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Remaining Useful Life Prediction of Lithium Battery via Neural network Ensemble 16

Remaining Useful Life Prediction of Lithium Battery via Neur...

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16th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021

作者： Zhang, Hengshan Wu, Di Wang, Zhongmin Chen, Yanping Xu, Jiaxuan Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an710000 China Shanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710000 China

ISBN: (纸本)9781665405539

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is critical for accelerating the technology development. The neural network via data driven can avoid manual feature extraction and release the difficulty of model construction. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. The single neural network cannot tackle these challenges efficiently. In this paper, the RUL prediction of lithium battery via neural network ensemble is developed to improve the accuracy and generalization of the prediction model. The predicting results of multiple neural networks are considered as the base vectors, and the liner combination on the base vectors are conducted to obtain the ensemble RUL prediction of lithium-ion battery. The experiment is performed on the MIT-Stanford University lithium battery accelerated degradation dataset, and the experimental results show that the developed method has achieved better generalization and higher accuracy comparing with the other methods. © 2021 IEEE.

关键词： Lithium-ion batteries

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Design and Implementation of Analyzer Management System Based on Elasticsearch 19th

Design and Implementation of Analyzer Management System Ba...

引用

19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Sun, Jingqi Nie, Peng Xu, Licheng Zhang, Haiwei College of Cyber Science Nankai University Tianjin300350 China College of Computer Science Nankai University Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

ISBN: (纸本)9783031203084

Elasticsearch is one of the most popular full-text search and analytics engine. It can store, search, and analyze big volumes of data in near real time. Before searching, Elasticsearch will build multiple inverted indexes for the data. The analyzer plays a crucial role in this process. An appropriate analyzer can segment text into semantically meaningful words, which can significantly improve the query accuracy. However, the default analyzer has limited performance in the Chinese context. Existing methods generally replace the default analyzer with manual configuration to optimize the query effect. The cost of service downtime caused by manually updating analyzers is often unacceptable in production environments. Based on Elasticsearch’s Restful-API, we have implemented a framework for dynamic configuration of analyzers in a cluster environment. The framework supports common Chinese analyzers and provides a visual interface. Experiments show that the framework proposed in this paper reduces the update and maintenance time cost of the analyzer in the online environment by 94% compared to manual update. At the same time, compared with the default analyzer configuration of Elasticsearch, the accuracy of the system based on this framework is improved by 30%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information management

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Intelligence’s Dilemma: AI Algorithms May Cause Anti-Intelligence

Intelligence’s Dilemma: AI Algorithms May Cause Anti-Intell...

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4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018

作者： Zhang, Yuhong Nauman, Umer Network and Data Security Key Laboratory of Sichuan University of Electronic Science and Technology of China Chengdu610054 China Information Science and Engineering College Henan University of Technology Zhengzhou450001 China

ISBN: (纸本)9789811379826

As the intelligent age continues and technology gets more advanced, algorithms offer some of the solutions for our better work and life. However, research suggests that the proliferation and abuse of intelligent algorithms may lead us to a decline in cognitive performance. In this paper, we first discuss the role of intelligent algorithms in terms of highlighting the value of data. Next, we analyse the non-neutrality of these data-driven algorithms. Further, we analyze the intrinsic discrimination of AI algorithm based on prejudice psychology. Next, we argue that overusing AI algorithms and strengthening the dependency on them will lead to lower our cognitive level in terms of slow thinking. Thus, the conclusion is that, in the intelligent age, the more intelligent algorithms we use, the lower our cognitive level will be. This is the dilemma of intelligence. Finally, we offer some possible principles that may apply to alleviate the above disadvantageous scenarios. © 2019, Springer Nature Singapore Pte Ltd.

