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arXiv 2023年

作者： Wang, Zhaorui Liu, Ya-Feng Wang, Ziyue Liu, Liang Pan, Haoyuan Cui, Shuguang The Future Network of Intelligence Institute The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices K. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of K is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for K and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of K are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of K and the design of the ADC quantizer;2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol. © 2023, CC BY.

关键词： Stochastic systems

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A riemannian exponential augmented lagrangian method for computing the projection robust Wasserstein distance 23

A riemannian exponential augmented lagrangian method for com...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Bo Jiang Ya-Feng Liu Ministry of Education Key Laboratory of NSLSCS School of Mathematical Sciences Nanjing Normal University Nanjing China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

Projection robust Wasserstein (PRW) distance is recently proposed to efficiently mitigate the curse of dimensionality in the classical Wasserstein distance. In this paper, by equivalently reformulating the computation of the PRW distance as an optimization problem over the Cartesian product of the Stiefel manifold and the Euclidean space with additional nonlinear inequality constraints, we propose a Riemannian exponential augmented Lagrangian method (REALM) for solving this problem. Compared with the existing Riemannian exponential penalty-based approaches, REALM can potentially avoid too small penalty parameters and exhibit more stable numerical performance. To solve the subproblems in REALM efficiently, we design an inexact Riemannian Barzilai-Borwein method with Sinkhorn iteration (iRBBS), which selects the stepsizes adaptively rather than tuning the step-sizes in efforts as done in the existing methods. We show that iRBBS can return an ε-stationary point of the original PRW distance problem within O(ε-3) iterations, which matches the best known iteration complexity result. Extensive numerical results demonstrate that our proposed methods outperform the state-of-the-art solvers for computing the PRW distance.

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Metrically regular mappings and its application to convergence analysis of a confined Newton-type method for nonsmooth generalized equations

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science China Mathematics 2020年第1期63卷 39-60页

作者： Mohammed Harunor Rashid Ya-xiang Yuan Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China Department of Mathematics Faculty of ScienceUniversity of Rajshahi 6205Bangladesh

Notion of metrically regular property and certain types of point-based approximations are used for solving the nonsmooth generalized equation f(x)+F(x)?0,where X and Y are Banach spaces,and U is an open subset of X,f:U→Y is a nonsmooth function and F:X■Y is a set-valued mapping with closed *** introduce a confined Newton-type method for solving the above nonsmooth generalized equation and analyze the semilocal and local convergence of this ***,under the point-based approximation of f on U and metrically regular property of f+F,we present quadratic rate of convergence of this ***,superlinear rate of convergence of this method is provided under the conditions that f admits p-point-based approximation on U and f+F is metrically *** example of nonsmooth functions that have p-point-based approximation is ***,a numerical experiment is given which illustrates the theoretical result.

关键词： set-valued mappings generalized equations metrically regular mapping semilocal convergence point-based approximation

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Detection and Cancellation of Multiplicative FDI Attack on Bilateral Encrypted Control System in Variable Periodic Motion

Detection and Cancellation of Multiplicative FDI Attack on B...

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IEEE/SICE International Symposium on System Integration

作者： Katsumasa Kosha Tetsuro Miyazaki Kaoru Teranishi Kiminao Kogiso Kenji Kawashima Department of Information Physics and Computing The University of Tokyo Tokyo Japan Oden Institute for Computational Engineering and Sciences The University of Texas at Austin Austin Texas USA Japan Society for the Promotion of Science Tokyo Japan Department of Mechanical and Intelligent Systems Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Teleoperation of remote assist robots has recently advanced owing to the development of communication technology. However, anonymous malicious attacks may intercept or falsify the network control system. Therefore, it is necessary to improve the security against cyber-attacks. As a countermeasure, an encrypted control method has been applied to prevent intercepting and detect the falsification of control parameters, as well as control signals by performing control operations with encrypted signals and parameters. Furthermore, we have proposed the algorithm to detect and restore the attack exploiting a vulnerability of encryption, which falsifies the plaintext by multiplying a constant factor. However, the algorithm is only effective for periodic motions with a fixed operating frequency and amplitude. If the frequency or amplitude of the motion is smaller than those of the base motion, the algorithm cannot detect the attack. To solve the problem, we propose an improved algorithm to detect and restore the attack in periodic motion with variable operating frequency and amplitude. In the proposed method, the operating frequency and amplitude are obtained through the position frequency analysis. They contribute to update the base energy, which is the average energy of corresponding periodic motion. We verified the proposed method for the bilateral control system using ElGamal encryption and experimentally confirmed its effectiveness against the FDI attack in various periodic motion.

