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A backpropagation neural network improved by a genetic algorithm for predicting the mean radiant temperature around buildings within the long-term period of the near future

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Building Simulation 2022年第3期15卷 473-492页

作者： Yuquan Xie Yasuyuki Ishida Jialong Hu Akashi Mochida Department of Architecture and Building Science Graduate School of EngineeringTohoku UniversityJapan Institute of Technology Tokyu ConstructionJapan

This study aimed to develop a neural network(NN)-based method to predict the long-term mean radiant temperature(MRT)around buildings by using meteorological parameters as training data.The MRT dramatically impacts building energy consumption and significantly affects outdoor thermal comfort.In NN-based long-term MRT prediction,two main restrictions must be overcome to achieve precise results:first,the difficulty of preparing numerous training datasets;second,the challenge of developing an accurate NN model.To overcome these restrictions,a combination of principal component analysis(PCA)and K-means clustering was employed to reduce the training data while maintaining high prediction accuracy.Second,three widely used NN models(feedforward NN(FFNN),backpropagation NN(BPNN),and BPNN optimized using a genetic algorithm(GA-BPNN))were compared to identify the NN with the best long-term MRT prediction performance.The performances of the tested NNs were evaluated using the mean absolute percentage error(MAPE),which was≤3%in each case.The findings indicate that the training dataset was reduced effectively by the PCA and K-means.Among the three NNs,the GA-BPNN produced the most accurate results,with its MAPE being below 1%.This study will contribute to the development of fast and feasible outdoor thermal environment prediction.

关键词： backpropagation neural network principal component analysis mean radiant temperature K-means clustering genetic algorithm long-term prediction

Predicting buckling of carbon fiber composite cylindrical shells based on backpropagation neural network improved by sparrow search algorithm

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Journal of Iron and Steel Research(International) 2023年第12期30卷 2459-2470页

作者： Wei Guan Yong-mei Zhu Jun-jie Bao Jian Zhang School of Mechanical Engineering Jiangsu University of Science and TechnologyZhenjiang212003JiangsuChina

The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner diameter of 100 mm,were manufactured and tested.The buckling behavior of CF-CCSs was analyzed by finite element and experiment.Subsequently,the effects of ply angle and length–diameter ratio on buckling load of CF-CCSs were analyzed,and the dataset of the neural network was generated using the finite element method.On this basis,the SSA-BPNN model for predicting buckling load of CF-CCS was established.The results show that the maximum and average errors of the SSA-BPNN to the test data are 6.88%and 2.24%,respectively.The buckling load prediction for CF-CCSs based on SSA-BPNN has satisfactory generalizability and can be used to analyze buckling loads on cylindrical shells of carbon fiber composites.

关键词： Composite cylindrical shell:Carbon fiber backpropagation neural network Sparrow search algorithm Buckling

Output Prediction of Helical Microfiber Temperature Sensors in Cycling Measurement by Deep Learning

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Photonic Sensors 2023年第3期13卷 37-49页

作者： Minghui CHEN Jinjin HAN Juan LIU Fangzhu ZHENG Shihang GENG Shimeng TANG Zhijun WU Jixiong PU Xining ZHANG Hao DAI Fujian Key Laboratory of Light Propagation and Transformation College of Information Science and EngineeringHuaqiao UniversityXiamen 361021China Institute of Ocean Exploration Technology College of Ocean and Earth SciencesXiamen UniversityXiamen 361005China

The inconsistent response curve of delicate micro/nanofiber(MNF)sensors during cycling measurement is one of the main factors which greatly limit their practical application.In this paper,we proposed a temperature sensor based on the copper rod-supported helical microfiber(HMF).The HMF sensors exhibited different light intensity-temperature response relationships in single-cycle measurements.Two neural networks,the deep belief network(DBN)and the backpropagation neural network(BPNN),were employed respectively to predict the temperature of the HMF sensor in different sensing processes.The input variables of the network were the sensor geometric parameters(the microfiber diameter,wrapped length,coiled turns,and helical angle)and the output optical intensity under different working processes.The root mean square error(RMSE)and Pearson correlation coefficient(R)were used to evaluate the predictive ability of the networks.The DBN with two restricted Boltzmann machines(RBMs)provided the best temperature prediction results(RMSE and R of the heating process are 0.9705℃and 0.9969,while the values of RMSE and R of the cooling process are 0.7866℃and 0.9977,respectively).The prediction results obtained by the optimal BPNN(five hidden layers,10 neurons in each layer,RMSE=1.1266℃,R=0.9957)were slightly inferior to those obtained by the DBN.The neural network could accurately and reliably predict the response of the HMF sensor in cycling operation,which provided the possibility for the flexible application of the complex MNF sensor in a wide sensing range.

