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Conference on Vehicle control and Intelligence (CVCI)

作者： Xu Zhao Hongyan Guo Wanqing Shi College of Communication Engineering Jilin University Changchun China Department of Aircraft Control and Information Engineering Jilin Institute of Chemical Technology Changchun China State Key Laboratory of Automotive Simulation and Control College of Communication Engineering Jilin University Changchun China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

Designing a data-driven predictive controller for lateral stability of vehicles on compacted-snow rutted roads, especially when the data is subjected to non-stationary noise disturbances, is a significant challenge. This paper introduces a novel subspace predictive equation construction method that incorporates filtering using the inverse of a first-order disturbance model. Based on this, a filtering data-driven predictive controller is designed. The proposed method is tested through numerical simulations and MATLAB/CarSim co-simulation. Simulation results show that the proposed subspace predictive equation with filtering effectively mitigates the variance and bias introduced by non-stationary noise. The co-simulation results demonstrate that the controller ensures the stability and robustness of the vehicle system.

关键词： Filtering Roads Predictive models Numerical simulation Mathematical models Stability analysis Robustness Numerical models Numerical stability Predictive control

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Blockchain-Assisted Electronic Medical Data-Sharing:Developments,Approaches and Perspectives

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Computers, Materials & Continua 2024年第12期81卷 3421-3450页

作者： Chenquan Gan Xinghai Xiao Qingyi Zhu Deepak Kumar Jain Akanksha Saini School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Artificial IntelligenceDalian University of TechnologyDalian116024China Symbiosis Institute of Technology Symbiosis International UniversityPune412115India College of Business and Law RMIT UniversityMelbourneVIC 3000Australia

Medical blockchain data-sharing is a technique that employs blockchain technology to facilitate the sharing of electronic medical *** blockchain is a decentralized digital ledger that ensures data-sharing security,transparency,and traceability through cryptographic technology and consensus ***,medical blockchain data-sharing methods have garnered significant attention and research ***,current methods have different storage and transmission measures for original data in the medical blockchain,resulting in large differences in performance and ***,we divide the medical blockchain data-sharing method into on-chain sharing and off-chain sharing according to the original data storage *** them,off-chain sharing can be subdivided into on-cloud sharing and local sharing according to whether the data is ***,we provide a detailed analysis of basic processes and research content for each ***,we summarize the challenges posed by the current methods and discuss future research directions.

关键词： Electronic medical data data-sharing medical blockchain cloud federated learning

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Cross-receiver specific emitter identification based on deep adversarial neural network with separated batch normalization 3

Cross-receiver specific emitter identification based on deep...

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3rd International Conference on Computer Science and communication technology, ICCSCT 2022

作者： Huang, Keju Jiang, Bin Yang, Junan Wang, Jian Hu, Pengjiang Li, Yiming Shao, Kun Zhao, Dongxing College of Electronic Engineering National University of Defense Technology Hunan Changsha China Science and Technology on Communication Information Security Control Laboratory Zhejiang Jiaxing314000 China

ISBN: (纸本)9781510661240

Specific emitter identification (SEI) is the approach to identify emitter individuals using received wireless signals. Despite the fact that deep learning has been successfully applied in SEI, the performance is still unsatisfying when the receiver changes. In this paper, we introduce a domain adaptation method, namely deep adversarial neural network (DANN), for cross-receiver SEI. Furthermore, separated batch normalization (SepBN) is proposed to improve the performance. Results of experiments using real data show that the proposed SepBN-DANN method performs desirably for cross-receiver SEI. © 2022 SPIE.

