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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Yun Xing Yin Sheng The Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This article investigates global asymptotic synchronization of coupled neural networks with reaction diffusions and unbounded time-varying delays (RDUDCNNs). Through designing a centralized adaptive controller and corresponding updating laws of time-varying control gains, under two different upper bound of control gains, an asymptotic convergence lemma under unbounded time-varying delays and the Barbarat lemma are used to derive global asymptotic synchronization of RDUDCNNs, respectively. The effectiveness of the obtained conclusion is verified through a numerical simulation.

关键词： Upper bound Automation Neural networks Numerical simulation Delays Synchronization Convergence

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Community Detection in Temporal Biological Metabolic Networks Based on Semi-NMF Method with Node Similarity Fusion 9th

Community Detection in Temporal Biological Metabolic Networ...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Xuanming Yu, Jianxing Lin, Miaopei Wang, Shiqi Liu, Wei Yin, Jian School of Artificial Intelligence Sun Yat-sen University Zhuhai China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Pazhou Lab Guangzhou510330 China Key Laboratory of Sustainable Tourism Smart Assessment Technology Ministry of Culture and Tourism Zhuhai China

ISBN: (纸本)9783031466731

This paper aims to study the topological structure of biological metabolic networks, such as detecting their inherent communities to learn the useful patterns and characteristics. By partitioning a metabolic network into multiple communities and subgraphs, we can reveal their local structure and organization. That helps researchers better understand the structure and function of metabolic networks, identify metabolic pathways and functional modules, and discover mechanisms and patterns of metabolic regulation. However, community detection remains a challenging problem due to the dynamic nature of metabolic network structure and data sparsity. To address this problem, we propose a new temporal community detection method for biological metabolic networks based on a semi-NMF framework with node similarity fusion, called SNF-MNet. This method grasps the dynamic nature of metabolic networks by introducing temporal smoothness to estimate network evolution. Furthermore, SNF-MNet adopts a node similarity fusion method to comprehensively consider the relations between nodes in the network, effectively solving the limitations and data sparsity issues of existing methods. SNF-MNet can effectively reduce the impact of noise and improve discriminability in small communities, providing more accurate and reliable results. Experiments on multiple synthetic and real-world datasets demonstrate the effectiveness of this model in community detection and temporal evolution tracking, with an increase of NMI 1%–3.2%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Metabolism

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Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfaces

arXiv

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arXiv 2024年

作者： Meng, Lubin Jiang, Xue Jia, Tianwang Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. EEG-based BCIs have been successfully used in many applications, such as neurological rehabilitation, text input, games, and so on. However, EEG signals inherently carry rich personal information, necessitating privacy protection. This paper demonstrates that multiple types of private information (user identity, gender, and BCI-experience) can be easily inferred from EEG data, imposing a serious privacy threat to BCIs. To address this issue, we design perturbations to convert the original EEG data into privacy-protected EEG data, which conceal the private information while maintaining the primary BCI task performance. Experimental results demonstrated that the privacy-protected EEG data can significantly reduce the classification accuracy of user identity, gender and BCI-experience, but almost do not affect at all the classification accuracy of the primary BCI task, enabling user privacy protection in EEG-based BCIs. © 2024, CC BY.

关键词： Differential privacy

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A Novel Dynamic Peer-to-Peer Carbon Right Sharing Scheme for Microgrid Clusters

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IEEE Transactions on Smart Grid 2025年

作者： Zhou, Wei Lai, Jingang Li, Xi Zeng, Zhigang School of Artificial Intelligence and Automation China Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China

In this paper, a novel four-level distributed game decision-making framework of dynamic peer-to-peer (P2P) carbon emission right (CER) sharing is proposed for microgrid clusters (MGCs), which consists of four phases: exploration, selection, equilibrium and correction. Firstly, in the exploration stage, the tradable information is published by microgrids (MGs) and an initial set of tradable options is constructed. Secondly, in the selection stage, bilateral transaction coefficients are calculated according to the deficit and surplus of CERs in the P2P carbon market, and then an optimal sharing matching scheme of CERs is designed to reduce invalid transactions. Thirdly, in the equilibrium stage, a P2P carbon right sharing strategy based on the Stackelberg game is proposed to realize the dynamic change in carbon prices and the flexible conversion of master-slave roles in P2P carbon trading, which can promote the popularity of carbon market. Fourthly, in the correction stage, a carbon emission right balancer (CERB) is developed to avoid the imbalance of CERs, and then a billing scheme including incentives and penalties is established to achieve the benefit redistribution after P2P carbon trading. In the end, numerical results illustrate the effectiveness of the proposed multi-layer game decision-making framework for P2P carbon right sharing among MGs. © 2010-2012 IEEE.

