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9th International Conference on Mechanical engineering, Materials, and Automation Technology, MMEAT 2023

作者： Wang, Ruihan Zhan, Xuejuan Liang, Zijun Li, Yuqi Xiao, Yun Peng, Min School of Urban Construction and Transportation Hefei University China Anhui Province Transportation Big Data Analysis and Application Engineering Laboratory China Key Laboratory of Traffic Information and Safety of Anhui Higher Education Institutes Anhui Sanlian University China

ISBN: (纸本)9781510668584

Along with our country's continuous growth, rising rush hours traffic congestion, many scholars proposed setting variable lanes solution, but due to the variable lanes different times the direction of the lane change, which made the driver can not be correctly into the right lane. So we propose a traffic optimization control method for variable lanes combined with colorful asphalt materials. Firstly, enumeration method is used to list all the direction schemes and phase schemes of the variable lanes, and the optimal scheme is obtained according to the constraints. In order to better distinguish the variable lanes from the conventional lanes, the variable lanes are paved with colorful asphalt environmental materials. Then the phase timing is calculated according to the determined variable lanes, phase scheme and traffic flow. Finally, through VISSIM traffic simulation software to evaluate the results, the results show that combining with colorful asphalt materials clearly calibrate the position of the variable lanes, effective rule on the variable lanes traffic space, and on this basis, through phase timing optimization technology to further improve the intersection traffic efficiency, effectively alleviate the intersection rush hours traffic congestion. It provides a basis for the research of variable lanes optimization control combined with colorful asphalt materials. © 2023 SPIE.

关键词： Traffic congestion

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Revealing the Defect-Dominated Electron Scattering in Mg_(3)Sb_(2)-Based Thermoelectric Materials

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Research 2023年第1期2022卷 379-388页

作者： Jucai Jia Yan Zhou Xiaoxi Chen Wenhua Xue Hulei Yu Jing Li Shizhen Zhi Chen Chen Jian Wang Shuaihang Hou Xingjun Liu Yumei Wang Feng Cao Yue Chen Jun Mao Qian Zhang School of Materials Science and Engineering and Institute of Materials Genome Big DataHarbin Institute of Technology(Shenzhen)Shenzhen 518055China Center for Device Thermography and Reliability(CDTR) H.H.Wills Physics LaboratoryUniversity of BristolTyndall AvenueBristol BS81TLUK Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of Sciences100190BeijingChina 'Department of Mechanical Engineering The University of Hong KongPokfulam RoadHong Kong SARChina School of Science Harbin Institute of Technology(Shenzhen)Shenzhen 518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

The thermoelectric parameters are essentially governed by electron and phonon *** the carrier scattering mechanism plays a decisive role in electron transport,it is of great significance for the electrical properties of thermoelectric *** a typical example,the defect-dominated carrier scattering mechanism can significantly impact the room-temperature electron mobility of n-type Mg_(3)Sb_(2)-based ***,the origin of such a defect scattering mechanism is still ***,the existence of the Mg vacancies and Mg interstitials has been identified by synchrotron powder X-ray *** relationship among the point defects,chemical compositions,and synthesis conditions in Mg_(3)Sb_(2)-based materials has been *** further introducing the point defects without affecting the grain size via neutron irradiation,the thermally activated electrical conductivity can be *** results demonstrate that the point defects scattering of electrons is important in the n-type Mg_(3)Sb_(2)-based materials.

关键词： materials defect Electron

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VDFSR knowledge based Variable-Density Photographic Film to Sound Generation Method

VDFSR Knowledge based Variable-Density Photographic Film to ...

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Wang, Ruiqi Jin, Cong Yu, Yinan Zhai, Yutong National Key Laboratory of Scattering and Radiation Beijing China University of China School of Data Science and Intelligent Media Communication Beijing China China Norinco Company School of Aerospace Engineering Beijing Institute of Technology Armament Research and Development Institute Beijng China Casic Space Engineering Development Co. Ltd. Beijing China

ISBN: (纸本)9798350376739

Image domain film to sound generation (IDFSG) is a research hotspot in film industry and cross-modal learning. Existing researches neglect sound generation need for variable density film soundtrack. To solve the problem of constructing sound generation processes during variable density film soundtrack reproduction (VDFSR), we propose a PF2SG method based on the VDFSR knowledge, which can effectively achieve high-quality sound generation performance. Specifically, from the perspective of mining the intrinsic consistency of different types of signal capability transformations, we embed variable density film soundtrack reproduction knowledge into the corresponding sound generation process in stages. Moreover, to further enhance the stability of our IDFSG method, we design a sound reproduction module for variable density film soundtrack that integrates the photometric sampling interval based on the mechanical structure of the film projector. Our method effectively explores the intrinsic consistency between the image domain information and the audio domain information during VD film sound generation. Experiments have shown that our method can effectively generate higher quality audio compared to existing methods and commercial devices. © 2024 IEEE.

