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Infrared image segmentation algorithm for defect detection based on FODPSO

INFRARED PHYSICS & TECHNOLOGY 2019年 102卷 103051-000页

作者： Tang, Qingju Gao, Shuaishuai Liu, Yongjie Yu, Fengyun Heilongjiang Univ Sci & Technol Sch Mech Engn Harbin 150022 Heilongjiang Peoples R China

To address the problems of high noise and fuzzy edges of acquired infrared images when detecting debonding defective components of SiC thermal barrier coating structure by pulsed infrared thermal waves, an image segmentation method based on fractional Darwin particle swarm optimization algorithm (FODPSO) is adopted to reconstruct infrared image sequences. This algorithm adopts fractional calculus to control the convergence of the system, and can perform n - 1 threshold optimization calculation on n - 1 level images. Experimental results show that FODPSO algorithm effectively overcomes the shortcomings of traditional algorithm such as easy to enter local optimal and slow convergence speed, and can realize the edge identification of SiC coating defects. The processed image improves the contrast and signal-to-noise ratio.

关键词： Fractional order Fractional-order Darwin particle swarm algorithm Infrared image Edge detection Multi-scale segmentation algorithm comparison SiC coating

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Optimal Combination of Predictors and algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data

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REMOTE SENSING 2019年第4期11卷 414-414页

作者： Chen, Lin Wang, Yeqiao Ren, Chunying Zhang, Bai Wang, Zongming Chinese Acad Sci Northeast Inst Geog & Agroecol Key Lab Wetland Ecol & Environm Changchun 130102 Jilin Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Rhode Isl Dept Nat Resources Sci 1 Greenhouse Rd Kingston RI 02881 USA

Accurate forest above-ground biomass (AGB) mapping is crucial for sustaining forest management and carbon cycle tracking. The Shuttle Radar Topographic Mission (SRTM) and Sentinel satellite series offer opportunities for forest AGB monitoring. In this study, predictors filtered from 121 variables from Sentinel-1 synthetic aperture radar (SAR), Sentinal-2 multispectral instrument (MSI) and SRTM digital elevation model (DEM) data were composed into four groups and evaluated for their effectiveness in prediction of AGB. Five evaluated algorithms include linear regression such as stepwise regression (SWR) and geographically weighted regression (GWR);machine learning (ML) such as artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that the RF model used predictors from both the Sentinel series and SRTM DEM performed the best, based on the independent validation set. The RF model achieved accuracy with the mean error, mean absolute error, root mean square error, and correlation coefficient in 1.39, 25.48, 61.11 Mgha(-1) and 0.9769, respectively. Texture characteristics, reflectance, vegetation indices, elevation, stream power index, topographic wetness index and surface roughness were recommended predictors for AGB prediction. Predictor variables were more important than algorithms for improving the accuracy of AGB estimates. The study demonstrated encouraging results in the optimal combination of predictors and algorithms for forest AGB mapping, using openly accessible and fine-resolution data based on RF algorithms.

关键词： optimal predictors algorithm comparison Sentinel-1 SAR Sentinel-2 MSI SRTM DEM forest AGB mapping

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New Criteria for Comparing Global Stochastic Derivative-Free Optimization algorithms

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第7期10卷 614-625页

作者： McCart, Jonathan Almomani, Ahmad SUNY Coll Geneseo Dept Math Geneseo NY 14454 USA

For many situations, the function that best models a situation or data set can have a derivative that may be difficult or impossible to find, leading to difficulties in obtaining information about the optimal values of the function. Thus, numerical methods for finding these important values without the direct involvement of the derivative have been developed, making the representation and interpretation of the results for these algorithms of importance to the researchers using them. This is the motivation to use and compare between derivative-free optimization (DFO) algorithms. The comparison methods developed in this paper were tested using three global solvers: Genetic algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) on a set of 26 n-dimensional test problems of varying convexity, continuity, differentiability, separability, and modality. Each solver was run 100 times per problem at 2, 20, 50 and 100 dimensions. The formulation for each algorithm used comes from the MATLAB Optimization Toolbox, unedited or revised. New criteria for comparing DFO solver performance are introduced in terms defined as Speed, Accuracy, and Efficiency, taken at different levels of precision and dimensionality. The numerical results for these benchmark problems are analyzed using these methods.

