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arXiv 2024年

作者： He, Pengfei Hao, Jin-Kao Wu, Qinghua School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Computer Science LERIA University of Angers 2 Boulevard Lavoisier Angers49045 France School of Management Huazhong University of Science and Technology No. 1037 Luoyu Road Wuhan China

The capacitated location-routing problem involves determining the depots from a set of candidate capacitated depot locations and finding the required routes from the selected depots to serve a set of customers whereas minimizing a cost function that includes the cost of opening the chosen depots, the fixed utilization cost per vehicle used, and the total cost (distance) of the routes. This paper presents a multi-population integrated framework in which a multi-depot edge assembly crossover generates promising offspring solutions from the perspective of both depot location and route edge assembly. The method includes an effective neighborhood-based local search, a feasibility-restoring procedure and a diversification-oriented mutation. Of particular interest is the multi-population scheme which organizes the population into multiple subpopulations based on depot configurations. Extensive experiments on 281 benchmark instances from the literature show that the algorithm performs remarkably well, by improving 101 best-known results (new upper bounds) and matching 84 best-known results. Additional experiments are presented to gain insight into the role of the key elements of the algorithm. © 2024, CC BY-NC-ND.

关键词： Cost functions

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Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction

arXiv

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arXiv 2024年

作者： Du, Quancheng Wang, Xiao Yin, Shouguo Li, Lingxi Ning, Huansheng The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence Anhui University Hefei230031 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Electrical and Computer Engineering Indiana University Purdue University IndianapolisIN46202 United States

Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ample response time for other traffic participants. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These methods leverage relative distances to forecast the motion trajectories of a single class of agents. However, in complex traffic scenarios, the motion patterns of various types of traffic participants exhibit inherent randomness and uncertainty. Relying solely on relative distances may not adequately capture the nuanced interaction patterns between different classes of road users. In this paper, we propose a novel multi-class trajectory prediction method named the social force embedded mixed graph convolutional network (SFEM-GCN). The primary goal is to extract social interactions among agents more accurately. SFEM-GCN comprises three graph topologies: the semantic graph (SG), position graph (PG), and velocity graph (VG). These graphs encode various of social force relationships among different classes of agents in complex scenes. Specifically, SG utilizes one-hot encoding of agent-class information to guide the construction of graph adjacency matrices based on semantic information. PG and VG create adjacency matrices to capture motion interaction relationships between different classes agents. These graph structures are then integrated into a mixed graph, where learning is conducted using a spatio-temporal graph convolutional neural network (ST-GCNN). To further enhance prediction performance, we adopt temporal convolutional networks (TCNs) to generate the predicted trajectory with fewer parameters. Experimental results on publicly available datasets demonstrate that SFEM-GCN surpasses state-of-the-art methods in terms of accuracy and robustness. Copyright © 2024, The Authors.

关键词： Forecasting

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Time-Delay Feedback control of a Suspended Cable Driven by Subharmonic and Superharmonic Resonance

SSRN

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SSRN 2024年

作者： Peng, Jian Li, Yanan Li, Luxin Lenci, Stefano School of Civil Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China Hunan Provincial Key Laboratory of Structures for Wind Resistance and Vibration Control Hunan University of Science and Technology Hunan Xiangtan411201 China School of Civil Engineering and Communication North China University of Water Resources and Electric Power Zhengzhou450045 China Department of Civil and Building Engineering and Architecture Polytechnic University of Marche Ancona60131 Italy

The longitudinal time-delay feedback control strategy is adopted to suppress the subharmonic and superharmonic responses of the suspended cable. Based on the Hamilton variational principle and the longitudinal time-delay feedback strategy, the in-plane nonlinear differential equations for a suspended cable are formulated, and Galerkin method is utilized to transform the equations into delayed differential equations. By using the method of multiple scales, approximate solutions for the 1/2-subharmonic and second-order superharmonic resonance responses of the controlled suspended cable were derived. The effects of the time delay and control gain are obtained through numerical examples. The research results demonstrate that by adjusting the control gain and time delay, resonance regions can be avoided, effectively suppressing the large-amplitude vibrations of the suspended cable and achieving favorable control performance. © 2024, The Authors. All rights reserved.

