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arXiv 2023年

作者： Chen, Jinbiao Zhang, Zizhen Cao, Zhiguang Wu, Yaoxin Ma, Yining Ye, Te Wang, Jiahai School of Computer Science and Engineering Sun Yat-sen University China School of Computing and Information Systems Singapore Management University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands Department of Industrial Systems Engineering & Management National University of Singapore Singapore Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Pedestrian Fundamental Diagram in Between Normal Walk and Crawling 13th

Pedestrian Fundamental Diagram in Between Normal Walk and Cr...

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13th Conference on Traffic and Granular Flow, TGF 2019

作者： Ma, Jian Shi, Dongdong Li, Tao School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu610031 China Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu610031 China

ISBN: (纸本)9783030559724

Due to the increase of the number and frequency of major events in the past decades, studies on pedestrian dynamics have attracted attentions of researchers of various disciplines. Considering that escape environment, especially the ones under special circumstances including fire, can be greatly affected by factors such as smoke, pedestrian gait is as a result different from normal walking. Thus an experimental study on fundamental diagram of single-file pedestrian movement at different available heights is detailed in the present paper. In total five experimental scenarios with the available heights of 1.0, 1.2, 1.4, 1.6 and 2.0 m were investigated. The relationship between velocity and density, density and flow, and spatial headway and instantaneous velocity were analyzed. It is found that the pedestrian’s free walking speed is the largest at the height of 2 m. At high density scenarios, the speed of pedestrians presents two separated states, i.e., 0.5 m/s at the heights of 2.0 and 1.6 m and 0.25 m/s at the heights of 1.0, 1.2 and 1.4m. It is also found that there are three segments, namely laminar flow state, stop and go phenomenon and jammed state, in the fundamental diagram. The results presented in this paper is helpful in understanding pedestrian evacuation at different heights. © 2020, Springer Nature Switzerland AG.

关键词： Laminar flow

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Cascading Fault Prevention of Power Grid Based on Key Power Generation Nodes 1

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, applications and Tools, FITAT 2020

作者： Chang, Kuo-Chi Luo, Jie Deng, Hui-Qiong Li, Qin-Bin Zheng, Rong-Jin Li, Pei-Qiang School of Information Science and Engineering Fujian University of Technology Fuzhou350108 China Fujian Provincial University Engineering Research Genter of Smart Grid Simulation Analysis and Integrated Control Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

In the case of power grid cascading trip, this study puts forward a strategy to prevent cascading trip by changing the output of generator set. Firstly, according to the specific performance of the power grid under the cascade tripping, combined with the action behavior of the line current-type backup protection, and an expression equation for measuring the safety level index between the power grid and the cascading trip failure is given. Secondly, the vulnerable branch is found out through the safety level index, the output of the key power generation nodes on the branch is taken as the optimization variable, and the optimization model is established to improve the safety level index under the chain trip of the power grid. Finally, a combination of sensitivity analysis and particle swarm optimization(PSO) was used to analyze an example on the IEEE-39 node system. The results demonstrate the feasibility and effectiveness of the model and method, and provide a reference for preventing chain trip accidents. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sensitivity analysis

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Exploring the influencing factors on demand-responsive customized bus ridership using multi-source data 6

Exploring the influencing factors on demand-responsive custo...

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6th International Conference on Transportation Information and Safety, ICTIS 2021

作者： Huo, Yamin Qiu, Zhongquan Xin, Yi Dai, Shideng Pan, Renbin Wen, Yi Southwest Jiaotong University Hi-Tech Industrial Development Zone School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Chengdu611756 China China Academy of Urban Planning and Design Beijing100037 China Southwestern University of Finance and Economics School of Business Administration Faculty of Business Administration Chengdu611130 China University of Tennessee Department of Civil and Environmental Engineering KnoxvilleTN37996 United States

