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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

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Source-free Domain Adaptation with Multiple Alignment for Efficient Image Retrieval

Source-free Domain Adaptation with Multiple Alignment for Ef...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ni, Jinghui Cui, Hui Zhao, Lihai Li, Fengling Han, Xiaohui Xu, Lijuan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China Quan Cheng Laboratory China Shandong Open University China University of Technology Sydney Australia

ISBN: (纸本)9798350368741

Domain adaptation techniques help models generalize to target domains by addressing domain discrepancies between the source and target domain data distributions. These techniques are particularly valuable for cross-domain hashing retrieval, as they reduce training costs while maintaining high retrieval efficiency. However, existing unsupervised domain adaptative hashing methods often require access to both source and target domain data, which may raise privacy concerns regarding source domain data. To address these concerns, restricting access to source domain data is crucial, but this restriction also makes the alignment process between domains more challenging. In this paper, we propose a Source-free Domain Adaptive Hashing with Multiple Alignment (SFDAH-MA) approach for image retrieval. SFDAH-MA integrates model structure alignment, class moment alignment, and semantic relationship alignment to maximize inter-domain knowledge transfer. This comprehensive alignment strategy not only enhances retrieval performance across domains but also minimizes reliance on source domain data, thereby supporting data privacy protection. Extensive experiments show that SFDAH-MA achieves state-of-the-art performance in source-free settings and achieves comparable results to existing unsupervised domain adaptive hashing methods under conventional settings. The source codes of our method are available at: https://***/Nikphyc/SFDAH-MA. © 2025 IEEE.

关键词： Deep hashing image retrieval source-free domain adaptation

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Exploring the Use of Mobile Ad-hoc Networks for Real-Time Automotive Applications 5th

Exploring the Use of Mobile Ad-hoc Networks for Real-Time Au...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Raghavendra, R. Negi, Bichitra Singh Manasa, C.M. Mishra, Awakash Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper investigates using cell networks (MANETs) for real-time automotive packages. The car industry increasingly seeks to benefit from the latest technology to improve its merchandise, and cellular ad hoc networks can revolutionize the enterprise. This paper outlines the use of MANETs within the automobile industry and uses simulations to explore the benefits and constraints of implementing MANETs inside the automotive space. Additionally, the paper explores the safety and privacy concerns of MANETs and how automobile applications can use the era comfortably and competently. The findings of this observation provide the perception of how MANETs may be applied for automobile programs and highlight the potential of cell ad-hoc networks as a crucial technology for the industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile ad hoc networks

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Model-Restrictive Adaptive Dynamic Programming for Mechanical-Electromagnetic Vibration Control of Distributed Rotor System in Corner Module Architecture Vehicle

Model-Restrictive Adaptive Dynamic Programming for Mechanica...

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international conference on Applied Nonlinear Dynamics, Vibration and Control, ICANDVC 2024

作者： Liu, Jincan Xie, Zhengchao Li, Wenfeng Wong, Pak Kin Zhao, Jing School of Mechanical and Automotive Engineering South China University of Technology Guangzhou510641 China Department of Electromechanical Engineering University of Macau Taipa China School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China Foshan Graduate School of Innovation Northeastern University Foshan528000 China

ISBN: (纸本)9789819633166

The distributed rotor systems are widely applied in vehicle and the electric vehicle equipped with the rotor systems is viewed as the corner modulus architecture-distributed rotor (CMA-DR) system. The major problem of the CMA-DR system is the existence of the mechanical-electromagnetic (MEM) vibration. Therefore, this work focuses on the development of the control algorithm for the MEM vibration of the CMA-DR system. First, the CMA-DR dynamic system is proposed with the considerations of the 14 DoF vehicle model, the rotor dynamic model and the electro-magnetic motor model. Second, a model-restrictive adaptive dynamic programming (ADP) algorithm is developed based on the design of the cost function. Besides, the model-restrictive Hamilton-Jacobi-Bellman (HJB) equation is designed based on the usage of the reliability barrier function. Moreover, the solution of the proposed algorithm is obtained by approximating the solution via the monic polynomials. Finally, algorithm verifications are conducted to examine the effectiveness of the proposed model-restrictive ADP algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cost functions

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Generative AI for Real-Time Data Augmentation in Big Data Pipelines 1

