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Conference on Optics, Photonics, and Digital Technologies for Imaging Applications VIII

作者： Lagrange, Antoine Gilles, Antonin Heggarty, Kevin Fracasso, Bruno Res & Technol Inst B Com Image Vis & Immers Lab F-35510 Cesson Sevigne France IMT Atlantique Opt Dept Technopole Brest IroiseCS 83818 F-29285 Brest France

ISBN: (纸本)9781510673151;9781510673144

During the quantization of the hologram values part of the information is lost, which introduces quantization noise in the reconstruction. To enhance the hologram quality, we developed in a previous work a view-dependent error diffusion algorithm which rejects the quantization noise in specific views. However, this algorithm is slow which prevents its use in real-time applications. In this paper, we present a GPU implementation of this technique which uses the inherent parallelism of the error diffusion algorithm. Moreover, we introduce a technique to select diffusion weights, which allows a trade-off between computation time and reconstruction quality. Experimental results demonstrate the ability of our approach to quickly generate high-quality holograms.

关键词： Diffusion Holograms Quantization Visualization computation time Fourier transforms

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Distributed wireless sensor node localization based on penguin search optimization

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2022年第1期30卷 50-62页

作者： Al Shayokh, Md Shin, Soo Young Kumoh Natl Inst Technol Dept IT Convergence Engn Gumi South Korea

Wireless sensor networks (WSNs) have become popular for sensing areas-of-interest and performing assigned tasks based on information on the location of sensor devices. Localization in WSNs is aimed at designating distinct geographical information to the inordinate nodes within a search area. Biologically inspired algorithms are being applied extensively in WSN localization to determine inordinate nodes more precisely while consuming minimal computation time. An optimization algorithm belonging to the metaheuristic class and named penguin search optimization (PeSOA) is presented in this paper. It utilizes the hunting approaches in a collaborative manner to determine the inordinate nodes within an area of interest. Subsequently, the proposed algorithm is compared with four popular algorithms, namely particle swarm optimization (PSO), binary particle swarm optimization (BPSO), bat algorithm (BA), and cuckoo search algorithm (CS). The comparison is based on two performance metrics: localization accuracy and computation time to determine inordinate nodes. The results obtained from the simulation illustrate that PeSOA outperforms the other algorithms, achieving an accuracy higher than 30%. In terms of computation time to determine inordinate nodes, the proposed algorithm requires 28% less time (on average) than the other algorithms do.

关键词： Wireless sensor networks localization Penguin search algorithm optimization computation time

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DATA COMPLEXITY FOR IDENTIFYING SUITABLE ALGORITHMS 13

DATA COMPLEXITY FOR IDENTIFYING SUITABLE ALGORITHMS

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IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Ramalingam, Prem Anand Fathima, Nikhat Supriya, P. Shetty, Prathish Sanyal, Mainak Yeshaswini, P. Rao, Mukesh Darapaneni, Narayana Paduri, Anwesh Reddy PES Univ Bangalore Karnataka India Great Learning Bangalore Karnataka India

ISBN: (纸本)9798350332865

Suitability of an algorithm to the given job depends on the distribution characteristics of the two classes in a binary-class dataset. A linear modelling algorithm will be more optimal on a linearly separable dataset i.e., the one in which the two classes can be cleanly separated by a linear boundary. Whereas, for a dataset in which the classes are not linearly separable may need a complex kernel-based algorithm to get the job done. A prior idea about the data in terms of the degree of separability of the two classes, is likely to help zoom in on algorithms more suitable than others and in the process reduce model building time, minimize wastage of computational resources. For this, we plan to use a purity metric that helps categorize a dataset into simple, medium, complex (SMC). The value of this metric will be estimated from the underlying distribution characteristics of the data on the various attributes. Simple dataset is the one in which the classes can be linearly separated without any transformations. The purity metric will be high and on the other end of the spectrum, a complex dataset will be the one in which the classes are not linearly separable even after transformations. Such datasets will have low value in purity metric. Further, with the help of boxplots and quantile values, we analyze which model performs better in each category. The paper proposes a set of guidelines to choose an optimal classical Machine Learning algorithm for binary classification.

关键词： linear separability purity computation time optimal algorithm classification

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An exact and explicit PV panel curve computation assisted by two 2-port networks

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SOLAR ENERGY 2022年 240卷 280-289页

作者： Mathew, Linta Eliya Panchal, Ashish K. SV Natl Inst Technol Elect Engn Dept Surat 395007 India

The single-diode-model (SDM) is more often used for the photovoltaic module current-voltage (I-V) curve computation. However, the SDM being implicit, demands iterative methods and makes the computation tedious. Optionally, the Lambert-W function-based curve computation provides an explicit and accurate solution. The function evaluations and the associated problems are really mathematical challenges. This paper proposes a novel curve computation method derived from the SDM decomposition. The voltage across the diode in the SDM equivalent circuit acts as an independent variable and makes the curve computation explicit. Also, the I-V curve can be obtained using the dual approach, i.e. I-approach and V-approach. The method is employed for the silicon cell and modules' curves computations in uniform operating conditions and partial shading conditions. The novel method outperforms in comparison with the Newton-Raphson and the Lambert-W based curve computation in terms of an explicit, exact, and fast solution. Similar to the SDM, the method is successfully adopted for the double-diode-model photovoltaics.

