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2nd international conference on Frontiers of Intelligent Manufacturing and automation, CFIMA 2024

作者： Lv, Yongfeng Zhao, Jun Huang, Yingbo Taiyuan University of Technology Shanxi Province Taiyuan China Shandong University of Science and Technology Shandong Qingdao China Kunming University of Science and Technology Yunnan Kunmin China

ISBN: (纸本)9798400710681

Adaptive dynamic programming (ADP) is widely used for the optimal control design of both the continuous-time and discrete-time nonlinear system, where the accurate mathematical dynamics are necessary and complicated. This paper proposes a data-based adaptive neural dynamic programming (ANDP) to study the optimum policies of the unknown continuous-time nonlinear systems, where dynamic knowledge in the HJB equation is avoided. The data-based ANDP is designed based on the heuristic dynamic programming (HDP) and integral reinforcement signal. First, the neural network is used to approximate the multi-input model, which can be used to design an integral reinforcement signal. Then, two critic and one action networks are constructed based on the integral reinforcement signal to update ith optimum policy of the multi-input model such that nonzero-sum Nash equilibrium can be reached. All the neural network weights are learned with the back-propagation algorithm. Moreover, the weight convergence and system stability are analyzed. Finally, simulation studies are presented to verify that the proposed ANDP scheme can provide an effective optimal control design method for the multi-input continuous-time nonlinear system. © 2024 Copyright held by the owner/author(s).

关键词： Continuous time systems

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Deep learning based multiple regression analysis approach for designing medical image recognition system 2nd

Deep learning based multiple regression analysis approach fo...

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2nd international conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Singh, Shweta Jagadish, A.N. Vyas, Bhuman Wasim Bhatt, Mohammed Sameen, Aws Zuhair Degadwala, Sheshang Electronics and Communication Department IES College of Technology Bhopal India Department of Computer Science Bapuji Institute of Engineering and Technology Karnataka Davanagere India Credit Acceptance Canton United States Model Institute of Engineering and Technology J&K Jammu India College of Medical Techniques Al-Farahidi University Baghdad Iraq Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9781032738659

Machine learning models are optimization models that make it possible to gather data, evaluate it, and provide the experts and management with the reports they need to make the best decisions. This study uses quantitative research, as it seeks to gather information and analyse it with statistical models such as regression analysis and analysis of variance. Researchers want to use statistical techniques such as descriptive analysis, regression analysis, and ANOVA (analysis of variance) to analyse the hypothesis. The purposive sample technique is used by the authors to choose respondents who work in the healthcare sector. A non-probability sampling technique called purpose sampling is used. The R squared value is 0.744, or 74.4 percent, according to the analysis. According to the investigation, there is a strong correlation between improved picture quality and ML-based techniques in digital image recognition. © 2025 The Author(s).

关键词： Regression analysis

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When Crowdsensing Meets Smart Cities: A Comprehensive Survey and New Perspectives

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 1101-1151页

作者： Wang, Zhenning Cao, Yue Jiang, Kai Zhou, Huan Kang, Jiawen Zhuang, Yuan Tian, Daxin Leung, Victor C. M. Wuhan University School of Cyber Science and Engineering Wuhan430072 China Northwestern Polytechnical University College of Computer Xi’an710129 China Guangdong University of Technology School of Automation Guangzhou510006 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China Beihang University School of Transportation Science and Engineering Beijing100191 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518061 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Crowdsensing has received widespread attention in recent years. It is extensively employed in smart cities and intelligent transportation systems. This paper comprehensively surveys the latest research advancements in crowdsensing for smart cities from a novel perspective. Specifically, this paper is categorized according to sensing entities in smart cities, including human-oriented sensing, vehicle-oriented sensing, and infrastructure-oriented sensing. Meanwhile, the development of Unmanned Aerial Vehicle (UAV)-assisted sensing in recent years is also summarized, accompanied by a timeline of related research. To facilitate easy comprehension, we have positioned the reading flow into the corresponding architectures, resolved problems, existing technical solutions, and specific application scenarios for different sensing entities. In particular, the problems to be solved are further analyzed from four technical perspectives, namely mathematics and operational research, artificial intelligence and machine learning, incentive mechanisms, security and privacy protection. Based on the proposed taxonomy, recent studies are thoroughly investigated to illustrate the current state of research in crowdsensing. Furthermore, this paper highlights the emerging applications of human-oriented and vehicle-oriented sensing in smart cities, as well as the frameworks, platforms, simulators, and datasets involved in crowdsensing. Finally, this paper discusses research directions related to crowdsensing in smart cities, such as digital twins, metaverses, and artificial intelligence-generated content. The primary goal of this survey is to review and synthesize prior research, identify potential avenues for future research, and explore opportunities for collaboration with other relevant research domains. © 1998-2012 IEEE.

