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8th CAA International Conference on Vehicular control and Intelligence, CVCI 2024

作者： Zhang, Dongxue Liu, Huiwen Jiao, Xiaohong Zhang, Ting Yanshan University Hebei Key Laboratory of Industrial Computer Control Engineering Hebei Province Qinhuangdao066004 China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (纸本)9798331504892

Navigating the intricate traffic environment, lane-changing has emerged as a frequent and essential driving maneu-ver for intelligent vehicles. However, it is challenging to guarantee the safety of the intelligent vehicle and the trajectory tracking accuracy against the external environment during lane-changing. Considering these challenges to overcome, the model predictive control (MPC)-based trajectory tracking strategy incorporating an autonomous lane-changing mechanism is proposed in this paper. The autonomous lane-changing mechanism considering lane-changing intention is proposed to ensure that the intelligent vehicle generates safe lane-changing decisions and trajectories. Subsequently, the MPC strategy that considers the physical char-acteristics of the intelligent vehicle is proposed to guarantee the accuracy and smoothness of trajectory tracking. The validation of its effectiveness and advantages is established on the PreScan and MATLAB/Simulink co-simulation platforms. © 2024 IEEE.

关键词： Predictive control systems

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Differential Privacy Protection Based on Federated Learning in Mobile Crowdsensing

Differential Privacy Protection Based on Federated Learning ...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Liu, Qi Wang, Yingjie Zhao, Weitao Qiu, Xuqiang School of Computer and Control Engineering Yantai University Yantai China Yantai Chijiu Clock Co. Ltd. Yantai China

ISBN: (纸本)9798350304602

Mobile Crowdsensing (MCS), as a novel data acquisition paradigm in the Internet of Things (IoT), incentivizes a large number of participants to collaboratively sense data for providing real-time services and accomplishing complex sensing tasks to benefit society. However, a major challenge hindering the further development of MCS is the risk of privacy leakage of participant data. In this paper, an effective integration of Federated Learning (FL) with MCS is proposed. The classic Federated Averaging (FedAvg) algorithm is enhanced, and differential privacy (DP) is introduced to locally preserve the privacy of sensitive user data, referred to as DP-FAG. In DP-FAG, noise is applied to sensitive participant data prior to global model aggregation. Moreover, all sensitive training data is securely stored on participant devices, effectively addressing the trusted third-party issue and ensuring data confidentiality. We conduct extensive experimental analysis on image classification tasks to validate the soundness and effectiveness of our proposed methodology. © 2023 IEEE.

关键词： Data acquisition

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The Bayesian CNN-LSTM Mixed Hybrid Algorithm Model of the Photovoltaic Short-term Output Forecasting 3

The Bayesian CNN-LSTM Mixed Hybrid Algorithm Model of the Ph...

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3rd International Conference on New Energy and Power engineering, ICNEPE 2023

作者： Dong, Enchong Guo, Yuxi North China Electric Power University School of Control and Computer Engineering Beijing China Shenzhen College of International Education Shenzhen China

ISBN: (纸本)9798350393224

Due to the volatility, randomness, and intermittency of photovoltaic power generation, it is difficult to accurately forecast its output. This paper proposes a Bayesian-optimized CNN-LSTM mixed neural model for a short-term photovoltaic output prediction model. First of all, the Xinjiang photovoltaic power station dataset is selected and preprocessed in this study. Secondly, the CNN method is applied for data feature extraction. And then the Bayesian optimization algorithm is used to analyze the LSTM super parameter optimization. Finally, the data, after undergoing feature extraction, are input into the Bayes theorem-LSTM model for forecast generation. The results show that the proposed algorithm has realized the accurate prediction of photovoltaic power, getting better than most other single benchmark models. These findings not only highlight the improved efficacy of the proposed algorithm but also affirm its potential applicability and value for practical implementation. © 2023 IEEE.

