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作者： Liu, Shengwei Zhao, Tianyang Liu, Xuan Li, Yuanzheng Wang, Peng Energy and Electricity Research Center Jinan University Zhuhai China School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

To enhance the resilience of power systems toward the temporal and spatial impacts caused by extreme weather events, e.g., hurricanes, the flexibility of cloud data centers (CDCs) is treated as a kind of efficient demand response. Since the workloads of CDCs have the shifting capacity between different locations and time slots, a day-ahead unit commitment problem including data centers is proposed to explore the integrated spatial and temporal flexibility of the workloads to its full extent through task migration. Considering the uncertainty of probability density functions, the line failure rates, workload arrival rates, power loads are integrated into an ambiguity set. The scheduling process of generators and CDCs is modeled as a two-stage distributionally robust optimization problem, which is reformulated as a large-scale deterministic mixed-integer linear programming problem and solved by the multicuts Benders decomposition method. The performance of the proposed scheduling strategy is tested in both the IEEE 24-bus RTS system and the three-area RTS-96 system. The results reveal that the method could mitigate the adverse impacts of hurricanes by enhancing the resilience of power systems and decreasing the dropping workloads of CDCs. © 1972-2012 IEEE.

关键词： Hurricanes

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Selective Bokeh Effect Transformation

Selective Bokeh Effect Transformation

引用

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Juewen Peng Zhiyu Pan Chengxin Liu Xianrui Luo Huiqiang Sun Liao Shen Ke Xian Zhiguo Cao Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology S-Lab Nanyang Technological University

Bokeh effect transformation is a novel task in computer vision and computational photography. It aims to convert bokeh effects from one camera lens to another. To this end, we introduce a new concept of blur ratio, which represents the ratio of the blur amount of a target image to that of a source image, and propose a novel framework SBTNet based on this concept. For cat-eye simulation and lens type transformation, a two-channel coordinate map and a two-channel one-hot map are added as extra inputs. The core of the framework is a sequence of parallel FeaNets, along with a feature selection and integration strategy, which aims to transform the blur amount with arbitrary blur ratio. The effectiveness of the proposed framework is demonstrated through extensive experiments, and our solution has achieved the top LPIPS metric in NTIRE 2023 Bokeh Effect Transformation Challenge.

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Memristor-Based Circuit of Sequential Associative Memory with Memory Interactions and Stimulus Similarity Effect

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Xiong, Dongdong Wang, Xiaoping Jiang, Yiming Yang, Chao Jiang, Man Lai, Jingang Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Hubei Key Laboratory of Braininspired Intelligent Systems Wuhan430074 China Huazhong University of Science and Technology Institute of Artificial Intelligence School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Huazhong University of Science and Technology Department of Physiology School of Basic Medicine and Tongji Medical College Wuhan430030 China

Most proposed memristor-based circuits of associative memory consider various mechanisms in only one associative memory. Few works on circuit design of sequential associative memory have been reported. In this paper, a memristor-based circuit of sequential associative memory with memory interactions and stimulus similarity effect is designed. Two associative memories, the prior associative memory (PAM) and the later associative memory (LAM), are formed successively. The PAM modifies the rate of the formation of the LAM, and the LAM, in turn, affects the strength of the PAM, which are two interactions in sequential associative memory, called transfer and retroaction. In addition, the magnitudes of transfer and retroaction are determined by the similarity of the conditioned stimuli. The above functions are realized by the ring input processing module, memory module, transfer module and retroaction module. It has been identified in circuit analysis that the proposed circuit is power efficient and has good robustness. Furthermore, the proposed circuit has promising applications for fire rescue robots. © 2016 IEEE.

关键词： Associative storage

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An Integrated Intelligent Autonomous Driving Bulldozer System: Pose Estimation, Object Detection, and Work Planning for Dumping Operations

引用

Journal of Field Robotics 2025年第0期

作者： Peng, Gang Gao, Qiang Liu, Xingyu Zhou, Yicheng Duan, Hangqi Wu, ZhanGang Hu, Bin Zhu, Xukang Xu, Daosheng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Engineering Research Center of Autonomous Intelligent Unmanned Systems Ministry of Education Wuhan China Shantui Construction Machinery Co. Ltd. Jining China

In recent years, the rapid advancement of automation control and intelligent sensing technologies has positioned autonomous driving as a focal point of interest for both academia and industry. As core equipment in modern construction and industrial production, engineering machinery urgently requires intelligent transformation. To promote the intelligent development of engineering machinery, we have designed an integrated intelligent autonomous driving bulldozer system, which can be extended to various types of engineering machinery. For the specific mine dumping operation environment, we propose a multisensor fusion pose estimation algorithm framework based on the Global Navigation Satellite System, inertial measurement unit, visual cameras, and light detection and ranging (LiDAR) to address issues arising from sensor failures under harsh construction conditions. Furthermore, to ensure safety during task execution, we design a three-dimensional object detection method based on the optimal observation plane from LiDAR data. By integrating pose estimation and environmental perception results, we develop a comprehensive work planning and path-tracking algorithm to maintain task efficiency. Experimental results demonstrate that our intelligent autonomous driving bulldozer system performs excellently under various working conditions. The accuracy of its pose estimation, object detection, and path tracking meets the requirements of actual construction environments, showcasing its significant potential in engineering applications. © 2025 Wiley Periodicals LLC.

