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An efficient and economical storage and energy sharing model for multiple multi-energy microgrids

ENERGY 2022年第PartB期244卷 123124-123124页

作者： Cao, Wenzhi Xiao, Jiang-Wen Cui, Shi-Chang Liu, Xiao-Kang 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

Multi-energy microgrids are facing a dilemma that realizing high local energy efficiency requires large-capacity ESS with hefty investment costs. To address the dilemma, an efficient and economic hybrid storage and energy sharing model for multiple microgrids is proposed. Specificly, a hybrid energy storage system (HESS) is introduced, which contains an electrical battery and a heat storage tank and is able to realize energy conversion. The multiple microgrids can share energy through the HESS in a collaborative way. An energy optimization problem is formulated to minimize the overall energy costs including energy purchase cost and HESS operating cost. ADMM algorithm is used to solve the problem in a distributed manner to avoid privacy concerns. The storage and energy sharing benefits of the microgrids and the HESS are determined by Nash bargaining solution. Simulation results show that the model can effectively improve the utilization of the renewable energy, and lead to considerable economic benefits for both the microgrids and the HESS.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Multi-energy microgrids Storage sharing Energy sharing Optimization Nash bargaining

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Optimal decisions of modularity, prices and return policy in a dual-channel supply chain under mass customization

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2022年第0期160卷 102675-102675页

作者： Wang, Jian He, Shulin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Online customization is very popular among consumers in current e-commerce because of its flexible customization time and diverse product attributes. This paper establishes a dual-channel supply chain composed of one manufacturer and one retailer under mass customization. The manufacturer sells standard products offline via the retailer when providing modular customized products online. Two online distribution strategies of the manufacturer are considered: The manufacturer can choose direct or agency sales through the retailer's online channel. Additionally, the return policy of modular customized products available online is explored. A Stackelberg game model is proposed under each return scenario (with and without online consumer returns) to derive the optimal solutions for different channel strategies. Then, the influence of different parameters on the optimal modularity level and product prices is investigated under each model. Through sensitivity analysis, we found that the cross-price sensitivity coefficient of dual channels has a considerably positive impact on optimal decisions. Furthermore, the manufacturer's channel strategy and return policy decisions are examined. The result indicates that direct sales should be chosen if manufacturers' online channel operating costs are lower than a certain threshold that increases with the revenue allocation ratio under agency sales. When the market demand of the product that is allowed to be returned is much more sensitive to the refund than its return quantity, a full refund policy should be offered for online customized products. These conclusions also apply to the return scenario where full refunds are provided for both standard and customized products.

关键词： Dual-channel Mass customization Online distribution strategies Return policy Modularity level

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Design and application of a structure-varying coupled chaotic system with high security

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期226卷

作者： Ming, Hao Hu, Hanping Zheng, Jun Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Wuhan 430074 Peoples R China Minist Educ Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China

Chaos-based security applications are facing great challenges with various data analysis and prediction techniques. Focusing on the security risks in chaotic applications, this paper proposes a structure-varying delay -coupled chaotic model (SVDCCM), which has attack immunity and is proven to be chaotic mathematically. To choose suitable coupling structures for variation, the effects of different structures on chaotic performance are investigated in detail. We recommend loop structures for better chaotic behaviors and the structures in which the same coupling terms exist should be avoided to produce synchronous behaviors. Moreover, the general changing principles including random and fast varying are proposed to be the guidance on designing structure -varying systems, and a simple state-driven changing mechanism is given as an example. Experiment results show that SVDCCM has complex chaotic behaviors, good statistical properties, and high unpredictability. Its security when facing various attack challenges is also demonstrated, such as time-frequency attacks, phase space reconstruction attacks, change-point detection, etc. To illustrate its feasibility in the practical security application, a pseudo-random number generator (PRNG) based on SVDCCM is designed and implemented on the hardware platform. Performance tests indicate that the proposed PRNG can produce binary sequences with high reliability of randomness and unpredictability, which can be used in cryptosystems and other potential applications.

关键词： Coupled chaos Structure-varying Security Attack-immunity Pseudo-random number generator (PRNG)

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Scheduling Parallel Real-Time Tasks on Virtual Processors

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第1期34卷 33-47页

作者： Jiang, Xu Liang, Haochun Guan, Nan Tang, Yue Qiao, Lei Wang, Yi Northeastern Univ Key Lab Intelligent Comp Med Image Minist Educ Shenyang 110819 Liaoning Peoples R China City Univ Hong Kong Hong Kong Peoples R China Beijing Inst Control Engn Beijing 100124 Peoples R China Uppsala Univ S-75236 Uppsala Sweden

