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Multi-Objective Remora Optimization Algorithm for Efficient Routing in Cognitive Wireless Sensor Network

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Multi-Objective Remora Optimization Algorithm for Efficient ...

Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： J Rajaram Srinivas Aluvala Zainab Abed Almoussawi Muntather Almusawi Anitha D CSE Malla Reddy Engineering college for women (Autonomous) Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India College of Islamic Science Ahl Al Bayt University Karbala Iraq The Islamic University Najaf Iraq Department of Computational Intelligence School of computing SRM Institute of Science and Technology Kattankulathur India

Cognitive Wireless Sensor Networks (CWSNs) are exposed to severe energy constraints, requiring investigation into solutions for extended network lifetimes. Radio Frequency Wireless Energy Harvesting (RFWEH) has proven its ability to efficiently mitigate these limits. However, the dynamic nature of many CWSN applications, influenced by node mobility, causes issues as network topologies changes. A unique Multi-Objective Remora Optimization Algorithm (MROA) is developed to handle the combination of energy harvesting and node mobility. MROA is designed for mobility-aware CWSNs and incorporates energy harvesting effortlessly. To choose cluster heads, the protocol applies an R-factor parameter that incorporates residual node energy, residual node data, and node speed with appropriate weighting. As a result, the MROA framework improves the network's energy performance, which leads to an extended using lifetime. When compared to existing methods namely RMAC-M, R-MAC, and WORP the suggested method performed well in the 500 rounds. It achieved in terms, Alive nodes -3400, Throughput - 170.57 kbps and Delay - 45.00 seconds respectively.

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GENERALIZED TRUNCATED MOMENT PROBLEMS WITH UNBOUNDED SETS

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arXiv 2022年

作者： Huang, Lei Nie, Jiawang Yuan, Ya-Xiang Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics University of California 9500 Gilman Drive La Jolla San DiegoCA92093 United States Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100049 China

This paper studies generalized truncated moment problems with unbounded sets. First, we study geometric properties of the truncated moment cone and its dual cone of nonnegative polynomials. By the technique of homogenization, we give a convergent hierarchy of Moment-SOS relaxations for approximating these cones. With them, we give a Moment-SOS method for solving generalized truncated moment problems with unbounded sets. Finitely atomic representing measures, or certificates for their nonexistence, can be obtained by the proposed method. Numerical experiments and applications are also given. Copyright © 2022, The Authors. All rights reserved.

关键词： Polynomials

Force-based gradient descent method for ab initio atomic structure relaxation

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Physical Review B 2022年第10期106卷 104101-104101页

作者： Yukuan Hu Xingyu Gao Yafan Zhao Xin Liu Haifeng Song State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 China CAEP Software Center for High Performance Numerical Simulation Beijing 100088 China

Force-based algorithms for ab initio atomic structure relaxation, such as conjugate gradient methods, usually get stuck in the line minimization processes along search directions, where expensive ab initio calculations are triggered frequently to test trial positions before locating the next iterate. We present a force-based gradient descent method, WANBB, that circumvents the deficiency. At each iteration, WANBB enters the line minimization process with a trial step size capturing the local curvature of the energy surface. The exit is controlled by a nonrestrictive criterion that tends to accept early trials. These two ingredients streamline the line minimization process in WANBB. The numerical simulations on nearly 80 systems with good universality demonstrate the considerable compression of WANBB on the cost for the unaccepted trials compared with conjugate gradient methods. We also observe across the board significant and universal speedups as well as the superior robustness of WANBB over several widely used methods. The latter point is theoretically established. The implementation of WANBB is pretty simple, in that no a priori physical knowledge is required and only three parameters are present without tuning.