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A Lightweight Anonymous key Agreement Scheme in V2G networks 2

A Lightweight Anonymous Key Agreement Scheme in V2G Networks

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2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022

作者： Chendong, Huang Feng, Wang Zhongming, Huang Fujian University of Technology School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (数字)9781665454766

ISBN: (纸本)9781665454766

Electric vehicle access to the grid (V2G) technology enables bidirectional interaction of energy and information between electric vehicles and the grid. The energy transactions between electric vehicles and the power grid is completed by the aggregators. Energy transactions are generally completed through open wireless communication networks, which are vulnerable to network attacks such as eavesdropping, impersonation attack, etc. data transmission in the V2G networks requires to verify the identity of each communication entity to achieve confidential communication. The realization of confidential communication can enhance the security of energy transactions. key agreement is a key technique to solve this problem. Therefore, we propose a lightweight anonymous key agreement scheme for V2G networks. For efficiency, we hire a data center to assist electric vehicles and aggregators in our key agreement scheme. Security analysis shows that our scheme can provide anonymity, resist eavesdropping, impersonation and replay attacks, etc. Performance analysis shows that our scheme has lower overall computational cost and communication overhead. © 2022 IEEE.

关键词： network security

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network Security Situation Assessment Method Based on Absorbing Markov Chain

Network Security Situation Assessment Method Based on Absorb...

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2022 International Conference on networking and network Applications, NaNA 2022

作者： Gao, Hongbin Wang, Shangxing Zhang, Hongbin Liu, Bin Zhao, Dongmei Liu, Zhen Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Hebei University of Science and Technology School of Information Science and Engineering Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei University of Science and Technology School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

This paper has a new network security evaluation method as an absorbing Markov chain-based assessment method. This method is different from other network security situation assessment methods based on graph theory. It effectively refinement issues such as poor objectivity of other methods, incomplete consideration of evaluation factors, and mismatching of evaluation results with the actual situation of the network. Firstly, this method collects the security elements in the network. Then, using graph theory combined with absorbing Markov chain, the threat values of vulnerable nodes are calculated and sorted. Finally, the maximum possible attack path is obtained by blending network asset information to determine the current network security status. The experimental results prove that the method fully considers the vulnerability and threat node ranking and the specific case of system network assets, which makes the evaluation result close to the actual network situation. © 2022 IEEE.

关键词： network security

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EEG-Based Fatigue Detection Using PLI Brain network and Relief Algorithm

EEG-Based Fatigue Detection Using PLI Brain Network and Reli...

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2022 International Conference on networking and network Applications, NaNA 2022

作者： He, Yan Wang, Zhongmin Zhao, Yupeng Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

EEG-based fatigue driving monitoring has important application value in road traffic safety, and the ultimate goal of the research is the development and use of wearable devices, and too many EEG channels in practical application scenarios is detrimental to device portability, and it will lead to problems such as large amount of data, complex calculation and long processing time, so it is especially important to study how to select the EEG channels highly correlated with fatigue. In this paper, a PLI-Relief-based channel selection algorithm by combining the PLI functional connectivity and the weighting idea of Relief algorithm is proposed, and it is applied to the channel selection of fatigue driving EEG. First, the PLI functional connectivity matrix is constructed for the EEG signals after preprocessing, and the binarized PLI matrix is mapped into a brain functional network, and the prime channels are selected by the degree property of the brain network. Then, the power spectral density features are extracted from the EEG signals of the prime channels, and the weights of each prime channel are obtained using the relief algorithm, then the number and the names of optimal channels are determined according to the recognition accuracy of different channel combinations. The proposed method was validated on the publicly available SEED-VIG dataset, and the data of the seven optimal channels is finally selected and obtains a classification accuracy of 81.25%. The framework proposed in this paper takes into account both the correlation between channels and the characteristics of the channel signals themselves in channel selection, which is a reference value for the development and application of wearable devices. © 2022 IEEE.

关键词： Wearable technology

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Intention Recognition Method Based on Resting-state and P300 Task-state Dynamic Brain Functional network Features 15

Intention Recognition Method Based on Resting-state and P300...

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15th International Conference on Measurement technology and Mechanical Automation, ICMTMA 2023

作者： Yi, X. Wang, Z.M. Heng, X. School of Computer Science and Technology Xi'An University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi 'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

In order to reconfigure its structure from the static state in the vision odd ball task, so as to realize the intention recognition based on the characteristics of the brain functional network. The thesis proposes the intention recognition method based on resting state and P300 task state dynamic brain functional network features. First, the brain connectivity in each time window is constructed into a brain functional network using phase lock value (PLV). Then, extract the global features (global efficiency, transitivity) of the brain functional network, and use Louvain algorithm to obtain the brain functional network community. The experimental results show that in the (100-200) ms of P300 task status, the core nodes are mainly concentrated in the forehead region and the central region, while in the (300-500) ms of P300, the core nodes are concentrated in the temporal lobe. The recognition accuracy based on this method reaches 93%. © Published under licence by IOP Publishing Ltd.

