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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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Semantic recommendation through Reinforcement Learning and weighted meta-paths

Semantic recommendation through Reinforcement Learning and w...

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2022 International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2022

作者： Yu, Jing Lang, Yue Li, Xuewen Zhang, Jin Zheng, Shaojie Gong, Jibing School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Key Laboratory for Software Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9781510655133

Explainable recommendation systems, which can produce high-accuracy recommendations and help users make quick decisions, have become a hotspot in research field. Most of existing research algorithms committed to improving the accuracy of recommendation, but ignored the interpretability of recommendation. We propose a semantic recommendation algorithm through Reinforcement Learning and weighted meta-paths which analyzes and selects the meta-path, and uses Reinforcement Learning network to train the weight of meta-path. This method can effectively solve data sparsity, improve the accuracy of recommendation, and increase interpretability. Compared with the baseline method, indicators of the recommended methods for integrating Reinforcement Learning with Heterogeneous Information Networks have been greatly improved, as verified and compared on a real MovieLens 1M dataset. Experimental results prove that the algorithm can effectively improve the accuracy and interpretability of the recommendation. © 2022 SPIE.

关键词： Reinforcement learning

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Towards Planning Package Delivery Paths by Exploiting the Crowd of Private Cars 24

Towards Planning Package Delivery Paths by Exploiting the Cr...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Nie, Hejing Wen, Yiping Li, Tiancai Ouyang, Sipeng Tian, Muyang School of Computer Science and Engineering Hunan University of Science and Technology China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology China

ISBN: (纸本)9798350319934

Crowdsourced delivery can provide cost-effective deliveries and it is becoming increasingly important in practice. This paper considers a package delivery problem which exploits relays of private cars to transport packages via their commuter routes. Specifically, we propose a method of planning package delivery paths based on the commuter routes of private cars. Using the commuter routes and time constraints as the reference, it can find optimal delivery paths and assign drivers of private cars to transport packages. The simulation experiment results show the validity and effectiveness of such method. © 2022 IEEE.

关键词： Cost effectiveness

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Approximate Simulation for Transition Systems with Regular Expressions 2nd

Approximate Simulation for Transition Systems with Regular E...

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2nd International Conference on Artificial Intelligence Logic and Applications, AILA 2022

作者： Cui, Xinyu Li, Zhaokai Chang, Yuting Pan, Haiyu Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Engineering North Minzu University Yinchuan750000 China

ISBN: (纸本)9789811975097

Simulation is a well-established technique for verifying wheth-er the behaviors of one labeled transition system (LTS) can mimic all behaviors of another LTS. Transition systems with regular expressions (RE-TSs) are an extension of LTSs, which are used as semantic models in modal or temporal logics to solve model checking problems. This paper presents approximate simulation, an extension of simulation of an LTS by a RE-TS, by combining general simulation and metrics, and discusses its properties. First, the notion of approximate simulation is introduced. Then, we investigate properties and an equivalent formalism of approximate simulation. On the other hand, we propose two approaches of fixed point characterization for approximate simulation, and study the relationship between them. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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How Well Do Large Language Models Serve as End-to-End Secure Code Producers?

arXiv

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arXiv 2024年

作者： Gong, Jianian Duan, Nachuan Tao, Ziheng Gong, Zhaohui Yuan, Yuan Huang, Minlie School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Zhongguancun Laboratory Beijing100191 China Tsinghua University Zhongguancun Laboratory Beijing China