关键词： Crystal structure Structural properties Density functional theory development

PGAbB: A Block-Based Graph Processing Framework for Heterogeneous Platforms

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arXiv 2022年

作者： Yaşar, Abdurrahman Rajamanickam, Sivasankaran Berry, Jonathan W. Çatalyürek, Ümit V. Nvidia Santa ClaraCA United States The School of Computational Science and Engineering Georgia Institute of Technology United States The Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States

Designing flexible graph kernels that can run well on various platforms is a crucial research problem due to the frequent usage of graphs for modeling data and recent architectural advances and variety. In this work, we propose a novel graph processing framework, PGAbB (Parallel Graph Algorithms by Blocks), for modern shared-memory heterogeneous platforms. Our framework implements a block-based programming model. This allows a user to express a graph algorithm using kernels that operate on subgraphs. PGAbB support graph computations that fit in host DRAM but not in GPU device memory, and provides simple but effective scheduling techniques to schedule computations to all available resources in a heterogeneous architecture. We have demonstrated that one can easily implement a diverse set of graph algorithms in our framework by developing five algorithms. Our experimental results show that PGAbB implementations achieve better or competitive performance compared to hand-optimized implementations. Based on our experiments on five graph algorithms and forty-four graphs, in the median, PGAbB achieves 1.6, 1.6, 5.7, 3.4, 4.5, and 2.4 times better performance than GAPBS, Galois, Ligra, LAGraph Galois-GPU, and Gunrock graph processing systems, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic random access storage

IMPROVED LAGUERRE SPECTRAL METHODS WITH LESS ROUND-OFF ERRORS AND BETTER STABILITY

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arXiv 2022年

作者： Huang, Shenghe Yu, Haijun School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China NCMIS & LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China

Laguerre polynomials are orthogonal polynomials defined on positive half line with respect to weight e−x. They have wide applications in scientific and engineering computations. However, the exponential growth of Laguerre polynomials of high degree makes it hard to apply them to complicated systems that need to use large numbers of Laguerre bases. In this paper, we introduce modified three-term recurrence formula to reduce the round-off error and to avoid overflow and underflow issues in generating generalized Laguerre polynomials and Laguerre functions. We apply the improved Laguerre methods to solve an elliptic equation defined on the half line. More than one thousand Laguerre bases are used in this application and meanwhile accuracy close to machine precision is achieved. The optimal scaling factor of Laguerre methods are studied and found to be independent of number of quadrature points in two cases that Laguerre methods have better convergence speeds than mapped Jacobi *** Codes 65N35, 65D32, 65G50, 33F05 © 2022, CC BY-NC-ND.

关键词： Polynomials

FedHZone: Federated Learning Based Hazardous Zone Detection Using Internet of Drone Things

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FedHZone: Federated Learning Based Hazardous Zone Detection ...

Communications, computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Payel Biswas Debashis De Mohammad S. Obaidat Arnab Hazra Balqies Sadoun Department of Computer Science Jogesh Chandra Chaudhuri College Kolkata India Department of Computer Science and Engineering Centre of Mobile Cloud Computing Maulana Abul Kalam Azad University of Technology Nadia WB West Bengal India King Abdullah II School of Information Technology The University of Jordan Amman Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Computational Intelligence School of Computing SRM University SRM Nagar Kattankulathur TN India School of Engineering The Amity University Noida UP India Department of Computer Science and Engineering Dr. Sudhir Chandra Sur Institute of Technology & Sports Complex Kolkata India College of Engineering Al Balqa ‘ Applied University Al-Salt Jordan

ISBN: (数字)9798350349832

ISBN: (纸本)9798350349849

Air pollution can affect human health, so it is necessary to predict the air quality index (AQI) in advance. In this work, air quality data collected by the Internet of Drone Things (IoDT) is predicted and analyzed to detect highly polluted zones using a distributed privacy-preserving federated learning (FL) approach. Air quality data for a particular zone is collected and then fed into a Long short-term memory (LSTM) based model to predict the pollution level in local training. Global aggregation is done by an IoDT -mounted central server to aggregate the received models from the local IoDT level. Privacy-preserving FedHZone predicts and analyses the AQI values from the past time series dataset in a multi-drone environment. This work detects the most hazardous zone in a larger region of Jaipur, Rajasthan using real-time data. The result shows FedHZone with stacked LSTM having “'2%-7% better accuracy than the vanilla LSTM-based model.

