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A hybrid deep learning-based online energy management scheme for industrial microgrid

APPLIED ENERGY 2021年 304卷

作者： Lu, Renzhi Bai, Ruichang Ding, Yuemin Wei, Min Jiang, Junhui Sun, Mingyang Xiao, Feng Zhang, Hai-Tao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Minist Educ Key Lab Ind Internet Things & Networked Control Chongqing 400065 Peoples R China Norwegian Univ Sci & Technol Dept Energy & Proc Engn N-7034 Trondheim Norway Chongqing Univ Posts & Telecommun Coll Automat Chongqing 400065 Peoples R China Hanyang Univ Dept Elect Syst Engn Ansan 15588 South Korea Zhejiang Univ Dept Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China North China Elect Power Univ Sch Control & Comp Engn State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

The fluctuations in electricity prices and intermittency of renewable energy systems necessitate the adoption of online energy management schemes in industrial microgrids. However, it is challenging to design effective and optimal online rolling horizon energy management strategies that can deliver assured optimality, subject to the uncertainties of volatile electricity prices and stochastic renewable resources. This paper presents an adaptable online energy management scheme for industrial microgrids that minimizes electricity costs while meeting production requirements by repeatedly solving an optimization problem over a moving control window, taking advantage of forecasted future prices and renewable energy profiles implemented by a hybrid deep learning model. The predicted values over the control horizon are assumed to be uncertain, and a multivariate Gaussian distribution is used to handle the variations in electricity prices and renewable resources around their predicted nominal values. Simulation results under different scenarios using real-world data verify the effectiveness of the proposed online energy management scheme, assessed by the corresponding gaps with respect to several selected benchmark strategies and the ideal boundaries of the best and worst known solutions. Furthermore, the robustness of the scheme is verified by considering severe errors in forecasted electricity prices and renewable profiles.

关键词： Online energy management Demand response Industrial microgrid Deep learning Convolutional neural network Long short-term memory

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MP-MAT: A 3D-AND-INSTANCE-AWARE HUMAN MATTING AND EDITING FRAMEWORK WITH MULTIPLANE REPRESENTATION

arXiv

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arXiv 2025年

作者： Jiao, Siyi Zeng, Wenzheng Li, Yerong Zhang, Huayu Gao, Changxin Sang, Nong Shou, Mike Zheng Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Show Lab National University of Singapore Singapore

Human instance matting aims to estimate an alpha matte for each human instance in an image, which is challenging as it easily fails in complex cases requiring disentangling mingled pixels belonging to multiple instances along hairy and thin boundary structures. In this work, we address this by introducing MP-Mat, a novel 3D-and-instance-aware matting framework with multiplane representation, where the multiplane concept is designed from two different perspectives: scene geometry level and instance level. Specifically, we first build feature-level multiplane representations to split the scene into multiple planes based on depth differences. This approach makes the scene representation 3D-aware, and can serve as an effective clue for splitting instances in different 3D positions, thereby improving interpretability and boundary handling ability especially in occlusion areas. Then, we introduce another multiplane representation that splits the scene in an instance-level perspective, and represents each instance with both matte and color. We also treat background as a special instance, which is often overlooked by existing methods. Such an instance-level representation facilitates both foreground and background content awareness, and is useful for other down-stream tasks like image editing. Once built, the representation can be reused to realize controllable instance-level image editing with high efficiency. Extensive experiments validate the clear advantage of MP-Mat in matting task. We also demonstrate its superiority in image editing tasks, an area under-explored by existing matting-focused methods, where our approach under zero-shot inference even outperforms trained specialized image editing techniques by large margins. Code is open-sourced at https://***/JiaoSiyi/***. Copyright © 2025, The Authors. All rights reserved.

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Event-Triggered Safety-Critical Model Predictive control for Underactuated Overhead Cranes

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年

作者： Wang, Jiangtong Tian, Zheng Sun, Jiankun Yang, Jun Li, Shihua Univ Notre Dame Dept Elect Engn Notre Dame IN USA Southeast Univ Sch Automat Nanjing 210096 Peoples R China Minist Educ Key Lab Measurement & Control CSE Nanjing 210096 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU England

In overhead cranes, the inclination angle of the payload must be limited within an acceptable range to ensure safety, and the trolley reaches a desired position simultaneously. However, the payload can be disturbed by strong winds, which poses a certain threat to the safety of overhead crane operation. In addition, when multiple overhead cranes communicate with each other via a shared network, the communication and computational resources available to each overhead crane become constrained. Taking into account the above factors, a novel event-triggered safety-critical model predictive control (ESMPC) algorithm is proposed for underactuated overhead crane systems to achieve satisfactory performance. In the proposed ESMPC algorithm, disturbances acting on the payload are estimated by a discrete-time disturbance observer. Then, the discrete-time predictive control barrier function is devised to guarantee operational safety. Subsequently, the prediction model is derived and the quadratic programming (QP) problem is formulated. The optimal control sequence can be obtained by solving the QP problem at each event-triggering instant. After that, the predicted control inputs are fully exploited and applied to the trolley one by one chronologically. Finally, the experimental results show that the payload swing angle can be limited within the safe range and the trolley can reach the desired position by using fewer communication and computing resources under the proposed ESMPC method.