关键词： Brain Function network Intention Recognition P300 PLV Sliding Windows

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Block-gram: Mining Knowledgeable Features for Smart Contract Vulnerability Detection 7th

Block-gram: Mining Knowledgeable Features for Smart Contrac...

引用

7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Li, Tao Wang, Haolong Fang, Yaozheng Jian, Zhaolong Wang, Zichun Xie, Xueshuo Tianjin Key Laboratory of Network and Data Security Technology Tianjin China College of Computer Science Nankai University Tianjin China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031281235

Effective vulnerability detection of large-scale smart contracts is critical because smart contract attacks frequently bring about tremendous economic loss. However, code analysis requiring traversal paths and learning methods requiring many features training is too time-consuming to detect large-scale on-chain contracts. This paper focuses on improving detection efficiency by reducing the dimension of the features, combined with expert knowledge. We propose a feature extraction method Block-gram to form low-dimensional knowledgeable features from the bytecode. We first separate the metadata and convert the runtime code to opcode sequence, dividing the opcode sequence into segments according to some instructions (jump, etc.). Then, we mine extensible Block-gram features for learning-based model training, consisting of 4-dimensional block features and 8-dimensional attribute features. We evaluate these knowledge-based features using seven state-of-the-art learning algorithms to show that the average detection latency speeds up 25 to 650 times, compared with the features extracted by N-gram. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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QoS Prediction based on the Low-rank Autoregressive Tensor Completion

QoS Prediction based on the Low-rank Autoregressive Tensor C...

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2022 International Conference on networking and network Applications, NaNA 2022

作者： Xia, Hong Dong, Qingyi Chen, Yanping Zheng, Jiahao Gao, Cong Wang, ZhongMin School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

With the rapid development of network services and edge computing, Quality of Service (QoS) has become an important indicator to validate performances of a network. Recommend high-quality services to users based on QoS values. However, the high sparsity of QoS data is because users usually call certain services only at a given time. Missing QoS data is very common in various service recommendation systems. Therefore, it is essential to predict QoS data to accurately recommend high-quality services to users. For the QoS data prediction problem, we build a third-order data tensor 'User-Service-Time' for the time series characteristics of QoS data. And introduce time-series variation as a regularization term into third-order tensor data prediction. We propose a QoS prediction framework using Low-Rank Autoregressive Tensor Completion (LATC). In particular, constructing a third-order tensor data model can better capture the global consistency of the data structure. Time regularization is introduced to take into account the local correlation of the data. Finally, in order to solve the constrained optimization problem, we use the general Alternating Direction Method of Multipliers (ADMM) to minimize the iterative optimization of variables and autoregressive parameters to obtain the final prediction result. Meanwhile, we conduct extensive research experiments on the real dataset WS-Dream. Experiments show that the QoS data prediction accuracy of our proposed QoS prediction method is higher than that of existing prediction methods under different degrees of data density. © 2022 IEEE.

关键词： Forecasting

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Entity disambiguation method based on Graph Attention networks 14

Entity disambiguation method based on Graph Attention Networ...

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14th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2022

作者： Qiang, Chengyu Li, Xiaogo Ma, Xianyan Han, Wei Yao, Yi School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an71012 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi 'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9781665499781

Entity disambiguation based on entity-link is a technique which constructs the mappings between entity reference items appearing in the short text and target entity in knowledge base respectively. In this paper we propose an entity disambiguation method based on Graph Attention networks for semi-structured knowledge base data. First, a global Knowledge Graph is constructed from the semi-structured knowledge base, and the entity reference items are embedded by Bert pre-trained model meanwhile. Next, Graph Attention networks which leverages masked self-attention layers is applyed on candidate entity nodes of global Knowledge Graph to fetch a vector of node level. Furtherly, we compute similarity scores rank between the entity reference items and the candidate entity to complete the task of entity disambiguation. The experimental results on CCKS2019 dataset achieve state-of-the-art. © 2022 IEEE

关键词： Natural language processing systems

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