关键词： Training Federated learning Atmospheric modeling Time series analysis Real-time systems Internet Telecommunication computing Servers Long short term memory Drones

Probabilistic partition of unity networks for high-dimensional regression problems

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arXiv 2022年

作者： Fan, Tiffany Trask, Nathaniel D'Elia, Marta Darve, Eric Institute for Computational and Mathematical Engineering Stanford University CA United States Center for Computing Research Sandia National Laboratories New Mexico United States Data Science and Computing Sandia National Laboratories CA United States Department of Mechanical Engineering Stanford University CA United States

We explore the probabilistic partition of unity network (PPOU-Net) model in the context of high-dimensional regression problems and propose a general framework focusing on adaptive dimensionality reduction. With the proposed framework, the target function is approximated by a mixture of experts model on a low-dimensional manifold, where each cluster is associated with a local fixed-degree polynomial. We present a training strategy that leverages the expectation maximization (EM) algorithm. During the training, we alternate between (i) applying gradient descent to update the DNN coefficients;and (ii) using closed-form formulae derived from the EM algorithm to update the mixture of experts model parameters. Under the probabilistic formulation, step (ii) admits the form of embarrassingly parallelizable weighted least-squares solves. The PPOU-Nets consistently outperform the baseline fully-connected neural networks of comparable sizes in numerical experiments of various data dimensions. We also explore the proposed model in applications of quantum computing, where the PPOU-Nets act as surrogate models for cost landscapes associated with variational quantum circuits. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum computers

On the hyper-singular boundary integral equation methods for dynamic poroelasticity: three dimensional case

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arXiv 2022年

作者： Zhang, Lu Xu, Liwei Yin, Tao School of Mathematical Sciences University of Electronic Science and Technology of China Sichuan Chengdu611731 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

In our previous work [SIAM J. Sci. Comput. 43(3) (2021) B784-B810], an accurate hyper-singular boundary integral equation method for dynamic poroelasticity in two dimensions has been developed. This work is devoted to studying the more complex and difficult three-dimensional problems with Neumann boundary condition and both the direct and indirect methods are adopted to construct combined boundary integral equations. The strongly-singular and hyper-singular integral operators are reformulated into compositions of weakly-singular integral operators and tangential-derivative operators, which allow us to prove the jump relations associated with the poroelastic layer potentials and boundary integral operators in a simple manner. Relying on both the investigated spectral properties of the strongly-singular operators, which indicate that the corresponding eigenvalues accumulate at three points whose values are only dependent on two Lamé constants, and the spectral properties of the Calderón relations of the poroelasticity, we propose low-GMRES-iteration regularized integral equations. Numerical examples are presented to demonstrate the accuracy and efficiency of the proposed methodology by means of a Chebyshev-based rectangular-polar solver. Copyright © 2022, The Authors. All rights reserved.

关键词： Boundary integral equations

Analysis of Panoramic Images using Deep Learning For Dental Disease Identification

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Analysis of Panoramic Images using Deep Learning For Dental ...

Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： Mohamed Mallick Midhun Kumar A Sakthi Govindaraju A. Suresh Kumar Manivel Kandasamy P Anitha Rathinam Group of Institutions Coimbaotore Database Engineer Indian Biological Data Center Center for Biotechnology Faridabab School of Computing Science and Engineering Galogotias University Delhi NCR Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Unitedworld School of Computational Intelligence Karnavati University Gujarat India OASYS Institute of Technology Trichy India

Dental caries is a distinct disease strategy caused by the metabolic action of dental biofilm, which continually demineralizes lacquer and dentine. Its commonly known as tooth rot, is one of the most prevalent and long- lasting dental illnesses, affecting people at all stages of their lives. There are a few different sorts of dental x-rays, each of which records a different perspective on dental living systems for caries detection and has the highest symptomatic accuracy, whereas panoramic radiography has the lowest Despite this, Bitewing has a few downsides, such as patient unease. Because of the lack of knowledge among administrators, Bitewing imaging frequently results in increased patient radiation exposure as a result of the need for image retakes. On the other hand, panoramic x-ray scans, also known as Orthopantomogram (OPG), are commonly utilized to capture the whole mouth with a small amount of radiation. OPG has a low radiation content, is simple to use, takes less time, and provides exceptional patient comfort. In this way, when compared to intraoral frameworks, pediatric, impaired, and senior people would profit greatly from panoramic imaging frameworks. This research work explores panoramic photos to detect accurate dental disease using deep learning. As a result, most patients can benefit from panoramic imaging, which covers a large portion of the maxillofacial region, including all of the teeth.

关键词： Deep learning Lacquers Visualization Laplace equations Teeth X-rays Learning (artificial intelligence)