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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad guojiang xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed guojiang xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser guojiang xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

基于强化学习知识获取共享算法的太阳电池模型参数辨识

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太阳能学报 2024年第9期45卷 334-344页

作者：熊国江辜再宇贵州大学电气工程学院贵阳550025 贵州大学勘察设计研究院有限责任公司贵阳550025 国网四川省电力公司眉山供电公司眉山620020

为准确辨识太阳电池模型参数,提出一种基于强化学习的知识获取共享算法(RLGSK)。针对知识获取共享算法(GSK)初级阶段和高级阶段的选择机制过于死板,处理太阳电池模型参数辨识问题时难以充分平衡全局与局部搜索,存在收敛慢、精度低等问题... 详细信息

为准确辨识太阳电池模型参数,提出一种基于强化学习的知识获取共享算法(RLGSK)。针对知识获取共享算法(GSK)初级阶段和高级阶段的选择机制过于死板,处理太阳电池模型参数辨识问题时难以充分平衡全局与局部搜索,存在收敛慢、精度低等问题,一方面,通过强化学习调整迭代时初级和高级阶段的个体比例,实现不同情景下知识获取与共享的灵活调整;另一方面,依靠性能导向的种群规模缩减实现计算资源的高效利用,提高算法性能。将RLGSK应用于5种案例,并与其他算法进行比较。结果表明,与GSK相比,RLGSK的搜索精度、稳定度和收敛速度提升极大,与其他算法相比也有很强的竞争力。

关键词：参数辨识太阳电池光伏组件强化学习知识获取共享算法

基于RL-GSK算法的光储联合电站运行策略研究

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电网与清洁能源 2024年第7期40卷 28-37页

作者：杨莎熊国江贵州大学电气工程学院贵州贵阳550025 贵州大学勘察设计研究院有限责任公司贵州贵阳550025

光伏电站配置储能可以促进光伏的最大消纳,减少“弃光”。此外,针对光伏出力和负荷情况合理分配储能充放电功率不仅能满足系统运行要求,还能利用峰谷套利提高光伏并网收益。该文以光储联合电站为研究对象,以电力市场环境下光储联合电站... 详细信息

光伏电站配置储能可以促进光伏的最大消纳,减少“弃光”。此外,针对光伏出力和负荷情况合理分配储能充放电功率不仅能满足系统运行要求,还能利用峰谷套利提高光伏并网收益。该文以光储联合电站为研究对象,以电力市场环境下光储联合电站利润最大化为目标,充分考虑光伏系统与储能系统运行时的各项约束条件,建立了光储联合电站优化运行模型。同时,针对原始知识获取共享算法收敛性能较弱的问题,采用强化学习构建一套系统的个体更新选择机制,提出了强化学习知识获取共享算法,并用来求解光储联合电站的优化运行策略。通过对南方某地区实际数据进行仿真研究,分析了联合电站的优化策略和经济效益模式,并验证了所提方法的有效性。

关键词：光储联合电站优化运行模型强化学习知识获取共享算法运行策略

基于增强型鲸鱼优化算法的交直流互联配电网经济调度

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现代电子技术 2024年第18期47卷 15-21页

作者：叶方慧熊国江杨曌昕徐波贵州大学电气工程学院贵州贵阳550025

随着新能源的不断发展和电力电子技术的广泛应用,交直流互联配电网技术成为未来配电网的发展方向。合理有效的交直流互联配电网调度策略可以提高新能源消纳比例,降低运行成本。文中建立了一种以设备运行维护成本、储能系统运行成本、交... 详细信息

随着新能源的不断发展和电力电子技术的广泛应用,交直流互联配电网技术成为未来配电网的发展方向。合理有效的交直流互联配电网调度策略可以提高新能源消纳比例,降低运行成本。文中建立了一种以设备运行维护成本、储能系统运行成本、交直流双向换流器损耗成本以及配电网与大电网之间购售电成本最小的交直流互联配电网经济调度模型。在满足配电网系统安全运行的约束条件下,为了有效求解该模型,设计一种增强型鲸鱼优化算法,对限定对数螺旋形状的常数b值进行动态更新,提高了算法的精度并加快了其全局收敛。最后进行仿真计算,验证所构建模型的正确性以及算法的优势。结果表明,所提算法的收敛速度更快,全局收敛能力更强,并且有效降低了系统的运行经济成本。