关键词： Smart city

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Password-Based Authenticated Key Exchange for Cloud Storage from RLWE 24

Password-Based Authenticated Key Exchange for Cloud Storage ...

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2nd international conference on Electronics, computers and Communication technology, CECCT 2024

作者： Xie, Gang Xu, Chunxiang Jiang, Changsong Han, Yu Dong, Xinfeng School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China University of Electronic Science and Technology of China Huzhou China School of Computer Science and Engineering and Technology of China University of Electronic Science and Technology of China Sichuan Chengdu China China and National Key Laboratory of Security Communication Sichuan Chengdu China

ISBN: (纸本)9798400710193

Cloud storage enables users to store, retrieve, and share large amounts of data in a flexible and convenient way. To guarantee only authorized users can access the data, authenticated key exchange is a feasible solution, which provides both user authentication and session key negotiation. In authenticated key exchange, passwords are the most widely used authenticatin factor due to their easy-operation. However, most password-authenticated key exchange rely on traditional cryptographic assumptions, such as the discrete logarithm problem or integer factorization, which are vulnerable to quantum attacks. In this paper, we propose a novel password-authenticated key exchange protocol dubbed PAKER based on Ring Learning With Errors problem(RLWE) for cloud storage, which is a Diffie-Hellman-like protocol and achieves post-quantum security. In PAKER, all computations are performed in a polynomial ring, where an approximate verification key is obtained through polynomial multiplication, followed by the application of a reconciliation function and an auxiliary modular function, a user and the server can negotiate a session key. We provide the formal security proof of PAKER. The comprehensive performance evaluation demonstrates that PAKER is efficient in accordance with computation and communication costs. © 2024 Copyright held by the owner/author(s).

关键词： Authentication Protocol

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Online Network Intrusion Detection Based on Self-Encoder Integration 24

Online Network Intrusion Detection Based on Self-Encoder Int...

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3rd international conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Sun, Dongxu Li, Peng Pei, Lina Wei, Chuhang Co. Ltd. Hebei Shijiazhuang China School of School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China

ISBN: (纸本)9798400710834

With the development of information technology, network security problems have intensified, and the performance enhancement of network intrusion detection systems has attracted much attention In this paper, we propose an online system based on self-encoder integration that utilizes deep learning to improve detection accuracy and robustness Neural networks are popular for their ability to learn complex patterns, but training resources are in high demand and existing supervised schemes require expert labeling and regular updates. Therefore, this paper introduces Kitsune, which uses unsupervised, efficient online self-encoder integration to distinguish between normal and abnormal traffic and is optimized to improve detection efficiency In this paper, we experimentally validate that the optimized Kitsune achieves improvements in accuracy, recall, false-positive rate, false-negative rate. © 2024 Copyright held by the owner/author(s).

关键词： Network intrusion

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NETrack: A Lightweight Attention-Based Network for Real-Time Pose Tracking of Nasal Endoscope Based on Endoscopic Image 21st

NETrack: A Lightweight Attention-Based Network for Real-Tim...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Shao, Liangjing Chen, Benshuang Chen, Xinrong Academy for Engineering and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory for Medical Imaging Computing and Computer Assisted Intervention Fudan University Shanghai200032 China

ISBN: (纸本)9789819601271

Vision-based real-time pose tracking of endoscope is significant for navigation and automation of endoscopy. In this work, a deep learning-based framework is proposed for vision-based real-time pose tracking of nasal endoscope based on endoscopic images. Specifically, an attention-based module is introduced into the feature encoder to extract and integrate multi-dimensional and multi-scale information. Furthermore, a novel pose encoder is proposed to extract more complicated representation from the feature map for pose vector prediction. The proposed method is compared with state-of-the-art methods on the dataset, which consists of endoscopic videos and endoscope poses obtaining by an optical tracking system. The experimental results demonstrate that the proposed network achieves better performance than state-of-the-art works for real-time nasal endoscope pose estimation. The average tracking error of our method is 5.78 mm, while the relative translation error and rotation error are respectively 0.48 mm and 0.22 degree on average. In addition, the average inference speed of our network is 46 fps, which achieves the real-time requirement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Endoscopy

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Deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification 2nd

Deep learning-based cognitive digital twin system for wrist ...

