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Research on key technologies of edge cache in virtual data space across WAN

Frontiers of Computer science 2023年第1期17卷 15-34页

作者： Jiantong HUO Yaowen XU Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China College of Software Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China College of Computer Science and Technology Zhejiang UniversityHangZhou 310013China

The authors of this paper have previously proposed the global virtual data space system (GVDS) to aggregate the scattered and autonomous storage resources in China’s national supercomputer grid (National Supercomputing Center in Guangzhou, National Supercomputing Center in Jinan, National Supercomputing Center in Changsha, Shanghai Supercomputing Center, and Computer Network Information Center in Chinese Academy of sciences) into a storage system that spans the wide area network (WAN), which realizes the unified management of global storage resources in China. At present, the GVDS has been successfully deployed in the China National Grid environment. However, when accessing and sharing remote data in the WAN, the GVDS will cause redundant transmission of data and waste a lot of network bandwidth resources. In this paper, we propose an edge cache system as a supplementary system of the GVDS to improve the performance of upper-level applications accessing and sharing remote data. Specifically, we first designs the architecture of the edge cache system, and then study the key technologies of this architecture: the edge cache index mechanism based on double-layer hashing, the edge cache replacement strategy based on the GDSF algorithm, the request routing based on consistent hashing method, and the cluster member maintenance method based on the SWIM protocol. The experimental results show that the edge cache system has successfully implemented the relevant operation functions (read, write, deletion, modification, etc.) and is compatible with the POSIX interface in terms of function. Further, it can greatly reduce the amount of data transmission and increase the data access bandwidth when the accessed file is located at the edge cache system in terms of performance, i.e., its performance is close to the performance of the network file system in the local area network (LAN).

关键词： virtual data space system wide area network edge cache redundant data transmission

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Mobility-Aware Deep Reinforcement Learning with Seq2seq Mobility Prediction for Offloading and Allocation in Edge computing

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IEEE Transactions on Mobile computing 2024年第6期23卷 6803-6819页

作者： Wu, Chao-Lun Chiu, Te-Chuan Wang, Chih-Yu Pang, Ai-Chun Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan National Taiwan University Institute of Networking and Multimedia Taipei10617 Taiwan National Taiwan University High Performance and Scientific Computing Center Taipei10617 Taiwan

Mobile/multi-access edge computing (MEC) is developed to support the upcoming AI-aware mobile services, which require low latency and intensive computation resources at the edge of the network. One of the most challenging issues in MEC is service provision with mobility consideration. It has been known that the offloading decision and resource allocation need to be jointly handled to optimize the service provision efficiency within the latency constraints, which is challenging when users are in mobility. In this paper, we propose Mobility-Aware Deep Reinforcement Learning (M-DRL) framework for mobile service provision in the MEC system. M-DRL is composed of two parts: glimpse, a seq2seq model customized for mobility prediction to predict a sequence of locations just like a 'glimpse' of the future, and a DRL specialized in supporting offloading decisions and resource allocation in MEC. By integrating the proposed DRL and glimpse mobility prediction model, the proposed M-DRL framework is optimized to handle the MEC service provision with average 70% performance improvements. © 2002-2012 IEEE.

关键词： Reinforcement learning

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Situational Scene Graph for Structured Human-Centric Situation Understanding

Situational Scene Graph for Structured Human-Centric Situati...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Chinthani Sugandhika Chen Li Deepu Rajan Basura Fernando College of Computing and Data Science Nanyang Technological University Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High-Performance Computing Agency for Science Technology and Research Singapore

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Graph based representation has been widely used in modelling spatio-temporal relationships in video understanding. Although effective, existing graph-based approaches focus on capturing the human-object relationships while ignoring fine-grained semantic properties of the action components. These semantic properties are crucial for understanding the current situation, such as where does the action takes place, what tools are used and functional properties of the objects. In this work, we propose a graph-based representation called Situational Scene Graph (SSG) to encode both human-object relationships and the corresponding semantic properties. The semantic details are represented as predefined roles and values inspired by situation frame, which is originally designed to represent a single action. Based on our proposed representation, we introduce the task of situational scene graph generation and propose a multi-stage pipeline Interactive and Complementary Network (InComNet) to address the task. Given that the existing datasets are not applicable to the task, we further introduce a SSG dataset whose annotations consist of semantic role-value frames for human, objects and verb predicates of human-object relations. Finally, we demonstrate the effectiveness of our proposed SSG representation by testing on different downstream tasks. Experimental results show that the unified representation can not only benefit predicate classification and semantic role-value classification, but also benefit reasoning tasks on human-centric situation understanding. Code and data are available at https://***/LUNAProject22/SSG.

