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Game Theoretic application to Intersection Management: A Literature Review

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arXiv 2023年

作者： Qin, Ziye Ji, Ang Sun, Zhanbo Wu, Guoyuan Hao, Peng Liao, Xishun The School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu611756 China The National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Sichuan Chengdu611756 China The College of Engineering Center for Environmental Research and Technology University of California at Riverside RiversideCA92507 United States The University of California at Los Angeles Los AngelesCA90095 United States

The emergence of vehicle-to-everything (V2X) technology offers new insights into intersection management. This, however, has also presented new challenges, such as the need to understand and model the interactions of traffic participants, including their competition and cooperation behaviors. Game theory has been widely adopted to study rationally selfish or cooperative behaviors during interactions and has been applied to advanced intersection management. In this paper, we review the application of game theory to intersection management and sort out relevant studies under various levels of intelligence and connectivity. First, the problem of urban intersection management and its challenges are briefly introduced. The basic elements of game theory specifically for intersection applications are then summarized. Next, we present the game-theoretic models and solutions that have been applied to intersection management. Finally, the limitations and potential opportunities for subsequent studies within the game-theoretic application to intersection management are discussed. Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

Effect of calcium alumina silicate hydrate nano-seeds on the hydration of low clinker cement

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Journal of Building engineering 2023年 66卷

作者： Liu, Xiao Luo, Qifeng Xie, Hui Li, Shiyu Zhang, Jiale Xia, Chunlei Ding, Yungchin Chen, Yichen Gao, Ruijun Wei, Zhongyuan Zhou, Wu Wang, Ziming Cui, Suping Faculty of Materials and Manufacturing Key Laboratory of Advanced Functional Materials Ministry of Education Beijing University of Technology Beijing100124 China Faculty of Materials and Manufacturing National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China Beijing Municipal Engineering Research Institute Beijing100037 China Institute of Mineral Resources Engineering National Taipei University of Technology Taipei10608 China China Building Materials Academy Beijing100024 China Tangshan Longyi Tech. Co Ltd. Tangshan063004 China Guangxi Keda Building Material Chemicals Co Ltd. Nanning530221 China

The replacement of cement clinker with supplementary cementitious materials (SCMs) is considered a very effective way of carbon reduction. However, the slow pozzolanic reaction of SCMs weakens the early strength, which hinders its application. In this study, the dedicated nucleating agents for Al-rich SCMs, C-A-S-H nano-seeds, were synthesized by co-precipitation using polycarboxylate superplasticizer (PCE) as dispersant. The performances of four nano-seeds with different Al/Si and their effects on hydration in 50% metakaolin cement (MK50) were investigated in detail. The results showed that C-A-S-H nano-seeds had a greater strengthening effect than C-S-H nano-seeds especially at early ages, and also accelerated setting, with Al/Si = 0.10 being the most significant. The Al/Si = 0.10 nano-seeds advanced the second main exothermic peak by 6.7 h, promoting the production of stratlingite and causing the highest contents of CH and bound water in hydrated samples. It was also demonstrated by 27Al NMR that C-A-S-H nano-seeds enhanced the reaction degree of metakaolin by producing more C-A-S-H than AFt or AFm. The pozzolanic activity was further explored in the reaction between metakaolin and CH. This study aims to develop an efficient strengthening agent (i.e., C-A-S-H nano-seeds) for low clinker cement, and to reveal the mechanistic difference between them and common C-S-H seeds from the perspectives of hydration and pozzolanic reactions, which is of great significance for the carbon emission reduction of cement industries and solid waste utilization. © 2023 Elsevier Ltd

关键词： Hydration

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

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arXiv 2022年

作者： Feng, Xin-Ru Li, Heng-Chao Wang, Rui Du, Qian Jia, Xiuping Plaza, Antonio School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS39762 United States School of Engineering and Information Technology University of New South Wales CanberraACT2612 Australia Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura Cáceres10071 Spain

Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions including constrained NMF, structured NMF, and generalized NMF. Furthermore, several experiments are conducted to illustrate the effectiveness of associated algorithms. Finally, we conclude the article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

