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Characterizing the coaxial HPGe detector using Monte Carlo simulations and evolutionary algorithms

APPLIED RADIATION AND ISOTOPES 2021年 174卷 109748-109748页

作者： Prozorova, Irina, V Ghal-Eh, Nima Bedenko, Sergey, V Popov, Yury A. Prozorov, Andrey A. Vega-Carrillo, Hector R. Natl Nucl Ctr Republ Kazakhstan Inst Atom Energy Kurchatov Kazakhstan Ferdowsi Univ Mashhad Fac Sci Dept Phys Mashhad Razavi Khorasan Iran Tomsk Polytech Univ Sch Nucl Sci & Engn Tomsk Russia Autonomous Univ Zacatecas Acad Unit Nucl Studies Zac Mexico

A standard procedure for characterizing the high-purity germanium detector (HPGe), manufactured by Canberra Industries Inc., is performed directly by the company using patented methods. However, the procedure is usually expensive and must be repeated because the characteristics of the HPGe crystal changes over time. In this work, the principles of a technique for use in obtaining and optimizing the detector characteristics based on a costeffective procedure in a standard research laboratory were developed. The technique required the geometrical parameters of the detector to be determined as precisely as possible by the Monte Carlo method in parallel with the optimization process based on evolutionary algorithms. The development of this approach facilitated the modeling of the HPGe detector as a standardized procedure. The results would be also beneficial in the development of gamma spectrometers and/or their calibrations before routine measurements.

关键词： HPGe Detector Gamma spectrometer MCNP5 Monte Carlo code differential evolution algorithm Calibration Efficiency

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Optimization Control for Flapping Load Mitigation and Output Power Levelling of Wind Turbine 21st

Optimization Control for Flapping Load Mitigation and Output...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Peng, Chao Zou, Jianzou Li, Yan Geng, Hua Zhang, Zhenzhen Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Southwest Minzu Univ Coll Elect & Informat Engn Chengdu 610041 Peoples R China

Fatigue load affects the secure and stability of wind turbine operation. Flapping load is the main source of fatigue load endured by wind turbine operating above the rated wind speed. In this paper, a novel optimization control method for wind turbine flapping load mitigation and output power leveling is proposed. At first, wind turbine flapping load is analyzed and its calculation model is presented. Secondly, principle of the proposed control method is presented. Considering on coupling between flapping load and output power, a multi-objective optimization model and differential evolution based flapping load optimization controller are then designed. Additional PID-based output power leveling controller is employed to keep the output power leveling around the rated power. Finally, the effectiveness of the proposed control method is demonstrated by NREL 5MW wind turbine simulations. Copyright (C) 2020 The Authors.

关键词： wind turbine pitch angle flapping load calculation model output power leveling differential evolution algorithm

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ACCELERATION METHOD FOR evolutionARY OPTIMIZATION OF VARIABLE CYCLE ENGINE

ACCELERATION METHOD FOR EVOLUTIONARY OPTIMIZATION OF VARIABL...

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ASME Turbomachinery Technical Conference and Exposition (Turbo Expo)

作者： Hao, Wang Zhou, Li Zhang, Xiaobo Wang, Zhanxue Northwestern Polytech Univ Sch Power & Energy Shaanxi Key Lab Internal Aerodynam Aeroengine Xian 710072 Peoples R China

ISBN: (纸本)9780791884058

Variable cycle engine (VCE) is considered as one of the best options for advanced military or commercial supersonic propulsion system. Variable geometries enable the engine to adjust performance over the entire the flight envelope but add complexity to the engine. evolutionary algorithms (EAs) have been widely used in the design of VCE. The initial guesses of the engine model are generally set using design point information during evolutionary optimization. However, the design point information is not suitable for all situations. Without suitable initial guesses, the Newton-Raphson solver will not be able to reach the solution quickly, or even get a convergent solution. In this paper, a new method is proposed to obtain suitable initial guesses of VCE model during evolutionary optimization. differential evolution (DE) algorithm is used to verify our method through a series of optimization cases of a double bypass VCE. The result indicates that the method can significantly reduce the VCE model call number during evolutionary optimization, which means a dramatic reduction in terms of evolution time. And the robustness of the optimization is not affected by the method. The method can also be used in the evolutionary optimization of other engines.

关键词： variable cycle engine evolutionary algorithm differential evolution algorithm evolutionary optimization initial guesses acceleration method

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Estimation of strength of source in a 2D participating media using the differential evolution algorithm

Estimation of strength of source in a 2D participating media...

