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2024 International Conference on measurement, Communication, and Virtual Reality, MCVR 2024

作者： Tao, Xinyu Ma, Dezhong Xiong, Xiaotong College of Measurement-control Technology & Communications Engineering Harbin University of Science & Technology Harbin China Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781510691773

Driver inattention is the primary cause of highway accidents. Various distractions can impair driving performance, posing a significant threat to road safety. This study uses a VR driving simulator to examine distracted driving, creating a simulated driving environment. It involves designing main and sub-driving tasks to replicate the cognitive load and distractions experienced by drivers. Data are collected using NASA-TLX scales, eye-tracking technology, and physiological signal acquisition. The impact of different distraction workloads on driver performance was assessed using statistical analysis and support vector machines (SVM). A multi-source data fusion model was developed to discriminate distracted driving behaviors. This research aims to provide theoretical insights and practical guidance for improving safe driving practices. © 2025 SPIE.

关键词： Automobile driver simulators

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Construction of Pressure Vessel Accident Emergency Drill System and Design of Scoring Mechanism based on Virtual Reality technology

Construction of Pressure Vessel Accident Emergency Drill Sys...

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2024 International Conference on measurement, Communication, and Virtual Reality, MCVR 2024

作者： Wang, Xinmiao Ma, Dezhong Niu, Chenchen Han, Yichi Li, Zhichao College of Measurement-control Technology & Communications Engineering Harbin University of Science & Technology Harbin China Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781510691773

To address the issues of high risk, high cost, limited coverage, and low effectiveness in pressure vessel emergency drills, this study aims to construct a pressure vessel accident emergency drill system based on virtual reality technology. The system utilizes Unity3D to develop a human-computer interaction interface and implements various functional modules using C# scripting. The system incorporates features such as scenario simulation, safety education and training, interactive operations, multi-player collaboration, and feedback and evaluation, enabling drill participants to train and examine their emergency response skills within a virtual environment. To ensure the objectivity and comprehensiveness of the scoring system, this study employs a combined weighting method of the Entropy Weight Method and Analytic Hierarchy Process to analyze the weights of the operational steps in the exercise process. A comprehensive and objective scoring mechanism for the pressure vessel accident emergency exercise system has been designed, which evaluates the performance of the exercise personnel from multiple dimensions and levels. Furthermore, a comprehensive and objective rating mechanism for the emergency drill system of pressure vessels was designed to evaluate the performance of the drill personnel from multiple dimensions and hierarchical levels. This system effectively enhances participants' abilities in rapid response, emergency handling, and teamwork during accidents. © 2025 SPIE.

关键词： Virtual environments

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Terahertz time-of-flight tomography algorithm for different curvature samples

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Optics Express 2025年第7期33卷 15095-15109页

作者： Zhou, Yu Li, Quanyong Cui, Zongyu Zhang, Jiyang Ren, Jiaojiao Zhang, Dandan Gu, Jian Li, Haoxiang Lin, Zihao Key Laboratory of Optoelectronic Measurement and Control and Optical Information Transmission Technology Ministry of Education Changchun University of Science and Technology Changchun130022 China School of Optoelectronic Engineering Changchun University of Science and Technology Changchun130022 China Zhongshan Research Institute Changchun University of Science and Technology Zhongshan528403 China

Traditional guide rail-based terahertz reflection tomography works well with planar samples and complements transmission tomography. Most imaging methods perform effectively with plane-like or simple curvature samples. However, these methods are ineffective for complex shapes due to the challenges of dealing with internal corrections and stitching errors. To address the above issues, terahertz time-of-flight tomography is applied to the prepared samples with abnormal curvature using a rotating scanning array and "point cloud splicing" method. This approach enables comprehensive sample information collection and resolves the inner layer point cloud position correction challenge. A radial distance point cloud down-sampling method is proposed to reduce the impact of overlapping point clouds on reconstruction accuracy. After down-sampling, the outer layer reconstruction error reduces to 0.22%. Poisson surface reconstruction further enhances the intuitiveness of imaging results, and the reconstruction accuracy is 0.01 mm. This study broadens the application of terahertz time-of-flight tomography and offers a different approach for reflection tomography. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Surface reconstruction

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The Client-Level GAN-Based Data Reconstruction Attack and Defense in Clustered Federated Learning 18th

The Client-Level GAN-Based Data Reconstruction Attack and D...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wu, Han Shi, Lei Ye, Junyu Fan, Yuqi Lv, Zengwei School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei230009 China

