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5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Han, Donghai Yu, Yaowen He, Dingxin Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665434256

Security-constrained economic dispatch (SCED) is one of the most important problems in power system operations. Corrective SCED (CSCED) is a type of SCED that considers corrective capabilities of the power system and adjusts base case decisions according to post-contingency states. Because of the large scale of the CSCED problem, it is difficult for purely model-based approaches to meet the time limits in real-time practical operations. To leverage historical operation data, this paper develops a novel hybrid model-based and data-driven framework to accelerate the solution process of CSCED. In the offline stage, our previous model-based contingency filtering approach is utilized to label active statuses of contingencies against historical net load samples. In the online stage, a multi-label classifier based on the K-Nearest Neighbor (KNN) algorithm quickly generates an active contingency set corresponding to the real-time net load. This active contingency set can be used to calculate the economic dispatch decisions in one shot. In addition, the accuracy of our framework can be further improved by adding an optional contingency filtering procedure at the end. Numerical testing results on the IEEE RTS-96 system demonstrate the accuracy and computational efficiency of the hybrid framework as compared to a purely model-based approach. © 2021 IEEE

关键词： Computational efficiency

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Modified Nested Saturated control for Uncertain Multiple Integrators With High-Order Nonlinear Perturbation

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第4期54卷 2086-2098页

作者： Li, Meng Zeng, Zhigang Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Educ Minist China 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 studies the global asymptotic stability problem of multiple integrators subject to uncertain parameters and high-order nonlinear perturbation. By introducing a modified saturation function with positive tunable parameters, three types of modified nested saturated controllers with different parameter assignments are proposed to handle the uncertain integrators with high-order nonlinear perturbation. The specific mechanism of the modified nested saturated controllers with different parameter assignments, in which control parameters appear before states, saturation functions, or saturation levels, is comprehensively analyzed. With the help of a modified saturation function, the global convergence domain of each state can be kept invariable in the saturation reduction analysis. Meanwhile, a large saturation level is obtained by adjusting positive tunable parameters. Combining with derivatives calculation and $M$ -matrix-based comparison theory, proposed saturated control schemes are utilized to handle the considered system. Finally, an active magnetic bearing (AMB) system is revisited as an uncertain mechanical system and used to verify the feasibility of the proposed control designs.

关键词： Global asymptotic stability high-order nonlinear perturbation nested saturated control saturation functions uncertain integrators

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Power Network Smart Meter Data Driven Cross-Task Transfer Learning for Resident Characteristics Estimation

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN INDUSTRIAL E...

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IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN INDUSTRIAL ELECTRONICS 2024年第2期5卷 652-661页

作者： Zhang, Hai-Tao Wang, Zhiyue Liu, Xingjian Zhou, Wei Ding, Yizhou Li, Yuanzheng Hu, Jiabing Huazhong Univ Sci & Technol Engn Res Ctr Autonomous Intelligent Unmanned Syst Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Minist Educ China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Elect & Elect Engn Wuhan 430074 Peoples R China

To extract the household attribute information from the large volume of smart meter data, this study proposes a resident characteristics estimator. Such an estimator enables energy suppliers to provide personalized services whereas to assist customers to reduce energy consumption. By leveraging the potential connections among different characteristics, a deep convolutional neural network-based cross-task transfer learning scheme is designed, which makes full use of the knowledge learned from one characteristic (such as retirement status)-based classification to estimate another relevant characteristics (such as age). Extensive experiments are conducted on the Irish dataset with 4232 households to substantiate the superiority of the proposed scheme compared with conventional deep convolutional neural networks-based learning methods.

