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arXiv 2025年

作者： Jia, Ziye Cao, Yilu He, Lijun Wu, Qihui Zhu, Qiuming Niyato, Dusit Han, Zhu College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China State Key Laboratory of ISN Xidian University Xian710071 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical and Computer Engineering the University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

As an important component of the sixth generation communication technologies, the space-air-ground integrated network (SAGIN) attracts increasing attentions in recent years. However, due to the mobility and heterogeneity of the components such as satellites and unmanned aerial vehicles in multilayer SAGIN, the challenges of inefficient resource allocation and management complexity are aggregated. To this end, the network function virtualization technology is introduced and can be implemented via service function chains (SFCs) deployment. However, urgent unexpected tasks may bring conflicts and resource competition during SFC deployment, and how to schedule the SFCs of multiple tasks in SAGIN is a key issue. In this paper, we address the dynamic and complexity of SAGIN by presenting a reconfigurable time extension graph and further propose the dynamic SFC scheduling model. Then, we formulate the SFC scheduling problem to maximize the number of successful deployed SFCs within limited resources and time horizons. Since the problem is in the form of integer linear programming and intractable to solve, we propose the algorithm by incorporating deep reinforcement learning. Finally, simulation results show that the proposed algorithm has better convergence and performance compared to other benchmark algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Network function virtualization

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Selected Sample Retraining Semi-supervised Learning Method for Aerial Scene Classification 1st

Selected Sample Retraining Semi-supervised Learning Method f...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Tian, Ye Li, Ju Zhang, Liguo Sun, Jianguo Yin, Guisheng College of Computer Science and Technology Harbin Engineering University Harbin150001 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

ISBN: (纸本)9783030930455

The performance of scene classification for remote sensing images based on deep neural networks is limited by the number of labeled data. To alleviate this problem, a variety of methods have been proposed to apply semi-supervised learning to exploit both labeled and unlabeled samples for training classifiers, but most of them still require a certain number of labeled samples considering the complex context relationship and huge spatial differences of remote sensing images. In this paper, we proposed a novel selected sample retraining semi-supervised learning method (S2 R) that is simple but works efficiently on scene classification remote sensing. First, we train several models independently, each model is trained for only a few epochs, and use them to label samples in the unlabeled data set. Then, the labeled unlabeled data set is divided into low-noise labeled data set and sub-unlabeled data set through the high probability sample selection method. Finally, the two segmented data sets are combined with the labeled data sets to train a scene classifier based on the semi-supervised learning method. To verify the effectiveness of the proposed method, it is further compared with several state-of-the-art semi-supervised classification approaches. The results demonstrate that our method consistently outperforms the previous methods on the condition of only a few labeled samples over the scene classification for remote sensing images. © 2021, Springer Nature Switzerland AG.

关键词： Remote sensing

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Dynamic Optimization of Electric Vehicle Charging for Sustainable Power Grids using Sine Hunter-Prey Optimization with GRUs

Dynamic Optimization of Electric Vehicle Charging for Sustai...

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International Conference on Electrical Power and Energy Systems (ICEPES)

作者： Mohd Khizar Syed Prakash Chandra Behera P Joel Josephson Shamim Ahmad Khan Timur Kholmukhamedov Ponni Valavan M Department of Physics Kohinoor college Maharashtra India Department of Science St. Claret College Bangalore India Department of ECE Malla Reddy Engineering College Medchal Malkajgiri Telangana India Glocal School of Science & Technology Glocal University Uttar Pradesh India Automatic Control and Computer Engineering Department Turin Polytechnic University Tashkent Uzbekistan Department of AI & DS K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

The integration of electric vehicles (EVs) into power grids presents a significant challenge for sustainability due to their dynamic charging demands and the need to accommodate renewable energy sources. Existing optimization methods often fail to adequately address the complexity of this problem. In this study, propose a novel approach that combines Sine Hunter-Prey Optimization (SHPO) with Gated Recurrent Units (GRUs) to dynamically optimize EV charging for sustainable power grid operation. Traditional optimization methods struggle to capture the nonlinear and time-varying nature of EV charging demands and renewable energy availability. To overcome this limitation, SHPO's ability to efficiently explore complex solution spaces and GRUs' capability to model temporal dependencies in sequential data. This proposed method optimizes EV charging schedules by considering grid stability, renewable energy utilization, and cost minimization. Validating the approach using historical data on EV charging patterns, renewable energy generation, and grid conditions. Performance metrics include grid stability indices, renewable energy integration rates, and cost savings compared to baseline strategies. Experimental results demonstrate that the method outperforms traditional optimization techniques, leading to more sustainable and resilient power grid operation with 92% stability. Overall, this study contributes to advancing the modern methods in dynamic optimization of EV charging, paving the way for more sustainable transportation and energy systems.

关键词： Renewable energy sources Costs Stability criteria Power system dynamics Optimization methods Power system stability Electric vehicle charging

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Industrial Data Extraction Method Based on Correlation Matrix and Multidimensional Probability Distribution 6

Industrial Data Extraction Method Based on Correlation Matri...

