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Microscopic Disruption Management: Energy Consumption and Passenger Compensation Optimisation

Transportation Research Procedia 2022年 62卷 43-50页

作者： Luis Cadarso Esteve Codina Ricardo García-Ródenas Aerospace Systems and Transport Research Group European Institute for Aviation Training and Accreditation (EIATA) Rey Juan Carlos University Fuenlabrada 28942 Madrid Spain Department of Statistics and Operations Research Universitat Politècnica de Catalunya (UPC) Spain Department of Mathematics School of Computer Science Institute of Applied Mathematics in Science and Engineering (IMACI) University of Castilla-La Mancha Ciudad Real Spain

Rail operations are often disrupted by accidents that cause traffic to diverge from the scheduled operations, rendering it difficult to run the schedule as planned. In such a case, the operator must change the schedule to return to the original schedule. If passengers are delayed, a train operator may have a policy of economically compensating them (e.g., refunding ticket fare). Compensation amounts are usually determined by the length of the delay. As a result, it is critical to have a smart way of determining whether to accelerate trains to absorb delays, thus increasing energy usage, or to compensate passengers. This paper presents a mathematical model for determining the speed profile while taking passenger usage into account. The model determines the best sequence of operating regimes and switching points between them for a variety of different situations and train types, all while accounting for delays and passenger compensation policies. The aim is to reduce both the amount of energy consumed and the amount of compensation paid to passengers. There are constraints on traction and braking forces, train velocity, forces induced by vertical and horizontal track profile, and passenger compensation policy. The results of computational tests performed on practical problem instances of the Spanish rail operator RENFE are showed. The suggested approach is capable of producing strategies that strike an excellent balance between different managerial objectives.

关键词： Train re-scheduling Passenger compensation Delays Optimal control

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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Blockchain-Enabled Economic Transactions: Recurring Financial Accruals and Payments

Blockchain-Enabled Economic Transactions: Recurring Financia...

引用

Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Razvan Mihai Omer Faruk Ozkul Gora Datta Nicolae Goga Sergii Grybniak Constantin Viorel Marian Faculty of Electronics Telecommunications and Technology of Information (ETTI) Politehnica University of Bucharest Bucharest Romania Faculty of Engineering in Foreign Languages (FILS) Politehnica University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering University of California Berkeley Berkeley United States Molecular Dynamics Group University of Groningen Groningen Netherlands Institute of Computer Systems Odesa Polytechnic State University Odesa Ukraine

Economic transactions are based upon implicit or explicit contracts that set out the rights and obligations of the parties to the transactions. Blockchain technology can address fundamental financial, economic, and accounting challenges. We propose a blockchain-based prototype capable of capturing the essence of recurring economic transactions. For this purpose, we have devised an asset rental contract to show how economic transactions are recorded and tracked more effectively, efficiently, and in quasi-real-time, thus changing the traditional way of account keeping and auditing. We use the Ethereum blockchain as the most evolved and widely used smart contract platform. We showcase a significant discovery related to current blockchain technology limitations to making automatic non-custodial recurring payments. We show how the prototype can be extended to a range of recurring transactions.

关键词： Economics Smart contracts Prototypes Finance Blockchains Standards

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Attribute Based Collusion Resistant Controlled Secure Data Processing System Using Node Insertion Method

Research Square

引用

Research Square 2021年

作者： Dhanalakshmi, P. Rajarajeswari, S. Das, Nripendra Narayan Karthikeyan, C. Reddy, L. Chandra Sekhar Jain, Parita Department of Computer science and Systems Engineering Sree Vidyanikethan Engineering College A. Rangampet India School of Computer Science and Engineering Vellore Institute of technology Chennai Campus India Department of Information Technology Manipal University Jaipur Rajasthan India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Medchal501401 India Department of Computer Science and Engineering KIET Group of Institutions Delhi-Meerut Road Muradnagar Uttar Pradesh Ghaziabad India

In the fast-developing technological world, a large number of informational data is obtained out of different software applications and hardware sources. Data storing, sharing of data, and then processing is more effective through the cloud computing method. In practice, the issue faced is a lack of security where the user details are leaked due to the exposure of software and hardware conflict. Among different data sharing technique, cloud computing is the most convenient method to be adapted due to service satisfying features and economy. Attribute-Based-Encryption (ABE) has the potential to detect even a narrow-drawn data sharing. The drawback of the conventional attribute-based encryption (ABE) poor data security and high over-head computation. The customer side satisfaction is not achieved because of security lacking in narrow-drawn informational data sharing as well as processing. The difficulty in achieving a standardized form of data sharing is an unsolved challenge for secured data processing. The attribute-based data sharing method is more effective for secured data processing in cloud computing. Customer satisfaction can be achieved through the secured services of cloud. The customer’s data should be secured and also the customer’s data is under an authorized access. The protection of data in cloud computing includes the usage of hardware solutions for a secured data operation, provides support in data storage, access, sharing, and automated detecting of risk during data processing. © 2021, CC BY.