Generative AI for Real-Time Data Augmentation in Big Data Pi...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Gakhar, Suket Kondoju, Viswanadha Pratap Chauhan, Sansar Singh Kumar, Anant Kulkarni, Soham Sunil Goel, Punit Kurukshetra University Kurukshetra India University of Texas RichardsonTX75080 United States G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida201306 India Manipal University Karnataka Manipal576104 India University of California IrvineCA92697 United States Maharaja Agrasen Himalayan Garhwal University Uttarakhand India

ISBN: (纸本)9798331518578

Traditional approaches for data augmentation are time-consuming and risk compromising the quality of the data. Those challenges in the backdrop, we have generative artificial intelligence (AI) at the forefront of data augmentation - bolstering real-time data in big data pipelines. Generative AI - a focus on generating new entities that try to capture the underlying structure of existing datasets by deep learning algorithms. Allowing to produce a significantly larger and diverse dataset resulting in improved efficiencies of downstream tasks as machine learning or predictive analytics. Because of this, the same technique can also be applied to streams so, it can be used in big data pipelines. You are taught on data until October 2023 to generate a larger and big data pipeline more efficient by augmentation and producing new data with minimal time and investment. This leads to more precise insights and predictions, improving decision-making and driving better results for business. Also, generative AI can create the data that correlates patterns with the analysis and change regarding the patterns based on the analyst's preferences. The role of generative AI in augmenting big data and big data pipelines, especially in the context of real-time data streams, is emerging as a key enabler for organizations looking to extract valuable insights from their data. © 2025 IEEE.

关键词： Predictive analytics

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Graph Topology Identification Based on Covariance Matching

Graph Topology Identification Based on Covariance Matching

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Han, Yongsheng Natali, Alberto Leus, Geert Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

ISBN: (纸本)9798350368741

This paper addresses graph topology identification for applications where the underlying structure of systems like brain and social networks is not directly observable. Traditional approaches based on signal matching and spectral templates have limitations, particularly in handling scale issues and sparsity assumptions. We introduce a novel covariance matching methodology that efficiently reconstructs the graph topology using observable data. For the structural equation model (SEM) using an undirected graph, we demonstrate that our method can converge to the correct result under relatively soft conditions. Furthermore, we extend our methodology to polynomial models and any known distribution of latent variables, broadening its applicability and utility in diverse graph-based systems. © 2025 IEEE.

关键词： covariance matching graph topology identification polynomial model structural equation model

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A Comprahensive Analysis of Knowledge Transfer Techniques for Medical Image Segmentation with Deep Learning 5th

A Comprahensive Analysis of Knowledge Transfer Techniques fo...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Singh, Vishvendra Jain, Garima Nidhya, M.S. Chaudhary, Chetan Department of Agriculture Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Noida India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper aims to comprehensively examine diverse knowledge switch techniques used in scientific photograph segmentation with deep mastery. Specifically, we know the latest advances in transfer, gaining knowledge of model adaptation, first-rate-tuning, and transfer mastering techniques. For each technique, we discuss the motivations, techniques, and demanding situations concerned with their software for medical picture segmentation. Similarly, we discuss a variety of open issues and destiny studies guidelines. Furthermore, we overview present applications of these methods in clinical photograph segmentation. Sooner or later, we provide a complete contrast of these processes based on the evaluation metrics hired in the literature. This paper aims to shed light on the modern state of understanding switch techniques in medical picture segmentation and to provide a roadmap for future research on this unexpectedly evolving field.. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Investigating the Benefits of Multi-Task Transfer Learning for Medical Image Segmentation 5th

Investigating the Benefits of Multi-Task Transfer Learning f...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Chandani, Priyanka Jaison, Feon Pandeya, Megha Sharma, Neeraj Noida Institute of Engineering and Technology Uttar Pradesh Noida India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

Multi-challenge transfer getting to know (MTTL) is a technique utilized in machine studying to improve the performance of one or extra models employing leveraging understanding from associated tasks. This article examines the benefit of MTTLC for medical photo segmentation. They segmented clinical photos with a deep learning model to compare the two, using a multi-mission method rather than only one undertaking. The authors develop a framework to teach two models, one consistent with each task, and explore the usage of MTTLC to refine the second version. The results show that while the single project network can attain proper performance, the MTTLC technique can significantly enhance segmentation accuracy. The results suggest that MTTLC could be a beneficial tool in scientific imaging, and extra research is needed to explore its ability and benefits. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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IoT-Driven Agricultural Monitoring Architecture with Novel Light Machine Learning for Enhanced Precision Crop Health Analysis 3rd

