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Multi-layered medium ultrasonic phased array sparse TFM imaging based on self-adaptive differential evolution algorithm

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第11期35卷 115402-115402页

作者： Yao, Shuxin Zhao, Jianjun Du, Xiaozhong Zhang, Yanjie Zhang, Zhong Taiyuan Univ Sci & Technol Sch Mech Engn Taiyuan 030024 Peoples R China Taiyuan Univ Sci & Technol Sch Energy & Mat Engn Jincheng 048000 Peoples R China Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan 030024 Peoples R China Sunny Grp Co Ltd Ningbo 315400 Peoples R China

Multilayer Composite material structures have been widely used in modern engineering fields. However, defects within these materials can adversely affect mechanical properties. Ultrasonic phased array total focusing method (TFM) imaging has advantages of high precision and dynamic focusing over the entire range, achieving significant progress in homogeneous medium detection. However, heavy computational burdens of multilayer structures lead to inefficient imaging. To address this issue, a sparse-TFM imaging algorithm using ultrasonic phased arrays suitable for multilayer media is proposed in this paper. This method constructs a fitness function with constraints such as main lobe width and sidelobe peak. Its objective is to obtain the distribution of sparse array element positions using an self-adaptive differential evolution algorithm. Subsequently, the delay time of each array element in multilayer media sparse TFM is calculated using the root mean square (RMS) principle and combined with amplitude weighting, the method corrects the imaging results. Compared with the Ray-based full-matrix capture and TFM method (Ray-based FMC/TFM), the RMS-based full-matrix capture and TFM (RMS-based FMC/TFM), and the phase shift method, the experimental and simulation results demonstrate that the proposed method significantly reduces the imaging data volume, improves computational efficiency, and maintains quantitative errors within 0.2 mm.

关键词： multilayer media structures self-adaptive differential evolution algorithm sparse total focusing method ultrasonic phased array

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Integrated Renewable Energy Storage System with Enhanced self-adaptive differential evolution algorithm on Profit Maximization

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第8期52卷 1474页

作者： Kani, J. Shanmuga Ulagammai, M. Anna Univ Dept Elect & Elect Engn Chennai India Saveetha Engn Coll Dept Elect & Elect Engn Chennai 602105 India

In this work addresses the surge in greenhouse gas emissions and fuel costs resulting from heightened energy demand, especially in developing nations. To counter these challenges, the focus is on optimizing renewable energy sources, which, though advantageous, are weather-dependent and require intricate management. The study introduces the Enhanced self-adaptive differential evolution (SADE) algorithm, encompassing solar, battery, and thermal sources, to maximize profitability. Real-time (RT) load profiles are used for performance analysis, comparing the proposed algorithm with existing techniques like PSO, differential evolution (DE) algorithm, and SADE algorithm. Improved energy storage technologies complement the increased utilization of renewable energy, enabling electricity storage during off-peak hours and release during peak demand, providing promising solutions for the energy industry.

关键词： battery energy storage profit maximization self-adaptive differential evolution algorithm particle swarm optimization differential evolution algorithm

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Global optimization of a vapor compression refrigeration system with a self-adaptive differential evolution algorithm

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APPLIED THERMAL ENGINEERING 2021年 197卷 117427-117427页

作者： Kong, Dehao Yin, Xiaohong Ding, Xudong Fang, Ning Duan, Peiyong Qingdao Univ Sci & Technol Sch Automat & Elect Engn Qingdao 266061 Shandong Peoples R China Shandong Prov Key Lab Intelligent Buildings Techn Jinan 250101 Peoples R China Shandong Inst Commerce & Technol Dept Basic Courses Jinan 250103 Shandong Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China

