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A New Binary Encoding Method for Energy Consumption Patterns Quantification

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Fang, Hongliang Xiao, Jiang-Wen Wang, Yan-Wu Chung, C. Y. Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Hong Kong Polytech Univ PolyU Dept Elect Engn Hong Kong Peoples R China

Extracting users' energy consumption patterns (ECPs) from smart meter data is an important work for retailers. The existing literature usually describe these patterns by clustering the daily load curves (DLCs), but lack a clear and quantified representation to explain what the exact schema of a user is. Therefore, this article proposes a new binary encoding method for ECPs quantification. Specifically, first, both time and value intervals are divided for dimensionality reduction based on the similarity of adjacent timestamp loads. Then, a binary aggregate approximation (BAX) method is proposed to encode each DLC into a BAX word, and the BAX words of users are merged to obtain the schemas with a three-element alphabet. Finally, based on the schemas, the stability scores of users' patterns are quantified and are used to select target users for demand response (DR) measures. Case studies on a real dataset with 5566 users show that each target user averagely contributes to 0.172% of peak reduction, while each unselected user only contributes to 0.026%. Furthermore, to obtain target schemas and to find new users in future DR measures, a $K$ -means symbolic algorithm is designed to cluster BAX words of target users. The proposed encoding method and the findings can provide guidance of finding typical target users for DR measures.

关键词： Binary aggregate approximation (BAX) dimension reduction energy consumption pattern (ECP) K-means symbolic algorithm stability quantification

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Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures

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APPLIED ENERGY 2024年 355卷

作者： Xu, Maoshu Zhang, E. Wang, Sheng Shen, Yi Zou, Binchen Li, Haomiao Wan, Yiming Wang, Kangli Jiang, Kai Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 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 Minist Educ Wuhan Peoples R China

Accurate estimation of the State of Charge (SoC) plays a vital role in ensuring the efficient and safe operation of lithium iron phosphate (LFP) batteries. However, the flat open circuit voltage (OCV) curve of the LFP battery implies a low sensitivity to SoC, which results in large SoC estimation errors in the presence of noisy terminal voltage measurements. To address this challenge, an SoC estimation methodology utilizing an ultrasonic reflection response model is proposed, which is the first methodology regarding highly accurate and robust ultrasonic model-based SoC estimation under dynamic load profiles. Since ultrasound waves enable nondestructive acquisition of battery internal physical property changes directly associated with SoC, the ultrasonic battery near-surface reflection feature is extracted and demonstrated to exhibit a highly linear correlation with and higher sensitivity to SoC. We pioneeringly construct an empirical differential ultrasonic model to describe how the ultrasonic feature depends on the SoC and dynamic current. The advantage of such an ultrasonic model is demonstrated by theoretical and experimental results of an Adaptive Extend Kalman Filter (AEKF) and an Adaptive H-infinity Filter (AHIF) under various dynamic load profiles and temperatures. The Root Mean Square Error (RMSE) for ultrasonic model-based SoC estimation remains at approximately 1% across all tests, reducing by 36.7% compared to the voltage model, which shows its great potential in accurate SoC estimation.

关键词： Lithium -ion battery State of charge Battery ultrasonic response model Adaptive extend Kalman filter Adaptive H -infinity filter

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Exponential Stabilization of Semi-Markov Reaction-Diffusion Memristive NNs via Event-Based Spatially Pointwise-Piecewise Switching control

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第2期35卷 2655-2666页

作者： Man, Jingtao Zeng, Zhigang Xiao, Qiang Zhang, Hao 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 Huazhong Agr Univ Coll Informat Wuhan 430070 Peoples R China

This article considers both the semi-Markov jumping phenomenon and spatial distribution characteristics when investigating the exponential stabilization of memristive neural networks (MNNs). The introduction of the semi-Markov jumping parameters relaxes the restriction on the sojourn time of Markovian MNNs. To increase the operability while ensuring control effect, a novel event-based spatially pointwise-piecewise switching control scheme is presented under a unified spatial division criterion, in which the pointwise and piecewise control can switch according to the preset event condition for the applicability to different control requirements. Moreover, by constructing a semi-Markov Lyapunov functional and utilizing the properties of the considered cumulative distribution function, the final exponential stabilization criterion and two related corollaries are obtained. Finally, simulation results illustrate the effectiveness and superiority of the proposed control strategy.