关键词：交直流互联配电网经济调度鲸鱼优化算法新能源消纳储能电压源型换流器光伏发电系统

面向大容量风电并网的VSC-MTDC功率协调控制

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机械与电子 2024年第6期42卷 16-21页

作者：于滨傅晓帆龙凌熊国江贵州大学电气工程学院贵州贵阳550025 贵州大学勘察设计研究院有限责任公司贵州贵阳550025

基于电压源换流站的多端直流输电(VSC-MTDC)已成为大容量风电远距离传输并网的首要选择。针对含大规模风电场的VSC-MTDC系统的功率协调控制策略进行了深入研究。首先,分析大规模风电并网时下垂系数对有功功率分配的影响,指出采用固定下... 详细信息

基于电压源换流站的多端直流输电(VSC-MTDC)已成为大容量风电远距离传输并网的首要选择。针对含大规模风电场的VSC-MTDC系统的功率协调控制策略进行了深入研究。首先,分析大规模风电并网时下垂系数对有功功率分配的影响,指出采用固定下垂系数存在优先级电网功率缺额的可能性和缺乏有功功率分配灵活性等问题。然后,针对此类问题,提出一种基于主从控制与自适应下垂控制相结合的功率协调控制策略,该策略根据风电功率情况制定3种控制模式,从而实现风电功率灵活分配。最后,在MATLAB/Simulink上构建了风电并网的四端柔性直流输电系统,仿真结果验证了所提控制策略的可行性和有效性。

关键词：多端柔性直流输电有功功率分配协调控制策略自适应下垂控制风电并网

基于粒子群差分进化极限学习机的电力系统故障诊断模型

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机械与电子 2024年第3期42卷 60-64,70页

作者：张耀姚瑶陈卓袁子霞熊国江贵州电网有限责任公司电力调度控制中心贵州贵阳550002 贵州大学电气工程学院贵州贵阳550025

针对电力系统发生的故障进行快速诊断,对电网及时恢复供电、降低故障影响具有非同寻常的意义。为了有效处理电力系统故障中存在的保护继电器和断路器运行的不确定性,提出了一种基于多重随机变异粒子群差分进化算法(MRPSODE)的极限学习... 详细信息

针对电力系统发生的故障进行快速诊断,对电网及时恢复供电、降低故障影响具有非同寻常的意义。为了有效处理电力系统故障中存在的保护继电器和断路器运行的不确定性,提出了一种基于多重随机变异粒子群差分进化算法(MRPSODE)的极限学习机故障诊断模型,利用MRPSODE算法确定极限学习机最佳的隐含层节点个数,实现高效率的故障诊断。采用交叉验证方法降低噪声对原始样本数据的影响,确保诊断性能。实际故障案例的仿真分析结果表明,所提方法能够成功诊断复杂故障,与其他方法相比具有较强的竞争力。

关键词：故障诊断极限学习机进化算法交叉验证

电网故障诊断改进解析模型及其自适应生物地理学优化方法

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电工技术学报 2014年第4期29卷 205-211页

作者：熊国江石东源华中科技大学强电磁工程与新技术国家重点实验室武汉430074

针对电网故障诊断原有解析模型存在多解的问题,在详细剖析产生多解原因的基础上,根据保护和断路器的动作具有不确定性的特点,综合考虑故障发生时各类保护动作的优先权信息建立了新的改进解析模型。改进解析模型赋予不同保护和断路器不... 详细信息

针对电网故障诊断原有解析模型存在多解的问题,在详细剖析产生多解原因的基础上,根据保护和断路器的动作具有不确定性的特点,综合考虑故障发生时各类保护动作的优先权信息建立了新的改进解析模型。改进解析模型赋予不同保护和断路器不同的贡献因子,使模型更加合理,符合实际需求。此外,还给出了求解改进解析模型的自适应生物地理学优化方法,旨在实现模型的高效求解。算例仿真结果表明:改进解析模型诊断结果唯一,且诊断结果合理;自适应生物地理学优化方法具有全局快速收敛的良好优化性能。

关键词：电力系统故障诊断改进解析模型贡献因子自适应生物地理学优化算法