检索结果-内蒙古大学图书馆

Optimization of solid oxide fuel cell power generation voltage prediction based on improved neural network

INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2023年第18期18卷 464-472页

作者： Wei, Liming Wang, Yixuan Jilin Jianzhu Univ Sch Elect & Comp Engn Changchun Jilin Peoples R China

This paper proposes a method for predicting the generation voltage of a solid oxide fuel cell based on the data results of a stand-alone solid oxide fuel single cell simulation model under ideal conditions, with the aim of improving the generation efficiency and extending the service life of the solid oxide fuel cell. In this paper, a modified back propagation (bp) neural network algorithm is used to improve the prediction accuracy of the solid oxide fuel cell generation voltage by using the whale algorithm to optimize the bp neural network model to improve its convergence and achieve the effect of improving the prediction accuracy. First, the characteristics of the independent solid oxide fuel cell are introduced and simulated. Second, the long short-term memory network model, linear regression network model and bp neural network are simulated and compared, and the results show that the bp neural network prediction model is more accurate and can be optimized and improved. Finally, the bp neural network is optimized and simulated using the whale algorithm, and the simulation results show that the method has better convergence and higher prediction accuracy than the traditional bp neural network prediction model.

关键词： whale optimization algorithm bp neural network algorithm generation voltage prediction optimization solid oxide fuel cell

来源：评论

学校读者我要写书评

暂无评论

A physical model-free ant colony optimization network algorithm and full scale experimental investigation on ceiling temperature distribution in the utility tunnel fire

引用

INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2022年 174卷

作者： Sun, Bin Hu, Zhenbiao Liu, Xiaojiang Xu, Zhao-Dong Xu, Dajun Southeast Univ Jiangsu Key Lab Engn Mech China Pakistan Belt & Rd Joint Lab Smart Disaster Nanjing 210096 Peoples R China SoutheastUniv China Pakistan Belt & Rd Joint Lab Smart Disaster Nanjing 210096 Peoples R China Minist Emergency Management Tianjin Fire Res Inst Tianjin 300381 Peoples R China

To advance understanding of the ceiling temperature characters in tunnel fires, a physical model-free ant colony optimization network algorithm is developed. Compared to the traditional physical model-based methods, the algorithm is not limited to the specific fire conditions and the structure of tunnels. The main advantage and contribution of the algorithm is that a novel ant colony optimization (ACO) network is constructed and firstly used to predict the ceiling temperature distribution in the tunnel fire as well as the maximal ceiling temperature based on only some sensors data. In order to verify the effectiveness of the algorithm, full scale burning tests were investigated in the largest fire experiment platform of the utility tunnel at the Tianjin Fire Research Institute, China. In addition, the developed ACO network algorithm has excellent performance by contrast with the commonly used back propagation (bp) neural network algorithm. By compared with the experimental results and the results obtained from the bp neural network algorithm, the ability and the effectiveness of the algorithm were supported. The algorithm can be used to predict the ceiling temperature in the tunnel fires for rapid and efficient fire disaster evaluation.

关键词： Ceiling temperature Tunnel fire Ant colony optimization network Full scale burning tests bp neural network algorithm

来源：评论

学校读者我要写书评

暂无评论

Personalized Distance Education System Based on Data Mining

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

引用

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2018年第7期13卷 4-16页

作者： Qi, Zhimin Jilin Normal Univ Siping Jilin Peoples R China

To improve the poor intelligence and personalized service of the learning system in current distance education and training, a personalized learning system model based on data mining technology was proposed. Then, the method of applying the decision tree and bp neural network algorithm to design the system was described in detail. Finally, the core module of the personalized distance education system combined with Web was designed. The personalized function module introduced how the user operates the intelligent Web reasoning in the module according to the user input learning information and gave the most matching learning materials. Finally, the application results showed that the system greatly improved the personalized service module.

关键词： learning system data mining technology bp neural network algorithm

来源：评论

学校读者我要写书评

暂无评论

Determination and application of seasonal distribution coefficients of traffic volume in pavement design

引用

AUSTRALIAN JOURNAL OF CIVIL ENGINEERING 2022年第2期20卷 233-243页

作者： Yu, Tengjiang Zhang, Haitao Wu, Guangyuan Chen, Dan Northeast Forest Univ Coll Civil Engn Harbin 150040 Peoples R China Beijing Gen Municipal Engn Design & Res Inst Co L Beijing Peoples R China

