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multi-parameter optimization and design of self-triggered low voltage hybrid DC circuit breaker based on machine learning

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 244卷

作者： Zhang, Zhaozi Gao, Caizhi Cao, Weidong Wang, Qian Chen, Silei Li, Xingwen Huang, Yinfang Gong, Zuoyong Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China Xian Univ Technol Sch Sci Xian 710048 Peoples R China Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China Shanghai Liangxin Elect Co Ltd Nader Shanghai 201206 Peoples R China

The low-voltage direct current (LVDC) hybrid circuit breaker (HCB), with advantages of low conduction loss and high breaking performance is better to meet the application needs of photovoltaic system. Among these, selftriggered hybrid circuit breakers (STHCBs) based on natural commutation have become an important development direction for LVDC HCBs due to their simple structure and selective protection. However, existing design methods do not adequately consider the influence of multiple parameters, resulting in unreliable current commutation during interruption. To address these issues, this paper proposes a machine learning-based multiparameter optimization method. The method employed Long Short-Term Memory (LSTM) algorithm to predict the interruption waveforms of mechanical switches under different short-circuit fault conditions as an input to the optimization. In addition, an interruption model of HCBs was developed, which comprehensively considered power electronic device characteristics, arc behavior and drive circuit. Based on the theoretical analysis and predicted waveforms, we used Genetic Algorithm (GA) to determine the optimal design parameters for the corresponding interruption waveforms. Experiment results confirm that this method can effectively enhance the interruption reliability of STHCBs while also demonstrates adaptability. The proposed method provides technical reference for the design of LVDC HCBs.

关键词： LVDC Hybrid circuit breaker Arc prediction multi-parameter optimization Short-circuit interruption Machine learning algorithms

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multi-parameter optimization maximizes the performance of genetically engineered Geobacillus for degradation of high-concentration nitroalkanes in wastewater

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BIORESOURCE TECHNOLOGY 2022年第0期347卷 126690-126690页

作者： Sun, Shenmei Song, Xiaoru Bian, Ya Wan, Xuehua Zhang, Jingjing Wang, Wei Nankai Univ TEDA Inst Biol Sci & Biotechnol Key Lab Mol Microbiol & Technol Minist EducTEDA Tianjin 300457 Peoples R China Tianjin Key Lab Microbial Funct Genom Tianjin 300457 Peoples R China

Nitroalkanes are important toxic pollutants for which there is no effective removal method at present. Although genetic engineering bacteria have been developed as a promising bioremediation strategy for years, their actual performance is far lower than expected. In this study, important factors affecting the application of engineered Geobacillus for nitroalkanes degradation were comprehensively optimized. The deep-reconstructed engineered strains significantly raised the expression and activity level of catalytic enzymes, but failed to fully enhance the degradation efficiency. However, further debugging of a variety of key parameters effectively improved the performance of the engineering strains. The increased cell membrane permeability, trace supplementation of vital nutritional factors, synergy of multifunctional enzyme engineered bacteria, switch of oxygen-supply mode, and moderate initial biomass all effectively boosted the degradation efficiency. Finally, a low-cost and highly effective bioreactor test for high-concentration nitroalkanes degradation proved the multi-parameter optimization mode helps to maximize the performance of genetically engineered bacteria.

关键词： Geobacillus Genetic engineering Nitroalkanes multi-parameter optimization Bioreactor

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multi-parameter optimization Analysis of Hydrodynamic Performance for Rim-Driven Thruster

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ENERGIES 2023年第2期16卷 891-891页

作者： Nie, Yuanzhe Ouyang, Wu Zhang, Zhuo Li, Gaoqiang Zheng, Ruicong Wuhan Univ Technol Sch Naval Architecture Ocean & Energy Power Engn Wuhan 430063 Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Peoples R China Reliabil Engn Inst Natl Engn Res Ctr Water Transport Safety Wuhan 430063 Peoples R China East Lake Lab Wuhan 420202 Peoples R China Guangzhou Haigong Marine Equipment Co Ltd Guangzhou 510000 Peoples R China

