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5th International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2022

作者： Wenxin, Zhang Yilin, Yang Qingguo, Song Chengli, Zhu Ni, Shaoquan School of Transportation and Logistics Southwest Jiaotong University Chengdu611756 China China Railway Chengdu Bureau Group Co. Ltd. Chengdu610082 China Yibin Railway Industry Investment Co. Ltd. Yibin644000 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China National and Local Joint Engineering Laboratory for Intelligent Integrated Transportation Chengdu611756 China

ISBN: (纸本)9789819908479

The special function orientation and passenger flow characteristics of suburban railway put forward higher demands for its transportation organization, such as mass transit type, rapidness, diversification, and convenience. Under the condition of suburban railway of mass transit type, the characteristics of high density in and out of high-speed trains will affect the safety of waiting and landing at passenger platforms. Based on the current passenger organization and technical level, this paper discussed the necessity and technical feasibility of setting platform door in suburban railway. The results show that the installation of platform door can greatly improve the passenger experience, and the technical conditions for the development of platform door system in suburban railway have been met. It is suggested that the new suburban railway should consider the installation of platform gate or reserve the engineering conditions for the installation of platform gate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Railroads

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Solving a Locomotive Routing Problem of Heavy Haul Railways 5th

Solving a Locomotive Routing Problem of Heavy Haul Railways

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5th International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2022

作者： Li, Yongxin Wang, Meng Liu, Zhen Zhang, Chi Li, Xueting School of Transportation and Logistics Southwest Jiaotong University Chengdu610031 China National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation Southwest JiaoTong University Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu610031 China China Energy Railway Equipment Company Beijing100011 China China Railway Shanghai Design Institute Group Corporation Limited Shanghai200070 China

ISBN: (纸本)9789819908479

Locomotive routing problem of heavy haul railways is more difficult than that of regular railways because it has some unique characteristics including different numbers of locomotives utilized for different train type, mixed using of various locomotive types, and unpaired locomotive turn-around. This paper extends the existing model of LRP. Locomotive attached variable is introduced to ensure locomotive turn-around in pair. Tractive force supply and demand balance constrain is used to transform the complex effects of multi-locomotive traction and various locomotive types. Locomotive servicing and maintenance requirements is transformed into constrains utilizing conditional discrimination. Then we develop a MIP model which has a huge scale. It is difficult to solve it utilizing the exact algorithm. We design a two-stage heuristic algorithm. In the first stage, the initial solution is produced by the heuristic algorithm based on dynamic table of locomotives staying at stations. In the second stage, Tabu Search is used to optimize the initial solutions deeply with the search space limit method and the feasible solution transformation method which are used to handle the model constraints. We conduct many computational experiments and comparative experiments to prove the rationality of the model and the effectiveness of the algorithm. The results of this paper can provide an effective reference for the study on the complex locomotive routing problem. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Tabu search

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GK: A Graphormer Architecture Based Knowledge Graph Reasoning Model for Link Prediction

GK: A Graphormer Architecture Based Knowledge Graph Reasonin...

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IEEE International Conference on big data and Cloud computing (BdCloud)

作者： Yueiia Wu Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.

关键词： Training Knowledge engineering Knowledge graphs Predictive models Prediction algorithms Feature extraction Cognition

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arXiv

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arXiv 2024年

作者： Wang, Yuhe Wang, Yating National & Local Joint Engineering Laboratory for Big Data Analysis and Computing Technology Beijing100190 China Institute for Scientific Computation Texas A&M University College StationTX77843 United States School of Mathematics and Statistics Xi’an Jiaotong University Shaanxi Xi’an710049 China

