and changing trends of these three parameters are given in the paper. © 2022, The Authors. All rights reserved.

关键词： Diffusion

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How to secure distributed filters under sensor attacks

arXiv

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arXiv 2020年

作者： He, Xingkang Ren, Xiaoqiang Sandberg, Henrik Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Affiliated with Digital Futures Sweden Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China

—We study how to secure distributed filters for linear time-invariant systems with bounded noise under false-data injection attacks. A malicious attacker is able to arbitrarily manipulate the observations for a time-varying and unknown subset of the sensors. We first propose a recursive distributed filter consisting of two steps at each update. The first step employs a saturation-like scheme, which gives a small gain if the innovation is large corresponding to a potential attack. The second step is a consensus operation of state estimates among neighboring sensors. We prove the estimation error is upper bounded if the filter parameters satisfy a condition. We further analyze the feasibility of the condition and connect it to sparse observability in the centralized case. When the attacked sensor set is known to be time-invariant, the secured filter is modified by adding an online local attack detector. The detector is able to identify the attacked sensors whose observation innovations are larger than the detection thresholds. Also, with more attacked sensors being detected, the thresholds will adaptively adjust to reduce the space of the stealthy attack signals. The resilience of the secured filter with detection is verified by an explicit relationship between the upper bound of the estimation error and the number of detected attacked sensors. Moreover, for the noise-free case, we prove that the state estimate of each sensor asymptotically converges to the system state under certain conditions. Numerical simulations are provided to illustrate the developed results. Copyright © 2020, The Authors. All rights reserved.

关键词： State estimation

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Regret and cumulative constraint violation analysis for online convex optimization with long term constraints

arXiv

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Institute for Advanced Study Tongji University Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an O(Tmax{c,1-c}) bound for static regret and an O(T (1-c)/2) bound for cumulative constraint violation, where c ϵ (0, 1) is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to O(log(T )) when the loss functions are strongly convex, which also improves existing results. In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal O( p T (1 + PT )) regret and an O(√T) cumulative constraint violation, where PT is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Comparators (optical)

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Management of services of a hyperconverged infrastructure using the coordinator 1st

Management of services of a hyperconverged infrastructure us...

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1st International Conference on computer Science, Engineering and Education Applications, ICCSEEA2018

作者： Rolik, Oleksandr Telenyk, Sergii Zharikov, Eduard Department of Automation and Control in Technical Systems National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine Department of Theoretical Electrical Engineering and Computer Science Faculty of Electrical and Computer Engineering Cracow University of Technology Cracow Poland

ISBN: (纸本)9783319910079

Modern data centers’ providers are gradually moving away from traditional and multi-vendor IT infrastructures to open, standardized and interchangeable solutions that are based on a software defined approach to managing data center resources. The authors analyze the architectural features, requirements, limitations, hardware and software of hyperconverged infrastructures and their advantages in comparison with traditional and converged architectures deployed in data centers. The authors propose to employ two-level coordination schema to manage compute, storage, network and virtualization subsystems of hyperconverged infrastructure along with the self-management algorithms inside these subsystems. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Digital storage

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Online model composition for networked discrete-event systems

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IFAC-PapersOnLine 2019年第20期52卷 43-48页

作者： Markus Zgorzelski Jan Lunze Institute of Automation and Computer Control Ruhr-Universität Bochum Universitätsstrasse 150 D-44780 Bochum Germany

Networked discrete-event systems consist of physically coupled subsystems that utilise a digital communication network for solving their current tasks. Each subsystem has to be steered from its current state into a situation-dependent target state. Due to the physical couplings among the subsystems, certain state transitions have to be executed synchronously with other subsystems to reach the local target states. To satisfy all local tasks, the subsystems have to find a deadlock-free order of the synchronous state transitions by applying global model information. However, in the networked discrete-event system there does not exist any coordinator and the global model information is distributed among the subsystems. Hence, the subsystems have to exchange model information and to compose it online to find the deadlock-free order of synchronous state transitions. This paper proposes an online model composition algorithm based on the A ⁎ -Algorithm that determines the necessary local model information to be communicated and composed by the subsystems. The method is demonstrated by an example.

