检索结果-内蒙古大学图书馆

Sensorless intelligent second-order integral sliding mode maximum power point tracking control for wind turbine system based on wind speed estimation

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2021年第7期235卷 1046-1063页

作者： Ma, Leiming Xiao, Lingfei Yang, Jianfeng Huang, Xinhao Meng, Xiangshuo Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Room 402 Nanjing 210016 Peoples R China Lanzhou Jiaotong Univ Automat & Elect Engn Lanzhou Peoples R China

Aiming at the maximum power point tracking for wind turbine, a sensorless intelligent second-order integral sliding mode control based on wind speed estimation is proposed in this article. The maximum wind energy capture is realized by controlling permanent magnet synchronous motor to adjust the speed of wind turbine. First, an intelligent second-order integral sliding mode control is designed for the speed loop and current loop control, which has fast convergence speed, strong robustness and can effectively reduce chattering. Second, a novel cascade observer based on direct sliding mode observer and extended high-gain observer is used to estimate the rotor speed and position. Besides, combined radial basis function neural network is used to estimate the valid value of wind speed. Both simulation and experiment are implemented, which verify the effectiveness of the proposed strategy under the condition of considering both model uncertainty and external disturbance.

关键词： Wind turbine second-order integral sliding mode maximum power point tracking sensorless control wind speed estimation optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

The development trend of China's natural gas consumption: A forecasting viewpoint based on grey forecasting model

引用

ENERGY REPORTS 2021年 7卷 4308-4324页

作者： Zhang, Jun Qin, Yanping Duo, Huaqiong Inner Mongolia Agr Univ Coll Sci Hohhot 010018 Peoples R China Inner Mongolia Agr Univ Coll Mat Sci & Art Design Hohhot 010018 Peoples R China

In the context of global emission reduction, as a type of clean and eco-friendly energy, natural gas has attracted wide attention. Accurate prediction of natural gas consumption can provide reference for relevant departments to formulate the corresponding development plans and strategies. At present, there are many forecasting tools for processing and analysing natural gas consumption, but most of them have the disadvantages of complex modelling or low precision. In order to solve this problem, inspired by the polynomial regression prediction model and the discrete grey prediction model, a novel polynomial discrete grey prediction model with fractional order accumulation (abbreviated as FPDGM(1,1) model) is established in this paper. In order to find the optimal parameters of the FPDGM(1,1) model, three most popular intelligent algorithms are used to solve the parameters of the model, and then the optimal parameters from the three intelligent algorithms are selected in this paper. In addition, the advantages of the FPDGM(1,1) model over other four forecasting models are confirmed through two parts in this paper. First, the natural gas consumptions of four municipalities in China are used as cases to verify the effectiveness of the FPDGM(1,1) model in this paper. Subsequently, the FPDGM(1,1) model and several kinds of frequently used forecasting models are applied to analyse and predict China's natural gas consumption, and the FPDGM(1,1) model is used to predict China's natural gas consumption in the next 5 years. The results of these two parts indicate that the FPDGM(1,1) model shows the best predictive performance in all cases. Lastly, based on the predicted results of the FPDGM(1,1) model, some reasonable suggestions are put forward for China's natural gas development plan. (C) 2021 Published by Elsevier Ltd.

关键词： Natural gas consumption Polynomial regression prediction model Discrete grey prediction model optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A new algorithm for load shifting operation of water pumping stations

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

引用

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2021年第4期36卷 2081-2093页

作者： Kucuk, Hayri Turan, Mustafa Yarali, Kadir Al-Sanabani, Hussein Iskefiyeli, Murat Sakarya Univ Fac Engn Dept Elect & Elect Engn TR-54050 Sakarya Turkey Sakarya Univ Fac Comp & Informat Sci Dept Comp Engn TR-54050 Sakarya Turkey

The highest cost item in the operation of water distribution systems is the energy cost used for pumping. With regard to Time-of-Use (TOU) electricity tariffs and urban water demand, the optimization of the pumping schedule of the stations in the water distribution systems can be modeled mathematically. In this study, a new algorithm (SDPA- simplified dynamic programming algorithm) was used to solve the pumping schedule optimization problem in order to reduce the pumping energy cost. By shifting the energy demand during the on-peak period to the off-peak period, SDPA was applied to a pumping station belonging to a water distribution system in Sakarya province, a 12% saving on the cost of energy was realized. In comparison to the IDPA (Improved dynamic programming algorithm), and CWLC (conventional water level control) approach, the simulation results showed that SDPA can achieve more savings in energy cost generated. In addition, SDPA managed to keep the count of pump on-off decisions quite small by preventing the problem of increasing pump operating frequency while the decision period getting very smaller than the decision period in IDPA approach. Therefore, we can conclude that using SDPA to reconfigure the pumping schedule without any additional investment in the pumping stations of water distribution systems, can achieve significant energy savings.

