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Image Processing, Electronics and computers (IPEC), Asia-Pacific Conference on

作者： Zexiang Shao Shibin Zhang Sachin Kumar School of Cyberspace Security Chengdu University of Information Technology Chengdu China Sichuan Key Laboratory of Advanced Cryptography and System Security Chengdu China Department of Computer Science and Engineering Ajay Kumar Garg Engineering College India

The Quantum K-means clustering algorithm offers the advantage of quantum parallel computing, but suffers from issues related to cluster center initialization and sensitivity to noisy data due to its similarity with the K-means clustering algorithm. To address these challenges, we propose a quantum K-means model based on an optimization framework. Specifi-cally, we use a quantum genetic algorithm with pin optimization to adjust the fitness function for the Quantum K-means clustering algorithm. The optimally adapted quantum genetic algorithm reduces the sensitivity of cluster center initialization and improves the noise robustness of the Quantum K-means clus-tering algorithm using a noise reduction self-encoder. As a result, the proposed model achieves better clustering performance even in the presence of noise. Experimental results demonstrate that our proposed quantum K-means model has fewer clustering iterations, lower probability of falling into local optima, and improved noise robustness after optimizing the clustering cen-ters. The model's effectiveness in clustering is also demon-strated experimentally.

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Utilization of Computational Intelligence in the Development of a Health Monitoring system for Induction Machines

Utilization of Computational Intelligence in the Development...

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International Conference on Futuristic Technologies (INCOFT)

作者： J.N Singh Brijraj Singh Solanki Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Sciences School of Computer & System Sciences Jaipur National University Jaipur India

Technology advancement makes significant contributions to the healthcare system. By automating the patient monitoring task, smart health monitoring will reduce human effort while improving patient work flow management. Computational intelligence plays a vital part in intelligent failure diagnosis and prediction since it is one of the most potent data processing methods for resolving challenging nonlinear issues. The continued use of computational intelligence in machinery condition monitoring and problem diagnosis has, however, only been briefly covered in a few thorough papers. This paper describes the creation of an induction machine health monitoring system. It is briefly explained how artificial neural networks work and how they might be used for machine diagnostics. It is covered the general situation of designing intelligent systems based on neural networks. The effectiveness and accuracy of the proposed intelligent system for identifying machine health are demonstrated.

关键词： Vibrations Fault diagnosis Condition monitoring Induction motors Smart healthcare Stator windings Synchronous motors

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Implementation of Soft Computing-Based Optimization Strategies to Enhance Power system Stability

Implementation of Soft Computing-Based Optimization Strategi...

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International Conference on Futuristic Technologies (INCOFT)

作者： Gautam Kumar Mukesh Kataria Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Sciences School of Computer % System Sciences Jaipur National University Jaipur India

The improvement of large, interconnected power systems relies basically upon the stability of the power system. Because of the critical robotized needs of both home and modern requirements, the present power system keeps on developing dramatically. Soft computing approaches have accomplished very high rates, astounding adaptation to internal failure, and versatility. These variables make counterfeit brain network answers for probably the most troublesome or perplexing issues in the controls and power systems spaces appealing. For a solitary machine boundless transport bar (SMIB) system, a fuzzy based power stabilizer is to be built in the recommended paper. A reproduction software must be utilized to follow through with the whole responsibility. Within the sight of an unobtrusive system unsettling influence, recreations of the SMIB Energy system state space model with fuzzy logic regulator and regular regulator are to be run. When contrasted with traditional and fuzzy logic power system stabilizers, this clear plan approach and more modest rule base are simpler to carry out and possibly offer better execution.