关键词： Networked control systems System integration Cyber-physical systems Approximation algorithms Polynomials Encryption Usability Robots Frequency control Cyberattack

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Enhancing Lung Disease Identification with Multimodal Data Fusion and Deep Learning CNN Approach

Enhancing Lung Disease Identification with Multimodal Data F...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： T. Grace Shalini G. Susan Shiny R. Saranya P Suresh Babu R Kavitha Athiraja Atheeswaran Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of EEE Velammal college of engineering and technology Madurai India Department Of Computer Science And Engineering Bannari Amman Institute Of Technology Sathyamangalam Erode India Department of information technology Sethu Institute of Technology Kariapatti India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of CSE (AIML) Malla Reddy College of Engineering Hyderabad India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Lung diseases, encompassing a wide spectrum from chronic conditions to acute infections, present a significant burden on global healthcare systems, necessitating precise and timely diagnostic methodologies. In this study, we propose a pioneering approach leveraging multimodal data fusion and deep learning Convolutional Neural Network (CNN) algorithms for enhanced identification of lung diseases. Our methodology integrates diverse data sources including medical images, clinical reports, and patient demographics to capture a comprehensive representation of lung pathology. We employ a state-of-the-art CNN architecture for medical image analysis, trained on a large-scale dataset encompassing various lung diseases and healthy lung tissue. Through extensive experimentation and validation, the approach achieved remarkable performance, with an accuracy of 94%, sensitivity of 92%, specificity of 93%, and an $F 1$-score of 93% in accurately identifying and classifying lung abnormalities. Furthermore, we demonstrate the clinical utility of our method in providing valuable insights to healthcare professionals, aiding in timely diagnosis, prognosis, and treatment planning. Our findings highlight the potential of multimodal data fusion coupled with deep learning CNN algorithms in advancing the field of lung disease identification, offering a promising avenue for improving healthcare practices and patient outcomes in respiratory medicine.

关键词： Deep learning Pathology Image analysis Pulmonary diseases Soft sensors Lung Data integration Medical services Convolutional neural networks Medical diagnostic imaging

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Enhancing Medical Image Diagnosis with Convolutional Neural Networks

Enhancing Medical Image Diagnosis with Convolutional Neural ...

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International Conference on Advances in Information Technology (ICAIT)

作者： K. Mahadevan S. Pushparani K. SashiRekha V.M. Sivagami R. Usharani Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Chennai Tamil Nadu India Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

This paper is a comparative analysis of medical image diagnosis algorithms with Convolutional Neural Networks (CNNs) and other methods; such as Support Vector Machine (SVM), Random Forest, and k-nearest Neighbors (k-NN) The study performs evaluations on datasets with multi-modalities like medical images and diagnostic and various imaging modalities. Results shows that CNNs have the upper hand over traditional, static algorithms across several metrics that assesses the performance of the system such as accuracy, precision, recall, and F1. In particular, CNN precision was 90.5% while the recall was 93.7%, and the accuracy was 92.1% as well as F1-score 92.0%. In addition, the high area under the Receiver Operating Characteristic curve (AUC-ROC) of over 0.95 for the CNN also signed for its excellent discriminatory power. These outcomes only exemplify CNN usefulness in the diagnostic area because it is an exact and accurate method for analyzing medical images. Although the nets CNNs are deployed in clinical cases may help address concerns regarding interpretability, compliance with regulations, and ethical opinions, such issues still exist and require further exploration. Overall, this research takes the first steps in directing algorithmic approach towards medical diagnosis and show the feasibility in deployment of CNNs as inseparable pieces of equipment in modern healthcare.

关键词： Support vector machines Ethics Accuracy Recurrent neural networks Machine learning algorithms Nearest neighbor methods Convolutional neural networks

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Securing Neural Network in the Cloud: A Systematic Approach of Cloud Security Based on ANN-SVM Model

Securing Neural Network in the Cloud: A Systematic Approach ...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Ar Arunarani V. Vijayagopal L. Kartheesan Vijay Kumar Dwivedi Rajesh Kumar A Neerav Nishant Department of Computational Intelligence School of Computing College of Engineering SRM Institute of Science and Technology Chennai Tamilnadu India Faculty of Computer Applications Dr. M.G.R. Educational and Research Institute Chennai Tamilnadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Mathematics Vishwavidyalaya Engineering College Surguja Chhattisgarh India Department of Computer Science and Engineering N.S.N. College of Engineering and Technology Karur Tamilnadu India Department of Computer Science and Engineering School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Many services have made the transition to the cloud because of the increased reliability and efficiency of cloud computing. Due to their capacity to ease service access while also securing communications in the public network, the three-factor Mutual Authentication and Key Agreement (MAKA) protocols are seeing increased use in multi-server designs. A lack of formal security proof or high communication and computation costs makes the limited three-factor MAKA protocols that are currently available susceptible to a wide range of attacks. The proposed approach consists of three phases, which are preprocessing, feature selection, and model training. Text scheduling and normalization make up preprocessing. In feature selection, the PSO is an optimization method that uses a population-based approach to find optimal solutions. An ANN-SVM was employed throughout the model's training phase. With an average accuracy of 93.30%, the proposed technique surpasses ANN and SVM.