ISBN: (纸本)9781665497138

Demand-responsive customized bus (DRCB) is a specific type of demand-responsive transit (DRT), which can flexibly customize services according to travel demand. However, few papers explored the influence factors on it. The purpose of this paper is to use the DRCB trip data from November 15 to December 15, 2020, in Xiongan New Area, China, to investigate the influence of built environment, weather, and time factors on DRCB ridership by using a negative binomial regression model. According to the spatiotemporal analysis, DRCB ridership is mainly concentrated in the peak hours of 07:00-09:00 and 17:00-20:00;the spatial distribution is primarily centered in areas such as government service center, railway station, and residential area. The model results show that scenic spot, governmental agency, morning peak period, and evening peak period are positively correlated with the DRCB ridership. Company, medical facility, visibility, rain, and weekend have a negative correlation with DRCB ridership. The results of this paper can provide insights for relevant companies to better understand the demand for DRCB and better plan the DRCB systems. © 2021 IEEE.

关键词： Buses

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Optimal Power Generation Output Considering Cascading Failure 1

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作者： Deng, Hui-Qiong Luo, Jie Li, Qin-Bin Zheng, Rong-Jin Li, Pei-Qiang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou350108 China Fujian Provincial University Engineering Research Genter of Smart Grid Simulation Analysis and Integrated Control Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

Aiming at the cascading fault phenomenon of power system, this paper analyzes the disturbed branches involved in the initial fault and their relationship with the power generating nodes. Firstly, according to the performance of the initial fault branch in the cascading fault phenomenon and the action of relay protection, an expression form of evaluating the severity of the initial fault branch is given. Secondly, the relationship between the current of the residual system branch and the injection power of the nodes after the initial fault removal is analyzed. Then, on the basis of synthesizing the severity of the disturbed branch and its correlation with the power generating nodes, the key power generating nodes on the seriously disturbed branch are selected by clustering analysis. Finally, based on the key power generation nodes, the optimal power generation output model considering the power generation operation cost was established, the model was solved by particle swarm optimization (PSO), the model was simulated by IEEE39 bus system in MATLAB environment, and the correctness of the established model and method was verified. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Particle swarm optimization (PSO)

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Screening of energy-saving technologies for cement production based on boston consulting group matrix 21st

Screening of energy-saving technologies for cement productio...

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21st Chinese Materials Conference, CMC 2020

作者： Zhang, Yuchen Gong, Xianzheng Liu, Yu Zhou, Liwei Faculty of Materials and Manufacturing Beijing University of Technology No.100 Ping Le Yuan Chaoyang District Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology No.100 Ping Le Yuan Chaoyang District Beijing100124 China China Building Material Federation No.11 San Li He Road Haidian District Beijing100831 China

ISBN: (纸本)9783035717389

Based on the analysis of the development situation of China’s cement industry and the BCG Matrix method, this paper classifies and screens 31 energy-saving technologies in China's cement production, and puts forward guiding suggestions for the application of energy-saving technologies for cement enterprises. The screening results show that the "Energy-efficient powder separation technology" and "New low-calcium cement clinker and production technology" are "double-high" technologies with excellent on both screening indicators. The investment energy saving rates are 17.5 tce/10,000 yuan (tons of standard coal equivalent per 10,000 yuan) and 10 tce/10,000 yuan. The promotion ratio increments are 35% and 25%. "Cement clinker energy-saving nitrogen reduction firing technology", "Fan drive mode adopts high voltage frequency conversion speed control technology", "Pure low temperature waste heat power generation technology for cement kiln" are "single-high" technologies with excellent on one of screening indicators. The investment energy saving rates are 0.3tce/10,000 yuan, 4.1tce/10,000 yuan, 3.9tce/10,000 yuan respectively. The promotion ratio increments are 19%, 35%, 31.5% respectively. © 2021 Trans Tech Publications Ltd, Switzerland.

