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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Quanyi Yan, Kedong Yin, Dan Huang, Chanying Xiao, Shan Yang, Yuxin School of Cyber Science and Engineering Nanjing University of Science and Technology Jiangyin China School of Computer Science and Engineering Nanjing Univ. of Science and Technology Nanjing China School of Computer Science and Engineering Beijing Univ. of Civil Engineering and Architecture Beijing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Fiberhome Communication Tech. Co. Wuhan China

ISBN: (纸本)9798350330717

The swift advancement of distributed computing has enhanced the support for big data and massive-scale models. Yet, delivering superior services to manage large and intricate network flows in data center networks remains a formidable challenge. In this paper, we present ReQ-tank, an intricate flow scheduling approach based on a multi-level feedback queue (MLFQ) devised to achieve flow prioritization and efficient flow scheduling. ReQ-tank employs a two-tier scheduling strategy: On the flow scheduling layer, priority queues are segmented into two categories, and the flows within high-priority queues follow a strict priority scheduling, while those in low-priority queues adhere to differential weighted Round-robin scheduling;On the packet scheduling layer, ReQ-tank modifies the priority of initially high-priority re-transmitted packets to facilitate fine-grained data packet scheduling. We carry out simulation experiments on web search workloads and data mining workloads. Experimental results demonstrate that ReQ-tank can curtail packet wait time in the network, significantly truncate the flow completion time (FCT) of delay-sensitive flows and counteract the issue of flow starvation in traditional strict priority queues. Consequently, ReQ-tank is deemed more suitable for complex distributed network applications. © 2023 IEEE.

关键词： Data Center Networking Flow scheduling packet scheduling SDN

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Research on Attention-Based Multigranular Remote Sensing Image Extreme Object Detection Algorithm 23

Research on Attention-Based Multigranular Remote Sensing Ima...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Li, Meng Tsang, Eric C.C. Xu, Wei-Hua He, Qiang School of Computer Science and Engineering Macau University of Science and Technology Taipa China College of Artificial Intelligence Southwest University Chongqing400715 China School of Science Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9798331528041

The detection of small objects and high aspect ratio objects is a challenge for remote sensing image object detection. In this paper, we refer to these objects as extreme objects. The Attention-Based Multigranular Extreme Object Detection Network (AMEODNet) is proposed to address these problems. It consists of two parts. The first part is a multi-granularity information fusion module. The second part is an extreme object extraction section. The proposed method is tested on the NWPU VHR-10 datasets. The results are compared with mainstream object detection algorithms to verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Optical remote sensing

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Evaluating Road Network Resilience under Recurring and Non-Recurring Incidents 23

Evaluating Road Network Resilience under Recurring and Non-R...

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23rd COTA International Conference of Transportation Professionals: Emerging Data-Driven Sustainable Technological Innovation in Transportation, CICTP 2023

作者： Tan, Erlong Liu, Bing Luan, Sen Ma, Xiaolei Zhang, Zhao School of Transportation Science and Engineering Beihang Univ. Beijing China Faculty of Architecture Civil and Transportation Engineering Beijing Univ. of Technology Beijing China School of Transportation Science and Engineering Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control Beihang Univ. Beijing China

ISBN: (数字)9780784485040

ISBN: (纸本)9780784485040

Resilience is one of the most representative indicators in the transportation system analysis. Some of the researchers have evaluated the resilience utilizing a performance metric. However, the same weight of long-term and short-term performance leads to inaccuracy in resilience evaluation, and is particularly producing a hysteresis problem when the resilience is calculated with long-term performance only. In addition, few studies compared the road network resilience under recurring and non-recurring incidents. In this paper, we propose a metric for evaluating resilience that considers both long-term and short-term performance to overcome the hysteresis problem. Meanwhile, a resilience assessment framework is proposed for evaluating road network resilience based on the road topology and dynamic performance. In addition, we compared the resilience between recurring and non-recurring incidents using the log data of North 5th Ring Road in beijing, China. The experimental results show that (1) the sensitive analysis demonstrates that appropriate importance factor β and period m of short term can effectively evaluate section resilience and overcome the hysteresis problem;(2) from the perspective of the road network, the minimal resilience of non-recurring incidents is approximately 35.42% lower than that of recurring incidents;and (3) to increase the resilience of the road network, the management should focus on improving the performance of relief road sections for recurring incidents and diverting the vehicles about to enter the entrance ramps in advance for non-recurring incidents. © ASCE.