The seasonal distribution coefficients of traffic volume have not been yet taken into account in asphalt pavement design in China. Through comprehensive analysis on the assessed data and the factors affecting monthly distribution coefficients of traffic volume in different areas, the monthly and seasonal distribution coefficients of traffic volume in Harbin city, China has been predicted and determined using a back propagation (bp) neural network algorithm. The research contents involve data investigation and analysis of monthly distribution coefficients of traffic volume, analysing the factors affecting the mentioned coefficients, prediction and determination of monthly and seasonal distribution coefficients of traffic volume, and application of those coefficients, etc. Through the application of monthly and seasonal distribution coefficients, the seasonal traffic volumes in a year were calculated, and the application results demonstrated that the seasonal distribution of traffic volume has a scientific rationality. In addition, this study indicated that the seasonal traffic volume could accurately evaluate the actual conditions about axle loads on the pavement, resulting in the asphalt pavement design to be more rational. Eventually, the findings could appropriately meet the requirements of actual vehicle loads passing on the road surface.

关键词： Asphalt pavement design Traffic volume Seasonal distribution Monthly and seasonal distributing coefficients bp neural network algorithm

来源：评论

学校读者我要写书评

暂无评论

The application of the intelligent algorithm in the prevention and early warning of mountain mass landslide disaster

引用

ARABIAN JOURNAL OF GEOSCIENCES 2020年第2期13卷 1-6页

作者： Yan, Yuan Ashraf, Muhammad Aqeel Xi An Jiao Tong Univ Sch Humanities & Social Sci Xian 710049 Shaanxi Peoples R China China Univ Geosci Sch Environm Studies Wuhan 430074 Peoples R China

The objective is to realize the early warning of disasters caused by mountain mass landslide. The probable causes of mountain mass landslide were predicted and analyzed in prior by constructing the back propagation (bp) neural network algorithm, which included earthquake, rainstorm, human activity, landslide displacement, slope gradient, and soil texture. In terms of the early warning of mountain mass landslide caused by earthquakes, rainstorm, or human activities, the accuracy of bp neural network algorithm was relatively high;especially, given the relevant data such as the free slope gradient, slide surface slope gradient, and soil texture of the mountain mass, the accuracy of bp neural network algorithm in the early warning of mountain mass landslide could reach 94.7%;the proposed algorithm could predict not only the occurrence possibility of mountain mass landslide but also the severity and possible range of mountain mass landslide. In addition, in terms of stability, the standard deviation of the proposed algorithm was 0.0062, which indicated the fairly good stability of the algorithm. The possible mountain mass landslide was predicted based on the comprehensive analysis of probable causes of mountain mass landslide and their proportions of weights by using the bp neural network algorithm;the accuracy and stability of the proposed algorithm were excellent, which also showed that the occurrence of mountain mass landslide was a comprehensive consequence caused by various factors and could never be predicted through singular factor analysis. Thus, the understanding of both bp neural network algorithm and mountain mass landslide was greatly improved.

关键词： bp neural network algorithm Model Early warning Rainstorm Mountain mass

来源：评论

学校读者我要写书评

暂无评论

Design of Low Scale Pressure Sensor Intelligent System with Temperature Compensation Based on STM32 4

Design of Low Scale Pressure Sensor Intelligent System with ...

引用

IEEE 4th International Conference on Control Science and Systems Engineering (ICCSSE)

作者： Li, Yanwen Ma, Shaojie Nanjing Univ Sci & Technol Sch Mech Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538678879

This article designs and implements a pressure sensor intelligent system with temperature compensation, which is based on STM32F103RCT6 microcontroller. The system is used to measure accurate small range signals of pressure sensor. Designing the functional circuit makes the output of the system digitized. Utilizing the improved bp neural network algorithm compensates the digital signal of the pressure sensor to ensure the accuracy of the pressure sensor. The repeated test shows that the intelligent system compensates the temperature drift of low scale pressure sensor effectively and ensures the property and the measurement accuracy of the pressure sensor.

关键词： low scale pressure sensor bp neural network algorithm temperature compensation temperature drift

来源：评论

学校读者我要写书评

暂无评论

Experimental investigation on processing of fused silica microchannels by high repetition rate femtosecond laser

Experimental investigation on processing of fused silica mic...