关键词： Cranes Payloads Safety Event detection control systems Predictive control Optimal control Prediction algorithms Electronic mail Force control barrier function (CBF) disturbance observer event-triggered safety-critical control overhead crane systems

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Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement

arXiv

引用

arXiv 2023年

作者： Lei, Shanglin Wang, Xiaoping Dong, Guanting Li, Jiang Liu, Yingjian School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China Wuhan430074 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China

Emotion Recognition in Conversation (ERC) has attracted widespread attention in the natural language processing field due to its enormous potential for practical applications. Existing ERC methods face challenges in achieving generalization to diverse scenarios due to insufficient modeling of context, ambiguous capture of dialogue relationships and overfitting in speaker modeling. In this work, we present a Hybrid Continuous Attributive Network (HCAN) to address these issues in the perspective of emotional continuation and emotional attribution. Specifically, HCAN adopts a hybrid recurrent and attention-based module to model global emotion continuity. Then a novel Emotional Attribution Encoding (EAE) is proposed to model intra- and inter-emotional attribution for each utterance. Moreover, aiming to enhance the robustness of the model in speaker modeling and improve its performance in different scenarios, A comprehensive loss function emotional cognitive loss LEC is proposed to alleviate emotional drift and overcome the overfitting of the model to speaker modeling. Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work. Another extensive comparative experiments and ablation studies on three benchmarks are conducted to provided evidence to support the efficacy of each module. Further exploration of generalization ability experiments shows the plug- and-play nature of the EAE module in our method. © 2023, CC BY-SA.

关键词： Emotion Recognition

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A Two-Phase Learning-Based Swarm Optimizer for Large-Scale Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第12期51卷 6284-6293页

作者： Lan, Rushi Zhu, Yu Lu, Huimin Liu, Zhenbing Luo, Xiaonan Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu Fukuoka 8048550 Japan Guilin Univ Elect Technol Natl Local Joint Engn Res Ctr Satellite Nav & Loc Guilin 541004 Peoples R China

In this article, a simple yet effective method, called a two-phase learning-based swarm optimizer (TPLSO), is proposed for large-scale optimization. Inspired by the cooperative learning behavior in human society, mass learning and elite learning are involved in TPLSO. In the mass learning phase, TPLSO randomly selects three particles to form a study group and then adopts a competitive mechanism to update the members of the study group. Then, we sort all of the particles in the swarm and pick out the elite particles that have better fitness values. In the elite learning phase, the elite particles learn from each other to further search for more promising areas. The theoretical analysis of TPLSO exploration and exploitation abilities is performed and compared with several popular particle swarm optimizers. Comparative experiments on two widely used large-scale benchmark datasets demonstrate that the proposed TPLSO achieves better performance on diverse large-scale problems than several state-of-the-art algorithms.

关键词： Optimization Convergence Particle swarm optimization Cultural differences Cybernetics Next generation networking Random variables Competitive swarm optimizer (CSO) cooperative coevolution large-scale optimization swarm intelligence

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Robust Tightly Coupled Pose Measurement Based on Multi-Sensor Fusion in Mobile Robot System

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SENSORS 2021年第16期21卷 5522页

作者： Peng, Gang Lu, Zezao Peng, Jiaxi He, Dingxin Li, Xinde Hu, Bin Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430070 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430070 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China Shantui Construct Machinery Co Ltd Jining 272000 Peoples R China

Currently, simultaneous localization and mapping (SLAM) is one of the main research topics in the robotics field. Visual-inertia SLAM, which consists of a camera and an inertial measurement unit (IMU), can significantly improve robustness and enable scale weak-visibility, whereas monocular visual SLAM is scale-invisible. For ground mobile robots, the introduction of a wheel speed sensor can solve the scale weak-visibility problem and improve robustness under abnormal conditions. In this paper, a multi-sensor fusion SLAM algorithm using monocular vision, inertia, and wheel speed measurements is proposed. The sensor measurements are combined in a tightly coupled manner, and a nonlinear optimization method is used to maximize the posterior probability to solve the optimal state estimation. Loop detection and back-end optimization are added to help reduce or even eliminate the cumulative error of the estimated poses, thus ensuring global consistency of the trajectory and map. The outstanding contribution of this paper is that the wheel odometer pre-integration algorithm, which combines the chassis speed and IMU angular speed, can avoid the repeated integration caused by linearization point changes during iterative optimization;state initialization based on the wheel odometer and IMU enables a quick and reliable calculation of the initial state values required by the state estimator in both stationary and moving states. Comparative experiments were conducted in room-scale scenes, building scale scenes, and visual loss scenarios. The results showed that the proposed algorithm is highly accurate-2.2 m of cumulative error after moving 812 m (0.28%, loopback optimization disabled)-robust, and has an effective localization capability even in the event of sensor loss, including visual loss. The accuracy and robustness of the proposed method are superior to those of monocular visual inertia SLAM and traditional wheel odometers.