关键词： Seebeck effect

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OS-SEI: Open-Set Specific Emitter Identification Based on Outlier Exposure and Label Smoothing

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IEEE Internet of Things Journal 2025年

作者： Wang, Juzhen Wang, Hanhong Wang, Wei Zha, Haoran Hangzhou Dianzi University School of Communication Engineering Hangzhou310018 China Binjiang Institute of Artificial Intelligence of Zhejiang University of Technology Hangzhou310052 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China Harbin Engineering University College of Information and Communication Engineering Harbin150009 China

Specific Emitter Identification (SEI), based on the inevitable hardware imperfections of emitters, plays a crucial role in physical layer security, independent of upper-layer cryptographic methods. In recent years, SEI combined with deep learning has demonstrated exceptional performance and considerable potential. However, most SEI methods are designed for closed, independent identically distributed environments, which contrasts with real-world open scenarios. This discrepancy leads to significant security vulnerabilities when deploying models in practical applications. To address this challenge, we propose a novel Open-Set SEI (OS-SEI) framework, Outlier Exposure with Label Smoothing (OELS), which utilizes partial exposure to auxiliary anomalous data during the training phase and introduces specific loss functions to mitigate the open-set risk. By constructing smooth decision boundaries through label smoothing and applying thresholds to class confidence, our SEI framework demonstrates robust open-set authentication capabilities. Extensive experiments conducted on ADS-B and WiFi datasets show that the OELS method achieves outstanding open-set recognition performance, with F1 scores of 83.77% and 93.14% at an openness level of 0.10557. Furthermore, ablation experiments confirm the effectiveness and potential of our framework’s components. By increasing the diversity of auxiliary unknown data, we further improve performance in OS-SEI tasks. © 2014 IEEE.

关键词： Hardware security

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Comparative Analysis of Machine Learning Models for Solar Power Prediction Using Time Series Weather Data

Comparative Analysis of Machine Learning Models for Solar Po...

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IEEE International Conference on Power Electronics, Intelligent control and Energy Systems (ICPEICES)

作者： Sumedh Nilesh Kane Akshit Rathore Abhijeet Sanya Anees Arati Kane Electronics and Communication Engineering Netaji Subhas University of Technology Delhi India Instrumentation and Control Engineering Bharati Vidyapeeth College of Engineering Delhi India

ISBN: (数字)9798350387193

ISBN: (纸本)9798350387209

Smart Grids preside over traditional power grids due to their efficiency and effectiveness. Smart grid implementation has benefited all components of the power system. These grids usually generate energy from renewable energy resources. However, the stability, reliability, and seamless functioning of the grid are adversely affected by the highly weather-dependent characteristics of renewable energy sources. As a result, accurate energy generation forecasting is an essential requirement for the grid's steady and reliable operation. The forecasting techniques for the quantity of electricity produced by solar power plants are discussed in this study. First, the impact of weather on solar power generation is investigated. Second, a machine learning method for solar panel output power prediction based on weather conditions is proposed. Finally, an ensemble approach utilizing different algorithms for machine learning is utilized to boost the accuracy of the proposed model. For the objective of creating prediction models, we utilized a variety of techniques, including temporal convolutional neural networks, fine trees, support vector machines with different kernel functions, artificial neural networks, and long-short-term memory RNNs. Each model's prediction accuracy has been assessed by comparing and contrasting these different methods. A comparative investigation indicated that the Temporal Convolutional Network-based model trumps the others in terms of efficiency. The proposed ensemble model exceeded the conventional individual models after performance comparison with the standard approaches.

关键词： Support vector machines Renewable energy sources Accuracy Predictive models Power system stability Smart grids Power system reliability Reliability Forecasting Meteorology

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Solar Radiation Forecasting Based on Random Forest and XGBoost 7

Solar Radiation Forecasting Based on Random Forest and XGBoo...

