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.
As a complicated human-machine environment system, cabin is the working space to perform the task of observation and manipulation, centralizing display instrument, manipulator, signal, alarm, and other terminal interfaces, for example, aerospace manned cabin, deep-sea submersibles cabin, engineering machinery cab, drilling rig control room, control room in nuclear power plant, and automobile cabin. The internal structure of these cabins is complex; the operators need to rely on vision, hearing, and touching to get information from the instruments and the outside world and make judgments quickly then immediately through locomotive organ such as hand and foot to manipulate correctly. In this state, the comfort of operating posture is an important factor to affect the operators’ work load, fatigue, health, and even safety, which should be considered in the human-machine interface design with emphasis. In the human-machine interface design for cabin, the layout design of all sorts of manipulators directly determines different operating postures, and the different operating postures directly affect operating comfort, convenience, and efficiency. Comfortable operating posture is advantageous to keep good matching relation between locomotive organ and manipulators. Therefore, the operating comfort is an important basis to evaluate layout design of human-machine interface for cabin [
Comfortable feeling is a kind of subjective feeling combined with the experience between physiological and psychological perception and affected by various factors such as work environment, duration, and different task [
At present, operating posture research focuses on using camera, driving posture monitoring system, 3D motion analysis, and other equipment to conduct the measurement, statistics, and analysis of postures. This kind of method is time-consuming and expensive and cannot be used in the design process early. Moreover, there is lack of effective modeling method in the comfort evaluation of operating posture. In order to evaluate the layout design scheme of human-machine interface for cabin in the early stage of design process and reduce the times of rework and production of physical prototype, shorten the cycle from design to manufacturing and cost [
The implementation steps of the proposed method are shown in Figure
Prediction method of operating comfort of human-machine interface layout for cabin based on GEP.
Through limb angles to describe the postures, joint angles are taken as independent variables to establish a comfort model of operating posture. Using mathematical method to describe each part size of the human body and relative position, it is used for the analysis of working posture and operating range and has nothing to do with the characteristics of the human body volume [
The simplified model of human skeletal system.
The human body’s different joint angles form different postures. To study comfortable working posture, the comfortable joint angles of the human body should be studied firstly. Most operation in cabin is given priority to sitting posture, so this paper chooses six parameters which are the most close to operating comfort of upper limb as variables of ergonomic characteristic. The six parameters are head, chest, waist, shoulder, upper arm, and forearm. The joint angles between adjacent limbs are used to describe the working posture.
The human body has many joints, and every joint has multiple degrees of freedom (DOF); thus many motions can be realized precisely. Joint motion can be regarded as the rotation around axis; the type of joints determines its form of motion. According to the DOF of the activity of articular surface relative to the joints of each other, the joints are divided into three kinds: DOF 1, DOF 2, and DOF 3. The DOF of joints involved in upper limb operating is shown in Figure
Degrees of freedom of joints.
Range of motion (ROM) of joints depends on statistical numerical [
Driving posture is an important issue in vehicle design process; many scholars and institutions have carried out a number of studies on optimal driving posture, preferred angles, seat comfort, and so on. Referring to the research results in vehicle driving posture and ergonomics [
Divide the ROM of joints (°).
