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An efficient online energy distribution management system based on predictive control

JOURNAL OF ENERGY STORAGE 2022年 51卷

作者： Hong, Shengping Xu, Hao Qin, Hongchuan Cheng, Zhe Jiang, Jianhua Li, Jian Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Educ Minist Image Proc & Intelligent Control Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Shenzhen Res Inst Shenzhen Guangdong Peoples R China Huazhong Univ Sci & Technol Sch Mat Sci & Engn Wuhan Hubei Peoples R China

An efficient online energy-distribution management system (EMS) that ensures cells operate under optimized conditions, is critical for the high performance and long-term service of large energy storage or generation systems. In this work, a novel two-level structure EMS based on model predictive control (MPC) is proposed to achieve fast energy balance with high-efficiency. The topology of the EMS is that cells are divided into several modules and each module connected in parallel with a bi-directional DC-DC converter, where the outputs of DC-DC converters are serially connected to obtain a higher DC bus voltage. Then the issues of energy deviations among cells are resolved by a two-level structure EMS which consists of an energy-distributing intra-group balance layer and a cell package current-distributing inter-group balance layer. Based on the EMS topology, an MPC is employed in each module to improve the balance speed while maintaining the stability of the DC-DC converter output voltage. Moreover, the parameters of the controller are optimized via simulation study. Finally, the EMS performance is validated by a hardware-in-loop platform and compared with existing EMS. The experimental results show that the proposed EMS can realize quick and accurate energy balance with low energy loss.

关键词： Energy distribution management Time-distributing structure Battery module current-distributing structure Battery access timing algorithm Model predictive control

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Statistical Feasibility Robust Optimization for Hydrogen-Data Center Microgrid Operation

SSRN

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SSRN 2024年

作者： Duan, Juntao Li, Yuanzheng Li, Yang Liu, Zhiwei Zhao, Yong School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

The increasing processing demands of information technology (IT) workloads cause the booming of data centers (DCs) in recent years. The servers in the DC emit much heat when processing the IT workloads and thus the DC temperature rises. The power consumption when controlling the DC temperature using the air conditioning (A/C) of the DC is high, which leads to expensive power bills. To reduce the power cost, using renewable energy (RE) to power DC has raised much attention in recent years. However, the intermittency of RE and the uncertainties of outside temperature pose operational challenges. Therefore, in this paper, a data-driven statistical-feasibility-based robust rolling optimization framework for the operation of hydrogen-DC microgrid (H2-DCMG) is proposed to control the DC temperature considering these uncertainties and the intermittency of RE. In the framework, the hydrogen energy storage system (HESS) is introduced to deal with the intermittency of RE, and robust optimization (RO) is adopted to deal with the uncertainty of outside temperature. The statistical feasibility is introduced into the framework to decrease the conservatism of RO, through relaxing the DC temperature constraints without harming normal DC operations. Moreover, we propose the Statistical-Guarantee-based Vertex Link (SGVL) algorithm to construct a polyhedron uncertain set of outside temperatures that can ensure the results of dispatch are economical and satisfy statistical feasibility. The constructed polyhedron uncertain set is linear, which reduces computational complexity in solving. A case study based on the real-world temperature dataset verifies the superiority of the SGVL algorithm on the aspect of the economic cost. It further validates the effectiveness of the proposed robust optimization framework for DC temperature control. © 2024, The Authors. All rights reserved.

关键词： Air conditioning

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Few-Shot Domain Adaptation via Mixup Optimal Transport

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IEEE TRANSACTIONS ON image processing 2022年 31卷 2518-2528页

作者： Xu, Bingrong Zeng, Zhigang Lian, Cheng Ding, Zhengming Wuhan Univ Technol Sch Automat Wuhan 430070 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 Tulane Univ Dept Comp Sci New Orleans LA 70118 USA

Unsupervised domain adaptation aims to learn a classification model for the target domain without any labeled samples by transferring the knowledge from the source domain with sufficient labeled samples. The source and the target domains usually share the same label space but are with different data distributions. In this paper, we consider a more difficult but insufficient-explored problem named as few-shot domain adaptation, where a classifier should generalize well to the target domain given only a small number of examples in the source domain. In such a problem, we recast the link between the source and target samples by a mixup optimal transport model. The mixup mechanism is integrated into optimal transport to perform the few-shot adaptation by learning the cross-domain alignment matrix and domain-invariant classifier simultaneously to augment the source distribution and align the two probability distributions. Moreover, spectral shrinkage regularization is deployed to improve the transferability and discriminability of the mixup optimal transport model by utilizing all singular eigenvectors. Experiments conducted on several domain adaptation tasks demonstrate the effectiveness of our proposed model dealing with the few-shot domain adaptation problem compared with state-of-the-art methods.