关键词： Cryptography

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Recognition of food-texture attributes using an in-ear microphone

arXiv

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arXiv 2021年

作者： Mars, Monica Diou, Christos Papapanagiotou, Vasileios van den Boer, Janet Delopoulos, Anastasios Multimedia Understanding Group Dpt. Electrical and Computer Engineering Faculty of Engineering Aristotle University of Thessaloniki Greece Department of Informatics and Telematics Harokopio University of Athens Greece Dpt. of Biomedical Signals and Systems Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Division of Human Nutrition and Health Wageningen University Netherlands

Food texture is a complex property;various sensory attributes such as perceived crispiness and wetness have been identified as ways to quantify it. Objective and automatic recognition of these attributes has applications in multiple fields, including health sciences and food engineering. In this work we use an in-ear microphone, commonly used for chewing detection, and propose algorithms for recognizing three food-texture attributes, specifically crispiness, wetness (moisture), and chewiness. We use binary SVMs, one for each attribute, and propose two algorithms: one that recognizes each texture attribute at the chew level and one at the chewing-bout level. We evaluate the proposed algorithms using leave-one-subject-out cross-validation on a dataset with 9 subjects. We also evaluate them using leave-one-food-type-out cross-validation, in order to examine the generalization of our approach to new, unknown food types. Our approach performs very well in recognizing crispiness (0.95 weighted accuracy on new subjects and 0.93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness. Copyright © 2021, The Authors. All rights reserved.

关键词： Wearable technology

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TraCemop: Towards Federated Learning with Traceable Contribution Evaluation and Model Ownership Protection

引用

IEEE Transactions on Mobile Computing 2025年

作者： Liu, Jingwei Zhou, Zihan Sun, Rong Liu, Lei Lu, Rongxing Dustdar, Schahram Niyato, Dusit Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Queen's University School of Computing KingstonONK7L 2N8 Canada TU Wien Distributed Systems Group Austria UPF ICREA Barcelona Spain Nanyang Technological University School of Computer Science and Engineering Singapore

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models without the need to share their local private data. As a result, it can effectively address the issue of data fragmentation. Nevertheless, insufficient evaluation of individual contributions and the lack of protections for both the intellectual property rights (IPR) of models and client privacy can greatly reduce clients' motivations in federated training. To address these challenges, this paper introduces the Traceable Contribution Evaluation and Model Ownership Protection (TraCemop) framework for federated learning, which allows each client to swiftly assess the contributions of others in each round, with integrated support for the traceability of evaluation results. To safeguard the intellectual property of models, a collective watermark is embedded in the global model. Additionally, a secure mechanism for verifying model ownership is also available in case of disputes. Security analysis indicates that TraCemop is capable of resisting data reconstruction attacks as well as various types of model copyright infringements. Finally, we evaluate the proposed framework using two commonly-used datasets, and the experimental results show a significant improvement in the efficiency of contribution evaluation compared to existing methods. Meanwhile, IPR infringement tests on TraCemop reveal that the proposed framework is resilient against malicious efforts to monopolize model ownership. © 2002-2012 IEEE.

关键词： Federated learning

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Relabel the noise: Joint extraction of entities and relations via cooperative multiagents

arXiv

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arXiv 2020年

作者： Chen, Daoyuan Li, Yaliang Lei, Kai Shen, Ying Alibaba Group School of Electronics and Computer Engineering Peking University School of Intelligent Systems Engineering Sun Yat-Sen University

Distant supervision based methods for entity and relation extraction have received increas-ing popularity due to the fact that these meth-ods require light human annotation efforts. In this paper, we consider the problem of shifted label distribution, which is caused by the in-consistency between the noisy-labeled training set subject to external knowledge graph and the human-annotated test set, and exacer-bated by the pipelined entity-then-relation ex-traction manner with noise propagation. We propose a joint extraction approach to address this problem by re-labeling noisy instances with a group of cooperative multiagents. To handle noisy instances in a fine-grained manner, each agent in the cooperative group eval-uates the instance by calculating a continuous confidence score from its own perspective;To leverage the correlations between these two ex-traction tasks, a confidence consensus module is designed to gather the wisdom of all agents and re-distribute the noisy training set with confidence-scored labels. Further, the confi-dences are used to adjust the training losses of extractors. Experimental results on two real-world datasets verify the benefits of re-labeling noisy instance, and show that the proposed model significantly outperforms the state-of-the-art entity and relation extraction methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Extraction

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Ultra-sensitive vibrational resonance induced by small disturbances

arXiv

引用

arXiv 2023年

作者： Li, Shangyuan Wang, Zhongqiu Yang, Jianhua Sanjuán, Miguel A.F. Huang, Shengping Lou, Litai Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment School of Mechatronic Engineering China University of Mining and Technology Jiangsu Xuzhou221116 China School of Computer Science and Technology China University of Mining and Technology Jiangsu Xuzhou221116 China Nonlinear Dynamics Chaos and Complex Systems Group Departamento de Física Universidad Rey Juan Carlos Tulipán s/n Móstoles Madrid28933 Spain

