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A Service Mining Scheme Based on Semantic for Internet of Things

Chinese Journal of Electronics 2025年第2期23卷 236-242页

作者： Xinming Li Yan Sun The Science and Technology on Complex Electronic System Simulation Laboratory Academy of Equipment Beijing China The School of Computer Science Beijing University of Posts and Telecommunication Beijing China

As the development of Internet of things (IOT), massive sensors have been deployed as the public infrastructure. With the development of in-depth applications in IOT, service discovery and composition are challenges to the end users. To handle this challenge, we develop a service mining scheme based on semantic for IOT to provide users with interesting composite services. In this scheme, services can be combined and recommended to users actively according to the calculation of service similarity and an updatable semantic database. By the results of service similarity, useless compositions can be filtered out so that energy consumption on service flooding will be reduced. The update strategy of semantic database is also given out, by which the composite services can keep up with time and be more applicative. The benefits of the proposed method are that all operations such as calculating, filtering, and updating are simple enough to be performed in sensor networks.

关键词： Smart buildings Databases Filtering Semantics Public infrastructure Ontologies Sensor systems Internet of Things Floods Intelligent sensors

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Interpretable Time Series Data Prediction for Severe Nuclear Accidents 3

Interpretable Time Series Data Prediction for Severe Nuclear...

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3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Zhang, Dazhi Jiang, Mengyuan Fu, Yongjie Zhou, Huabing China Nuclear Power Operation Technology Corporation Ltd Cnnc Key Laboratory on Nuclear Industry Simulation Wuhan430040 China College of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9798350393224

In this study, we employ a Gated Recurrent Unit (GRU) neural network model to predict changes in coolant flow in small-scale nuclear power plants under Loss-of-Coolant Accident (LOCA) conditions with high precision and speed. We quantitatively compare this model with the traditional Long Short-Term Memory (LSTM) method to validate the accuracy of the GRU model. Additionally, we perform an interpretability analysis on the predictive model using the SHAP (SHapley Additive exPlanations) method for the first time, revealing the inherent influencing factors in L O C A accidents. This research aims to enhance trust in artificial intelligence models in nuclear power scenarios, facilitate a better understanding of prediction results and the current system state, and promote the practical application of artificial intelligence models in nuclear power systems. © 2023 IEEE.

关键词： GRU LOCA Model interpretability Neural networks Nuclear energy SHAP Time series forecasting

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An anonymous and fair auction system based on blockchain

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Journal of Supercomputing 2023年第13期79卷 13909-13951页

作者： Ye, Zongli Chen, Chin-Ling Weng, Wei Sun, Hongyu Tsaur, Woei-Jiunn Deng, Yong-Yuan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung City413310 Taiwan Department of Computer Science Jilin Normal University Siping136000 China State Key Laboratory of Numerical Simulation Siping136000 China Computer Center National Taipei University New Taipei City237303 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City237303 Taiwan

Consumer approval of the online auction paradigm is demonstrated by rapidly expanding online auction transaction volumes. Bidders can access a wider variety of products, and sellers can reach a larger audience through online auction platforms. However, privacy concerns have reduced users’ trust in auction platforms, and complicated disputes have led consumers to lose faith in online auctions, negatively impacting market growth. The emergence of blockchain technology has brought a new approach to solving these problems. Blockchain is a new generation of information technology that is decentralized, transparent, traceable, anti-tampering, and unforgeable, following big data, cloud computing, and artificial intelligence. In this paper, we present a blockchain-based online auction solution as well as an auction protocol that uses ring signatures to guarantee anonymity. Three features summarize this essay: (1) Using blockchain technology to ensure that the auction transaction process is transparent, traceable, tamper-proof, and unforgeable. (2) The ring signature-based anonymous auction protocol allows for concealing bidders’ public keys in a group of public keys. Because malicious individuals are prevented from evaluating data by tracking numerous transactions of a single public key, this significantly protects bidders’ privacy. (3) The proposed scheme effectively resolves disputes that may arise during the auction. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Blockchain

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Identifying possible lncRNA-disease associations based on deep learning and positive-unlabeled learning