关键词： Fuzzy logic Sensitivity Regulators Computational modeling Stability criteria Static VAr compensators Power system stability

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Joint Secure and Covert Communications for Active STAR-RIS Assisted ISAC systems

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IEEE Transactions on Wireless Communications 2025年

作者： Guo, Liang Jia, Jie Mu, Xidong Liu, Yuanwei Chen, Jian Wang, Xingwei Northeastern University School of Computer Science and Engineering China Northeastern University Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China BelfastBT3 9DT United Kingdom Department of Electrical and Electronic Engineering China

This paper investigates the design of jointly supporting physical layer security (PLS) and covert communications (CCs) in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (a-STAR-RIS) assisted integrated sensing and communication (ISAC) system. Due to the unified waveform design of ISAC signals, we consider a challenging scenario with two targets being suspicious attackers, where one warden target potentially detects the confidential transmission behavior of covert users and another eavesdropper target attempts to intercept the broadcasted confidential information of security users. We investigate the joint beamforming design at the base station (BS) and the a-STAR-RIS to achieve a high-quality sensing beampattern while meeting covertness and security communication requirements. (1) For the ideal scenario with perfect channel state information (CSI) and precise target locations, we propose an alternative optimization (AO) method to address the optimization problem involving highly coupled variables. Specifically, the optimal beamforming design at the BS is handled using the semi-definite relaxation (SDR) technique, while the beamforming design at the a-STAR-RIS is addressed through a penalty-based iterative algorithm. (2) A more practical case with uncertain target locations and imperfect CSI is considered to achieve a robust beamforming design, where the non-deterministic outage probability constraints are effectively transformed by employing the Bernstein-type inequality. Numerical results demonstrate the superiority of the a-STAR-RIS over the baseline cases and certify that the proposed algorithms can effectively balance the tradeoff among the sensing quality, covert and secure communication requirements. Besides, results also show that the proposed robust beamforming scheme can construct adequate sensing beampattern, even with imperfect CSI and uncertain target locations. © 2002-2012 IEEE.

关键词： Beamforming

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Deep Learning Techniques for Advanced Large-Scale Data Processing

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Mp Sunil Jyoti Shekhawat T. Vanaja Vazeer Ali Mohammed Shailesh J. Thaware Vaishali Singh Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer & Information System Lewis University Illinois USA Department of Computer Engineering Vishwakarma Institute of Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information.

关键词： Deep learning Accuracy Scalability Information processing Predictive models Data processing Prediction algorithms Hardware Natural language processing Optimization

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Optimization for Task Offloading and Downloading in UAV-Assisted MEC systems with Aerial to Aerial Collaboration

Optimization for Task Offloading and Downloading in UAV-Assi...

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International Conference on computer Supported Cooperative Work in Design

作者： Jiawei Jiang Xiang Tian Yubing Han Chunyu Hu Bin Feng Jiguo Yu 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 School of Information Science and Technology Taishan University Taian China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Owing to the easy deployment and mobile flexibility, Unmanned Aerial Vehicle (UAV) assisted Mobile Edge Computing (MEC) has been deemed as one potential technology for handling the computation-intensive tasks at terminal devices (TDs). In this work, a MEC architecture assisted by UAVs is designed which achieves efficient offloading, computing, and downloading for tasks from multiple TDs via aerial to aerial collaboration of two UAVs. In this architecture, the task offloading process contains two parts, i.e., the offloading from TDs to a mobile UAV which flies around TDs, and the offloading from the mobile UAV to a hovering UAV which hovers in the air. The computing tasks from TDs will be divided into three parts allocated to the TDs themselves, and both two UAVs. Upon completion of computation, the computation results are downloaded to the TDs. The optimization objective is to seek for an optimal task division strategy to attain the weighted total energy consumption minimization for all devices. Since the formulated optimization problem is not convex, we develop a two-step iteration algorithm which jointly optimizes computing frequency, task allocation volume, as well as UAV's trajectory based on the method of block coordinate descent. Simulation results confirm the effectiveness and performance advantages of the designed algorithm.

关键词： Energy consumption Simulation Collaboration computer architecture Autonomous aerial vehicles Minimization Resource management Jamming Trajectory optimization Radio spectrum management

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Towards high-throughput and low-latency billion-scale vector search via CPU/GPU collaborative filtering and re-ranking 25

Towards high-throughput and low-latency billion-scale vector...

