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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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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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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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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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Investigation of Security Attacks in IoMT Devices and Federated Learning as a Mitigation Strategy

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Procedia Computer science 2025年 258卷 3426-3435页

作者： Kiruthika M T Poongodi Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu 603203 India Professor Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bangalore Karnataka India

Internet of Medical Things (IoMT) is transforming the healthcare business with an increasing number of wearables, gadgets, and sensing devices. As a result, several patients are under daily monitoring for their cardio health, blood glucose level, blood pressure, etc. The data generated by these devices are very voluminous, and there is an increasing need for safeguarding these sensitive data in order to protect them from malicious attacks. This paper suggests to incorporate Federated learning model to IoMT as a mitigative measure. It is apt to induct FL into IoMT as it operates on a collaborative environment which do not allow transfer of the raw data. Patients’ data are stored at the client end itself and only model updates are shared to the server. In this way, the data and network attacks can be reduced to a great extent. This work proposes an integrated architecture where the layers of FL and IoMT can be knotted up for ensuring privacy and security. This proposed architecture model, further elaborates a distributed working environment. This paper illustrates how FL helps in securing the data produced by the different IoMT devices through the proposed architecture. The challenges involved in the deployment of FL in the framework of IoMT along with potential research directions are discussed further in this research.

关键词： : Federated Learning IoMT Security Internet of Medical Things IoMT Data Attacks Optimization

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Exploring Composite Dark Matter with an SU(4) gauge theory with 1 fermion flavor

arXiv

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arXiv 2024年

作者： Ayyar, Venkitesh Hariri Institute of Computing and Computational science and Engineering Boston University 3 Cummington Mall BostonMA United States

Several SU(N) gauge theories have been explored as candidates for producing stable dark matter particles that can explain their relative abundance, while also evading current constraints from direct, indirect and collider searches. In this talk, I will present the confinement and spectral properties of a new model we name "Hyper Stealth Dark Matter", which involves an SU(4) gauge theory with 1 quark flavor. The lightest baryon in this theory can be a potential dark matter candidate as it is protected from decay and hence can evade detection with a mass of just a few GeV. Existence of a first order confinement transition would open the possibility of potential detection of gravitational waves from such a transition at future observatories. © 2024, CC BY.

关键词： Galaxies

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Oral Cancer Detection and Classification Using Deep Learning with DenseNet121-CatBoost Classifier 2

Oral Cancer Detection and Classification Using Deep Learning...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Anitha, D. Soujanya, Thota Chakraborty, Subhra Alkhayyat, Ahmad Revathi, R. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Sr University School of Agricultural Sciences Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350372892

Oral cancer is a growing universal health challenge, which is rising in the current situation. Early detection is vital for effective treatment and better patient results. However, current diagnostic methods, notably manual histopathological analysis, suffer from subjectivity and time limitations due to classifying the images according to their type. The dependence on manual analysis can lead to delays in accurately diagnosing oral cancer. The proposed method DenseNet121-CatBoost classifier addresses this challenge by using the Histopathological Images dataset. The DenseNet121 for the extracting process and the CatBoost classifier for classifying the images according to their classes. DenseNet121 is utilized to extract features and patterns from images and the CatBoost classifier in the classification process helps to classify the dataset according to its classes using the extracted information. The performance of the DenseNet121-CatBoost method is analyzed using accuracy, specificity, sensitivity, f1score, precision, and Area Under Curve (AUC). This paper shows that the proposed DenseNet121-CatBoost outperforms the existing methods such as ResNet50+CatBoost, DenseNet201+SVM (Support Vector Machine), and GoogLeNet+XGBoost in terms of accuracy of 97.62%, specificity of 98.96%, sensitivity of 94.55%, f1-score of 93.87%, precision of 98.65%, and AUC of 95.26%. © 2024 IEEE.

关键词： Gabor filters

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Leveraging Deep Learning and Blockchain for Enhanced Transparency and Traceability in the Indian Herbal Product Supply Chain

Leveraging Deep Learning and Blockchain for Enhanced Transpa...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Prateek Uniyal Charanjeet Singh G. Dinesh Dept of Computational Intelligence School of Computing School of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Medicinal plants have been a cornerstone of healthcare practices globally, serving a significant population in developing countries who rely on affordable herbal remedies. However, the current marketing system through Mandis and wholesale markets suffers from opacity. Numerous intermediaries create an information gap for farmers regarding fair pricing and market trends. Additionally, concerns exist about misidentification and adulteration of medicinal plants due to limited knowledge among collectors and traders. The National Medicinal Plants Board (NMPB) recognizes the need for a robust supply chain. This paper proposes a blockchain-based platform ‘Indian Herbal Blockchain Network (IHBN)’ integrated with Deep Learning for image processing. The deep learning model has achieved a decent accuracy of 90.24% which decreases the chance of misidentification. The platform ensures transparent traceability of medicinal plants from source to consumer, along with accurate identification using image analysis. This system promotes transparency within the medicinal plant supply chain, reducing the risk of adulteration and improving overall trust.

关键词： Medicinal plants Deep learning Accuracy Supply chains Sociology Pricing Medical services

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