检索结果-内蒙古大学图书馆

A hybrid forecasting method for cooling load in large public buildings based on improved long short term memory

JOURNAL OF BUILDING ENGINEERING 2023年 76卷

作者： Liu, Zongyi Yu, Junqi Feng, Chunyong Su, Yucong Dai, Junwei Chen, Yufei Xian Univ Architecture & Technol Sch Bldg Serv Sci & Engn Xian 710055 Peoples R China Xian Univ Architecture & Technol Sch Mech & Elect Engn Xian 710055 Peoples R China

Air conditioning system in large public buildings have great potential for energy saving. In order to improve the operational efficiency of the air conditioning system, it is crucial to establish an accurate and effective cooling load forecasting model. This paper proposes a hybrid model based on random forest-improved sparrow search algorithm-long short term memory (RF-ISSA-LSTM) for forecasting the cooling load of large public buildings. Firstly, random forest (RF) is used to select high-impact parameters for building cooling load and reduce the dimensionality of the original input data. Secondly, three strategies are introduced to improve the location update in different stages of the sparrow search algorithm (ISSA), and then an ISSA-optimized LSTM forecasting model is established. Finally, two case studies are selected to evaluate the predictive performance of the model in detail. The experimental results show that the mean average percentage error (MAPE) and root mean square error (RMSE) of the RF-ISSA-LSTM model predicting the cooling load for these two buildings are 0.6072%, 3.3552 and 0.7939%, 4.4227, respectively. Compared with other neural network models, the RF-ISSA-LSTM model has higher prediction accuracy and shorter running time. Meanwhile, with the reduced training sample size, the RFISSA-LSTM model can still effectively predict the cooling load, which indicates its strong generalization ability. Therefore, this work is enlightening and the proposed hybrid prediction model can serve as a technical tool to support the energy efficiency management of large public buildings.

关键词： Large public building Cooling load forecasting Random forest sparrow search algorithm Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN

引用

ENERGIES 2021年第21期14卷

作者： Zhou, Ling Zhao, Qiancheng Wang, Xian Zhu, Anfeng Hunan Univ Sci & Technol Engn Res Ctr Hunan Prov Min & Utilizat Wind Turbi Xiangtan 411201 Peoples R China Hunan Univ Technol Coll Elect & Informat Engn Zhuzhou 412000 Peoples R China

When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been established based on the Random Walk Improved sparrow search algorithm to optimize auto-associative neural network (RWSSA-AANN), and is used for fault diagnosis of wind turbine group anemometers. Using the cluster analysis, six wind turbines are determined to be used as a wind turbine group. The 20,000 sets of normal historical data have been used for training and simulating of the model, and the single and multiple fault states of the anemometer are simulated. Using this model to analyze the wind speed supervisory control and data acquisition system (SCADA) data of six wind turbines in a wind farm from 2013 to 2017, can effectively diagnose the fault state and reconstruct the fault data. A comparison of the results obtained using the model developed in this work has also been made with the corresponding results generated using AANN without optimization and AANN optimized by genetic algorithm. The comparison results indicate that the model has a higher accuracy and detection rate than AANN, genetic algorithm auto-associative neural network (GA-AANN), and principal component analysis (PCA).

关键词： auto-associative neural network sparrow search algorithm anemometer fault diagnosis cluster analysis

来源：评论

学校读者我要写书评

暂无评论

Classification and prediction of deformed steel and concrete bond-slip failure modes based on SSA-ELM model

引用

STRUCTURES 2023年 57卷

作者： Fan, Congcong Zheng, Yuanxun Wen, Yongchao Sun, Meng Zhengzhou Univ Sch Water Conservancy & Civil Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Yellow River Lab Zhengzhou 450001 Peoples R China Zhengzhou Engn Co Ltd China Railway Grp 7 Zhengzhou 450052 Henan Peoples R China

