检索结果-内蒙古大学图书馆

A new hybrid optimization prediction model for PM2.5 concentration considering other air pollutants and meteorological conditions

引用

CHEMOSPHERE 2022年第Part3期307卷 135798页

作者： Yang, Hong Liu, Zehang Li, Guohui Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

With the development of economy, the problem of air pollution has become increasingly serious. As an important detection index of air pollutants, how to accurately and effectively predict PM2.5 concentration is a significant issue related to human health and development. In this paper, a new hybrid optimization prediction model for PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEM-DAN), variational mode decomposition optimized by COOT optimization algorithm (COOT-VMD), and least square support vector machine (LSSVM) optimized by the jaya optimization algorithm (jaya-LSSVM), named CEEMDAN-COOT-VMD-jaya-LSSVM, is proposed. To avoid artificially setting the limits of the decomposition layer and penalty factor of VMD parameters, an improved VMD by COOT optimization algorithm, named COOT-VMD, is proposed. First, the original sequence of PM2.5 concentration is decomposed by CEEMDAN. Second, the high complexity component with low prediction accuracy after once decomposition is decomposed by COOT-VMD. Third, a prediction model of optimized LSSVM by jaya optimization algorithm, named jaya-LSSVM, is proposed. jaya-LSSVM is used to predict all components considering other air pollutants such as PM10, SO2, NO2, CO, and O3 and meteorological conditions such as wind speed, temperature, sunlight, relative humidity and average air pressure. Finally, all predicted values are reconstructed to obtain the final prediction results. PM2.5 concentration from April 1, 2016 to March 29, 2021 in Xi'an and Shenyang is used as the experimental data to verify the proposed model. The results of experiment in Xi'an show that the RMSE, MAE, MAPE and R(2 )are 2.843, 1.8344, 2.94%, and 0.99525 respectively. The results of experiment in Shenyang show that the RMSE, MAE, MAPE and R2 are 2.2714, 1.673, 3.13%, and 0.99573 respectively. Compared to other single and hybrid models, the proposed model can accurately predict PM2.5 concentration. Diebold Mar

关键词： PM2.5 concentration prediction Secondary decomposition COOT optimization algorithm Least square support vector machine jaya optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement

引用

ENGINEERING WITH COMPUTERS 2021年第3期37卷 1943-1959页

作者： Chen, Siyu Gu, Chongshi Lin, Chaoning Zhang, Kang Zhu, Yantao Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utili Nanjing 210098 Peoples R China

The observation data of dam displacement can reflect the dam's actual service behavior intuitively. Therefore, the establishment of a precise data-driven model to realize accurate and reliable safety monitoring of dam deformation is necessary. This study proposes a novel probabilistic prediction approach for concrete dam displacement based on optimized relevance vector machine (ORVM). A practical optimization framework for parameters estimation using the parallel jaya algorithm (PJA) is developed, and various simple kernel/multi-kernel functions of relevance vector machine (RVM) are tested to obtain the optimal selection. The proposed model is tested on radial displacement measurements of a concrete arch dam to mine the effect of hydrostatic, seasonal and irreversible time components on dam deformation. Four algorithms, including support vector regression (SVR), radial basis function neural network (RBF-NN), extreme learning machine (ELM) and the HST-based multiple linear regression (HST-MLR), are used for comparison with the ORVM model. The simulation results demonstrate that the proposed multi-kernel ORVM model has the best performance for predicting the displacement out of range of the used measurements dataset. Meanwhile, the ORVM model has the advantages of probabilistic output and can provide reasonable confidence interval (CI) for dam safety monitoring. This study lays the foundation for the application of RVM in the field of dam health monitoring.

关键词： Optimized relevance vector machine Multi-kernel jaya optimization algorithm Dam health monitoring Prediction model

来源：评论

学校读者我要写书评

暂无评论

A novel algorithm for computing interior eigenpairs of large non-symmetric matrices

引用

SOFT COMPUTING 2021年第17期25卷 11865-11876页

作者： Jamalian, Ali Aminikhah, Hossein Univ Guilan Dept Comp Sci Rasht Iran Univ Guilan Dept Appl Math Rasht Iran Univ Guilan Ctr Excellence Math Modelling Optimizat & Combina POB 41938-19141 Rasht Iran

One challenging problem in numerical linear algebra is computing eigenvalues from the interior of the spectrum of large matrices. The harmonic Arnoldi method is one of the methods that can be used to find interior eigenpairs of a large non-symmetric matrix. However, it has been shown that this method may converge erratically and even may fail. In this paper, we present a novel and efficient method for computing interior eigenpairs of large non-symmetric matrices. The proposed eigensolver is a hybrid of the harmonic Arnoldi algorithm and the jaya optimization algorithm, which is called jaya-HA. Numerical experiments have tested the accuracy and performance of the jaya-HA, and computational results show that this algorithm converges faster and provides superior performance.

