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Adaptive Particle Swarm optimization for Enhanced Convolutional Neural Network Model Training 9

Adaptive Particle Swarm Optimization for Enhanced Convolutio...

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9th International conference on Information Technology Research

作者： Atugoda, A. W. C. K. Fernando, Subha Univ Moratuwa Dept Computat Math Colombo Sri Lanka

ISBN: (纸本)9798331511425;9798331511432

Deep Neural Network (DNN) models find extensive application in many domains such as engineering, research, biomedical science, and autonomous systems. Among the various DNN models, Deep Convolutional Neural Networks (DCNNs) are particularly effective for solving nonlinear problems, such as image classification tasks. Optimizing neural networks is crucial in these applications to achieve high accuracy and reliable outcomes. Traditionally, many studies have focused on weight optimization, employing techniques like Stochastic Gradient Descent (SGD) and the Adam optimizer. However, these methods often suffer from issues like getting trapped in local minima and requiring large memory storage, leading to suboptimal performance. To address these challenges, researchers have turned to Swarm Intelligence (SI) approaches, known for their speed and robustness in global optimization. Among the various SI algorithms, Particle Swarm optimization (PSO) is particularly favored due to its simplicity and lack of derivative requirements. However, some studies have shown that standard PSO may struggle with slow convergence and high memory demands. Therefore, this study proposes using an Adaptive PSO method to improve the efficiency of CNNs. The performance of the model is evaluated on three benchmark datasets-MNIST, CIFAR-10, and CIFAR-100. The results demonstrate that the Adaptive PSO method significantly outperforms conventional optimization techniques on these datasets.

关键词： particle swarm optimization weight optimization deep convolutional neural network

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Automating Data Reuse in high-Level Synthesis

Automating Data Reuse in High-Level Synthesis

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Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Meeus, Wim Stroobandt, Dirk IMEC Ghent Belgium Univ Ghent B-9000 Ghent Belgium

ISBN: (纸本)9783981537024

Current high-Level Synthesis (HLS) tools perform excellently for the synthesis of computation kernels, but they often don't optimize memory bandwidth. As memory access is a bottleneck in many algorithms, the performance of the generated circuit will benefit substantially from memory access optimization. In this paper we present an automated method and a toolchain to detect reuse of array data in loop nests and to build hardware that exploits this data reuse. This saves memory bandwidth and improves circuit performance. We make use of the polyhedral representation of the source program, which makes our method computationally easy. Our software complements the existing HLS flows. Starting from a loop nest written in C, our tool generates a reuse buffer and a loop controller, and preprocesses the loop body for synthesis with an existing HLS tool. Our automated tool produces designs from unoptimized source code that are as efficient as those generated by a commercial HLS tool from manually-optimized source code.

关键词： buffer storage data handling high level synthesis source code (software) storage management HLS tools array data circuit performance computation kernel synthesis data reuse automation data reuse buffer generation high-level synthesis loop controller loop nests manually-optimized source code memory access memory access optimization polyhedral representation reuse buffer source program Algorithm design and analysis Arrays Buffer storage Hardware Image edge detection Indexes optimization

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A Task Scheduling Algorithm for HPC Applications using Colored Stochastic Petri Net Models 13

A Task Scheduling Algorithm for HPC Applications using Color...

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13th IEEE International conference on Intelligent Computer Communication and Processing (ICCP)

作者： Mironescu, Ion Dan Vintan, Lucian Lucian Blaga Univ Sibiu Sibiu Romania

ISBN: (纸本)9781538633687

The increase in demand for high performance Computing (HPC) scientific applications motivates the efforts to reduce costs of running these applications. The problem to solve is that of dynamical multi-criterial optimal scheduling of an application on a HPC platform with a high number of heterogeneous nodes. The solution proposed by the authors is a HPC hardware-software architecture that includes the infrastructure for two level (node and inter-node level) adaptive load balancing. The article presents the development of an Coloured Petri Net(CPN) for such an architecture. The model was used for the development of a dynamic distributed algorithm for the scheduling problem. The CPN allowed a holistic hardware-software formal verification and analysis. Some simple properties were formally proofed. Simulations were performed to assess performance and the results were in the performance range of other load balancing algorithms with significant benefits in reducing the optimization's complexity.

关键词： high performance Computing Task Scheduling Load Balancing Colored Petri Net Formal Verification performance Evaluation

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Design, Analysis & performance Improvement of A Modified Asymmetrical H-Bridge Multilevel Inverter By Metaheuristic algorithms 3

Design, Analysis & Performance Improvement of A Modified Asy...

