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Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP.

Deng L, Wang G, Chen B - Comput Intell Neurosci (2015)

Bottom Line: Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture.With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively.The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

View Article: PubMed Central - PubMed

Affiliation: School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China.

ABSTRACT
In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming), using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model's input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators' operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

No MeSH data available.


The sub-ET of optimal individual.
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fig9: The sub-ET of optimal individual.

Mentions: Determine the genetic control parameters before the algorithm running, including the size of population, the upper limit of evolution generation, and the probability of each genetic operator. The main operating parameters of GEP model are shown in Table 7. In function set, Sqrt represents square root; Ln represents return to natural logarithm of a number; X2 represents square; Avg2 represents mean of two variables. In terminal set, x1, x2, x3, and x4 represent four comfort impact factors extracted by factor analysis. 15 groups of simulation data are randomly selected from 22 groups of data in Table 6 as training set and the rest 7 groups as validation set. Through Visual Basic programming, the best individual is obtained after running multiple times, using computer with Inter Core i7-4500U CPU and 8 GB RAM. The best individual expression tree is shown in Figure 9; each expression tree represents a gene, and 3 genes are connected by linking function “+” to form a chromosome. The best individual translated into mathematical expression is as follows:(7)fx=127.850+x1−12x1+12x2+x2+x2+1/21.406−5.9560.990−x12−x1−7.005+x1x2+1212x3+x2x4−2.590+3.303+x3+x1.


Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP.

Deng L, Wang G, Chen B - Comput Intell Neurosci (2015)

The sub-ET of optimal individual.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581542&req=5

fig9: The sub-ET of optimal individual.
Mentions: Determine the genetic control parameters before the algorithm running, including the size of population, the upper limit of evolution generation, and the probability of each genetic operator. The main operating parameters of GEP model are shown in Table 7. In function set, Sqrt represents square root; Ln represents return to natural logarithm of a number; X2 represents square; Avg2 represents mean of two variables. In terminal set, x1, x2, x3, and x4 represent four comfort impact factors extracted by factor analysis. 15 groups of simulation data are randomly selected from 22 groups of data in Table 6 as training set and the rest 7 groups as validation set. Through Visual Basic programming, the best individual is obtained after running multiple times, using computer with Inter Core i7-4500U CPU and 8 GB RAM. The best individual expression tree is shown in Figure 9; each expression tree represents a gene, and 3 genes are connected by linking function “+” to form a chromosome. The best individual translated into mathematical expression is as follows:(7)fx=127.850+x1−12x1+12x2+x2+x2+1/21.406−5.9560.990−x12−x1−7.005+x1x2+1212x3+x2x4−2.590+3.303+x3+x1.

Bottom Line: Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture.With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively.The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

View Article: PubMed Central - PubMed

Affiliation: School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China.

ABSTRACT
In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming), using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model's input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators' operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

No MeSH data available.