关键词： Hysteresis

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Navigating Green Transportation in Urban Agglomerations: A Refined Approach for Carbon Emissions Assessment 24

Navigating Green Transportation in Urban Agglomerations: A R...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Chen, Boyou Wang, Yuanqing Abolarin, Emmanuel Zhang, Shuangyan Liu, Xihao Guo, Hanlin Ren, Baolong Dept. of Built Environment Bartlett School of Planning Univ. College London London United Kingdom Dept. of Transportation Engineering College of Transportation Engineering Chang’an Univ. Xi’an China Dept. of Industrial and Manufacturing Systems Engineering College of Engineering and Computer Science Univ. of Michigan–Dearborn Dearborn United States School of Civil and Transportation Engineering Beijing Univ. of Civil Engineering and Architecture Beijing China Sino-French Engineer School Beihang Univ. Beijing China Dept. of Instrument Science and Technology College of Mechanical Engineering Nanjing Univ. of Science and Technology Nanjing China Sino-Danish College Univ. of Chinese Academy of Sciences Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Rapid urbanization has expanded urban agglomerations, escalating carbon emissions from increased transportation needs. These agglomerations have distinct multi-modal transportation systems. Analyzing their carbon footprints and eco-friendly transportation evolution is essential for sustainability. However, research in this domain is limited. This research refines emissions estimation methods, introducing models tailored for urban agglomerations. An evaluation framework for green transportation detailing a computational method for future policies is proposed. The research highlights primary transportation structures and energy consumption. Using a "bottom-up" approach, carbon estimation models for inter- and intra-city transportation are drafted. Assessment indicators are redefined by merging the DPSIR model with urban metrics, forming five main modules with 19 indicators. Using the Regression Random Forest method and focusing on the Yangtze River Delta in China, the link between annual emissions and these indicators is examined, providing a theoretical analysis approach for general cases. © ASCE.

关键词： Carbon sequestration

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Which Kinds of Older People Will Pay More for Community Home-Based Elderly Care Services? Evidence from Age-Friendly Communities in beijing

Which Kinds of Older People Will Pay More for Community Home...

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2024 International Conference on Construction and Real Estate Management: ESG Development in the Construction Industry, ICCREM 2024

作者： Li, Xiaoman Zhang, Zhao School of Marxism Guangdong Univ. of Science and Technology Dongguan China College of Civil Engineering and Architecture Zhejiang Univ Hangzhou China

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

It is unrealistic to rely solely on the government to provide community home-based elderly care services (CHECS) for the elderly due to a significant financing disparity. The elderly must be encouraged to purchase CHECS on their initiative. Consequently, it is essential to comprehend the willingness of senior citizens to pay for CHECS. Our study proposes five hypotheses regarding the factors that influence elderly individuals' willingness to pay for CHECS. Using the ordered multiple logistic model, a regression analysis demonstrates that the elderly's individual characteristics (IC), economic conditions (EC), subjective norms (SN), attitude factors (AF), and behavior factors (BF) can influence their willingness to pay for CHECS. The article concludes that the relevant departments should do a good job of publicity while regulating the CHECS market and implementing precise measures for differentiated elderly groups to fully stimulate older people's willingness to pay for CHECS and promote the construction of age-friendly communities. © ASCE.

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MHANDTI: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Multi-Hop Attention Networks

MHANDTI: Drug-Target Interaction Prediction Model Based on H...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Chen Sun, Jiaqi Xing, Linlin Zhang, Longbo Cai, Hongzhen Guo, Maozu Shandong University of Technology School of Computer Science and Technology Zibo China Shandong University of Technology College of Agricultural Engineering and Food Science Zibo China Beijing University of Civil Engineering and Architecture College of Electrical and Information Engineering Beijing China

ISBN: (纸本)9798350386226

Predicting drug-target interactions (DTI) has become an important step in the drug discovery and drug repositioning process. The biological identification of DTI incurs significant financial and temporal costs, and the deployment of computational methodologies for DTI prediction can substantially curtail both the duration and economic expenditure of drug discovery or repositioning. Drawing from disparate data sources can offer a comprehensive overview and diverse perspectives for predicting drug-target interactions. However, existing methods for processing drug and target information are unable to capture long-range dependency across heterogeneous graphs. To capture more comprehensive drug and target features for DTI prediction, this article proposes a drug-target interaction prediction model based on heterogeneous graph multi-hop attention networks, called MHANDTI. Concretely, the word-level deep convolutional neural network is used to obtain the structural features of drugs and targets as attributes of drug and target entity nodes in heterogeneous graphs. For the processing of heterogeneous graphs, MHANDTI designs a multi-hop attention diffusion layer (MHADL) to establish a connection between the non-direct connection nodes, and summarize the characteristics of the adjacent nodes of the center nodes in several hops. A series of results indicate that the performance of this method is superior to the latest existing frameworks. © 2024 IEEE.

