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Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

International Journal of Intelligent Computing and Cybernetics 2023年第3期16卷 629-646页

作者： Haoning Pu Zhan Wen Xiulan Sun Lemei Han Yanhe Na Hantao Liu Wenzao Li Chengdu University of Information Technology ChengduChina Education Department of Sichuan Province Education Informatization and Big Data CenterChengduChina Network and Data Security Key Laboratory of Sichuan Province UESTCChengduChina

Purpose–The purpose of this paper is to provide a shorter time cost,high-accuracy fault diagnosis method for water *** pumps are widely used in industrial equipment and their fault diagnosis is gaining increasing *** the time-consuming empirical mode decomposition(EMD)method and the more efficient classification provided by the convolutional neural network(CNN)method,a novel classification method based on incomplete empirical mode decomposition(IEMD)and dual-input dual-channel convolutional neural network(DDCNN)composite data is proposed and applied to the fault diagnosis of water ***/methodology/approach–This paper proposes a data preprocessing method using IEMD combined with mel-frequency cepstrum coefficient(MFCC)and a neural network model of ***,the sound signal is decomposed by IEMD to get numerous intrinsic mode functions(IMFs)and a residual(RES).Several IMFs and one RES are then extracted by MFCC ***,the obtained features are split into two channels(IMFs one channel;RES one channel)and input into ***–The Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection(MIMII dataset)is used to verify the practicability of the *** results show that decomposition into an IMF is optimal when taking into account the real-time and accuracy of the *** with EMD,51.52% of data preprocessing time,67.25% of network training time and 63.7%of test time are saved and also improve *** limitations/implications–This method can achieve higher accuracy in fault diagnosis with a shorter time ***,the fault diagnosis of equipment based on the sound signal in the factory has certain feasibility and research ***/value–This method provides a feasible method for mechanical fault diagnosis based on sound signals in industrial applications.

关键词： Fault identification Neural nets DDCNN IEMD MFCC

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OPTIMIZING MULTI-INPUT SCHEDULING FOR FUZZING TEST USING BAYESIAN ALGORITHM WEIGHT OPTIMIZATION 10

OPTIMIZING MULTI-INPUT SCHEDULING FOR FUZZING TEST USING BAY...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Fang, Junzhe Wang, Liyong Ji, Haojie Guo, Teng Hu, Te Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China Zaozhuang Network Security Guarantee Center Zaozhuang China

ISBN: (纸本)9781837242672

As a major software testing method, fuzzing has the potential to enhance testing coverage and detect a great number of software vulnerabilities in complex code programs. The current fuzzy testing for multiple input parameters lacks an effective calling mechanism and treats all variant parameter equally, resulting in low efficiency. This paper presents a fuzzy testing method based on Bayesian algorithm for scheduling parameters. Firstly, it combines the characteristics of input parameters to match the appropriate variation strategy. Secondly, it utlizes the Bayesian algorithm to automatically generate weight values for the objective function index during sufficient testing. It evaluates test cases with multi-parameter inputs according to these weights and selects parameters with high utilization value for multiple tests. Finally, in order to evaluate the effectiveness of the method of this paper, an application programming interface of Amercian fuzzy lop (AFL-API) is developed based on the typical fuzzy testing tool AFL++, and compared with AFL++ as a fuzzy testing tool. The experimental results demonstate that when using the AFL-API for program testing, there was 47% increase in coverage rate, 52% increase in the number of discovered crashes, and more program vulnerabilities were identified during actual testing. © The Institution of Engineering & Technology 2024.

关键词： Software testing

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Enhanced PIR Scheme Combining SimplePIR and Spiral: Achieving Higher Throughput Without Client Hints 6th

Enhanced PIR Scheme Combining SimplePIR and Spiral: Achievi...

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6th International Conference on Machine Learning for Cyber security, ML4CS 2024

作者： Xu, Haoyao Li, Yitong Tian, Haibo School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9789819645657

In recent years, Private information Retrieval (PIR) schemes capable of rapid response have gotten significant attention, with SimplePIR, for instance, achieving a throughput of approximately 10 GB/s in a single-threaded setting. However, these schemes often require users to download data related to the database, known as client hints, making them unsuitable for scenarios where the database undergoes frequent updates. This paper, building upon the Spiral scheme, incorporates the precomputation approach of SimplePIR to design a query method that leverages simulatable homomorphic ciphertexts for offline precomputation. This enhancement not only improves the efficiency of the Spiral scheme but also addresses the issue of SimplePIR’s necessity for downloading client hints. Experimental results demonstrate that, on a database with 220 entries of 256 bytes each, the proposed scheme achieves a throughput 1.87 times higher than that of the original Spiral scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ciphertext

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key Management and Authentication Scheme Forward Trusted Pervasive Container Computing 2

Key Management and Authentication Scheme Forward Trusted Per...