In many popular parallel programming models, e.g., OpenMP (OpenMP, 2013), applications are usually dispatched into several dedicated scheduling entities (named "threads " in common) for which the processor time of physical platform is provided through the OS schedulers. This behavior requires for a hierarchical scheduling framework, considering each thread as a virtual processor (VP). Moreover, hierarchical scheduling allow separate applications to execute together on a common hardware platform, with each application having the "illusion " of executing on a dedicated component. However, the problem for scheduling parallel real-time tasks on virtual multiprocessor platform has not been addressed yet. An analogous approach to virtual scheduling for parallel real-time tasks is federeted scheudling, where each task exclusively executes on a set of dedicated physical processors. However, federated scheduling suffers significant resource wasting. In this article, we study the scheduling of real-time parallel task on virtual multiprocessors. As a physical processor is shared by virtual processors, tasks effectively share processors with each other. We conduct comprehensive performance evaluation to compare our proposed approach with existing methods of different types. Experiment results show that our approach consistently outperforms existing methods to a considerable extent under a wide range of parameter settings.

关键词： Real-time scheduling vitual processors parallel tasks multiprocessor

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Fast Fixed-Time Output Multi-Formation Tracking of Networked Autonomous Surface Vehicles: A Mathematical Induction Method

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第5期72卷 5769-5781页

作者： Ding, Teng-Fei Xu, Kun-Ting Ge, Ming-Feng Park, Ju H. Liang, Chang-Duo China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Yeungnam Univ Sch Dept Elect Engn Gyongsan 38541 South Korea

In this paper, we aim to exploit an effective way to solve the output multi-formation tracking problem of the networked autonomous surface vehicles (ASVs) in a fast fixed time manner. Specifically, addressing the output multi-formation tracking problem implies that 1) the networked ASVs are divided into multiple interconnected subnetworks with respect to multiple targets;2) for each subnetwork, the outputs of the networked ASVs form a desired geometric formation with exchanging the local interactions. Besides, solving the fast fixed-time tracking problem in this paper implies that 1) the convergence time is independent of the initial conditions;2) the system states are forced to reach the employed nonsingular fixed-time sliding surface in a prescribed time, which thus called fast fixed-time control. Then, based on a time-related function and a nonsingular fixed-time sliding surface, a hierarchical fast fixed-time control algorithm is proposed to solve the aforementioned problem within a fast fixed time being independent of the initial conditions. Furthermore, by employing the Lyapunov argument and mathematical induction, we present the sufficient conditions for fast fixed-time convergence of the tracking errors with respect to multiple targets. Finally, numerous simulation examples are presented to demonstrate the effectiveness of the proposed control algorithm.

关键词： Target tracking Convergence Formation control Asymptotic stability Germanium Task analysis Standards Output multi-formation tracking hierarchical fast fixed-time control networked autonomous surface vehicles (ASVs)

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Joint load prediction of multiple buildings using multi-task learning with selected-shared-private mechanism

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ENERGY AND BUILDINGS 2023年第1期293卷

作者： Xiao, Jiang-Wen Cao, Minghui Fang, Hongliang Wang, Jinsong 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 Case Western Reserve Univ Case Sch Engn Cleveland OH 44106 USA

Short-term load forecasting for residential buildings is of great significance in ensuring the safe and economic operation of the power grid. However, most existing prediction methods focus solely on the temporal characteristics of individual buildings and ignore the spatial correlation between different buildings in a neighborhood. To tackle this problem and realize joint prediction for multiple buildings, a novel multi-task learning model with a selected-shared-private mechanism is proposed in this paper. Firstly, the temporal and spatial characteristics hidden in electricity consumption patterns are analyzed and represented by auto -correlation and cross-correlation, respectively. Then, a correlation-oriented combination strategy is proposed to build input feature set for the prediction model, and the temporal convolutional network is adopted to extract features. Furthermore, a novel selected-shared-private mechanism is designed for multi-task learning to improve the prediction accuracy, which can selectively utilize information from related tasks while learning private features. The proposed model is compared with other methods on the public dataset, and the results demonstrate that the proposed model can make satisfactory joint predictions for multiple buildings with more than 35% accuracy improvement over the support vector machine.

关键词： Multiple residential buildings Short-term load prediction Multi-task learning Selected-shared-private mechanism

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Stationary Mapping Based Generalized Monitoring Scheme for Industrial Processes With Mixed Operational Stages

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1-13页

作者： Wang, Zhaojing Zheng, Ying Wong, David Shan-Hill Wang, Yang Yang, Weidong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Natl Tsing Hua Univ Dept Chem Engn Hsinchu 300 Taiwan

Frequent operation changes are inevitable to achieve different production aims, which leads to mixed periods of stationarity and nonstationarity in industrial processes and increases the monitoring difficulty. In this article, a generalized monitoring scheme is proposed for industrial processes with stationary and nonstationary operational stages. First, the local average similarity (LAS) and distance average similarity (DAS) are developed on offline training data to divide operational stages and identify repeating stages. The equilibrium relationship between variables in each stage can be guaranteed rather than in the whole process, which is the premise of refined modeling. Then, multiple cointegration analysis (CA) and detrended fluctuation regression (DFR) models are proposed to handle nonstationary variables with different integrated orders, so as to map all stages into stationary space. For online monitoring, the real-time stage identification is performed based on the comprehensive similar index (CSI) that combines LAS and DAS, and the process monitoring is realized by a local static principal component analysis (PCA). Finally, the effectiveness of our proposed method is verified by an extended Tennessee Eastman simulation and batch-fed penicillin fermentation process.