关键词： Bayesian algorithm Convolutional Neural Network algorithm Long Short-Term Memory algorithm Photovoltaic power output prediction

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3D pulmonary vessel segmentation based on improved residual attention u-net

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Medicine in Novel Technology and Devices 2023年第4期20卷 64-75页

作者： Jiachen Han Naixin He Qiang Zheng Lin Li Chaoqing Ma School of Computer and Control Engineering Yantai UniversityYantai264005ShandongChina Yantaishan Hospital Affiliated Binzhou Medical UniversityYantai264003ShandongChina

Automatic segmentation of pulmonary vessels is a fundamental and essential task for the diagnosis of various pulmonary vessels *** accuracy of segmentation is suffering from the complex vascular *** this paper,an Improved Residual Attention U-Net(IRAU-Net)aiming to segment pulmonary vessel in 3D is *** extract more vessel structure information,the Squeeze and Excitation(SE)block is embedded in the down sampling *** in the up sampling stage,the global attention module(GAM)is used to capture target features in both high and low *** two stages are connected by Atrous Spatial Pyramid Pooling(ASPP)which can sample in various receptive fields with a low computational *** the evaluation experiment,the better performance of IRAU-Net on the segmentation of terminal vessel is *** is expected to provide robust support for clinical diagnosis and treatment.

关键词： Pulmonary vessel segmentation RAU-Net Squeeze and excitation Atrous spatial pyramid pooling Deep learning

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Parallel control Strategy for Flexible Operation of Ultra-supercritical Units Under Digital Twin Theory 2

Parallel Control Strategy for Flexible Operation of Ultra-su...

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2nd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2022

作者： Huang, Ting Hou, Guolian School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781665492270

With the rapid development of big data and cloud technology, the coordinate control of thermal power plants is fast changing to a flexible and adaptive intelligent control model. In order to improve the operational flexibility of ultra supercritical (USC) units, a parallel control scheme based on ACP method is constructed in this paper. Firstly, an artificial control system that can reflect the operational conditions of units in real-time is constructed by the digital twin technology. Then, the precise model and active disturbance rejection control method are chosen for artificial system to guide the operation of USC units using computation experiments. Afterwards, the operational stability is ensured by the parallel execution of the artificial control system and actual USC units. Finally, the remarkable control performance of USC units based on the proposed parallel control scheme is successfully confirmed on a 1000MW units. © 2022 IEEE.

关键词： Disturbance rejection

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Self-supervised Semantic Soft Label Learning Network for Deep Multi-view Clustering

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IEEE Transactions on Multimedia 2025年

作者： Yan, Weiqing Yang, Tingyu Tang, Chang Yantai University School of Computer and Control Engineering Yantai261400 China China University of Geosciences School of Computer Science Wuhan430074 China

Multi-view clustering, which identifies shared semantics from different perspectives and classifies data samples into distinct categories using unsupervised methods, is gaining increasing interest. This task primarily focuses on learning consistent multi-view feature representations and clustering labels. Current approaches for achieving consistent multi-view feature representations often use techniques such as cascading, weight fusion, and attention mechanism fusion. These methods reconstruct features based on original low-level features via encoder-decoder, which often contain visual private information, leading to misleading feature representations. Furthermore, in the clustering label learning process, many methods use a two-stage approach: first, they achieve consistent feature representations, and then they apply hard labeling methods like K-means or spectral clustering to obtain clustering labels. Single-stage methods typically derive consistent labels through a linear coding layer based on consistent representation learning. These methods do not fully utilize the multi-view view semantic information, and consistent representation learning may be impaired when some low-quality views are present, leading to the generation of inaccurate semantic labels. To address these issues, we propose a Self-supervised Semantic Soft Label Learning Network for Deep Multi-view Clustering. Specifically, we introduce a consensus high-level feature learning module that uses a shared MLP layer to transform low-level features into a high-level feature space. To enhance the consistency between high-level features from different views, we maximize mutual information between these features and introduce the U-Projection module, which improves the expressive power of the consensus feature via resampling the features and concatenating the fused features before and after sampling operations. Additionally, we propose a self-supervised semantic label learning module that employs a dual-br

关键词： Self-supervised learning

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Event Recommendation Based on Heterogeneous Social Network Information and Time Information 4

Event Recommendation Based on Heterogeneous Social Network I...