关键词： Adaptation models Training Numerical models Couplings Feature extraction Deep learning Automation Few-shot learning domain adaptation optimal transport data augmentation

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Flocking with nonlinear systems based on distributed observers

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NEUROCOMPUTING 2025年 619卷

作者： Chi, Jun An, Qing Wang, Xiaoling Su, Housheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Luoyu Rd 1037 Wuhan 430074 Hubei Peoples R China Wuchang Univ Technol Articial Intelligence Sch 16 Jiangxia Ave Wuhan 430223 Hubei Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Wenyuan Rd 9 Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Wenyuan Rd 9 Nanjing 210023 Jiangsu Peoples R China

This paper investigates the output feedback-based flocking control problem for nonlinear dynamical systems (NDSs). To address this, distributed observers and consensus-based leader-following control algorithms are incorporated into each sensor in the network. By combining state estimation with flocking control algorithms, the observed state information of the NDS serves as the virtual leader for each sensor. To handle scenarios involving obstacles, we propose enclosing obstacles of various shapes within circles and developing a flocking control algorithm using artificial potential functions. Simulation results validate the effectiveness of the proposed algorithm.

关键词： Nonlinear observers Flocking Mobile sensor network Output feedback

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Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells

Constraining Depth Map Geometry for Multi-View Stereo: A Dua...

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International Conference on Computer Vision (ICCV)

作者： Xinyi Ye Weiyue Zhao Tianqi Liu Zihao Huang Zhiguo Cao Xin Li Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Albany Albany NY

Learning-based multi-view stereo (MVS) methods deal with predicting accurate depth maps to achieve an accurate and complete 3D representation. Despite the excellent performance, existing methods ignore the fact that a suitable depth geometry is also critical in MVS. In this paper, we demonstrate that different depth geometries have significant performance gaps, even using the same depth prediction error. Therefore, we introduce an ideal depth geometry composed of Saddle-Shaped Cells, whose predicted depth map oscillates upward and downward around the ground-truth surface, rather than maintaining a continuous and smooth depth plane. To achieve it, we develop a coarse-to-fine framework called Dual-MVSNet (DMVSNet), which can produce an oscillating depth plane. Technically, we predict two depth values for each pixel (Dual-Depth), and propose a novel loss function and a checkerboard-shaped selecting strategy to constrain the predicted depth geometry. Compared to existing methods, DMVSNet achieves a high rank on the DTU benchmark and obtains the top performance on challenging scenes of Tanks and Temples, demonstrating its strong performance and generalization ability. Our method also points to a new research direction for considering depth geometry in MVS.

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Resilient Delayed Impulsive control for Consensus of Multiagent Networks Subject to Malicious Agents

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 7196-7205页

作者： Zhai, Yang Liu, Zhi-Wei Guan, Zhi-Hong Gao, Zhiwei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Northumbria Univ Fac Engn & Environm Newcastle Upon Tyne NE1 8ST Tyne & Wear England

Impulsive control is widely applied to achieve the consensus of multiagent networks (MANs). It is noticed that malicious agents may have adverse effects on the global behaviors, which, however, are not taken into account in the literature. In this study, a novel delayed impulsive control strategy based on sampled data is proposed to achieve the resilient consensus of MANs subject to malicious agents. It is worth pointing out that the proposed control strategy does not require any information on the number of malicious agents, which is usually required in the existing works on resilient consensus. Under appropriate control gains and sampling period, a necessary and sufficient graphic condition is derived to achieve the resilient consensus of the considered MAN. Finally, the effectiveness of the resilient delayed impulsive control is well demonstrated via simulation studies.

关键词： Delays Consensus algorithm Topology Graphics Upper bound Safety Linear matrix inequalities Communication delays impulsive control multiagent network (MAN) resilient consensus trusted agent

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Multi-scale convolutional neural networks for brain disease diagnosis

Multi-scale convolutional neural networks for brain disease ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhongyi Hu Libin Gao Yinsheng Tong Xingjin Lu Rui Li Lei Xiao Zuoyong Li College of Computer Science and Aritificial Intelligence Wenzhou University Wenzhou China Key Laboratory of Intelligent Image Processing and Analysis Wenzhou China College of Computer and Control Engineering Minjiang University Fuzhou China