关键词： computation time Current-voltage(I-V) curve Explicit method Photovoltaic (PV) Single-diode-model (SDM)

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HeterSim: Enable Flexible Simulation for Heterogeneous Distributed Deep Learning Platform

HeterSim: Enable Flexible Simulation for Heterogeneous Distr...

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2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Tang, Yinan Zhang, Hongwei Wang, Li Guo, Zhenhua Zhao, Yaqian Li, Rengang IEIT SYSTEM Co. Ltd. Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Distributed deep learning has become an essential technique for accelerating deep learning, but its performance is often influenced by the heterogeneous computing nodes and heterogeneous communication networks within the distributed deep learning platform. Due to the high costs of practical deployment and running of distributed deep learning, it is almost impossible for researchers to optimize the training strategy of distributed deep learning tasks in real-world environments. In this paper, we propose HeterSim, a simulator specifically designed for heterogeneous distributed deep learning platforms. HeterSim enables flexible configuration of computing node performance and network connections, and supports to define and simulate distributed deep learning workloads using graph-based representations. HeterSim also allows for the modification of communication strategies in the distributed deep learning process, thereby assisting researchers in validating their designs for distributed deep learning. We verify the feasibility and flexibility of HeterSim, by generating a simulation platform at the scale of millions of nodes, and successfully simulate the distributed deep learning process of Resnet50. We aim to provide HeterSim as a flexible and user-friendly simulator for researchers, targeting heterogeneous distributed deep learning platforms, and helping researchers evaluate and optimize the strategy of distributed deep learning tasks at a lower cost. © 2025 SPIE.

关键词： Computer simulations Switches Deep learning Education and training Data modeling Modeling computation time Computer architecture Manufacturing Mathematical modeling

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Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics

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JOURNAL OF SUPERCOMPUTING 2022年第9期78卷 11975-12023页

作者： Mohar, Satinder Singh Goyal, Sonia Kaur, Ranjit Punjabi Univ Patiala Dept Elect & Commun Engn Patiala Punjab India

Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have been collected is revealed. Localization of sensor nodes in WSNs plays a significant role in several applications such as detecting the enemy movement in military applications. The major aim of localization problem is to find the coordinates of all target nodes with the help of anchor nodes. In this paper, two variants of bat optimization algorithm (BOA) are proposed to localize the sensor nodes in a more efficient way and to overcome the drawbacks of original BOA, i.e. being trapped in local optimum solution. The exploration and exploitation features of original BOA are modified in the proposed BOA variants 1 and 2 using improved global and local search strategies. To validate the efficiency of the proposed BOA variants 1 and 2, several simulations have been performed for various numbers of target nodes and anchor nodes, and the results are compared with original BOA and other existing optimization algorithms applied to node localization problem. The proposed BOA variants 1 and 2 outperform the other algorithms in terms of mean localization error, number of localized nodes and computing time. Further, the proposed BOA variants 1 and 2 and original BOA are also compared in terms of various errors and localization efficiency for several values of target and anchor nodes. The simulations results signify that the proposed BOA variant 2 is superior to the proposed BOA variant 1 and existing BOA in terms of several errors. The node localization based on the proposed BOA variant 2 is more effective as it takes less time to perform computations and has less mean localization error than the proposed BOA variant 1, BOA and other existing optimization algorithms.

关键词： Node localization Bat optimization algorithm computation time Target nodes Localization error

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A Task-Resource Joint Optimization Model in Distributed Computing 1st

A Task-Resource Joint Optimization Model in Distributed Comp...

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1st International Conference on Emerging Networking Architecture and Technologies (ICENAT)

作者： Su, Shi Hui, Hongwen Wei, Wenjie Beijing Normal Univ Expt High Sch 14 XiCheng Dist Beijing 100032 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn 30 Xueyuan Dist Beijing 100083 Peoples R China

ISBN: (纸本)9789811996962;9789811996979

Distributed computing decomposes the user tasks into many small tasks and distributes them to multiple servers for processing to save the overall computing time. There is an important challenge that how to efficiently divide the tasks to achieve the goal. In this paper, aiming at solving the complex relationship between multiple tasks and multiple servers, we propose a resource-task joint optimization model in a distributed computing environment. The optimization goal of this model is to minimize the total computation time, while considering the transmission time and execution time of task data. Based on the task size, intensity, and computing power of the server, we propose a task-resource joint optimization algorithm to solve the proposed model. Numerical simulation verified the feasibility of the model and the correctness of the algorithm.

