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SSRN 2024年

作者： Luo, Haibo Ye, Huaisheng Feng, Chao Shi, Xin Xie, Guoqi School of Computer and Data Science Minjiang University Fuzhou350018 China College of Computer and Data Science Fuzhou University Fuzhou350018 China College of Network and Information Security Fujian Agriculture and Forestry University Fuzhou350018 China Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha410082 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

Truck overload and over-limit are the primary causes of infrastructure damage and traffic safety accidents. In the past 2 years, researchers have started to deploy intelligent Internet of Things system at the source of truck delivery to enforce permit control. Truck type recognition is the core of such a system. However, existing methods suffer from limitations such as low recognition accuracy, high costs, difficult maintenance, and the need for road surface modifications. This study proposes a monocular camera-based visual truck type recognition method. First, the optimized lightweight deep neural network based on machine vision is utilized to detect truckaxles and recognize wheel hub categories. Next, a tracking algorithm and the video streamcontext information are employed to complete the robust statistics of the axle by neighborhoodcounting. Finally, the proposed method further achieves truck type recognition by leveragingavailable prior knowledge. Experimental results demonstrate that this method exhibits superiortime efficiency compared with existing algorithms, and it reduces average processing time by92.37% to 96.24% while ensuring high accuracy. Moreover, the method has been deployed onembedded devices with low computing power to enable real-time operation onsite. © 2024, The Authors. All rights reserved.

关键词： Object detection

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Transfer learning to decode brain states reflecting the relationship between cognitive tasks

arXiv

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arXiv 2022年

作者： Qu, Youzhi Jian, Xinyao Che, Wenxin Du, Penghui Fu, Kai Liu, Quanying Shenzhen Key Laboratory of Smart Healthcare Engineering Department of Biomedical Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

Transfer learning improves the performance of the target task by leveraging the data of a specific source task: the closer the relationship between the source and the target tasks, the greater the performance improvement by transfer learning. In neuroscience, the relationship between cognitive tasks is usually represented by similarity of activated brain regions or neural representation. However, no study has linked transfer learning and neuroscience to reveal the relationship between cognitive tasks. In this study, we propose a transfer learning framework to reflect the relationship between cognitive tasks, and compare the task relations reflected by transfer learning and by the overlaps of brain regions (e.g., neurosynth). Our results of transfer learning create cognitive taskonomy to reflect the relationship between cognitive tasks which is well in line with the task relations derived from neurosynth. Transfer learning performs better in task decoding with fMRI data if the source and target cognitive tasks activate similar brain regions. Our study uncovers the relationship of multiple cognitive tasks and provides guidance for source task selection in transfer learning for neural decoding based on small-sample data. Copyright © 2022, The Authors. All rights reserved.

关键词： Brain

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Research on Surface Quality Monitoring of Workpiece Based on CornerNet 7

Research on Surface Quality Monitoring of Workpiece Based on...

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7th Annual International Conference on Network and Information Systems for Computers, ICNISC 2021

作者： Xiong, Qiaoqiao Tang, Saihong Li, Yiting Chen, Qipeng Universiti Putra Malaysia Faculty of Engineering Department of Mechanical and Manufacturing Engineering Serdang43400 Malaysia GuiZhou University of Finance and Economics College of Big Data Statistics Guiyang550025 China Guizhou University Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guiyang550025 China

ISBN: (纸本)9781665402323

In order to monitor the surface quality of a workpiece in the production line in real-Time, a method based on CornerNet is proposed. First of all, this method uses the anchor free target detection framework, which avoids the problems of data imbalance and introduction of more hyperparameters caused by traditional deep learning models in the design of anchor. Secondly, the ResNet50-vd that incorporates the idea of deformable convolution is selected as the backbone network of CornerNet, and uses the feature pyramid structure to enhance the feature learning ability of the model for better real-Time performance and robustness. A machining production line in Guizhou Province was used as an experimental platform to verify the proposed method's feasibility and effectiveness. Experimental results show that the method has various advantages such as simple and clear structure, strong adaptability and so on. In addition, the detection accuracy and speed meet the needs of industrial scenarios. © 2021 IEEE.

关键词： Surface properties

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Practical Fixed-Time Event-Triggered Time-Varying Formation Tracking Control for Disturbed Multi-Agent Systems with Continuous Communication Free

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无人系统（英文） 2021年第1期9卷 23-34页

作者： Xiaofeng Chai Jian Liu Yao Yu Jianxiang Xi Changyin Sun Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of EducationSchool of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083P.R.China School of Automation Southeast UniversityNanjing 210096P.R.China Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of EducationAnhui Polytechnic UniversityWuhu 241000P.R.China Rocket Force University of Engineering Xi'an 710025P.R.China School of Automation Southeast UniversityNanjing 210096P.R.China

In this paper,we study the practical fixed-time event-triggered time-varying formation tracking problem of leader-follower multi-agent systems with multi-dimensional ***-time event-triggered control schemes with continuous communication and intermittent communication are developed,*** communication and measurement are avoided,and computation cost is reduced greatly in the latter *** the settling time is to be specified regardless of initial states of ***,tracking errors are adjustable as desired with expected settling *** is demonstrated that time-varying formation tracking can be achieved under the two proposed control schemes and Zeno behavior can be ***,numerical examples are provided to illustrate the effectiveness of the proposed control strategies.