Parts of the body | Mode of activity | DOF | Comfortable range | Less comfortable range | Uncomfortable range |
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Head | Flexion (+)/extension (−) | DOF 1 | 0~12 | −5~0, 12~20 | −20~−5, 20~25 |
Lateral left (+)/lateral right (−) | DOF 2 | −5~5 | −10~−5, 5~10 | −20~−10, 10~20 | |
Rotation right (+)/rotation left (−) | DOF 3 | −25~25 | −35~−25, 25~35 | −75~−35, 35~70 | |
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Chest | Flexion (+)/extension (−) | DOF 1 | −10~10 | 10~15 | — |
Lateral left (+)/lateral right (−) | DOF 2 | −5~5 | −10~−5, 5~10 | −20~−10, 10~20 | |
Rotation right (+)/rotation left (−) | DOF 3 | −10~10 | −20~−10, 10~20 | −70~−20, 20~70 | |
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Waist | Flexion (+)/extension (−) | DOF 1 | −5~10 | −10~−5, 10~20 | 20~40 |
Lateral left (+)/lateral right (−) | DOF 2 | −5~5 | −10~−5, 5~10 | — | |
Rotation right (+)/rotation left (−) | DOF 3 | −5~5 | −10~−5, 5~10 | — | |
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Shoulder | Flexion (+)/extension (−) | DOF 1 | −2~2 | −5~−2, 2~5 | −8~−5, 5~20 |
Elevation (+)/depression (−) | DOF 2 | −3~10 | 10~20 | −8~−3, 20~53 | |
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Upper arm | Flexion (+)/extension (−) | DOF 1 | −5~35 | −20~−5, 35~90 | −60~−20, 90~170 |
Abduction (+)/adduction (−) | DOF 2 | −5~25 | −10~−5, 25~60 | −18~−10, 60~80 | |
Medical rotation (+)/lateral rotation (−) | DOF 3 | −5~5 | −10~−5, 5~15 | −20~−10, 15~97 | |
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Forearm | Flexion (+)/extension (−) | DOF 1 | 80~110 | 0~80, 110~115 | 115~140 |
Pronation (+)/supination (−) | DOF 2 | 0~80 | 80~90 | 90~160 |
Refer to biological genetic gene expression patterns, Portuguese scientist Candida Ferreira proposed a revolutionary new member in the evolutionary computation family—GEP, which combined with the advantages of genetic algorithm and genetic programming [
GEP is a kind of new data mining technique, which has ideal efficiency. Because GEP combines the advantages of genetic algorithm and genetic programming, its efficiency is higher than GA or GP 2~4 orders of magnitude in solving complex problems. Using adaptive random search method, GEP is able to discover formula which can describe data inherent law from the data, without relying on any prior knowledge, showing strong accuracy and universality. Research on improvement and application of GEP algorithm has been attracting more and more attention, but the GEP has not been applied in the field of operating comfort prediction. So, this paper attempts to carry out the research on operating comfort prediction model based on GEP.
In GEP, a computer program is coded into fixed length of linear symbol strings and then when calculating the individual fitness, chromosomes (genotype) are expressed as different shapes and sizes of expressing tree (phenotype). Gene is the basic unit to constitute chromosome. Chromosome represents the feasible solution to solve the problem and consists of one or more genes. Formalization definition of gene can be expressed as a six-tuple:
As one kind of evolutionary algorithm, the operation process of GEP is similar to genetic algorithm. First of all, randomly generate initial population containing a certain number of individuals, and evaluate the fitness of these chromosomes. Then, on the basis of the fitness valve, choose the individuals as the next generation of population, conduct genetic operation on the selected individuals, and generate offspring with new features. The new individuals enter into the next round of the survival of the iterative process, and the process is repeated until the terminal condition is satisfied. The main steps of function mining by GEP are as follows [
Code the individuals and create the initial population. Population contains a number of individuals (chromosomes), and chromosome is composed of more than one gene (see Table
The genetic structure of GEP.
Gene 1 | Gene 2 | ⋯ | Gene | |||
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Head | Tail | Head | Tail | Head | Tail | |
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⋯ |
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Calculate the fitness value. The fitness value of each individual is calculated by fitness function. And the fitness value reflects the extent of excellence of individual to achieve the optimal solution in the course of evolution. If the optimal individual meets ending condition, it should be transferred to the output and if not, it should be transferred to genetic operation steps, and then it produces offspring with new characteristics. In GEP, in order to evaluate the matching degree between the data calculated by the expression and training data, Ferreira put forward two evaluation models: the fitness function based on absolute error and the fitness function based on relative error.
