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Unraveling adaptive changes in electric vehicle charging behavior toward the postpandemic era by federated meta-learning

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The Innovation 2024年第2期5卷 21-23页

作者： Linlin You Rui Zhu Mei-Po Kwan Min Chen Fan Zhang Bisheng Yang Man Sing Wong Zheng Qin School of Intelligent Systems Engineering Sun Yat-Sen UniversityShenzhen 518107China Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis WaySingapore 138632Republic of Singapore Institute of Space and Earth Information Science Chinese University of Hong KongHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhan 430079China Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong KongChina

Dear Editor,Electric vehicle(EV)sales have significantly grown over the years to fulfill growing demands for economic travel and greenhouse gas mitigation.1 However,the surge in the number of EVs has led to charging anxiety as users struggle to find an available charging station before running out of electricity,resulting in longer reserve and waiting times.2 Moreover,severe mobility restrictions caused by infectious diseases,such as coronavirus disease 2019(COVID-19),have greatly affected people’s travel behavior3,4 and hindered their willingness to use EVs,given that charging in public spaces consumes time and increases the risk of contracting the virus.5 This implies that in the postpandemic era,in which individuals coexist with the virus,the interplay between the two important trends,namely vehicle electrification and mobility restrictions,can extensively affect people’s daily commuting by using EVs.

关键词： travel behavior charging

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A generalized reliability analysis of 3-ary n-cubes

A generalized reliability analysis of 3-ary n-cubes

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International Conference on Communication Software and Networks, ICCSN

作者： Zefang Bai Guijuan Wang Meijie Ma School of Mathematics and Statistics Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331506322

ISBN: (纸本)9798331506339

Evaluating the reliability of interconnection networks is crucial. The edge-connectivity is a parameter used to assess reliability. In this paper, we discuss a conditional edge-connectivity called the h-extra g-component edge-connectivity. It combines the extra edge-connectivity and the component edge-connectivity. First, this paper proved that $c \lambda_{3}^{h}\left(Q_{n}^{3}\right)=4 n h-$ $4 e_{h}\left(Q_{n}^{3}\right)-h$ for $1 \leq h \leq \frac{1}{2} \cdot 3^{\left\lceil\frac{h}{2}\right\rceil}, n \geq 3$. Then, the paper given the upper bound of h-extra g-component edge-connectivity of the 3 -ary n-cubes for $g \in\left\{4,3^{c}+1\right\}, 1 \leq c \leq\left\lceil\frac{n}{2}\right\rceil-1, n \geq 3$.

关键词： Upper bound Multiprocessor interconnection Software Reliability

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Sequential end-to-end network for efficient person search

arXiv

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arXiv 2021年

作者： Li, Zhengjia Miao, Duoqian Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China

Person search aims at jointly solving Person Detection and Person Re-identification (re-ID). Existing works have designed end-to-end networks based on Faster R-CNN. However, due to the parallel structure of Faster R-CNN, the extracted features come from the low-quality proposals generated by the Region Proposal Network, rather than the detected high-quality bounding boxes. Person search is a fine-grained task and such inferior features will significantly reduce re-ID performance. To address this issue, we propose a Sequential End-to-end Network (SeqNet) to extract superior features. In SeqNet, detection and re-ID are considered as a progressive process and tackled with two sub-networks sequentially. In addition, we design a robust Context Bipartite Graph Matching (CBGM) algorithm to effectively employ context information as an important complementary cue for person matching. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our method achieves state-of-the-art results. Also, our model runs at 11.5 fps on a single GPU and can be integrated into the existing end-to-end framework easily. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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A Dirty Data Identification Method in Mechanical Equipment Condition Monitoring

A Dirty Data Identification Method in Mechanical Equipment C...

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Smart World Congress (SWC), IEEE

作者： Huijuan Hao Hongge Zhao Sijian Zhu Yue Feng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The rise of the Industrial Internet of Things (IIOT) has brought new opportunities for the mechanical equipment condition monitoring. The trend towards intelligent and automated production of mechanical equipment has become mainstream. However, in the actual operational environment, mechanical equipment is often mixed with a large amount of dirty data unrelated to equipment failures, which seriously affects equipment fault diagnosis and wastes maintenance resources. To improve the reliability of mechanical equipment health status analysis, this paper proposes a new method for dirty data identification suitable for mechanical equipment condition monitoring. First, utilizing the time-space position encoding (TSPE) to map positional relationships between sequences. Next, designing a time-dimension attention (TDA) mechanism to capture the correlation between multiple temporal attribute features and the temporal dependencies between preceding and subsequent data. Then, building a dirty data identification model using an improved Transformer network and Generative Adversarial Network (GAN). Finally, employing an adaptive thresholding method to dynamically set thresholds, determining the degree of data anomalies by comparing anomaly scores with the threshold. The effectiveness of the proposed method are validated through one simulation experiment and one real-world example. The F1 Score of this method on the simulated missing data and drifting data is 0.9874 and 0.9838. On the drive-end and fan-end is $\mathbf{0. 9 7 0 0}$ and 0.9882 at Case Western Reserve University (CWRU). Compared to other dirty data identification methods in the field, this method has better dirty data identification capability.