Bond-slip failure modes between reinforcement and concrete can seriously affect the load-bearing performance and safety of reinforced concrete (RC) structures. The pull-out test is widely used to analyze RC bond-slip and failure modes because of its simplicity. Therefore, a prediction model based on sparrow search algorithm (SSA) Optimized Extreme Learning Machine (SSA-ELM) is proposed in this study to quickly and effectively evaluate the failure forms of RC structures. The bond-slip test samples containing 399 sets of deformed bar and concrete deformation pull-out tests were first collected as a database, and the various characteristic parameters were preprocessed. Then, the features significantly influencing the failure pattern were screened out using random forest, and the plausibility tests were performed using the Pearson correlation coefficient and mutual information. Subsequently, a classification prediction model of the pull-out failure pattern was developed based on standard machine learning (ML) algorithms. In addition, nine well-known ML algorithms (LR, K-NN, DT, SVC, BPNN, NB, RF, AdaBoost, and XGBoost) are considered. This model's accuracy and generalization ability are compared based on the Precision and Recall confusion matrix. Finally, the optimized ELM was trained by SSA using the training set data with optimized ELM weights and thresholds. The results showed that the concrete protective layer thickness ratio to reinforcement diameter had the highest sensitivity to the failure pattern of RC drawn specimens. The accuracy of the prediction results of the SSA-ELM model was better than other ML algorithms (Accuracy = 95.8%), such as BP Neural Network. In addition, SSA has better stability and higher accuracy than Gray Wolf Optimization (GWO) algorithm, Particle Swarm Optimization (PSO) algorithm, Genetic algorithm (GA), Ant Colony Optimization (ACO), and other ML classification algorithms.

关键词： Reinforced concrete structure Bond-slip Pull-out test Failure mode Extreme learning machine sparrow search algorithm

来源：评论

学校读者我要写书评

暂无评论

Short-Term Electricity Price Forecasting Based on the Two-Layer VMD Decomposition Technique and SSA-LSTM

引用

ENERGIES 2022年第22期15卷

作者： Guo, Fang Deng, Shangyun Zheng, Weijia Wen, An Du, Jinfeng Huang, Guangshan Wang, Ruiyang Foshan Univ Sch Mechatron Engn & Automat Foshan 528231 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou 313098 Peoples R China Xuzhou Vocat Coll Ind Technol Sch Informat Xuzhou 221140 Jiangsu Peoples R China

Accurate electricity price forecasting (EPF) can provide a necessary basis for market decision making by power market participants to reduce the operating cost of the power system and ensure the system's stable operation. To address the characteristics of high frequency, strong nonlinearity, and high volatility of electricity prices, this paper proposes a short-term electricity price forecasting model based on a two-layer variational modal decomposition (VMD) technique, using the sparrow search algorithm (SSA) to optimize the long and short-term memory network (LSTM). The original electricity price sequence is decomposed into multiple modal components using VMD. Then, each piece is predicted separately using an SSA-optimized LSTM. For the element with the worst prediction accuracy, IMF-worst is decomposed for a second time using VMD to explore the price characteristics further. Finally, the prediction results of each modal component are reconstructed to obtain the final prediction results. To verify the validity and accuracy of the proposed model, this paper uses data from three electricity markets, Australia, Spain, and France, for validation analysis. The experimental results show that the proposed model has MAPE of 0.39%, 1.58%, and 0.95%, RMSE of 0.25, 0.9, and 0.3, and MAE of 0.19, 0.68, and 0.31 in three different cases, indicating that the proposed model can well handle the nonlinear and non-stationarity characteristics of the electricity price series and has superior forecasting performance.

关键词： electricity price forecasting two-layer variational modal decomposition sparrow search algorithm long short-term memory networks hybrid model

来源：评论

学校读者我要写书评

暂无评论

Study on the intermediate crack-induced debonding strain of FRP-strengthened concrete members using the updated BP neural network

引用

AIN SHAMS ENGINEERING JOURNAL 2024年第11期15卷

作者： Rui, Yu Xuan Chongqing Jiaotong Univ Sch Civil Engn 66 Xuefu Rd Chongqing 400074 Peoples R China

Fiber reinforced plastics (FRP) are often used to enhance the capacity of the reinforced concrete beam (RC beam). However, the debonding failure is often observed due to the effect of the complex environment and the random loads. The debonding failure includes three types: plate end interfacial debonding (PE debonding), critical diagonal crack-induced debonding (CDC debonding), and intermediate flexural crack-induced interfacial debonding (IC debonding). In order to investigate the IC debonding strain of RC beam strength by FRP, this paper proposed some data-driven models to explore the IC debonding strain, based on the machine learning approaches The concrete strength, shear span proportion, the proportion of anchorage length to shear width, tensile reinforcement proportion, steel yield strength, stirrup reinforcement ratio, FRP stiffness, and the proportion of sheet span to beamwidth were regarded as the input parameters. The IC debonding strain was regarded as the output. It was found that the BP model can predict the IC debonding strain well. However, the BP data-driven model is easy to fall into a local minimum, and it is very difficult to converge, which has a negative effect on the accuracy of the model. The sparrow search algorithm (SSA) was proposed to update it. The results indicated that the neural network optimized by SSA with lowest relative error, which can predict the IC debonding strain well. In addition, a study on the importance of each input found that the concrete strength, shear span proportion, and reinforcement yield strength will have a big impact on the IC debonding strain. (c) 2023 THE AUTHOR. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-ncnd/4.0/).

