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arXiv 2024年

作者： He, Zhuohang Ma, Junjie Yuan, Xiaojun The National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China The Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100864 China

Compressive Robust Principal Component Analysis (CRPCA) naturally arises in various applications as a means to recover a low-rank matrix low-rank matrix L and a sparse matrix S from compressive measurements. In this paper, we approach the problem from a Bayesian inference perspective. We establish a probabilistic model for the problem and develop an improved turbo message passing (ITMP) algorithm based on the sum-product rule and the appropriate approximations. Additionally, we establish a state evolution framework to characterize the asymptotic behavior of the ITMP algorithm in the large-system limit. By analyzing the established state evolution, we further propose sufficient conditions for the global convergence of our algorithm. Our numerical results validate the theoretical results, demonstrating that the proposed asymptotic framework accurately characterize the dynamical behavior of the ITMP algorithm, and the phase transition curve specified by the sufficient condition agrees well with numerical simulations. © 2024, CC BY.

关键词： Asymptotic analysis

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Marine Ecosystem Monitoring: Applying Remote Sensing and AI to Track and Predict Coral Reef Health

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 486-499页

作者： Veeranjaneyulu, Rayavarapu Govindarajan, Dinesh Subramanian, Chandramohan Devi, Deva Uma Banerjee, Sudipta Edpuganti, Sai Krishna Upadhyay, Shrikant Department of Computer Science & amp Engineering PACE Institute of Technology and Sciences Andhra Pradesh Ongole India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of ECE SCSVMV Deemed University Tamil Nadu Kanchipuram India Department of Computer Science and Engineering Gayatri Vidya Parishad College of Engineering (A) Andhra Pradhesh Visakhapatnam India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur Dist. India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

Coral reefs are essential ecosystems, supporting a diverse range of marine life and offering considerable ecological, economic, and cultural benefits. However, they face increasing threats from climate change, pollution, and other human activities. Thus, effective monitoring and management of coral reefs are crucial for their conservation and sustainability. In this study, we employed a hybrid model, HCNN-SVM, which combines convolutional neural networks (CNNs) for feature extraction and support vector machines (SVMs) for classification. We utilized a coral-reef dataset from Kaggle, containing images from CoralNet and the Moorea Coral Reef Long Term Ecological Research (MCR LTER). To enhance image quality and ensure accurate reflectance values, we applied radiometric, geometric, and water column corrections during preprocessing. Feature extraction involved the use of spectral indices such as the Coral Bleaching Index (CBI) and Normalized Difference Vegetation Index (NDVI) to detect healthy and stressed corals, along with texture analysis to differentiate substrates. The model was trained on this diverse dataset, which captures various environmental conditions and reef types, achieving an accuracy of 98.55%. Cross-validation methods were employed to evaluate model performance, ensuring robustness and generalizability. The HCNN-SVM model demonstrated high accuracy in identifying and mapping coral reef components, making it a powerful tool for monitoring coral reef health and supporting conservation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks (CNNs) Coral Bleaching Index (CBI) Coral reef monitoring Feature extraction HCNN-SVM Moorea Coral Reef Long Term Ecological Research (MCR LTER) Normalized Difference Vegetation Index (NDVI) Support vector machines (SVMs)

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Projective Exploration on Individual Stress Levels using Machine Learning 2

Projective Exploration on Individual Stress Levels using Mac...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Nagendra Prabhu, S. Singh, Amarjeet Bhavya, B. Sreeja, K. Bhargavi, P. Srm Institute of Science Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India New Horizon College of Engineering Department of Computer Science and Engineering Department Bangalore India

ISBN: (纸本)9781665400527

Recently, Stress Prediction in every individual based on their profile and behaviour is a challenging task in the current sector. Current system is a manual process where it is difficult to identify the stress in the college students or employees. There is no automation for any stress prediction. System uses machine learning algorithms or AI algorithms to find out the stress levels, these technologies are used for application development. The purpose of this project is to reduce stress in students and employees. Computer science has come a long way in the last few years. It is massive and multifaceted. It has been used in a variety of applications to meet the basic needs of human society. In the field of healthcare, machine learning has made significant progress. Stress is a deadly disease that kills a large number of people worldwide. How machine learning techniques can assist in reducing the risk of stress prediction, is examined in this study. © 2022 IEEE.