关键词： carbon emission right balancer Microgrid cluster peer-to-peer carbon trading Stackelberg game

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A multi-scale attention network for grading of invasive ductal cancer 3

A multi-scale attention network for grading of invasive duct...

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3rd International Conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Zhang, Xinxin Lu, Cheng Bai, Jiayang Chen, Yuli School of Computer Science Shaanxi Normal University Xi'an710119 China Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Southern Medical University Guangzhou510080 China

ISBN: (纸本)9781510681279

Invasive ductal carcinoma (IDC) represents the most prevalent form of breast cancer. Automatic and precise IDC grading is essential for the clinical assessment of tumor status. However, current methodologies struggle with accurate grading due to the intricate spatial structure within IDC tumor regions, coupled with significant similarities between different classes of images and high variability within the same class, posing a substantial challenge for IDC grading. To tackle these challenges, we propose a novel multi-scale attention model named MS-HiFuse. This model integrates the multi-scale convolution and the multi-head attention mechanism into the local feature block of the HiFuse network, creating an enhanced multi-scale local feature block that more effectively captures the nuanced features of the tumor area and facilitates the learning of fine-grained feature representations. Furthermore, to overcome the limited number of original data and to ensure data quality, we augment the dataset through operations such as maximum rectangle cropping, subdivision of plots, and image flipping for each sample. Comparison experiments demonstrate that our proposed MSHiFuse network attains an AUC of 80.84%, outperforming both the current competing networks and the original model. Hence, the application of the MS-HiFuse network proposed herein holds significant promise for the grading of IDC pathology images. © 2024 SPIE.

关键词： Grading

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Research on attention mechanism based assisted diagnosis of pulmonary embolism 1

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19th Chinese Intelligent Systems Conference, CISC 2023

作者： Li, Hua Tao Hu, Zhong Yi Hu, Ming Zhe College of Computer and Artificial Intelligence WenZhou University Wenzhou325035 China WenZhou Intelligent Information Processing and Key Analysis Laboratory Wenzhou325035 China Radiology and Imaging Department of WenZhou People’s Hospital WenZhou China

ISBN: (数字)9789819968824

ISBN: (纸本)9789819968817

The traditional diagnosis of pulmonary embolism (PE) requires doctors to distinguish between computed tomography (CT) images, which is a very timeconsuming task and may result in patients not receiving timely and effective treatment. Therefore, the use of computer intelligence to assist in diagnosis is crucial. In this paper, we propose a Coordination and spatial attention convNext (CSACNet) based on CSA attention mechanism to realize early intelligent auxiliary diagnosis of pulmonary embolism disease. First, Convolutional neural network uses ConvNext network as the backbone network for feature extraction, which can solve the gradient problem caused by network deepening. Secondly, the CSA attention module introduced is a fusion of CoordAttention and Spatial Attention, extracting channel, position, and spatial information between features to obtain more discriminative features and improve the network’s classification accuracy for pulmonary embolism images. The proposed method was tested on the largest publicly contested PE dataset(RSNASTR), and the experimental results showed that it outperformed the current best method and improved the performance of pulmonary embolism assisted diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Diseases

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A Time-Independent Control Method for Variable-Speed Load-Bearing Walking with Suspended Backpack

A Time-Independent Control Method for Variable-Speed Load-Be...