关键词： Condition monitoring Correlation Buildings Production Transformers Generative adversarial networks Market research Data models Monitoring Industrial Internet of Things

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Research on Generating Xi'an Drum Music Based on Generative Adversarial Network

Research on Generating Xi'an Drum Music Based on Generative ...

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： Tianmian Liang Peng Li Yumei Cao Xiaojun Wu Huifang Zhao Chenye Qi Ministry of Culture and Tourism Key Laboratory of Intelligent Computing and Service Technology for Folk Song Xi’an China School of Computer Science Shaanxi Normal University Xi'an China Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi’an China Xi'an Key Laboratory of Cultural Tourism Resources Development and Utilization Xi’an China School of Music Shaanxi Normal University Xi'an China

Currently most AI music generator platforms mainly focus on creating modern music, without focusing on the creation of folk music. And most music generation neural network models use recursive neural networks and their variants, resulting in excessively repetitive music information, greatly reducing the fun of music. When using only generative adversarial networks (GANs) to generate music, it is easy to experience unstable training, vanishing gradients, and mode collapse. Failure to consider time dependence can reduce the authenticity of generated music. In order to solve the problem that most of the conventional deep learning GAN music generation network models do not consider music theory rules in music generation and Xi'an drum music faces the scarcity of tracks and difficulties in composition, we proposed a GAN network music generative model based on music theory rules and characteristics of Chinese Folk Songs. When generating music, the generator CNN and the regulator CNN train together, and generate music melodies under the constraints of the voice column CNN module. The generated results are sent to the discriminator CNN, which submits feedback to the generator CNN for competition, to compensate for the shortcomings of previous technology and ultimately generate better melodies, thereby improving the authenticity, sweetness, and rhythm of the generated music.

关键词：

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MGCL: Multiorder Graph Neural Network With Cross-Learning for Multivariate Time-Series Anomaly Detection

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhe Cui Di Zang Junqi Zhang Keshuang Tang College of Computer Science and Technology and the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Key Laboratory of Road and Traffic Engineering Ministry of Education and the College of Transportation Engineering Tongji University Shanghai China

Multivariate time-series (MTS) anomaly detection is critical for industrial systems. However, for inter-variable dimensional modeling, existing work mainly focuses on low-order dependencies between variable pairs, overlooking high-order interactions among multiple variables. Additionally, for temporal dimension modeling, time series are influenced by various system factors, posing challenges in simultaneously capturing long-term and short-term dependencies. To address these challenges, we propose a multiorder graph neural network with cross-learning (MGCL). First, we design a multiscale temporal module that captures long-term seasonal dependencies and short-term trend dependencies by employing a temporal global attention network (TGAN) and a gated temporal convolution network (GTCN), respectively. Second, we construct an adaptive hypergraph generator to explicitly represent the dynamic high-order interactions among multiple variables. Finally, we develop a multiorder graph cross-learning module, which integrates a dynamic memory graph convolution network (DMGCN), a tri-directional diffusion hypergraph convolution network (3D-HGCN), and a cross-learner to comprehensively capture multiorder inter-variable dependencies. Extensive experiments on eight real-world datasets demonstrate that MGCL outperforms state-of-the-art baselines.

关键词： Time series analysis Anomaly detection Market research Graph neural networks Transformers Adaptation models Convolution Sensor phenomena and characterization Transportation Power system dynamics

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Design and Implementation of DSP Cache 21

Design and Implementation of DSP Cache

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21st IEEE International Conference on Communication technology, ICCT 2021

作者： Liang, Kunkun Wu, Jun Ren, Haoqi Zhang, Zhifeng Tan, Bin Tongji University Electronics and Information Engineering College Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China School of Computer Science Fudan University Shanghai200438 China College of Electronics and Information Engineering Jinggangshan University Ji'an343009 China

ISBN: (纸本)9781665432061

The cache can improve the DSP processor's access speed to external memory and solve the 'Storage Wall' problem. Designing an efficient and flexible cache module plays an vital role in improving the memory access efficiency and overall performance of the DSP. Based on the analysis of DSP storage level design requirements, combined with the actual pipeline structure of the DSP processor SWIFT independently developed by our laboratory, in this paper, we design and implement first- level instruction and data cache. For L1 D-Cache, the relevant conflict detection and processing module is designed to support four parallel Load/Store access requests. The size of the cache can be flexibly configured according to actual application requirements to achieve a balance between power consumption and latency. To write back the modified data left in the cache before the cache size is changed, the Cacheclean instruction is designed. Finally, module-level functional verification and logic synthesis are carried out. The results show that the cache function meets expectations, and the critical path after logic synthesis optimization meets the requirement of 1GHz frenuency. © 2021 IEEE.

关键词： Pipelines

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An Approach to New Bearing Fault Diagnosis Utilizing Deep Transfer Learning

An Approach to New Bearing Fault Diagnosis Utilizing Deep Tr...