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3rd IEEE International conference on Sustainable Energy and Future Electric Transportation, SeFet 2023

作者： Nanda, Kausik Guru, Neelakantha Barisal, Ajit Kumar Pani, Siddhanta Odisha University of Technology and Research Department of Electrical Engineering Bhubaneswar India

ISBN: (纸本)9798350319972

The performance improvement of multilevel inverters has attracted the attention of researchers. The conventional topologies of multilevel inverters require many power-electronic switches which leads to a significant amount of energy being lost as a cause of high-frequency switching. Hence, the trade-off between the losses occurring in the switches of the converter and the efficiency has introduced the concept of reduced component count new multilevel inverter topologies. This paper proposes a modified single-phase inverter, a development of a conventional H-bridge structure. The proposed structure can generate a 15-levels of single-phase voltage output. The proposed inverter is simulated using MATLAB/Simulink software. A simple PWM strategy i.e., Selective Harmonic Elimination is employed to eliminate the lowest-order significant harmonics. Harmonic elimination and THD minimization are nonlinear problems that can be effectively handled by metaheuristic approaches. In this paper four metaheuristic techniques i.e., Teaching Learning Based optimization, Flower Pollination Algorithm, Modified Particle Swarm optimization & Invasive Weed optimization are implemented to obtain the switching angles of the SHE scheme for THD minimization and IWO beats the output of others. © 2023 IEEE.

关键词： Invasive weed optimization

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A nonlinear state space approach to nonlinear blind source separation

A Nonlinear state space approach to Nonlinear blind source s...

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15th International conference on Digital Signal Processing

作者： Zhang, Jingyi Woo, W. L. Dlay, S. S. Univ Newcastle Sch Elect Elect & Comp Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England

ISBN: (纸本)9781424408818

This paper addresses the prominent problem of separating noisy signals that have been convolutively mixed and nonlinearly distorted. The mixed signals are characterized by a nonlinear state space model which models both the statistical properties of the source signals and the overall nonlinear mixing process. A novel algorithm based on maximum likelihood framework has been rigorously developed for estimating the parameters in the model as well as inferring the source signals. In the proposed model, the nonlinear distortion function is modeled by using high order polynomials which enable the model to be formulated and optimized in a tractable manner. The strength of the proposed approach lies in the closed estimation of the source signals and the adaptive optimization procedure of the model parameters. This has resulted in high performance accuracy, fast convergence and efficient implementation of the estimation algorithm. Simulation has been conducted to verify the effectiveness of the proposed algorithm and the obtained results have shown 50% better accuracy than conventional nonlinear algorithms.

关键词： nonlinear signal processing blind source separation blind equalization machine learning for signal processing

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SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design 7

SmOOD: Smoothness-based Out-of-Distribution Detection Approa...

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37th IEEE/ACM International conference on Automated software Engineering (ASE)

作者： Ben Braiek, Houssem Tfaily, Ali Khomh, Foutse Reid, Thomas Guida, Ciro Polytech Montreal Montreal PQ Canada Sycodal Inc Montreal PQ Canada Bombardier Aerosp Inc Montreal PQ Canada

ISBN: (纸本)9781450394758

Aircraft industry is constantly striving for more efficient design optimization methods in terms of human efforts, computation time, and resources consumption. Hybrid surrogate optimization maintains high results quality while providing rapid design assessments when both the surrogate model and the switch mechanism for eventually transitioning to the HF model are calibrated properly. Feedforward neural networks (FNNs) can capture highly nonlinear input-output mappings, yielding efficient surrogates for aircraft performance factors. However, FNNs often fail to generalize over the out-ofdistribution (OOD) samples, which hinders their adoption in critical aircraft design optimization. Through SmOOD, our smoothnessbased out-of-distribution detection approach, we propose to codesign a model-dependent OOD indicator with the optimized FNN surrogate, to produce a trustworthy surrogate model with selective but credible predictions. Unlike conventional uncertainty-grounded methods, SmOOD exploits inherent smoothness properties of the HF simulations to effectively expose OODs through revealing their suspicious sensitivities, thereby avoiding over-confident uncertainty estimates on OOD samples. By using SmOOD, only high-risk OOD inputs are forwarded to the HF model for re-evaluation, leading to more accurate results at a low overhead cost. Three aircraft performance models are investigated. Results show that FNN-based surrogates outperform their Gaussian Process counterparts in terms of predictive performance. Moreover, SmOOD does cover averagely 85% of actual OODs on all the study cases. When SmOOD plus FNN surrogates are deployed in hybrid surrogate optimization settings, they result in a decrease error rate of 34.65% and a computational speed up rate of 58.36x, respectively.

关键词： Neural network out-of-distribution detection surrogate modeling aircraft design optimization

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optimization of an external perforated screen for improved daylighting and thermal performance of an office space

Optimization of an external perforated screen for improved d...