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2nd International Conference on Big Data and Privacy Computing, BDPC 2024

作者： Hei, XinHong Xue, YuZhan Wang, YiChuan Liu, YiFan Peng, Chen Sun, Xiang School of Computer Science and Engineering Xi'an University of Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9798350307597

Cloud computing technology is an internet-based computing model that provides high reliability and security measures, including data backup, secure access control, etc. As Docker containers play a increasingly important role in traditional cloud computing architectures, the communication needs between containers become prominent. However, existing container to container communication mainly relies on network protocols for file transfer, making it difficult to achieve the expected results in cloud cluster security. Therefore, this paper primarily focuses on container based keys and proposes a container identity trusted key management method based on Pervasive container computing architecture. The experimental results demonstrate that the container identity trusted key management method not only meets the security transmission characteristics required in the container cloud architecture, but also adopts a strategy combined with trusted hardware, providing security guarantees. In addition, the method proposed in this article that combines trusted hardware for signature verification can defend against multiple network attacks compared to traditional container architectures without using security policies. © 2024 IEEE.

关键词： Cloud computing architecture

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Chip-integrated quantum signature network over 200 km

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Light(Science & Applications) 2025年第4期14卷 1061-1069页

作者： Yongqiang Du Bing-Hong Li Xin Hua Xiao-Yu Cao Zhengeng Zhao Feng Xie Zhenrong Zhang Hua-Lei Yin Xi Xiao Kejin Wei Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China National Laboratory of Solid State Microstructures and School of Physics Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China National Information Optoelectronics Innovation Center(NOEIC) Wuhan 430074China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronicsand InformationGuangxi UniversityNanning 530004China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing 100872China Peng Cheng Laboratory Shenzhen 518055China

The development of quantum networks is paramount towards practical and secure *** digital signatures(QDS)offer an information-theoretically secure solution for ensuring data integrity,authenticity,and nonrepudiation,rapidly growing from proof-of-concept to robust ***,previous QDS systems relied on expensive and bulky optical equipment,limiting large-scale deployment and reconfigurable networking ***,we introduce and verify a chip-based QDS network,placing the complicated and expensive measurement devices in the central relay while each user needs only a low-cost *** demonstrate the network with a three-node setup using an integrated encoder chip and decoder *** developing a 1-decoy-state one-time universal hashing-QDS protocol,we achieve a maximum signature rate of 0.0414 times per second for a 1 Mbit messages over fiber distances up to 200 km,surpassing all current state-of-the-art QDS *** study validates the feasibility of chip-based QDS,paving the way for large-scale deployment and integration with existing fiber infrastructure.

关键词： secure communications quantum networks chip based network large scale deployment digital signatures qds offer quantum digital signatures fiber distances decoy state protocol

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Uncertainty-aware coarse-to-fine alignment for text-image person retrieval

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Visual Intelligence 2025年第1期3卷 1-14页

作者： Deng, Yifei Chen, Zhengyu Li, Chenglong Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei 230601 China

Text-to-image person retrieval, a fine-grained cross-modal retrieval problem, aims to search for person images from an image library that match a given textual caption. Existing text-to-image person retrieval methods usually use fixed-point embedding to express the semantics of the two modalities and perform multi-granularity alignment between modalities in the embedding space. However, owing to the inherent mutual one-to-many correspondence between images and texts, it is often difficult for fixed-point embedding methods to adequately capture this relationship, leading to erroneous retrieval results. To address this problem, we propose a novel uncertainty-aware coarse-to-fine alignment method, which first maps fixed-point embedding to probability distributions and then aligns two modalities in terms of distributions and sampling points at a coarse-to-fine granularity, for accurate text-to-image person retrieval. Specifically, we first introduce two contrastive learning tasks of distribution contrast learning and point contrast learning, to achieve coarse-grained inter-modal alignment with uncertainty-aware. The distribution contrast learning task ensures that distributions with the same identity are as similar as possible across modalities through distribution-based contrastive learning. The point contrast learning task performs the contrastive learning of inter-modal and intra-modal sampling points, which not only models rich and diverse cross-modal associations, but also optimizes the learning of distributions. For the fine-grained association requirements of text-to-image person retrieval, we design the task of uncertainty-aware attribute masking language reconstruction, which achieves fine-grained alignment by randomly masking attribute words in the text and reconstructing them via inter-modal sample point interactions. Extensive experiments on two public datasets demonstrate the superior performance of our method. © The Author(s) 2025.