The rapid advancement of large language models (LLMs) such as GPT-4 has revolutionized the landscape of software engineering, positioning these models at the core of modern development practices. As we anticipate these models to evolve into the primary and trustworthy tools used in software development, ensuring the security of the code they produce becomes paramount. How well can LLMs serve as end-to-end secure code producers? This paper presents a systematic investigation into LLMs’ inherent potential to generate code with fewer vulnerabilities. Specifically, We studied GPT-3.5 and GPT-4’s capability to identify and repair vulnerabilities in the code generated by four popular LLMs including themselves (GPT-3.5, GPT-4, Code Llama, and CodeGeeX2). By manually or automatically reviewing 4,900 pieces of code, our study reveals that: (1) large language models lack awareness of scenario-relevant security risks, which leads to the generation of over 75% vulnerable code on the SecurityEval benchmark;(2) LLMs such as GPT-3.5 and GPT-4 are unable to precisely identify vulnerabilities in the code they generated;(3) GPT-3.5 and GPT-4 can achieve 33.2%~59.6% success rates in repairing the insecure code produced by the 4 LLMs, but they both perform poorly when repairing self-produced code, indicating self-repair "blind spots". To address the limitation of a single round of repair, we developed a lightweight tool that prompts LLMs to construct safer source code through an iterative repair procedure based on the insights gained from our study. Experiments show that assisted by semantic analysis engines, our tool significantly improves the success rates of repair to 65.9%~85.5%. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Learners' States Monitoring Method Based on Face Recognition Technology 12

Learners' States Monitoring Method Based on Face Recognition...

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12th International Conference on Identification, Information and Knowledge in the internet of Things, IIKI 2021

作者： Qiao, Xiaoyi Zheng, Xiangwei Sun, Xiaofang Li, Shuqin Zhang, Yuang School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014 China

At present, many scholars focus on the learners' states in online learning, however, a complete real-time monitoring system of learners' states has not been proposed. In order to better detect the learning process and provide learners with a basis for self-reflection, this paper describes a learners' states monitoring method based on face recognition technology (LSMFR). A learner monitoring system is developed to collect the learners' states in real-time through external cameras and record the tired blinking, yawning, head posture, and emotional expression of learners respectively. After a comprehensive analysis, the evaluation score of learners' states is obtained. In the running process of the system, information feedback is completed by recording the detection content in real-time and then states of learning effectiveness are generated. After learning for a short time, learners check the data record for self-reflection. The simulation test is carried out on the system and the analysis shows that the system achieves the correct analysis of the learners' states, and generates accurate evaluation scores. © 2022 The Authors. Published by Elsevier B.V.

关键词： face recognition learners' states procedure analysis real-time monitoring

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Query Provenance Analysis: Efficient and Robust Defense Against Query-Based Black-Box Attacks

Query Provenance Analysis: Efficient and Robust Defense Agai...

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IEEE Symposium on Security and Privacy

作者： Shaofei Li Ziqi Zhang Haomin Jia Yao Guo Xiangqun Chen Ding Li Key Laboratory of High-Confidence Software Technologies (MOE) School of Computer Science Peking University University of Illinois Urbana-Champaign School of Electronics Engineering and Computer Science Peking University

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

Query-based black-box attacks have emerged as a significant threat to machine learning systems, where adversaries can manipulate the input queries to generate adversarial examples that can cause misclassification of the system. To counter these attacks, researchers have proposed Stateful Defense Models (SDMs) such as BlackLight and PIHA, which can reject queries that are “similar” to historical queries. However, recent studies show that existing approaches are vulnerable to a stronger adaptive attack, Oracle-guided Adaptive Rejection Sampling (OARS). OARS can be easily integrated with existing attack algorithms to evade the SDMs by generating queries with fine-tuned direction and step size of perturbations utilizing the leaked decision boundary from the SDMs. In this paper, we propose a novel approach, Query Provenance Analysis (QPA), for defending against query-based black-box attacks robustly (against both non-adaptive and adaptive attacks) and efficiently (in real-time). Our key insight is that, instead of focusing on individual queries, utilizing features from the query sequence (termed query provenance) can distinguish malicious queries from benign queries more effectively. We construct a query provenance graph to capture the relationship between a new query and prior historical queries, and then design efficient algorithms to detect malicious queries based on the query provenance graphs. We evaluate QPA on four datasets against six query-based attacks and compare QPA with state-of-the-art SDM defenses. The results show that QPA outperforms the baselines regarding defense robustness and efficiency on both non-adaptive and adaptive attacks. Specifically, QPA reduces the Attack Success Rate (ASR) of OARS to 4.08%, which is roughly 20× lower than the baselines. Moreover, QPA achieves higher throughput (up to 7.67×) and lower latency (up to 11.09×) than baselines.