The efficiency of rim-driven thrusters (RDT) has always been the focus of attention in the context of energy conservation and environmental protection. A multi-parameter collaborative optimization framework is proposed to improve the efficiency of RDT based on the response surface method (RSM). The common structural parameters of RDT, including pitch ratio, disk ratio and rake angle, are selected as design variables to carry out the Box-Behnken experimental design combined with the simulation data obtained through CFD calculations. The response surface second-order model is employed to evaluate the extent to which different parameters can affect the target variable and obtain the optimal hydraulic efficiency. The results show that the established model has high precision, good reproducibility and strong anti-interference ability. The influence of the pitch ratio, rake angle and disk ratio on open water efficiency decreases in sequence. Compared with the prototype RDT, the maximum efficiency of the optimized RDT is increased by 13.8%, and the surface pressure distribution and flow field characteristics are also significantly modified.

关键词： rim-driven thrusters (RDT) response surface method (RSM) hydrodynamic performance computational fluid dynamics (CFD) multi-parameter optimization

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A multi-parameter optimization Setting Method of Reactive Power Control for Fault Recovery Process of Doubly-Fed Wind Farm 5

A Multi-parameter Optimization Setting Method of Reactive Po...

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5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)

作者： Xiao, Qiang Zhang, Shiling Ye, Fan Wu, Jungang Xie, Lu Hu, Jian State Grid Chongqing Econ & Technol Res Inst Co L Chongqing Peoples R China Southwest Univ Coll Engn & Technol Chongqing Peoples R China

ISBN: (纸本)9798350363272;9798350363265

After the grid-connected system of doubly-fed wind farm fails, it typically undergoes the stages of fault period, fault removal and normal operation. At present, the related research primarily focuses on reactive power control (RPC) for low voltage ride-through (LVRT) of wind farms during the fault period. In fact, the problem of high voltage off-grid caused by transient overvoltage during wind power recovery after fault removal also needs attention. This paper provides a multi-parameter optimization setting method of RPC for the fault recovery process of doubly-fed wind farm. The implementation of RPC by this method can fully consider the limit capacity and response speed of different means such as rotor side converter (RSC), grid side converter (GSC) of doubly-fed induction generator (DFIG) and reactive power compensation device, and coordinate with the fault protection action of the transmission line of wind power system, and improve the voltage transient recovery trajectory of wind farm. The effectiveness of the proposed method is verified by a multiple parallel doubly-fed wind farm test system.

关键词： doubly-fed wind farm fault recovery converter multi-parameter optimization reactive power control

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Monitoring soil salinization and its spatiotemporal variation at different depths across the Yellow River Delta based on remote sensing data with multi-parameter optimization

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第16期29卷 24269-24285页

作者： Cheng, Tiantian Zhang, Jiahua Zhang, Sha Bai, Yun Wang, Jingwen Li, Shuaishuai Javid, Tehseen Meng, Xianglei Sharma, Til Prasad Pangali Qingdao Univ Coll Comp Sci & Technol Remote Sensing Informat & Digital Earth Ctr Qingdao 266071 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Earth & Planetary Sci Beijing Peoples R China

Soil salinization is recognized as a key issue negatively affecting agricultural productivity and wetland ecology. It is necessary to develop effective methods for monitoring the spatiotemporal distribution of soil salinity at a regional scale. In this study, we proposed an optimized remote sensing-based model for detecting soil salinity in different depths across the Yellow River Delta (YRD), China. A multi-dimensional model was built for mapping soil salinity, in which five types of predictive factors derived from Landsat satellite images were exacted and tested, 94 in-situ measured soil salinity samples with depths of 30-40 cm and 90-100 cm were collected to establish and validate the predicting model result. By comparing multiple linear regression (MLR) and partial least squares regression (PLSR) models with considering the correlation between predictive factors and soil salinity, we established the optimized prediction model which integrated the multi-parameter (including SWIR1, SI9, MSAVI, Albedo, and SDI) optimization approach to detect soil salinization in the YRD from 2003 to 2018. The results indicated that the estimates of soil salinity by the optimized prediction model were in good agreement with the measured soil salinity. The accuracy of the PLSR model performed better than that of the MLR model, with the R-2 of 0.642, RMSE of 0.283, and MAE of 0.213 at 30-40 cm depth, and with the R-2 of 0.450, RMSE of 0.276, and MAE of 0.220 at 90-100 cm depth. From 2003 to 2018, the soil salinity showed a distinct spatial heterogeneity. The soil salinization level of the coastal shoreline was higher;in contrast, lower soil salinization level occurred in the central YRD. In the last 15 years, the soil salinity at depth of 30-40 cm experienced a decreased trend of fluctuating, while the soil salinity at depth of 90-100 cm showed fluctuating increasing trend.