ISBN: (纸本)9783031714634

Clustered Federated Learning (CFL) is an innovative architecture that effectively tackles the challenge of data heterogeneity in Federated Learning (FL). However, such a setup also introduces vulnerabilities that can be exploited by attackers to execute more severe attacks. This paper compares the efficacy of data reconstruction attacks utilizing Generative Adversarial Nets (GANs) in FL and CFL, respectively. It also proposes a general client-level data reconstruction attack scheme whose key is in that the attackers can eavesdrop the victim’s partial uploaded parameters. Experimental results demonstrate that the sample data generated in this kind of attack within the scenario of CFL exhibit a higher degree of similarity with the targeted data compared to FL. And it is observed that the attacks involving eavesdropping in CFL perform more effectively with a client-level attack accuracy. In addition, we design a lightweight and adaptive local differential privacy (LDP) mechanism to defend against this kind of attack. In this method, under the premise that the differential privacy budget decreases as the number of training round adds, each client dynamically adjusts the probability of adding differential privacy noise and the size of DP’s parameters according to the training accuracy and the resemblance to the reconstructed data generated by attackers. Our defense method can effectively alleviate the loss of training accuracy for each client caused by the accumulation of the differential privacy budget while ensuring the defense effect, and it also reduces clients’ computational burden. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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A Method for Abnormal Detection and Poisoned Data Recovery in Clustered Federated Learning 18th

A Method for Abnormal Detection and Poisoned Data Recovery...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Chen, Yingying Shi, Lei Xu, Hao Ye, Junyu Xu, Juan School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei230009 China

ISBN: (纸本)9783031714665

Clustered Federated Learning (CFL), as a type of Federated Learning (FL) paradigm, divides the clients into multiple clusters through the clustering process and trains them within the clusters, thus improving the overall model training accuracy. But the clustering process is also the weak link, which can lead to the failure of the whole model training if it is interfered or destroyed by the attacker. In this paper, we study the attack methods and defense strategies for attackers to implement data poisoning by tampering with client data, resulting in overall clustering failure. Our defense approach is designed to identify anomalous clients and recover their poisoned data. In order to resist the malicious behavior of attackers, we propose a dual-threshold pixel difference and watermark authentication defense method for detecting the presence of anomalous clients. The method can accurately identify the abnormal fluctuations in the data and effectively screen out the abnormal clients. Meanwhile, we propose a self-embedding watermarking defense algorithm based on shuffling idea for recovering poisoned data. Among them, our self-embedding watermarking algorithm not only accurately locates and recovers the tampered region of the image, but also improves the watermark security to prevent attackers from cracking it easily. Simulation results show that our proposed algorithm can accurately identify the abnormal client and recover the data. Even if the client’s data is massively tampered with, we can recover the tampered data images with high quality and ensure that the CFL clustering is not corrupted. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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ICEEMDAN and LSTM Spindle System Fault Diagnosis Method

ICEEMDAN and LSTM Spindle System Fault Diagnosis Method

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Wang, Nan Wang, Hongjun Zhang, Zhuangzhuang Chen, Baisheng School of Mechanic and Electric Engineering BISTU Beijing100192 China Key Laboratory of Modern Measurement and Control Technology BISTU Beijing100192 China

ISBN: (纸本)9783031494123

Research on fault diagnosis of rotating machinery is crucial as it serves as a core component. In order to diagnose spindle system fault types quickly and accurately, Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Long Short-Term Memory (LSTM) fault diagnosis method is presented. Firstly, collect the fault vibration, then ICEEMDAN is used for vibration signals to extract seven times domain features and four frequency domain features. Finally, the time domain and frequency domain features’ parameters are input into the LSTM network for fault classification. The method is verified for the practical industrial CNC machine center spindle system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Empirical mode decomposition

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Spatiotemporal mode-locking and dissipative solitons in multimode fiber lasers

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Light(Science & Applications) 2023年第11期12卷 2309-2324页

作者： Bo Cao Chenxin Gao Kewei Liu Xiaosheng Xiao Changxi Yang Chengying Bao State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentsTsinghua UniversityBeijing 100084China State Key Laboratory of Information Photonics and Optical Communications School of Electronic EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China

Multimode fiber(MMF)lasers are emerging as a remarkable testbed to study nonlinear spatiotemporal physics with potential applications spanning from high energy pulse generation,precision measurement to nonlinear *** underlying mechanism for the generation of ultrashort pulses,which can be understood as a spatiotempoal dissipative soliton(STDS),in the nonlinear multimode resonators is the spatiotemporal mode-locking(STML)with simultaneous synchronization of temporal and spatial *** this review,we first introduce the general principles of STML,with an emphasize on the STML dynamics with large intermode ***,we present the recent progress of STML,including measurement techniques for STML,exotic nonlinear dynamics of STDS,and mode field engineering in MMF *** conclude by outlining some perspectives that may advance STML in the near future.