Identifying possible lncRNA-disease associations based on de...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Peng, Lihong Huang, Liangliang Lu, Yuankang Liu, Guangyi Chen, Min Han, Guosheng Hunan University of Technology School of Computer Science Zhuzhou China Xiangtan University Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan China

ISBN: (纸本)9781665468190

lncRNAs are involved in many biological processes, and their mutations and disorders are closely related to many diseases. Identification of LncRNA-Disease Associations (LDAs) helps us understand the pathogenesis of diseases and improve their diagnosis and treatment. However, experiments to determine LDAs are expensive, so it is essential to exploit effective computational methods to screen possible LDAs. In this study, we developed an LDA prediction model (LDA-DLPU) based on deep learning and positive-unlabeled (PU) learning. First, LDA-DLPU extracted features of lncRNAs and diseases based on singular value decomposition and regression model. Second, it selected negative LDAs based on PU learning and graph autoencoder. Finally, it classified unknown lncRNA-disease pairs based on deep neural network. LDA-DLPU obtained the best performance on two datasets. We predict that BCYRN1 and IFNG-AS1 may associate with leukemia. © 2022 IEEE.

关键词： Diseases

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MD-TransUNet: TransUNet with Multi-attention and Dilated Convolution for Brain Stroke Lesion Segmentation 19th

MD-TransUNet: TransUNet with Multi-attention and Dilated C...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Xu, Jie Wan, Jian Zhang, Xin School of Computer Science and Technology Hangzhou Dianzi University Zhejiang310018 China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Ministry of Education Beijing China School of Information and Electronic Engineering Zhejiang University of Science and Technology Zhejiang China Key Laboratory of Marine Ecosystem Dynamics Second Institute of Oceanography Ministry of Natural Resources Hangzhou China

ISBN: (纸本)9783031545276

The accurate segmentation of stroke lesion regions holds immense significance in shaping treatment strategies and rehabilitation protocols. Due to the large difference in the volume of stroke lesion areas and the great similarity between lesion areas and normal tissues, most of the existing methods for lesion segmentation cannot deal with these problems well. This paper proposes a novel network named MD-TransUNet for the segmentation of stroke lesions, whose framework is based on the UNet architecture. To fully obtain deep image features, it uses ResNet50 for downsampling. MD (multi-dilated) module is employed as the skip connection to gain more receptive fields. Different receptive fields can adapt to varying volumes of lesion areas. Then, a feature extraction module with multi-level attention mechanism is designed using ConvLSTM, non-local spatial attention, and channel attention modules to suppress useless information expression in skip connections and upsampling processes while focusing more on effective spatial and channel information in features. The experiments show that our proposed network gets superior performance than benchmark methods and indicates the generalization and effectiveness of the proposed model. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Convolution

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Control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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An Energy-Constrained Optimization-Based Structured Pruning Method for Deep Neural Network Compression 22

An Energy-Constrained Optimization-Based Structured Pruning ...

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22nd IEEE International Conference on Communication technology, ICCT 2022

作者： Liu, Siping Li, Qiang Peng, Bo Qiao, Yanping Lin, Maosong Zeng, Qiang School of Information Engineering Southwest University of Science and Technology Mianyang China Science and Technology on Altitude Simulation Laboratory Mianyang China School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9781665470674

Deep neural networks are widely used in modern intelligent applications due to their superior ability to express reality, and these intelligent applications run on highly energy-constrained edge devices. Neural network structured pruning is an efficient method to reduce the energy consumption of neural networks. This paper proposes an energy-constrained optimization-based structured pruning method to compress deep neural network. This method uses the energy budget provided by edge devices as a constraint for neural network pruning. The key idea is to formulate deep neural network structured pruning as an optimization problem, in which the energy estimate of each layer is used as the Frobenius norm optimization constraint for the convolution kernel weights. Then the importance of all convolution kernels in the network can be evaluated, thereby the neural network structure can be pruned according to the importance to reduce the energy consumption and memory size of the neural network. Compared with traditional heuristic structured pruning methods, our proposal enables neural networks to achieve higher accuracy with the same or lower energy budget. © 2022 IEEE.