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Smart World Congress (SWC), IEEE

作者： HuiJuan Hao Yue Feng HongGe Zhao SiJian Zhu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Bearings, serving as crucial components in mechanical equipment, exert a direct influence on the overall performance of the system. Deep transfer learning has garnered significant attention in the realm of intelligent fault diagnosis for machines. Nevertheless, extant deep transfer learning algorithms encounter challenges when detecting newly emerging faults within the target domain. To address this limitation, this study introduces a novel bearing fault diagnosis approach for newly emerging faults, leveraging deep transfer learning principles. Initially, a multi-grained hierarchical feature extraction network is constructed to effectively capture intricate data characteristics. Subsequently, these extracted features are employed to train a fault classifier, thereby establishing a fault diagnosis network grounded in deep transfer learning. This network is capable of discriminating unlabeled new faults from labeled known faults with remarkable precision. Furthermore, an adaptive hierarchical clustering algorithm, informed by the elbow method, is incorporated to categorize the newly emerging faults. The clustered samples are then utilized to train the online sequential extreme learning machine (OS-ELM), facilitating the construction of a new fault diagnosis network tailored for diagnosing these faults in the target domain. The efficacy of the proposed methodology was rigorously verified through experiments executed on both the CWRU and XJTU-SY datasets. The outcomes conclusively demonstrated an accuracy of $\mathbf{9 8. 7 5 \%}$ on the CWRU dataset and $\mathbf{9 7. 9 2 \%}$ on the XJTU-SY dataset. This model portends immense potential in tackling fault diagnosis transfer scenarios characterized by the emergence of multiple novel faults within the target domain, thereby markedly broadening the applicability of deep transfer learning.

关键词： Fault diagnosis Accuracy Adaptive systems Extreme learning machines Transfer learning Clustering algorithms Feature extraction Classification algorithms Data mining Elbow

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$k$k-TEVS: A $ k$k-Times E-Voting Scheme on Blockchain With Supervision

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IEEE Transactions on Dependable and Secure computing 2024年第3期22卷 2326-2337页

作者： Yang Liu Debiao He Min Luo Lianhai Wang Cong Peng School of Cyber Science and Engineering Wuhan University Wuhan China Institute of Information Technology Shenzhen Institute of Information Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Shanghai China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

The e-vote is regarded as a way to express the opinion that the voters ask for. Actually, the e-vote could be applied wildly like questionnaire, survey and feedback. Moreover, the coexistences of efficiency and security as well as transparency and privacy could be considered as building blocks in the e-vote system. The blockchain could provide a public access board to reduce the storage costs for the field consisted of the vote group manager (GM) with its vote assistants (VA). Particularly the $k$ -times anonymous authentication ( $k$ -TAA) could also be a practical approach to preserve voters’ privacy and reduce the computation costs during the vote process. However, the e-vote scheme with pure $k$ -TAA strategy could damage either the supervision of voting or the efficiency and consistency of authentication process. What’s more, the impacts of dishonest voters couldn’t be stopped until the vote end. To tackle these problems, we apply the accumulator technology to add or revoke the voters at any time and extend the framework of $k$ -TAA with the update process for the e-vote on blockchain for supervision ( $ k$ -TEVS). In our scheme, the voter updates his membership witness and proves the fact that he is still a valid member with respective VA under the latest accumulator value. What’s more, this witness update operation is not contained in the authentication process, which means that the authentication process is still constant and efficient. Moreover, our add or delete update process with signature of knowledge needs only one pairing operation. For the security, we prove that the relaxed anonymity still holds in the $ k$ -TEVS framework. Finally, We implement $ k$ -TEVS scheme, the Emura’s work [1] and the Huang’s work [2] for comparison. Then we make time cost and communication cost experiments, which present the feasibility and practicality of this scheme.

关键词： Blockchains Authentication Costs Protocols Security Privacy Surveys Electronic voting Computer science Information security

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Base Station Network Traffic Prediction Approach Based on LMA -DeepAR 6

Base Station Network Traffic Prediction Approach Based on LM...

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6th IEEE International Conference on Computer and Communication systems, ICCCS 2021

作者： Zhang, Jiachen Zuo, Xingquan Xu, Mingying Han, Jing Zhang, Baisheng Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing Uinversity of Posts and Telecommunications Beijing China Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing Uinversity of Posts and Telecommunications Beijing China Network Interface Virtualization Shanghai System Design Deptment Zhongxing Telecommunication Equipment Corporation Shanghai China

ISBN: (纸本)9780738126043

Accurate network traffic prediction of base station cell is very vital for the expansion and reduction of wireless devices in base station cell. The burst and uncertainty of base station cell network traffic makes the network traffic nonlinear and nonstationary, which brings challenges to the long-term prediction of network traffic. In this paper, the traffic model LMA-DeepAR for base station network is established based on DeepAR. Acordding to the distribution characteristics of network traffic, this paper proposes an artificial feature sequence calculation method based on local moving average (LMA). The feature sequence is input into DeepAR as covariant, which makes the statistical characteristics of network traffic near a period of time in the past be considered when updating parameters, and the interference of non-stationary network traffic on model training will be reduced. Experimental results show that the proposed prediction approach (LMA-DeepAR) outperforms other methods in the overall long-term prediction performance and stability of multi cell network traffic. © 2021 IEEE.

关键词： Base stations

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