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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Zhao, Yi Bai, Muchen Ni, Panpan Jiang, Yunan Feng, Xuan Zhang, Xiaobo School of Design SouthWest Jiaotong University Chengdu611756 China School of Computing and Artificial Intelligence SouthWest Jiaotong University Chengdu611756 China Sichuan Institute of Computer Sciences Chengdu610042 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9783031425141

In recent years, high-speed trains are one of the popular research topics in the field of transportation and have attracted much attention from scholars at home and abroad in view of their high efficiency and safety features. High-speed trains are susceptible to unexpected situations of delayed arrival due to the disturbance of external factors including wind and temperature during the trip. The delay of high-speed trains has a vital impact on people's work efficiency and living standards. To solve the problem of intelligent forecast of high-speed train delay, we design and implement a clustering ensemble algorithm based on text data of bullet train operation environment monitoring. We firstly combine the concepts of cosine similarity, Jaccard similarity and information entropy, then fuse a bidirectional weighting formula, and further propose a novel Clustering Ensemble algorithm based on Bidirectional Weighted Co-association (CE-BWC). This approach consists of an inter-cluster weighting formula used to obtain the implicit generalized information by weighting each cluster between clusters, and a reliability weighting formula transferring the cluster information to the data item itself to measure the correlation degree of the data group. Experiments prove that our CE-BWC has better prediction performance for high-speed train delay and outperforms the state-of-the-art benchmark algorithms. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Efficiency

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Cypher4BIM: Releasing the Power of Graph for Building Knowledge Discovery

arXiv

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arXiv 2024年

作者： Zhu, Junxiang Nisbet, Nicholas Yin, Mengtian Wei, Ran Brilakis, Ioannis Department of Engineering University of Cambridge Cambridge United Kingdom Bartlett School of Sustainable Construction University College London London United Kingdom School of Computing and Communications Lancaster University Lancaster United Kingdom School of Design and the Built Environment Curtin University Bentley Australia

Graph is considered a promising way for managing building information. A new graphic form of IFC (Industry Foundation Classes) data has just been developed, referred to as IFC-Graph. However, understanding of IFC-Graph is insufficient, especially for information query. This study aims to explore graphic building information query and develop a graph query language tailored for IFC-Graph. A series of tasks were carried out, including a) investigating the structure of IFC data and the main types of information in IFC, b) investigating the graph query language Cypher, and c) developing a set of tailored functional query patterns. The developed language is referred to as Cypher4BIM. Five IFC models were used for validation, and the result shows that Cypher4BIM can query individual instances and complex relations from IFC, such as spatial structure, space boundary, and space accessibility. This study contributes to applications that require effective building information query, such as digital twin. Copyright © 2024, The Authors. All rights reserved.

关键词： Query languages

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Research on Fault Diagnosis Method Based on Structural Causal Model in Tennessee Eastman Process 7th

Research on Fault Diagnosis Method Based on Structural Causa...

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7th International Conference on computing, Control and Industrial engineering, CCIE 2023

作者： Pu, Haoyuan Liu, Jie Chen, Zhi Yang, Xiaohua Ren, Changan Xu, Zhuoran Jian, Yifan School of Computer Science University of South China Hengyang China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hengyang China School of Nuclear Science and Technology University of South China Hengyang China

ISBN: (纸本)9789819927296

In the practical application of fault diagnosis in large-scale chemical systems, due to the danger of faults, the machine learning diagnosis methods used in current research often face the problem of scarcity of fault samples. This makes it difficult for the model to be trained effectively, thus affecting the fault detection rate of the model. Therefore, for the fault diagnosis of chemical system with small sample data, this paper proposes a diagnostic method that uses structural causal model to further combine the diagnostic results of the two machine learning methods, thereby improving global diagnostic performance. With the help of powerful system description ability of structural causal model, we can establish a causal graph for diagnostic process of the chemical system, and accurately construct the corresponding structural equations for the various diagnostic results of the two machine learning methods to combine them, thus improve the fault detection rate. The global results make up for the shortcomings of single machine learning method in small sample chemical system fault diagnosis. The verification on the chemical system simulation platform Tennessee Eastman process shows that, compared with the diagnosis results of the selected Gaussian Naive Bayes method and the K-nearest neighbour method, the results obtained by our method are effectively improved in fault detection rate of each fault in TEP. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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Cooperative Channel Assignment for VANETs Based on Dual Reinforcement Learning

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Computers, Materials & Continua 2021年第2期66卷 2127-2140页

作者： Xuting Duan Yuanhao Zhao Kunxian Zheng Daxin Tian Jianshan Zhou Jian Gao Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191China School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Engineering and Design University of SussexBrightonBN19RHUK Research Institute of Highway Ministry of Transport Beijing100088China