关键词： Adaptation models Privacy Heuristic algorithms Perturbation methods Closed box Machine learning Throughput Real-time systems Robustness Security

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Understanding and Predicting the Burst of Burnout via Social Media

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Proceedings of the ACM on Human-computer Interaction 2021年第CSCW3期4卷 1-27页

作者： Wu, Jue Ma, Junyi Wang, Yasha Wang, Jiangtao Key Laboratory of High Confidence Software Technologies Ministry of Education China School of Electronics Engineering and Computer Science Peking University China National Engineering Research Center of Software Engineering Peking University China Center for Intelligent Healthcare Coventry University United Kingdom

Job burnout is a special type of work-related stress that is prevalent in our modern society, and constant burnout is extremely harmful for people's physical health and emotional wellbeing. Traditional studies for burnout mainly rely on surveys/questionnaires, which have revealed several interesting findings but are of high cost and very time consuming. With the prevalence of social networking applications, we aim to re-investigate the burnout phenomenon in a novel perspective. In this paper, we collected a dataset consisting of 1532 burnout Weibo users with their postings. Based on the previous literature, we propose a number of hypotheses about what might be the burst signal of the burnout from the perspective of language, time and interaction. Furthermore, extensive correlation analysis is conducted to investigate if these hypotheses are supported, which leads to a number of interesting findings. Finally, we develop machine learning models to predict the burst of burnout based on extracted features and achieve a relatively high accuracy, which reveals potential implications in early-stage intervention. © 2021 ACM.

关键词： Machine learning

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TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System 25

TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Lear...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhang, Jinsong Mao, Xiaoxuan Li, Lu Wu, Hanqian Joint Graduate School Southeast University Nanjing China College of Software Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration of Ministry of Education Nanjing China

ISBN: (纸本)9798350330014

In today's evolving society, the increasing complexity and frequency of meetings necessitate advanced scheduling systems. Traditional methods are constrained by rigid prede-fined strategies, lack intelligent negotiation mechanisms, and often compromise user privacy. Addressing these challenges, we introduce the Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System (TPMARL-MSS). Unlike conventional systems, TPMARL-MSS autonomously learns and refines its strategies through continuous feedback. It features automated negotiation and adaptive decision-making, offering a more nuanced scheduling approach. Importantly, our Thought-Perception module protect privacy, allowing the agent to deduce preferences from participant behavior without revealing personal data. Evaluations on the real-world dataset shows that TPMARL-MSS surpasses traditional methods in efficiency and schedule quality, highlighting its practical applicability. © 2023 IEEE.

关键词： Decision making

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Wireless Channel Estimation and Equalization Based on Deep Learning

Wireless Channel Estimation and Equalization Based on Deep L...

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Intelligent Data and Security (IDS), IEEE International Conference on

作者： Xueting Huang Linshu Chen Xiaoyan Chen Mo Wang Yuxin Liu Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Software Engineering Xiamen University of Technology Xiamen China

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

Nowadays, the research on wireless communication is hot, and it has achieved leapfrog development in the past few decades and has made astonishing breakthroughs in emerging applications such as 5G technology, Internet of Things (lOT), and smart cities. Channel estimation and equalization in wireless communication are very important technologies, which are cru-cial for ensuring reliable data transmission and improving system performance. However, with the increasing demand for wireless high-speed data communication, traditional channel estimation and equalization technologies are facing enormous challenges. Nowadays, with the development of artificial intelligence, deep learning has been successfully applied to wireless communication systems. Compared to traditional channel estimation and equalization techniques, deep learning-based channel estimation and equalization techniques have many advantages. For example, traditional channel estimation and equalization methods typically require modeling of channel models and are only applicable to specific channel conditions, making it difficult to achieve optimal performance under different channel conditions. Deep learning technology can improve the performance of channel estimation and equalization by learning a large amount of data and training under different channel conditions, especially in nonlinear and complex channel conditions. Therefore, this article will provide a detailed description of the research on channel estimation and equalization based on deep learning, in order to demonstrate its ultra-high performance and development prospects.

关键词： Wireless communication Deep learning Training Smart cities System performance Channel estimation Data communication

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