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第43届中国控制会议

作者： Zhiyuan He Yu Cao Yifei Guo Jian Huang Hubei Key Laboratory of Brain-inspired Intelligent Systems Key Laboratory for Image Processing and Intelligent ControlSchool of Artificial Intelligence and AutomationHuazhong University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Walking with a load brings a significant burden to human shoulders,resulting in increasing metabolic energy consumption and the risk of skeleton and muscle *** backpack has been widely conducted for load-bearing walking,but exiting time-dependent control methods and event-based control methods are unable to exploit the characteristics of human gait *** this case,a time-independent control(TIC) method capable of eliminating the time dependence and adapting to variable speed is *** on the dynamical model of human-backpack system,an assistance profile along with the parameter optimization method are introduced which allows the backpack to reduce the burden on human shoulders during load-bearing *** the simulation evaluation,the proposed TIC method is compared with an impedance controller based time-dependent method and a locked backpack,under both constant speed and variable speed *** results demonstrates that the TIC case achieves an 89.1% reduction in dynamic load on shoulder under the condition of constant speed and a 78.2% reduction under the condition of variable speed,compared with the LOCKED case.

关键词： suspended backpack load carriage time-independent control

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A Knowledge-Enhanced Inferential Network for Cross-Modality Multi-hop VQA 9th

A Knowledge-Enhanced Inferential Network for Cross-Modality...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Wang, Shiqi Yu, Jianxing Lin, Miaopei Qiu, Shuang Luo, Xiaofeng Yin, Jian Guangdong Key Laboratory of Big Data Analysis and Processing School of Artificial Intelligence Sun Yat-sen University Zhuhai China Guangdong University of Education Guangzhou510303 China Guangdong Industry Polytechnic Guangzhou510300 China

ISBN: (纸本)9783031466632

This paper focuses on cross-modality multi-hop visual questions, which require multi-hop reasoning over different sources of knowledge from multiple modalities, such as image and text. Due to the lack of cross-modality reasoning ability, it is difficult for the traditional model to make correct predictions. To solve this problem, we propose a new knowledge-enhanced inferential framework. We first build a reasoning graph to capture the topological relations between the objects in the given image and the logical relations of entities corresponding to these objects. To align the visual objects and textual entities, we design a cross-modality retriever with the help of an external multimodal knowledge graph. Based on the logical and topological relations on the graph, we can derive the answer by decomposing a complex multi-hop question into a series of attention-based reasoning steps. The result of the previous step acts as the context of the next step. By linking the results of all steps, we can form an evidence chain to the answer. Extensive experiments conducted on the popular KVQA dataset demonstrate the effectiveness of our approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge graph

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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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Spatiotemporal distance embedded hybrid ant colony algorithm for a kind of vehicle routing problem with constraints

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Frontiers of Information Technology & Electronic Engineering 2023年第7期24卷 1062-1079页

作者： Zhenhui FENG Renbin XIAO School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Institute of Artificial Intelligence Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Information Processing and Intelligent Control Ministry of EducationWuhan 430074China

We investigate a kind of vehicle routing problem with constraints(VRPC)in the car-sharing mobility environment,where the problem is based on user orders,and each order has a reservation time limit and two location point transitions,origin and *** is a typical extended vehicle routing problem(VRP)with both time and space *** consider the VRPC problem characteristics and establish a vehicle scheduling model to minimize operating costs and maximize user(or passenger)*** solve the scheduling model more accurately,a spatiotemporal distance representation function is defined based on the temporal and spatial properties of the customer,and a spatiotemporal distance embedded hybrid ant colony algorithm(HACA-ST)is *** algorithm can be divided into two ***,through spatiotemporal clustering,the spatiotemporal distance between users is the main measure used to classify customers in categories,which helps provide heuristic information for problem ***,an improved ant colony algorithm(ACO)is proposed to optimize the solution by combining a labor division strategy and the spatiotemporal distance function to obtain the final scheduling *** analysis is carried out based on existing data sets and simulated urban *** with other heuristic algorithms,HACA-ST reduces the length of the shortest route by 2%–14%in benchmark *** VRPC testing instances,concerning the combined cost,HACA-ST has competitive cost compared to existing VRP-related ***,we provide two actual urban scenarios to further verify the effectiveness of the proposed algorithm.

关键词： Vehicle routing problem with constraints(VRPC) Spatiotemporal distance function Labor division strategy Ant colony algorithm(ACO)

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