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3rd International Conference on Digital Society and Intelligent systems, DSInS 2023

作者： An, Yaning Guan, Wei Qi, Geqi Yu, Jixiang Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China School of Systems Science Beijing Jiaotong University Beijing100044 China Key Laboratory of Brain-Machine Intelligence for Information Behavior- Ministry of Education Shanghai International Studies University Shanghai200083 China

ISBN: (纸本)9798350331387

para>Driving styles can be used to characterize the behavioral characteristics of drivers, and provide a personalized foundation for the development of intelligent driving systems. The electrophysiological analysis is one of the emerging technologies for driving style recognition. However, most studies focused on classifying driving styles based on electroencephalogram signals have predominantly extracted the time-frequency features, which ignore the spatially coordinated activities of multiple brain regions. Therefore, this paper proposes a graph-based driving style classification method by implementing electrophysiological analysis on brain functional connectivity networks. By adopting graph convolution network on the time-frequency and functional connectivity features, the constructed model attains an accuracy of 88.3% in recognizing three car-following driving styles. This approach can help to promote the electrophysiological recognition of driving styles with a network perspective. © 2023 IEEE.

关键词： Brain

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Hierarchical microstructures and strengthening mechanisms of nano-TiC reinforced CoCrFeMnNi high-entropy alloy composites prepared by laser powder bed fusion

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Journal of Materials science & Technology 2023年第5期136卷 245-259页

作者： Hongyu Chen Konrad Kosiba Tiwen Lu Ning Yao Yang Liu Yonggang Wang Konda Gokuldoss Prashanth Challapalli Suryanarayana Key Laboratory of Impact and Safety Engineering of Ministry of Education of China Ningbo UniversityNingbo 315211China Leibniz Institute for Solid State and Materials Research Dresden Institute for Complex MaterialsHelmholtzstr.2001069 DresdenGermany Key Laboratory of Pressure Systems and Safety Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Department of Mechanical and Industrial Engineering Tallinn University of Technology19086 TallinnEstonia Erich Schmid Institute of Materials Science Austrian Academy of SciencesA-8700 LeobenAustria CBCMT Vellore Institute of TechnologyVellore 632014Tamil NaduIndia Department of Mechanical and Aerospace Engineering University of Central FloridaOrlandoFL 32816-2450United States

High entropy alloys(HEAs)have recently received extensive attention due to their appealing mechani-cal performance given their simple phase *** study utilized laser powder bed fusion(LPBF)to fabricate high-performance HEA *** processing respective powder blends,LPBF enabled the fabrication of stronger composites with a uniformly distributed reinforcing ***,the impact of varying content of nano-scale TiC(1-3 wt%)particles for strengthening the CoCrFeMnNi HEA was *** microstructural features and mechanical properties of the HEA composites were investigated in *** introduction of nano-scale TiC into the HEA matrix encouraged the development of cross-scale hierarchical microstructure and eliminated the formation of oxide *** more nano-TiC led to a higher dislocation density and more refined microstructure in the HEA composites,whereas it posed little influence on the anisotropy of the HEA matrix which typically featured a<001>texture along the building *** an optimized content of nano-TiC(1-2 wt%),the strength-ductility trade-offcan be overcome by exploiting multiple strengthening mechanisms encompassing grain boundary strengthening,solid solution strengthening,Orowan strengthening,and dislocation *** HEA composites showed a favored strength-ductility combination with a yield strength of 748-882 MPa,ultimate tensile strength of 931-1081 MPa,and fracture elongation of 23%-29%.This study demonstrates that the introduction of nano-scale TiC is an effective way to simultaneously improve the strength and ductility of additively manufactured HEA materials.