Fluid flow through bimodal porous media, characterized by a distinct separation in pore size distribution, is critical in various scientific and engineering applications, including groundwater management, oil and gas production, and carbon sequestration. This note delves into the physical similarity of fluid flow within such media, bridging the gap between microscale phenomena and macroscale observations. We present a representative mathematical model that conceptualizes bimodal porous media as a double-continuum system, distinguishing between macroporous and microporous regions. The model captures the complex interactions between these regions, particularly focusing on the challenges of modeling fluid flow when there is significant disparity in pore sizes. By employing a heuristic approach grounded in pore-scale tomography, we derive governing equations that describe fluid flow and analyze the solution characteristics. The results reveal unique features of the fluid flow in bimodal systems, such as the occurrence of boundary discontinuities and the delayed transient response, which are not observed in conventional porous media. This work provides ground for further studies in bimodal porous media, offering insights that could enhance predictive modeling and optimization in various applications concerning porous media with similar bimodal pore size distributions. © 2024, CC BY.

关键词： Transient analysis

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A Hybrid Framework for Spatial Interpolation: Merging data-driven with Domain Knowledge

arXiv

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arXiv 2024年

作者： Zhang, Cong Du, Shuyi Song, Hongqing Wang, Yuhe National & Local Joint Engineering Lab for Big Data Analysis and Computing Technology Beijing100190 China School of Civil and Resource Engineering University of Science and Technology Beijing Beijing100083 China Institute for Scientific Computation Texas A&M University College StationTX77843 United States

Estimating spatially distributed information through the interpolation of scattered observation datasets often overlooks the critical role of domain knowledge in understanding spatial dependencies. Additionally, the features of these datasets are typically limited to the spatial coordinates of the scattered observation locations. In this paper, we propose a hybrid framework that integrates data-driven spatial dependency feature extraction with rule-assisted spatial dependency function mapping to augment domain knowledge. We demonstrate the superior performance of our framework in two comparative application scenarios, highlighting its ability to capture more localized spatial features in the reconstructed distribution fields. Furthermore, we underscore its potential to enhance nonlinear estimation capabilities through the application of transformed fuzzy rules and to quantify the inherent uncertainties associated with the observation datasets. Our framework introduces an innovative approach to spatial information estimation by synergistically combining observational data with rule-assisted domain knowledge. © 2024, CC BY.

关键词： Domain Knowledge

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analysis of parallel helix tension measurement based on frequency domain reflection method 5

Analysis of parallel helix tension measurement based on freq...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Li, Chaopeng Li, Qing Chen, Huamin Xu, Xiajie Miao, Xinyu Zhang, Yangke College of Mechanical and Electrical Engineering China Jiliang University Zhejiang Hangzhou310018 China National and Local Joint Engineering Research Center for Disaster Monitoring Technology and Instruments China Jiliang University Zhejiang Hangzhou310018 China Zhejiang Key Laboratory of Intelligent Manufacturing Quality Big Data Traceability and Application Zhejiang Hangzhou310061 China Jinhua Geological Environment Monitoring Station Zhejiang Jinhua321000 China

ISBN: (纸本)9781510668621

Distributed measurement of underground rock and soil deformation is an important part of geological hazard monitoring. Using parallel helical transmission lines as sensors and combining frequency domain reflection (FDR) analysis enables continuous distributed measurement of larger deformation quantities. It is known that the characteristic impedance of the parallel helical transmission line increases with the increase of tensile force. Through FDR, the measurement and location of the tensile point can be determined. This paper explains the principle of FDR measurement of tensile deformation in parallel helical transmission lines. In response to practical application issues of FDR in parallel helical transmission lines, this paper analyzes the influence of sweep range, frequency, and number of sampling points on measurement results through experimental data. The paper also measures different incident signals under different circumstances, identifies and analyzes factors that may affect measurement results, and selects a better measurement scheme. The proposed method is used to simulate the actual use of parallel helical transmission lines and obtain actual measurement data, which is analyzed for better understanding. © 2023 SPIE.