关键词： Personnel

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Isogeometric analysis for free vibration of bidirectional functionally graded plates in the fluid medium

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Defence Technology（防务技术） 2022年第8期18卷 1311-1329页

作者： Quoc-Hoa Pham Phu-Cuong Nguyen Van Ke Tran Trung Nguyen-Thoi Faculty of Civil Engineering Ho Chi Minh City Open UniversityHo Chi Minh CityViet Nam Faculty of Mechanical Engineering Le Quy Don Technical UniversityHanoiViet Nam Division of Computational Mathematics and Engineering Institute for Computational ScienceTon Duc Thang UniversityHo Chi Minh CityViet Nam Faculty of Civil Engineering Ton Duc Thang UniversityHo Chi Minh CityViet Nam

This paper for first time proposes an isogeometric analysis (IGA) for free vibration response of bi-directional functionally graded (BDFG) rectangular plates in the fluid medium. Material properties of the BDFG plate change in both the thickness and length directions via power-law distributions and Mori-Tanaka model. The governing equation of motion of BDFG plate in the fluid-plate system is formulated basing on Hamilton's principle and the refined quasi three-dimensional (3D) plate theory with improved function f(z). The fluid velocity potential is derived from the boundary conditions of the fluid-plate system and is used to determine the added mass. The discrete system of equations is derived from the Galerkin weak form and numerically analyzed by IGA. The accuracy and reliability of the proposed solutions are verified by comparing the obtained results with those published in the literature. Moreover, the effects of the various parameters such as the interaction boundary condition, geometric parameter, submerged depth of plate, fluid density, fluid level, and the material volume control coefficients on the free vibration behavior of BDFG plate in the fluid medium are investigated in detail. Some major findings regarding the numerical results are withdrawn in conclusions.

关键词： Contact plate-fluid Refined quasi 3D plate theory Mori-Tanaka model Isogeometric analysis Bidirectional functionally graded plate

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LEAST H2 NORM UPDATING QUADRATIC INTERPOLATION MODEL FUNCTION FOR DERIVATIVE-FREE TRUST-REGION ALGORITHMS

arXiv

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arXiv 2023年

作者： Xie, Pengcheng Yuan, Ya-Xiang Institute of Computational Math and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences China

Derivative-free optimization methods are numerical methods for optimization problems in which no derivative information is used. Such optimization problems are widely seen in many real applications. One particular class of derivative-free optimization algorithms is trust-region algorithms based on quadratic models given by underdetermined interpolation. Different techniques in updating the quadratic model from iteration to iteration will give different interpolation models. In this paper, we propose a new way to update the quadratic model by minimizing the H2 norm of the difference between neighbouring quadratic models. Motivation for applying the H2 norm is given, and theoretical properties of our new updating technique are also presented. Projection in the sense of H2 norm and interpolation error analysis of our model function are proposed. We obtain the coefficients of the quadratic model function by using the KKT conditions. Numerical results show advantages of our model, and the derivative-free algorithms based on our least H2 norm updating quadratic model functions can solve test problems with fewer function evaluations than algorithms based on least Frobenius norm *** Codes 90C56, 90C30, 65K05, 90C90 Copyright © 2023, The Authors. All rights reserved.

