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An embedded vertical‐federated feature selection algorithm based on particle swarm optimisation

CAAI Transactions on Intelligence Technology 2023年第3期8卷 734-754页

作者： Yong Zhang Ying Hu Xiaozhi Gao Dunwei Gong Yinan Guo Kaizhou Gao Wanqiu Zhang School of Information and Control Engineering China University of Mining and TechnologyXuzhouChina The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina School of Computing University of Eastern FinlandKuopioFinland The Macao Institute of Systems Engineering Macao University of Science and TechnologyTaipaChina

In real life,a large amount of data describing the same learning task may be stored in different institutions(called participants),and these data cannot be shared among par-ticipants due to privacy *** case that different attributes/features of the same instance are stored in different institutions is called vertically distributed *** pur-pose of vertical‐federated feature selection(FS)is to reduce the feature dimension of vertical distributed data jointly without sharing local original data so that the feature subset obtained has the same or better performance as the original feature *** solve this problem,in the paper,an embedded vertical‐federated FS algorithm based on particle swarm optimisation(PSO‐EVFFS)is proposed by incorporating evolutionary FS into the SecureBoost framework for the first *** optimising both hyper‐parameters of the XGBoost model and feature subsets,PSO‐EVFFS can obtain a feature subset,which makes the XGBoost model more *** the same time,since different participants only share insensitive parameters such as model loss function,PSO‐EVFFS can effec-tively ensure the privacy of participants'***,an ensemble ranking strategy of feature importance based on the XGBoost tree model is developed to effectively remove irrelevant features on each ***,the proposed algorithm is applied to 10 test datasets and compared with three typical vertical‐federated learning frameworks and two variants of the proposed algorithm with different initialisation ***-mental results show that the proposed algorithm can significantly improve the classifi-cation performance of selected feature subsets while fully protecting the data privacy of all participants.

关键词： Evolutionary optimization feature selection privacy protection vertical‐federated learning

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Optimal Configuration of Multitype Energy Storage for Integrated Energy Systems Considering System Reserve Value

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 2114-2126页

作者： Xun Dou Jun Wang Zhenyuan Zhang Min Gao College of Electrical Engineering and Control Science Nanjing Tech UniversityNanjing 211816China Research Institute of NARI Technology Co. Ltd.Nanjing 211106China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Falcon Computing Solutions 10880 Wilshire Blvd Suite 1132Los AngelesCA 90024USA

An integrated energy system(IES)contributes to improving energy efficiency and promoting sustainable energy *** different dynamic characteristics of the system,such as demand/response schemes and complex coupling characteristics among energy sources,siting and sizing of multitype energy storage(MES)are very important for the economic operation of the *** the effect of the diversity of the IES on system reserve based on electricity,gas and heat systems in different scenarios,a two-stage MES optimal configuration model,considering the system reserve value,is *** the first stage,to determine the location and charging/discharging strategies,a location choice model that minimizes the operating cost,considering the system reserve value,is *** the second stage,a capacity choice model,to minimize the investment and maintenance cost of the MES,is ***,an example is provided to verify the effectiveness of the MES configuration method in this paper in handling operational diversity and ensuring system *** with the configuration method that disregards the system reserve value,the results show that the MES configuration method proposed in this paper can reduce the annual investment cost and operating cost and improve the system reserve value.

关键词： Integrated energy system multitype energy storage optimal configuration renewable source system reserve value

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Secure Semantic Communication for Image Transmission in the Presence of Eavesdroppers

Secure Semantic Communication for Image Transmission in the ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Tang, Shunpu Liu, Chen Yang, Qianqian He, Shibo Niyato, Dusit Zhejiang University College of Information Science and Electronic Engineering Hangzhou China Zhejiang University College of Control Science and Engineering Hangzhou China Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798350351255

Semantic communication (SemCom) has emerged as a key technology for the forthcoming sixth-generation (6G) network, attributed to its enhanced communication efficiency and robustness against channel noise. However, the open nature of wireless channels makes them vulnerable to eavesdropping, which poses a serious threat to privacy. To address this issue, we propose a novel secure semantic communication (SemCom) approach for image transmission, which integrates steganography technology to conceal private information within non-private images (host images). Specifically, we propose an invertible neural network (INN)-based signal steganography approach that embeds channel input signals of a private image into those of a host image before transmission. This ensures that the original private image can be reconstructed from the received signals at the legitimate receiver, while the eavesdropper can only decode the information of the host image. Simulation results demonstrate that the proposed approach maintains comparable reconstruction quality of both host and private images at the legitimate receiver, compared to scenarios without any secure mechanisms. Moreover, the results indicate that the eavesdropper is only able to reconstruct host images, showcasing the enhanced security provided by our approach. © 2024 IEEE.