关键词： Feature extraction Smart meters Task analysis Estimation Transfer learning Data models Convolutional neural networks control engineering learning smart grids system identification

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Lightweight LiDAR-Camera Alignment With Homogeneous Local-Global Aware Representation

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2024年第11期25卷 15922-15933页

作者： Zhu, Angfan Xiao, Yang Liu, Chengxin Tan, Mingkui Cao, Zhiguo China Acad Engn Phys Inst Comp Applicat Beijing 100873 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China

In this paper, a novel LiDAR-Camera Alignment (LCA) method using homogeneous local-global spatial aware representation is proposed. Compared with the state-of-the-art methods (e.g., LCCNet), our proposition holds 2 main superiorities. First, homogeneous multi-modality representation learned with a uniform CNN model is applied along the iterative prediction stages, instead of the state-of-the-art heterogeneous counterparts extracted from the separated modality-wise CNN models within each stage. In this way, the model size can be significantly decreased (e.g., 12.39M (ours) vs. 333.75M (LCCNet)). Meanwhile, within our proposition the interaction between LiDAR and camera data is built during feature learning to better exploit the descriptive clues, which has not been well concerned by the existing approaches. Secondly, we propose to equip the learned LCA representation with local-global spatial aware capacity via encoding CNN's local convolutional features with Transformer's non-local self-attention manner. Accordingly, the local fine details and global spatial context can be jointly captured by the encoded local features. And, they will be jointly used for LCA. On the other hand, the existing methods generally choose to reveal the global spatial property via intuitively concatenating the local features. Additionally at the initial LCA stage, LiDAR is roughly aligned with camera by our pre-alignment method, according to the point distribution characteristics of its 2D projection version with the initial extrinsic parameters. Although its structure is simple, it can essentially alleviate LCA's difficulty for the consequent stages. To better optimize LCA, a novel loss function that builds the correlation between translation and rotation loss items is also proposed. The experiments on KITTI data verifies the superiority of our proposition both on effectiveness and efficiency. The source code will be released at https://***/Zaf233/Light-weight-LCA upon acceptance.

关键词： LiDAR-camera alignment deep learning homogeneous multi-modality representation local-global spatial awareness transformer

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Bridging Feature Gaps to Improve Multi-Organ Segmentation on Abdominal Magnetic Resonance image

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第3期27卷 1477-1487页

作者： Kang, Susu Yang, Muyuan Sharon Qi, X. Jiang, Jun Tan, Shan Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Minist Educ China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Innovat Inst Wuhan 430074 Peoples R China Univ Calif Los Angeles Sch Med Dept Radiat Oncol Los Angeles CA 90001 USA

Accurate segmentation of abdominal organs on MRI is crucial for computer-aided surgery and computer-aided diagnosis. Most state-of-the-art methods for MRI segmentation employ an encoder-decoder structure, with skip connections concatenating shallow features from the encoder and deep features from the decoder. In this work, we noticed that simply concatenating shallow and deep features was insufficient for segmentation due to the feature gap between shallow features and deep features. To mitigate this problem, we quantified the feature gap from spatial and semantic aspects and proposed a spatial loss and a semantic loss to bridge the feature gap. The spatial loss enhanced spatial details in deep features, and the semantic loss introduced semantic information into shallow features. The proposed method successfully aggregated the complementary information between shallow and deep features by formulating and bridging the feature gap. Experiments on two abdominal MRI datasets demonstrated the effectiveness of the proposed method, which improved the segmentation performance over a baseline with nearly zero additional parameters. Particularly, the proposed method has advantages for segmenting organs with blurred boundaries or in a small scale, achieving superior performance than state-of-the-art methods.

关键词： Semantics image segmentation Decoding Bioinformatics Logic gates Fuses Feature extraction MRI segmentation feature aggregation convolutional neural networks deep learning

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Low-Rank Optimal Transport for Robust Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1667-1680页

作者： Bingrong Xu Jianhua Yin Cheng Lian Yixin Su Zhigang Zeng IEEE the School of Automation Wuhan University of TechnologyWuhan 430070China Intelligent Transportation Systems Research Center Wuhan University of TechnologyWuhan 430063 Chongqing Research Institute Wuhan University of TechnologyChongqingChina the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074China

When encountering the distribution shift between the source(training) and target(test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are welllabeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation:distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.

关键词： Domain adaptation low-rank constraint noise corruption optimal transport

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Selection of Cancer Biomarkers from Microarray Gene Expression Data Utilizing the Bi-Objective Optimization Method

Selection of Cancer Biomarkers from Microarray Gene Expressi...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Hanjing Lin, Jianqing Zhu, Huachao Huang, Yabing Huazhong University of Science and Technology Key Lab. of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Wuhan430074 China Wuhan University School of Computer Science Wuhan430064 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350337488

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy. © 2023 IEEE.