关键词： knowledge representation

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SQLStateGuard: Statement-Level SQL Injection Defense Based on Learning-Driven Middleware 15

SQLStateGuard: Statement-Level SQL Injection Defense Based o...

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15th Annual ACM Symposium on Cloud Computing, SoCC 2024

作者： Liu, Xin Huang, Yuanyuan Wang, Tianyi Li, Song Niu, Weina Shen, Jun Zhou, Qingguo Zhou, Xiaokang School of Information Science and Engineering Lanzhou University Gansu Lanzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of Computing and Information Technology University of Wollongong WollongongNSW Australia Kansai University Osaka Suita Japan

ISBN: (纸本)9798400712869

SQL injection is a significant and persistent threat to web services. Most existing protections against SQL injections rely on traffic-level anomaly detection, which often results in high false-positive rates and can be easily bypassed by attackers. This paper introduces SQLStateGuard, the world's first middleware-driven statement-level SQL injection defense approach, to address these issues. The SQLStateGuard uses a custom SQL middleware based on the idea of Runtime Application Self-Protection to capture raw SQL statements. These statements are then analyzed by SQLSG-Net, a database-oriented detection network based on gated linear units. If SQLSG-Net detects malicious SQL statements, the SQL middleware will block them. Experiments show that the detection accuracy of SQLStateGuard exceeds 99%, outperforming existing approaches, and it can identify the type of a specific SQL injection. Additionally, SQLStateGuard has no fingerprint and does not respond to SQL syntax errors, making it more challenging for attackers to gather information. This paper also presents a novel dataset generation process for SQLStateGuard and shares two statement-level SQL injection datasets with the research community, including over 145,000 malicious SQL statements categorized by the type of SQL injection. © 2024 ACM.

关键词： Middleware

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FM-FCN:A Neural Network with Filtering Modules for Accurate Vital Signs Extraction

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Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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ISAR Imaging and Cross-Range Scaling Based on Image Rotation Correlation

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Journal of Beijing Institute of Technology 2022年第2期31卷 196-207页

作者： Fangming Liu Cong Du Yujiao Ding Minghai Wang Wei Dong State Key Laboratory on Integrated Optoelectronics College of Electronic Science&EngineeringJilin UniversityChangchun 130012China. Big Data and Network Management Center Jilin UniversityChangchun 130012China.

With the rapid advancement of technology,not only do we need to acquire a clear in-verse synthetic aperture radar(ISAR)image,but also the real size of the target on the imaging plane,so it’s particularly important for the ISAR to rescale the *** is,the ISAR image which is in the range-Doppler domain is converted into the range-azimuth ***,the key point to solving the problem is to estimate the rotation *** this paper,a new scheme to rescale the images is *** the sake of solving the problem of two-dimensional image blur and target high-speed,the instantaneous range instantaneous Doppler(IRID)method is used to obtain ISAR images,and the rotation parameters are estimated by comparing the rotation correlation of the two *** this method,the error of the estimated rotation parameters is greatly reduced,so that the target can be rescaled *** simulation results verify the ef-fectiveness of the proposed algorithm.

关键词： cross-range scaling instantaneous range instantaneous Doppler(IRID) inverse synthet-ic aperture radar(ISAR) rotational correlation rotation parameters

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Deep Learning for Semantic Segmentation of Football Match Image 2

Deep Learning for Semantic Segmentation of Football Match Im...