关键词： Computer vision Codes Annotations Semantics Pipelines Semisupervised learning Cognition Testing

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AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection 37

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis o...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large dataset

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Unsupervised Anomalous Sound Detection Using Hybrid Machine Learning Techniques

Unsupervised Anomalous Sound Detection Using Hybrid Machine ...

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International Conference on Big data and Smart computing (BIGCOMP)

作者： Eunsun Yun Minjoong Jeong Supercomputing Application Center Korea Institute of Science and Technology Information Data and High Performance Computing Science University of Science and Technology (UST) Daejeon Republic of Korea

The early detection of defects in mechanical equipment is of paramount importance in the industrial field. Research into data analysis methodologies for extracting valuable data from mechanical equipment has highlighted the significant value of these technologies. One such study is the early and accurate detection of anomalies in the acoustic data of machinery. In this paper, we propose an effective technique for the detection and classification of rare event anomalies in the data derived from the rotational noise of automotive motors. MFCC extraction and smoothing techniques were used to select minimal features for optimal performance, and Principal Component Analysis (PCA) was applied to extract salient features. These features are capable of distinguishing between normal and anomalous data. Additionally, an unsupervised learning algorithm was applied to the dataset to differentiate between normal and anomaly data. Experimental results showed that the proposed method can effectively detect sound anomalies with a high accuracy of 99.4% and is also capable of detailed classification of anomalous data.

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Numerical Predication and Experimental Validation of Wind Load on a Jack-up Platform 32nd

Numerical Predication and Experimental Validation of Wind Lo...

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32nd International Ocean and Polar Engineering Conference, ISOPE 2022

作者： Zhang, Baili Dao, My Ha Xing, Xiuqing Lou, Jing Tan, Wei Siang Cui, Yongdong Khoo, Boo Cheong Institute of High Performance Computing Agency for Science Technology and Research Singapore Department of Mechanical Engineering National University of Singapore Singapore

ISBN: (纸本)9781880653814

In a complex offshore environment, wind load is among the most important design factors for the jack-up platform. In this study, we have conducted a detailed numerical analysis of wind loading on a jack-up platform in normal operation and extreme conditions at both model scale and full scale for wind headings from 0 to 360 degrees. The wind load in the form of coefficients of three force components and three moment components are in good agreement between numerical prediction and experimental measurements. In addition, we measured surface pressures at 22 different locations of the platform, and they match well with CFD calculations. © 2022 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Wind stress

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Application of Generative Artificial Intelligence for Epidemic Modeling

Application of Generative Artificial Intelligence for Epidem...

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Simulation Winter Conference

作者： Hannah Danielle Ladera Villaplana Jaeyoung Kwak Michael H. Lees Hongying Li Wentong Cai College of Computing and Data Science Nanyang Technological University Singapore Singapore Informatics Institute University of Amsterdam Amsterdam The Netherlands Department of Fluid Dynamics Institute of High Performance Computing Singapore Singapore

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Epidemic models have become increasingly useful, especially in the wake of the recent COVID-19 pandemic, emphasizing the crucial role of human behavior in the spread of disease. There has been a recent rise in the usage and popularity of generative artificial intelligence (GenAI), such as ChatGPT especially with its ability to mimic human behavior. In this study, we demonstrate a novel application of GenAI for epidemic modeling. We employed GenAI for creating agents living in a hypothetical town in simulations and simulating their behavior within the context of an ongoing pandemic. We performed a series of simulations to quantify the impact of agent traits and the availability of information for health condition, virus, and government guidelines on the disease spread patterns in terms of peak time and epidemic duration. We also characterized the most influential factors in agents' decision-making using random forest model.

关键词： Pandemics Generative AI Computational modeling Government Decision making Behavioral sciences Random forests Diseases Guidelines Context modeling

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Frameworked electrolytes:Ionic transport behavior and high mobility for solid state batteries

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InfoMat 2024年第2期6卷 76-89页

作者： Jianguo Sun Hao Yuan Jing Yang Tuo Wang Yulin Gao Qi Zhao Ximeng Liu Haimei Wang Yong-Wei Zhang John Wang Department of Materials Science and Engineering National University of SingaporeSingaporeSingapore Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore ST Engineering Advanced Material Engineering Pte.Ltd. SingaporeSingapore National University of Singapore(Chongqing)Research Institute Chongqing Liang Jiang New AreaChongqingthe People's Republic of China Institute of Materials Research and Engineering(IMRE) Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore