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7th International Conference on Green technology and Sustainable Development, GTSD 2024

作者： Nguyen, Nhat-Truong Tuong Dao, Thai Cat Tuyen Nguyen, Le Nhut Nguyen, Thanh-Dat Le, Minh-Vuong French, Isobel Timothea Doan, Van-Tuong Kim Vo, Van Hoang Tran, Nghi C. Pham, Duc-Hung Nguyen, Van-Trung Ho Chi Minh City University of Technology and Education Department of Renewable Energy Viet Nam Ho Chi Minh City Industry and Trade College Department of Control Engineering and Automation Viet Nam LILAMA2 International Technology College Dongnai Viet Nam Institute of Cognitive Neuroscience National Central University Taiwan College of Information and Communication Technology Can Tho University Viet Nam Hung Yen University of Technology and Education Faculty Electrical and Electronic Engineering Viet Nam

ISBN: (纸本)9798350386073

Renewable energy offers a viable alternative to depleting fossil fuels, contributing to global warming and greenhouse gas emissions. Thus, solar energy is widely favored as a renewable energy source. However, the output power of solar systems is unstable and is highly dependent on environmental circumstances such as radiation, humidity, temperature, and wind. Therefore, accurately estimating solar radiation is crucial for successful integration of the solar system into existing electrical grid systems. In this study, two different machine learning models, XGBoost and Random Forest, were used to predict solar radiation data in Ho Chi Minh City, Vietnam. The results show that RMSE and MAE of the Random Forest model (61.09 and 24.37) is smaller compared to XGBoost (61.31 and 25.08). Regression value of the two models is close to 0.955. In summary, this paper demonstrates that these two machine learning algorithms effectively forecast solar radiation, with the Random Forest model showing better performance. © 2024 IEEE.

关键词： Solar irradiance

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Fe_(2)O_(3)-decorated boron/nitrogen-co-doped carbon nanosheets as an electrochemical sensing platform for ultrasensitive determination of paraquat in natural water

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Chinese Chemical Letters 2023年第1期34卷 446-451页

作者： Meng Li Xinzhong Wang Yelin Zhu Xiuxiu Jia Shusheng Zhang Huaisheng Wang Yongtao Li Guangzhi Hu School of Electronic Communication Technology Shenzhen Institute of Information TechnologyShenzhen 518172China College of Chemistry Zhengzhou UniversityZhengzhou 450000China Institute for Ecological Research and Pollution Control of Plateau Lakes School of Ecology and Environmental ScienceYunnan UniversityKunming 650504China School of Materials Science and Engineering Liaocheng UniversityLiaocheng 252000China Joint Institute for Environmental Research and Education College of resources and environmentSouth China Agricultural UniversityGuangzhou 510642China

Boron/nitrogen-co-doped carbon(BCN)nanosheets decorated with Fe_(2)O_(3) nanocrystals(Fe_(2)O_(3)–BCN)were cast on a glassy carbon electrode(GCE)and applied as an electrochemical sensor to effectively detect paraquat(PQ),a toxic herbicide,in aqueous environments.A linear experiment performed using square wave voltammetry(SWV)under optimized experimental conditions produced a decent linear relationship and a low detection limit(LOD)of 2.74 nmol/L(S/N=3).Repeatability,reproducibility,stability,and interference experiments confirmed that the Fe_(2)O_(3)–BCN/GCE system exhibited decent electrochemical sensing performance for PQ ***,the designed sensor showed high selectivity and a decent linear relationship with PQ concentration in natural water *** the best of our knowledge,this is the first study on the preparation of Fe_(2)O_(3)–BCN nanosheets for PQ *** proposed sensor can be employed as an effective alternative tool for distinguishing and processing PQ.

关键词： Paraquat Fe_(2)O_(3)-BCN Electrochemical sensor Contaminant detection

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AUV Underwater Docking Guidance Method Based on Rotating Current Field

AUV Underwater Docking Guidance Method Based on Rotating Cur...

