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An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR

REMOTE SENSING 2022年第1期14卷 210-210页

作者： Li, Yongkang Huo, Tianyu Yang, Chenxi Wang, Tong Wang, Juan Li, Beiyu Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China China North Ind Grp Corp Ltd North Automat Control Technol Inst Taiyuan 030006 Peoples R China

This paper studies the imaging of a ground moving target with airborne circular stripmap synthetic aperture radar (CSSAR). First, the range equation of a target moving with accelerations is developed. Then, a new range model of high accuracy is proposed, since the commonly used second-order Taylor-approximated range model is inaccurate when the azimuth resolution is relatively high or the target moves with accelerations. The proposed range model also makes it easy to derive an accurate analytical expression for the target's 2-D spectrum. Third, based on the proposed range model, the target's 2-D spectrum is derived and an efficient imaging method is proposed. The proposed imaging method implements focusing via a phase multiplication in the 2-D frequency domain and utilizes the genetic algorithm to accomplish an efficient search of the parameters of the proposed range model. Finally, numerical experiments are conducted to validate the proposed range model and the proposed imaging method.

关键词： circular stripmap synthetic aperture radar imaging method range equation model ground moving target imaging generic algorithm

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An enhanced breeding swarms algorithm for high dimensional optimisations

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2020年第3期15卷 181-193页

作者： Hansen, Jon A. Sund, Jorgen Tollemache, Dylan Arefi, Ali Nourbakhsh, Ghavameddin Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld Australia Murdoch Univ Discipline Engn & Energy Perth WA Australia

This paper proposes a metaheuristic optimisation algorithm named enhanced breeding swarms (EBS), which combines the strengths of particle swarm optimisation (PSO) with those of genetic algorithm (GA). In addition, EBS introduces three modifications to the original breeding swarms to improve the performance and the accuracy of the optimisation algorithm. These modifications are applied on the acceptance criteria based on the improved glowworm swarm optimisation, velocity impact factor, and the mutation operator. The EBS algorithm is tested and compared against GA, PSO, and original BS algorithms, using unrotated and rotated six recognised optimisation benchmark functions. Results indicate that the EBS outperforms GA, PSO, and BS in most cases in terms of accuracy and speed of convergence, especially when the dimension of optimisation increases. As an application of the proposed EBS algorithm, a load flow analysis on a 6-bus network is performed, and the comparison results against another heuristic algorithm and the Newton-Raphson are reported.

关键词： enhanced breeding swarms EBS particle swarm optimisation PSO generic algorithm metaheuristic improved glowworm swarm optimisation IGSO computational intelligence

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Solder Joint Reliability Risk Estimation by AI-Assisted Simulation Framework with Genetic algorithm to Optimize the Initial Parameters for AI Models

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MATERIALS 2021年第17期14卷 4835-4835页

作者： Yuan, Cadmus Fan, Xuejun Zhang, Gouqi Feng Chia Univ Dept Mech & Comp Aided Engn Taichung 407082 Taiwan Lamar Univ Dept Mech Engn Beaumont TX 77710 USA Delft Univ Technol Dept Elect Components Technol & Mat NL-2628 CD Delft Netherlands

Solder joint fatigue is one of the critical failure modes in ball-grid array packaging. Because the reliability test is time-consuming and geometrical/material nonlinearities are required for the physics-driven model, the AI-assisted simulation framework is developed to establish the risk estimation capability against the design and process parameters. Due to the time-dependent and nonlinear characteristics of the solder joint fatigue failure, this research follows the AI-assisted simulation framework and builds the non-sequential artificial neural network (ANN) and sequential recurrent neural network (RNN) architectures. Both are investigated to understand their capability of abstracting the time-dependent solder joint fatigue knowledge from the dataset. Moreover, this research applies the genetic algorithm (GA) optimization to decrease the influence of the initial guessings, including the weightings and bias of the neural network architectures. In this research, two GA optimizers are developed, including the "back-to-original" and "progressing" ones. Moreover, we apply the principal component analysis (PCA) to the GA optimization results to obtain the PCA gene. The prediction error of all neural network models is within 0.15% under GA optimized PCA gene. There is no clear statistical evidence that RNN is better than ANN in the wafer level chip-scaled packaging (WLCSP) solder joint reliability risk estimation when the GA optimizer is applied to minimize the impact of the initial AI model. Hence, a stable optimization with a broad design domain can be realized by an ANN model with a faster training speed than RNN, even though solder fatigue is a time-dependent mechanical behavior.