This paper presents a global optimization strategy for the vapor compression refrigeration system (VCRS) based on self-adaptive differential evolution (SADE) algorithm to minimize the system energy consumption with the indoor cooling requirements fulfilled. The simplified hybrid VCRS model is established based on the thermodynamics and heat transfer theory, together with the parameter regression method, to predict the system performance and energy consumption under different working conditions. Then considering the interactions between components, the influences of varying indoor loads and outdoor air conditions, a global optimization problem with constraints is formulated. The SADE algorithm is used to solve the global optimization problem, and the optimal variables settings are obtained. Simulation results demonstrate that the proposed global optimization strategy can improve the VCRS energy efficiency in different time periods while satisfying the cooling demands. Averagely 15.57% energy saving on the typical testing day can be achieved. Moreover, significant energy savings can be obtained during the morning and evening periods with partial indoor cooling loads. Furthermore, to show the effectiveness of proposed SADE strategy, the DE and classical PSO algorithms are compared, and the results indicate that the SADE algorithm can significantly reduce the calculation time, and can avoid local minimum. This research brings a feasible methodology, to reduce the energy consumption of air conditioning systems effectively.

关键词： Vapor compression refrigeration system Global optimization strategy Energy savings self-adaptive differential evolution algorithm

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A self-adaptive differential evolution algorithm for Transmission Network Expansion Planning with System Losses Consideration

A Self-Adaptive Differential Evolution Algorithm for Transmi...

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IEEE International Conference on Power and Energy (PECon)

作者： Sum-Im, Thanathip Ongsakul, Weerakorn Srinakharinwirot Univ Dept Elect Engn Fac Engn Nakhon Nayok 26120 Thailand Asian Inst Technol Sch Environm Energy Field Study Resources & Dev Pathum Thani 12120 Thailand

ISBN: (纸本)9781467350198;9781467350174

In this paper, a self-adaptive differential evolution algorithm (SaDEA) is applied directly to the DC power flow based model in order to efficiently solve transmission network expansion planning (TNEP) problem. The purpose of TNEP is to minimize the transmission investment cost associated with the technical operation and economical constraints. The TNEP problem is a large-scale, complex and nonlinear combinatorial problem of mixed integer nature where the number of candidate solutions to be evaluated increases exponentially with system size. In addition, the TNEP problem with system losses consideration is also investigated in this paper. The efficiency of the proposed method is initially demonstrated via the analysis of low and medium complexity transmission network test cases. A detailed comparative study among conventional genetic algorithm (CGA), tabu search (TS), artificial neural networks (ANNs), hybrid artificial intelligent techniques and the proposed method is presented. From the obtained experimental results, the proposed technique provides the accurate solution, the feature of robust computation, the simple implementation and the satisfactory computational time.

关键词： Transmission network expansion planning self-adaptive differential evolution algorithm transmission line losses

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Synthesis and Design of 5G Duplexer Based on Optimization Method

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ZTE Communications 2022年第3期20卷 70-76页

作者： WU Qingqiang CHEN Jianzhong WU Zengqiang GONG Hongwei National Key Laboratory of Antennas and Microwave Technology Shaanxi Joint key Laboratory of GrapheneXidian UniversityXi'an 710071China ZTE Corporation Shenzhen 518057China

A new optimization method is proposed to realize the synthesis of *** traditional optimization method takes all the variables of the duplexer into account,resulting in too many variables to be optimized when the order of the duplexer is too high,so it is not easy to fall into the local *** order to solve this problem,a new optimization strategy is proposed in this paper,that is,two-channel filters are optimized separately,which can reduce the number of optimization variables and greatly reduce the probability of results falling into local *** optimization method combines the self-adaptive differential evolution algorithm(SADE)with the Levenberg-Marquardt(LM)algorithm to get a global solution more easily and accelerate the optimization *** verify its practical value,we design a 5 G duplexer based on the proposed *** duplexer has a large external coupling,and how to achieve a feed structure with a large coupling bandwidth at the source is also *** experimental results show that the proposed optimization method can realize the synthesis of higher-order duplexers compared with the traditional methods.