关键词： Training Support vector machines Protocols Accuracy Systematics Cloud computing security Neural networks Feature extraction Vectors Reliability

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Self-Supervised Learning with Variational Autoencoders for Anomaly Detection in Autonomous Drone Fleets

Self-Supervised Learning with Variational Autoencoders for A...

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Smart Technologies, Communication and Robotics (STCR)

作者： Kirubhakaran M M.Sundarrajan Gowtham T D. Agila Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Surya Engineering College Erode Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Master of Computer Application Excel Engineering College Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Agni College of Technology Chennai Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

However, Unmanned Aerial Vehicles (UAVs) are increasingly being employed for different applications such as surveillance, logistics, disaster response, and smart city management, and thereby, the requirement for robust detection of anomalies and path optimization techniques has arisen. Existing traditional UAV monitoring systems typically depend on anomaly detection based on a rule or an unsupervised clustering method, i.e., DBSCAN and One-Class SVM, balancing high false positives and slow response time, correction, and suboptimal trajectory. To tackle these issues, we develop an AI-powered UAV anomaly detection and adaptive navigation framework where Isolation forests and MDPs are used to predict UAV trajectory, and DQNs are used for adaptive navigation. Experimental results in a simulated smart city arena with a multi-UAV fleet show that our proposed framework achieves 94% accuracy, 92% precision, and 91% recall, which are much superior to the existing approaches, such as One Class SVM (87% accuracy) and DBSCAN (80% accuracy). Additionally, the system compensates for false positives by 30%, improves energy efficiency by 15%, and reduces UAV collision risks by 20%, with high compatibility with real-time UAV fleet management. In the future, the scalability and the adaptability of the UAV-based smart city operation will be furthered through federated learning-based decentralised UAV coordination, reinforcement learning for autonomous swarm intelligence, and hybrid optimisation techniques for improving energy efficiency.

关键词： Support vector machines Accuracy Smart cities Navigation Scalability Autonomous aerial vehicles Energy efficiency Trajectory Anomaly detection Optimization

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A new framework of high-order unfitted finite element methods using ALE maps for moving-domain problems

arXiv

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arXiv 2024年

作者： Lu, Wenhao Ma, Chuwen Zheng, Weiying LSEC NCMIS Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Science University of Chinese Academy of Sciences Beijing100049 China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China

As a sequel to our previous work [C. Ma, Q. Zhang and W. Zheng, SIAM J. Numer. Anal., 60 (2022)], [C. Ma and W. Zheng, J. Comput. Phys. 469 (2022)], this paper presents a generic framework of arbitrary Lagrangian-Eulerian unfitted finite element (ALE-UFE) methods for partial differential equations (PDEs) on time-varying domains. The ALE-UFE method has a great potential in developing high-order unfitted finite element methods. The usefulness of the method is demonstrated by a variety of moving-domain problems, including a linear problem with explicit velocity of the boundary (or interface), a PDE-domain coupled problem, and a problem whose domain has a topological change. Numerical experiments show that optimal convergence is achieved by both third- and fourth-order methods on domains with smooth boundaries, but is deteriorated to the second order when the domain has topological changes. Copyright © 2024, The Authors. All rights reserved.

关键词： Finite element method

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Optimizing Routing Paths in Mobile Wireless Sensor Networks: A Sub-Flow Adaptive Multipath Approach for Energy Efficiency and Delay Sensitivity

Optimizing Routing Paths in Mobile Wireless Sensor Networks:...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： R. Vinston Raja Shavej Ali Siddiqui Avilasha BG L. Mohana Kannan Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Deparment of Applied Science and Humanities Faculty of Engineering and Technology Khwaja Moinuddin Chishti Language University Lucknow India Deparment of Mechanical Engineering Dayanandasagar College of Engineering Bangalore India Department of Biomedical Engineering Erode Sengunthar Engineering College Perundurai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Wireless Sensor Networks (WSNs) have become crucial in various domains such as social applications, healthcare, and the military. However, their diversity makes them susceptible to a wide range of attacks, leading to challenges like poor throughput, high network latency, and excessive energy consumption. This study proposes a novel approach that combines residual Riemannian neural networks with the Sub-Flow Adaptive Multipath Routing algorithm for enhanced routing and attack detection in WSNs. By leveraging node position data, the Sub-Flow Adaptive Multipath Routing algorithm optimizes energy consumption and extends network lifespan. Simultaneously, residual Riemannian neural networks analyze residual patterns in network traffic data to accurately detect rogue nodes, thereby enhancing security. Blockchain technology is then employed to securely store classified data, ensuring data integrity and security. This integrated approach significantly improves network performance, maintains robust security against potential threats, and enhances the reliability of WSNs in demanding environments.

关键词： Wireless sensor networks Energy consumption Adaptive systems Neural networks Routing Throughput Delays Blockchains Security Reliability

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