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6th International Conference on Power and Renewable Energy, ICPRE 2021

作者： Peng, Su Yue, Sun Zhihong, Luo China Ship Development and Design Center Wuhan China Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin China North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9781665449618

The operation data of power plant has the characteristics of multivariable and big data, which is widely used in the research of data-driven modeling. Effective data extraction is an important part of data-driven modeling, and the performance of data extraction algorithm directly affects the accuracy of modeling. In this paper, an effective data extraction method based on correlation matrix and vine structure is proposed. The rank correlation coefficient is used to measure the correlation between variables and construct the correlation matrix. According to the correlation matrix, important variables are selected and grouped. Vine structure is used to establish a multi-dimensional joint distribution model for each group of variables. The data with high probability are extracted by confidence interval as effective data. Finally, the extracted results of multiple combinations are combined to form an effective data set. The SCR data are used for example verification, and the results show that the proposed method can extract effective data and improve the correlation between variables significantly. © 2021 IEEE.

关键词： Data mining

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Trajectory Learning and Demonstration of a Robot Manipulator Based-on Sensor Fusion from a Hand-held Tool

Trajectory Learning and Demonstration of a Robot Manipulator...

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International Automatic control Conference

作者： Kai-Tai Song Szu-Chen Ou Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Yang Ming Chiao Tung University Hsinchu Taiwan ROC Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan ROC

ISBN: (数字)9798350354904

ISBN: (纸本)9798350354911

This paper presents a programming system for a robot manipulator using a hand-held demonstration tool. In this system, a user holds the self-designed tool to demonstrate a desired motion path, and the robot arm replicates the learned trajectory. The path-demonstration tool, including optical tags (OT) and an IMU, is designed to collect trajectory data from hand motion. Sensor data fusion allows the system to track accurately the demonstrated trajectory. A calibration procedure is proposed to transform the trajectory from tool’s coordinate system to robot’s coordinate system. A cubic spline-based trajectory planning method is proposed to ensure smooth reproduction of the learned trajectory in both time and space. Experimental results show that the robot is able to replicate squared and curved trajectories collected from the path-demonstration tool. The average position error is within 2.435 mm in XYZ directions and the average orientation error is within 0.65 degrees.

关键词： Trajectory planning Tracking Robot kinematics Transforms Sensor fusion Programming Robot sensing systems Manipulators Trajectory Splines (mathematics)

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An Assessment of Multilevel Converters Submodules EMI Emissions Considering Three Configurations Including Parasitic Parameters

An Assessment of Multilevel Converters Submodules EMI Emissi...

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IEEE International Symposium on Electromagnetic Compatibility - EMC Europe

作者： Amr Madi Waseem Elsayed Ahmed Hebala Michal Przybylski Piotr Lezynski Robert Smolenski Institute of Automatic Control Electronics and Electrical Engineering Doctoral School of Exact and Technical Sciences University of Zielona Gora Zielona Gora Poland Electrical Engineering Mathematics & Computer Science (EEMCS) University of Twente Enschede Netherlands Electrical and Control Engineering College of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Institute of Automatic Control Electronics and Electrical Engineering University of Zielona Gora Zielona Gora Poland

With the expanding use of modular multilevel converters in power installations it became more important to lower the total conducted emissions. One of the most important key factors in controlling electromagnetic interference emissions is the type of submodules used and the utilized switches. This paper compares three well-known submodules which are half bridge, full bridge and double-clamped half bridge effect on the electromagnetic interference emissions. For making the model more accurate the parasitic components were included in all the circuits Additionally, the same configurations are carried out and compared under two switches which are insulated gate bipolar transistors and metal oxide semiconductor field effect transistors operating at two different switching frequencies. The results were obtained by a simulation in a Matlab/Simulink environment.

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LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection

arXiv

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arXiv 2024年

作者： Tian, Jiwei Shen, Chao Wang, Buhong Xia, Xiaofang Zhang, Meng Lin, Chenhao Li, Qian Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China Air Traffic Control and Navigation College Air Force Engineering University Xi’an China Information and Navigation College Air Force Engineering University Xi’an China School of Computer Science and Technology Xidian University Xi’an China

—Deep learning methods can not only detect false data injection attacks (FDIA) but also locate attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep learning vulnerabilities have been studied in the field of single-label FDIA detection, the adversarial attack and defense against multi-label FDIA locational detection are still not involved. To bridge this gap, this paper first explores the multi-label adversarial example attacks against multi-label FDIA locational detectors and proposes a general multi-label adversarial attack framework, namely muLti-labEl adverSarial falSe data injectiON attack (LESSON). The proposed LESSON attack framework includes three key designs, namely Perturbing State Variables, Tailored Loss Function Design, and Change of Variables, which can help find suitable multi-label adversarial perturbations within the physical constraints to circumvent both Bad Data Detection (BDD) and Neural Attack Location (NAL). Four typical LESSON attacks based on the proposed framework and two dimensions of attack objectives are examined, and the experimental results demonstrate the effectiveness of the proposed attack framework, posing serious and pressing security concerns in smart grids. © 2024, CC BY.