关键词： control systems Switches Sensors Actuators Memristors Markov processes Synchronization Event-based switching law exponential stabilization pointwise-piecewise control reaction-diffusion memristive neural networks (MNNs) semi-Markov jumping parameters

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TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2022年 30卷 2567-2576页

作者： Peng, Ruimin Zhao, Changming Jiang, Jun Kuang, Guangtao Cui, Yuqi Xu, Yifan Du, Hao Shao, Jianbo Wu, Dongrui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430000 Peoples R China Childrens Hosp Wuhan 430016 Peoples R China

Electroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. However, manual seizure subtype classification is expensive and time-consuming, whereas automatic classification usually needs a large number of labeled samples for model training. This paper proposes an EEGNet-based slim deep neural network, which relieves the labeled data requirement in EEG-based seizure subtype classification. A temporal information enhancement module with sinusoidal encoding is used to augment the first convolution layer of EEGNet. A training strategy for automatic hyper-parameter selection is also proposed. Experiments on the public TUSZ dataset and our own CHSZ dataset with infants and children demonstrated that our proposed TIE-EEGNet outperformed several traditional and deep learning models in cross-subject seizure subtype classification. Additionally, it also achieved the best performance in a challenging transfer learning scenario. Both our code and the CHSZ dataset are publicized.

关键词： Convolution Kernel Electroencephalography Brain modeling Epilepsy Feature extraction Encoding EEG seizure subtype classification EEGNet deep learning positional encoding

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Complementary Learning Subnetworks Towards Parameter-Efficient Class-Incremental Learning

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第6期37卷 3240-3252页

作者： Li, Depeng Zeng, Zhigang Dai, Wei Suganthan, Ponnuthurai Nagaratnam Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist China Key Lab Image Proc & Intelligent Control Educ Wuhan 430074 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Qatar Univ Coll Engn KINDI Ctr Comp Res Doha 2713 Qatar

In the scenario of class-incremental learning (CIL), deep neural networks have to adapt their model parameters to non-stationary data distributions, e.g., the emergence of new classes over time. To mitigate the catastrophic forgetting phenomenon, typical CIL methods either cumulatively store exemplars of old classes for retraining model parameters from scratch or progressively expand model size as new classes arrive, which, however, compromises their practical value due to little attention paid to parameter efficiency. In this paper, we contribute a novel solution, effective control of the parameters of a well-trained model, by the synergy between two complementary learning subnetworks. Specifically, we integrate one plastic feature extractor and one analytical feed-forward classifier into a unified framework amenable to streaming data. In each CIL session, it achieves non-overwritten parameter updates in a cost-effective manner, neither revisiting old task data nor extending previously learned networks;Instead, it accommodates new tasks by attaching a tiny set of declarative parameters to its backbone, in which only one matrix per task or one vector per class is kept for knowledge retention. Experimental results on a variety of task sequences demonstrate that our method achieves competitive results against state-of-the-art CIL approaches, especially in accuracy gain, knowledge transfer, training efficiency, and task-order robustness. Furthermore, a graceful forgetting implementation on previously learned trivial tasks is empirically investigated to make its non-growing backbone (i.e., a model with limited network capacity) suffice to train on more incoming tasks.

关键词： Adaptation models Feature extraction Training Data mining Knowledge engineering Data models Accuracy Vectors Transformers Robustness Non-stationary data streaming data modeling class-incremental learning complementary learning system

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Prescribed Performance-Based Secondary control for DC Microgrid

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2022年第4期37卷 2610-2619页

作者： Yuan, Qi-Fan Wang, Yan-Wu Liu, Xiao-Kang Liu, Zhi-Wei Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence andAutomat Wuhan 430074 Peoples R China

In this article, based on the prescribed performance control approach, a distributed secondary control scheme is developed for an islanded DC microgrid, to achieve current sharing among the converters and voltage regulation of the DC bus while guaranteeing the prescribed transient performance of the system. With the proposed controller, the evolution of both the bus voltage error and the current sharing error can be constrained within a predefined imposed bound. In this way, the safety of the electrical devices can be ensured and the dynamic performance of the microgrid can be improved. In addition, the controller is quite simple for implementation without sampling and delivering the information of the DC bus voltage. Simulation studies and experiment tests are carried out to demonstrate the effectiveness of the proposed secondary control scheme.

关键词： DC microgird distributed secondary control prescribed performance current sharing voltage regulation

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Predictor-Based Global Sampled-Data Output Feedback Stabilization for Nonlinear Uncertain Systems Subject to Delayed Output

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IEEE TRANSACTIONS ON AUTOMATIC control 2023年第3期68卷 1839-1846页

作者： Sun, Jiankun Yang, Jun Li, Shihua Zeng, Zhigang 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 Leics England Southeast Univ Sch Automat Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Peoples R China

In this article, we address the problem of global sampled-data output feedback stabilization for a class of nonlinear uncertain systems with delayed output using the continuous-discrete method. Thanks to the prediction technique and feedback domination method, a novel coupled design method of predictor-based continuous-discrete observer and linear controller is proposed when only delayed sampled-data output is accessible. The proposed predictor-based observer can effectively estimate the unknown state by compensating the influences of sampling and output delay. The main advantage of the proposed control method is that the full state information and accurate model nonlinearities do not need to be known any more. The global exponential stability of the overall hybrid control system can be ensured when there hold some sufficient conditions with respect to the maximum allowable sampling period and output delay.