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4th International Conference on Advanced Information Science and System, AISS 2022

作者： Zhao, Xiaofan Ma, Wenming School of Computer and Control Engineering Yantai University Shandong Province Yantai City China

ISBN: (纸本)9781450397933

In recent years, event-based social networks have developed rapidly, and event recommendation has attracted more and more attention. At present, for event recommendation, it is centered on the event, and aims to help users get the events which they are interested in from a large number of events. However, compared with traditional recommendation problems, event recommendation has many challenges. First of all, there is no obvious explicit rating of users' response to events, but implicit feedback. Secondly, the recommendation of events has heterogeneous social network relations. At the same time, most users participate in few events, which leads to a very serious data sparsity problem. In order to address these challenges and improve the effectiveness of event recommendation, this paper proposes an event recommendation model that integrates users' online and offline heterogeneous social network information and time information. The model uses Bayesian personalized ranking as the framework to process the implicit feedback information of users and events, and simultaneously combines online and offline social network information and time information to model together to improve the accuracy of recommendation. Experimental results based on real data sets show that the performance of the proposed model is better than other methods. © 2022 ACM.

关键词： Recommender systems

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Hierarchical Multimodal Decoupling-Fusion Framework for offline Multiple Appropriate Facial Reaction Generation

Hierarchical Multimodal Decoupling-Fusion Framework for offl...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lv, Qincheng Liu, Xiaofeng Li, Jie Ni, Rongrong Xue, Pujun Song, Siyang College of Information Science and Engineering Hohai University Changzhou China College of Artificial Intelligence and Automation Hohai University Changzhou China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China School of Microelectronics and Control Engineering Changzhou University Changzhou China School of Computer Science University of Exeter Exeter United Kingdom

ISBN: (纸本)9798350368741

Facial reactions convey crucial emotional information and coordinating interpersonal relationships in human dyadic interactions. While existing Multiple Appropriate Facial Reaction Generation (MAFRG) methods focus on generating multiple reasonable facial reactions, none of these approaches combines 2D and 3D facial behaviour information nor account for the influence of individuals' facial identities, leading to inconsistencies in the generated facial reactions and limited capability in capturing subtle variations in facial depth and expression dynamics. This paper proposes a novel Hierarchical Multimodal Decoupling-Fusion (HMDF) framework that decouples 3D facial identity from expression behaviors, eliminating identity-based interference in the reaction generation process, which are integrated with audio-visual features through a cross-attention mechanism. Experiments show that our framework achieved the enhanced diversity and synchrony in the generated facial reactions. © 2025 IEEE.

关键词： 2D 3D facial behaviour fusion Facial identity decoupling Multiple Appropriate Facial Reaction Generation

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An Active Suspension System Design for a Racing Car 3

An Active Suspension System Design for a Racing Car

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2021 3rd International Conference on Robotics, Intelligent control and Artificial Intelligence, ICRICA 2021

作者： Shi, Kailai School of Control and Computer Engineering North China Electric Power University Beijing China

This paper investigated the PID controller for an active suspension system for a racing car. A two-wheel half-car model is used and simulated in the Simulink environment. This model allows us to have two outputs and separately change the demand ride height of the front axle and the rear axle. Considering it is a racing car, downforce plays an important part in the suspension system and makes it a bit different from a private car that can only produce little downforce that can be ignored. To improve the control quality, a feedforward controller is added to compensate for the downforce. © Published under licence by IOP Publishing Ltd.

关键词： Automobile suspensions

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A detection method of lost assets based on feature optimization and active-passive detection

A detection method of lost assets based on feature optimizat...

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2021 International Conference on computer Application and Information Security, ICCAIS 2021

作者： Yan, Jingchen Cai, Chenxi Du, Zhe Li, Jianbin School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781510655218

With the development of Internet technology, various network attacks have emerged one after another, seriously affecting the security of many key infrastructures such as finance, energy, and transportation. Therefore, the importance of network asset management is self-evident. How to judge the security of assets and detect lost assets has become an important research topic. This paper proposes a method for detecting lost assets based on feature optimization and activepassive detection. Firstly, it achieves the classification of abnormal traffic by extracting important features of the traffic data. And then, it detects the network assets using the combined active and passive detection method. The experiments show that this method can effectively detect the lost assets in the network and effectively provide an analysis basis for threat analysis and emergency response. © The Authors.

关键词： Feature extraction

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