引用

International high-performance Built Environment conference (iHBE)

作者： Lavin, Cristian Fiorito, Francesco Univ Sydney Fac Architecture Design & Planning 148 City Rd Sydney NSW 2008 Australia Univ New South Wales Fac Built Environm Sydney NSW 2052 Australia

Glazed towers are predominant among commercial buildings built in recent decades. They advance the use of natural light, increase the visual contact with the outside and have a contemporary look. However, issues related to excessive daylight illuminances, glare and direct solar gains affect the visual and thermal comfort, and potentially increase the building energy demand. Passive design of protections is a key element to tackle these problems. A shading device consisting of a perforated screen is presented in this research with the aim of improving the Useful Daylight Illuminance (UDI), and reducing energy consumption and Daylight Glare Probability (DGP). The screen has been supposed to be used in an office space in Australia with windows on its north and west facades. Integrated daylight and thermal simulations have been carried out with the use of Rhinoceros/Grasshopper integrated specialist software. The geometry of the shading device has been optimized using Grasshopper integrated evolutionary optimization tool based on Genetic algorithms. Size and distribution of the perforations in the solar screens have been chosen as optimization variables. Results, then, have been compared to a base case with no shadings and show significant improvements in UDI values, together with substantial reductions of energy consumptions and glare probability. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Shading device perforated screen (PS) optimization genetic algorithms (GA) useful daylight illuminance (UDI) energy consumption daylight glare probability (DGP)

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Design optimization of Dense Urban Neighbourhood with Computational Simulation and Genetic Algorithm for Improving Outdoor Thermal Environment 17

Design Optimization of Dense Urban Neighbourhood with Comput...

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17th conference of International-Building-performance-Simulation-Association (IBPSA)

作者： Hao, Tongping Huang, Jianxiang Jones, Phil Univ Hong Kong Fac Architecture Dept Urban Planning & Design Hong Kong Peoples R China Cardiff Univ Welsh Sch Architecture Cardiff Wales

ISBN: (纸本)9781775052029

The outdoor thermal environment is of growing concern in the urban planning and design field, especially in tropical/subtropical regions. The layout and design of urban neighbourhood may have significant environmental impacts. Although computer simulation has been increasingly used to assess outdoor thermal conditions, it is yet to be fully adopted by practitioners in design thinking and processes. This paper proposes a design optimization method to reduce outdoor heat stress and increase the thermal comfortable area in urban spaces. The thermal comfort simulation software CityComfort+ is linked with genetic algorithms to optimize the thermal conditions of open space defined by a cluster of buildings. The method is tested with a hypothetical case in Hong Kong. The algorithm is able to identify high-performing design schemes within a reasonable period, given the site and project constraints. The method can be useful for practice and the further development of simulation-based optimization methods.

关键词： Genetic algorithms

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Application of genetic algorithms for design optimization of a Bragg cell

Application of genetic algorithms for design optimization of...

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Optomechanical Design and Engineering 2001 conference

作者： Nikulin, VV Skormin, VA Binghamton Univ Watson Sch Eng Laser Commun Res Lab Binghamton NY USA

ISBN: (纸本)0819441589

Non-mechanical beam steering technologies, utilizing acousto-optics, allow for achieving the high bandwidth laser beam positioning required for optical communications, laser scanners, LADARs, etc. Properties of the Bragg cell, chiefly responsible for the efficiency and attainable characteristics of the entire positioning system, are assured by successful design of this optical component. However, design of Bragg cells is dominated by experience and intuition of the designers and the potential of this technology is not fully utilized. An optimal design problem of a Bragg cell is formulated on the basis of known equations of the underlying physical phenomena, and a genetic optimization scheme is applied for the solution of the resultant formidable problem. The approach not only yields a design solution, but also allows for the variation of the design criterion and emphasizing particular properties of the resultant component. The prowess of the proposed approach has been demonstrated by design optimization examples.

关键词： mathematical model nonlinear constraint optimization genetic algorithms loss function penalty function performance criterion

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A Comparative Study of Different Biogeography Based optimization Migration Models performance on Antenna Array Thinning Problems 14

A Comparative Study of Different Biogeography Based Optimiza...

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14th International conference on high performance Computing & Simulation (HPCS)

作者： Goudos, Sotirios K. Siakavara, Katherine Sahalos, John N. Aristotle Univ Thessaloniki Dept Phys Thessaloniki Greece Univ Nicosia Dept Elect & Comp Engn Nicosia Cyprus

ISBN: (纸本)9781509020881

Biogeography-Based optimization (BBO) which is inspired by the science of biogeography, is a stochastic population-based Evolutionary Algorithm (EA). The original BBO uses a linear migration model to describe how species migrate from one island to another. Other non-linear migration models have been proposed in the literature. A popular combinatorial optimization problem in antenna design is array thinning. The purpose of this paper to study the performance of these models on antenna array thinning problems. We apply BBO with four different migration models to five different array design cases. The problems dimensions range from 50 to 300. The results show that BBO with sinusoidal migration model generally performs better than the other models. However, these results are considered to be indicative and do not generally apply to all optimization problems in antenna design.

关键词： Linear array synthesis array thinning optimization methods evolutionary algorithms biogeography based optimization migration models

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