引用

Optifab Conference

作者： Liao, Kai Wang, Wenjun Mei, Xuesong Liu, Bin Pan, Aifei Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn 99 Yanxiang Rd Xian 710054 Shaanxi Peoples R China Xi An Jiao Tong Univ Shaanxi Key Lab Intelligent Robots 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Mech Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

ISBN: (数字)9781510630642

ISBN: (纸本)9781510630642

Femtosecond laser processing has been extensively used in micromachining, especially for the precision processing of hard and brittle materials. However, the precision of the materials ablated by femtosecond laser is not easy to control. This paper reports an experimental and theoretical study on the ablation characteristics of fused silica using high repetition rate femtosecond laser. An experimental study of microchannels milling on the fused silica was carried out. The influence of pulse energy, repetition rate, scanning velocity, scanning times on the size and morphology of the microchannels was obtained. Simultaneously, the experimental data on the depth and width of microchannels under different parameter combinations were acquired through the orthogonal experiment. The prediction model of aspect ratio was obtained by bp neural network algorithm. Finally, the verification test was established and showed that the experimental results were consistent with the theoretical results. It would provide a theoretical basis for further study on the microchannels fabrication of femtosecond laser.

关键词： Microchannels processing Femtosecond laser Aspect ratio bp neural network algorithm

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Prediction of Generation Power in Photovoltaic Microgrid 8

Comprehensive Prediction of Generation Power in Photovoltaic...

引用

IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Tu, Kunquan Xiong, Hejin Chen, Feihu Wuhan Univ Technol Coll Automat Wuhan Hubei Peoples R China Wuhan Univ Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781538681787

The output power of distributed photovoltaic power supply is mainly affected by environmental and meteorological conditions. The output power has great randomness and fluctuation. If the output power of photovoltaic cells can be accurately predicted, it will be of great significance to the optimal dispatch of distribution network. In this paper, based on the improved bp neural network algorithm, considering the correction of solar radiation intensity of photovoltaic cloud cover and the correction of output power fluctuation of similar day history, a comprehensive forecasting method of output power of photovoltaic power generation is established, and satisfactory forecasting results are obtained through simulation and analysis.

关键词： photovoltaic power generation bp neural network algorithm power prediction grid connection simulation

来源：评论

学校读者我要写书评

暂无评论

Research on the Load_breast_cancer data set under Multiple Machine Learning algorithms 24

Research on the Load_breast_cancer data set under Multiple M...

引用

International Conference on Modeling, Natural Language Processing and Machine Learning (CMNM)

作者： Song, Meirui Wu, Shixiao Huang, Rujuan Wuhan Business Univ Wuhan Peoples R China

ISBN: (纸本)9798400709760

breast cancer ranks first in female malignant tumors. Early detection and diagnosis is the key to treatment. This paper uses the open-source load_break_cancer breast cancer data set, mainly uses random forest, support vector machine, logical regression, Gauss naive Bayesian algorithm, bp neural network algorithm, k-neighborhood algorithm and XGBoost algorithm to classify and predict the breast cancer data set, conducts a lot of training and testing on the data set under a variety of machine learning algorithms, analyzes the learning curve in the training process, analyzes the training and testing results, and analyzes the performance of the algorithm processing data, which is of great significance for breast cancer diagnosis and treatment.

关键词： Random Forest algorithm Support Vector Machine algorithm Logistic Regression algorithm Gaussian Naive Bayes algorithm bp neural network algorithm k-Neighbor algorithm XGBoost algorithm

来源：评论

学校读者我要写书评

暂无评论

Study on optimization of atmospheric correction process based on 6S radiative transfer model

Study on optimization of atmospheric correction process base...

引用

Conference on AOPC - Novel Technologies and Instruments for Astronomical Multi-Band Observations

作者： Li, Han Chen, Yueting Feng, Huajun Xu, Zhihai Li, Qi Zhejiang Univ State Key Lab Modern Opt Instrumentat Hangzhou Peoples R China

ISBN: (数字)9781510650145

ISBN: (纸本)9781510650145;9781510650138

6s(Second Simulation of the Satellite Signal in the Solar Spectrum) radiative transfer model is one of the atmospheric correction algorithms based on the atmospheric radiative transmission model. It is widely used because of its high correction accuracy. Meanwhile, it is criticized for the complexity of the parameters and the efficiency of the correction process. 6S model needs to establish a look-up table based on the geometric conditions and aerosol conditions which directly determines the accuracy of the atmospheric correction. This paper analyzes the limitations of traditional look-up table method and uses artificial intelligence algorithms such as the support vector regression(SVR) algorithm and the back propagation (bp) algorithm to instead the traditional look-up table method. The experiments' results show that the output value and predictive value fit well. Both are better than the traditional linear interpolation performance results, and the bp algorithms performs better, which verifies the feasibility of bp neural network algorithms prediction model instead of linear interpolation method for table lookup. Finally, this paper takes Landsat-8 data as an example, uses the method proposed in this article to perform atmospheric correction, and compares the FLAASH model correction results. The visual performance results of the two are roughly the same.

关键词： Atmospheric Correction 6S Radiative Transfer Model Machine Learning bp neural network 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：