We have found two kinds of ultra-sensitive vibrational resonance in coupled nonlinear systems. It is particularly worth pointing out that this ultra-sensitive vibrational resonance is a transient behavior caused by transient chaos. Considering long-term response, the system will transform from transient chaos to periodic response. The pattern of vibrational resonance will also transform from ultra-sensitive vibrational resonance to conventional vibrational resonance. This article focuses on the transient ultra-sensitive vibrational resonance phenomenon. It is induced by a small disturbance of the high-frequency excitation and the initial simulation conditions, respectively. The damping coefficient and the coupling strength are the key factors to induce the ultra-sensitive vibrational resonance. By increasing these two parameters, the vibrational resonance pattern can be transformed from an ultra-sensitive vibrational resonance to a conventional vibrational resonance. The reason for different vibrational resonance patterns to occur lies in the state of the system response. The response usually presents transient chaotic behavior when the ultra-sensitive vibrational resonance appears and the plot of the response amplitude versus the controlled parameters shows a highly fractalized pattern. When the response is periodic or doubly-periodic, it usually corresponds to the conventional vibrational resonance. The ultra-sensitive vibrational resonance not only occurs at the excitation frequency, but it also occurs at some more nonlinear frequency components. The ultra-sensitive vibrational resonance as a transient behavior and the transformation of vibrational resonance patterns are new phenomena in coupled nonlinear systems. © 2023, CC BY-NC-ND.

关键词： Nonlinear systems

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Indoor 3D Visible Light Positioning Analysis with Channel Estimation Errors

Indoor 3D Visible Light Positioning Analysis with Channel Es...

引用

International Conference on systems, Signals and Image Processing, IWSSIP

作者： Dimitrios Bozanis Nikos G. Evgenidis Vasilis K. Papanikolaou Pavlos S. Bouzinis Sotiris A. Tegos Alexis A. Dowhuszko Panagiotis D. Diamantoulakis George K. Karagiannidis Department of Electrical and Computer Engineering Wireless Communications and Information Processing Group (WCIP) Aristotle University of Thessaloniki Thessaloniki Greece Institute for Digital Communications (IDC) Friedrich-Alexander Universitat Erlangen-Nurnberg (FAU) Germany Department of Applied Informatics University of Macedonia Thessaloniki Greece Department of Communications and Networking Aalto University Espoo Finland Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon

Since radio-frequency signals are affected signifi-cantly by multipath fading, the localization accuracy they offer cannot meet the sixth generations (6G) demanding specifications. On the other hand, visible light positioning (VLP) has emerged as a low cost alternative that takes into account the highly deterministic channel characteristics of visible light communications to provide cm-level accuracy provided that the investigated spaces are adequately covered by LEDs. However, imperfect channel state information and modeling can lead to positioning errors. In this paper, localization accuracy is quantified in terms of channel estimation errors via low complexity lateration algorithm. The channel estimation errors can be caused by a variety of issues and the provided analysis is general so that it can be easily applied to a plethora of applications. Finally, Monte Carlo simulations validate the proposed method and offer valuable insights for the design and performance bounds of VLP systems.

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V2X-BGN: Camera-based V2X-Collaborative 3D Object Detection with BEV Global Non-Maximum Suppression

V2X-BGN: Camera-based V2X-Collaborative 3D Object Detection ...

引用

IEEE Symposium on Intelligent Vehicle

作者： Caiji Zhang Bin Tian Shi Meng Shuangying Qi Yang Sun Yunfeng Ai Long Chen School of Artificial Intelligence University of Chinese Academy of Sciences and With the National Key Laboratory for Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Bin Tian is With the National Key Laboratory for Multi-Modal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and With the School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China National Key Laboratory for Multi-Modal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and With the School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Chongqing Iron and Steel Group Mining Co. Ltd. Chongqing China School of Mechanical and Equipment Engineering Hebei University of Engineering Handan China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Waytous Inc. Qingdao China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Waytous Ltd. Qingdao China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In recent years, research on Vehicle-to-Everything (V2X) cooperative perception algorithms mainly focuses on the fusion of intermediate features from LiDAR point clouds. Since the emergence of excellent single-vehicle visual perception models like BEVFormer, collaborative perception schemes based on camera and late-fusion have become feasible approaches. This paper proposes a V2X-collaborative 3D object detection structure in Bird's Eye View (BEV) space, based on global non-maximum suppression and late-fusion (V2X-BGN), and conducts experiments on the V2X-Set dataset. Focusing on complex road conditions with extreme occlusion, the paper compares the camera-based algorithm with the LiDAR-based algorithm, validating the effectiveness of pure visual solutions in the collaborative 3D object detection task. Additionally, this paper highlights the complementary potential of camera-based and LiDAR-based approaches and the importance of object-to-ego vehicle distance in the collaborative 3D object detection task.

关键词： Point cloud compression Visualization Three-dimensional displays Laser radar Roads Collaboration Object detection

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