Identification of soybean in Argentina using Sentinel-2 composite images

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International Journal of Agricultural and Biological engineering 2024年第5期17卷 266-274页

作者： Linsheng Huang Yue Chen Yuhao Pan Zihang Lou Shijun Zheng Xiaoyang Zhang Le Yu Shengwei Liu Dailiang Peng National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China Research Area of Ecology and Biodiversity School of Biological SciencesThe University of Hong KongPokfulamHong KongChina College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Geospatial Sciences Center of Excellence Department of Geography&Geospatial SciencesSouth Dakota State UniversityBrookingsSD 57007USA Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing 100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing 100084China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000HenanChina

Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google Earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models;2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest;3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security.

关键词： soybean machine learning time window feature selection Sentinel-2 Google Earth Engine

Research on Pre-view Method of Safety Level of Cascading Trip for Power Grid 6th

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Research on Pre-view Method of Safety Level of Cascading Tri...

6th International Conference on Advanced Machine Learning Technologies and applications, AMLTA 2021

作者： Deng, Huiqiong Li, Qinbin Zheng, Rongjin Li, Peiqiang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China Fujian Provincial University Engineering Research Center of Smart Grid Simulation Analysis and Integrated Control Fuzhou Fujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China College of Mechanical & Electrical Engineering National Taipei University of Technology No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan Department of Business Administration North Borneo University College Lot 47 Block-F Alamesra Permai Plaza 2 Jln Sulaman Kota Kinabalu Sabah88400 Malaysia

ISBN: (纸本)9783030697167

This article introduces a method by which the power grid's security level can be observed in advance based on the expected initial failure. Firstly, based on the general form of cascading trip, this paper gives a mathematical expression of grid tripping based on cascading trip relay protection action behavior. According to the mathematical expression, the relationship between grid tripping and grid node injection power is discussed. According to this relationship, the distance between the node injection power under the current operation state and the nearest critical state node injection power is taken as the index to measure the power grid's security for cascading trip. According to this index, combined with various power grid constraints before and after the initial fault, a mathematical model for obtaining this safety index is given. This model is an optimized mathematical model. For the model, this paper gives a solution idea based on particle swarm. Finally, a few cases are performed in the IEEE-14 node system, and the calculations of the cases proved the accuracy of the proposed model and method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electric load flow

On efficient and robust metrics for RANSAC hypotheses and 3D rigid registration

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arXiv 2020年

作者： Yang, Jiaqi Huang, Zhiqiang Quan, Siwen Zhang, Qian Zhang, Yanning Cao, Zhiguo National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University Xi’an710129 China School of Electronic and Control Engineering Chang’an University Xi’an710064 China School of Resources and Environment Hubei University Wuhan430062 China National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration. Estimating six-degree-of-freedom (6-DoF) pose from feature correspondences remains a popular approach to 3D rigid registration, where random sample consensus (RANSAC) is a de-facto choice to this problem. However, existing metrics for RANSAC hypotheses are either time-consuming or sensitive to common nuisances, parameter variations, and different application scenarios, resulting in performance deterioration in overall registration accuracy and speed. We alleviate this problem by first analyzing the contributions of inliers and outliers, and then proposing several efficient and robust metrics with different designing motivations for RANSAC hypotheses. Comparative experiments on four standard datasets with different nuisances and application scenarios verify that the proposed metrics can significantly improve the registration performance and are more robust than several state-of-the-art competitors, making them good gifts to practical applications. This work also draws an interesting conclusion, i.e., not all inliers are equal while all outliers should be equal, which may shed new light on this research problem. Copyright © 2020, The Authors. All rights reserved.