关键词： FRP Concrete structure Machine learning approaches sparrow search algorithm Feature importance

来源：评论

学校读者我要写书评

暂无评论

A novel approach of fault diagnosis for gearbox based on VMD optimized by SSA and improved RCMDE

引用

JOURNAL OF VIBRATION AND CONTROL 2024年

作者： Cao, Jiahao Zhang, Xiaodong Wang, Hongwei Yin, Runsheng Xinjiang Univ Sch Mech Engn Urumqi Peoples R China Xi An Jiao Tong Univ Sch Mech Engn 28 Xianning West Rd Xian 710049 Peoples R China

Gearboxes play a vital role in the power transmission of mechanical equipment, and studying fault diagnosis methods is essential to ensure the normal operation of rotating machines. Since the vibration signal of the gearbox has unstable characteristics with strong background noise, a novel approach of fault diagnosis for wind turbine gearbox based on variational mode decomposition (VMD) optimized by sparrow search algorithm (SSA) and improved refined composite multi-scale dispersion entropy (IRCMDE) is proposed in this paper. Firstly, for reducing background noise, sample signals are decomposed by the model of SSA-VMD, and the denoised signals are recomposed according to the correlation coefficient. Then, the proposed IRCMDE under a certain scale factor is calculated to extract initial feature information of the recomposed signal. In the next step, the initial features are reduced to 3 dimensions by the algorithm of the Gaussian process latent variable model (GPLVM). Finally, a support vector machine (SVM) is used to diagnose the different states of gearbox faults. Experimental and comparative experimental results from the wind turbine drivetrain diagnostics simulator (WTDDS) show that the proposed method can quickly and accurately identify the fault of gear transmission.

关键词： Fault diagnosis improved refined composite multi-scale dispersion entropy refined composite multi-scale dispersion entropy variational modal decomposition sparrow search algorithm

来源：评论

学校读者我要写书评

暂无评论

MulOER-SAN: 2-layer multi-objective framework for exercise recommendation with self-attention networks

引用

KNOWLEDGE-BASED SYSTEMS 2023年 260卷

作者： Ren, Yimeng Liang, Kun Shang, Yuhu Zhang, Yiying Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin Peoples R China

Diagnosing the knowledge mastery level of students on required concepts and providing tailored exercises to them is an essential task in e-learning environments. Due to the different knowledge level of students and the large scale of exercise banks, it is difficult for general recommenders to recommend suitable exercises for each student, which decreases their learning efficiency. In this paper, we develop a 2-layer multi-objective framework for exercise recommendation with self-attention networks, abbreviated as MulOER-SAN, to capture the change in students' knowledge acquisition and thus provide customized exercise recommendations. Note that MulOER-SAN is a 2-layer framework. Via the bottom layer, a self-attention mechanism is adopted to predict the coverage in the next exercise, which can lead the learning process in the right direction. Moreover, we implement a novel knowledge tracing model with an enhanced self-attention sub-layer, thus tracing students' dynamic knowledge state evolution. As the top layer of the 2-layer model, exercises are filtered according to the bottom layer's predicted results, and the candidate subsets with appropriate difficulty and novelty are generated. From the perspective of diversity, we also carefully develop a chaotic sparrow search algorithm to further filter the candidate subset to avoid redundancy of recommended results. On the above basis, a simple yet effective difficulty smoothness factor is implemented to generate a high -quality ranking list in a real teaching application scenario. Comprehensive experiments conducted on three real-world datasets have demonstrated that the proposed MulOER-SAN framework significantly outperforms state-of-the-art methods in terms of various evaluation metrics.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Exercise recommendation Self -attention mechanism Knowledge tracing sparrow search algorithm

来源：评论

学校读者我要写书评

暂无评论

Simulation study of neutron multiplicity of plutonium samples of different shapes

引用

FRONTIERS IN ENERGY REsearch 2022年 10卷

作者： Lu, Yunfeng Zhang, Quanhu Yao, Qingxu Wang, Yu Xian Res Inst High Technol Xian Peoples R China

In this paper, an optimized boron-coated straw tube neutron multiplicity counter is used to simulate neutron multiplicity for six different shapes of weapons-grade plutonium samples. It was found that under the point model equation, the mass simulation results for samples of different shapes showed different degrees of negative deviations with increasing mass, and the deviations increased with increasing mass. On this basis, the reasons for the deviation are analyzed and a multilayer perceptron is introduced, and three counting rates and preset qualities of samples of different qualities under six shapes are trained. The results showed that the multi-layer perceptron could better fit the relationship between the counting rates of different shapes and the preset quality. The sparrow search algorithm was introduced to optimize the initial weights and thresholds of the multilayer perceptron, and the optimized model was used to predict another four samples of different shapes. The results show that the optimized multilayer perceptron can better fit the relationship between the counting rate and the mass of samples with different shapes. When the sample mass is less than 3 kg, the relative deviation of the three counting rates for samples with four shapes to the sample mass prediction is less than 2%, which meets the requirements of neutron multiplicity measurement.