关键词： solder joint fatigue risk estimation wafer level chip-scaled packaging artificial neural network recurrent neural network generic algorithm principle component analysis time temperature-dependent nonlinearity

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Off-Line Time Aware Scheduling of Bag-of-Tasks on Heterogeneous Distributed System

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IEEE ACCESS 2019年 7卷 104777-104788页

作者： Xuan, Hejun Wei, Shiwei Li, Yanling Guo, Huaping Xinyang Normal Univ Sch Comp & Informat Technol Xinyang 464000 Peoples R China Guilin Univ Aerosp Technol Sch Comp & Technol Guilin 541000 Peoples R China Xinyang Normal Univ Henan Key Lab Anal & Applicat Educ Big Data Xinyang 464000 Peoples R China

The resource allocation for bag-of-tasks in the heterogeneous distributed system is to distribute the tasks to proper processors such that the makespan is minimized. It is a well-known NP-hard problem, and is even more complex and challenging when the processors have off-line time. To tackle this challenging problem, first, we set up a mathematical model for this problem which minimizes the makespan of the bag-of-tasks with the off-line time segment of the processors. Second, to solve the model efficiently, we propose two new algorithms: a new scheduling algorithm referred to as sorting-allocation-pulling scheduling algorithm which first allocate the tasks to available time segment on proper processors and then pulls them to the formerly available time segment for the sake of minimizing the makespan, and an effective genetic algorithm with a novel local search operator and a well-designed modify operator. Finally, the numerical simulation experiments are conducted, and the two proposed algorithms are compared. The experimental results indicate the effectiveness of the proposed model and algorithms.

关键词： Distributed computing task scheduling bag-of-tasks generic algorithm

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Genetic algorithm-based adaptive weight decision method for motion estimation framework

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JOURNAL OF SUPERCOMPUTING 2019年第4期75卷 1909-1921页

作者： Chae, Jeongsook Jin, Yong Wen, Mingyun Zhang, Weigiang Sung, Yunsick Cho, Kyungeun Dongguk Univ Seoul Dept Multimedia Engn 26 Pildong 3 Ga Seoul 100715 South Korea

Recently, the diverse virtual reality devices are developed and utilized. Particularly, the devices that recognize the motions of users such as griping hands and opening hands are issued to utilize the motions of the users as input methodology. Traditional research about motion recognition suggests user's motion estimation methods by calculating Bayesian probability after measuring the orientation of the motions by a Myo, which is one of contact-type motion recognition devices. However, the motion estimation methods have the problem of low motion estimation accuracy, given that orientation is defined by x, y, and z, which are calculated separately only considering the values of the corresponding axis. In order to improve motion estimation accuracy, motions should be estimated by considering the values of all axis. This paper proposes a method using genetic algorithm to calculate weights, which are applied to estimate motions through Bayesian probability by considering the values of all axis after measuring user's motions with a Myo. The proposed method consists of three steps. First, the Bayesian probability is calculated by considering the correlations of x, y, and z of the orientation of a Myo. Second, weights are determined by applying genetic algorithm. Third, motions are estimated through the Bayesian probability with the determined weights. Experiments were conducted to compare the Bayesian probability between the traditional method based on min/max and the proposed method, which showed that the proposed method had reduced the difference of the orientations by 32%.

关键词： Bayesian probability generic algorithm Motion estimation Weight

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Loan Default Prediction Model Improvement through Comprehensive Preprocessing and Features Selection

Loan Default Prediction Model Improvement through Comprehens...

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International Arab Conference on Information Technology (ACIT)

作者： Al-qerem, Ahmad Al-Naymat, Ghazi Alhasan, Mays Zarqa Univ Comp Sci Dept Zarqa Jordan Princess Sumaya Univ Technol King Hussein Sch Comp Sci Amman Jordan

ISBN: (纸本)9781728130101

For financial institutions and the banking industry, it is very crucial to have predictive models for their financial activities, as they play a major role in risk management. Predicting loan default is one of the critical issues that they focus on, as huge revenue loss could be prevented by predicting customer's ability to pay back on time. In this paper, different classification methods (Naive Bayes, Decision Tree, and Random Forest) are being used for prediction, comprehensive different pre-processing techniques are being applied on the dataset, and three different feature extractions algorithms are used to enhance the accuracy and the performance. Results are compared using F1 accuracy measure, and an improvement of over 3% has been obtained.