关键词： Additive manufacturing Laser powder bed fusion High entropy alloy Microstructure Mechanical property

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Evolutionary Direction Learning With Multivariate Gaussian Probabilistic Model for Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2025年

作者： Wang, Xianpeng Zhang, Jingchuan Tang, Lixin Liu, Yaxue Ministry of Education National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China

In recent years, utilizing data from the evolutionary process of multiobjective evolutionary algorithms (MOEAs) to learn knowledge and guide evolutionary search has become a popular research topic. However, existing knowledge learning (KL) frameworks often suffer from the low quality of collected datasets and the inefficiency of model construction, which significantly limits their effectiveness. To address this issue, this paper proposes a novel evolutionary direction learning (EDL) framework, which aims to learn the evolutionary direction (ED) knowledge for each objective to enhance the population generation of MOEAs. The proposed EDL incorporates an effective data collection method based on objective improvement to generate high-quality datasets, based on which a multivariate Gaussian probabilistic model is employed to learn ED knowledge for each objective through a data fusion modeling approach. Besides, a knowledge assignment method is designed to select the most suitable ED knowledge to guide the evolution of solutions. Experimental results on both synthetic and real-world problems demonstrate that the proposed EDL framework can accelerate the convergence of MOEAs and significantly improve their performance. A comparison of the proposed EDL with three state-of-the-art KL frameworks indicates that EDL is a highly competitive learning framework, achieving superior performance with larger datasets and impressive efficiency. © 1997-2012 IEEE.

关键词： Contrastive Learning

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TAO-Based data Governance in Intelligent Transportation systems

TAO-Based Data Governance in Intelligent Transportation Syst...

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International Conference on Intelligent Transportation

作者： Xiaolong Liang Juanjuan Li Rui Qin Wenwen Ding Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems and State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences and Collaborative Laboratory for Intelligent Science and Systems Beijing China

Intelligent Transportation systems (ITS) are heavily dependent on private user data. However, most ITS fails to harness the potential of this invaluable data due to the absence of effective data governance mechanisms promoting users' contributions. Moreover, data contributors face security risks such as privacy preservation, data leakage, etc., as well as high costs in data sharing, while benefits are disproportionately reaped by system operators and interaction. This systemic imbalance could indirectly incentivize a surge in inactions and even malicious actions. In response to these challenges, we propose the design of a True Autonomous Organization (TAO) for ITS, namely ITS TAO. Utilizing the newly designed decision models with decentralized organization structures and the three-power structure, ITS TAO aims to realize the fair distribution of rights and benefits for ITS data contributors. Furthermore, we design a real-time evaluation system based on parallel intelligence capable of identifying potential hazards.

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Multi-scale discriminative regions attention network for fine-grained vehicle classification

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第2期6卷 164-177页

作者： Rong, Wen-Zhong Han, Jin Cai, Ying-Hao Liu, Gen College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Fine-grained vehicle classification is a challenging task in computer vision due to the low intra-class variance. Some methods have been developed to improve the accuracy of fine-grained vehicle classification by improving the ability of discriminative features extraction, but there is still room for further improvement in the localization accuracy of vehicle discriminative regions. Based on deep convolutional neural networks, we focus on finding a more efficient structure that pays more attention to the discriminative image regions to enhance the ability of fine-grained vehicle classification. We propose a novel Multi-Scale Discriminative Regions Attention Network (MS-DRAN), which extracts feature maps through ResNet-50 backbone network and generates multi-scale feature maps by a Feature Pyramid Network (FPN). Then, MS-DRAN generates discriminative regions attention maps on the multi-scale feature maps. The attention maps from shallow layer perform pixel-level multiplication with the feature maps from deeper layer. By this way, the network gradually extracts a more discriminating feature maps for classification. We also design a multi-task loss function to associate the classification results for each scale and optimize the network parameter in training. Lastly, we validate the MS-DRAN on Stanford Cars-196 dataset and CompCars dataset and achieves 94.3% and 98.1% in accuracy, respectively. ©2021 ISSN 2414-8105 (Online).