关键词： Helical microfiber temperature sensors deep belief network backpropagation neural network response prediction cycling measurement

Bias correction of sea surface temperature retrospective forecasts in the South China Sea

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Acta Oceanologica Sinica 2022年第2期41卷 41-50页

作者： Guijun Han Jianfeng Zhou Qi Shao Wei Li Chaoliang Li Xiaobo Wu Lige Cao Haowen Wu Yundong Li Gongfu Zhou School of Marine Science and Technology Tianjin UniversityTianjin 300072China Tianjin Key Laboratory for Oceanic Meteorology Tianjin 300074China

Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature(SST) forecasts have been developed in this study: a backpropagation neural network(BPNN) algorithm, and a hybrid algorithm of empirical orthogonal function(EOF) analysis and BPNN(named EOF-BPNN). The performances of these two methods are validated using bias correction experiments implemented in the South China Sea(SCS), in which the target dataset is a six-year(2003–2008) daily mean time series of SST retrospective forecasts for one-day in advance, obtained from a regional ocean forecast and analysis system called the China Ocean Reanalysis(CORA),and the reference time series is the gridded satellite-based SST. The bias-correction results show that the two methods have similar good skills;however, the EOF-BPNN method is more than five times faster than the BPNN method. Before applying the bias correction, the basin-wide climatological error of the daily mean CORA SST retrospective forecasts in the SCS is up to-3°C;now, it is minimized substantially, falling within the error range(±0.5°C) of the satellite SST data.

关键词： sea surface temperature retrospective forecasts bias correction backpropagation neural network empirical orthogonal function analysis South China Sea

Total organic carbon content logging prediction based on machine learning:A brief review

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能源地球科学(英文)

能源地球科学(英文) 2023年第2期4卷 100-107页

作者： Linqi Zhu Xueqing Zhou Weinan Liu Zheng Kong Institute of Deep-sea Science and Engineering Chinese Academy of SciencesSanyaHainan572000China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai ZhuhaiGuangdong519000China CNOOC China Limited LtdShenzhen BranchShenzhen518054China Exploration and Development Research Institute Shengli Oilfield CompanySINOPECDongyingShandong257029China

The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.

关键词： Total organic carbon content Well logging Machine learning backpropagation neural network Support vector regression

Cascade Optimization Control of Unmanned Vehicle Path Tracking Under Harsh Driving Conditions

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 114-125页

作者：黄迎港罗文广黄丹蓝红莉 School of Electrical and Information Engineering、Guangxi Key Laboratory of Auto Parts and Vehicle Technology Guangxi University of Science and TechnologyLiuzhou 545006GuangxiChina School of Mechanical and Automotive Engineering South China University of TechnologyGuangzhou 510641China

Under ultra-high-speed and harsh conditions,conventional control methods struggle to ensure the path tracking accuracy and driving stability of unmanned vehicles during the turning process.Therefore,this study proposes a cascade control to solve this problem.Based on the new vehicle error model that considers vehicle tire sideslip and road curvature,the feedforward-parametric adaptive linear quadratic regulator(LQR)and proportional integral control-based speed-keeping controllers are used to compose the path-tracking cascade optimization controller for unmanned vehicles.To improve the adaptability of the unmanned vehicle path-tracking control under harsh driving conditions,the LQR controller parameters are automatically adjusted using a back-propagation neural network,in which the initial weights and thresholds are optimized using the improved grey wolf optimization algorithm according to the driving conditions.The speed-keeping controller reduces the impact on the curve-tracking accuracy under nonlinear vehicle speed variations.Finally,a joint model of MATLAB/Simulink and CarSim was established,and simulations show that the proposed control method can achieve stable entry and exit curves at ultra-high speeds for unmanned vehicles.Under strong wind and ice road conditions,the method exhibits a higher tracking accuracy and is more adaptive and robust to external interference in driving and variable curvature roads than methods such as the feedforward-LQR,preview and pure pursuit controls.