关键词： Electric lines

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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A fine-grained perspective on the robustness of global cargo ship transportation networks

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Journal of Geographical Sciences 2018年第7期28卷 881-899页

作者：彭澎程诗奋陈金海廖梦迪吴琳刘希亮陆锋 State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research CAS University of Chinese Academy of Sciences Navigation Aids Technology Research Center of Jimei University National & Local Joint Engineering Research Center for Marine Navigation Aids Services College of Geomatics Shandong University of Science and Technology Institute of Computing Technology CAS Fujian Collaborative Innovation Center for Big Data Applications in Governments Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application

The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System（AIS） data available. First, we subdivide three typical cargo ship transportation networks（i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks（i.e., degree-based attack, betweenness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation ***： The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current resear

关键词： complex network fine-grained cargo ship transportation network robustness automatic identification system

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Digital Memristor-Based Neural Network for Gas Recognition in Electronic Noses

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IEEE Sensors Journal 2024年

作者： Wang, Jiajun Yang, Yu Tan, Jinpei Wang, Lidan Duan, Shukai Yan, Jia Ministry of Education Southwest University College of Artificial Intelligence Southwest University National Local Joint Engineering Research Center of Intelligent Transmission and Control Technology Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Key Laboratory of Luminescence Analysis and Molecular Sensing Chongqing400715 China State Key Laboratory of Intelligent Vehicle Safety Technology China

The electronic nose (e-nose) is widely used in environmental monitoring, product quality control, and medical diagnostics. Deep learning has greatly improved pattern recognition, but high-performance neural networks often require many parameters. Traditional computing architectures, which involve frequent data transmission and storage, are inefficient and consume a lot of energy, making them unsuitable for rapid-response e-nose applications like on-site detection or mobile devices. Memristors, which combine storage and computation in one device, offer a solution by reducing power consumption and increasing efficiency. However, real memristor chips and large-scale arrays are still hard to obtain. Therefore, this paper proposes an efficient digital memristor neuron circuit design based on Field-Programmable Gate Array (FPGA). Additionally, we introduce a resource-efficient digital memristor array design scheme to maximize the implementation of digital memristor array with limited resources. We also develop a gas data acquisition system that utilizes sensor arrays for data collection and performs two-dimensional processing on the data. To achieve gas recognition, we present a lightweight mixed-precision quantized convolutional neural network (LHPQ-CNN). We successfully deployed the LHPQ-CNN model on an FPGA-based digital memristor array, resulting in the creation of an advanced e-nose pattern recognition system. Our experimental results demonstrate an impressive accuracy rate of 99.42% in identifying ten distinct gases, underscoring its significant advantages for practical applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Integrated Optimization of Maintenance Window and Train Timetable: An Innovation Modeling Approach and Alternating Direction Method of Multipliers Methods

SSRN

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SSRN 2022年

作者： Yang, Haonan Ni, Shaoqun Huo, Haoyang Yang, Yuhua Liu, Su Zhang, Qingpeng Chen, Dingjun School of Transportation and Logistics Southwest Jiaotong University Chengdu610031 China National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation Southwest Jiaotong University Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu610031 China School of Data Science City University of Hong Kong Hong Kong

Train timetable plays an important role in improving passenger service and transportation efficiency. The arrangement of the maintenance window directly affects the quality of maintenance, which interacts with the train timetable and constrains each other. This paper studies the integrated optimization of the maintenance window and train timetable. We abstract creatively the occupation of track capacity by maintenance activity as arc and construct the integrated time-space network of maintenance activities and trains. The headway constraints of different types of arcs are determined by the influences of trains and maintenances, and the original model is converted into a multi-commodity network flow model containing multiple types of arcs. Then we decomposed the problem into two sub-problems: (i) the arrangement of major maintenance activities;(ii) the integrated optimization of the maintenance window and train timetable for fixed maintenance activities. The heuristic algorithms based on VNS and ADMM framework were designed to solve the sub-problems, respectively. The practical case with the data of the Wuhan-Guangzhou Railway is analyzed. The results show that, in the scenario where the speed of the fast and slow trains is 160km/h and 120km/h, respectively, compared with common forms of the maintenance window, the optimized train operation cost is reduced by 21.7% with the completion of all maintenance activities. © 2022, The Authors. All rights reserved.

关键词： Heuristic algorithms

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