关键词： Steganography

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PITD3-based Autonomous Evasion Decision-making Method for Aircraft 43

PITD3-based Autonomous Evasion Decision-making Method for Ai...

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43rd Chinese control Conference, CCC 2024

作者： Liu, Chang Dou, Liqian Zhang, Ruilong Tang, Yifan Ren, Mengyuan Zhang, Lun School of Electrical Engineering and Automation Tianjin300072 China Beijing Aerospace Automatic Control Institute China Tianjin Aerospace Institute of Computing Technology Tianjin300308 China

ISBN: (纸本)9789887581581

Outstanding autonomous evasion decision-making capability has an important significance for ensuring flight safety and enhancing the autonomy of the aircraft. In this paper, a prediction information-based TD3 (PITD3) evasion decision-making algorithm for aircraft is proposed, which can autonomously evade the interceptors' attack in an antagonistic environment. Firstly, an antagonistic environment is described according to environmental constraints and the dynamics of the aircraft and interceptor. Secondly, the autonomous decision-making process of the aircraft facing the threat target is modelled as a Markov decision process to describe the state information of the aircraft and interceptor. Finally, an autonomous evasion decision-making method based on the twin delayed deep deterministic policy gradient (TD3) is designed to find the optimal strategy, and the effectiveness of the proposed autonomous evasion decision-making method is demonstrated by simulation results and comparative experiments. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Markov processes

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Research on Geospatial Object Recognition Algorithm in Power Transmission and Transformation engineering Based on FIseg-YOLO 1

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Ouyang, Peng Pi, Yufei Zhang, Pengcheng Zhang, Kun School of Electrical Engineering and Automation Nantong University Nantong China Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong Institute of Technology Jiangsu Nantong China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

The preliminary work of power transmission and transformation engineering design tasks relies on the precise survey of terrain to ensure the high-quality completion of design tasks. Therefore, developing a technology that can accurately and efficiently survey the construction area has become a hot topic in the field of electrical design research. However, identifying the location and precise boundaries of geospatial targets of different scales and densities from high-resolution natural environment images captured by drones remains a challenge. To address this issue, we improved upon the YOLOv8 algorithm by incorporating the concept of the YOLACT algorithm, proposing a novel instance segmentation algorithm—FIseg-YOLO, specifically designed for recognizing geospatial targets in power transmission and transformation line projects. This algorithm integrates various attention mechanism modules in the backbone network, effectively improving the accuracy of target recognition against complex backgrounds. Simultaneously, we optimized the neck network structure by enhancing the multi-path fusion of the network, achieving multi-level feature fusion between different layers. Additionally, the algorithm adds a multi-scale detection head to the basic network structure to extract shallow information from the input image and combine it with the feature fusion network to detect objects across a broader scale. Experimental validation on the geospatial orthophoto dataset shows that the mAP@.5 of the FIseg-YOLO model reached 67.3%, a 6.6% increase compared to the YOLOv8 base model, indicating a significant improvement in model performance. This improvement is of great significance for enhancing the efficiency of preliminary survey tasks in power transmission and transformation engineering. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Analysis of Mimetic Muscle Rehabilitation: Multi-platform Facemarks Detection 1

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6th Computational Methods in Systems and Software, CoMeSySo 2022

作者： Tomeš, Jakub Vácha, Ondřej Štícha, Karel Kohout, Jan Mareš, Jan Department of Computing and Control Engineering University of Chemistry and Technology in Prague Technická 1905/5 Praha 616628 Czech Republic

ISBN: (数字)9783031214356

ISBN: (纸本)9783031214349

The paper deals with frameworks for the description of mimetic muscle rehabilitation of patients with facial palsy. Detection models on different platforms (Android, iOS, Windows) are compared: ARCore Augmented Faces, ARKit Face Tracking, and MediaPipe Face Mesh. They can provide data for further quantitative analysis of mimetic muscle dysfunction. The real-time nature of these facemark detectors allows for direct feedback in the form of automatic mimetic muscle detection to the patient during the rehabilitation session. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： ARCore ARKit Facemarks Facial palsy MediaPipe Mimetic exercises Rehabilitation

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Research on Fault Diagnosis of Spacecraft control System Based on Graph Neural Network 8

Research on Fault Diagnosis of Spacecraft Control System Bas...