关键词： Location

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Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection

arXiv

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arXiv 2024年

作者： Adams, Steven Patanè, Andrea Lahijanian, Morteza Laurenti, Luca Delft Center for Systems and Control Technical Universiy of Delft Delft2628 CD Netherlands School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Department of Aerospace Engineering Sciences and Computer Science University of Colorado Boulder BoulderCO80303 United States

Infinitely wide or deep neural networks (NNs) with independent and identically distributed (i.i.d.) parameters have been shown to be equivalent to Gaussian processes. Because of the favorable properties of Gaussian processes, this equivalence is commonly employed to analyze neural networks and has led to various breakthroughs over the years. However, neural networks and Gaussian processes are equivalent only in the limit;in the finite case there are currently no methods available to approximate a trained neural network with a Gaussian model with bounds on the approximation error. In this work, we present an algorithmic framework to approximate a neural network of finite width and depth, and with not necessarily i.i.d. parameters, with a mixture of Gaussian processes with error bounds on the approximation error. In particular, we consider the Wasserstein distance to quantify the closeness between probabilistic models and, by relying on tools from optimal transport and Gaussian processes, we iteratively approximate the output distribution of each layer of the neural network as a mixture of Gaussian processes. Crucially, for any NN and ǫ > 0 our approach is able to return a mixture of Gaussian processes that is ǫ-close to the NN at a finite set of input points. Furthermore, we rely on the differentiability of the resulting error bound to show how our approach can be employed to tune the parameters of a NN to mimic the functional behavior of a given Gaussian process, e.g., for prior selection in the context of Bayesian inference. We empirically investigate the effectiveness of our results on both regression and classification problems with various neural network architectures. Our experiments highlight how our results can represent an important step towards understanding neural network predictions and formally quantifying their uncertainty. © 2024, CC BY.

关键词： Deep neural networks

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Vision Transformers(ViT) Pretraining on 3D ABUS Image and Dual-CapsViT: Enhancing ViT Decoding via Dual-Channel Dynamic Routing

Vision Transformers(ViT) Pretraining on 3D ABUS Image and Du...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingwang Xu Wei Wang Kuanquan Wang Suyu Dong Pengzhong Sun Jinwei Sun Gongning Luo Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China

Breast cancer continues to be a pressing global health concern, emphasizing the essential need for effective diagnostic techniques. Automated Breast Ultrasound Systems (ABUS) provide a promising advance in breast tumor detection, yet they require significant expertise in interpreting 3D ABUS images, a task fraught with distinctive challenges. Although Vision Transformers (ViT) display remarkable potential for image processing, their low inductive bias and significant data requirements pose obstacles, particularly in the data-constrained medical field. To mitigate these issues, we introduce a Mask-Recover strategy for pretraining Transformer models on 3D ABUS images, enhancing model adaptability and reducing the data demands of the ViT model. Moreover, recognizing the risk that ViTs’ average pooling approach may unintentionally mask small but vital features, we propose Dual-CapsViT, an inventive model combining Transformers and Capsule Networks. This integration affords efficient token routing while preserving fine-grained details. To reconcile potential inconsistencies between capsules and tokens, we engineer a novel dual-channel routing algorithm, strengthening the decoder’s performance. We benchmarked our models against well-known standards such as ResNet and ViT for classifying breast tumors in ABUS images. Our models exhibited superior performance, as evidenced by improved accuracy, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC) metrics, thereby affirming Dual-CapsViT’s potential to enhance breast cancer diagnostics.

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Toward parallel intelligence: An interdisciplinary solution for complex systems

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The Innovation 2023年第6期4卷 152-164页

作者： Yong Zhao Zhengqiu Zhu Bin Chen Sihang Qiu Jincai Huang Xin Lu Weiyi Yang Chuan Ai Kuihua Huang Cheng He Yucheng Jin Zhong Liu Fei-Yue Wang College of Systems Engineering National University of Defense TechnologyChangsha 410073China Hunan Institute of Advanced Technology Changsha 410073China Shanghai Institute of Infectious Disease and Biosecurity Shanghai 200032China Institute of Epidemiology Helmholtz Zentrum Minchen-German Research Center for Environmental Health(GmbH)NeuherbergGermany Department of Computer Science Hong Kong Baptist UniversityHong KongChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling,analysis,management,and *** meet these demands,the parallel systems method rooted in the artificial systems,computational experiments,and parallel execution(ACP)approach has been *** method cultivates a cycle termed parallel intelligence,which iteratively creates data,acquires knowledge,and refines the actual *** the past two decades,the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines,offering vers atile interdisciplinary solutions for complex systems across diverse *** review explores the origins and fundamental concepts of the parallel systems method,showcasing its accomplishments as a diverse array of parallel technologies and applica-tions while also prognosticating potential *** posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation.

关键词： execution artificial offering

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