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2024 OES China Ocean Acoustics, COA 2024

作者： Liu, Jiahui Wang, Zuoshuai Yang, Wentie Liu, Lanyi Xu, Yidong Harbin Engineering University College of Information and Communication Engineering Harbin China Wuhan Second Ship Design and Research Institute Hubei Key Laboratory of Marine Electromagnetic Detection and Control Wuhan China Hygon Information Technology Co. Ltd. Wuhan China Yantai Research Institute of Harbin Engineering University Yantai China

ISBN: (纸本)9798350340297

Autonomous underwater vehicles (AUV) have been increasingly employed in marine resource exploration and development tasks;however, maintaining of long-term operations requires energy supplementation, which mandates docking with platforms. To address the challenges of AUV docking in intricate oceanic environments, this study proposes a novel docking guidance solution, grounded in the rotating current field mechanism. This approach employs multiple pairs of dipoles to transmit fixed phase difference signals, thereby creating a stable and uniform low-frequency current field region. Furthermore, based on the signal amplitude and phase characteristics relative to position, this paper introduces three AUV docking guidance methods able to achieve precise positioning and expedite the docking process. Experimental findings demonstrate that the proposed docking guidance method offers notable advantages in terms of positioning accuracy and feasibility of implementation, thus presenting a novel technical avenue for AUV underwater docking guidance. © 2024 IEEE.

关键词： Marine navigation

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Underwater Electric Dipole Source Localization Based on Hybrid Particle Swarm Optimization-Gravitational Search Algorithm

Underwater Electric Dipole Source Localization Based on Hybr...

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2024 OES China Ocean Acoustics, COA 2024

作者： Liu, Jiahui Yang, Wentie Wang, Zuoshuai Chen, Jiong Xu, Yidong College of Information and Communication Engineering Harbin Engineering University Harbin China Wuhan Second Ship Design and Research Institute Hubei Key Laboratory of Marine Electromagnetic Detection and Control Wuhan China SiEngine Technology Co. Ltd. Wuhan China Yantai Research Institute of Harbin Engineering University Yantai China

ISBN: (纸本)9798350340297

The localization process of the underwater electric dipole source involves solving the global optimal solution of a nonlinear function, which necessitates a multidimensional search and remarkable computational resources. Challenges including slow solution speed and the risk of missing the global optimal solution also exist. To address these challenges, in this study, a global search and localization method based on normalized electric field amplitude, which simultaneously matches and solves the target source position and dipole moment, is proposed. Moreover, the particle swarm optimization (PSO) algorithm is integrated with the gravitational search (GSA) algorithm to propose the improved time-varying inertia-weighted PSO and GSA hybrid optimization algorithm (TVIW-PSO-GSA), enabling rapid optimization and solution of underwater electric dipole source localization. The proposed algorithm exhibits strong convergence capabilities while ensuring local search accuracy. The simulation results reveal that the proposed algorithm achieves faster convergence speed and more accurate estimation performance when compared with traditional optimization algorithms. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Primary User Adversarial Attacks on Deep Learning-Based Spectrum Sensing and the Defense Method

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China communications 2021年第12期18卷 94-107页

作者： Shilian Zheng Linhui Ye Xuanye Wang Jinyin Chen Huaji Zhou Caiyi Lou Zhijin Zhao Xiaoniu Yang Science and Technology on Communication Information Security Control Laboratory Jiaxing314000China College of Information Engineering Zhejiang University of TechnologyHangzhou310012China Institute of Cyberspace Security Zhejiang University of TechnologyHangzhou310012China School of Communication Engineering Hangzhou Dianzi UniversityHangzhou310018China

The spectrum sensing model based on deep learning has achieved satisfying detection per-formence,but its robustness has not been *** this paper,we propose primary user adversarial attack(PUAA)to verify the robustness of the deep learning based spectrum sensing *** adds a care-fully manufactured perturbation to the benign primary user signal,which greatly reduces the probability of detection of the spectrum sensing *** design three PUAA methods in black box *** or-der to defend against PUAA,we propose a defense method based on autoencoder named *** apply the long short-term memory network and the convolutional neural network together to DeepFilter,so that it can extract the temporal and local features of the input signal at the same time to achieve effective *** experiments are conducted to eval-uate the attack effect of the designed PUAA method and the defense effect of *** show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing *** addition,the experimen-tal results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA with-out affecting the detection performance of the model.

关键词： spectrum sensing cognitive radio deep learning adversarial attack autoencoder defense

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