关键词： Investments

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Spatiotemporal Effects of Built Environment on Varying Rail Transit Station Ridership Patterns

SSRN

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SSRN 2022年

作者： Wang, Jing Wan, Feng Dong, Chunjiao Yin, Chaoying Chen, Xiaoyu Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China Hikvision Research Institute Hangzhou310012 China College of Automobile and Traffic Engineering Nanjing Forestry University Nanjing210037 China School of Computer and Information Technology Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China

Although many studies have explored the impacts of the built environment on rail transit station ridership, spatiotemporal heterogeneity of the effects and different ridership patterns are rarely considered. Using multi-source data from China, three direct models (DRMs) based on the geographically and temporally weighted regression (GTWR) model are proposed to reveal the spatiotemporal effects of daily, weekday hourly and weekend hourly ridership patterns. The results show that employment density, entertainment places, housing prices, and entrances/exits are the dominant variables affecting ridership in the three models;however, their effects vary in spatiotemporal ranges. For example, the positive effect of employment density decreases from Monday to Sunday, and its coefficients during the peak hours are highest in the northwest center, where is the largest technology park situated. The results also highlight the importance of bus stations and parking lots in attracting commuter ridership. The findings provide insights into urban rail transit planning. © 2022, The Authors. All rights reserved.

关键词： Housing

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A cross Transformer for image denoising

arXiv

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arXiv 2023年

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Shichao Zhang, Yanning Lin, Chia-Wen School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://***/hellloxiaotian/CTNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Image denoising

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Federated Learning for Medical Image Classification: Advances, Challenges and Opportunities

SSRN

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SSRN 2023年

作者： Zhang, Xiaobo Zhao, Xiaole Wu, Yunyang Zheng, Hailong Li, Yang School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Artificial Intelligence Research Institute Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

Medical images are private integrations comprising private patient information owned by various hospitals and relevant research institutes, and the generated image data can be utilized to infer physical health conditions. In recent years, more and more AI researchers have used medical images as datasets for their analysis purposes, which will cause relevant personnel to worry about their privacy. As a distributed machine learning framework, federated learning can effectively solve the problem of data privacy. It participates in and completes model training without sharing personal data, with strong universality and good results. In this work, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Wilhelm Conrad Röntgen (X-Ray) as well as other typical medical imaging modes are used as research objects to summarize, analyze and look forward to the previous work related to federal learning in the field of medical image analysis. On the one hand, this survey classifies and compares the existing federal learning framework and algorithm for different medical image datasets;on the other hand, it discusses the relevant federal studies that are widely used in medical images, analyses the problems that need to be solved in the field of medical images, and looks forward to future studies. © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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Efficient and Environmentally Friendly Operation of Intermittent Dedicated Lanes for Connected Autonomous Vehicles in Mixed Traffic Environments

SSRN

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SSRN 2022年

作者： Chen, Shuiwang Hu, Lu Yao, Zhihong Zhu, Juanxiu Zhao, Bin Jiang, Yangsheng School of Transportation and Logistics Southwest Jiaotong University Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu610031 China School of Management Xihua University Sichuan Chengdu610039 China School of Automobile & Transportation Engineering Xihua University Sichuan Chengdu610039 China

The emergence of connected autonomous vehicles (CAVs) provides a possibility to develop an efficient and sustainable mobility option. To foster the adoption of CAVs, the dedicated CAV lanes have been widely discussed in terms of optimal planning and allocating infrastructures for CAVs. Meanwhile, a sensible operation manner of dedicated lanes is also crucial to reap the maximum benefits. In this paper, we propose a novel idea, i.e., the intermittent dedicated CAV lanes (IDCLs), which dynamically provide a separate right-of-way for human-driven vehicles (HVs) to maximize traffic efficiency and minimize environmental costs. To this end, we first tailor two multi-class queuing models as fundamental tools to evaluate the traffic state. A traffic emission model is proposed based on the proposed queuing models. Then, a queuing-based bi-objective optimization model, which aims to maximize the traffic throughput and minimize the green tax simultaneously, is developed for the optimal operation of IDCL. Subsequently, the non-dominated sorting genetic algorithm-II (NSGA-II) is employed to find the Pareto optimal solutions. Finally, numerical experiments are conducted under different market penetration rates (MPRs) of CAVs and various traffic intensities. The results reported in this paper help policymakers and authorities draw insights into the IDCL policy. © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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