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2nd international conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Chaudhary, Jitendra Kumar Parimalam, T. Alghayadh, Faisal Yousef Keshta, Ismail Soni, Mukesh Degadwala, Sheshang School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand India Department of Computer Science Nandha Arts and Science College Tamilnadu Erode India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9781032738659

In analyzing and recognizing wrist pulse signals, it isn’t easy to mine the nonlinear information of wrist pulse signals using analysis methods such as time and frequency. Traditional machine learning methods require the manual definition of features and cannot perform self-learning of features. A cognitive digital twin technique for pulse analysis and recognition based on threshold-less recursive graph and CNN is proposed. The wrist pulse signal is converted into a threshold-free recursive graph based on the nonlinear dynamics’ theory. The VGG-16 CNN automatically extracts the nonlinear features of the recursive graph, and a pulse condition classification model is established. Experimental finding several that the classification of the proposed method accuracy can reach 98.14%, as compared with the existing pulse classification methods. This study offers a novel concept and strategy for classifying pulse signals, and it has application to the objectification of pulse diagnosis. © 2025 The Author(s).

关键词： Graphic methods

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Brillouin Frequency Shift Extraction for BOTDA Based on Gramian Angular Fields 4

Brillouin Frequency Shift Extraction for BOTDA Based on Gram...

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4th international Computational Imaging conference, CITA 2024

作者： Zhu, Borong Zhang, Zhihao Qian, Yuhao Ma, Xiaole Wang, Liang Yu, Kuanglu Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510688834

To accurately extract Brillouin frequency shift of BOTDA with large sweeping step sizes, a novel structure of GAFCNN is proposed, combining time series coding with convolutional neural networks. The experimental data validation results demonstrate that the GAFCNN is capable of obtaining more accurate results under large sweep steps and low signal-to-noise ratios (SNRs) than other current methods. © 2025 SPIE.

关键词： Convolutional neural networks

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A Study on the Universality of a Variational Mode Parameter Optimization Method Based on an Improved Hippopotamus optimization Algorithm 24

A Study on the Universality of a Variational Mode Parameter ...

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2nd international conference on Frontiers of Intelligent Manufacturing and automation, CFIMA 2024

作者： Li, Jiahao Lv, Donghao Dong, Bo Xi, Keyang School of Automation and Electrical Engineering Inner Mongolia University of Science & Technology Baotou China

ISBN: (纸本)9798400710681

Parameter selection in Variational Mode Decomposition (VMD) often faces challenges where existing empirical methods, trial-and-error approaches, or single-index optimization fail to achieve global optimal performance or are limited to specific signal domains. To tackle this issue, this paper proposes an approach based on an Improved Hippopotamus Optimization Algorithm (IHO) for optimizing VMD parameters. To comprehensively address the characteristics of various signals, a quality factor composed of relevance, permutation entropy, and distinctiveness is employed as the objective function, optimized using the Hippopotamus Optimization Algorithm (IHO) algorithm for VMD parameter tuning. Results demonstrate that IHO significantly enhances VMD parameter optimization compared to the original Hippopotamus Algorithm (HO). Across different signal types, IHO-VMD exhibits superior performance in gear fault detection compared to Ensemble Empirical Mode Decomposition (EEMD), and in surface electromyography (sEMG) signal denoising, it outperforms Second-Generation Wavelet Soft Thresholding methods, highlighting its universality and effectiveness. © 2024 Copyright held by the owner/author(s).

关键词： Variational mode decomposition

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A Tamper-Proof Smart Contract Metamodel for Blockchain to Optimise Computational Latency 3rd

A Tamper-Proof Smart Contract Metamodel for Blockchain to Op...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Sengupta, Ratul Srivastava, Ruchika Mishra, Sushruta Abualigah, Laith Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al-Albayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Gelugor11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

ISBN: (纸本)9789819726707

In recent years, blockchain technology has garnered considerable interest on account of its peer-to-peer, distributed consensus, and anonymity functionalities. A smart contract functions as a verifiable, self-executing, and self-validating computing layer. This study seeks to identify the key concepts and associated future research goals pertaining to smart contracts based on the blockchain. Smart contracts enable the implementation of programmable assets, including money, and the automation of business logic processes that were previously executed manually. In response to this disclosure, a comprehensive examination of smart contracts employing blockchain technology is undertaken. In this article, we propose an innovative and practical metamodel for ensuring the integrity of protocols within smart contracts facilitated by blockchain technology. This article begins with an explanation of smart contracts’ operational models, functionalities, and applications. Analysis of the results reveals that the utilisation of the model alleviates the workload associated with numerous measurements. It was determined that the mean response latency, computation time, and overhead were each 1.47, 1.19, and 3.04 s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart contract

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