关键词： optimization self-adaptive differential evolution algorithm LM optimization algorithm filter synthesis duplexer

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A Two-Phase Coverage Control algorithm for self-Orienting Heterogeneous Directional Sensor Networks

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IEEE ACCESS 2020年 8卷 88215-88226页

作者： Li, Ming Hu, Jiangping Cao, Xiaoli Chongqing Technol & Business Univ Coll Comp Sci & Informat Engn Engn Lab Detect Control & Integrated Syst Chongqing 400067 Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

Maximizing network coverage is very important for heterogeneous directional sensor networks (HDSNs). HDSNs consist of directional sensor nodes with different parameters in terms of the sensing radius, the communication radius and the angle of view. After the initial deployment of the HDSNs, coverage problems occur, such as overlapped areas and holes in the coverage, which are not covered by any one of the sensor nodes. To solve these coverage problems, motility and mobility are often utilized. Motility means the ability to adjust the working direction of the nodes, while mobility presents the physical movement of the nodes. The coverage problem that we solved in this paper is characterized by the heterogeneity of the directional sensor nodes in terms of the sensing radius, the field of angle, the motility, the mobility, and the communication radius. A two-phase algorithm was proposed to solve the coverage optimization problem of the HDSNs. In the first phase of the proposed algorithm, a stepwise method was used to adjust the working direction of the nodes;in the second phase of the proposed algorithm, a virtual force-directed self-adaptive differential evolution algorithm was proposed to achieve a tradeoff between the coverage and the energy consumption. Extensive simulation experiments were performed to demonstrate the effectiveness of our proposed algorithm over alternative algorithms in terms of the coverage rate and energy consumption.

关键词： Heuristic algorithms Sensors Energy consumption Optimization Wireless sensor networks Cameras Business Directional sensor networks coverage control heterogeneous network self-adaptive differential evolution algorithm virtual force

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An improved deep forest classification algorithm

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2022年第4期40卷 305-314页

作者： Ding, Jiaman Liu, Cuihua Li, Runxin You, Jinguo Jia, Lianyin Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China

Recent studies suggest that deep forest is also a kind of deep learning while overcoming the problems of excessive parameters and hard-to-adjust parameters in deep neural networks. However, the deep forest does not consider the classification contributions of each forest or the time and memory consumption caused by high-dimensional features, thus leading to inefficiency. To solve these problems, an improved deep forest (IgcForest) classification algorithm is proposed in this paper. IgcForest first preserves the class distribution vectors of the main features by a pooling strategy to realise the feature reduction and reuse. Next, an adaptive weighting strategy is proposed to calculate the weight of each forest in the cascade structure. Experiments are carried out on multiple UCI datasets to verify the effectiveness of IgcForest and the results demonstrate that IgcForest achieves better results in different evaluation indexes than other algorithms.

关键词： deep learning deep forest ensemble methods pooling strategy self-adaptive differential evolution algorithm

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A self-adaptive differential evolution algorithm for Dynamic Economic Dispatch with Valve-Point Effects

A Self-Adaptive Differential Evolution Algorithm for Dynamic...

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International Universities Power Engineering Conference

作者： Thanathip Sum-Im Department of Electrical Engineering Faculty of Engineering Srinakharinwirot University

ISBN: (纸本)9781467328548

In this paper, a self-adaptive differential evolution algorithm (SaDEA) is proposed for solving dynamic economic dispatch (DED) problem with valve-point effects consideration. The purpose of DED problem is to minimize the total generation costs of thermal power plants associated with the technical and economical constraints. The software development has been performed within the mathematical programming environment of MATLAB in this work. The efficiency and effectiveness of the proposed technique is initially demonstrated via the analysis of 3-unit and 10-unit test systems considering valve-point loading and ramp rate constraints. A detailed comparative study among an evolutionary programming (EP), a particle swarm optimization (PSO), an enhanced particle swarm optimization (EPSO), an enhanced particle swarm optimization with Gaussian mutation (EPSO-GM), a hybrid method between evolutionary programming and sequential quadratic programming (EP-SQP), a modified hybrid EP-SQP (MHEP-SQP) and the proposed method is presented. From the experimental results, the proposed method has the achieved solutions with good accuracy, stable convergence characteristics, simple implementation and satisfactory computational time.