关键词： Coarse-to-fine alignment Contrastive learning Cross-modal retrieval Probalility distribution Uncertainty-aware

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Enhanced Public-key Searchable Encryption Scheme for Cloud-Based EHR Systems

Enhanced Public-Key Searchable Encryption Scheme for Cloud-B...

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2023 IEEE International Conference on E-Health networking, Application and Services, Healthcom 2023

作者： Kong, Xiangqian Chen, Lanxiang City University of Macau Macau999078 China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (纸本)9798350302301

Due to the swift development of cloud computing technology, an increasing number of healthcare organizations tend to store electronic health records (EHR) on cloud storage servers. To ensure patient privacy, sensitive information within EHR data must undergo encryption prior to being uploaded to the cloud server. Nonetheless, this encryption process alters the data's original structural characteristics, presenting a significant challenge in achieving effective and flexible EHR utilization, such as plaintext keyword search. Considering the privacy and practicality of EHR. This paper introduces a new cloud-based EHR system that integrates public-key encryption with the keyword search (PEKS) scheme. This system enables authorized users to conduct searches for specific keywords on encrypted EHR data, all while maintaining the highest level of data security. Compared to existing PEKS-based EHR system solutions, our approach is not limited by a secure channel, reducing communication overhead while taking full advantage of the large storage space and high computational performance of the cloud server to realize flexible and effective search capabilities. Furthermore, our solution effectively prevents online keyword guessing attacks from outside adversaries. © 2023 IEEE.

关键词： Cloud storage

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BLAC: A Blockchain-Based Lightweight Access Control Scheme in Vehicular Social networks 25th

BLAC: A Blockchain-Based Lightweight Access Control Scheme i...

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25th International Conference on information and Communications security, ICICS 2023

作者： Zuo, Yuting Xu, Li Zhang, Yuexin Kang, Zhaozhe Zhao, Chenbin Fujian Normal University Fuzhou China Fujian provincial Key Laboratory of Network Security and Cryptology Fuzhou China Wuhan University Wuhan China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan China

ISBN: (纸本)9789819973552

Vehicular Social networks (VSNs) rely on data shared by users to provide convenient services. Data is outsourced to the cloud server and the distributed roadside unit in VSNs. However, roadside unit has limited resources, so that data sharing process is inefficient and is vulnerable to security threats, such as illegal access, tampering attack and collusion attack. In this article, to overcome the shortcomings of security, we define a chain tolerance semi-trusted model to describe the credibility of distributed group based on the anti tampering feature of blockchain. We further propose a Blockchain-based Lightweight Access Control scheme in VSNs that resist tampering and collusion attacks, called BLAC. To overcome the shortcomings of efficiency, we design a ciphertext piece storage algorithm and a recovery one to achieve lightweight storage cost. In the decryption operation, we separate a pre-decryption algorithm based on outsourcing to achieve lightweight decryption computation cost on the user side. Finally, we present the formal security analyses and the simulation experiments for BLAC, and compare the results of experiments with existing relevant schemes. The security analyses show that our scheme is secure, and the results of experiments show that our scheme is lightweight and practical. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Access control

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Enhancing Stance Detection on Social Media via Core Views Discovery 27

Enhancing Stance Detection on Social Media via Core Views Di...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Yan, Yu Shen, Yinghan Liu, Teli Jiang, Xuhui Yin, Dechun School of Information and Network Security People Public Security University of China China Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781643685489

Stance detection aims to identify the expressed attitude towards a target from the text, which is significant for learning public cognition from social media. The short and implicit nature of social media users' expressions potentially results in the stance understanding bias of the model. To address this problem, introducing external background information is helpful to mitigate these biases and enhance explainability. The core view, reflecting the motivations and reasons behind an individual's stance toward the target, can be summarized and extracted from collective tweets, which can serve as a reference for stance detection. In this study, we propose the Stance Detection via Core View Discovery (SD-CVM), where the core views are used for background information modeling. Specifically, we construct a joint classifier combining the semantic understanding of tweets and their relevant core views from the public. We utilize the Large Language Model (LLM) to extract core views with stances from tweets and use these core views as background references for tweets. To further optimize the tweet understanding, we develop the contrastive and rebalancing mechanism by incorporating stance supervision signals for training. Experiments on two representative datasets demonstrate the excellent performance of our method. © 2024 The Authors.

关键词： Tweets

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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