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Memristor-based BAM circuit implementation for image associative memory and filling-in

NEURAL COMPUTING & APPLICATIONS 2021年第13期33卷 7929-7942页

作者： Yang, Zijia Wang, Xiaoping Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Associative memory and filling-in are two essential functions of the human brain. To implement these two brain-inspired functions in hardware, we proposed a memristor-based bidirectional associative memory (BAM) circuit in this paper. This circuit combines an online algorithm with a memristor array adjustment process, thus makes the circuit more universal for various tasks. The proposed circuit is constructed out of memristive synaptic circuits, IN modules and ACT modules. The memristive synaptic circuits utilize memristor arrays to represent weight matrix and operate corresponding operations hence make computing-in-memory and process information in parallel, which simplifies the complexity of circuit and improves the processing speed. The IN modules employ transistors as switches to choose the input layer hence can get initial information flow bidirectionally. The ACT modules perform activation function and can output continuous arbitrary real numbers. Thereby, both binary and gray-scale images can be tested in the proposed circuit. In addition to the hetero-association and filling-in results given in detail, the retrieval rates of the proposed circuit with the impact of different degrees of noise and the number of stored patterns are also evaluated and compared with software-based BAM. The simulation has experimented via MATlab and PSpice, and the corresponding results show a remarkable performance of the proposed circuit. The influence of memristor's stuck-at-fault is also considered. In comparison with software-based BAM and similar memristor-based neural network circuit, the proposed circuit performs better in processing speed.

关键词： Memristors Bidirectional associative memory Associative memory Filling-in

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A Sensitive Nanothermometer Based on DNA Triplex Structure 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Chen, Zhekun Hu, Yingxin Xie, Chun Chen, Kuiting Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang050043 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

DNA has been used to construct a variety of nanoscale thermometers due to its unique thermodynamic properties. It has proven to be one of the most promising, yet challenging aspects of DNA-based thermometers, to obtain significant signal changes when the temperature changes slightly. Here, we propose a strategy to construct nanoscale thermometers with sensitive temperature responses based on DNA triplex. The thermometers consist of a CT-rich DNA strand and stabilizing strands, whose conformations vary with temperature. By adjusting the sequence design or introducing mismatch bases in stabilizing strands, the temperature response interval of the thermometer can be reduced to 7 ∘C, which lead to higher sensitivity compared to the interval of 12–15 ∘C for conventional designs. The temperature responses of the thermometer are characterized by fluorescence experiments. The fluorescence kinetics experiments demonstrate the good repeatability. The design of the nanothermometer will be helpful for constructing advanced nanosystem with sensitive temperature responses as well as long service times. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Thermometers

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Tissue P Systems with Look-Ahead Mode

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Chinese Journal of Electronics 2025年第1期23卷 81-86页

作者： Yun Jiang Tao Song Zheng Zhang School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Department of Control Science and Engineering Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China

Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system with look-ahead mode, is discussed for decreasing the inherent non-determinism of tissue P systems and helping implementing tissue P systems on computers. Such systems are proved to be universal by simulating register machine, and they are also proved to be able to efficiently solve computationally hard problems by means of a space-time tradeoff, which is illustrated with a polynomial solution to 3-coloring problem.

关键词： Computers Computational modeling Medical services Parallel processing Polynomials Computational efficiency Registers Business

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Deep Reinforcement Learning-Based Cooperative Frequency controller for Hydropower Dominated Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年

作者： Hu, Xiong Yang, Xiaoyun Nie, Xing Yao, Chi Liu, Zhi-Wei Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Nanchang Univ Sch Infrastruct Engn Nanchang 330031 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 430074 Peoples R China

Hydropower dominated systems often experience negative damping and severe power flow oscillations due to improperly tuned governor parameters, even with power system stabilizers. Conventional methods optimize these parameters in a single-machine infinite bus model, neglecting interactions between generations during primary frequency control. This article proposes a deep reinforcement learning based cooperative primary frequency controller using a goal representation heuristic dynamic programming model to address this challenge. The controller minimizes power flow oscillations and frequency deviation through cooperative action among hydrogenerations. Case studies on an IEEE 9 bus system demonstrate that the proposed method achieves superior damping of power oscillations and achieves a much shorter regulation time in the primary frequency control compared to the traditional PID controllers.