关键词： Interpolation

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Algorithms for the Prize-Collecting k-Steiner Tree Problem

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Tsinghua science and Technology 2022年第5期27卷 785-792页

作者： Lu Han Changjun Wang Dachuan Xu Dongmei Zhang School of Science Beijing University of Posts and TelecommunicationsBeijing 100876China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China Beijing Institute for Scientific and Engineering Computing Beijing University of TechnologyBeijing 100124China School of Computer Science and Technology Shandong Jianzhu UniversityJinan 250101China

In this paper,we study the prize-collecting k-Steiner tree(PCkST) *** are given a graph G=(V,E) and an integer *** graph is connected and undirected.A vertex r ∈ V called root and a subset R?V called terminals are also given.A feasible solution for the PCkST is a tree F rooted at r and connecting at least k vertices in *** a vertex from the tree incurs a penalty cost,and including an edge in the tree incurs an edge *** wish to find a feasible solution with minimum total *** total cost of a tree F is the sum of the edge costs of the edges in F and the penalty costs of the vertices not in *** present a simple approximation algorithm with the ratio of 5.9672 for the *** algorithm uses the approximation algorithms for the prize-collecting Steiner tree(PCST) problem and the k-Steiner tree(kST) problem as *** we propose a primal-dual based approximation algorithm and improve the approximation ratio to 5.

关键词： prize-collecting Steiner tree approximation algorithm

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An Improved Cuttlefish Optimization Algorithm Based Faster R-CNN for Brain Tumour Detection 4

An Improved Cuttlefish Optimization Algorithm Based Faster R...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Arunarani, A.R. Bhenia, Vedant Abirami, T. Aparna, N. Abul Ala Walid, Md. Sharada, K.A. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Kattankulathur Campus Department of Computational and Intelligence Tamilnadu Chennai India Ips Academy Institute of Engineering and Science Department of Computer Science and Engineering Madhya Pradesh Indore India Kongu Engineering College Information Technology Tamilnadu Erode India Dhaanish Ahmed Institute of Technology Department of Biomedical Engineering Tamilnadu Coimbatore India Department of Computer Science and Engineering Khulna Bangladesh Hkbk College of Engineering Vtu Department of Cse Karnataka Bengaluru India

ISBN: (纸本)9798350300888

Brain tumor is caused due to the anomalous development of tissues in the brain. Precise identification of the tumor in premature stage is essential to provide effective medication and curtail further impact of the disease in affected individuals. This present research includes four main stages for the diagnosis of brain tumor including segmentation and classification based on deep learning approaches. The preprocessing stage makes use of filters to reduce the noise impact in the brain imagery. The segmentation stage is responsible to segment the tumor portions from the images using Enhanced Region Growth algorithm. Feature selection stage utilizes Improved Cuttlefish Optimization (ICO) algorithm to extricate the optimal features from the segmented images. The final stage is classification in which Faster R-CNN (FRCNN) algorithm is employed to categorize the brain images as tumor affected or not. BRA TS-2020 dataset is used for the experimental investigation of the proposed FRCNN-ICO method. Performance of this method is assessed using parameters such as Accurate Prediction Rate (APR), Imprecise Prediction Rate (IPR) and Imprecise Denial Rate (IDR). The proposed FRCNN-ICO produces the highest APR value of 99.2% compared to the existing researches on brain tumor detection and proves to perform superior than the methods considered for comparison purposes. © 2023 IEEE.

关键词： Tumors

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Stabilizing oxygen vacancies and promoting electrostrain in leadfree potassium niobate-based piezoelectrics over wide temperature ranges

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Journal of Advanced Ceramics 2024年第12期13卷 1965-1973页

作者： Bingcheng Luo Wei Feng Suwei Dai Hongzhou Song Yunyi Wu Jie Zhang College of Science China Agricultural UniversityBeijing 100083China State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China School of Materials Science and Technology China University of Geosciences BeijingBeijing 100083China Institute of Applied Physics and Computational Mathematics Beijing 100094China Research Center for Comprehensive Energy Technology CTG Science and Technology Research InstituteBeijing 100038China Research Center for Metamaterials Wuzhen LaboratoryJiaxing 314500China