Carry out a series of genetic operations to produce a new generation of population using evolution principles to guide the evolution, including
Take the operating comfort evaluation of console layout of a certain type drilling rig as an example to illustrate the implementation of the proposed method. Use CATIA software to establish the human body and the product model, integrate the human physiological characteristics, simulate the operating postures, and realize the visualization of dynamic process in human-computer interaction. At the same time, making full use of ergonomic evaluation criteria and algorithm, operating comfort is analyzed and evaluated quantitatively. Using the data obtained from the simulation evaluation as training sample and validation sample, the relation model of joint angles and operating comfort is established based on GEP. Through limb angles to predict the comfort of operating posture, the basis of evaluation and optimization of human-machine interface layout design for cabin is provided.
The ergonomic design module of CATIA integrates four submodules: the human builder, human measurements editor, human activity analysis, and human posture analysis [
Create a data file of the human body model which must follow certain form. A population file contains four segments; the form is as follows: MEAN_STDEV M (): this segment lists each part of the body size of the male. MEAN_STDEV F (): this segment lists each part of the body size of the female. CORR M (): this segment lists the correlative numerical values between each part of size variable of the male. CORR F (): this segment lists the correlative numerical values between each part of size variable of the female.
The segment of MEAN_STDEV needs each measurement numerical value of Chinese adult body size, including the mean and the standard deviation. Each item takes up one line with the pattern of “
The segment of CORR needs correlative numerical values between any pairs of variables; the correlation between any two variables is defined in −1.0~1.0. It expresses the dependencies between two variables. The absolute value of correlation is higher; the dependencies between variables are higher.
According to the above format, the human body dimension data from the Chinese standard GB10000-88 is wrote in order; a complete database file of the human body dimension can be established. Take .sws as extension name, the file can be uploaded by user defined population database in CATIA. Detailed constructive process can be found in [
Imbedding the Chinese virtual human body model into human-machine interface layout scheme model, the human body model can be adjusted according to the selected percentile in real time. For different layout scheme, the human also has different operating posture. CATIA can adjust the virtual human body model to different operating posture fast and conveniently.
Before the posture evaluation, the preferred angle and corresponding score of each DOF of joints must be defined. When evaluating the comfort of body parts, based on the angle of DOF and score in current posture, the software will conclude evaluation score by interpolation and weighted arithmetic.
Set up the preferred angle of upper arm.
The simulation of operating posture.
Certain operating posture.
The corresponding comfort score of operating posture.
The statistics of comfort score.
Limb | Head | Chest | Waist | Right shoulder | Right upper arm | Right forearm | Overall comfort score | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
DOF | DOF 1 | DOF 2 | DOF 3 | DOF 1 | DOF 2 | DOF 3 | DOF 1 | DOF 2 | DOF 3 | DOF 1 | DOF 2 | DOF 1 | DOF 2 | DOF 3 | DOF 1 | DOF 2 | |
Angle (°) |
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0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | −2 | 10 | 35 | 4 | −13 | 63 | 84 | 8.91 |
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0 | 1 | 8 | 2 | 0 | 0 | 7 | 0 | 0 | 1 | 34 | 62 | 15 | 29 | 0 | 84 | 8.63 |