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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Bing Tian Haikun Liu Yuhang Tang Shihai Xiao Zhuohui Duan Xiaofei Liao Hai Jin Xuecang Zhang Junhua Zhu Yu Zhang National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd Towards high-throughput and low-latency billion-scale vector search via CPU/GPU collaborative filtering and re-ranking

ISBN: (纸本)9781939133458

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern ANNS systems can address these issues simultaneously. In this paper, we present Fusion-ANNS, a high-throughput, low-latency, cost-efficient, and high-accuracy ANNS system for billion-scale datasets using SSDs and only one entry-level GPU. The key idea of Fusion-ANNS lies in CPU/GPU collaborative filtering and reranking mechanisms, which significantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically, we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We implement FusionANNS and compare it with the state-of-the-art SSD-based ANNS system-SPANN and GPU-accelerated in-memory ANNS system-RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN; 2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy.

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Exploring Low Rate Dos Attack Detection Methods: A Bibliometric Analysis and Roadmap for Further Research

Exploring Low Rate Dos Attack Detection Methods: A Bibliomet...

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International Conference on Data and Software engineering (ICODSE)

作者： Anteng Widodo Adian Fatchur Rochim Budi Warsito Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Department of Information System Faculty of Engineering Muria Kudus University Kudus Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future.

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Review of the Ionospheric Error Correction Techniques in Satellite Navigation

Review of the Ionospheric Error Correction Techniques in Sat...

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第十四届中国卫星导航年会

作者： Xihao Han Jianlei Yang Shuaiyong Zheng Mengzhi Gao Zhigang Huang Xiaoqin Jin Kun Li Peng Yang Zhonghao Zuo Department School of Integrated Circuit Science and Engineering Tianjin University of Technology The 54th Research Institute of China Electronics Technology Group Corporation State Key Laboratory of Satellite Navigation System and Equipment Technology School of Electronic and Information Engineering Beihang University College of Artificial Intelligence Nankai University School of Computer Science and Engineering Tianjin University of Technology

Ionospheric error seriously affects the positioning accuracy of the global navigation satellite ***,BDS,Galileo and GLONASS all adopt different ionospheric error correction *** ionospheric error correction methods in satellite navigation are introduced,and the principle and development of single frequency ionospheric error correction,dual-frequency ionospheric error correction and multi-frequency ionospheric error correction are summarized in this *** the single frequency correction,the grid ionospheric model,Klobuchar model,IRI constraint model and Ne Quick model are summarized,the BDGIM model further improves the ionospheric error correction accuracy on the base of the grid ionospheric model correction by ameliorating spherical harmonics,the Kriging interpolation method improves the interpolation accuracy of the grid ionospheric model correction;In the dualfrequency correction,the ionosphere-free model and the ionospheric error correction methods in the PPP-RTK technology are summarized;In the multifrequency correction,the optimization and improvement of ionospheric error correction technique with the high order correction and geomagnetic field model are ***,the ionospheric error correction techniques and their derivative are analyzed and encapsulated,the development trend and future hotspots of ionospheric error correction technology in satellite navigation are listed and analyzed.

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Neural Topic Modeling with Deep Mutual Information Estimation

arXiv

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arXiv 2022年

作者： Xu, Kang Lu, Xiaoqiu Li, Yuan-Fang Wu, Tongtong Qi, Guilin Ye, Ning Wang, Dong Zhou, Zheng School of Computer Science Nanjing University of Posts and Telecommunications China School of Computer Science and Engineering Southeast University China Faculty of Information Technology Monash University Australia Department of Earth System Science Tsinghua University Beijing100084 China

The emerging neural topic models make topic modeling more easily adaptable and extendable in unsupervised text mining. However, the existing neural topic models is difficult to retain representative information of the documents within the learnt topic representation. In this paper, we propose a neural topic model which incorporates deep mutual information estimation, i.e., Neural Topic Modeling with Deep Mutual Information Estimation(NTM-DMIE). NTM-DMIE is a neural network method for topic learning which maximizes the mutual information between the input documents and their latent topic representation. To learn robust topic representation, we incorporate the discriminator to discriminate negative examples and positive examples via adversarial learning. Moreover, we use both global and local mutual information to preserve the rich information of the input documents in the topic representation. We evaluate NTM-DMIE on several metrics, including accuracy of text clustering, with topic representation, topic uniqueness and topic coherence. Compared to the existing methods, the experimental results show that NTM-DMIE can outperform in all the metrics on the four datasets. © 2022, CC BY.

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