关键词： Derivative-free optimization algorithm comparison test problem benchmarking

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Model Prediction of Defects in Sheet Metal Forming Processes 19th

Model Prediction of Defects in Sheet Metal Forming Processes

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19th International Conference on Engineering Applications of Neural Networks (EANN)

作者： Dib, Mario Ribeiro, Bernardete Prates, Pedro Univ Coimbra Ctr Informat & Syst Univ Coimbra CISUC Coimbra Portugal Univ Coimbra Ctr Mech Engn Mat & Proc CEMMPRE Coimbra Portugal

ISBN: (纸本)9783319982045;9783319982038

Predicting defects is a challenge in many processing steps during manufacturing because there is a great number of variables involved in the process. In this paper, we take a machine learning perspective to choose the best model for defects prediction of sheet metal forming processes. An empirical study is presented with the objective to choose the best machine learning algorithm that will be able to perform accurately this task. For building the model, three distinct datasets were created using numerical simulation for three mild steel materials: mild steel, DH600, HSLA340. The numerical simulation was performed on the basis of sixteen input features representing characteristics of the materials. Moreover, two kinds of defects, springback and maximum thinning, each one is binary with 1 (defects) and 0 (non-defects) were considered in the simulator. The experimental setup consists of running MLP, CART, NB, RF and SVM algorithms using cross-validation for correctly choosing model parameters. The results were averaged in 30 runs and the standard deviations recorded. The initial conclusion is that the learning algorithm scores differently depending on the type of defect and conditions of the experiment. Although the preliminary results show good performance of the algorithms in simulated environment, a further study with real data will be addressed in future work.

关键词： Machine learning Manufacturing process Predictive model Defect prediction algorithm comparison

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Best practices for comparing optimization algorithms

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OPTIMIZATION AND ENGINEERING 2017年第4期18卷 815-848页

作者： Beiranvand, Vahid Hare, Warren Lucet, Yves Univ British Columbia Dept Comp Sci Kelowna BC Canada Univ British Columbia Dept Math Kelowna BC Canada

Comparing, or benchmarking, of optimization algorithms is a complicated task that involves many subtle considerations to yield a fair and unbiased evaluation. In this paper, we systematically review the benchmarking process of optimization algorithms, and discuss the challenges of fair comparison. We provide suggestions for each step of the comparison process and highlight the pitfalls to avoid when evaluating the performance of optimization algorithms. We also discuss various methods of reporting the benchmarking results. Finally, some suggestions for future research are presented to improve the current benchmarking process.

关键词： Benchmarking algorithm comparison Guidelines Performance Software Testing Metric Timing Optimization algorithms

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Performance Analysis of Security algorithms for IoT devices 5

Performance Analysis of Security Algorithms for IoT devices

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5th IEEE-Region-10 Humanitarian Technology Conference (R10-HTC)

作者： Khan, Nuzhat Sakib, Nazmus Jerin, Ismot Quader, Shaela Chakrabarty, Amitabha BRAC Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781538621752

Internet of things (IoT) has gained tremendous attention in today's world, IoT ecosystem is growing and showing us a glimpse of how the future will look in the coming days. However, the IoT sector is not impeccable, security is still a threat for the IoT devices as these devices are meant for low power usage for small scale setups. Security algorithms are not abrupt, but many of them don't fit the IoT systems as their compatibility can only rely on products with larger form factor (Which usually means better performance, storage etc.). In this paper, the main concern has been to investigate the performance of different security algorithms and compare them in terms of processing cycle and execution timein Raspberry Pi. The paper has worked with FLECC_IN_C and Crypto++(R), two different cryptographic libraries and measured their performance in a constrained environment. It is the first of its kind, to this paper's knowledge, to use raspberry pi which is established as black box device and implemented security algorithms on it. The paper in the end shows a summarised view of several key algorithms and decides which is better in the terms of IoT constraints.

关键词： IoT security algorithm cryptography Crypto plus plus (R) flecc_in_c algorithm comparison security analysis data security embedded device security

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Is a comparison of results meaningful from the inexact replications of computational experiments?