关键词： Classification (of information)

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Modeling viral evolution:A novel SIRSVIDE framework with application to SARS-CoV-2 dynamics

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hLife 2024年第5期2卷 227-245页

作者： Kaichun Jin Xiaolu Tang Zhaohui Qian Zhiqiang Wu Zifeng Yang Tao Qian Chitin Hon Jian Lu State Key Laboratory of Protein and Plant Gene Research Center for BioinformaticsSchool of Life SciencesPeking UniversityBeijingChina NHC Key Laboratory of Systems Biology of Pathogens Institute of Pathogen BiologyChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangdongChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacaoChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangdongChina Macao Center for Mathematical Sciences Macao University of Science and TechnologyMacaoChina Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacaoChina

Understanding evolutionary trends in emerging viruses,such as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is crucial for effective public health management and ***,extensive debates have arisen concerning viral evolutionary trends,particularly the interplay between transmissibility,pathogenicity,and immune *** this context,we have developed a novel computational model named SIRSVIDE(Susceptible-Infected-Recovered-Susceptible-Variation-Immune Decay-Immune Escape)to simulate the transmission and evolutionary dynamics of viral *** simulation results indicate that under conditions of high mutation rates,elevated transmission rates,and larger susceptible host populations,viral populations exhibit prolonged increases in transmissibility and immune escape,accompanied by reductions in pathogenicity and noticeable short-term fluctua***,when the total susceptible population size and mutation rate decrease,substantial uncertainty in the evolutionary trends of viral populations becomes *** summary,the SIRSVIDE model establishes a comprehensive framework for generating both short-and long-term viral epidemiological and evolutionary *** simulation outcomes align with existing evidence indicating that SARS-CoV-2 is undergoing selection for heightened transmissibility,decreased pathogenicity,and enhanced immune ***,the model sheds light on the possible evolutionary dynamics of other viruses.

关键词： viral evolution severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) transmissibility-pathogenicity trade-off immune escape

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data-Driven Intersection Safety Evaluation Methodology and Analysis 23

Data-Driven Intersection Safety Evaluation Methodology and A...

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23rd COTA International Conference of Transportation Professionals: Emerging data-Driven Sustainable Technological Innovation in Transportation, CICTP 2023

作者： Wu, Pingping Lu, Guangquan Liu, Miaomiao Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang Univ. Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Southeast Univ. Nanjing China

ISBN: (数字)9780784485040

ISBN: (纸本)9780784485040

With the development of information and sensing technologies, a wide variety of intersection traffic data is available. The purpose of this paper is to make full use of video and commercial vehicle trajectory data to deeply explore the intersection safety status. In this paper, based on different sample sizes, different sampling frequencies, and different accuracies of data, the safety analysis of intersections is carried out from micro and macro aspects based on post-encroachment time (PET) and vehicle deceleration behavior, respectively. On the one hand, we use the full-sample and high-precision trajectory data extracted from video to analyze the safety of each region of intersection based on PET. On the other hand, using the trajectory data with low penetration rate and low sampling frequency extracted from the positioning data of the online car-hailing, a distribution fit, and non-parametric test are conducted for the vehicle deceleration behavior, and then the overall safety state of the intersection is evaluated. The results can provide ideas for the identification of intersection risk points and the application of different kinds of data on intersection safety evaluation. © ASCE.

关键词： Commercial vehicles

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Effect of oxygen enrichment on sintering behavior of high proportion vanadium-titanium magnesite concentrates

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Journal of Iron and Steel Research International 2023年第11期30卷 2122-2132页

作者： Shi-hong Peng Hao Liu Ze-zheng Sun Chang-wei Li Yue-lin Qin Wei-qiang Liu Guang Wang School of Metallurgy and Materials Engineering Chongqing University of Science and TechnologyChongqing401331China Value-Added Process and Clean Extraction of Complex Metal Mineral Resources Chongqing Municipal Key Laboratory of Institutions of Higher EducationChongqing401331China Chongqing Chuanyi Automation Co. Ltd.Chongqing401120China Process and Systems Engineering Åbo Akademi UniversityÅbo20500Finland Department of Materials Science and Engineering McMaster UniversityOntarioL8S 4L7Canada