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8th International Conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Ning, Zhicheng Liu, Xiaoxiang Li, Xinyuan Liu, Jun Beijing Insitute of Control Engineering Science and Technology on Space Intelligent Control Laboratory Beijing China Beijing University of Posts and Telecommunications Intelligent Perception and Computing Research Center Beijing China

ISBN: (纸本)9798350302455

The fault diagnosis technology of spacecraft control systems is a crucial component of spacecraft health management. Traditional fault diagnosis technologies, such as model-based diagnosis and signal processing, are relatively mature but are limited in their ability to fully utilize information in big data environments. Neural network-based fault diagnosis methods often overlook the sequential nature of spacecraft data as time series data. Given that the state of a spacecraft at any given time is closely related to preceding data, it is possible to conduct fault diagnosis by extracting relevant features from fault sample data. In this paper, we propose a graph neural network-based fault diagnosis method for spacecraft control systems. To address the issue of limited fault samples, we construct a spacecraft control system model and generate various types of gyro and flywheel fault data through fault injection. Utilizing the Graphstar model - a powerful graph neural network with strong information mining capabilities - we transform one-dimensional fault data into graph data with spatial structure and extract relevant characteristics through deep learning to diagnose faults. Simulation results demonstrate that our method can effectively diagnose faults with high accuracy. © 2023 IEEE.

关键词： Failure analysis

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Research on Mixing of Virtual and Actual Reality for Motor Augmented Assembly 23

Research on Mixing of Virtual and Actual Reality for Motor A...

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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Zhang, Haowei Shao, Xuqiang Wu, Xuan School of Control and Computer Engineering North China Electric Power University China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education China

ISBN: (纸本)9781450398572

The effect of the mixing of virtual and actual reality will directly affect the realism and immersion of the augmented assembly system. The correct occlusion relationship and collision interaction between virtual and real objects are crucial to the fusion effect. The existing virtual-real occlusion handling methods cannot be applied to the augmented assembly system with complex contours and changeable scenes. Therefore, an occlusion handling method based on "virtual avatar"of motor equipment is proposed, which has strong robustness and real-time performance. Aiming at the problem that the number of wrong matching pairs of feature points is too large in the 3D registration process, we combine the method of Cosine similarity measurement on the basis of ORB algorithm to improve the accuracy of feature point matching. The current virtual and real object collision detection methods have large computation and low detection accuracy. We propose an octree hierarchical collision detection optimization algorithm based on model surface voxelization representation and volume difference, which improves the detection accuracy on the basis of ensuring computational efficiency. The experimental results show that the proposed methods have high real-time performance and accuracy, and greatly improve the realism and immersion of the augmented assembly system. © 2023 ACM.

关键词： Augmented reality

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A General Method for Power Equipment State Classification Using Time Series Data Based on Attention Mechanism

A General Method for Power Equipment State Classification Us...

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2023 IEEE International Conference on Power Science and Technology, ICPST 2023

作者： Zhao, Wenqing Wang, Jifa Chen, Rui Liu, Liang Xu, Lijiao North China Electric Power University School of Control and Computer Engineering Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education Baoding China North China Electric Power University School of Control and Computer Engineering Baoding China

ISBN: (纸本)9798350311358

With refinement of power equipment, the identification and classification of its operation states is quite important for the stable operation of the system. As the power equipment states are rather complicated, it is difficult to realize general classification due to the limitation of data types and model characteristics when using deep learning. In the light of this, based on the time series data of various power equipment, an equipment state classification method is proposed by combining Recurrent Neural Network (RNN), Long short-term memory (LSTM) and attention mechanism. Firstly, RNN is used as the bottom frame network, and the LSTM core network integrating attention mechanism is added to construct the complex Feature Extraction Module (FEM). Secondly, main feature extraction is implemented in the LSTM network integrated with attention mechanism. Then, the extracted features are input into the complex FEM model and trained to match the evaluation state and obtain the evaluation results. In this paper, confusion matrix, F1micro and accuracy are used as evaluation indexes. Three cases were selected to verify the method and five groups of comparison models were set for each case. Results show that the proposed method has good accuracy in power equipment state classification and also has strong generalization ability in other data classification. © 2023 IEEE.

关键词： Long short-term memory

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Chinese Image Description Evaluation Method Based on Target Domain Semantic Constraints 3

Chinese Image Description Evaluation Method Based on Target ...

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3rd International Conference on Image Processing and Intelligent control, IPIC 2023

作者： Wang, Zhenhao Sun, Wenyi Wang, Zhengsong Yang, Le School of Computing and Communication Engineering Northeastern University at Qinhuangdao Haigang Hebei Qinhuangdao China School of Control Engineering Northeastern University at Qinhuangdao Haigang Hebei Qinhuangdao China

ISBN: (纸本)9781510668140

To address the problems of insufficient accuracy and difficulty of application in the current Chinese image description field, this paper proposes an evaluation method based on semantic constraints in the target domain. Unlike previous research, this method acts on the output stage of the model, and based on the extraction of key semantics in the target application domain, it is constrained by the macroscopic semantic space of that domain or by introducing external semantic information from other visual tasks. The experiments show that the proposed method effectively improves the semantic coherence between the model output description sentences and the input images in the target domain, and is helpful for the practical application of image description in specific domains. © 2023 SPIE.

关键词： Semantics

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