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2nd International Conference on Innovations and Development of Information Technologies and Robotics, IDITR 2023

作者： Wu, Yutian Zhao, Wuqi Huang, Chen Xi, Yaming Li, Qing Wang, Heng University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing China Beijing International Studies University Department of Physical Education Beijing China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing China

ISBN: (纸本)9798350345575

As one of the most popular sports, football has been a subject to growth and advancements in technology. The combination of football and artificial intelligence is expected to be used for intelligent football analysis. Image semantic segmentation is an important basis for image analysis and understanding. This paper proposes a deep learning-based image segmentation model for pixel-level classification of the video recordings frames of football matches. Every pixel of football video frame is classified into one of the 10 classes, e.g., players, ball, goal bar and several background scenes. In this paper, we first test a variety of CNN architectures and pre-Trained models and select the MobileNet-UNet architecture as our baseline. We note the severe unbalanced data distribution in football scene segmentation. To solve this problem, the weighted multi-class cross-entropy loss is adopted in training of MobileNet-UNet to redistribute the weights of classification loss, focusing on smaller foreground object classes and improving segmentation accuracy. We also propose to use image transformations and a random mixture sampling technique for training data augmentation to reduce model overfitting. The model is trained and validated in the well-Annotated Football Semantic Segmentation Open dataset. The proposed best model achieves 0.96 frequency weighted IoU and 0.90 mean IoU segmentation accuracy on validation set. © 2023 IEEE.

关键词： Semantic Segmentation

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Integrated Energy Management Platform for Monitoring, Operation and Maintenance 16th

Integrated Energy Management Platform for Monitoring, Operat...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Zhang, Tao Wang, Hailun Ye, Jun Yang, Xu Zhang, Yixin Li, Dongze Shi, Zinan School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education Beijing100083 China Xi’an Jiaotong University Xi’an710000 China Beijing SyiTsing Energy Technology Co. LTD. Beijing100083 China

ISBN: (纸本)9789819617760

A seamless and comprehensive system for the clean utilization of energy remains elusive, with energy dispatch failing to harmonize the overall picture and the integration of clean energy sources. The judicious employment of holistic smart energy holds immense importance for societal advancement. To foster a more rational and efficient approach to the development and administration of smart energy, and to ensure the optimal utilization of new energy resources, the intelligent integrated energy management platform's development and design methodologies are being optimized in tandem with the Internet of Things technology. This paper outlines the construction of an integrated energy intelligent management system and platform, leveraging the ‘cloud management edge side’ approach. It bridges the gap between the various tiers of the integrated energy service system, achieving a comprehensive integration of energy flow, information flow, and value flow within the system. This platform stands poised to support comprehensive energy operators, data, and network services across the region, spurring regional economic growth, and offering a diverse array of financial products and services, grounded in high-quality user resources and industrial structure service capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Adaptive neural prescribed performance control for PMSM with time delays

Adaptive neural prescribed performance control for PMSM with...

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2022 Chinese Automation Congress, CAC 2022

作者： Li, Tandong Li, Shaobo Zhang, Junxing GuiZhou University School of Mechanical Engineering Guiyang China GuiZhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9781665465335

In this paper, an adaptive neuro-prescribed performance control is proposed for the PMSM with time delays. First, a novel prescribed performance function is proposed, and the constrained system is converted into unconstrained. Then, the time delays and the unknown nonlinearities are addressed by combining the proper Lyapunov-Krasovskii functionals with the neuro-learning techniques. Furthermore, the first-order filters are introduced to overcome the "explosion of complexity"generated from the backstepping procedures. After that, stability analysis shows that the closed-loop system are gradually stable. Finally, the simulations illustrate the effectiveness of the suggested scheme. © 2022 IEEE.

关键词： Time delay

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Structure-Based Uncertainty Estimation for Source-Free Active Domain Adaptation

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IET Computer Vision 2025年第1期19卷

作者： Ouyang, Jihong Zhang, Zhengjie Meng, Qingyi Chi, Jinjin College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Active domain adaptation (active DA) provides an effective solution by selectively labelling a limited number of target samples to significantly enhance adaptation performance. However, existing active DA methods often struggle in real-world scenarios where, due to data privacy concerns, only a pre-trained source model is available, rather than the source samples. To address this issue, we propose a novel method called the structure-based uncertainty estimation model (SUEM) for source-free active domain adaptation (SFADA). To be specific, we introduce an innovative active sample selection strategy that combines both uncertainty and diversity sampling to identify the most informative samples. We assess the uncertainty in target samples using structure-wise probabilities and implement a diversity selection method to minimise redundancy. For the selected samples, we not only apply standard-supervised loss but also conduct interpolation consistency training to further explore the structural information of the target domain. Extensive experiments across four widely used datasets demonstrate that our method is comparable to or outperforms current UDA and active DA methods. © 2025 The Author(s). IET Computer Vision published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： computer vision convolutional neural nets image classification

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