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A Bio-Inspired Decision-Making Memristive Circuit Based on Classical and Operant Conditioning

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2023年 22卷 4501-4513页

作者： Yang, Chao Wang, Xiaoping Chen, Zhanfei Wang, Zilu Zhang, Sen Zeng, Zhigang Huazhong Univ Sci & Technol Inst Artificial Intelligence 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 Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Hubei Key Lab Brain Inspired Intelligent Syst Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Harbin Inst Technol Shenzhen Sch Mech Engn & Automat Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China

This work proposes a bio-inspired decision-making memristive circuit drawing on Hull's secondary learning system. This circuit can not only mimic the decision-making initiated by secondary drive stimuli and shaped and guided by secondary reinforcers via integrating classical conditioning (CC) and operant conditioning (OC), but also consider the factors that influence decision-making, such as demand states, incentive motivation, and habit strength. These bionic functions have not yet been implemented by existing memristive circuits. Our circuit primarily includes CC module, drive regulation module, habit memory module, incentive generation module, and winner-takes-all module, which is designed through a modular hierarchical circuit design method. Memristors play a core role in our circuit and enable the circuit to perform brain-like online learning in an in-memory computing way, which has power and area advantages. The PSPICE-based simulations in various scenarios show that our circuit has a strong adaptive decision-making ability since more bionic features are considered. The proposed circuit can be applied to a bionic intelligent robot, enabling the robot capable of autonomous associative learning abilities to perform complex tasks such as detection and *** to Practitioners-This work is motivated by the problem of neuromorphic circuit design for bio-inspired learning and decision-making. To realize brain-like online in-situ learning in an in-memory computing way and enhance the adaptability of the circuit in dynamic environment, a memristive circuit integrating CC and OC is proposed. Referring to Hull's secondary learning system, the proposed circuit takes into account factors that affect decision-making, such as demand states, incentive motivation, and habit strength, as well as the fact that decision-making processes can be evoked by secondary drive stimuli and shaped by secondary reinforcers, which is unaddressed by existing memristor-based works. The bi

关键词： Memristor operant conditioning classical conditioning associative memory decision-making bionic learning

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Machine learning-assisted solid waste life-cycle management: Applications, constrains, and future opportunities

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RESOURCES CONSERVATION AND RECYCLING 2025年 219卷

作者： Zou, Qiuxia Duan, Huabo Yang, Zeguo Wang, Xiaoping Tian, Yu Hou, Huijie Yang, Jiakuan Huazhong Univ Sci & Technol Inst Artificial Intelligence Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Hubei Prov Engn Lab Solid Waste Treatment Disposal & Recycle Technol Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Environm Sci & Engn Hubei Key Lab Multimedia Pollut Cooperat Control Y Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Harbin Inst Technol Sch Municipal & Environm Engn State Key Lab Urban Water Resource & Environm Harbin 150090 Peoples R China

The ever-increasing amount of solid waste generated globally has given rise to issues of environmental pollution and climate change impact associated with the resource recovery and ultimate disposal. Given the complexity of waste categories and multi-objective management needs both in developed and developing countries, it is urgent to employ innovative strategies, such as the machine learning techniques. In present study, we conduct a systematic review of the application of machine learning in the waste life-cycle management with consideration of advancement, challenges, limitations, and future directions. We found that the challenges currently faced by solid waste management systems are extremely complex, necessitating the use of knowledge in the field to constrain machine learning models to enable a deep integration between machine learning and sustainable waste management. Due to its proficiency in modeling complex nonlinear processes, particularly the great advantages in prediction and multi-objective optimization, existing studies on machine learning mainly focus on waste generation projection, collection and classification, transportation, recycling, and disposal route optimization. However, the performance of machine learning applications in the field of solid waste is hindered by limitations in data quality and quantity, as well as the insufficient interpretability of individual models. These challenges can be addressed by integrating methods such as multi-source data fusion, data augmentation techniques, and ensemble learning, along with the development of highly interpretable machine learning models. Overall, the key to achieving environmentally sound management of solid waste lies in optimizing designs and balancing multidimensional evaluation criteria.

关键词： Solid waste Life-cycle management Machine learning Multi-objective optimization Sustainability

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Two-Archive-Based Competitive Swarm Optimizer for Constrained Multiobjective Optimization 5

Two-Archive-Based Competitive Swarm Optimizer for Constraine...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Li, Lianghao He, Cheng Lin, Jianqing Pan, Linqiang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Wuhan China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350393521

Constrained multiobjective optimization problems (CMOPs) are prevalent in various real-world applications, presenting a formidable challenge to existing evolutionary algorithms when faced with intricate constraints. When solving CMOPs, a crucial concern is achieving a balance between convergence, diversity, and feasibility. To address these challenges, this paper proposes a two-archive-based constrained multiobjective competitive swarm optimizer. The algorithm preserves two collaborative and complementary archives to improve population convergence and feasibility. By implementing a competitive particle swarm mechanism, offspring solutions are generated by drawing upon solutions from both archives, thus capitalizing on their synergistic effect and exceptional information. An adaptive parameter is also used to adjust the bias in choosing the winner in the paired competition during the evolution. The experimental results demonstrate the effectiveness of the proposed algorithm in handling constrained multiobjective optimization problems. © 2023 IEEE.