All solid-state batteries(ASSBs)are the holy grails of rechargeable batteries,where extensive searches are ongoing in the pursuit of ideal solid-state ***,there is still a long way off to the satisfactorily high(enough)ionic conductivity,long-term stability and especially being able to form compatible interfaces with the solid ***,we have explored ionic transport behavior and high mobility in the sub-nano pore networks in the framework ***,the frameworked electrolyte behaves as a solid,and however in the(sub)-nano scales,the very limited number of solvent molecules in confinement makes them completely different from that in liquid *** from a liquid-electrolyte counterpart,the interactions between the mobile ions and surrounding molecules are subject to dramatic changes,leading to a high ionic conductivity at room temperature with a low activation ***+ions in the sub-nano cages of the network structure are highly mobile and diffuse rather independently,where the rate-limiting step of ions crossing cages is driven by the local concentration gradient and the electrostatic interactions between Li^(+)*** new class of frameworked electrolytes(FEs)with both high ionic conductivity and desirable interface with solid electrodes are demonstrated to work with Li-ion batteries,where the ASSB with LiFePO_(4)shows a highly stable electrochemical performance of over 450 cycles at 2℃ at room temperature,with an almost negligible capacity fade of 0.03‰ each *** addition,the FE shows outstanding flexibility and anti-flammability,which are among the key requirements of large-scale applications.

关键词： frameworked electrolyte macroscopically solid with 3D ionic channels in sub-nano-scales solid-state battery space confinement of Li ions

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Quantifying model uncertainty of exterior convective heat transfer coefficient models in building energy simulation by onsite measurement 18

Quantifying model uncertainty of exterior convective heat tr...

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18th IBPSA Conference on Building Simulation, BS 2023

作者： Zheng, Long Sekhar, Chandra Poh, Hee Joo Chong, Adrian Department of the Built Environment National University of Singapore 4 Architecture Drive 117566 Singapore Institute of High Performance Computing Agency for Science Technology and Research 1 Fusionopolis Way 138632 Singapore

Given the fact that model uncertainty, usually due to the imperfection of models, can lead to additional biases that are not captured by parameter uncertainty, this study aims to quantify the model uncertainty of exterior convective heat transfer coefficient (CHTC) models in building energy simulation (BES). Four basic CHTC models in EnergyPlus are selected, and high-quality onsite measurements at a low-rise porous building in Singapore are used to compare predicted and measured exterior CHTCs. Local wind velocity and temperature difference between the surface and ambient air are correlated with model discrepancy using linear regression. Results demonstrate that the developed correlations effectively reduce Model Bias Errors and Root Mean Square Errors, indicating the effectiveness of quantifying the model uncertainty of existing exterior CHTC models and highlighting the potential of onsite measurement as an alternative to high-fidelity models for quantifying model uncertainty. © 2023 *** rights reserved.

关键词： Heat transfer coefficients

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SmartShip: data Decoding Optimization for Onboard AI Anomaly Detection

SmartShip: Data Decoding Optimization for Onboard AI Anomaly...

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International Symposium on Parallel and Distributed computing

作者： Pavle Ivanovic Alexander Windmann Philipp Neumann Chair for High Performance Computing Helmut Schmidt University Hamburg Germany Chair for Computer Science in Mechanical Engineering Helmut Schmidt University Hamburg Germany Deutsches Elektronen-Synchroton Research Group High Performance Computing and Data Science Universität Hamburg Hamburg Germany

ISBN: (数字)9798350369199

ISBN: (纸本)9798350369205

Maritime rescue operations rely on efficient and resilient sea vessels for responding to offshore emergencies. To ensure the availability and prompt deployment of rescue cruisers, system operators need to recognize (and resolve) the failure patterns before they occur onboard. However, many SAR organizations do not have sufficient computing resources for advanced anomaly detection and future fault prediction. This paper details our performant and cost-effective solution for sensor data acquisition, CAN bus decoding, and AI analysis, deployable on typical maritime edge devices. The main focus of our work is enhancing the decoding speed, which is a prerequisite for efficient AI analyses and anomaly detection. Besides elaboration on different optimization strategies, we provide the actual use case for AI anomaly detection based on anomaly scores of electrical, fuel, and cooling system sensors. As a result, we improved the decoding speed by more than two orders of magnitude, allowing edge outlier detection in minutes rather than days.

关键词： performance evaluation System testing Image edge detection data acquisition Decoding Pattern recognition Artificial intelligence Anomaly detection Optimization Tuning

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