关键词： Frequency control Oscillators Damping Hydroelectric power generation Load flow Optimal control Power system stability Transfer functions Stability analysis Blades Cooperative control deep reinforcement learning hydropower dominated system power flow oscillations primary frequency control

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A Parallel Read-Write Circuit Design for Driving Memristor Crossbar Array

A Parallel Read-Write Circuit Design for Driving Memristor C...

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2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Jiang, Ningye Jiang, Mingxuan Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

ISBN: (纸本)9798350316889

Memristor crossbar array is considered as a promising circuit module for accelerating neural networks. Because the memristor is tunable and multi-state, it is important to design applicable Read-Write (RW) circuit for driving memristor crossbar array. In this paper, a novel RW circuit based on analog circuits is proposed for tuning memristors in high-dimensional array parallelly, which is scalable and low-overhead. The standalone RW circuit schematic is presented for reading/writing single memristor and verified in circuit simulation. Then, we extend and apply this circuit into a two-dimensional memristor crossbar array. The performance of memristor crossbar array controlled by designed RW circuits is verified via the simulations under various machine learning datasets. The comparison with related works is presented, which demonstrates the advantages of the proposed RW circuit regarding overhead and anti-error. © 2023 IEEE.

关键词： Memristors

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Gaussian Mixture Model Uncertainty Modeling for Power Systems Considering Mutual Assistance of Latent Variables

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2025年第2期16卷 1483-1486页

作者： Yang, Xiao Li, Yuanzheng Zhao, Yong Li, Yang Hao, Guokai Wang, Yan-Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Minist Educ China Wuhan 430074 Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin 132000 Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China

Gaussian mixture model (GMM) and Dirichlet process mixture model (DPMM) are the primary techniques used to characterize uncertainties in power systems, which are commonly solved by expectation-maximization (EM) algorithm. However, for the massive data of uncertain variables, the algorithm encounters challenges in accurately obtaining GMM and DPMM with a lower time consumption. To address this issue, we propose a method for GMM uncertainty modeling in power systems considering the mutual assistance of latent variables. Specifically, the GMM of uncertain variables is first constructed, and the conditional probability is employed to characterize the mutual assistance of latent variables. Then, an improved EM algorithm is developed to obtain the optimal GMM parameters, in which the expectation step (E-step) and maximization step (M-step) of the algorithm are revised using the conditional probability. Importantly, the closed-form solutions for GMM parameters are rederived in the revised E-step and M-step. Finally, the proposed uncertainty modeling method is compared with the traditional GMM and DPMM on actual wind power and load data from Australia. The proposed method performs the efficiency and accuracy in characterizing uncertainty.

关键词： Uncertainty Correlation Probability distribution Load modeling Wind power generation Renewable energy sources Power system stability Gaussian mixture model uncertainty modeling latent variables EM algorithm

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A Predefined-Time Consensus Algorithm of Multi-Agent System for Distributed Constrained Optimization

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 957-968页

作者： Li, Kaixuan Hu, Qilong Liu, Qingshan Zeng, Zhigang Cheng, Fan Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Sch Math Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Anhui Univ Sch Artificial Intelligence Inst Informat Mat & Intelligent Sensing Lab Anhui Hefei 230601 Peoples R China

This article proposes a multi-agent system with predefined-time consensus to solve distributed optimization problem subject to inconsistent constraint sets. Under some derived conditions, the predefined-time consensus of multi-agent system is guaranteed. Meanwhile, the states of agents are convergent with consensus to an optimal solution of the distributed constrained optimization. Compared to the system with finite time consensus, the proposed system has a fixed time consensus that can be estimated in advance. The suggested system for distributed constrained optimization not requires objective functions have a specific form and smooth. It suitable for the problem with bounded sub-differential of objective function and unbound gradient of objective function. Simulation results validate the theoretical analysis.