关键词： Networked discrete-event systems model composition A ⁎ -Algorithm

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Vehicle control in Highway Traffic by Using Reinforcement Learning and Microscopic Traffic Simulation

Vehicle Control in Highway Traffic by Using Reinforcement Le...

引用

International Symposium on Intelligent Systems and Informatics (SISY)

作者： Laszlo Szoke Szilard Aradi Tamas Becsi Peter Gaspar Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Megyetem rkp. 3 Budapest Hungary Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences Kende u. 13-17 Budapest Hungary

ISBN: (数字)9781728173528

ISBN: (纸本)9781728173535

The paper presents a simple yet powerful and intelligent driver agent, designed to operate in a preset highway situation using Policy Gradient Reinforcement Learning (RL) agent. The goal is to navigate safely in dense highway traffic and proceed through the defined length with the shortest time possible. The algorithm uses a dense neural network as a function approximator for the agent with discrete action space on the control level, e.g., acceleration and steering. The developed simulation environment uses the open-source traffic simulator called Simulation of Urban MObility (SUMO), integrated with an interface, to interact with the agent in real-time. With this tool, numerous driving and highway situations can be created and fed to the agent from which it can learn. The environment opens the opportunity to randomize and customize the other road users' behavior. Thus the experience can be more diverse, and thus the representation becomes more general. The article describes the modeling environment, the details on the learning agent, and the rewarding scheme. After evaluating the experiences gained from the training, some ideas for optimization and further development goals are also proposed.

关键词： Learning (artificial intelligence) Task analysis Vehicles Roads Training Microscopy

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Parallel Building: A Complex System Approach for Smart Building Energy Management

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1452-1461页

作者： Abdulaziz Almalaq Jun Hao Jun Jason Zhang Fei-Yue Wang Department of Electrical Engineering Engineering CollegeUniversity of HailHail 55476Saudi Arabia Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of DenverDenverCO 80208 USA State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.

关键词： ACP theory artificial intelligence data acquisition deep learning(DL) energy consumption machine learning parallel energy prediction prediction algorithms smart grid

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Fast and accurate single-image depth estimation on mobile devices, mobile AI 2021 challenge: Report

arXiv

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arXiv 2021年

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Computer Vision Lab ETH Zurich Switzerland Ltd AI Witchlabs Switzerland Tencent GY-Lab China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Nanjing Artificial Intelligence Chip Research Institute of Automation Chinese Academy of Sciences China Black Sesame Technologies Inc. Singapore Singapore Visual Media Lab KAIST Korea Republic of Harbin Institute of Technology China Peng Cheng Laboratory China Multimedia and Computer Vision Laboratory National Cheng Kung University Taiwan Samsung Research UK United Kingdom OPPO Research Institute China ETH Zurich Switzerland

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2021, The Authors. All rights reserved.

关键词： computer operating systems

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Memory-efficient GPU-based exact and parallel triangle counting in large graphs 21

Memory-efficient GPU-based exact and parallel triangle count...

引用

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Ouyang, Zhiyou Wu, Shanni Zhao, Tongtong Yue, Dong Zhang, Tengfei Institute of Advanced Technology School of Automation Nanjing University of Posts and Telecommunications Nanjing210003 China School of Computer Nanjing University of Posts and Telecommunications Nanjing China Institute of Advanced Technology School of Automation Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing University of Posts and Telecommunications Nanjing China School of Automation Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728120584

Triangle counting is one of the basic research topics in many practical problems, such as clustering coefficients, transitivity ratio and traffic network complexity, etc., all of which can be converted into triangle counting issues. In this paper, a memory efficient GPU-based parallel triangle counting algorithm is proposed for large undirected graphs, which based on Polak's parallel forward triangle counting algorithm and improved the algorithm flow, edges sorting and triangle counting kernel function to increase the memory usage efficiency and run time performance. Algorithm comparison with the description of proposed algorithm steps and the implementation details is also introduced in the paper. Performance evaluations show that the proposed algorithm achieves 5 to 10 times speedup over the existing parallel forward algorithm, moreover, the GPU memory usage is reduced to a maximum of 50% compared to the existing algorithm. © 2019 IEEE.

关键词： Graphics processing unit

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