Optimization of a neuro-human thermal model using a genetic algorithm

2020 ◽  
pp. 1420326X2097519
Author(s):  
Mohamad El Kadri ◽  
Fabrice De Oliveira ◽  
Christian Inard ◽  
François Demouge
Keyword(s):  
Genetic Algorithm ◽  
Thermal Model ◽  
Absolute Error ◽  
Maximum Error ◽  
Male Students ◽  
Local Skin ◽  
Active Systems ◽  
Physiological Variables ◽  
Human Thermal Model ◽  
Skin Temperatures

A neuro-human thermal model was optimized to increase the prediction accuracy of the physiological variables of a group of 15 healthy male students exposed to transient environmental conditions. The effect of both the passive and active systems parameters was studied using a sensitivity analysis, and the parameters that had the most influence on the neuro-human thermal model outputs were established. A genetic algorithm was then used to optimize the model in order to determine the parameters that corresponded to the studied population. The results showed that the optimization increased the precision of the neuro-human thermal model. The mean absolute error and the maximum error between the experimental data and the numerical results for mean skin temperature were 0.13°C and 0.56°C, respectively, and we obtained 0.03°C and 0.11°C, respectively, for rectal temperature. These results show that the neuro-human thermal model can be accurately adjusted for the rectal, mean and local skin temperatures of a targeted population by using a genetic algorithm to determine the values of the parameters that correspond to this population.

scholarly journals High-Performance Workwear for Coal Miners in Northern China: Design and Performance Evaluation

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ying Ke ◽  
Qing Zheng ◽  
Faming Wang ◽  
Min Wang ◽  
Yi Wang
Keyword(s):  
Performance Evaluation ◽  
Thermal Comfort ◽  
High Performance ◽  
Northern China ◽  
Coal Miners ◽  
Climate Chamber ◽  
Local Skin ◽  
Worker Performance ◽  
And Performance ◽  
Skin Temperatures

Abstract The design of workwear has significant effects on worker performance. However, the current workwear for coal miners in Northern China is poor in fitness and thermal comfort. In this study, new workwear (NEW) for coal miners was developed with the design features providing better cold protection and movement comfort performance, as compared with a commonly worn workwear (CON). To evaluate the effectiveness of NEW, we conducted human trials which were performed using simulated work movements (i.e., sitting, shoveling, squatting, and crawling) in a climate chamber (10°C, 75% RH). Physiological measurements and perceptual responses were obtained. The results demonstrated that the local skin temperatures at chest, scapula, thigh, and calf; mean skin temperatures,; and thermal comfort in NEW were significantly higher than those in CON. NEW also exerted an improvement in enhancing movement comfort. We conclude that NEW could meet well with the cold protective and mobility requirements.

scholarly journals Short-Term Traffic Flow Forecasting Model Based on GA-TCN

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rongji Zhang ◽  
Feng Sun ◽  
Ziwen Song ◽  
Xiaolin Wang ◽  
Yingcui Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Traffic Flow ◽  
Absolute Error ◽  
Forecasting Model ◽  
Average Absolute Error ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Fitness Value

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme.

Spatial Dynamic Analysis of a Mangeove Patch Based on Unmanned Aerial Vehicle: A Case Study in Pear Bay in Guangxi, China

2021 ◽  
Author(s):  
Xin Lin ◽  
Chungan Li ◽  
Mei Zhou ◽  
Wenhai Liang ◽  
Biao Li
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Spatial Dynamics ◽  
Absolute Error ◽  
Maximum Error ◽  
Canopy Height ◽  
Accurate Information ◽  
Maximum Absolute Error ◽  
Patch Area ◽  
Maximum Increase ◽  
Aerial Vehicle

Abstract This study investigated the short-term spatial variability of an mangrove patch, located in the Pearl Bay in Guangxi, China. Unmanned aerial vehicle (UAV) imagery covering the period from March 2015 to October 2017 were used and the following models were developed: two annual ultra-high resolution spatial resolution digital orthophoto maps (DOMs), two digital elevation models (DEMs), two digital surface models (DSMs), two canopy height models (CHMs), and a canopy height difference model (d-CHM). Using these models, the spatial dynamics of the extent and canopy height of the patch were analyzed. The resolution of the DOMs was 0.1 m, with an average geometrical error of 0.17 m and a maximum error of 0.44 m. The resolutions of DEMs, DSMs, CHMs, d-CHM were all 1 m. The average elevation errors of CHM in 2015 and 2017 were 0.002 m and -0.001 m, respectively, with maximum absolute errors of 0.034 m and 0.030 m, respectively. The average elevation error of d-CHM was -0.003 m and the maximum absolute error was 0.036 m, and the data quality were rated as good. From 2015 to 2017, the area of the mangrove patch increased from 8.16 ha to 8.79 ha, with an average annual increase of 3.7%. Specifically, the areas of expansion, shrinkage, and maximum seaward expansion were 6356 m2, 19 m2, and 24 m, respectively. The driving factor for the variability was natural processes. Stand canopy height exhibited a particular trend of decrease from northwest to southeast (horizontal; parallel to the seawall) and from the land to the sea (vertically; perpendicular to the seawall). From 2015 to 2017, 88.2% of the patch area showed increased canopy height, with an average increase of 0.78 m and a maximum increase of 3.2 m. In contrast, 11.8% of the patch area showed decreased canopy height with a maximum decrease of 3.1 m. The main reason for the decrease in canopy height was the death of trees caused by serious insect plagues. On the other hand, the reason for the increase in height could be attributed to the natural growth of mangrove trees, but further studies are required to verify the cause. UAV remote sensing has an incomparable advantage over traditional methods in that it provides extremely detailed and highly accurate information for in-depth study of the spatial evolution of mangrove patches, which would significantly contribute towards the protection and management of mangroves.

