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32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016

作者： Sun, Wen Capobianco, Roberto Gordon, Geoffrey J. Bagnell, J. Andrew Boots, Byron School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Computer Control and Management Engineering Sapienza University of Rome Italy School of Interactive Computing Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9781510827806

We present the Smoothing Machine (SMACH, pronounced "smash"), a dynamical system learning algorithm based on chain Conditional Random Fields (CRFs) with latent states. Unlike previous methods, SMACH is designed to optimize prediction performance when we have information from both past and future observations. By leveraging Predictive State Representations (PSRs), we model beliefs about latent states through predictive states-an alternative but equivalent representation that depends directly on observable quantities. Predictive states enable the use of well-developed supervised learning approaches in place of local-optimumprone methods like EM: we learn regressors or classifiers that can approximate message passing and marginalization in the space of predictive states. We provide theoretical guarantees on smoothing performance and we empirically verify the efficacy of SMACH on several dynamical system benchmarks.

关键词： Dynamical systems

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State Estimation of Baker's Yeast Fed-Batch Cultivation by Extended Kalman Filter Using Alternative Models 1

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IFAC Proceedings Volumes 1998年第11期31卷 601-606页

作者： P. Hrnčiřík J. Náhlík V. Havlena Dept. of Computing and Control Engineering Institute of Chemical Technology Prague Czech Republic Trnka Laboratory for Automatic Control Czech Technical University Prague and Honeywell Technology Center Prague Czech Republic

The implementation of a new estimation method with alternative process models for the state estimation of the baker’s yeast fermentation is presented. Some of the implementation issues are discussed using the data ob... 详细信息

关键词： Estimation Extended Kalman Filter Fermentation processes Nonlinear model

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Event-Triggered Distributed Multisensor Multitarget Tracking with GM-PHD Filter

Event-Triggered Distributed Multisensor Multitarget Tracking...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Qi, Jialin Zheng, Zhang Dong, Jin Hong, Jiang Xiwang, Dong Yu, Jianglong Zhang, Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Beijing Academy of Blockchain and Edge Computing Beijing100086 China

ISBN: (纸本)9789811966125

This paper focuses on a distributed event-triggered Gaussian mixture probability hypothesis density (GM-PHD) filtering algorithm. In this algorithm, sensors transmit their Gaussian components to neighbors basing on the event-triggered conditions. Then Gaussian components are fused for certain times by matrix reformulation method. This algorithm can reduce data transmission and energy consumption with guaranteeing the estimation performance. Simulation experiment verifies the performance of this algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Matrix algebra

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Distributed Time-Varying Optimization for a Class of Second-Order Multi-agent Systems with Disturbance

Distributed Time-Varying Optimization for a Class of Seco...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Su, Piaoyi Zheng, Hongwei Yu, Jianglong Hua, Yongzhao Dong, Xiwang Ren, Zhang School of Automation Science and Electronic Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Beijing Academy of Blockchain and Edge Computing Beijing100086 China Institute of Artificial Intelligence Beihang University Beijing100191 China

ISBN: (纸本)9789811966125

This paper investigates the distributed convex time-varying optimization issue for multi-agent systems described by second-order with local disturbances produced by exosystems. Assuming each agent possessing a local cost function, the objective of this paper is to realize zero optimal tracking error considering the influences of extra disturbances and time-varying cost function simultaneously. Firstly, construst a local observer for each agent to obtain the accurate value of disturbance. Then, a distributed optimization algorithm is proposed to consensus optimal realization term. Thirdly, the system stability is confirmed by some Lyapunov-based analysis method. At last, in order to demonstrate the effectiveness of the proposed algorithm, a numerical simulation is constructed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cost functions

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An Obstacle Avoidance Path Planning Algorithm for Manipulator in Deep Crate Grasping

An Obstacle Avoidance Path Planning Algorithm for Manipulato...