关键词： Observers control systems Nonlinear systems Delays Uncertain systems Uncertainty Output feedback Feedback domination global output feedback stabilization nonlinear uncertain systems output predictor

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Pose-Guided Hierarchical Semantic Decomposition and Composition for Human Parsing

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第3期53卷 1641-1652页

作者： Yang, Beibei Yu, Changqian Yu, Jin-Gang Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China Ctr Brain Comp Interface Pazhou Lab Guangzhou 510335 Peoples R China

Human parsing is a fine-grained semantic segmentation task, which needs to understand human semantic parts. Most existing methods model human parsing as a general semantic segmentation, which ignores the inherent relationship among hierarchical human parts. In this work, we propose a pose-guided hierarchical semantic decomposition and composition framework for human parsing. Specifically, our method includes a semantic maintained decomposition and composition (SMDC) module and a pose distillation (PC) module. SMDC progressively disassembles the human body to focus on the more concise regions of interest in the decomposition stage and then gradually assembles human parts under the guidance of pose information in the composition stage. Notably, SMDC maintains the atomic semantic labels during both stages to avoid the error propagation issue of the hierarchical structure. To further take advantage of the relationship of human parts, we introduce pose information as explicit guidance for the composition. However, the discrete structure prediction in pose estimation is against the requirement of the continuous region in human parsing. To this end, we design a PC module to broadcast the maximum responses of pose estimation to form the continuous structure in the way of knowledge distillation. The experimental results on the look-into-person (LIP) and PASCAL-Person-Part datasets demonstrate the superiority of our method compared with the state-of-the-art methods, that is, 55.21% mean Intersection of Union (mIoU) on LIP and 69.88% mIoU on PASCAL-Person-Part.

关键词： Semantics Task analysis image segmentation Pose estimation Legged locomotion Hierarchical systems Merging Human parsing pose distillation (PC) semantic maintained decomposition and composition (SMDC)

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Prescribed Performance control of Mobile Wheeled Inverted Pendulum Systems under Arbitrary Initial Conditions 8

Prescribed Performance Control of Mobile Wheeled Inverted Pe...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Zhang, Mengshi Cao, Yu Yang, Bo Huang, Jian School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute ShenZhen518060 China

ISBN: (纸本)9798350300178

Mobile wheeled inverted pendulum (MWIP) is a typical naturally unstable underactuated system and it is very necessary to constrain its transient response to improve the overall safety. Prescribed performance control (PPC) is an effective method, however the system initial conditions are usually required to be known and constraint, otherwise it can lead to singularity problems. In practical systems, it is often difficult to obtain the exact initial state leading to limitations in the application of PPC. To overcome this issue, this paper presented a composite error transformation function to address the problem of initial condition dependence. A continuous bounded function was designed to compress the arbitrary initial error into the predefined bound to avoid singularities. After a set time, it was degraded to an original system error. Based on this composite transformation, a state feedback controller was proposed for the underactuated MWIP system to achieve the constraint outputs. Then, by employing the Lyapunov theorem, it was guaranteed that the closed-loop system would remain stable. Various numerical simulations under different initial conditions were carried out to prove the validity of the proposed controller. © 2023 IEEE.

关键词： Closed loop systems

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image encryption algorithm based on Hilbert sorting vector and new spatiotemporal chaotic system

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OPTICS AND LASER TECHNOLOGY 2023年第1期167卷

作者： Zhang, Hangming Hu, Hanping Ding, Weiping Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

The design of the lattice coupling mode in current spatiotemporal chaotic systems lacks dynamic characteristics. When it is applied to the cryptosystem, its chaotic performance defects weaken the security of the cryptosystem. In this context, it is urgent to design new coupling rules and secure cryptosystems based on fractal and chaos theory. In this paper, a kind of sorting vector based on the fractal sorting matrix and fractal curve, the Hilbert sorting vector (HSV), is proposed creatively, and its iterative generation process is introduced. HSV is irregular and infinitely iterable. According to the requirements of the actual situation, HSV has a flexible adjustment vector length, which improves the multiplicity and efficiency of changing information positions. Then, HSV is used to reconstitute the the interaction of nodes during iteration in a new spatiotemporal chaotic system named Hilbert-sorting-vector coupled map lattice (HSVCML). Using this new sorting vector, the dynamic characteristics of spatiotemporal chaotic systems can be effectively improved. This is proved by comparing their Lyapunov exponent, Kolmogorov-Sinai entropy, bifurcation diagram, and information entropy with the coupled map lattice (CML). Moreover, the rich spatio-temporal behaviours of HSVCML are studied. Therefore, HSVCML is more appropriate for image encryption than CML. Finally, HSV is combined with a spatiotemporal chaotic system to frame an image encryption method. For the purpose of proving the effectiveness and security of this algorithm against different types of attacks, a large number of tests related to security analysis and time complexity analysis are carried out. Simulation results prove that our encryption algorithm is more secure and efficient than the previous algorithms and can resist various attacks.

关键词： Hilbert sorting vector Fractal sorting matrix Fractal curve Spatiotemporal chaotic system Coupled map lattice image encryption

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