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−3 | 1 | 8 | 6 | 0 | 10 | 15 | 0 | 7 | 5 | 39 | 70 | 24 | 29 | 0 | 84 | 8.21 |
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0 | 2 | 9 | 10 | 0 | 12 | 22 | 0 | 10 | −3 | 36 | 84 | 28 | 12 | 0 | 99 | 8.10 |
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5 | 1 | 6 | 2 | 0 | 0 | 2 | 0 | 0 | 1 | 6 | 50 | 28 | 12 | 32 | 99 | 8.94 |
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5 | 1 | 10 | 6 | 0 | 1 | 4 | 0 | 1 | 3 | 17 | 70 | 28 | 12 | 0 | 125 | 8.57 |
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8 | 1 | 10 | 4 | 0 | 0 | 3 | 0 | 0 | 1 | 17 | 34 | 24 | 12 | 49 | 124 | 8.83 |
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10 | 1 | 11 | 4 | 0 | 0 | 3 | 0 | 0 | 1 | 12 | 26 | 39 | 12 | 53 | 101 | 8.88 |
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10 | 1 | 14 | 4 | 0 | 0 | 3 | 0 | 0 | 4 | 10 | 22 | 50 | 12 | 47 | 101 | 8.77 |
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15 | 1 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | −1 | 5 | −16 | 34 | −20 | 88 | 108 | 8.68 |
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17 | 1 | 16 | 0 | 0 | 0 | 1 | 0 | 0 | −2 | 2 | −27 | 56 | −20 | 81 | 108 | 8.49 |
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17 | 1 | 17 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 2 | −16 | 54 | −20 | 67 | 108 | 8.52 |
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0 | 2 | 11 | 4 | −4 | 2 | 7 | 0 | 2 | 16 | 15 | 66 | 49 | −6 | 0 | 135 | 8.22 |
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−1 | 2 | 9 | 4 | −8 | 2 | 10 | −2 | 2 | 13 | 25 | 68 | 57 | −9 | 0 | 104 | 8.10 |
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−1 | 2 | 9 | 3 | −8 | 2 | 10 | −2 | 2 | 12 | 21 | 68 | 57 | −9 | 5 | 105 | 8.13 |
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−1 | 2 | 11 | 4 | −8 | 2 | 10 | −4 | 2 | 12 | 20 | 68 | 56 | −9 | 7 | 105 | 8.11 |
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1 | 2 | 11 | 4 | −6 | 2 | 10 | 0 | 2 | 8 | 21 | 50 | 47 | 2 | 7 | 105 | 8.42 |
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1 | 2 | 11 | 6 | −6 | 2 | 13 | −2 | 2 | 11 | 19 | 50 | 61 | 2 | 7 | 100 | 8.30 |
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7 | −1 | 13 | 6 | 0 | 2 | 13 | 0 | 5 | 3 | 19 | 22 | 39 | −11 | 38 | 101 | 8.57 |
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8 | −1 | 19 | 6 | 0 | 2 | 13 | −1 | 6 | 2 | 19 | 42 | 39 | −10 | 15 | 101 | 8.46 |
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11 | −1 | 21 | 0 | 0 | 2 | 8 | −1 | 6 | −2 | 9 | 30 | 39 | −11 | 21 | 122 | 8.57 |
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10 | −1 | 23 | 1 | 0 | 2 | 8 | −3 | 6 | −3 | 19 | 42 | 39 | −11 | 11 | 122 | 8.38 |
First of all, check whether the data is suitable for factor analysis. Input the data in Table
According to the principle of “usually select the number of eigenvalues as factor number when cumulative variance contribution rate is greater than 0.85,” the factors are extracted by the method of principal component analysis. Four factors are extracted; the corresponding cumulative variance contribution rate reaches 86.311% (shown in Table
Total variance explained.
Component | Initial eigenvalues | Extraction sums of squared loadings | Rotation sums of squared loadings | ||||||
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Total | % of variance | Cumulative % | Total | % of variance | Cumulative % | Total | % of variance | Cumulative % | |
1 | 6.716 | 41.975 | 41.975 | 6.716 | 41.975 | 41.975 | 3.974 | 24.835 | 24.835 |
2 | 3.498 | 21.864 | 63.839 | 3.498 | 21.864 | 63.839 | 3.851 | 24.069 | 48.904 |
3 | 2.496 | 15.597 | 79.437 | 2.496 | 15.597 | 79.437 | 3.351 | 20.947 | 69.851 |
4 | 1.100 | 6.875 | 86.311 | 1.100 | 6.875 | 86.311 | 2.634 | 16.460 | 86.311 |
5 | 0.865 | 5.405 | 91.716 | ||||||
6 | 0.431 | 2.694 | 94.409 | ||||||
⋮ | ⋮ | ⋮ | ⋮ | ||||||
15 | 0.008 | 0.048 | 99.992 | ||||||
16 | 0.001 | 0.008 | 100.000 |
In Table
Rotated component matrix.