关键词： Construction equipment

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Complementary Learning Subnetworks for Parameter-Efficient Class-Incremental Learning

arXiv

引用

arXiv 2023年

作者： Li, Depeng Zeng, Zhigang The School of Artificial Intelligence and Automation The Institute of Artificial Intelligence Huazhong University of Science and Technology China The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

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. However, CIL models are challenged by the well-known catastrophic forgetting phenomenon. Typical methods such as rehearsal-based ones rely on storing exemplars of old classes to mitigate catastrophic forgetting, which limits real-world applications considering memory resources and privacy issues. In this paper, we propose a novel rehearsal-free CIL approach that learns continually via the synergy between two Complementary Learning Subnetworks. Our approach involves jointly optimizing a plastic CNN feature extractor and an analytical feed-forward classifier. The inaccessibility of historical data is tackled by holistically controlling the parameters of a well-trained model, ensuring that the decision boundary learned fits new classes while retaining recognition of previously learned classes. Specifically, the trainable CNN feature extractor provides task-dependent knowledge separately without interference;and the final classifier integrates task-specific knowledge incrementally for decision-making without forgetting. In each CIL session, 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. Extensive experiments on a variety of task sequences show that our method achieves competitive results against state-of-the-art methods, especially in accuracy gain, memory cost, training efficiency, and task-order robustness. Furthermore, to make the non-growing backbone (i.e., a model with limited network capacity) suffice to train on more incoming tasks, a graceful forgetting implementation on previously learned trivial tasks is empirically investigated. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Event-triggered Nonlinear control with Reset Dynamics

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IEEE Transactions on Automatic control 2025年

作者： Zhu, Lijun Chen, Zhiyong The Ministry of Education Key Laboratory of Imaging Processing and Intelligence Control China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China The University of Newcastle School of Engineering CallaghanNSW2308 Australia

In this paper, we propose an improved event-triggered control law based on Lyapunov function method for a class of nonlinear systems by integrating a monotonically decreasing dynamics with a reset mechanism at each sampling instance. It is rigorously guaranteed that the sampling interval can be increased compared to a recently designed Lyapunov-function based event-triggered control law when adopted at each step while the advantages compared with other existing event-triggered control laws are maintained. Then, the improved event-triggered law is applied in the adaptive control scheme. Finally, it is verified by numerical examples that the sampling efficiency is improved when the design parameter is appropriately chosen. © 1963-2012 IEEE.

关键词： Lyapunov functions

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Solving Nonlinear Equations Systems with a Two-Phase Root-Finder Based on Niching Differential Evolution 15th

Solving Nonlinear Equations Systems with a Two-Phase Root-Fi...

引用

15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Duan, Lianlang Xu, Fei Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

Solving nonlinear equation systems (NESs) is a challenging problems in numerical computation. Two goals should be considered for solving NESs. One is to locate as many roots as possible and the other is to improve the solving precision. In this work, in order to achieve these two goals, a two-phase niching differential evolution (TPNDE) is proposed. The innovation of TPNDE is mainly reflected in the following three aspects. First, a probabilistic mutation mechanism is designed to increase the diversity of the population. Second, a novel reinitialization procedure is proposed to detect converged individuals and explore more new regions that may contain global optima. Third, distribution adjustment local search (DALS) is proposed to deal with the isolated solution and enhance the convergence of the algorithm. To evaluate the performance of TPNDE, we test our TPNDE and 5 state-of-the-art algorithms on 20 NESs with diverse features. The experimental results show that the proposed TPNDE algorithm is better than the other methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Nonlinear equations

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Cortical Neural P Systems 15th

Cortical Neural P Systems

引用

15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Fu, Zhongzheng Zhang, Luping Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

P systems are distributed and parallel computing models inspired from living cells. In this work, a variant of spiking neural P systems, cortical neural P systems, are proposed in the frame work of P systems combining the topology structure of basic P system and information processing style in spiking neurons. The computational power of cortical neural P systems is investigated. It is proved that cortical neural P systems are equivalent to Turing machines as number generators. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Turing machines

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HSDet: A Representative Sampling Based Object Detector in Cervical Cancer Cell images 15th

HSDet: A Representative Sampling Based Object Detector in Ce...

引用

15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Yan, Xiaoli Zhang, Zhongsi Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

Automated detection of cervical cancer cells has the potential to reduce error and increase productivity in cervical cancer screening. However, the existing object detection methods to detect the cervical cancer cells make inadequate use of negative samples and fail to balance the number of hard samples and easy samples. In this work, we propose a image-level sampling method, called pair sampling, which can extract the representative negative samples and generate the sample pair image. Then, based on the sample pair image, we propose the hybrid sampling, a proposal-level sampling method, to balance the number of hard samples and easy samples. Combining the proposed sampling methods, we design a representative sampling based cervical cell detector, which can detect the cervical cancer cells effectively. In order to comprehensively evaluate our method, we use a dataset which is consisted of 16000 annotated cervical cell images with size of 1024 × 1024. The experimental result shows that our method achieves 57.1% mean Average Precision (mAP), and it is higher than cascade R-CNN and Faster R-CNN by 4.7% and 5.8%, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Trimmed Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles 15th

Trimmed Data-Driven Evolutionary Optimization Using Selectiv...

引用

15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Shan, Yawen Hou, Yongjin Wang, Mengzhen Xu, Fei Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

Data-driven evolutionary algorithms (DDEAs) have attracted much attention in recent years, due to their effectiveness and advantages in solving expensive and complex optimization problems. In an offline data-driven evolutionary algorithm, no additional information is available to update surrogate models, so we need to ensure the quality of the surrogate models built before optimization. Based on the offline data-driven evolutionary algorithm using selective surrogate ensembles (DDEA-SE), we propose a trimmed bagging based DDEA-SE (TDDEA-SE) to construct a more accurate model pool. To this end, we use trimmed bagging to prune models with large errors calculated by out-of-bag samples, thus improving the accuracy of the selective surrogate ensembles and promoting the optimization. The experimental results on benchmark problems show that the proposed algorithm can strike a balance between diversity and accuracy of models, and its competitiveness on solving offline data-driven optimization problems. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Evolutionary algorithms

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