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2023 China Automation Congress, CAC 2023

作者： Yao, Zhuobin Gan, Yahui Han, Zao Gao, Huan Li, Ang School of Automation Southeast University Key Laboratory of Measurement and Control of Systems of Engineering Ministry of Education NanJing China Intel Joint Research Institute on Intelligent Edge Computing Nanjing YingQi Intelligent Technology Co. Ltd. NanJing China

ISBN: (纸本)9798350303759

The deep crate grasping problem is a major challenge for manipulator in industrial applications. In order to solve the issue of deep crate grasping and ensure the path quality of the algorithm, this paper proposes a new hybrid obstacle avoidance planning algorithm based on goal-biased rapidly exploring random tree and improved artificial potential field (RRT-APF). Initially, the algorithm strategizes an efficient trajectory for the working element within the operational area. Subsequently, it dissects this trajectory through sampling, creating a sequence of sub-target points. These points serve as the basis for obstacle avoidance planning within the manipulator's configuration space, mitigating planning complexities. Moreover, the RRT algorithm is employed to address the challenge of local optima within the artificial potential field method. The stochastic nature of the RRT algorithm plays a crucial role in enabling the APF algorithm to break free from local optima. Ultimately, the efficacy of the algorithm is demonstrated through the planning of a path for obstacle avoidance on the DOBOT CR5 manipulator using MATLAB. © 2023 IEEE.

关键词： Trees (mathematics)

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Vision-Based PM2.5 Concentration Estimation with Natural Scene Statistical Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2805-2815页

作者： Wang, Guangcheng Shi, Quan Wang, Han Gu, Ke Wei, Mengting Wong, Lai Kuan Wang, Mingxing Nantong University School of Transportation and Civil Engineering Nantong226019 China Beijing University of Technology Faculty of Information Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Artificial Intelligence Institute Beijing100124 China Chinese Academy of Sciences Institute of Software Beijing100045 China Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia Environmental Science and Technology Company Ltd. Beijing102100 China

As the primary pollutant in China's urban atmosphere, particulate matter (PM)2.5 poses a great threat to the health of residents and ecological stability. An efficient and effective PM2.5 concentration monitoring is essential. Nonetheless, the popular devices for PM2.5 monitoring are developed based on two standards: the microoscillation balance method and the β-ray method, which have high purchase and maintenance costs and slow calculation rates. To this end, we put forward a real-time and reliable vision-based estimation algorithm of PM2.5 concentration. To be specific, the proposed method first develops two natural scene statistical analysis-based visual priors to measure saturation and structural information losses caused by the 'haze' formed by PM2.5. Moreover, we develop a lightweight deep belief network-deep neural network-based PM2.5 concentration estimation model, which learns the mapping from the designed visual priors to PM2.5 concentrations. Experiments confirm the superiority of our vision-based PM2.5 concentration estimation method by comparison with state-of-the-art photo-based PM2.5 monitoring methods. © 2020 IEEE.

关键词： Entropy

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Bio-inspired Swarm Intelligence: A Flocking Project with Group Object Recognition 3

Bio-inspired Swarm Intelligence: A Flocking Project with Gro...

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3rd International Conference on Consumer Electronics and Computer engineering, ICCECE 2023

作者： Tianbo, Song Weijun, Hu Jiangfeng, Cai Weijia, Liu Quan, Yuan Kun, He Ira A. Fulton Schools of Engineering Arizona State University Tempe United States Saint Petersburg State University Faculty of Applied Mathematics and Control Processes Saint Petersburg Russia Lomonosov Moscow State University Department of Chemistry Moscow Russia Allen School of Engineering and Computing Trine University Angola United States The Open University of China Faculty of Engineering Hangzhou China College of Computing Illinois Institute of Technology Chicago United States

ISBN: (纸本)9798350331578

With the help of the adaptive gain control algorithm and the flocking SWARM algorithm with adversarial agents, the project described in this article makes the entire system communication-free while maintaining the same functionality and performance. It outlines the entirety of this biologically inspired swarm intelligence's thought process, from inception to conclusion (Flocking algorithm). The entire project is dependable and practically impenetrable under a variety of conditions. The neighbor observation for group conjecture is then strengthened by the use of the adaptive gain control technique and a partial Kalman filter. After the system has been stabilized, use the built-in camera of the SWARM drone to introduce the Object Recognition method in computer vision. As a result, the system enters the real world from the simulation level. © 2023 IEEE.

关键词： Object recognition

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Exploiting Free LUT Entries to Mitigate Soft Errors in SRAM-based FPGAs

Exploiting Free LUT Entries to Mitigate Soft Errors in SRAM-...