关键词： multi-sensor fusion robot pose measurement simultaneous localization and mapping visual inertia system

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Robust Stability of Recurrent Neural Networks With Time-Varying Delays and Input Perturbation

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第6期51卷 3027-3038页

作者： Zhang, Fanghai Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This paper addresses the robust stability of recurrent neural networks (RNNs) with time-varying delays and input perturbation, where the time-varying delays include discrete and distributed delays. By employing the new psi-type integral inequality, several sufficient conditions are derived for the robust stability of RNNs with discrete and distributed delays. Meanwhile, the robust boundedness of neural networks is explored by the bounded input perturbation and L-1-norm constraint. Moreover, RNNs have a strong anti-jamming ability to input perturbation, and the robustness of RNNs is suitable for associative memory. Specifically, when input perturbation belongs to the specified and well-characterized space, the results cover both monostability and multistability as special cases. It is revealed that there is a relationship between the stability of neural networks and input perturbation. Compared with the existing results, these conditions proposed in this paper improve and extend the existing stability in some literature. Finally, the numerical examples are given to substantiate the effectiveness of the theoretical results.

关键词： Input perturbation recurrent neural networks (RNNs) robust stability time-varying delays

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Multiple Mittag-Leffler Stability of Delayed Fractional-Order Cohen-Grossberg Neural Networks via Mixed Monotone Operator Pair

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第12期51卷 6333-6344页

This article mainly investigates the multiple Mittag-Leffler stability of delayed fractional-order Cohen-Grossberg neural networks with time-varying delays. By using mixed monotone operator pair, the conditions of the coexistence of multiple equilibrium points are obtained for fractional-order Cohen-Grossberg neural networks, and these conditions are eventually transformed into algebraic inequalities based on the vertex of the divided region. In particular, when the symbols of these inequalities are determined by the dominant term, several verifiable corollaries are given. And then, the sufficient conditions of the Mittag-Leffler stability are derived for fractional-order Cohen-Grossberg neural networks with time-varying delays. In addition, two numerical examples are provided to illustrate the effectiveness of the theoretical results.

关键词： Neural networks Stability criteria Delays Asymptotic stability Numerical stability MONOS devices Fractional-order Cohen-Grossberg neural networks Mittag-Leffler stability mixed monotone operator pair time-varying delays

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Asymptotic Stability and Synchronization of Fractional-Order Neural Networks With Unbounded Time-Varying Delays

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第9期51卷 5547-5556页

作者： Zhang, Fanghai Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

This article deals with the asymptotic stability and synchronization of fractional-order neural networks (FONNs) with unbounded time-varying delays, in which FONNs can be the general nonlinear system. First, the existence of the equilibrium point of the nonlinear system is obtained, and the mu-type stability of fractional-order nonlinear systems is proved by the analytical method. Second, several sufficient conditions are derived to guarantee the mu-type stability of FONNs by taking easily implemented parameters. Moreover, the mu-type synchronization of drive-response systems is established by designing the linear controller. Especially, mu-type stability is the general stability, including Mittag-Leffler stability and power-stability, which is the extension of the Mittag-Leffler stability of FONNs. Finally, two numerical examples are presented to show the effectiveness of the results.

关键词： Asymptotic stability fractional-order neural networks (FONNs) synchronization unbounded time-varying delays

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Positivity and stability of coupled differential-difference equations with time-varying delay on time scales

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AUTOMATICA 2021年 131卷

作者： Xiao, Qiang Zeng, Zhigang Huang, Tingwen Lewis, Frank L. Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Texas A&M Univ Qatar Doha Qatar Univ Texas Arlington UTA Res Inst Ft Worth TX USA

This paper investigates positivity and asymptotic stability for a class of timescale-type differential-difference equation with time-varying delay. When the delay is bounded, a necessary and sufficient condition is obtained based on timescale theory and comparison principle. While for the case of unbounded delay, a sufficient criterion is acquired. Further, for the corresponding continuous-time system with unbounded delay, a necessary and sufficient condition is secured under an extra constraint. This work relaxes a previous assumption on the unbounded time-varying delay and also generalizes and deepens some existing results. (C) 2021 Published by Elsevier Ltd.

关键词： Differential-difference equation Positivity Stability Time scale Bounded and unbounded delay

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