关键词： Constraint handling

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Gaze and Environmental Context-Guided Deep Neural Network and Sequential Decision Fusion for Grasp Intention Recognition

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 3687-3698页

作者： Yang, Bo Chen, Xinxing Xiao, Xiling Yan, Pei Hasegawa, Yasuhisa Huang, Jian Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Peoples R China Shenzhen Key Lab Biomimet Robot & Intelligent Syst Shenzhen 518055 Peoples R China Huazhong Univ Sci & Technol Union Hosp Tongji Med Coll Dept Rehabil Wuhan 430022 Peoples R China Nagoya Univ Dept Micronano Mech Sci & Engn Chikusa Ku Nagoya 4648603 Japan

Grasp intention recognition plays a crucial role in controlling assistive robots to aid older people and individuals with limited mobility in restoring arm and hand function. Among the various modalities used for intention recognition, the eye-gaze movement has emerged as a promising approach due to its simplicity, intuitiveness, and effectiveness. Existing gaze-based approaches insufficiently integrate gaze data with environmental context and underuse temporal information, leading to inadequate intention recognition performance. The objective of this study is to eliminate the proposed deficiency and establish a gaze-based framework for object detection and its associated intention recognition. A novel gaze-based grasp intention recognition and sequential decision fusion framework (GIRSDF) is proposed. The GIRSDF comprises three main components: gaze attention map generation, the Gaze-YOLO grasp intention recognition model, and sequential decision fusion models (HMM, LSTM, and GRU). To evaluate the performance of GIRSDF, a dataset named Invisible containing data from healthy individuals and hemiplegic patients is established. GIRSDF is validated by trial-based and subject-based experiments on Invisible and outperforms the previous gaze-based grasp intention recognition methods. In terms of running efficiency, the proposed framework can run at a frequency of about 22 Hz, which ensures real-time grasp intention recognition. This study is expected to inspire additional gaze-related grasp intention recognition works.

关键词： Hidden Markov models Feature extraction Object detection image recognition Fuses Three-dimensional displays Task analysis Grasp intention recognition gaze environmental context object detection hidden Markov model deep neural networks

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Tradeoff Analysis Between control Time and Energy Consumption for Delayed Neural Networks With Discontinuous Activation Functions

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第8期34卷 5012-5023页

作者： Chen, Chongyang Zhu, Song 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 China Univ Min & Technol Sch Math Xuzhou 221116 Jiangsu Peoples R China

This article studies finite-time stabilization of delayed neural networks (DNNs) whose activation functions are discontinuous. Several sufficient conditions for guaranteeing finite-time stabilization of considered DNNs are obtained by constructing appropriate controllers with giving upper bounds of control time. Subsequently, based on the existing definition of energy consumption, the required energy to achieve stabilization is estimated. To quantify the cost of control, an evaluation index function is constructed to analyze the tradeoff between control time and consumed energy. Ultimately, acquired results are verified by simulating two numerical examples.

关键词： Artificial neural networks Costs control systems Switches Upper bound Energy consumption Delays Energy consumption finite-time stabilization neural networks time-varying delay

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Emergence mechanism of lion pride cooperative behaviours for task allocation problem and its implementation

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2024年第4期24卷 252页

作者： Wu, Bowen Xiao, Renbin Feng, Zhenhui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

Task allocation is a key challenge in various fields due to increasing tasks and UAVs, leading to high computational costs. To tackle this, we employ swarm intelligence inspired by cooperative behaviours in lions, which is decentralised and adaptive. Initially, we abstract lions' cooperation behaviours and establish a formal model based on attraction-repulsion mechanisms. Then, we use multi-agent technology to simulate these behaviours, exploring parameter effects on rationality, scalability, and adaptation. We apply this model to UAV task allocation, proposing a distributed self-organising algorithm validated through simulations. Finally, we analyse factors influencing lions' cooperative behaviours, demonstrating the efficacy of the attraction-repulsion mechanism in task allocation.

关键词： attraction-repulsion collective intelligence cooperative behaviour emergence mechanism lions task allocation UAVs

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Accessibility and Vulnerability Analysis of the Integrated Road, High-speed Rail and Airline System in China 13

Accessibility and Vulnerability Analysis of the Integrated R...