To achieve high-efficiency utilization of complex and unmanageable iron-containing minerals,the effects of oxygen enrichment on productivity,yield,flame front speed,exhaust gas peak temperature,and desulphurization reaction of the vanadium-titanium magnetite sintering process as well as sinter tumble index and mineralogy were clarified,with oxygen enrichment concentrations ranging from 21 to 29 vol.%.Results indicated that with increasing the oxygen enrichment concentration from 21 to 27 vol.%,the flame front speed increased from 30.3 to 40.0 mm min^(-1),the yield enhanced from 72%to 77%,and the productivity augmented from 1.83 to 2.67t m^(-2)h^(-1);in the meantime,the tumble index was improved from 73.7%to 77.9%,and the exhaust gas peak temperature rose from 376.4 to 484.8℃.The main reason for the improvement in sintering properties was the increased combustibility of fuels and the generation of proper liquid phase that improved the permeability of the packed *** improved sinter strength is mainly due to the increase in the phase fraction of silico-ferrites of calcium and *** addition,oxygen enrichment sintering could significantly increase the desulphurization level of vanadium-titanium magnetite sinter and the rate of desulphurization reaction during sintering process.

关键词： Sinter Oxygen enrichment Vanadium-titanium magnetite Sintering behavior

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Event-Triggered Energy Optimization of Wireless Sensor Networks 19

Event-Triggered Energy Optimization of Wireless Sensor Netwo...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Liu, Lu Song, Ruizhuo Wei, Qinglai School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665472432

Aiming at limited communication and energy re-sources in wireless sensor networks (WSN s), this paper proposes an energy management scheme of WSNs via adaptive dynamic programming (ADP) based on event-triggered mecha-nism (ETM). The optimal control strategy obtained by iteration can schedule the sensor nodes and make the nodes switch between working and sleeping situations, thus improving the energy utilization and extending the service life of the energy-constrained WSNs. Firstly, the mathematical model of WSNs is established, and the state is estimated by extended Kalman filter (EKF) algorithm to improve the measurement accuracy. Then, ADP solves the designed value function to achieve the scheduling plan. On the premise of system stability, ETM is applied to activate the controller on demand, which can reduce communication burden and save WSNs energy consumption. Finally, the simulation experiment reveals that the proposed algorithm can reduce the unnecessary triggering times of the controller effectively while ensuring the requirements, and avoid data congestion and interaction resource waste. © 2022 IEEE.

关键词： Energy utilization

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Universal Speaker Embedding Free Target Speaker Extraction and Personal Voice Activity Detection

arXiv

引用

arXiv 2025年

作者： Zeng, Bang Li, Ming School of Computer Science Wuhan University Wuhan China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Data Science Research Center Duke Kunshan University Kunshan China

Determining "who spoke what and when" remains challenging in real-world applications. In typical scenarios, Speaker Diarization (SD) is employed to address the problem of "who spoke when," while Target Speaker Extraction (TSE) or Target Speaker Automatic Speech Recognition (TSASR) techniques are utilized to resolve the issue of "who spoke what." Although some works have achieved promising results by combining SD and TSE systems, inconsistencies remain between SD and TSE regarding both output inconsistency and scenario mismatch. To address these limitations, we propose a Universal Speaker Embedding Free Target Speaker Extraction and Personal Voice Activity Detection (USEF-TP) model that jointly performs TSE and Personal Voice Activity Detection (PVAD). USEF-TP leverages frame-level features obtained through a cross-attention mechanism as speaker-related features instead of using speaker embeddings as in traditional approaches. Additionally, a multi-task learning algorithm with a scenario-aware differentiated loss function is applied to ensure robust performance across various levels of speaker overlap. The experimental results show that our proposed USEF-TP model achieves superior performance in TSE and PVAD tasks on the LibriMix and SparseLibriMix datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Embeddings

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