scholarly journals Parameter identification of Bouc-Wen model for Magnetorheological (MR) fluid Damper by a Novel Genetic Algorithm

2020 ◽  
Vol 12 (8) ◽  
pp. 168781402095054
Author(s):  
Birhan Abebaw Negash ◽  
Wonhee You ◽  
Jinho Lee ◽  
Kwansup Lee
Keyword(s):  
Genetic Algorithm ◽  
Parameter Identification ◽  
Mr Damper ◽  
Absolute Error ◽  
Mr Fluid ◽  
Fitness Evaluation ◽  
Damper Control ◽  
Fluid Dampers ◽  
Mr Fluid Damper ◽  
Crossover And Mutation

In this research, novel genetic algorithm (nGA) is proposed for Bouc-Wen modle parameters esstimation for magnetorheological (MR) fluid dampers. The optimization efficiency is improved by modifying the crossover and mutation steps of a GA. In the crossover stage, the probability of reproducing offspring from the same parent (same mother and father chromosome) is done to be zero, which may happen in the standard GA, and the probability of a chromosome to be selected for mating is based on error rank weighting of the chromosomes. Additional fitness evaluation of chromosomes will take place in between the crossover and mutation steps to save the best chromosome found so far, which is not implemented in the standard genetic algorithm (GA). The model is validated by comparing its simulation output force ( Fsim) with experimentally generated MR damper force ( Fexp). The mean absolute error, standard deviation and number of generations for convergence are taken as a criterias for performance evaluation. With these ctriterias, the proposed novel GA outperform better than the other researches. The accuracy is improved by 46.67% compared to standard GA. The proposed novel GA for Bouc-Wen model parameter identification can be used for any MR damper control system with better accuracy.

scholarly journals Evaluation of the Mass Diffusion Coefficient and Mass Biot Number Using a Nondominated Sorting Genetic Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 260 ◽  
Author(s):  
Radosław Winiczenko ◽  
Krzysztof Górnicki ◽  
Agnieszka Kaleta
Keyword(s):  
Genetic Algorithm ◽  
Diffusion Coefficient ◽  
Biot Number ◽  
Moisture Diffusion ◽  
Absolute Error ◽  
Mass Diffusion ◽  
Precise Determination ◽  
Nsga Ii ◽  
Moisture Diffusion Coefficient ◽  
Nondominated Sorting

A precise determination of the mass diffusion coefficient and the mass Biot number is indispensable for deeper mass transfer analysis that can enable finding optimum conditions for conducting a considered process. The aim of the article is to estimate the mass diffusion coefficient and the mass Biot number by applying nondominated sorting genetic algorithm (NSGA) II genetic algorithms. The method is used in drying. The maximization of coefficient of correlation (R) and simultaneous minimization of mean absolute error (MAE) and root mean square error (RMSE) between the model and experimental data were taken into account. The Biot number and moisture diffusion coefficient can be determined using the following equations: Bi = 0.7647141 + 10.1689977s − 0.003400086T + 948.715758s2 + 0.000024316T2 − 0.12478256sT, D = 1.27547936∙10−7 − 2.3808∙10−5s − 5.08365633∙10−9T + 0.0030005179s2 + 4.266495∙10−11T2 + 8.33633∙10−7sT or Bi = 0.764714 + 10.1689091s − 0.003400089T + 948.715738s2 + 0.000024316T2 − 0.12478252sT, D = 1.27547948∙10−7 − 2.3806∙10−5s − 5.08365753∙10−9T + 0.0030005175s2 + 4.266493∙10−11T2 + 8.336334∙10−7sT. The results of statistical analysis for the Biot number and moisture diffusion coefficient equations were as follows: R = 0.9905672, MAE = 0.0406375, RMSE = 0.050252 and R = 0.9905611, MAE = 0.0406403 and RMSE = 0.050273, respectively.

scholarly journals Genetic Algorithm Based PID Controller Tuning Approach for Continuous Stirred Tank Reactor

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
A. Jayachitra ◽  
R. Vinodha
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Absolute Error ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Operating Range ◽  
Continuous Stirred Tank ◽  
Dynamic Nonlinearity

Genetic algorithm (GA) based PID (proportional integral derivative) controller has been proposed for tuning optimized PID parameters in a continuous stirred tank reactor (CSTR) process using a weighted combination of objective functions, namely, integral square error (ISE), integral absolute error (IAE), and integrated time absolute error (ITAE). Optimization of PID controller parameters is the key goal in chemical and biochemical industries. PID controllers have narrowed down the operating range of processes with dynamic nonlinearity. In our proposed work, globally optimized PID parameters tend to operate the CSTR process in its entire operating range to overcome the limitations of the linear PID controller. The simulation study reveals that the GA based PID controller tuned with fixed PID parameters provides satisfactory performance in terms of set point tracking and disturbance rejection.

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