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SOFT COMPUTING 2016年第1期20卷 223-235页

作者： Crepinsek, Matej Liu, Shih-Hsi Mernik, Luka Mernik, Marjan Univ Maribor SLO-2000 Maribor Slovenia Calif State Univ Fresno Fresno CA 93740 USA CALTECH Pasadena CA 91125 USA

The main objective of this paper is to correct the unreasonable and inaccurate criticism to our previous experiments using Teaching-Learning-Based Optimization algorithm and to quantify the amount of error that may arise due to incorrect counting of fitness evaluations. It is shown that inexact experiment replication should be avoided in comparisons between meta-heuristic algorithms whenever possible. Otherwise, an inexact replication and margin of error should be explicitly reported.

关键词： Teaching-learning-based optimization algorithm comparison Replication of experiments

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Unveiling the development of intracranial injury using dynamic brain EIT: an evaluation of current reconstruction algorithms

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PHYSIOLOGICAL MEASUREMENT 2017年第9期38卷 1776-1790页

作者： Li, Haoting Chen, Rongqing Xu, Canhua Liu, Benyuan Tang, Mengxing Yang, Lin Dong, Xiuzhen Fu, Feng Fourth Mil Med Univ Dept Biomed Engn Xian 710032 Shaanxi Peoples R China Imperial Coll London Dept Biomed Engn London SW7 2AZ England

Objective: Dynamic brain electrical impedance tomography (EIT) is a promising technique for continuously monitoring the development of cerebral injury. While there are many reconstruction algorithms available for brain EIT, there is still a lack of study to compare their performance in the context of dynamic brain monitoring. Approach: To address this problem, we develop a framework for evaluating different current algorithms with their ability to correctly identify small intracranial conductivity changes. Firstly, a simulation 3D head phantom with realistic layered structure and impedance distribution is developed. Next several reconstructing algorithms, such as back projection (BP), damped least-square (DLS), Bayesian, split Bregman (SB) and GREIT are introduced. We investigate their temporal response, noise performance, location and shape error with respect to different noise levels on the simulation phantom. The results show that the SB algorithm demonstrates superior performance in reducing image error. To further improve the location accuracy, we optimize SB by incorporating the brain structure-based conductivity distribution priors, in which differences of the conductivities between different brain tissues and the inhomogeneous conductivity distribution of the skull are considered. We compare this novel algorithm (called SB-IBCD) with SB and DLS using anatomically correct head shaped phantoms with spatial varying skull conductivity. Main results and Significance: The results showed that SB-IBCD is the most effective in unveiling small intracranial conductivity changes, where it can reduce the image error by an average of 30.0% compared to DLS.

关键词： dynamic electrical impedance tomography brain injury algorithm comparison inhomogeneous brain conductivity distribution

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Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study

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ENERGIES 2017年第2期10卷 245-245页

作者： Li, Kangji Pan, Lei Xue, Wenping Jiang, Hui Mao, Hanping Jiangsu Univ Sch Elect Informat Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Inst Agr Engn Zhenjiang 212013 Peoples R China

Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. This paper presents an efficient optimization framework to facilitate optimization designs with the aid of commercial simulation software and MATLAB. The performances of three optimization strategies, including the proposed approach, GenOpt method and artificial neural network (ANN) method, are investigated using a case study of a simple building energy model. Results show that the proposed optimization framework has competitive performances compared with the GenOpt method. Further, in another practical case, four popular multi-objective algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO), the multi-objective genetic algorithm (MOGA) and multi-objective differential evolution (MODE), are realized using the propose optimization framework and compared with three criteria. Results indicate that MODE achieves close-to-optimal solutions with the best diversity and execution time. An uncompetitive result is achieved by the MOPSO in this case study.

关键词： building performance design multi-objective optimization residential building algorithm comparison

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A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses

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SWARM INTELLIGENCE 2016年第2期10卷 99-121页

作者： Ampellio, Enrico Vassio, Luca Politecn Torino Dipartimento Ingn Meccan & Aerosp I-10129 Turin TO Italy Politecn Torino Dipartimento Elettron & Telecomunicaz I-10129 Turin TO Italy

In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results show remarkable competitiveness and robustness for AsBeC.

关键词： Modified Artificial Bee Colony Engineering optimization Interpolation strategies algorithm comparison

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