关键词： Deterioration

Two-Stream Regression Network for Dental Implant Position Prediction

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arXiv 2023年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Chen, Wenting Shen, Linlin Li, Xin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China Department of Electrical Engineering City University of Hong Kong Hong Kong

In implant prosthesis treatment, the design of surgical guide requires lots of manual labors and is prone to subjective variations. When deep learning based methods has started to be applied to address this problem, the space between teeth are various and some of them might present similar texture characteristic with the actual implant region. Both problems make a big challenge for the implant position prediction. In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue. For the training of IRD, we extend the original annotation to provide additional supervisory information, which contains much more rich characteristic and do not introduce extra labeling costs. A multi-scale patch embedding module is designed for the MSPENet to adaptively extract features from the images with various tooth spacing. The global-local feature interaction block is designed to build the encoder of MSPENet, which combines the transformer and convolution for enriched feature representation. During inference, the RoI mask extracted from the IRD is used to refine the prediction results of the MSPENet. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TSIPR achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

Integrated Public Transport Timetable Coordination and Vehicle Scheduling with Even Headways

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SSRN

SSRN 2022年

作者： Liu, Tao Ji, Wen Gkiotsalitis, Konstantinos Cats, Oded National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Transportation and Logistics Southwest Jiaotong University Chengdu611756 China Institute of System Science and Engineering School of Transportation and Logistics Southwest Jiaotong University Chengdu611756 China Department of Civil Engineering Faculty of Engineering Technology University of Twente Horst Complex Z222 P.O. Box 217 Enschede 7500 AE Netherlands Department of Transport & Planning Delft University of Technology Netherlands

Timetabling and vehicle scheduling are two important activities in public transport (PT) operations planning. Traditionally, the timetabling problem is solved first before proceeding to the vehicle scheduling problem. The integration of these two problems can help further reduce the total operation cost and improve the level of service, especially when timetables of different bus lines are well-coordinated at transfer stations. This work addresses the integrated PT timetable coordination and vehicle scheduling problem while ensuring that each PT line is dispatched with an even headway. We first separately formulate two integer linear programming models for the timetable coordination and vehicle scheduling problems. Next, the two models are combined into a bi-objective integer linear programming model for the integrated timetable coordination and vehicle scheduling problem. For small size PT networks, the model can be solved by using an ɛ -constraint method, together with off-the-shelf optimization solvers. For large-size problems, two constraint reduction procedures are developed to reduce the number of redundant constraints so as to reduce the computation complexity and improve the solution process. Finally, the models and solution method are applied to a numerical example and a real-world bus rapid transit (BRT) network in Chengdu, China. Computation results show that the solution generated by the sequential optimization approach is usually dominated by the Pareto-optimal solutions generated by the integrated optimization approach. Our findings suggest that it is not a wise decision to use the solution generated by the sequential optimization approach or the solution with the minimum fleet size generated by the integrated optimization approach. For practical implementation, it is recommended to choose the solution that has a fleet size of one more vehicle than the minimum fleet size. © 2022, The Authors. All rights reserved.

关键词： Integer programming

Domain-Adversarial Transformer Network for Multiphase Liver Tumor Segmentation

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Domain-Adversarial Transformer Network for Multiphase Liver ...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yangfan Ni Geng Chen Zhan Feng Heng Cui Dimitris Metaxas Shaoting Zhang Wentao Zhu Zhejiang Lab Hangzhou China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi’an China Department of Radiology College of Medicine The First Affiliated Hospital Zhejiang University Hangzhou China Department of Computer Science Rutgers University Piscataway NJ USA Shanghai Artificial Intelligence Laboratory SenseTime Research Shanghai China

Accurate liver tumor segmentation is a prerequisite for data-driven tumor analysis. Multiphase computed tomography (CT) with extensive liver tumor characteristics is typically used as the most crucial diagnostic basis. However, the large variations in contrast, texture, and tumor structure between CT phases limit the generalization capabilities of the associated segmentation algorithms. Inadequate feature integration across phases might also lead to a performance decrease. To address these issues, we present a domain-adversarial transformer (DA-Tran) network for segmenting liver tumors from multiphase CT images. A DA module is designed to generate domain-adapted feature maps from the non-contrast-enhanced (NC) phase, arterial (ART) phase, portal venous (PV) phase, and delay phase (DP) images. These domain-adapted feature maps are then combined with 3D transformer blocks to capture patch-structured similarity and global context attention. The experimental findings show that DA-Tran produces cutting-edge tumor segmentation outcomes, making it an ideal candidate for this co-segmentation challenge.

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