关键词： boron-coated straw tube neutron multiplicity plutonium sample mass property measurement multilayer perceptron sparrow search algorithm

来源：评论

学校读者我要写书评

暂无评论

A new method to investigate the intermediate crack-induced debonding strain

引用

CASE STUDIES IN CONSTRUCTION MATERIALS 2022年 17卷

作者： Rui, Yu Xuan Chongqing Jiaotong Univ Sch Civil Engn 66 Xuefu Rd Nanan Dist Chongqing 400074 Peoples R China

In order to investigate the interfacial debonding (IC) strain of the reinforced concrete structures strengthened by fiber-reinforced polymer (FRP), this paper proposed some data-driven models to explore it, considering the effect of random factors both in material and environment. The concrete strength, shear span proportion, the proportion of anchorage length to shear width, tensile reinforcement proportion, steel yield strength, stirrup reinforcement ratio, FRP stiffness, the proportion of sheet span to beamwidth, ratios of environmental temperature to the maintenance standard temperature, and ratios of environmental humidity to the relative humidity were regarded as the inputs, the IC debonding strain was regarded as the output. The results indicate that the neural network optimized by the sparrow search algorithm not only has a relatively small error but also has stronger robustness. In addition, a study on the importance of the inputs on the IC debonding strain indicates that the concrete strength, FRP stiffness, and ratios of environmental temperature to the maintenance standard temperature are the most significant factor to influence the IC debonding strain.

关键词： FRP Reinforced concrete structure BP neural network model sparrow search algorithm

来源：评论

学校读者我要写书评

暂无评论

An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2022年 144卷 105383-105383页

作者： Baghdadi, Nadiah A. Malki, Amer Abdelaliem, Sally F. Balaha, Hossam Magdy Badawy, Mahmoud Elhosseini, Mostafa Princess Nourah Bint Abdulrahman Univ Coll Nursing POB 84428 Riyadh 11671 Saudi Arabia Taibah Univ Coll Comp Sci & Engn Yanbu 46421 Saudi Arabia Mansoura Univ Fac Engn Comp & Control Syst Engn Dept Mansoura 46421 Egypt Princess Nourah Bint Abdulrahman Univ Riyadh Saudi Arabia

Researchers have developed more intelligent, highly responsive, and efficient detection methods owing to the COVID-19 demands for more widespread diagnosis. The work done deals with developing an AI-based framework that can help radiologists and other healthcare professionals diagnose COVID-19 cases at a high level of accuracy. However, in the absence of publicly available CT datasets, the development of such AI tools can prove challenging. Therefore, an algorithm for performing automatic and accurate COVID-19 classification using Convolutional Neural Network (CNN), pre-trained model, and sparrow search algorithm (SSA) on CT lung images was proposed. The pre-trained CNN models used are SeresNext50, SeresNext101, SeNet154, MobileNet, MobileNetV2, MobileNetV3Small, and MobileNetV3Large. In addition, the SSA will be used to optimize the different CNN and transfer learning(TL) hyperparameters to find the best configuration for the pre-trained model used and enhance its performance. Two datasets are used in the experiments. There are two classes in the first dataset, while three in the second. The authors combined two publicly available COVID-19 datasets as the first dataset, namely the COVID-19 Lung CT Scans and COVID-19 CT Scan Dataset. In total, 14,486 images were included in this study. The authors analyzed the Large COVID-19 CT scan slice dataset in the second dataset, which utilized 17,104 images. Compared to other pre-trained models on both classes datasets, MobileNetV3Large pre-trained is the best model. As far as the three-classes dataset is concerned, a model trained on SeNet154 is the best available. Results show that, when compared to other CNN models like LeNet-5 CNN, COVID faster R-CNN, Light CNN, Fuzzy + CNN, Dynamic CNN, CNN and Optimized CNN, the proposed Framework achieves the best accuracy of 99.74% (two classes) and 98% (three classes).

关键词： COVID-19 Convolutional neural network (CNN) Deep learning (DL) Metaheuristic optimization sparrow search algorithm

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：