关键词： Time-varying formation tracking fixed-time control event-triggered control multi-agent systems bounded disturbance

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A new method for predicting plant proteins function based on multi label classification algorithm

A new method for predicting plant proteins function based on...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuai Chen Juan Wang Maozu Guo School of Computer Science Inner Mongolia University Hohhot China School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Protein function annotation is an important content of bioinformatics. It is unrealistic to experimentally annotate a large number of protein functions, so automated prediction of the functions of proteins is required. Studying plant proteins can help us cultivate new plant varieties. However, the existing methods mainly aim at predicting the single function of plant proteins. Most proteins only have sequence information. Therefore, the multi-function prediction method of plant protein based on sequence has become the focus of research. In this study, we propose a method based on sequence to predict the function of plant proteins, called PlantGO. We describe the functional annotation problem as a multi-label classification problem using Gene Ontology (GO) terminology, and predict multiple functions for plant proteins. PlantGO extracts features from three aspects and performs feature fusion respectively. To avoid redundant information in features, PlantGO uses the random forest to select features and obtains three groups of optimal features. PlantGO uses a multi-label learning algorithm MLKNN to predict protein function, which proves the effectiveness and accuracy of the algorithm. Finally, PlantGO adopts the ensemble learning method to integrate all models into a unified model, further improving the prediction performance. Therefore, PlantGO has achieved excellent performance. The performance metrics are Accuracy (0.936), F1 Score (0.944), Ranking loss (0.011), which is better than the previous method in the independent test. We have developed an online server to predict the functions of plant proteins.

关键词： Proteins Protein engineering Annotations Terminology Predictive models Prediction algorithms Feature extraction

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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Automatically Labeling Aging Scenarios with a Machine Learning Approach 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Xu, Yang Gao, Qinglin Zhu, Wenjie Wu, Aoran Chen, Honglin School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shanghai Tianyu Senior Living Service Co. Ltd. Shanghai China School of Basic Courses Bengbu Medical College Bengbu China Management and Business Department Skidmore College Saratoga SpringsNY United States Department of Social Sciences University of Eastern Finland Kuopio70150 Finland

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Older adults have diversified needs often associated with particular aging scenarios. People started to use aging scenarios to provide better Smart Eldercare services and develop innovative solutions. The problem is how to manage these scenarios effectively. To our best knowledge, this study is the first to develop a model to provide a structured framework of aging scenarios for research and teaching of eldercare services. Labeling aging scenarios can facilitate their collection, classification, and consolidation within this model. This study uses the automatic labeling approach to manage and utilize aging scenarios. Under each class, there are labels with different numbers and smaller granularity to reflect the requirements in each scenario case. Since a scenario case often involves more than one label, properly labeling it becomes a multi-label text classification problem. We extend the multi-label text classification model LSAN for labeling aging scenario cases. For evaluation purposes, we collected 938 scenario cases as the dataset. Experimental results show that our proposed method can achieve an average accuracy of 61.66%, better than other classical methods. © 2022, Springer Nature Switzerland AG.

关键词： Classification (of information)

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A Localization Method of High Energy Transients for All-sky Gamma-ray Monitor

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Research in Astronomy and Astrophysics 2024年第10期24卷 25-35页

作者： Yi Zhao Wangchen Xue Shaolin Xiong Qi Luo Yuanhao Wang Jiacong Liu Heng Yu Xiaoyun Zhao Yue Huang Jinyuan Liao Jianchao Sun Xiaobo Li Qibin Yi Ce Cai Shuo Xiao Shenglun Xie Chao Zheng Yanqiu Zhang Chenwei Wang Wenjun Tan Zhiwei Guo Chaoyang Li Zhenghua An Gang Chen Yanqi Du Min Gao Ke Gong Dongya Guo Jiang He Jianjian He Bing Li Gang Li Xinqiao Li Jing Liang Xiaohua Liang Yaqing Liu Xiang Ma Rui Qiao Liming Song Xinying Song Xilei Sun Jin Wang Ping Wang Xiangyang Wen Hong Wu Yanbing Xu Sheng Yang Dali Zhang Fan Zhang Hongmei Zhang Peng Zhang Shu Zhang Zhen Zhang Shijie Zheng Keke Zhang Xingbo Han Haiyan Wu Hu Tai Hao Geng Gaopeng Lu Wei Xu Fanchao Lyu Hongbo Zhang Fangjun Lu Shuangnan Zhang Department of Astronomy Beijing Normal UniversityBeijing 100875China Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China School of Physics and Optoelectronics Xiangtan UniversityXiangtan 411105China College of Physics Hebei Normal UniversityShijiazhuang 050024China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang 550001China School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550001China Institute of Astrophysics Central China Normal UniversityWuhan 430079China College of physics Sciences Technology Hebei UniversityBaoding 071002China Physics and Space Science College China West Normal UniversityNanchong 637002China School of Computing and Artificial Intelligence Southwest Jiaotong UniversityChengdu 611756China College of Electronic and information Engineering Tongji UniversityShanghai 201804China Innovation Academy for Microsatellites of Chinese Academy of Sciences Shanghai 201304China National Space Science Center Chinese Academy of SciencesBeijing 100190China School of Earth and Space Sciences University of Science and Technology of ChinaHefei 230026China Electronic Information School Wuhan UniversityWuhan 430072China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric SciencesNanjing 210000China Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China