Dynamic channel assignment(DCA)is significant for extending vehicular ad hoc network(VANET)capacity and mitigating ***,the un-known global state information and the lack of centralized control make channel assignment performances a challenging task in a distributed vehicular direct communication *** our preliminary field test for communication under V2X scenario,we find that the existing DCA technology cannot fully meet the communication performance requirements of *** order to improve the communication performance,we firstly demonstrate the feasibility and potential of reinforcement learning(RL)method in joint channel selection decision and access fallback adaptation design in this ***,a dual reinforcement learning(DRL)-based cooperative DCA(DRL-CDCA)mechanism is ***,DRL-CDCA jointly optimizes the decision-making behaviors of both the channel selection and back-off adaptation based on a multi-agent dual reinforcement learning ***,nodes locally share and incorporate their individual rewards after each communication to achieve regional consistency *** results show that the proposed DRL-CDCA can better reduce the one-hop packet delay,improve the packet delivery ratio on average when compared with two other existing mechanisms.

关键词： Vehicular ad hoc networks reinforcement learning dynamic channel assignment

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Examining the Potential of Micro-Aerial Vehicles for Tree Inspections as Climate Change Education Tools

Examining the Potential of Micro-Aerial Vehicles for Tree In...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Thomas Menkhoff Benjamin Gan Foong Shaohui Lee Kong Chian School of Business Singapore Management University Singapore School of Computing and Information Systems Singapore Management University Singapore Engineering Product Development Singapore University of Technology & Design Singapore

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Our research objective is to examine the impact of aerial tree inspections with micro-aerial vehicles (in short: micro drones) on students' climate education. Climate change threatens the health and survival of trees and forests. Aerial tree inspections represent an important climate change education approach to explain the urgency of climate change adaptation and climate solutions to draw down greenhouse gases. They are also useful tools to appreciate the technical components of micro drones such as external proximity sensors for obstacle avoidance, the fully enclosed propulsion system that allows flight inside dense jungle canopy or the macro-focused high-definition video feed with onboard 10W illumination so that dark areas can be inspected. With such a system, arborists no longer need to manually climb and scale trees. Our research goal is to examine the impact of climate education with micro-aerial vehicles for tree inspections on achieving important learning outcomes such as catalyzing students' climate change awareness and action. We regard micro drones as quality climate change teaching & learning tools that can improve students' engagement with climate change and increase their capacity to adapt to climate change by enhancing sustainability-related competencies and motivating them towards more pro-environmental, carbon offsetting behavior in the context of climate action.

关键词： Autonomous aerial vehicles Climate change Education Behavioral sciences Inspection Environmental monitoring Learning systems Sustainable development Drones Autonomous aerial vehicles Greenhouse gases Sensor systems Propulsion

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Hierarchical Micro-Segmentations for Zero-Trust Services via Large Language Model (LLM)-enhanced Graph Diffusion

arXiv

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arXiv 2024年

作者： Liu, Yinqiu Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong Shen, Xuemin College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Department of Electrical and Computer Engineering University of Waterloo Canada

—In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning zero-trust services in NGNs poses significant challenges, primarily due to the environmental complexity and dynamics. Motivated by these challenges, this paper explores efficient zero-trust service provisioning using hierarchical micro-segmentations. Specifically, we model zero-trust networks via hierarchical graphs, thereby jointly considering the resource- and trust-level features to optimize service efficiency. We organize such zero-trust networks through micro-segmentations, which support granular zero-trust policies efficiently. To generate the optimal micro-segmentation, we present the Large Language Model-Enhanced Graph Diffusion (LEGD) algorithm, which leverages the diffusion process to realize a high-quality generation paradigm. Additionally, we utilize policy boosting and Large Language Models (LLM) to enable LEGD to optimize the generation policy and understand complicated graphical features. Moreover, realizing the unique trustworthiness updates or service upgrades in zero-trust NGN, we further present LEGD-Adaptive Maintenance (LEGD-AM), providing an adaptive way to perform task-oriented fine-tuning on LEGD. Extensive experiments demonstrate that the proposed LEGD achieves 90% higher efficiency in provisioning services compared with other baselines. Moreover, the LEGD-AM can reduce the service outage time by over 50%. © 2024, CC BY.

关键词： Diffusion

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Family of Mutually Uncorrelated Codes for DNA Storage Address design

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Liu, Zhenlu Cao, Ben Shao, Qi Zheng, Yanfen Wang, Bin Zhou, Shihua Zheng, Pan Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China University of Canterbury Upper Riccarton Department of Accounting and Information Systems Christchurch8140 New Zealand

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system. © 2002-2011 IEEE.