The brain is the most sophisticated and complex organ in the human body. Nowadays, diagnosing complex and diverse brain diseases is a hot topic. Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), and others are common brain diseases. With the increased usage of Artificial Intelligence (AI) in medical image analysis, the endeavor to make AI comprehend brain images for assisting doctors in making objective diagnoses of brain diseases has gained considerable attention. Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used tool for diagnosing and analyzing brain ***, the Pearson correlation coefficient (PCC) method constructs a dynamic functional connectivity (dFC) network using a fixed window size. However, this method has limitations as it is challenging to extract potential high-level features from the dFC and determine local feature relevance. This paper presents a method for constructing dFC based on multi-scale sliding windows and proposes Multi-scale Convolutional Neural Networks (MsCNN) for learning and analyzing dFC at various scales. Finally, a deep fusion of features learned at different scales is employed for the diagnostic classification of brain *** to the rs-fMRI dataset from ADNI, the classification accuracy was 84.0% for eMCI/lMCI, 84.4% for lMCI/AD, and 58.4% for NC/eMCI/lMCI/AD. Applied to rs-fMRI data from ABIDE, the accuracy was 74.7% for ASD/NC. The proposed method exhibits robust classification performance and introduces a new approach to diagnosing other brain diseases.

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Formation-containment control of multiple underactuated surface vessels with sampling communication via hierarchical sliding mode approach

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ISA TRANSACTIONS 2022年 124卷 458-467页

作者： Liu, Zhi-Wei Hou, Huazhou Wang, Yan-Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China RMIT Univ Sch Engn Melbourne Vic 3000 Australia

The formation-containment control problem of multiple underactuated surface vessels (USVs) is investigated in this paper. A hierarchical sliding mode control strategy is proposed to solve this problem under sampling communication. The proposed control comprises two layers: a local sliding model control layer and a distributed coordination layer. The local control layer is designed to drive each USV tracking the reference trajectories, and the distributed coordination layer is proposed to generate the reference trajectories satisfying the control objective of formation-containment control. To achieve the formation-containment control of the closed-loop multiple USVs, a sufficient condition is obtained by utilizing the Lyapunov stability and eigenvalue analysis. Finally, a simulation result is provided to show the effectiveness of the proposed hierarchical sliding mode approach. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Sliding mode control (SMC) Hierarchical control Formation-containment control Underactuated surface vessels (USV) Multi-agent systems (MASs)

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Adversarial Refinement Network for Human Motion Prediction 15th

Adversarial Refinement Network for Human Motion Prediction

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Chao, Xianjin Bin, Yanrui Chu, Wenqing Cao, Xuan Ge, Yanhao Wang, Chengjie Li, Jilin Huang, Feiyue Leung, Howard City University of Hong Kong Hong Kong China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China

ISBN: (纸本)9783030695316

Human motion prediction aims to predict future 3D skeletal sequences by giving a limited human motion as inputs. Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend, but motion details such as limb movement may be lost. To predict more accurate future human motion, we propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation. Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation. During training, we deliberately introduce the error distribution by learning through the adversarial mechanism among different subjects. In testing, our cascaded refinement network alleviates the prediction error from the coarse predictor resulting in a finer prediction robustly. This adversarial error augmentation provides rich error cases as input to our refinement network, leading to better generalization performance on the testing dataset. We conduct extensive experiments on three standard benchmark datasets and show that our proposed ARNet outperforms other state-of-the-art methods, especially on challenging aperiodic actions in both short-term and long-term predictions. © 2021, Springer Nature Switzerland AG.

关键词： Errors

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Observer-based consensus for fractional-order multi-agent systems with positive constraint

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NEUROCOMPUTING 2022年 501卷 489-498页

作者： Chen, Siyu An, Qing Zhou, Hongtao Su, Housheng Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore Wuchang Univ Technol Artificial Intelligence Sch Wuhan 430223 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Wuhan 430074 Peoples R China

This paper is concerned with consensus for fractional-order multi-agent systems by observer-type output feedback with positive constraint depicted by general linear model. First, an observer-based protocol is presented and the sufficient conditions for the consensus for fractional-order multi-agent systems with positive constraint are derived. Then, the simplified conditions without utilizing the detailed topology of the graph are given. In order to simplify the process of the solution of the feedback gain matrix and the observer gain matrix, the linear programming approach is used. When the system matrix is strictly Metzler, semidefinite programming approach is adopted. Finally, some illustration examples are presented as the verification of the obtained methods. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Fractional-order systems consensus multi-agent systems general linear model positive constraint observer

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