关键词： Energy efficiency load shifting optimal pumping scheduling optimization algorithm water distribution system

来源：评论

学校读者我要写书评

暂无评论

Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification

引用

IEEE ACCESS 2021年 9卷 36019-36037页

作者： El-Kenawy, El-Sayed M. Mirjalili, Seyedali Ibrahim, Abdelhameed Alrahmawy, Mohammed El-Said, M. Zaki, Rokaia M. Eid, Marwa Metwally Delta Higher Inst Engn & Technol DHIET Dept Commun & Elect Mansoura 35111 Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea Mansoura Univ Fac Engn Comp Engn & Control Syst Dept Mansoura 35516 Egypt Mansoura Univ Fac Comp & Informat Dept Comp Sci Mansoura 35516 Egypt Mansoura Univ Fac Engn Elect Engn Dept Mansoura 35516 Egypt Delta Higher Inst Engn & Technol DHIET Mansoura 35111 Egypt Benha Univ Shoubra Fac Engn Dept Elect Engn Banha 11629 Egypt

The chest X-ray is considered a significant clinical utility for basic examination and diagnosis. The human lung area can be affected by various infections, such as bacteria and viruses, leading to pneumonia. Efficient and reliable classification method facilities the diagnosis of such infections. Deep transfer learning has been introduced for pneumonia detection from chest X-rays in different models. However, there is still a need for further improvements in the feature extraction and advanced classification stages. This paper proposes a classification method with two stages to classify different cases from the chest X-ray images based on a proposed Advanced Squirrel Search optimization algorithm (ASSOA). The first stage is the feature learning and extraction processes based on a Convolutional Neural Network (CNN) model named ResNet-50 with image augmentation and dropout processes. The ASSOA algorithm is then applied to the extracted features for the feature selection process. Finally, the Multi-layer Perceptron (MLP) Neural Network's connection weights are optimized by the proposed ASSOA algorithm (using the selected features) to classify input cases. A Kaggle chest X-ray images (Pneumonia) dataset consists of 5,863 X-rays is employed in the experiments. The proposed ASSOA algorithm is compared with the basic Squirrel Search (SS) optimization algorithm, Grey Wolf Optimizer (GWO), and Genetic algorithm (GA) for feature selection to validate its efficiency. The proposed (ASSOA + MLP) is also compared with other classifiers, based on (SS + MLP), (GWO + MLP), and (GA + MLP), in performance metrics. The proposed (ASSOA + MLP) algorithm achieved a classification mean accuracy of (99.26%). The ASSOA + MLP algorithm also achieved a classification mean accuracy of (99.7%) for a chest X-ray COVID-19 dataset tested from GitHub. The results and statistical tests demonstrate the high effectiveness of the proposed method in determining the infected cases.

关键词： X-ray imaging Feature extraction Diseases optimization Lung COVID-19 Classification algorithms Chest X-ray transfer learning convolutional neural network squirrel search optimization multilayer perceptron optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Group-based multiple pipe routing method for aero-engine focusing on parallel layout

引用

Frontiers of Mechanical Engineering 2021年第4期16卷 798-813页

作者： Hexiang YUAN Jiapeng YU Duo JIA Qiang LIU Hui MA School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion System(Ministry of Education) Northeastern UniversityShenyang 110819China Shenyang Engine Research Institute of AECC Shenyang 110015China School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China

External pipe routing for aero-engine in limited three-dimensional space is a typical nondetermini-stic polynomial hard problem,where the parallel layout of pipes plays an important role in improving the utilization of layout space,facilitating pipe assembly,and *** paper presents an automatic multiple pipe routing method for aero-engine that focuses on parallel *** compressed visibility graph construction algorithm is proposed first to determine rapidly the rough path and interference relationship of the pipes to be *** on these rough paths,the information of pipe grouping and sequencing are obtained according to the difference degree and interference degree,***,a coevolutionary improved differential evolution algorithm,which adopts the coevolutionary strategy,is used to solve multiple pipe layout optimization *** using this algorithm,pipes in the same group share the layout space information with one another,and the optimal layout solution of pipes in this group can be obtained in the same evolutionary ***,to eliminate the minor angle deviation of parallel pipes that would cause assembly stress in actual assembly,an accurate parallelization processing method based on the simulated annealing algorithm is ***,the simulation results on an aero-engine demonstrate the feasibility and effectiveness of the proposed method.