关键词： Time delay

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TOWARDS TRUSTWORTHY MULTI-LABEL SEWER DEFECT CLASSIFICATION VIA EVIDENTIAL DEEP LEARNING

arXiv

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arXiv 2022年

作者： Zhao, Chenyang Hu, Chuanfei Shao, Hang Wang, Zhe Wang, Yongxiong Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

An automatic vision-based sewer inspection plays a key role of sewage system in a modern city. Recent advances focus on utilizing deep learning model to realize the sewer inspection system, benefiting from the capability of data-driven feature representation. However, the inherent uncertainty of sewer defects is ignored, resulting in the missed detection of serious unknown sewer defect categories. In this paper, we propose a trustworthy multi-label sewer defect classification (TMSDC) method, which can quantify the uncertainty of sewer defect prediction via evidential deep learning. Meanwhile, a novel expert base rate assignment (EBRA) is proposed to introduce the expert knowledge for describing reliable evidences in practical situations. Experimental results demonstrate the effectiveness of TMSDC and the superior capability of uncertainty estimation is achieved on the latest public benchmark. Copyright © 2022, The Authors. All rights reserved.

关键词： Sewers

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Dual-Branch Transfer Learning in Raman Spectroscopy for Bacterial Quantitative Analysis

SSRN

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SSRN 2023年

作者： Li, Qifeng Yang, Yunpeng Wu, Jianing Wei, Chunsheng Xia, Hua Han, Yangguang Ma, Xiangyun School of Precision Instrument and Opto-electronics Engineering Tianjin University Tianjin300072 China State Key Laboratory of Precision Measurement Technology and Instruments Tianjin University Tianjin300072 China School of Narcotics Control and Public Order Studies Criminal Investigation Police University of China Shenyang110854 China Research and Development Center of Narcotics Control Technology and Equipment Shenyang110854 China

Accurate quantification of bacteria is critical for ensuring food safety, advancing biomedical research, and a range of other pressing concerns. Raman spectroscopy is a popular technique for quantitative analysis due to its benefits of being fast, non-destructive, and highly sensitive. However, the accuracy of the transfer model is often limited by factors such as differences in equipment and environmental noise, which limits the popularization of Raman spectroscopy. In this paper, we propose a approach that overcomes this challenge by introducing a dual branch network based on Continuous Wavelet Transform (CWT) for model transfer. Our model comprises dual branches that perform distinct tasks. The spectral learning branch is responsible for extracting features from the spectral domain. The time-frequency map learning branch employs CNNs for extracting the multi-scale information-rich features. The proposed method is used for the quantitative analysis of Escherichia coli. The proposed approach significantly outperforms traditional methods in improving prediction accuracy. It offers a much-needed solution to the long-standing challenge of Raman spectroscopy in the field of bacterial quantitative analysis. With our approach, we can expect to see Raman spectroscopy more widely adopted in the future. © 2023, The Authors. All rights reserved.

关键词： Raman spectroscopy

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Sputtered Sn-doped Ga2O3 films under balance controlled of energy supply and ion bombardment for solar-blind detection application

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Vacuum 2024年 225卷

作者： Wang, Chen Fan, Wei-Hang Cao, Rong-Jun Fan, Hui-Chen Xu, Yi-Hong Kang, Pin-Chun Wu, Ting-Zhu Kuo, Hao-Chung Lien, Shui-Yang Zhu, Wen-Zhang Xiamen Key Laboratory of Development and Application for Advanced Semiconductor Coating Technology School of Opto-electronic and Communication Engineering Xiamen University of Technology Xiamen361024 China National Measurement and Testing Center for Flat Panel Display Industry Xiamen Institute of Measurement and Testing Xiamen361004 China National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen University Xiamen361005 China Department of Photonics & Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Biomedical Engineering Da-Yeh University Changhua51591 Taiwan

The characteristics of amorphous Sn-doped Ga2O3 films deposited using radio frequency magnetron sputtering at room temperature under different sputter powers have been demonstrated. A balance mechanism of energy supply and ion bombardment controlled by sputter power has been found during the deposition. The increasing growth rate as power can be due to the increased yield sputtering species indicated by an in situ optical emission spectroscopy. As the power increases to 700 W, an obvious change from discrete nanoparticles to flat-continuous film can be observed because of sufficient energy supply for oxidation reaction, which leads to the maximum ratio of lattice oxygen and Sn4+ indicating the optimal film quality and conductive property. However, a non-negligible high energy ion bombardment presented at 900 W leads to the degraded film quality. The influence of sputtering power on Sn-doped Ga2O3 solar-blind photodetectors is demonstrated. It can obtain a good performance with an ultra-low dark current density of 2.2 × 10−11 A/cm2, a substantial light-dark current ratio of 1.08 × 103 and fast rise/decay time of 1.59 s/0.54 s at 700 W. The revelation of the role of sputtering power on the properties of Sn-doped Ga2O3 films may offer an interesting direction for low-cost and high-performance solar-blind photodetectors. © 2024 Elsevier Ltd

关键词： Gallium compounds

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Heterogeneous influence of individuals’ behavior on mask efficacy in gathering environments

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Frontiers of engineering Management 2022年第4期9卷 550-562页