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Eurotherm Conference 95 - Computational Thermal Radiation in Participating Media IV

作者： Parwani, Ajit K. Talukdar, Prabal Subbarao, P. M. V. Indian Inst Technol Delhi Dept Mech Engn New Delhi 110016 India

A numerical model is developed to estimate the time varying strength of a heat source without prior information about the unknown function. The medium is considered to be gray, absorbing and emitting and the heat transfer is assumed to take place by both radiation and conduction. All the boundaries of the 2-D enclosure are considered to be black with known temperature. The finite-volume method is used for discretization of the energy as well as the radiative transfer equation. This inverse radiation-conduction problem is solved with the minimization of a performance function, which is expressed by the sum of square residuals between calculated and observed temperature, utilizing the differential evolution algorithm. The prediction of the strength of the source by the present algorithm is found to be quite reasonable.

关键词： Inverse radiation finite volume method differential evolution algorithm participating medium

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The Modified differential evolution algorithm (MDEA)

The Modified Differential Evolution Algorithm (MDEA)

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4th International Scientific Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Ramezani, Fatemeh Lotfi, Shahriar Coll Nabi Akram Dept Comp Engn Tabriz Iran Univ Tabriz Dept Comp Sci Tabriz Iran

ISBN: (纸本)9783642284939

differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms. DE has drawn the attention of many researchers resulting in a lot of variants of the classical algorithm with improved performance. This paper presents a new modified differential evolution algorithm for minimizing continuous space. New differential evolution operators for realizing the approach are described, and its performance is compared with several variants of differential evolution algorithms. The proposed algorithm is based on the idea of performing biased initial population. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed differential evolution algorithms. The results indicate that the proposed algorithm is able to arrive at high quality solutions in a relatively short time limit: for the largest publicly known problem instance, a new best solution could be found.

关键词： Modified differential evolution algorithm differential evolution algorithm Optimization Biased Population

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A novel weighted graph representation-based method for structural topology optimization

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ADVANCES IN ENGINEERING SOFTWARE 2021年 153卷 102977-102977页

作者： Jie, Xing Ping, Xu Shuguang, Yao Hui, Zhao Ziliang, Zhao Zhangjun, Wang Cent South Univ Minist Educ Key Lab Traff Safety Track Changsha 410075 Peoples R China Joint Int Res Lab Key Technol Rail Traff Safety Changsha 410075 Peoples R China Natl & Local Joint Engn Res Ctr Safety Technol Ra Changsha 410075 Peoples R China

This paper proposes a novel weighted graph representation for structural topology optimization. Based on the graph theory, a weighted adjacency matrix is first introduced to collect the connectivity information and the corresponding width value of the edges. Accordingly, each edge with different width is symbolized as a rectangle to represent the mapped topology for a regular meshed design domain. To reduce the computational cost, an improved differential evolution (DE) process with a dual self-adaptive mutation operator which is named as the DSADE is proposed to utilize as an optimizer. Finally, three classical numerical tests are carried out. The results indicate that the present method can effectively deal with a series of structural topology optimization problem with different boundary constraints. In addition, by comparing with the related methods in literatures, it is found that the present method can achieve an optimized solution without complex initial definitions.

关键词： Topology optimization Graph theory Weighted adjacency matrix differential evolution algorithm Dual self-adaptive

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The Improved differential evolution algorithm for Partner Selection in Agile Manufacturing

The Improved Differential Evolution Algorithm for Partner Se...

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World Automation Congress (WAC)

作者： Li Xia Zhang Jingmin Liu Kunqi Shijiazhuang Univ Econ Sch Informat Engn Shijiazhuang Peoples R China

ISBN: (纸本)9781467344975

To solve the partner selection problem in agile manufacturing, the paper comes up with a Improved differential evolutionary algorithm for the problem, which compares with the classic differential evolution algorithm, The algorithm has the advantages of simple structure, fast convergence rate and good robustness. Experiment indicates that the Multi-child differential evolutionary algorithm to solve the partner selection problem is excellent in quality, stability and convergence rate.