关键词： multiple pipe routing optimization algorithm aero-engine pipe grouping parallel layout

来源：评论

学校读者我要写书评

暂无评论

Global Steering for Control Moment Gyroscope Clusters Using Heuristic Variable Search Techniques

引用

JOURNAL OF SPACECRAFT AND ROCKETS 2021年第4期58卷 972-983页

作者： Papakonstantinou, Charalampos Lappas, Vaios J. Schaub, Hanspeter Kostopoulos, Vassilis Univ Patras Dept Mech Engn & Aeronaut Rion 26504 Greece Univ Patras Rion 26504 Greece Univ Patras Appl Mech & Vibrat Lab Rion 26504 Greece Univ Colorado Colorado Ctr Astrodynam Res Aerosp Engn Sci Dept Boulder CO 80303 USA

This paper addresses the problem of singularity avoidance in a four-control-moment-gyroscope cluster as used for the attitude control of a satellite. A global search algorithm is developed that adjusts the null motion added upon the singularity robust inverse steering law. Its principal characteristic is that it uses global information gathered from the whole maneuver, compared to most conventional techniques that consider only some local information, near the current gimbal configuration for the optimization. The method is implemented using a ternary tree structure, and a heuristic algorithm optimizes a cost function that depends on the manipulability index, the time spent in the vicinity of singularity, the quaternion error, and the gimbal rates. Specific measures are taken to decrease the execution time of the algorithm, even for long maneuvers, without the need of a visit histogram. In addition, the tuning of only two variables can drastically change the execution time and the computational resources needed. The numerical simulation used to evaluate the algorithm indicates that the elliptic singularity can sufficiently be avoided and the algorithm drives the system fast away from the singularity with an overall performance improvement.

关键词： Control Moment Gyroscope Quaternions Singularity Avoidance Steering Laws Search algorithm Satellites Histograms Numerical Simulation optimization algorithm Yaw Polynomial Interpolation

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence in Geopolymer Concrete Mix Design: A Comprehensive Review of Techniques and Applications

引用

Iranian Journal of Science and Technology - Transactions of Civil Engineering 2025年 1-33页

作者： Mushthofa, Malik Thedy, John Teguh, Mochamad Purwanto Pratama, Adjie Gemilang Han, Ay Lie Department of Civil Engineering Islamic University of Indonesia Yogyakarta 55584 Indonesia Department of Bioenvironmental Systems Engineering National Taiwan University Taipei 10617 Taiwan Department of Civil Engineering Diponegoro University Semarang 50275 Indonesia

This systematic review explores the application of Artificial Intelligence (AI) in optimizing the mix design of fly ash-based geopolymer concrete (FABGC). Analyzing studies published between 2014 and 2025, it examines key methodologies, including machine learning models, optimization algorithms, and multi-criteria decision-making approaches. Critical aspects such as data preprocessing, AI model selection, hyperparameter tuning, explainable AI (XAI), and optimization strategies are synthesized to provide a comprehensive perspective on AI-driven FABGC research. The review identifies Deep Residual Networks (ResNet) and Extreme Gradient Boosting (XGB) as the most accurate models for predicting FABGC strength, consistently outperforming others due to their lower error metrics. Backpropagation Neural Networks (BPNN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) also demonstrate competitive performance, while Random Forest (RF) and Decision Tree (DT) models excel in computational efficiency with shorter training times. Despite being the most widely implemented, Artificial Neural Networks (ANN) rarely achieve the highest predictive accuracy. Traditional regression methods, though straightforward, lag behind in performance. These findings underscore the need for standardized datasets, enhanced collaboration, and innovative AI-driven approaches to improve FABGC mix design optimization. Addressing these challenges will facilitate more reliable and efficient AI applications in sustainable concrete technology. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Artificial intelligence Fly ash Geopolymer concrete Mix design optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

An advanced optimization strategy for enhancing the performance of a hybrid pressure-swing distillation process in effective binary-azeotrope separation

引用

SEPARATION AND PURIFICATION TECHNOLOGY 2022年 282卷

作者： Miao, Guang Zhuo, Kaisheng Li, Guoqing Xiao, Jing South China Univ Technol Key Lab Enhanced Heat Transfer & Energy Conservat Minist Educ Guangzhou 510640 Peoples R China South China Univ Technol Sch Chem & Chem Engn Guangzhou 510640 Peoples R China South China Univ Technol Sch Chem & Chem Engn Guangdong Prov Key Lab Green Chem Prod Technol Guangzhou 510640 Peoples R China