Component | ||||
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1 | 2 | 3 | 4 | |
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0.350 | −0.127 | 0.145 |
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−0.387 | 0.213 | 0.031 |
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−0.358 | 0.270 | −0.383 |
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0.357 |
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−0.166 | 0.013 |
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0.113 |
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0.070 | −0.396 |
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0.221 |
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0.095 | 0.125 |
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0.621 |
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0.091 | 0.238 |
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0.422 |
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0.040 | 0.292 |
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−0.330 | −0.077 |
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−0.014 |
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−0.319 | 0.008 |
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−0.241 |
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0.432 | −0.095 |
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0.323 |
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−0.240 | −0.113 |
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0.357 |
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0.560 | 0.119 |
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0.485 |
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−0.458 | 0.014 | −0.040 |
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0.210 | −0.093 | −0.423 |
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0.143 | −0.453 | −0.166 |
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By regression method, the factor score coefficient is estimated. Component sore coefficient matrix is calculated, and the calculation formula of factor score is expressed as follows:
Factor analysis is used to reduce the variable (namely, joint angle) dimension and eliminate the correlation between the variables so as to reduce the independent variable inputting in GEP later. The data after dimension reduction are shown in Table
The data about the joint angles after dimension reduction.
Factor 1 | Factor 2 | Factor 3 | Factor 4 | Overall comfort score | |
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−0.168 | 0.062 | 0.456 | 0.337 | 8.91 |
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0.301 | 0.068 | 0.532 | 0.391 | 8.63 |
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0.142 | 0.699 | 0.401 | 0.406 | 8.21 |
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0.035 | 0.957 | 0.322 | 0.283 | 8.10 |
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0.041 | −0.084 | 0.380 | 0.338 | 8.94 |
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0.423 | −0.101 | 0.365 | −0.019 | 8.57 |
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0.122 | −0.128 | 0.409 | 0.110 | 8.83 |
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−0.196 | 0.047 | 0.316 | 0.332 | 8.88 |
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−0.231 | 0.061 | 0.212 | 0.295 | 8.77 |
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−0.581 | 0.020 | 0.209 | 0.188 | 8.68 |
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−0.720 | 0.082 | 0.087 | 0.192 | 8.49 |
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−0.627 | 0.109 | 0.064 | 0.107 | 8.52 |
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0.379 | −0.090 | −0.168 | 0.040 | 8.22 |
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0.173 | 0.193 | −0.421 | 0.279 | 8.10 |
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0.149 | 0.166 | −0.416 | 0.265 | 8.13 |
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0.191 | 0.186 | −0.515 | 0.134 | 8.11 |
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0.067 | 0.185 | −0.112 | 0.344 | 8.42 |
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0.018 | 0.296 | −0.331 | 0.332 | 8.30 |
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−0.232 | 0.465 | 0.230 | −0.036 | 8.57 |
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−0.086 | 0.487 | 0.186 | −0.292 | 8.46 |
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−0.036 | 0.206 | 0.224 | −0.571 | 8.57 |
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0.118 | 0.246 | 0.145 | −0.737 | 8.38 |
Through the analysis of the data in Table
Determine the symbols representing chromosome; namely, choose function set and terminal set which suited the solution. The coding environment of GEP can be described as
The evolution results in every generation are evaluated by fitness function; the individuals with bigger fitness value are retained and have a higher chance to reproduce. According to the characteristics of the problem and based on the mean square error (MSE), this paper constructed the fitness function. The smaller the MSE value the bigger the fitness value of individual. The largest fitness value is 1000. The fitness function is defined as follows:
The larger the fitness value of individual the better the individual. Stop condition of algorithm is the fact that the fitness value of best individual achieves the required accuracy or the program achieves the maximum evolutionary generations.