关键词： Harmonic Arnoldi method jaya optimization algorithm Interior eigenproblem Eigenpair Non-symmetric matrix

来源：评论

学校读者我要写书评

暂无评论

A Hybrid DL Architecture for Improved Generalizability with Self-Adaptive jaya Optimizer for Diabetic Retinopathy

引用

Procedia Computer Science 2024年 235卷 2090-2100页

作者： Akhilesh Rawat Rajeev Kumar Data to Knowledge (D2K) Lab School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi 110 067 India

Anomalies such as microaneurysms, exudates, and hemorrhages are diagnosed by retinal vessel segmentation to identify different phases of Diabetic Retinopathy (DR); several typical ways to detect Hard Exudates (HE) in retinal images have been used to determine the severity of diabetes. This work proposes a hybrid approach to better segment such abnormal cells. It optimizes the learning parameters of a Convolutional Neural Network (CNN) with a nature-inspired optimizer, the Self-Adaptive jaya optimization algorithm (SAJOA). The SAJOA helps in avoiding trapping in local minima. It uses the SAJOA to steer the search for obtaining near-optimal parameter values for the CNN training. We choose the learning parameters such as batch size, dropout rate, learning rate, etc. The batch size affects the batch normalization performance, and the dropout rate involves the regularization property of the model. The near-optimal values of these two parameters help improve the generalization ability of the deep learning architecture. Thus, this hybrid approach improves the CNN performance and generalizability, leading to precise segmentation of unseen data, which aids in diagnosing diabetic retinopathy. The results of many trials were evaluated against the DRIVE dataset; the proposed method achieved accuracy and F1 score on average, higher by 7 to 8% than the state-of-the-art methods.

关键词： Diabetic Retinopathy Hard Exudates Deep Learning Convolutional Neural Network Nature-Inspired algorithms jaya optimization algorithm Generalization

来源：评论

学校读者我要写书评

暂无评论

Optimal Walking Gait Generator for Biped Robot Using Modified jaya optimization Technique

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 382-399页

作者： Ho Pham Huy Anh Tran Thien Huang VNU HCM Fac Elect & Elect Engn FEEE HCM Univ Technol Ho Chi Minh City Vietnam HCM City Univ Technol & Educ Fac Appl Sci FAS Ho Chi Minh City Vietnam

This paper treats the optimization of the biped walking trajectory that can be used as a reference trajectory for control. The biped robot is modeled as a kinetic chain of 11 links connected by 10 joints. The inverse kinematics of the biped is derived for the specified positions of the hips and feet. The objective is to optimize the biped robot able to stably and naturally walking with preset foot-lift magnitude (or preset hip-shift, or preset step-length). The stability of the biped robot is quantified by the distance between the ZMP and the foot center in the step cycle, which represents the first objective function. Additionally, for the biped robot to follow the preset foot-lift value, the difference between the magnitude of foot-lift value and the foot-lift preset value represents the second objective function. Specifically, we minimize the value of the two objective functions by considering the gait parameters of biped robot as variables. The new jaya optimization algorithm is innovatively applied to optimize the biped gait parameters as to ensure the biped robot walking robustly and steadily. The efficiency of the proposed jaya-based identification method is compared with the Genetic algorithm (GA), the Particle Swarm optimization (PSO), the Central Force optimization (CFO) and improved Differential Evolution (DE) [Modified Differential Evolution (MDE)] algorithms. The simulation results tested on the real small-sized biped robot system HUBOT-4 demonstrate that the novel proposed algorithm offers an efficient and stable gait for biped robot with precise foot-lift value. (C) 2020 The Authors. Published by Atlantis Press SARL.