关键词： unmanned vehicles path tracking harsh driving conditions cascade control improved gray wolf optimization algorithm backpropagation neural network

Evaluation of the use of sublevel open stoping in the mining of moderately dipping medium-thick orebodies

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International Journal of Mining Science and Technology 2021年第2期31卷 333-346页

作者： Shuai Xu Ruiyu Liang Fidelis TSuorineni Yuanhui Li Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China School of Mining and Geosciences Nazarbayev UniversityAstana 010000Kazakhstan School of Natural Resources and Civil Engineering Northeastern UniversityShenyang 110819China

The flow of blasted ore during mining of moderately dipping medium-thick orebodies is a challenge.Selecting a suitable mining system for such ore bodies is difficult.This paper proposes a diamond layout sublevel open stoping system using fan blastholes with backfilling to mine such orebodies.To evaluate the performance of system the relationships between ore recovery and stope footwall dip angle,footwall surface roughness,drawpoint spacing and production blast ring burden were investigated.An ore recovery data set from 81 laboratory physical model experiments was established from combinations of the listed factors.Various modules in a back propagation neural network structure were compared,and an optimal network structure identified.An ore recovery backpropagation neural network(BPNN)forecast model was developed.Using the model and sensitivity analysis of the factors affecting the proposed open stope mining system,the significance of each factor on ore recovery was studied.The study results were applied to a case study at the Shandong Gold Group Jiaojia Gold Mine.The results showed that the application of a BPNN and sensitivity analysis models for ore recovery prediction in the proposed mining system and field experimental results confirm that the suggested mining method is feasible.

关键词： Moderately dipping medium-thick orebody Sublevel open stoping Fan blastholes Ore recovery backpropagation neural network

Estimating the Texture of Purple Soils Using Vis-NIR Spectroscopy and Optimized Conversion Models

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Agricultural Sciences 2023年第2期14卷 202-218页

作者： Baina Chen Jie Wei Qiang Tang Yu Gou Chunhong Liu Chongqing Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area School of Geography and Tourism Chongqing Normal University Chongqing China Chongqing Field Observation and Research Station of Surface Ecological Process in the Three Gorges Reservoir Area School of Geography and Tourism Chongqing Normal University Chongqing China Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical Sciences Southwest University Chongqing China

Soil texture is an indicator of soil physical structure which delivers many ecological functions of soils such as thermal regime, plant growth, and soil quality. However, traditional methods for soil texture measurement are time-consuming and labor-intensive. This study attempts to explore an indirect method for rapid estimating the texture of three subgroups of purple soils (i.e. calcareous, neutral, and acidic). 190 topsoil (0 - 10 cm) samples were collected from sloping croplands in Tongnan and Beibei Districts of Chongqing Municipality in China. Vis-NIR spectrum was measured and processed, and stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), and back propagation neural network (BPNN) models were constructed to inform the soil texture. The clay fractions ranged from 4.40% to 27.12% while sand fractions ranged from 0.34% to 36.57%, hereby soil samples encompass three textural classes (i.e. silt, silt loam, and silty clay loam). For the original spectrum, the texture of calcareous and neutral purple soils was not significantly correlated with spectral reflectance and linear models (SMLR and PLSR) exhibited low prediction accuracy. The correlation coefficients and the goodness-of-fits between soil texture and the transformed spectra of all soil groups increased by continuum-removal (CR), first-order differential (R'), and second-order differential (R") transformations. Among them, the R" had the best performance in terms of improving the correlation coefficients and the goodness-of-fits. For the calcareous purple soil, the SMLR exceeds PLSR and BPNN with a higher coefficient of determination (R2) and the ratio of performance to inter-quartile distance (RPIQ) values and lower root mean square error of validation (RMSEV), but for the neutral and acidic purple soils, the PLSR model has a better prediction accuracy. In summary, the linear methods (SMLR and PLSR) are more reliable in estimating the texture of the three purple soil groups when using Vis-NIR spectroscopy in

关键词： Soil Texture Vis-NIR Spectra Stepwise Multiple Linear Regression Partial Least Squares Regression backpropagation neural network