关键词： Classification Pre-processing Prediction Features selection generic algorithm PSO algorithm Naive Bayes decision tree SVM Random Forest

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A hybrid PSO-GA algorithm for optimization of laminated composites

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2017年第6期55卷 2111-2130页

作者： Barroso, Elias Saraiva Parente, Evandro, Jr. Cartaxo de Melo, Antonio Macario Univ Fed Ceara Dept Engn Estrutural & Construcao Civil Lab Mecan Computac & Visualizacao Campus PiciBloco 728 BR-60455760 Fortaleza Ceara Brazil

This paper presents a new hybrid Particle Swarm algorithm (PSO) for optimization of laminated composite structures. The method combines the standard PSO heuristics with Genetic algorithm operators in order to improve the algorithm performance. Thus, operations that are important to the optimization of laminated composites such as mutation and layer swap are incorporated into the method. A specially designed encoding scheme is used to represent the laminate variables and the associated velocities. A study is carried-out to select the best variant of the proposed method for the optimization of laminated composites, considering different swarm topologies and genetic operators. Both strength maximization and weight minimization problems are considered. A meta-optimization procedure is used to tune the parameters of each variant in order to avoid biased results. The results showed that the proposed method led to excellent results for both traditional and dispersed laminates, representing a significant improvement over the standard PSO algorithm.

关键词： Laminated composites Particle swarm optimization generic algorithm Meta-Optimization

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Sensitivity analysis of acquisition granularity of photovoltaic output power to capacity configuration of energy storage systems

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APPLIED ENERGY 2017年 203卷 794-807页

作者： Han, Xiaojuan Liu, Dahe Liu, Jian Kong, Lingda North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Baoding 071003 Hebei Peoples R China China Elect Power Res Inst Beijing 100092 Peoples R China

Acquisition granularity and acquisition span are two important indexes to analyze the active power of renewable energy power stations, and it is important for the analysis of the intermittent energy output power to determine the acquisition granularity of the data. An acquisition granularity calibration method of the photovoltaic output power based on data mining technology is proposed in this paper. The sensitivity changing characteristics of the time series and the sum of the output power are respectively extracted by the multi-scale descriptive statistical analysis and interpolation method. A "power trapezoidal continuous changing state" method is proposed to establish a multi-objective optimization model for the acquisition granularity calibration of the photovoltaic output power. Genetic algorithm (GA) and Particle swarm optimization (PSO) algorithm are respectively used to solve the model and determine the optimal acquisition granularity of the photovoltaic output power. The sensitivity of the acquisition granularity of the data to the capacity of the energy storage system is analyzed, and the energy storage system with the optimal acquisition granularity can't only effectively smooth the fluctuation of the photovoltaic output power but also keep the main information of the data. The simulation tests of the annual actual operation data at a photovoltaic power station with the installed capacity of 14 MW in China verify the validity of the model. The simulation results show when the acquisition granularity of the photovoltaic output power takes 45 s, it can satisfy the accuracy of the required data for the capacity configuration of the energy storage system. The method proposed in this paper provides a theoretical basis for the intermittent energy applications and has a certain engineering application prospects. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multi-objective optimization generic algorithm Acquisition granularity calibration Capacity configuration Trapezoidal continuous changing state

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generické algoritmy

Generické algoritmy

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作者： Snítilá, Jitka Charles University of Prague

This thesis focuses on the lower bounds for generic algorithms for discrete logarithms problem and Diffie-Hellmans problems. This thesis introduces two diffrent models of Black-Box for that purpose. On these models thesis approxi- mates and compares success probability of generic algorithms for given problems including Maurers reduction. This reduction solves discrete logarithms problem using a appropriate elliptic curve and a Diffie-Hellmans oracle. This thesis also researches generic algorithm for identifiaction schemes, that are based on discrete logarithms problem. 1

关键词： generic algorithm Black-Box Diffie-Hellmans problems discrete logarithms problem Maurers reduction

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An integrated multi-objective optimization model for determining the optimal solution in the solar thermal energy system

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ENERGY 2016年 102卷 416-426页

作者： Kim, Jimin Hong, Taehoon Jeong, Jaemin Lee, Myeonghwi Koo, Choongwan Lee, Minhyun Ji, Changyoon Jeong, Jaewook Yonsei Univ Dept Architectural Engn Seoul 03722 South Korea Purdue Univ Div Construct Engn & Management W Lafayette IN 47906 USA

The STE (solar thermal energy) system is considered an important new renewable energy resource. While various simulations are used as decision-making tools in implementing the STE system, it has a limitation in considering both diverse impact factors and target variables. Therefore, this study aimed to develop an integrated multi-objective optimization model for determining the optimal solution in the STE system. As the optimization algorithm, this study utilizes GA (genetic algorithm) to select optimal STE system solution. Using crossover and mutation, GA investigates optimal STE system solution. The proposed model used GA based on the software program Evolver 5.5. The proposed model presents high available and efficient results as decision-making tools. First, to determine the optimal solution, a total of 30,407,832 possible scenarios were generated by considering various factors in terms of their high availability. Second, in terms of efficiency, an average of 131 s were used to determine the optimal solution out of the previously proposed various scenarios. The proposed model can become a tool for consumers to decide on the optimal solution for the design of the STE system. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Solar thermal energy system Multi-objective optimization generic algorithm Economic and environmental assessment

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