关键词： Deep neural networks

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User-level failure detection and auto-recovery of parallel programs in HPC systems

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Frontiers of computer Science 2021年第6期15卷 31-42页

作者： Guozhen ZHANG Yi LIU Hailong YANG Jun XU Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China Sino-German Joint Software Institute Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854China

As the mean-time-between-failures(MTBF)continues to decline with the increasing number of components on large-scale high performance computing(HPC)systems,program failures might occur during the execution period with high *** successful execution of the HPC programs has become an issue that the unprivileged users should be *** the user perspective,if the program failure cannot be detected and handled in time,it would waste resources and delay the progress of program ***,the unprivileged users are unable to perform program state checking due to execution control by the job management system as well as the limited ***,automated tools for supporting user-level failure detection and autorecovery of parallel programs in HPC systems are *** paper proposes an innovative method for the unprivileged user to achieve failure detection of job execution and automatic resubmission of failed *** state checker in our method is encapsulated as an independent job to reduce interference with the user *** addition,we propose a dual-checker mechanism to improve the robustness of our *** implement the proposed method as a tool named automatic re-launcher(ARL)and evaluate it on the Tianhe-2 *** results show that ARL can detect the execution failures effectively on Tianhe-2 *** addition,the communication and performance overhead caused by ARL is *** good scalability of ARL makes it applicable for large-scale HPC systems.

关键词： high performance computing parallel program failure detection failure auto-recovery

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A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning 19th

A Synchronous Parallel Method with Parameters Communication...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yanguo Xue, Meiting Xu, Peiran Shi, Yukun Zeng, Kaisheng Zhang, Jilin Yue, Lupeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China National University of Defense Technology Changsha China School of Cyberspace HangZhou Dianzi University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031545306

With the development of machine learning technology in various fields, such as medical care, smart manufacturing, etc., the data has exploded. It is a challenge to train a deep learning model for different application domains with large-scale data and limited resources of a single device. The distributed machine-learning technology, which uses a parameter server and multiple clients to train a model collaboratively, is an excellent method to solve this problem. However, it needs much communication between different devices with limited communication resources. The stale synchronous parallel method is a mainstream communication method to solve this problem, but it always leads to high synchronization delay and low computing efficiency as the inappropriate delay threshold value set by the user based on experience. This paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning. It predicts the optimal timing for synchronization, which can solve the problem of long synchronization waiting time caused by the inappropriate threshold settings in the stale synchronous parallel method. Moreover, it allows fast nodes to continue local training while performing global synchronization, which can improve the resource utilization of work nodes. Experimental results show that compared with the delayed synchronous parallel method, the training time and quality, and resource usage of our method are both significantly improved. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Forecasting

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Adaptive watermark generation mechanism based on time series prediction for stream processing

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Frontiers of computer Science 2021年第6期15卷 59-73页

作者： Yang SONG Yunchun LI Hailong YANG Jun XU Zerong LUAN Wei LI Sino-German Joint Software Institute Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854China College of Life Sciences and Bioengineering Beijing University of TechnologyBeijing 100083China

The data stream processing framework processes the stream data based on event-time to ensure that the request can be responded to in *** reality,streaming data usually arrives out-of-order due to factors such as network *** data stream processing framework commonly adopts the watermark mechanism to address the data *** is a special kind of data inserted into the data stream with a timestamp,which helps the framework to decide whether the data received is late and thus be *** watermark generation strategies are periodic;they cannot dynamically adjust the watermark distribution to balance the responsiveness and *** paper proposes an adaptive watermark generation mechanism based on the time series prediction model to address the above *** mechanism dynamically adjusts the frequency and timing of watermark distribution using the disordered data ratio and other lateness properties of the data stream to improve the system responsiveness while ensuring acceptable result *** implement the proposed mechanism on top of Flink and evaluate it with realworld *** experiment results show that our mechanism is superior to the existing watermark distribution strategies in terms of both system responsiveness and result accuracy.

关键词： data stream processing watermark time series based prediction dynamic adjustment

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