关键词： Power generation Dynamic economic dispatch self-adaptive differential evolution algorithm

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High-temperature oxidation of methyl isopropyl ketone: A shock tube experiment and a kinetic model

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COMBUSTION AND FLAME 2019年 209卷 376-388页

作者： Cheng, Jia Zou, Chun Lin, Qianjin Liu, Shibo Wang, Yunpeng Liu, Yang Huazhong Univ Sci & Technol State Key Lab Coal Combust Wuhan 430074 Hubei Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518057 Peoples R China

Pentanone has three types of isomers: 3-methyl-2-butanone (methyl isopropyl ketone, MIPK), 3-pentanone (diethyl ketone, DEK) and 2-pentanone (methyl propyl ketone, MPK). Since MIPK is the smallest branched ketone and a promising biofuel, it is important to fully understand its combustion kinetics. In this work, the ignition delay times were measured in the temperature range from 1150 to 1600 K at pressures from 1 to 5 bar and for the equivalence ratios of 0.5, 1.0 and 1.5 (Mixes 1-9). A high-temperature detailed combustion kinetic model for MIPK was developed via a hierarchical method. Previous theoretical computations of the H-atom abstraction by H(O)over dot(2) and (O)over dotH *** the tertiary carbon of MIP were adopted, and the reaction rate coefficients of H-abstraction by (H)over dot from the MIPK tertiary carbon were calculated via quantum chemical techniques. The rate coefficients of the reactions that are related to the acetyl group and the isopropyl group of MIPK were estimated by analogy with MPK and DIPK, respectively. Eleven elementary reactions in the submodel that are related to MIPK were selected for the optimization due to the poor prediction performance of the raw model. The self-adaptive differential evolution algorithm was applied to optimize the raw kinetic model based on the ignition delay times of Mixes 1-6. The optimized MIPK model was validated against the ignition delay times of Mixes 7-9. A brute-force sensitivity analysis, in combination with a flux analysis, demonstrates that the amount of unsaturated fuel-related stable intermediate products is the key factor that determines the reactivity of MIPK and DEK. (C) 2019 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

关键词： Methyl isopropyl ketone Kinetic model Ignition delay time self-adaptive differential evolution algorithm

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Estimation of Static Young's Modulus for Sandstone Formation Using Artificial Neural Networks

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ENERGIES 2019年第11期12卷 2125页

作者： Mahmoud, Ahmed Abdulhamid Elkatatny, Salaheldin Ali, Abdulwahab Moussa, Tamer King Fand Univ Petr & Minerals Coll Petr Engn & Geosci Dhahran 31261 Saudi Arabia King Fand Univ Petr & Minerals Ctr Integrat Petr Res Dhahran 31261 Saudi Arabia

In this study, we used artificial neural networks (ANN) to estimate static Young's modulus (E-static) for sandstone formation from conventional well logs. ANN design parameters were optimized using the self-adaptive differential evolution optimization algorithm. The ANN model was trained to predict E-static from conventional well logs of the bulk density, compressional time, and shear time. The ANN model was trained on 409 data points from one well. The extracted weights and biases of the optimized ANN model was used to develop an empirical relationship for E-static estimation based on well logs. This empirical correlation was tested on 183 unseen data points from the same training well and validated using data from three different wells. The optimized ANN model estimated E-static for the training dataset with a very low average absolute percentage error (AAPE) of 0.98%, a very high correlation coefficient (R) of 0.999 and a coefficient of determination (R-2) of 0.9978. The developed ANN-based correlation estimated E-static for the testing dataset with a very high accuracy as indicated by the low AAPE of 1.46% and a very high R and R-2 of 0.998 and 0.9951, respectively. In addition, the visual comparison of the core-tested and predicted E-static of the validation dataset confirmed the high accuracy of the developed ANN-based empirical correlation. The ANN-based correlation overperformed four of the previously developed E-static correlations in estimating E-static for the validation data, E-static for the validation data was predicted with an AAPE of 3.8% by using the ANN-based correlation compared to AAPE's of more than 36.0% for the previously developed correlations.

关键词： static young's modulus artificial neural networks self-adaptive differential evolution algorithm sandstone reservoirs

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