Determine the genetic structure and the length of gene head. According to the complexity of the problem to define the length of gene head
The corresponding expression tree is shown in Figure
Gene expression tree.
In GEP coding, the length of each gene is fixed, including the front effective K-expression and the back of filler components. From top to bottom and left to right, the expression tree in Figure
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
GEP parameter set.
Parameter names | Parameter values |
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Function set |
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Terminal set |
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Generation | 1000 |
Number of individuals | 30 |
Number of genes | 3 |
Linking function | + |
Head size | 8 |
Mutation rate | 0.002 |
One-point/two-point/gene recombination rate | 0.003 |
IS/RIS/gene transposition rate | 0.005 |
Numerical constant | (−10, 10) |
The sub-ET of optimal individual.
Figure
The fitting curve of training set.
The range of
The fitting curve of validation set.
Back propagation neural network model and GEP algorithm model are used to predict the above 22 sets of data, respectively, and the predicted values and the relative error are shown in Table
Comparison of predicted results by GEP and BP.
Number | Sample | Factor 1 | Factor 2 | Factor 3 | Factor 4 | Overall comfort score | Relative error | |||
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Actual value | Predicted value | GEP | BP | |||||||
GEP | BP | |||||||||
1 |
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0.423 | −0.101 | 0.365 | −0.019 | 8.57 | 8.5630 | 8.5353 | −0.0008 | −0.0040 |
2 |
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0.118 | 0.246 | 0.145 | −0.737 | 8.38 | 8.3658 | 8.5124 | −0.0017 | 0.0158 |
3 |
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0.122 | −0.128 | 0.409 | 0.110 | 8.83 | 8.8769 | 8.6929 | 0.0053 | −0.0155 |
4 |
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0.018 | 0.296 | −0.331 | 0.332 | 8.30 | 8.2269 | 8.4456 | −0.0088 | 0.0175 |
5 |
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−0.036 | 0.206 | 0.224 | −0.571 | 8.57 | 8.5408 | 8.6613 | −0.0034 | 0.0107 |
6 |
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−0.232 | 0.465 | 0.230 | −0.036 | 8.57 | 8.5690 | 8.3524 | −0.0001 | −0.0254 |
7 |
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0.173 | 0.193 | −0.421 | 0.279 | 8.10 | 8.0867 | 7.9501 | −0.0016 | −0.0185 |
8 |
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−0.196 | 0.047 | 0.316 | 0.332 | 8.88 | 8.8441 | 8.8638 | −0.0040 | −0.0018 |
9 |
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0.149 | 0.166 | −0.416 | 0.265 | 8.13 | 8.1654 | 8.3935 | 0.0044 | 0.0324 |
10 |
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−0.168 | 0.062 | 0.456 | 0.337 | 8.91 | 8.9099 | 8.7646 | 0.0000 | −0.0163 |
11 |
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−0.231 | 0.061 | 0.212 | 0.295 | 8.77 | 8.7820 | 8.8782 | 0.0014 | 0.0123 |
12 |
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−0.720 | 0.082 | 0.087 | 0.192 | 8.49 | 8.5195 | 8.2823 | 0.0035 | −0.0245 |
13 |
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0.301 | 0.068 | 0.532 | 0.391 | 8.63 | 8.6264 | 8.4704 | −0.0004 | −0.0185 |
14 |
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−0.581 | 0.020 | 0.209 | 0.188 | 8.68 | 8.6929 | 8.5924 | 0.0015 | −0.0101 |
15 |
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0.035 | 0.957 | 0.322 | 0.283 | 8.10 | 8.1147 | 8.1305 | 0.0018 | 0.0038 |
16 |
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−0.627 | 0.109 | 0.064 | 0.107 | 8.52 | 8.5734 | 8.3132 | 0.0063 | −0.0243 |