关键词： Distributed computing Resource-task joint optimization computation time

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An Efficient Method for the computation of Electromagnetic Fields Associated With Tortuous Lightning Channels

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IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 2022年第5期64卷 1431-1441页

作者： Brignone, Massimo Nicora, Martino Mestriner, Daniele Procopio, Renato Petrarca, Carlo Formisano, Alessandro Barmada, Sami Delfino, Federico Univ Genoa ICT I-16145 Genoa Italy Univ Genoa Elect Engn Dept I-16145 Genoa Italy Univ Naples Federico II Dept Elect Engn I-80125 Naples Italy Univ Campania Luigi Vanvitelli Dept Engn I-81031 Aversa Italy Univ Pisa Sch Engn I-56122 Pisa Italy

Lightning electromagnetic (EM) fields are typically computed by assuming the current propagation within a straight vertical channel of negligible cross section. However, it is well known that the lightning path is tortuous and this can significantly affect the radiated fields and the lightning-induced voltages on power lines. Lupo et al. (2000) proposed an analytical method evaluating the EM fields radiated by a tortuous channel assuming a step current source;to consider an arbitrary (realistic) current source, a slow-converging convolution integral was applied. The aim of this article is to reduce the computation time of such procedure by extending to the tortuous channel case the approach proposed by Brignone et al. (2021) for the evaluation of the lightning EM fields with arbitrary current source, originally developed for a straight vertical channel. The performances of the present method are evaluated by means of comparison with the solution proposed by Lupo et al. (2000), showing an excellent agreement with a significant reduction of the CPU effort. This in principle will allow the computation of lightning-induced overvoltages at both individual and statistical level without a prohibitive computational effort.

关键词： Lightning Discharges (electric) Surges computational modeling Aerospace electronics Geometry Fractals computation time lightning channel tortuosity lightning electromagnetic fields

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Emulating AC OPF Solvers With Neural Networks

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第6期37卷 4950-4953页

作者： Baker, Kyri Univ Colorado Boulder CO 80309 USA

Using machine learning to obtain solutions to AC optimal power flow has recently been a very active area of research due to the astounding speedups that result from bypassing traditional optimization techniques. However, generally ensuring feasibility of the resulting predictions while maintaining these speedups is a challenging, unsolved problem. In this letter, we train a neural network to emulate an iterative solver in order to cheaply and approximately iterate towards the optimum. Once we are close to convergence, we then solve a power flow to obtain an overall AC-feasible solution. Results shown for networks up to 1,354 buses indicate the proposed method is capable of finding feasible, near-optimal solutions to AC OPF in milliseconds on a laptop computer. In addition, it is shown that the proposed method can find "difficult" AC OPF solutions that cause flat-start or DC-warm started algorithms to diverge.

关键词： Artificial neural networks Training Testing Convergence Load modeling Learning systems Loading Machine learning optimal power flow computation time

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A compressed sensing and CNN-based method for fault diagnosis of photovoltaic inverters in edge computing scenarios

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IET RENEWABLE POWER GENERATION 2022年第7期16卷 1434-1444页

作者： Wang, Xinyi Yang, Bo Wang, Zhaojian Liu, Qi Chen, Cailian Guan, Xinping Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Control & Manag Shanghai Peoples R China

Accurate and real-time diagnosis of the inverter is crucial for the reliability, safety and generation efficiency of the photovoltaic (PV) system. Recently, deep learning (DL) is widely used for accurate diagnosis, which automatically extracts useful features instead of relying on experts. For real-time diagnosis, in an emerging edge computing paradigm, massive data generated by end-device are processed in nearby edge nodes, which reduces energy consumption and latency. However, large amounts of data are required for model training in the cloud, which leads to tremendous burdens of transmission and computation. To tackle these issues, a data-driven diagnosis method based on compressed sensing (CS) and convolutional neural network (CNN) is proposed for open-circuit faults of PV inverters. Based on CS, raw signals are compressed in edge nodes with the optimal compression ratio determined by experiments. The proposed CS-CNN is evaluated on an edge-cloud semi-physical experiment platform. Compared with common compression methods, the accuracy of CS is improved by more than 3%, and the time consumption is only one-fifth. Compared with the CNN of the same structure, the transmission and computation time is largely reduced by an order of magnitude. The developed CS-CNN compresses 85% of data with a test accuracy of 99.18%.

关键词： inverter photovoltaic power systems end-device edge computing scenarios generation efficiency power engineering computing PV inverters energy consumption deep learning (artificial intelligence) cloud computing learning (artificial intelligence) safety massive data common compression methods convolutional neural nets compressed sensing nearby edge nodes deep learning computation time edge-cloud semiphysical experiment platform photovoltaic inverters optimal compression ratio feature extraction open-circuit faults edge computing time consumption convolutional neural network CNN-based method data-driven diagnosis method fault diagnosis real-time diagnosis emerging edge computing paradigm photovoltaic system developed CS-CNN

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