关键词： Linear programming Optimization Convergence Resource management Privacy Multi-agent systems Sensors Optimal solution multi-agent system distributed constrained optimization fixed-time consensus communication network

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Locating and sizing charging station in multi-period to promote electric vehicles adoption in urban areas

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ENERGY REPORTS 2024年 11卷 3581-3598页

作者： Hu, Dandan Huang, Liu Liu, Chen Liu, Zhi-Wei South Cent Minzu Univ Sch Management 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 430074 Peoples R China RMIT Univ Sch Engn Melbourne Vic 3001 Australia

The layout of public fast charging stations (CSs) often fails to meet the charging demand of electric vehicle (EV) users, resulting in charging anxiety. To address this issue and promote the widespread use of EVs in urban areas, this paper presents a multi-period locating and sizing optimization model under dynamic demand. The model incorporates GPS trajectory extraction data to identify potential charging demand points and considers the mobile load characteristics of EVs for fusion modeling. A multi-period change formula is proposed to reflect the interaction between public fast CSs and EV market penetration, incorporating charging opportunity and waiting time satisfaction as measures of charging service quality. To solve the model, a heuristic algorithm combining genetic algorithm (GA) and k-medoids clustering algorithm is designed. We apply the model to an actual case of Shenzhen and find that increasing investment in charging infrastructure can boost EV market penetration in the later planning period. Moreover, the gap between charging opportunity and waiting time satisfaction tends to narrow as the planning progresses. Our findings highlight the importance of optimizing the location and size of public fast CSs to improve the charging service quality and promote the adoption of EVs.

关键词： Charging station Location Capacity Queuing

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Tuning Geometric Conformations of Curved DNA Structures by controlling Positions of Nicks 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Xie, Chun Hu, Yingxin Chen, Kuiting Chen, Zhekun Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang050043 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

DNA origami is one of the powerful techniques that utilize DNA as building blocks to synthesize nanostructures. Traditionally, through introducing different numbers of insertions and deletions of base pairs in DNA helices, the in-plane bending angle of curved DNA structures could be roughly tuned. Here, we explored a strategy that used the position patterns of nicks in staple strands to tune the geometric conformation of curved DNA origami structures, including in-plane bending, out-of-plane bending, and twisting angles. When the structure adopted different patterns of nicks positions, great difference appeared in the geometric properties. Further, by combining subunits of different nicks position patterns, the bending and twisting of the combined structure was effectively tuned. The strategy increases the design accuracy of curved DNA origami structures and expands the toolbox for designing DNA structures. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Molecular dynamics

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Saliency Regularization for Self-Training with Partial Annotations

Saliency Regularization for Self-Training with Partial Annot...

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IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Wang, Shouwen Wan, Qian Xiang, Xiang Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China Wuhan Res Inst Posts & Telecommun Wuhan Peoples R China

ISBN: (纸本)9798350307184

Partially annotated images are easy to obtain in multi-label classification. However, unknown labels in partially annotated images exacerbate the positive-negative imbalance inherent in multi-label classification, which affects supervised learning of known labels. Most current methods require sufficient image annotations, and do not focus on the imbalance of the labels in the supervised training phase. In this paper, we propose saliency regularization (SR) for a novel self-training framework. In particular, we model saliency on the class-specific maps, and strengthen the saliency of object regions corresponding to the present labels. Besides, we introduce consistency regularization to mine unlabeled information to complement unknown labels with the help of SR. It is verified to alleviate the negative dominance caused by the imbalance, and achieve state-of-the-art performance on Pascal VOC 2007, MS-COCO, VG-200, and Openimages V3.

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