IoT-Driven Agricultural Monitoring Architecture with Novel L...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Andi, Hari Krishnan Ramasamy, Lokeshkumar Nramban Kannan, Senthil Kumar Murugesan, Sasikumar Benarji, Bejjam S. N. Manogaran, Umamaheswari Sengan, Sudhakar Centre for Postgraduate Studies Asia Metropolitan University Johor81750 Malaysia School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Deemed to Be University Vel Tech Rangarajan Dr Sagunthala R&H12D Institute of Science and Technology Tamil Nadu Chennai600062 India C. Abdul Hakeem College of Engineering & Technology Tamil Nadu Melvisharam Ranipet632509 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

In the age of digital transformation, agriculture is harnessing the potential of advanced technologies to address global challenges related to food security and environmental sustainability. While Smart Agriculture and Precision Agriculture lay the foundation, there remains a pressing need for real-time, granular data processing to realize their full potential. Edge computing, coupled with Machine Intelligence (MI), promises a solution, yet existing models often lack the adaptability and real-time efficiency demanded by diverse agricultural scenarios. Addressing these challenges, this paper proposes the Precision Intensive Crop Health Observer (PICHO). PICHO is an innovative three-tier architecture comprising the Field Data Interface Unit (FDIU) for seamless sensor data collection, the Edge Analytics Processor (EAP) for real-time edge-based analytics, and the Cloud Coordination Hub (CCH) for data storage and in-depth analysis. Central to the EAP's efficacy is the lightweight learning model LENet, which is designed with dual pipelines for processing sensor and image data. Specifically crafted to be trained on resource-constrained edge devices, LENet ensures that PICHO remains adaptable, efficient, and poised to redefine the future of precision agriculture. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Fault-Tolerant Control for Semi-active Air Suspension via Adaptive Memory-Event-Triggered Mechanism

Fault-Tolerant Control for Semi-active Air Suspension via Ad...

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international conference on Applied Nonlinear Dynamics, Vibration and Control, ICANDVC 2024

作者： Li, Wenfeng Liang, Liqiong Xie, Zhengchao Zhao, Jing Wong, Pak Kin Department of Electromechanical Engineering University of Macau Macau Taipa999078 China Zhuhai UM Science and Technology Research Institute Zhuhai519085 China School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China School of Mechanical and Automotive Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9789819633166

This paper presents a fault-tolerant control method for the semi-active air suspension using adaptive memory-event triggered mechanism. Firstly, considering the parameter uncertainties and nonlinearities of the air spring and magnetorheological damper, the Takagi-Sugeno fuzzy method is employed to describe the nonlinear air suspension system. Secondly, an adaptive memory-event-triggered mechanism is proposed for the purpose of deciding whether to trigger sampled data packets by utilizing the recently released historical data. Meanwhile, the triggered threshold can be adaptively adjusted in response to the change of the air suspension system states. Moreover, considering the inevitable actuator failures in control systems, a robust fault-tolerant control method is introduced to maintain system stability and mitigate the effect on system performance under actuator failure condition. Simulations are conducted to assess the superiority of the proposed controller under multiple working pavements, despite the existence of actuator failures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Control nonlinearities

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Optimized Task Scheduling in Diverse Distributed Systems with a Hybrid Genetic Algorithm Approach 3rd

Optimized Task Scheduling in Diverse Distributed Systems wit...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Muthusamy, Ramesh Kumar Kadu, Swati Ayyasamy, Karthikeyan Pichuka Veera Venkata Satya, Srinivas Arumugam, Ramachandran Ammasai, Viswanathan Sengan, Sudhakar Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of AI&DS AISSMS IOIT Pune411001 India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai602105 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627012 India

ISBN: (纸本)9789819783540

In Heterogeneous Distributed Systems (HDS), efficient task scheduling (TS) across diverse resources remains a pressing challenge. This paper introduces a Hybrid Genetic Algorithm (HGA) as a solution, blending heuristic techniques with the evolutionary strengths of Genetic Algorithms (GAs) to optimize Task Scheduling (TS). Traditional algorithms often fall short in balancing computational speed with solution quality, especially in large-scale environments. This proposed HGA addresses this by leveraging domain-specific heuristics to provide a promising starting point for the GA's optimization process. The TS dilemma has been successfully defined by an all-encompassing logical system, which ensures the accuracy of the design of algorithms. In comparison tests, the HGA frequently comes out on top, demonstrating less makespan and a more excellent distribution of tasks among the resources available. Also, resources with more efficient processing capabilities will be provided priority in this recommended heuristic method, which focuses on tasks with higher computational demands. The findings of the present investigation demonstrate that the HGA may transform TS approaches by providing high-quality solutions while increasing performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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