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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin Cancer Classification 27th

EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ray, Amartya Gayen, Soumyajit Kaplun, Dmitrii Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031781032

Skin cancer poses a serious global health challenge, where timely and precise diagnosis is essential to improve patient outcomes. Recently, neural networks have proven to be highly effective tools for automated skin cancer classification, significantly advancing the field of dermatology. This paper introduces a novel approach to generate edge maps from dermoscopic images using a holistically nested edge detector model. These edge maps enhance the detection of shape and symmetry irregularities, which are key indicators of malignancy, and improve the focus on relevant regions of interest. We then propose an edge-guided dual-branch neural network, called EDB-Net, for the classification task. Branch 1 handles edge maps, while Branch 2 processes original dermoscopic images. To highlight significant regions and focus on specific lesion areas, we incorporate a novel channel-spatial synergistic attention block within Branch 2. Additionally, we introduce a unique strategy to modulate the generated attention maps using edge features extracted from the edge maps in Branch 1, creating edge-guided features that refine the overall feature representation. In the final stage, both edge-guided and attention-aided features are combined, producing more distinct and contextually relevant outputs, thereby significantly enhancing classification performance. Our model achieves accuracies of 0.927 and 0.848 on the challenging HAM10000 and ISIC 2016 datasets, respectively, without employing any data augmentation. The source code of the proposed model is available at: https://***/Cmatermedicalimageanalysis/EDB_Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Semi-Markov Telecommunication Networks Model

Semi-Markov Telecommunication Networks Model

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2024 International Conference on Actual Problems of Electron Devices Engineering, APEDE 2024

作者： Svetlov, Mikhail S. L’vov, Alexey A. Martirosyan, Arman M. Martirosyan, David M. Bagaev, Igor S. Khasanov, Semen T. Laboratory of Control and Automation Problems in Mechanical Engineering Institute of Precision Mechanics and Control of RAS Saratov Russia Institute of Institute of Electronic Engineering and Instrumentation Yuri Gagarin State Technical University of Saratov Saratov Russia Institute of Applied Information Technologies and Communications Yuri Gagarin State Technical University of Saratov Saratov Russia Phystech-School of Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Moscow Russia

ISBN: (纸本)9798350393316

This paper proposes a semi-Markov model of telecommunication network (TCN). The variant of dynamic traffic adaptive control of queuing system as a special case of TCN is considered. The main purpose of control is to minimize the average cost per unit of time to service the incoming flow of information (packets). This takes into account the different bandwidth of the channels, the processing speed of information in the channel and the information capacity of the buffers. The approach to the organization of dynamic control taking into account noise immunity (information reliability) and information security is discussed. © 2024 IEEE.

关键词： Bandwidth

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Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural Networks for Medical Image Classification 5th

BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural ...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Ghosh, Triyas Chakraborty, Soham Kaplun, Dmitrii Gulvanskii, Vyacheslav Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9789819638628

In this paper, we have proposed a novel model, called Bonferroni Mean Operator-aided Fusion of Neural Networks (BFuse-Net). Here, we have taken advantage of the capabilities of four deep learning models as the base learners and then applied a customized attention method to prioritize different fine-grained features by passing their outputs via four parallel pipelines. Next, we have employed a novel aggregation method, a modified Bonferroni Mean operator, to merge the decision scores acquired from every pipeline, prioritizing their individual contributions above their interactions with others in the decision scores. Three medical image datasets— LC25000 (colon cancer), Oral Squamous Cell Carcinoma, and Malaria Cell Images have been used for experimentation. The model yields state-of-the-art outcomes for the three datasets. The source codes and additional results can be found at GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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An Ensemble of UNet Frameworks for Lung Nodule Segmentation

An Ensemble of UNet Frameworks for Lung Nodule Segmentatio...

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International Conference on Actual Problems of Applied Mathematics and computer Systems, APAMCS 2022

作者： Gautam, Nandita Basu, Abhishek Kaplun, Dmitry Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur India Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031341267

One of the most common types of cancer in the world is lung cancer, which is a cause of increasing mortality. It is most often discovered in the middle and later stages as it does not have obvious symptoms due to which its treatment is often missed. Studies show that most lung cancers are in the form of lung nodules, which can be categorized as benign or malignant. Thus, accurate early identification of malignant lung nodules that might later become cancerous is essential for the prevention of lung cancer. Computed Tomography (CT) images can be useful for identifying and segmenting these nodules. In this paper, we propose an ensemble model, called BUS-UNet++, to segment lung nodules using CT images. We use a combination of the ConvLSTM up-sampling architecture from BUS-UNet and skip connections in aggregation blocks from the UNet++ model to build the BUS-UNet++ ensemble. The dataset for this study is collected from the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), where we have selected the modality of these images as CT images. These are further preprocessed to obtain nodule masks and nodule images, which constitute the Region of Interest (RoI). The accuracy achieved with the Adam Optimizer is 0.9731, the Intersection over Union (IoU) of about 0.8439, and the dice score coefficient (DSC) of about 0.958 are obtained by the proposed system. This ensemble model outperforms several state-of-the-art models used for the same purpose. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image segmentation

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 356-368页

作者： Jiaxing Li Zidong Wang Jun Hu Hongli Dong Hongjian Liu the Department of Applied Mathematics Harbin University of Science and Technology IEEE the Department of Computer Science Brunel University London the School of Automation Harbin University of Science and Technology the Artificial Intelligence Energy Research Institute Northeast Petroleum University the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University the National Key Laboratory of Continental Shale Oil of China Northeast Petroleum University the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University the School of Mathematics and Physics Anhui Polytechnic University

In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.

关键词： Amplify-and-forward (AaF) relays covariance intersection fusion cubature Kalman filtering multi-sensor systems uniform boundedness

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A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

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computers, Materials & Continua 2023年第4期75卷 1157-1177页

作者： Guo-Wen Sun Gang Xiang Wei He Kai Tang Zi-Yi Wang Hai-Long Zhu School of Computer Science and Information Engineering Harbin Normal UniversitySongbei DistrictHarbin150025China Beihang University School of Automation Science and Electrical Engineering Beijing100191China Beijing Aerospace Automatic Control Institute Beijing100074China Rocket Force University of Engineering Xi’anShaanxi Province710025China

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

关键词： Self-adaptive quality factor belief rule base wireless sensor networks fault diagnosis

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Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage

Shifting Spotlight for Co-supervision: A Simple yet Efficien...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hu, Yang Zhang, Jinxia Zhang, Kaihua Yuan, Yin Huang, Jiale Zhan, Zechao Wang, Xin Key Laboratory of Measurement and Control of CSE School of Automation Southeast University China School of Computer Science Nanjing University of Information Science and Technology China Advanced Ocean Institute of Southeast University China Alibaba Group China

ISBN: (纸本)9798350368741

Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial computational costs. To address this, we propose the Co-Supervised Spotlight Shifting Network (CS3Net), a compact single-branch framework inspired by how shifting light source exposes camouflage. Our spotlight shifting strategy replaces multi-branch designs by generating supervisory signals that highlight boundary cues. Within CS3Net, a Projection Aware Attention (PAA) module is devised to strengthen feature extraction, while the Extended Neighbor Connection Decoder (ENCD) enhances final predictions. Extensive experiments on public datasets demonstrate that CS3Net not only achieves superior performance, but also reduces Multiply-Accumulate operations (MACs) by 32.13% compared to state-of-the-art COD methods, striking an optimal balance between efficiency and effectiveness. © 2025 IEEE.

关键词： Camouflaged Object Detection Efficient Network Design Network Co-supervision

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Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

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