关键词： Agile Manufacturing Partner Selection differential evolution algorithm Improved differential evolutionary algorithm

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EA-based hyperparameter optimization of hybrid deep learning models for effective drug-target interactions prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 185卷 115525-115525页

作者： Mahdaddi, Abla Meshoul, Souham Belguidoum, Meriem Abdelhamid Mehri Constantine 2 Univ NTIC Fac IFA Dept El Khroub Algeria Princess Nourah Bint Abdulrahman Univ IT Dept RC CCIS Riyadh Saudi Arabia

The identification of drug-target interactions (DTIs) is an important process in drug repositioning and drug discovery. However, it is very expensive and time-consuming to determine all possible DTIs with experimental approaches. Most existing machine learning-based methods formulate the DTIs prediction problem as a binary classification problem. Nevertheless, the lack of experimentally validated negative samples results in imbalanced class distribution within the datasets, which may have a negative influence on the DTI prediction performance. Casting DTI prediction task as a regression problem seems an interesting alternative to avoid this issue especially with the recent increase in protein structural data and DTI datasets. Within this context, a twofold contribution is described in this paper. First, we propose a novel deep learning model for predicting drug-target binding affinities called "Convolution Neural Network with Attention-based bidirectional Long Short-Term Memory network" (CNN-AbiLSTM), which combines a CNN with an attention-based biLSTM. Second, building a powerful hybrid CNN-AbiLSTM model can be highly complicated and requires a suitable setting of the model's hyper parameters. To handle this problem, we propose an evolutionary algorithm-based framework more specifically a differential evolution (DE) algorithm to find the optimal configuration of the proposed model. Experimental results show that the proposed DE-based CNN-AbiLSTM model achieves better performance compared with baseline methods.

关键词： Drug-Target binding affinity prediction Convolutional Neural Network Bidirectional LSTM Attention mechanism differential evolution algorithm

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Modeling and Application of Complex Soil Structure Influenced by Grounding Device of Tower

Modeling and Application of Complex Soil Structure Influence...

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IEEE-Industry-Applications-Society Annual Meeting

作者： Zhang, Dong Zhou, Lijun Huang, Lin Wu, Tongshuai Hu, Chen Liu, Xintong Chen, Sixiang Southwest Jiaotong Univ Coll Elect Engn Chengdu Peoples R China Guangdong Power Grid Co Ltd Foshan Power Supply Bur Foshan Peoples R China

ISBN: (纸本)9781728171920

In transmission lines tower earthing device assessment and reconstruction, the existing tower earthing device will lead to inversion error of soil electric parameters. As a result, the inversion data can't represent the real electric parameters of soil. In order to obtain the accurate soil structure around the tower earthing device, this paper proposes an inversion strategy combining finite element method (FEM) with differential evolution (DE) algorithm. Firstly, FEM is used to calculate the theoretical value of soil apparent resistivity without earthing device, and the effectiveness of FEM is verified by complex image method (CIM). Then, DE algorithm is used to optimize the parameters of soil with earthing device and the optimal values of soil parameters are obtained, the validity of the inversion method is verified by comparing it with the set model. The method has been applied to the reconstruction of earthing device of tower in Foshan city, Guangdong province. The test results show that the reconstruction plan guided by the soil structure obtained by the inversion method can effectively reduce the impulse grounding resistance of the tower in power transmission networks.

关键词： Multi-layer soil parameters inversion earthing device reconstruction finite element method differential evolution algorithm

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Objective Resource Beamforming in Broadband SWIPT System by evolution algorithms 2

Objective Resource Beamforming in Broadband SWIPT System by ...

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2nd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (IEEE ECBIOS) - Biomedical Engineering, Healthcare and Sustainability

作者： Chien, Wei Hsieh, Tzong-Tyng Chiu, Chien-Ching Cheng, Yu-Ting Huang, Chun-Lian Beibu Gulf Univ Dept Sch Elect & Informat Engn Qinzhou Guangxi Peoples R China Ningde Qianwei Ind Technol Co Ltd Ningde Fujian Peoples R China Tamkang Univ Dept Elect & Comp & Engn Dept New Taipei Taiwan

ISBN: (纸本)9781728187129

The evolutionary algorithm beamforming technology of multi-user resource allocation in wireless information and power transmission (SWIPT) system is studied. In large-capacity broadband communication systems, there are many users with wearable devices. The transmitter provides wireless information and power to a specific receiver at the same time, while other receivers can obtain energy from the radio frequency when idle. Under the constraint of bit error rate (BER), by adjusting the length of the feed line of the transmitting antenna and the excitation current, the energy capture efficiency is maximized. Asynchronous particle swarm optimization (APSO) and adaptive dynamic differential evolution algorithm (SADDE) are used to synthesize the optimal radiation pattern. Both APSO and SADDE can form a good pattern to receive energy. However, APSO has a faster convergence rate than SADDE.

关键词： beamforming wireless information and power transfer broadband asynchronous particle swarm optimization differential evolution algorithm

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