关键词： Uncertain nonlinear biped robot system Genetic algorithm (GA) Particle Swarm optimization (PSO) jaya optimization algorithm Central Force optimization (CFO) algorithm Modified Differential Evolution (MDE) algorithm Gait optimization for biped robot Zero moment point (ZMP) concept

来源：评论

学校读者我要写书评

暂无评论

Real-time measurement method of drilling fluid rheological parameters based on multi-objective inversion

引用

MEASUREMENT 2023年 223卷

作者： Zou, Jialing Ni, Yuanlei Liang, Haibo Southwest Petr Univ Sch Mech Engn Chengdu 610500 Peoples R China Southwest Petr Univ Mech Engn 8 Xindu Rd Chengdu Sichuan Peoples R China

Real-time online fast and accurate measurement of drilling fluid performance parameters is very important in the petroleum extraction process. At present, there are few studies on the measurement of drilling fluid performance parameters, and the measurement of drilling fluid performance parameters on the well site mainly uses a rotational viscometer, which cannot achieve online real-time measurement. Therefore, this study developed a real-time online measurement system for drilling fluid rheological performance parameters based on the principle of pipeline differential pressure measurement, and realized online measurement of parameters such as yield stress, liquidity index, and consistency coefficient. In order to obtain accurate three rheological parameters, this study conducted a numerical simulation study of the drilling fluid measurement system based on the finite element method to realize the analysis of fluid pressure distribution in the pipeline. Secondly, the surrogate model between the differential pressure and rheological parameters, which is based on an extreme learning machine (ELM) optimized by particle swarm algorithm (PSO) is set up. Finally, based on the accuracy and robustness of the multi-objective function of inversion calculation, an intelligent inversion model for rheological parameters with a multi-objective jaya algorithm is proposed to realize real-time online fast and accurate acquisition of drilling fluid rheological parameters.

关键词： Drilling fluids Rheological properties parameters Multi -target inversion Extreme Learning Machine jaya optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Consideration of the Single-Phase Photovoltaic and Energy Storage Joint Regulation of a Three-Phase Unbalanced Control Strategy in a Power Distribution System

引用

ENERGIES 2023年第12期16卷 4817页

作者： Wang, Min Wang, Huilin Zuo, Fanglin Zou, Jie Chen, Yuan Yu, Zixuan Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China

With a large amount of distributed power and energy storage access, the traditional three-phase unbalanced treatment of a power distribution system is mainly aimed at the three-phase unbalance of a load, which cannot effectively address the three-phase unbalance problem of a power distribution network after a large number of single-phase photovoltaic access. Therefore, this paper proposes a three-phase unbalanced treatment strategy for the distribution network, which considers the joint regulation ability of single-phase photovoltaic and energy storage and the regulation ability of a reactive power compensation device. Firstly, the joint regulation ability of single-phase photovoltaic and energy storage under different photovoltaic permeability is analyzed. Secondly, according to the joint regulation ability of single-phase photovoltaic and energy storage and the regulation ability of reactive power compensation device, the three-phase power optimization model is constructed to minimize the three-phase unbalance degree and regulation cost, and the jaya optimization algorithm is used to solve the model. Finally, the 33-node distribution system is used to verify the effectiveness of the proposed strategy.

关键词： single-phase photovoltaic and energy storage power distribution system three-phase unbalance jaya optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Selection and evaluation of FOPID criteria for the X-15 adaptive flight control system (AFCS) via Lyapunov candidates: Optimizing trade-offs and critical values using optimization algorithms

引用

e-Prime - Advances in Electrical Engineering, Electronics and Energy 2023年 6卷

作者： Basil, Noorulden Marhoon, Hamzah M. Department of Electrical Engineering College of Engineering Mustansiriyah University Baghdad Iraq Department of Systems Engineering College of Information Engineering Al-Nahrain University Baghdad Jadriya Iraq