Piezoelectric ceramics provide high strain and large driving forces in actuators.A large electrostrain can be realized by the introduction of point defects such as vacancies,interstitial defects,and substitution *** Mn doping,a significant increase in the reversible electrostrain from 0.05%to 0.17%could be achieved in potassium niobite lead-free piezoelectric *** origins of the large electrostrain were analyzed via in situ X-ray diffraction(XRD)under an electric *** electrostrain and other typical electrical properties of the samples were measured at various temperatures,which enabled the ceramics to perform under a very wide temperature range,such as−80–130℃ for the 0.5 mol%Mn-doped sample with low dielectric loss(≤0.02).More importantly,combined with characterizations of the defect behavior by thermally stimulated depolarization current(TSDC),the failure mechanisms of electrostrain in a hightemperature environment could be revealed,which was associated with synergistic damage to the defects caused by the electric field and high *** results can provide good ideas and a basis for the design of piezoelectric materials with good electrostrain stability over a wide temperature range.

关键词： piezoelectric ceramic lead-free point defect electrostrain thermally stimulated depolarization current(TSDC)

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Efficient Global Algorithms for Transmit Beamforming Design in ISAC Systems

arXiv

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arXiv 2024年

作者： Wu, Jiageng Wang, Zhiguo Liu, Ya-Feng Liu, Fan The School of Mathematics Jilin University Changchun130012 China The College of Mathematics Sichuan University Sichuan Chengdu610064 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 Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China

In this paper, we propose a multi-input multi-output transmit beamforming optimization model for joint radar sensing and multi-user communications, where the design of the beamformers is formulated as an optimization problem whose objective is a weighted combination of the sum rate and the Cramér-Rao bound, subject to the transmit power budget. Obtaining the global solution for the formulated nonconvex problem is a challenging task, since the sum-rate maximization problem itself (even without considering the sensing metric) is known to be NP-hard. The main contributions of this paper are threefold. Firstly, we derive an optimal closed-form solution to the formulated problem in the single-user case and the multi-user case where the channel vectors of different users are orthogonal. Secondly, for the general multi-user case, we propose a novel branch and bound (B&B) algorithm based on the McCormick envelope relaxation. The proposed algorithm is guaranteed to find the globally optimal solution to the formulated problem. Thirdly, we design a graph neural network (GNN) based pruning policy to determine irrelevant nodes that can be directly pruned in the proposed B&B algorithm, thereby significantly reducing the number of unnecessary enumerations therein and improving its computational efficiency. Simulation results show the efficiency of the proposed vanilla and GNN-based accelerated B&B algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： computational efficiency

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Validation of Yamaguchi-Ichikawa water hammer model 26

Validation of Yamaguchi-Ichikawa water hammer model

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26th Fluid Mechanics Conference, FMC 2024

作者： Urbanowicz, K. Di Nucci, C. Sharma, B.K. Ashok, K. Krajewski, S. Department of Mechanical Engineering and Mechatronics West Pomeranian University of Technology in Szczecin Szczecin Poland University of l'Aquila L'Aquila Italy Department of Mathematics Birla Institute of Technology and Science Pilani Rajasthan Pilani India Advanced Computational Facility School of Mechanical Engineering Vellore Institute of Technology Vellore India

This study comprehensively analyses the Yamaguchi-Ichikawa (YI) model for water hammer phenomena in hydraulic systems, alongside recent theoretical studies and experiments. While promising initially, comparative investigations reveal limitations in the YI model's alignment with experimental data, particularly regarding initial conditions and pulsation periodicity. These shortcomings, including the absence of a phase shift, may hinder its applicability in complex pressure systems with multiple wave reflections. The research focuses on analyzing analytical models that describe the phenomenon of water hammer. These models enable the proposal of future research directions, integrating theoretical models with experimental observations. © Published under licence by IOP Publishing Ltd.

关键词： Water hammer

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