The separation of binary azeotropes with low cost and energy consumption is important and challenging for the production of chemicals. A hybrid heat-integrated pressure-swing distillation process (HHIPSD) with heat pump was proposed for the separation of both maximum-and minimum-boiling azeotropes in this work. Compared to the conventional fully heat-integrated pressure-swing distillation process (FHIPSD), the HHIPSD process showed lower total annual cost and higher thermodynamic efficiency in the separation of seven minimum-boiling azeotropes of benzene-ethanol, methanol-acetone, methanol-ethyl acetate (EA), ethanol-EA, acetonitrileethanol, isobutanol (IBA)/isobutyl acetate (IBAc) and diisopropyl ether (DIPE)-isopropyl alcohol (IPA), and two maximum-boiling azeotropes of water-ethylenediamine (EDA) and acetone-chloroform. For the best performance of HHIPSD process, robustness and algorithm optimization were studied in this paper. Scenario analysis method was proven to greatly enhance the convergence of HHIPSD models. The improved genetic algorithm (GA) was modified by particle-swarm optimization and pattern search method and validated by 13 benchmark functions. Meanwhile, different constraint handling methods were studied to ensure the better performance of the improve GA method. With the improvement of model robustness and optimization algorithm, the HHIPSD process achieved 21.6 % and 4.2 % decrease of TAC for water-EDA and methanol-acetone separation, respectively, compared to FHIPSD process. The proposed HHIPSD process together with the optimization strategy suggests the pressure-swing distillation technology can be highly effective in azeotrope separation.

关键词： Pressure-swing distillation Binary azeotropes Heat integration Robustness optimization optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

HFSA: hybrid feature selection approach to improve medical diagnostic system

引用

PEERJ COMPUTER SCIENCE 2025年 11卷 e2764-e2764页

作者： Rabie, Asmaa H. Aldawsari, Mohammed Saleh, Ahmed I. Saraya, M. S. Rashad, Metwally Mansoura Univ Fac Engn Comp & Control Syst Engn Dept Mansoura Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Alddawasir Comp Engn & Informat Dept Al Kharj Saudi Arabia Benha Univ Fac Comp & Artificial Intelligence Banha Egypt

Thanks to the presence of artificial intelligence methods, the diagnosis of patients can be done quickly and accurately. This article introduces a new diagnostic system (DS) that includes three main layers called the rejection layer (RL), selection layer (SL), and diagnostic layer (DL) to accurately diagnose cases suffering from various diseases. In RL, outliers can be removed using the genetic algorithm (GA). At the same time, the best features can be selected by using a new feature selection method called the hybrid feature selection approach (HFSA) in SL. In the next step, the filtered data is passed to the naive Bayes (NB) classifier in DL to give accurate diagnoses. In this work, the main contribution is represented in introducing HFSA as a new selection approach that is composed of two main stages;fast stage (FS) and accurate stage (AS). In FS, chi-square, as a filtering methodology, is applied to quickly select the best features while Hybrid optimization algorithm (HOA), as a wrapper methodology, is applied in AS to accurately select features. It is concluded that HFSA is better than other selection methods based on experimental results because HFSA can enable three different classifiers called NB, K-nearest neighbors (KNN), and artificial neural network (ANN) to provide the maximum accuracy, precision, and recall values and the minimum error value. Additionally, experimental results proved that DS, including GA as an outlier rejection method, HFSA as feature selection, and NB as diagnostic mode, outperformed other diagnosis models.

关键词： Feature selection Diagnosis NB classifier optimization algorithm Diseases Artificial intelligence Machine learning Filter methods Wrapper methods Healthcare

来源：评论

学校读者我要写书评

暂无评论

Simulation optimization framework for dynamic probabilistic safety assessment

引用

RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年 220卷

作者： Park, Jong Woo Lee, Seung Jun UNIST Dept Nucl Engn 50 UNIST Gil Ulsan 44919 South Korea

Probabilistic safety assessment (PSA) based on event trees and fault trees has been widely used in the risk assessment of nuclear power plants. A static approach by nature, PSA has limitations to consider dynamic scenarios with time-dependent sequences and interactions. In contrast to static-based PSA, dynamic PSA has been introduced as a complementary methodology that considers dynamic scenarios between the system and human operations by interfacing physical simulation with thermal-hydraulic models for risk assessment. However, the various research on dynamic PSA has a common challenge in that the number of dynamic scenarios to be simulated increases impractically. An approach is therefore necessary to manage the number of simulations for performing dynamic PSA efficiently. The objective of this paper is to propose a simulation optimization framework using an optimization algorithm to reduce, as much as reasonably achievable, the large number of dynamic scenarios to be evaluated. The optimization algorithm is proposed to optimize the large numbers of generated dynamic scenarios while maintaining accurate risk quantification in the performance of dynamic PSA. To demonstrate the application of the proposed framework to dynamic PSA, two case studies were conducted considering loss of coolant accidents.

关键词： Dynamic PSA Simulation optimization framework optimization algorithm TH simulation Loss of coolant accident

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：