关键词： Soil salinization Remote sensing multi-parameter optimization Yellow River Delta Spatiotemporal dynamic

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The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations

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JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN 2024年第1期38卷 14-14页

作者： Jones, Jeremy Clark, Robert D. Lawless, Michael S. Miller, David W. Waldman, Marvin Simulat Plus Inc 42505 10th St West Lancaster CA 93534 USA Indiana Univ Luddy Sch Informat Comp & Engn 700 N Woodlawn Ave Bloomington IN 47408 USA

Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. In the last decade, several programs have been developed that incorporate these properties into the drug design and optimization process and to varying degrees, allowing for multi-parameter optimization. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with an advanced evolutionary algorithm that is quite different than current generative models. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine inhibitors of the dihydroorotate dehydrogenase from Plasmodium falciparum to illustrate how AIDD generates novel sets of molecules.

关键词： Artificial intelligence De novo drug design Evolutionary algorithms multi-parameter optimization Pareto optimization Pharmacokinetic simulation QSAR QSPR Molecular modeling Generative chemistry

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A multi-parameter optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10652页

作者： Hao, Jiaxing Yang, Sen Gao, Hongmin Shijiazhuang Tiedao Univ Sch Elect & Elect Engn Shijiazhuang 050043 Peoples R China Army Engn Univ Dept UAV Engn Shijiazhuang 050003 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

Electromagnetic technology is widely applied in numerous fields, and precise electromagnetic characteristic fitting technology has become a crucial part for enhancing system performance and optimizing design. However, it faces challenges such as high computational complexity and the difficulty in balancing the accuracy and generalization ability of the model. For example, the Radar Cross Section (RCS) distribution characteristics of a single corner reflector model or Luneberg lens provide a relatively stable RCS value within a certain airspace range, which to some extent reduces the difficulty of radar target detection and fails to truly evaluate the radar performance. This paper aims to propose an innovative multi-parameter optimization method for electromagnetic characteristic fitting based on deep learning. By selecting common targets such as reflectors and Luneberg lens reflectors as optimization variables, a deep neural network model is constructed and trained with a large amount of electromagnetic data to achieve high-precision fitting of the target electromagnetic characteristics. Meanwhile, an advanced genetic optimization algorithm is introduced to optimize the multiple parameters of the model to meet the error index requirements of radar target detection. In this paper, by combining specific optimization variables such as corner reflectors and Luneberg lenses with the deep learning model and genetic algorithm, the deficiencies of traditional methods in handling electromagnetic characteristic fitting are effectively addressed. The experimental results show that the 60 degrees corner reflector successfully realizes the simulation of multiple peak characteristics of the target, and the Luneberg lens reflector achieves the simulation of a relatively small RCS average value with certain fluctuations in a large space range, which strongly proves that this method has significant advantages in improving the fitting accuracy and optimization efficiency, opening up

关键词： deep learning electromagnetic characteristics multi-parameter optimization neural network

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Correlation analysis based multi-parameter optimization of the organic Rankine cycle for medium- and high-temperature waste heat recovery

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APPLIED THERMAL ENGINEERING 2021年 188卷 116626-116626页

作者： Zhang, Tao Liu, Liuchen Hao, Junhong Zhu, Tong Cui, Guomin Univ Shanghai Sci & Technol Sch Energy & Power Engn Shanghai 200093 Peoples R China China Acad Bldg Res Beijing 100013 Peoples R China North China Elect Power Univ Sch Energy Power & Mech Engn Minist Educ Key Lab Power Stn Energy Transfer Convers & Syst Beijing 102206 Peoples R China Tongji Univ Sch Mech Engn Shanghai 201803 Peoples R China

