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Solving non-cyclic dynamic multi-objective optimization problems via GRU prediction and multi-information hybrid exploration

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Journal of Supercomputing 2025年第8期81卷 1-34页

作者： Sun, Hao Bai, Xiaochuang Wang, Cong Yang, He Hu, Ziyu School of Electrical and Engineering Yanshan University Hebei Qinhuangdao066004 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei Qinhuangdao066004 China Electrical automation department Tianjin Research Institute of Electric Science Co. Ltd. Tianjin China

In non-cyclic dynamic multi-objective optimization problems, the non-cyclic nature of environmental changes may cause the Pareto optimal front (PF) to be different from historical times. In addition, changes may also occur on the Pareto optimal solutions set (PS). However, predictions based solely on a single space show lower accuracy due to insufficient information. Therefore, solving non-cyclic dynamic multi-objective optimization problems via gate recurrent unit (GRU) prediction and multi-information hybrid exploration (GPMHE) is designed to overcome the above challenges. In the objective space, the GRU-based prediction strategy (GP) captures patterns of population change, predicts its distribution in new environments, and subsequently maps the results to the decision space;The multi-information hybrid exploration strategy (MHE) takes the feature individual as the representative individual, adaptively guides the population evolution direction in the decision space, thereby enhancing the algorithm’s adaptability. Compared with five advanced algorithms on the non-cyclic dynamic benchmark test set (NCD), the proposed algorithm GPMHE has shown robust adaptation to dynamic environmental changes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Multiobjective optimization

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An Ensemble of UNet Frameworks for Lung Nodule Segmentation 1

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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: (数字)9783031341274

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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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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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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Virtual Synchronous Generator Droop control for Renewable Energy Sources

Virtual Synchronous Generator Droop Control for Renewable En...

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2024 IEEE Global Energy Conference, GEC 2024

作者： Cakmak, Fevzi Aykat, Sukru Kazanbaş, Mehmet Cemil Akgul, Sabahattin Department of Electricity and Energy Mardin Atuklu University Mardin Turkey Department of Computer Engineering Mardin Atuklu University Mardin Turkey Department of Control and Automation Engineering Yildiz Technical University Istanbul Turkey Department of Handicrafts Mardin Atuklu University Mardin Turkey

ISBN: (纸本)9798331532611

The connection of large-scale RES (renewable energy source) to the grid and the increase in the efficiency of energy production also causes different power quality problems. It has become mandatory for these inverter-based systems to participate in load frequency control. For this purpose, the design and simulation of a virtual synchronous generator (VSG), which enables RES to operate like synchronous generators, has been carried out in Matlab/Simulink environment. It is observed that the VSG keeps the load frequency stable according to different load conditions. ©2024 IEEE.

关键词： Synchronous generators

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The supplemental definition of dynamic behavior of partially observable object for phasily limited control task 11

The supplemental definition of dynamic behavior of partially...

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11th IEEE International Conference on Intelligent Systems, IS 2022

作者： Ivashchuk, Viacheslav National University of Food Technologies Dacitcs Department of Automation and Computer-Integrated Technologies of Control Systems Kyiv Ukraine

ISBN: (纸本)9781665456562

each dynamically active object, regardless of its own nature, has limited energy, which limits its ability to change state coordinates. The research has scientific importance for non-stationary objects, which do not allow to estimate the state coordinates directly by measuring system, and the prediction of behavior of these coordinates is complicated by interparametric relationships in the object. The solution is possible by combining the dynamic game on minmax estimates and by the next descending by quadratic criterion, which does not disturb the limitations, which are adequate to real capabilities of the object. The rough of control system are explained by providing only the necessary descending conditions as a formal approximation of a confidence space terms of a complex of space sets, which give the lower and the upper approximation of the original behavior of space of object's variables. © 2022 IEEE.

关键词： minmax phasily limited Riccati Savage risk

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Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 231-239页

作者： Yahui Liu Bin Tian Yisheng Lv Lingxi Li Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 IEEE the Transportation and Autonomous Systems Institute(TASI)and the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis(IUPUI)Indianapolis 46202 USA

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://***/yahuiliu99/PointC onT.

关键词： Content-based Transformer deep learning feature aggregator local attention point cloud classification

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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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On non-von Neumann flexible neuromorphic vision sensors

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npj Flexible Electronics 2024年第1期8卷 638-663页

作者： Hao Wang Bin Sun Shuzhi Sam Ge Jie Su Ming Liang Jin Institute for Future Shandong Key Laboratory of Industrial Control TechnologySchool of AutomationQingdao UniversityQingdao 266071China College of Electronic and Information Engineering Qingdao UniversityQingdao 266071China Department of Electrical and Computer Engineering and Institute for Functional Intelligent MaterialsNational University of SingaporeSingapore 117583Singapore

The structure andmechanismof thehuman visual system contain rich treasures,and surprising effects can be achieved by simulating the human visual *** this article,starting from the human visual system,we compare and discuss the discrepancies between the human visual system and traditional machine vision *** the wide variety and large volume of visual information,the use of nonvon Neumann structured,flexible neuromorphic vision sensors can effectively compensate for the limitations of traditional machine vision systems based on the von Neumann ***,this article addresses the emulation of retinal functionality and provides an overview of the principles and circuit implementation methods of non-von Neumann computing ***,in terms of mimicking the retinal surface structure,this article introduces the fabrication approach for flexible sensor ***,this article analyzes the challenges currently faced by non-von Neumann flexible neuromorphic vision sensors and offers a perspective on their future development.

关键词： Neumann system starting

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Hand Gesture Recognition Using a Multi-modal Deep Neural Network 13th

Hand Gesture Recognition Using a Multi-modal Deep Neural Net...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Fulsunder, Saneet Umar, Saidu Taherkhani, Aboozar Liu, Chang Yang, Shengxiang School of Computer Science and Informatics De Montfort University Leicester United Kingdom Key Laboratory of Networked Control Systems and Digital Factory Department Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9783031579189

As devices around us get more intelligent, new ways of interacting with them are sought to improve user convenience and comfort. While gesture-controlled systems have existed for some time, they either use additional specialized imaging equipment, require unreasonable computing resources, or are simply not accurate enough to be a viable alternative. In this work, a reliable method of recognizing gestures is proposed. The built model correctly classifies hand gestures for keyboard typing based on the activity captured by an ordinary camera. Two models are initially developed for classifying video data and classifying time-series sequences of the skeleton data extracted from a video. The models use different strategies of classification and are built using lightweight architectures. The two models are the baseline models which are integrated to form a single multi-modal model with multiple inputs, i.e., video and time-series inputs, to improve accuracy. The performances of the baseline models are then compared to the multimodal classifier. Since the multimodal classifier is based on the initial models, it naturally inherits the benefits of both baseline architectures and provides a higher testing accuracy of 100% compared to the accuracy of 85% and 75% for the baseline models respectively. © IFIP International Federation for Information Processing 2024.

关键词： Deep neural networks

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