Recently, there has been a growing interest among academics worldwide in studying flight control systems. The advancement of tracking technologies, such as the X-15 adaptive flight control system developed at NASA (National Aeronautics and Space Administration), has sparked significant exploration efforts by scientists. The vast availability of aerial resources further contributes to the importance of studying adaptive flight control systems (AFCS). The successful operation of AFCS relies on effectively managing the three fundamental motions: pitch, roll, and yaw. Therefore, scientists have been diligently working on developing optimization algorithms and models to assist AFCS in achieving optimal gains during motion. However, in real-world scenarios, each motion requires its own set of criteria, which presents several challenges. Firstly, there are multiple criteria available for selecting appropriate optimization values for each motion. Secondly, the relative importance of these criteria influences the selection process. Thirdly, there is a trade-off between the performance of the criteria within a single optimization case and across different cases. Lastly, determining the critical value of the criteria poses another obstacle. Consequently, evaluating and selecting optimum methods for AFCS trajectory controls becomes a complex operation. To address the need for optimizing AFCS for various maneuvers, this study proposes a new selection process. The suggested approach involves utilizing black hole optimization (BHO), jaya optimization algorithm (JOA), and sunflower optimization (SFO) as methods for detecting and correcting trajectories in adaptive flight control systems. These methods aim to determine the best launch of missiles from the AFCS based on the coordinate location for both long and short distances. Additionally, the methods determine the optimal gains for the FOPID (fractional order proportional integral derivative) controller and enhance protection agai

关键词： Adaptive flight control system Black hole optimization FOPID controller criteria jaya optimization algorithm Sunflower optimization

来源：评论

学校读者我要写书评

暂无评论

optimization of radioactive sources to achieve the highest precision in three-phase flow meters using jaya algorithm

引用

APPLIED RADIATION AND ISOTOPES 2018年 139卷 256-265页

作者： Roshani, G. H. Karami, A. Khazaei, A. Olfateh, A. Nazemi, E. Omidi, M. Kermanshah Univ Technol Elect Engn Dept Kermanshah Iran Razi Univ Mech Engn Dept Kermanshah Iran Islamic Azad Univ Kermanshah Branch Young Researchers & Elite Club Kermanshah Iran Nucl Sci & Technol Res Inst Tehran Iran

Gamma ray source has very important role in precision of multi-phase flow metering. In this study, different combination of gamma ray sources ((Ba-133-Cs-137), (Ba-133-Co-60), ((AmCs)-Am-241-Cs-137), (Am-241-Co-60), (Ba-133-Am-241) and ((CoCs)-Co-60-Cs-137)) were investigated in order to optimize the three-phase flow meter. Three phases were water, oil and gas and the regime was considered annular. The required data was numerically generated using MCNP-X code which is a Monte-Carlo code. Indeed, the present study devotes to forecast the volume fractions in the annular three-phase flow, based on a multi energy metering system including various radiation sources and also one NaI detector, using a hybrid model of artificial neural network and jaya optimization algorithm. Since the summation of volume fractions is constant, a constraint modeling problem exists, meaning that the hybrid model must forecast only two volume fractions. Six hybrid models associated with the number of used radiation sources are designed. The models are employed to forecast the gas and water volume fractions. The next step is to train the hybrid models based on numerically obtained data. The results show that, the best forecast results are obtained for the gas and water volume fractions of the system including the (Am-241-Cs-137) as the radiation source.

关键词： Three-phase flow Gamma ray sources Artificial neural network jaya optimization algorithm MCNP-X code

来源：评论

学校读者我要写书评

暂无评论

Multi-parameter inverse analysis of concrete dams using kernel extreme learning machines-based response surface model

引用

ENGINEERING STRUCTURES 2022年 256卷 113999-113999页

作者： Kang, Fei Liu, Xi Li, Junjie Li, Hongjun Dalian Univ Technol Fac Infrastruct Engn Sch Hydraul Engn Dalian 116024 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100048 Peoples R China

Inverse analysis by finite element model (FEM) based on measured displacement data is a popular approach for parameter identification of concrete dams. FEM-based inverse analysis method is economical and effective, but with some limitations such as complex computation and time-consuming. This paper presents a kernel extreme learning machine (KELM)-based response surface model (RSM) for parameter inverse analysis of concrete dams. The FEM is replaced by KELM-based RSM to explore the relationships between material parameters and displacement response of dam-foundation systems. Sample data of KELM-based RSM are generated by the efficient sampling technique Latin hypercube sampling. Subsequently, a novel optimization algorithm jaya is adopted to minimize the objective function for material parameters identification. The effectiveness and practicability of the proposed approach are verified by two cases and two real concrete gravity dams in operation with sufficient monitoring data. Comparative studies with direct finite element method and several optimization algorithms were also performed to demonstrate the superiority of the proposed approach over commonly used techniques. Results show that the proposed KELM-based RSM is a simple and efficient way to achieve high accuracy in parameter inverse analysis of concrete dams at a low computation cost.

关键词： Concrete dams Inverse back analysis Kernel extreme learning machines jaya optimization algorithm Displacements

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：