关键词： Convolutional neural networks

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A Decision-Making Support Model Based on Case-Based Reasoning for Utilizing Building Renewable Energy Technologies

A Decision-Making Support Model Based on Case-Based Reasonin...

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2024 International Conference on Construction and Real Estate Management: ESG Development in the Construction Industry, ICCREM 2024

作者： Ma, Dingyuan Li, Xiaodong Zhao, Songbo Liu, Mei Dept. of Construction Management School of Urban Economics and Management Beijing Univ. of Civil Engineering and Architecture Beijing China Dept. of Construction Management School of Civil Engineering Tsinghua Univ. Beijing China Architectural Design and Research Institute HIT Co. Ltd. Harbin China

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

Due to climate change and high energy consumption, green building has become a potential and effective method in the construction sector. However, there is still a challenge of selecting the most appropriate renewable energy technologies for specific building characteristics that depend heavily on the experience and knowledge of experts. To tackle this issue, this research proposed a decision-making model based on Case-Based Reasoning (CBR). In addition, a set of 358 certified green building cases is utilized, and two methods of similarity calculation are employed to improve the quality of decision results. The finding demonstrates the model's applicability by comparison of the predicted and actual results of the test set. The proposed method provides a solution for sustainable technological decision-making for the renewable energy technology selection in green building, providing guidance for decision-makers without relying solely on expert experience and knowledge. © ASCE.

关键词： Green buildings

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Rockburst prediction using artificial intelligence techniques:A review

Rock Mechanics Bulletin

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Rock Mechanics Bulletin 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

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Research on Building Energy Consumption Prediction and Optimization Based on Artificial Intelligence and DeST Simulation

Research on Building Energy Consumption Prediction and Optim...

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2024 International Conference on Construction and Real Estate Management: ESG Development in the Construction Industry, ICCREM 2024

作者： Pan, Junhao Su, Han Li, Weinan Li, Yulong School of Computer Science and Technology Hainan Univ. Haikou China School of Management Science and Engineering Central Univ. of Finance and Economics Beijing China Steel Structure Branch China Jingye Engineering Technology Co. Ltd. Beijing China

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

The construction industry, as a sector with huge output value and energy intensity, is crucial for energy conservation and carbon reduction. The current research mostly focuses on structural adjustment and use efficiency. Still, the cost of structural adjustment is high in actual operation, and the use of facilities needs to meet the needs of personnel, not just energy conservation. Research often relies on idealized data and lacks realistic dynamism. This study proposes an energy consumption simulation method with human behavior as the core, using the DeST model to create a basic building model, combining facial recognition and AI to predict the impact of human behavior on energy consumption, dynamically adjusting facility usage, evaluating energy-saving solutions, aiming to achieve energy consumption minimization while maintaining satisfaction. © ASCE.

关键词： Carbon capture and utilization

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Road Network Accessibility Analysis Based on Dynamic Diversity Entropy 24

Road Network Accessibility Analysis Based on Dynamic Diversi...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Hu, Dingkang Zhao, Tingting Zhang, Yu Ma, Wei School of Systems Science Beijing Jiaotong Univ. Beijing China Dept. of Civil and Environmental Engineering Univ. of South Florida TampaFL United States Dept. of Civil and Environmental Engineering Hong Kong Polytechnic Univ. Kowloon Hong Kong

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

In this work, we propose a novel accessibility measure based on Dynamic Diversity Entropy to evaluate the difficulty of citizens accessing various nodes with resources based on network structure. Furthermore, the self-avoiding random walk is adopted for improving the computational efficiency of evaluating accessibility of nodes in a scaled urban street network. Dynamic Diversity Entropy is introduced to measure the outcome of self-avoiding random walks. Then, we introduce link travel times representing the time-varying traffic status, and spatial heterogeneous Point of Interest’s density representing nodes’ resource weights in the accessibility assessment is used. A case study on a partial road network inside beijing’s third ring road is conducted. The spatial accessibility distribution and the relationship between accessibility to restaurants and time limit are visualized to investigate the performance of the proposed accessibility assessment indicators. © ASCE.

关键词： Roads and streets

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