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Asian Test Symposium (ATS)

作者： Keheng Huang Yu Hu Xiaowei Li Gengxin Hua Hongjin Liu Bo Liu State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Beijing Institute of Control Engineering China

As the feature size of FPGA shrinks to nanometers, SRAM-based FPGAs are more vulnerable to soft errors. During logic synthesis, reliability of the design can be improved by introducing logic masking effect. In this work, we observe that there are a lot of not-fully occupied look-up tables (LUTs) after logic synthesis. Hence, we propose a functional equivalent class based soft error mitigation scheme to exploit free LUT entries in the circuit. The proposed technique replaces not fully-occupied LUTs with corresponding functional equivalent classes, which can improve the reliability while preserve the functionality of the design. Experimental results show that, compared with the baseline ABC mapper, the proposed technique can reduce the soft error rate by 21%, and the critical-path delay increase is only 4.25%.

关键词： Table lookup Circuit faults Forward error correction Field programmable gate arrays Vectors Reliability engineering

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Impact of Parametric Uncertainties on the Steady-State Performances of DFIG-Based WECS 10th

Impact of Parametric Uncertainties on the Steady-State Perfo...

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10th EAI International Conference on Advancements of Science and Technology, EAI ICAST 2022

作者： Ayenew, Endalew Biru, Getachew Mulatu, Asrat Berhanu, Milkias Electrical Power and Control Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia School of Electrical and Computer Engineering Addis Ababa Institute of Technology Addis Ababa University Addis Ababa Ethiopia Center of Excellence for Sustainable Energy College of Electrical and Mechanical Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia

ISBN: (纸本)9783031411724

This chapter presents the study carried out to discover uncertain parameters in a model of a doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS) and to investigate their effects on WECS plant while harvesting energy from wind at varying speeds. This is carried out by analytically formulating the steady-state models of a DFIG-based WECS and computing the parameters of these models at different wind speeds that drive wind turbines at rotor speed from sub-synchronous to super-synchronous. The main uncertain parameters in this system are the mutual reactances between stator and rotor windings, and the reactance of rotor winding. These variations happen due to the varying wind speed. The effect of parametric uncertainties on the steady-state performances of DFIG-based WECS is examined. The results depicted that the steady-state performances of DFIG-based WECS are influenced by uncertain phenomena. Typically a 0.5% change in mutual reactances caused up to 10 kW change in the generator output power. Mainly, it causes instability in the performances of the DFIG-based WECS and slightly reduces overall efficiency at a higher value of the wind speed. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DFIG-based WECS HAWT Performances of WECS Uncertain parameter

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Terminal Sliding Modes-Based PLLs for Three-Phase Grid-Connected Inverters 49

Terminal Sliding Modes-Based PLLs for Three-Phase Grid-Conne...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Hosseinabadi, Pooyan Alinaghi Kermadi, Mostefa Pota, Hemanshu Mekhilef, Saad Abadi, Ali Soltani Sharif The School of Engineering and Information Technology The University of New South Wales CanberraACT Australia Kuala Lumpur Malaysia The School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia Institute of Automatic Control and Robotics Warsaw University of Technology Faculty of Mechatronics Warsaw Poland

ISBN: (纸本)9798350331820

This paper proposes and experimentally implements new phase-locked loop algorithms using non-adaptive and adaptive terminal sliding modes for the three-phase grid-connected inverters. The proposed algorithms aim to extract the grid voltage phase and frequency and provide them for the inverter controller. The proposed sliding mode-based PLL algorithms provide faster, more robust, and more accurate detection of the grid voltage information compared to the conventional PI-PLL. This leads to better current controller performance and subsequently, improves the quality of the injected currents into the grid. The finite-time stability notion is incorporated with robust sliding modes to ensure a fast convergence rate. An adaptive law is proposed to determine a proper value for the switching gain which leads to a better overall performance of the PLL compared with a non-adaptive one. The Lyapunov stability theory is used to obtain the stability proofs of the closed-loop systems under both designed sliding modes-based PLLs. The proposed PLL algorithms are validated by simulation and experimental results for the three-phase grid-connected inverter under normal and various abnormal grid conditions. © 2023 IEEE.

关键词： Phase locked loops

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