17 |
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−0.086 | 0.487 | 0.186 | −0.292 | 8.46 | 8.4013 | 8.2050 | −0.0069 | −0.0301 |
18 |
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0.142 | 0.699 | 0.401 | 0.406 | 8.21 | 8.1966 | 8.0616 | −0.0016 | −0.0181 |
19 |
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0.067 | 0.185 | −0.112 | 0.344 | 8.42 | 8.3905 | 8.6967 | −0.0035 | 0.0329 |
20 |
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0.191 | 0.186 | −0.515 | 0.134 | 8.11 | 8.0588 | 7.8540 | −0.0063 | −0.0316 |
21 |
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0.041 | −0.084 | 0.380 | 0.338 | 8.94 | 8.8832 | 9.2055 | −0.0064 | 0.0297 |
22 |
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0.379 | −0.090 | −0.168 | 0.040 | 8.22 | 8.3137 | 8.0311 | 0.0114 | −0.0230 |
Comparison chart of actual value and predicted value by GEP and BP prediction model.
In order to validate the presented operating comfort prediction method of human-machine interface layout, the method of questionnaire survey is adopted, and the operating comfort of operating 14 manipulators on console shown in Figure
The case for verification.
Table
The comparison of evaluation results.
Manipulator number | The name of the manipulator | The score of questionnaire | The score of this method | Deviation |
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1 | Gen E-Stop | 8.50 | 8.42 | 0.08 |
2 | VFD E-Stop | 8.00 | 8.21 | 0.21 |
3 | E-Brake | 9.00 | 8.95 | 0.05 |
4 | Parking Brake | 8.50 | 8.87 | 0.37 |
5 | Left Cathead | 8.00 | 8.04 | 0.04 |
6 | Right Cathead | 8.00 | 8.11 | 0.11 |
7 | RT Inertia Brake | 9.00 | 8.84 | 0.16 |
8 | RT Motor Control | 8.50 | 8.76 | 0.26 |
9 | RT Speed Setting | 8.50 | 8.81 | 0.31 |
10 | RT Torque Limit | 9.00 | 8.68 | 0.32 |
11 | Rotary Cathead | 8.50 | 8.33 | 0.17 |
12 | Air Spinner | 8.00 | 8.21 | 0.21 |
13 | Spare 1 | 8.00 | 8.15 | 0.15 |
14 | Spare 2 | 7.50 | 8.01 | 0.51 |
In addition, in accordance with the method of establishing the virtual human body model put forward in Section
Comfort is a kind of subjective feeling, and it is difficult to quantify. In the process of operation, controlled by the feedback mechanism of the human body movement, the body always keeps each joint at a high comfort level as much as possible. Utilizing this adjustment mechanism, the operating comfort evaluation data is obtained by CATIA software. Then, GEP algorithm is applied for ergonomic analysis of human-machine interface layout for cabin. With 22 groups of evaluation data as the prediction model’s data base, according to GEP algorithm to realize the complex functions’ automatic modeling, the operating comfort prediction model is established. The example of operating comfort prediction of human-machine interface layout for driller control room proves that GEP has strong nonlinear and global search ability to find function, with the predicted results close to the target, and it has high prediction accuracy. With the limited training samples, GEP also can get accurate results. The comfort prediction model constructs the coupling relationship between joint angles and operating comfort, providing a solution for rapid assessment.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This research was supported by Open Fund (OGE201403-23) of Key Laboratory of Oil & Gas Equipment, Ministry of Education (Southwest Petroleum University), and by the Open Research Subject (GY-14YB-31) of Key Laboratory (Research Base) of Industrial Design Industry Research Center, Humanities and Social Science, Education Department of Sichuan Province.