作者： Haochen SUN Xiaofan LIU Zhanwei DU Ye WU Haifeng ZHANG Xiaoke XU College of Information and Communication Engineering Dalian Minzu UniversityDalian 116600China Web Mining Laboratory Department of Media and CommunicationCity University of Hong KongHong KongChina WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong KongChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkHong KongChina Computational Communication Research Center Beijing Normal University(Zhuhai)Zhuhai 519087China School of Journalism and Communication Beijing Normal UniversityBeijing 100875China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical ScienceAnhui UniversityHefei 230601China

Wearing masks is an easy way to operate and popular measure for preventing *** masks can slow down the spread of viruses,their efficacy in gathering environments involving heterogeneous person-to-person contacts remains ***,we aim to investigate the epidemic prevention effect of masks in different real-life gathering *** study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering *** transmission of COVID-19 is simulated using the Monte Carlo simulation *** heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general ***,the heterogeneity of gathering environments causes the epidemic prevention effect of masks to *** masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools,high schools,and ***,the use of masks alone in primary schools and hospitals cannot control *** high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse,masks can meet the need for *** the heterogeneity of individual behavior,if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing,the epidemic prevention effect of masks can be ***,asymptomatic infection has varying effects on the prevention effect of masks in different *** effect can be weakened or eliminated by increasing the usage rate of masks in high schools and ***,the effect on primary schools and hospitals cannot be *** study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention.

关键词： COVID-19 masks behavioral heterogeneity asymptomatic infection

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Classification of Dunhuang Mural Image Based on Small-sample and Semi-supervised Learning

Classification of Dunhuang Mural Image Based on Small-sample...

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IEEE Information technology, Networking, Electronic and Automation control Conference

作者： Hui Cui Zhibin Su Luyue Zhang Liyang Bai School of Information and Engineering Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Center for Ethnic and Folk Literature and Art Development Ministry of Culture and Tourism P.R.C.

Dunhuang mural images classification belongs to the research task in the field of image recognition. In this paper, the semi-supervised model is established with multidimensional features extracted by transfer learning. A small number of labeled samples were used to obtain a large number of unlabeled data, combined with Active Learning and iterative strategy for multiple rounds of label transfer of selected samples. After several rounds of iterations, we can get a more powerful classification learner. Experiments on the self-built Dunhuang mural dataset have shown that the results can approach or even exceed some supervised learning methods when the number of known label samples is less than 4%. Our research implements the cultural resource image classification algorithm based on small samples, which is conductive to improving the accuracy when label samples are scarce.

关键词： Technological innovation Transfer learning Supervised learning Semisupervised learning Predictive models Feature extraction Classification algorithms

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A Novel Integer Frequency Offset Estimation Method for OFDM Based on Preamble

A Novel Integer Frequency Offset Estimation Method for OFDM ...

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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Xinxin Liu Zhan Xu Lu Tian Xiaolong Yang Key Laboratory of Modern Measurement and Control Technology Beijing Information Science and Technology University Beijing China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781665405836

Orthogonal frequency division multiplexing (OFDM) is very sensitive to frequency offset. Although the existence of integer frequency offset in OFDM will not destroy the orthogonality between subcarriers, it will cause a cyclic shift of the frequency domain data after FFT transformation at the receiving end, which will affect the demodulation of data. In this paper, we propose a novel integer frequency offset estimation method based on the similarity property of adjacent subcarriers of the preamble. This method not only guarantees the original accuracy and performance of the conventional method but also achieves less resource consumption and lower computational complexity.

关键词： OFDM Frequency-domain analysis Estimation Logic gates Frequency conversion Hardware Frequency estimation

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DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatial and Temporal Transformer Network

DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatia...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaokun Zhang Xinde Li Chuanfei Hu Jianping Xu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Science and Technology on Information Systems Engineering Laboratory Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human body at a single scale or cascading multiple scales, such as joints, bones, and body-parts. However, they are difficult to simultaneously capture spatial-temporal motion patterns of the human body at different scales due to the complex motion patterns. To address this issue, we propose Dual-scale Spatial and Temporal transFormer (DSTFormer), which can concurrently explore the spatial dependencies and temporal motion patterns of human joints and bones. Additionally, we introduce a Gcn-Spatial Transformer Block (GSTB), which introduces Graph Convolutional Networks (GCN) into transformer to enhance the exploitation of local relationships and global information between adjacent joints or bones. Extensive experiments are conducted on the Human3.6M benchmark dataset, and superior results are reported when comparing to other state-of-the-art methods. More remarkably, our model achieves to-date the best published performance, with P1 errors of 37.9 mm and 15.6 mm, respectively.

关键词： Solid modeling Three-dimensional displays Mechatronics Graph convolutional networks Computational modeling Pose estimation Transformers Bones Joints Robots

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