关键词： dissipative nonlinear locking

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Observation and research of deep underground multi-physical fields—Huainan–848 m deep experiment

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Science China Earth Sciences 2023年第1期66卷 54-70页

作者： Yun WANG Yaxin YANG Heping SUN Chengliang XIE Qisheng ZHANG Xiaoming CUI Chang CHEN Yongsheng HE Qiangqiang MIAO Chaomin MU Lianghui GUO Jiwen TENG School of Geophysics and Information Technology China University of GeosciencesBeijing 100083China School of Geophysics and Measurement-control Technology East China University of TechnologyNanchang 330013China Innovation Academy for Precision Measurement Science and Technology Chinese Academy of SciencesWuhan 430077China Institution of Engineering Protection IDEAMSPLALuoyang 471023China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and TechnologyHuainan 232001China Institute of Geochemistry Chinese Academy of SciencesGuiyang 550081China

Compared with the surface,the deep environment has the advantages of allowing“super-quiet and ultra-clean”-geophysical field observation with low vibration noise and little electromagnetic interference,which are conducive to the realization of long-term and high-precision observation of multi-physical fields,thus enabling the solution of a series of geoscience *** the Panyidong Coal Mine,where there are extensive underground tunnels at the depth of 848 m below sea level,we carried out the first deep-underground geophysical observations,including radioactivity,gravity,magnetic,magnetotelluric,background vibration and six-component seismic *** concluded from these measurements that(1)the background of deep subsurface gravity noise in the long-period frequency band less than 2 Hz is nearly two orders of magnitude weaker than that in the surface observation environment;(2)the underground electric field is obviously weaker than the surface electric field,and the relatively high frequency of the underground field,greater than 1 Hz,is more than two orders of magnitude weaker than that of the surface electric field;the east-west magnetic field underground is approximately the same as that at the surface;the relatively high-frequency north-south magnetic field underground,below 10 Hz,is at least one order of magnitude lower than that at the surface,showing that the underground has a clean electromagnetic environment;(3)in addition to the highfrequency and single-frequency noises introduced by underground human activities,the deep underground space has a significantly lower background vibration noise than the surface,which is very beneficial to the detection of weak earthquake and gravity signals;and(4)the underground roadway support system built with ferromagnetic material interferes the geomagnetic *** also found that for deep observation in the“ultra-quiet and ultra-clean”environment,the existing geophysical equipment and observation technology hav

关键词： Multi-physical fields Radioactivity Gravity Geomagnetic Electromagnetic Earthquake Observations deep underground

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Intelligent Elevator Safety Supervision System Combined with Battery Car Identification 22

Intelligent Elevator Safety Supervision System Combined with...

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Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing

作者： Shibo Guo Kang Zhang Zhiyan Zhou Xinyu Zhou Weiyu Gong Zhenghao Wang Kaiyue Zhu Haibin Wu Aili Wang School of Measurement-Control Technology and Communications Engineering Harbin University of Science and Technology China

ISBN: (纸本)9781450397810

In recent years, elevator safety accidents occur frequently, and relevant news has aroused people's strong attention to elevator safety. Among them, electric vehicle explosion accident hot spot is the highest. In this paper, according to the elevator safety hazard source (including illegal vehicle such as storage battery into, passengers trapped, robbed or distress due to illness, the elevator is high concentration flammable gases or burning smoke) design storage battery detection module, voice call module, combustible gas monitoring module for joint operations, monitoring, alarm, the outside world communication as one of the complete system. The system uses Arduino Mega 2560 as the main control chip, ESP32-CAM wireless camera module uses the improved YOLOv4 model for real-time analysis and processing of illegal vehicles, LD3320 speech recognition module and SYN6288 Chinese speech synthesis chip to achieve the voice call function. MQ-2 gas sensitive smoke sensor and LCD1602 LIQUID crystal display module to achieve gas monitoring function, HC-05 Bluetooth module to achieve communication function feedback alarm information to the elevator monitoring and management center, if necessary, can automatically call the alarm phone. The experiment shows that the detection confidence of battery car in elevator environment can reach 0.75, the recognition accuracy of voice call in quiet environment is 96%, and the recognition accuracy of voice call in noise environment is 90%. The alarm can be realized by combustible gas monitoring. The system can monitor the hidden danger in the elevator in real time, solve the problems such as the elevator supervision is weak, the emergency rescue is not timely, improve the efficiency of solving the elevator safety problems.

关键词： voice call combustible gas monitoring battery car identification ASPP-YOLOv4

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Combined Classification of Hyperspectral and LiDAR Data based on Dual-Channel Cross-Transformer 5

Combined Classification of Hyperspectral and LiDAR Data base...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Zhou, Binbin Wang, Qingyan Zhang, Junping Wang, Yujing School Of: Measurement-Control And Communication Engineering Harbin University Of Science And Technology Harbin150080 China School Of: Electronics And Information Engineering Harbin Institute Of Technology Harbin150001 China

ISBN: (纸本)9798400709272

In the face of complex scenes, single-modal dominant classification tasks encounter limitations in performance due to insufficient information. On the other hand, joint classification of multimodal remote sensing data faces challenges such as data sample differences and lack of correlation in physical features between modalities, which can impact classification performance. To fully integrate the heterogeneous information of multimodal data and improve classification performance, we propose a dual-channel cross-transformer feature fusion extraction network. The framework leverages self-attention mechanisms to aggregate features within each modality, and the feature fusion module based on cross-modal attention fully considers the complementary information between modalities. Classification tasks are performed using the fused spatial-spectral features obtained from the joint representation of modalities. Extensive experiments conducted on the Houston and MUUFL datasets demonstrate the effectiveness of the proposed model compared to existing methods. © 2023 ACM.

关键词： Classification (of information)

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