关键词： Bi-objective optimization Cancer biomarker Gene selection NSGA-II

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Practical Finite-Time Synchronization of Fractional-Order Complex Dynamical Networks With Application to Lorenz's Circuit

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年

作者： Wei, Chen Wang, Xiaoping Lai, Jingang Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain Inspired Intelligent Syst Wuhan 430074 Peoples R China

This paper focuses on addressing the practical finite-time synchronization (PFTS) problem of heterogeneous fractional-order complex dynamical networks (FCDNs) through event-triggered feedback control (ETFC). Firstly, a novel practical finite-time stability lemma is proposed based on the fractional-order differential inequality (c)(t0DtV)-V-alpha(t)<= -p(1)V(t)-p(2)V(beta)(t)+q, which plays a crucial role in analyzing PFTS. Secondly, a novel ETFC protocol is designed where the information transmission of the controller occurs at a sequence of state-dependent instants. Thirdly, using the aforementioned lemma and fractional Lyapunov theory, synchronization criteria for heterogeneous FCDNs can be derived, and Zeno behavior is excluded. Finally, the numerical example involving the PFTS of a fractional-order Lorenz's circuit is provided to demonstrate the effectiveness of the proposed theoretical results.

关键词： Synchronization Feedback control Circuits Calculus Vectors Accuracy Protocols Nonlinear systems System dynamics Symbols Fractional complex dynamical networks practical finite-time synchronization event-triggered feedback control

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Cluster Space control Method of Manned-Unmanned Aerial Team for Target Search Task

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第7期70卷 2545-2549页

作者： Huo, Mengzhen Duan, Haibin Zeng, Zhigang Beihang Univ Sch Automat Sci & Elect Engn State Key Lab Virtual Real Technol & Syst Beijing 100083 Peoples R China Peng Cheng Lab Dept Math & Theories Virtual Real Fundamental Res Lab Shenzhen 518000 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

This brief proposed a cluster space control method to help the operator of manned aerial vehicles (MAVs) to coordinate with unmanned aerial vehicles (UAVs) more efficiently. The operator of the MAV could change the states of the whole heterogeneous team only by issuing a single command. To verify its application performance, the method is employed to conduct search tasks under a complex environment with static and dynamic unknown targets. The receding horizon control (RHC) and backstepping control methods are utilized to complete the search task. Simulation results show the feasibility and efficiency of the proposed method and give a strong foundation for man-machine coordination in related fields.

关键词： Heterogeneous manned-unmanned aerial team cluster space control receding horizon control target search task

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Task Allocation and Path Planning for Multiple Destinations Oriented Towards Task Updates

Task Allocation and Path Planning for Multiple Destinations ...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Yong Yang Dingxin He Huantao Mao Hong Chen Hongtao Wu Zhi-Wei Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

This study addresses the complexities of orchestrating multi-target transportation tasks within multi-agent systems, constrained by load capacity. The primary objective is to engineer an advanced path planning framework that assures collision avoidance and optimal load distribution. Diverging from conventional single-path strategies, this research incorporates load factors and requires continuous dynamic multi-target point and path planning to ensure agents can efficiently navigate through a series of predetermined key points. To address this complex requirement, a two-stage multi-agent task allocation and path planning method is proposed. First, an initial solution is obtained using a suboptimal algorithm, followed by optimization iterations using the large neighborhood search algorithm to improve task allocation. In the second stage, an accelerated algorithm based on priority search is used to plan optimal paths for each agent in a predetermined order. The proposed algorithm's effectiveness is comprehensively evaluated through a series of experimental evaluations and comparisons with the commercial solver Gurobi within a limited time. The results show that the proposed method achieves optimality in both running time and minimum path cost while ensuring the load balance of agents.

关键词： Costs Navigation Heuristic algorithms Transportation Path planning Robustness Planning Resource management Optimization Multi-agent systems

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