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13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022

作者： Sun, Wenjing Zhang, Hui Ouyang, Min School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Lab. for Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

ISBN: (数字)9781665486903

ISBN: (纸本)9781665486903

National-scale transportation systems are critical infrastructures to ensure the normal operation of the nation and offer essential services to modern societies. And they face a constant barrage of external stresses or threats that challenge their operation. This article analyzes the accessibility and vulnerability of the Chinese road, high-speed rail and airline systems. Firstly, it models the complementary relationship among road, high-speed rail and airline systems and takes them as an integrated transportation system (IRHAS). Then, the accessibility map from (to) the center of Beijing via IRHAS is displayed. Finally, modeling the extreme storm recently occurred in Zhengzhou as a reginal disruption, the vulnerability of the IRHAS is calculated. The findings in this paper provide guidance for city administrators to plan the road, high-speed rail, and airline systems as a whole. © 2022 IEEE.

关键词： Storms

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Research on auto focusing algorithm of microscopic imaging system

Research on auto focusing algorithm of microscopic imaging s...

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2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2022

作者： Lv, Meini Yang, Qiuhui Ma, Wenjing Huang, Zeqiong College of Wuzhou Wuzhou China Wuzhou University Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou China

ISBN: (数字)9781665480901

ISBN: (纸本)9781665480901

In the intelligent microscopic imaging system, the focusing evaluation function is one of the important core links in the automatic focusing system. In order to solve the problem that the focusing curve loses the characteristics of the ideal curve caused by illumination change, an automatic focusing algorithm based on Robert and wavelet transform is proposed in this paper. The algorithm extracts the maximum value of the high-frequency component wavelet coefficients decomposed by the wavelet transform, and uses the extracted wavelet coefficients to weight the Robert evaluation value coefficients, increases the sensitivity factor of the algorithm, and makes an experimental analysis with five traditional focusing functions under the condition of different illumination intensity to verify the feasibility of the algorithm. The experimental results show that the algorithm can overcome the change of illumination, has strong anti noise, sensitivity and stability, and is suitable for the fine focusing process in the microscopic imaging system. © 2022 IEEE.

关键词： Imaging systems

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Interpretable data-driven contingency classification for real-time corrective security-constrained economic dispatch

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IET RENEWABLE POWER GENERATION 2024年第3期18卷 476-488页

作者： Yu, Yaowen Gao, Yijie Li, Yuanzheng Yan, Ying Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat C MEIC CICAEET Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat 219 Ningliu Rd Nanjing 210044 Peoples R China

High penetrations of renewable energy are crucial for low-carbon power systems. However, the higher volatility of renewable power generation pushes real-time operations closer to equipment limits. It is thus important to utilize flexibilities in the system through corrective security-constrained economic dispatch (SCED) that allows generators to take corrective adjustments after contingencies. The corrective SCED problem, containing a large number of contingencies, and corresponding post-contingency decisions and constraints, is very large in scale and difficult to solve using purely model-based methods within the strict time limits of real-time markets. To accelerate the solution process, this paper develops a novel interpretable data-driven contingency classification method. Historical data and their potentially useful patterns are utilized in interpretable data-driven decision tree classifiers. To directly consider continuous features, such as net load values, and to consider imbalanced datasets without much additional complexity, Improved Strong Optimal Classification Trees (ISOCTs) are developed with new branching threshold constraints and category weights in the objective function. ISOCTs are then embedded into a hybrid model-based and data-driven framework to guarantee the accuracy of the real-time active contingency set and the resulting security of dispatch decisions. Numerical testing results demonstrate the classification accuracy, computational efficiency, and interpretability of the proposed approach.

关键词： artificial intelligence decision trees power generation dispatch power system security renewable energy sources

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A multi-period ambulance location and allocation problem in the disaster

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2022年第5期43卷 909-932页

作者： Wang, Jian Wang, Yin Yu, Mingzhu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Shenzhen Univ Coll Management Inst Big Data Intelligent Management & Decis Shenzhen 518060 Peoples R China

To achieve quick response in the disaster, this paper addresses the issue of ambulance location and allocation, as well as the location problem of temporary medical centers. Considering budget and capacity limitations, a multi-period mixed integer programming model is proposed and two hybrid heuristic algorithms are designed to solve this complex problem. The proposed model and algorithm are further verified in a real case study, and the numerical experiments demonstrate the effectiveness of our proposed model. Specifically, we obtain several findings based on the computational results: (1) The best locations of ambulance stations should change in each period because the demand rate changes over time. (2) Involving temporary medical centers is necessary to reduce the average waiting time of injured people. (3) It may not be optimal to allocate ambulances from the nearest ambulance stations because of potentially limited station capacity.

关键词： Ambulance station location Ambulance allocation Disaster response

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