Fast and reliable localization of high-energy transients is crucial for characterizing the burst properties and guiding the follow-up *** based on the relative counts of different detectors has been widely used for all-sky gamma-ray *** are two major methods for this count distribution localization:χ^(2)minimization method and the Bayesian *** we propose a modified Bayesian method that could take advantage of both the accuracy of the Bayesian method and the simplicity of the χ^(2)*** comprehensive simulations,we find that our Bayesian method with Poisson likelihood is generally more applicable for various bursts than the χ^(2)method,especially for weak *** further proposed a location-spectrum iteration approach based on the Bayesian inference,which could alleviate the problems caused by the spectral difference between the burst and location *** method is very suitable for scenarios with limited computation resources or timesensitive applications,such as in-flight localization software,and low-latency localization for rapidly follow-up observations.

关键词： methods:data analysis methods:analytical (stars:)gamma-ray burst:general

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Animal Shapes, Modal Analysis, and Visualization of Motion (I): Horse and Camel

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Journal of Geometric Analysis 2023年第10期33卷 1-60页

作者： Chen, Goong Huang, Jingtong Wei, Chunqiu Yang, Jing Scully, Matthew M. Sergeev, Alexey Chen, Ming-Chieh Krantz, Steven G. Yao, Pengfei Guo, Tiexin Wang, Junmin Department of Mathematics Texas A &M University College Station 77843 TX United States Institute for Quantum Science and Engineering Texas A &M University College Station 77843 TX United States National Center for Theoretical Sciences National Tsing Hua University Hsinchu Branch Hsinchu Taiwan Department of Mathematics and Data Science Beijing University of Civil Engineering and Architecture Beijing 100044 China School of Mathematics Beijing Institute of Technology Beijing 100081 China Department of Mechanical Engineering Cheng Shiu University Kaohsiung 83347 Taiwan Department of Mathematics Washington University at St. Louis St. Louis 63130 MO United States Key Laboratory of Complex Systems and Data Science of Ministry of Education School of Mathematics Shanxi University Shanxi Taiyuan 030006 China Institute of Systems Science Academy of Mathematics and Systems Science Academia Sinica Beijing 100190 China School of Mathematics and Statistics Central South University Hunan Changsha 410083 China

Eigenfunctions and eigenvalues of physical systems and engineering structures can reveal many of the system’s fundamental features and, therefore, become a basis for the study of inverse problems. In this series of papers, we take a reverse, direct-problem point of view;namely, given the shapes of animals, can we see the patterns of their motions or behaviors through their eigenmode analysis? This modal analysis, we believe, has never been done for living animals. Our modal analysis emphasizes dynamics, which is achieved by visualization through video animation by incorporating the time-harmonic dependence of the eigenmodes. Furthermore, we intend our modal analysis to be more realistic by encompassing the situation of the presence of a floor. Certain physical interpretations of the motion patterns from modal analysis are made. In addition, by visualization, one can see that symmetry plays an important role in motion patterns. One of our main conclusions is that shapes alone can usefully reflect or explain some animal’s behavior or motion patterns. © 2023, Mathematica Josephina, Inc.

关键词： Animal shapes CAD Camel Horse Modal analysis Motion patterns

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Joint User and Target Tracking in Multi-Static Sensing Systems

Joint User and Target Tracking in Multi-Static Sensing Syste...

引用

IEEE International Conference on Communications in China Workshops (ICCC)

作者： Boyang Hu Hui Tian Wei Ni Shaoshuai Fan State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Data61 Commonwealth Scientific and Industrial Research Organisation Sydney NSW Australia School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Perceiving the mobility of the passive targets is critical in the context of Integrated Sensing and Communication (ISAC). While target localization has been extensively studied in radio sensing, the motion estimation and tracking of the targets have not been revealed. This paper proposes a new framework for joint UE and target tracking in the multi-static sensing system under the promising scenario that the user equipment (UE) serves as the receiver. We first estimate the UE's location using the direct paths between the UE and the base stations (BSs), and estimate the target's location of each scattering path to identify the targets by the mean-shift clustering algorithm. Then, we jointly estimate the motions of the UE and targets using the Taylor-least squares (LS) algorithm, and propose a joint UE and target tracking method based on the extended Kalman filter (EKF). Simulations demonstrate that the proposed joint motion estimation and tracking scheme improves the accuracy of location estimates by 42.34% w.r.t. the UE, and 71.29% w.r.t. the targets.

关键词： Location awareness Target tracking Accuracy Motion estimation Clustering algorithms Scattering Estimation Receivers Integrated sensing and communication Kalman filters

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