The present paper established a multi-parameter optimization model of the organic Rankine cycle (ORC) to recover medium- and high-temperature waste heat. The preliminary screening of 42 pure working fluids is completed according to the limitations of environment and safety requirements. Afterwards, the relationship between heat source temperature, working fluid physical properties, and system net output power are analyzed. It can be found that there is a parabola variation relationship between working fluid critical temperature and system net power output. When the net power output reaches its peak value, a further increase of working fluid critical temperature will causes the opposite effect. Furthermore, correlation analysis is introduce to investigate the variation relationship between system net output power and working fluid critical temperature, system evaporating temperature/pressure, and condensing temperature/pressure by different heat source temperatures is investigated in detail. Results show that it is not much effective to use a working fluid with great pressure ratio for the design of a high-efficiency ORC system. On the contrary, using working fluid with a high temperature ratio can always improve the system net power output. Besides, the working fluid specific heat difference/ratio, and the latent heat difference/ratio during the evaporation and condensation processes jointly influence the ORC output performance. As a result, the influence order of working fluid key properties to the system net power output is: temperature ratio > pressure ratio > latent heat > specific heat.

关键词： Organic Rankine cycle (ORC) Working fluid multi-parameter optimization Net power output Correlation analysis

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multi-parameter optimization of Vented CMUTs for Airborne Applications

Multi-Parameter Optimization of Vented CMUTs for Airborne Ap...

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IEEE International Ultrasonics Symposium (IUS)

作者： Ma, Bo Chang, Chienliu Oguz, Huseyin kagan Firouzi, Kamyar Khuri-Yakub, Butrus T. Stanford Univ Dept Elect Engn Edward L Ginzton Lab Stanford CA 94305 USA

ISBN: (纸本)9781538633830

This paper reports vented CMUTs with wide bandwidth and high sensitivity, optimized by multi-parameter optimization method. The dominated parameters, including the radius and thickness of the flexible plate, gap height, and the size and distribution of vented holes have been individually optimized. The impedance measurements well-match the simulation results. Then the multi-parameter optimization method in place of the individual parameter optimization method is used to optimize the design parameters all together. Finally, keeping the approximate bandwidth, the multi-parameter optimization makes the sensitivity improved by 11% at the same percentage of pull-in voltage, and driving voltage is lowered by 49% compared with the individual parameter optimization.

关键词： CMUTs individual parameter optimization multi-parameter optimization wide bandwidth high sensitivity

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A multi-parameter optimization Model for the Evaluation of Shale Gas Recovery Enhancement

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ENERGIES 2018年第3期11卷 654-654页

作者： Liu, Jia Wang, Jianguo Leung, Chunfai Gao, Feng China Univ Min & Technol State Key Lab Geomech & Deep Underground Engn Xuzhou 221116 Jiangsu Peoples R China Natl Univ Singapore Ctr Offshore Res & Engn E1A-07-031 Engn Dr 2 Singapore 117576 Singapore China Univ Min & Technol Sch Mech & Civil Engn Xuzhou 221116 Jiangsu Peoples R China

Although a multi-stage hydraulically fractured horizontal well in a shale reservoir initially produces gas at a high production rate, this production rate declines rapidly within a short period and the cumulative gas production is only a small fraction (20-30%) of the estimated gas in place. In order to maximize the gas recovery rate (GRR), this study proposes a multi-parameter optimization model for a typical multi-stage hydraulically fractured shale gas horizontal well. This is achieved by combining the response surface methodology (RSM) for the optimization of objective function with a fully coupled hydro-mechanical FEC-DPM for forward computation. The objective function is constructed with seven uncertain parameters ranging from matrix to hydraulic fracture. These parameters are optimized to achieve the GRR maximization in short-term and long-term gas productions, respectively. The key influential factors among these parameters are identified. It is established that the gas recovery rate can be enhanced by 10% in the short-term production and by 60% in the long-term production if the optimized parameters are used. Therefore, combining hydraulic fracturing with an auxiliary method to enhance the gas diffusion in matrix may be an effective alternative method for the economic development of shale gas.

关键词： shale gas reservoir multi-scale flow multi-parameter optimization response surface methodology (RSM) gas recovery rate (GRR)

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