关键词： DNA sequences

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Immersive Biography: Supporting Intercultural Empathy and Understanding for Displaced Cultural Objects in Virtual Reality 25

Immersive Biography: Supporting Intercultural Empathy and Un...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Zhao, Ke Chen, Ruiqi Zhang, Xiaziyu Wang, Chenxi Chen, Siling Wang, Xiaoguang Wang, Yujue Tong, Xin School of Information Management Wuhan University Wuhan China Duke Kunshan University Kunshan China Human Centered Design & Engineering University of Washington Seattle WA United States School of Design Politecnico di Milano Milan Italy Intelligent Computing Laboratory for Cultural Heritage Wuhan University Wuhan China Thrust of Computational Media and Arts Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Data Science Research Center Duke Kunshan University Suzhou Kunshan China

ISBN: (纸本)9798400713941

Displaced cultural objects often act as mediators of intercultural understanding due to their connection between the original and host communities. This study explores how immersive embodied VR biography enhances intercultural empathy and understanding of displaced cultural objects. We took the famous Chinese painting, the Admonitions Scroll, housed at the British Museum as an example to design an Immersive Biography in VR. We conducted an empirical study with 24 participants from source and non-source communities. Findings suggested that interacting with biographical narratives of displaced cultural objects in a personified embodied way can effectively promote intercultural empathy and understanding. Additionally, simulated intercultural scenarios and dialogues with personified cultural objects fostered intercultural empathy in both groups, with a stronger effect observed in non-source communities due to differences in cultural identity and personal connections. Our study provided the potential and practical insights of immersive technologies to inspire intercultural communication for displaced cultural objects. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Displaced cultural objects Embodied narratives Intercultural empathy Object biography Virtual Reality

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Atmospheror: Towards an Olfactory Interactive System for Enhancing Social Presence and Interaction in Synchronous Online Classes

Atmospheror: Towards an Olfactory Interactive System for Enh...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in computing Systems, CHI EA 2023

作者： Lu, Qi Zhang, Yuewei Zhang, Yuxin Ma, Shao-En Zhang, Yunfan Qin, Yuou Gao, Peizhong Zhang, Qiwei Xu, Yingqing The Future Laboratory Academy of Arts & Design Tsinghua University Beijing China The Future Laboratory Industiral Engineering Tsinghua University Beijing China The Future Laboratory School of Software Tsinghua University Beijing China The Future Laboratory Tsinghua University Beijing China The Future Laboratory Department of Automation Tsinghua University Beijing China College of Design and Innovation Tongji University Shanghai China Creative Computing Institute University of the Arts London London United Kingdom

ISBN: (纸本)9781450394222

Synchronous online classes have become increasingly common in recent years. However, students and instructors often face challenges when giving and receiving reactions and feedback. Existing solutions include audio and visual cues, which often act as distractions. A potential alternative is olfactory cues. This paper proposes Atmospheror, an interactive system that supports interaction and feedback between students and instructors through an ambient olfactory display. We performed a pilot study with two instructors and 13 students to evaluate the effectiveness of Atmospheror and its design and implementation, applying both quantitative and qualitative methods. Preliminary results indicate that Atmospheror can promote students' concentration and increase interactivity in online classes. User interviews provided suggestions for future enhancements of the selection and treatment of odor, hardware implementation, and interface system mechanisms. © 2023 Owner/Author.

关键词： Students

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Enhanced Food Classification System Using YOLO Models For Object Detection Algorithm

Enhanced Food Classification System Using YOLO Models For Ob...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Sudharson S Priyanka Kokil Annamalai R N V Sai Manoj Computer Science and Engineering Vellore Institute of Technology Chennai India Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Chennai India Computer Science and Engineering (AIE) Amrita School of Computing Chennai India

Food classification is crucial for the food industry and has a significant impact on public health and nutrition. Deep learning techniques such as YOLOv5 and YOLOv7 have recently shown significant improvement in food categorization accuracy. The results of this study’s analysis of both models is evaluated on a manually labeled dataset, evaluating their accuracy, recall, and mAP at the different intersections over union (IoU) criteria. The accuracy, recall, and mAP of both YOLOv5 and YOLOv7 were evaluated at various IoU criteria, and the results showed that YOLOv5 outperformed YOLOv7 in all three metrics. Specifically, YOLOv5 achieved an accuracy score of 0.851 and a recall score of 0.836, whereas YOLOv7 scored 0.67 and 0.675, respectively. Furthermore, YOLOv5’s mAP score at the 0.5 IoU threshold was 0.892, while YOLOv7 scored 0.669. At the 0.5-0.95 IoU threshold, YOLOv5’s mAP was 0.644 and YOLOv7’s was 0.426. This study demonstrates that YOLOv5 is a superior deep learning model for food categorization tasks, with potential implications for improving consumer nutrition and food safety in the food industry.

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