关键词： Microwave integrated circuits Correlation Process monitoring Principal component analysis Color Biological system modeling Fluctuations Operational stage identification process monitoring stationary and nonstationary operational stages

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Interior Attention-Aware Network for Infrared Small Target Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Wang, Kewei Du, Shuaiyuan Liu, Chengxin Cao, Zhiguo 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

Infrared small target detection plays an important role in target warning, ground monitoring, and flight guidance. Existing methods typically utilize local-contrast information of each pixel to detect infrared small targets, neglecting the interior relation between target pixels or background pixels. The mere use of the local information of one pixel, however, is not sufficient for accurate detection, which may lead to missing detection and false alarms. As a harmonious whole, information between pixels are necessary to determine if a pixel belongs to the target or the background. Motivated by the fact that pixels from targets or backgrounds are correlated with each other, we propose a coarse-to-fine interior attention-aware network (IAANet) for infrared small target detection. Specifically, a region proposal network (RPN) is first applied to obtain coarse target regions and filter out backgrounds. Then, we leverage a transformer encoder to model the attention between pixels in coarse target regions, outputting attention-aware features. Finally, predictions are obtained by feeding attention-aware features to a classification head. Extensive experiments show that our approach is capable of detecting targets precisely, of suppressing a variety of false alarm sources, and works effectively in various background environments and target appearances. We show that our IAANet outperforms the state-of-the-art methods by a large margin. Code will be made available at: https://***/kwwcv/iaanet.

关键词： Semantics Object detection Visualization Feature extraction Convolutional neural networks Proposals Clutter Attention-aware coarse-to-fine infrared small target detection

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Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification

Knowledge-Data Fusion Based Source-Free Semi-Supervised Doma...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ruimin Peng Jiayu An Dongrui Wu Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Electroencephalogram (EEG)-based seizure sub-type classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with no source data and limited labeled target data, can be used for privacy-preserving seizure subtype classification. This paper considers two challenges in SF-SSDA for EEG-based seizure subtype classification: 1) How to effectively fuse both raw EEG data and expert knowledge in classifier design? 2) How to align the source and target domain distributions for SF-SSDA? We propose a Knowledge-Data Fusion based SF-SSDA approach, KDF-MutuaISHOT, for EEG-based seizure subtype classification. In source model training, KDF uses Jensen-Shannon Diver-gence to facilitate mutual learning between a feature-driven Decision Tree-based model and a data-driven Transformer-based model. To adapt KDF to a new target dataset, an SF-SSDA algorithm, MutualSHOT, is developed, which features a consistency-based pseudo-label selection strategy. Experiments on the public TUSZ and CHSZ datasets demonstrated that KDF-MutualSHOT outperformed other supervised and source-free domain adaptation approaches in cross-subject seizure subtype classification.

关键词： Training Adaptation models Data privacy Fuses Brain modeling Transformers Data models Electroencephalography Decision trees Cybernetics

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Time-Weighted Kernel-Sparse-Representation-Based Real-Time Nonlinear Multimode Process Monitoring

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第4期18卷 2411-2421页

作者： Wang, Yang Zheng, Ying Wang, Zhaojing Yang, Weidong Huazhong Univ Sci & Technol Key Lab Educ Minist Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Real-time nonlinear multimode process monitoring of actual industrial systems has attracted increasing attention recently. In this article, the time-weighed kernel sparse representation (TWKSR) method is proposed to partition the mode of the training dataset by introducing the time-series-dependent characteristics into the kernel sparse representation algorithm. The alternating direction method of multipliers is utilized to solve the optimization problem of the proposed TWKSR method. Then, the representative samples from each identified mode are selected to update the dictionary matrix. Based on the updated dictionary matrix, the sparse coefficient is used for online mode identification, and the reconstruction error is utilized for fault detection. Finally, a numerical simulation case and the wastewater treatment process example verify the effectiveness of the proposed method.

关键词： Kernel Dictionaries Sparse matrices Process monitoring Fault detection Adaptation models Optimization Alternating direction method of multipliers (ADMM) fault detection nonlinear multimode processes sparse representation (SR)

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