Development of design system for product pattern design based on Kansei engineering and BP neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Daoling Chen ◽  
Pengpeng Cheng

PurposeIn order to help companies better grasp the perceptual needs of consumers for patterns, so as to carry out more accurate product pattern development and recommendation, this research develops a product pattern design system based on computer-aided design.Design/methodology/approachFirst, use the Kansei engineering theory and method to obtain the user's perceptual image, and deconstruct and encode the pattern based on the morphological analysis method, then through the BP neural network to construct the mapping relationship between the user's perceptual image and the pattern design elements, and finally calculate and find the corresponding design code combination according to the design goal to guide the pattern design.FindingsTaking costume paper-cut patterns as an example, the feasibility of this system is verified, the design system can well reflect the user's perceptual image in the pattern design and improve the efficiency of pattern customization service.Originality/valueCompared with the traditional method that relies on the designer's personal experience to propose a design plan, this research provides scientific and intelligent design methods for product pattern design.

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bingjun Li ◽  
Shuhua Zhang

PurposeThe purpose of this study to provide a reference basis for effectively managing the risk of agrometeorological disasters in Henan Province, speeding up the establishment of a scientific and reasonable system of agrometeorological disasters prevention and reduction and guaranteeing grain security.Design/methodology/approachFirstly, according to the statistical data of areas covered by natural disaster, areas affected by natural disaster, sown area of grain crops and output of grain crops from 1979 to 2018 in Henan Province, China. We have constructed an agrometeorological disaster risk assessment system for Henan province, China, which is composed of indicators such as rate covered by natural disaster, rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability. The variation characteristics of agrometeorological disasters in Henan Province and their effects on agricultural production are analyzed. Secondly, the grey relational analysis method is used to analyze the relation degree between the main agrometeorological disaster factors and the output of grain crops of Henan Province. Based on the grey BP neural network, the rate covered by various natural disaster and the rate affected by various natural disaster are simulated and predicted.FindingsThe results show that: (1) the freeze injury in the study period has a greater contingency, the intensity of the disaster is also greater, followed by floods. Droughts, windstorm and hail are Henan Province normal disasters. (2) According to the degree of disaster vulnerability, the ability to resist agricultural disasters in Henan Province is weak. (3) During the study period, drought and flood are the key agrometeorological disasters affecting the grain output of Henan Province, China.Practical implicationsThe systematic analysis and evaluation of agrometeorological disasters are conducive to the sustainable development of agriculture, and at the same time, it can provide appropriate and effective measures for the assessment and reduction of economic losses and risks.Originality/valueBy calculating and analyzing the rate covered by natural disaster, the rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability of crops in Henan Province of China and using grey BP neural network simulation projections for the rate covered by various natural disaster and the rate affected by various natural disaster, the risk assessment system of agrometeorological disasters in Henan is constructed, which provides a scientific basis for systematic analysis and evaluation of agrometeorological disasters.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinsong Tu ◽  
Yuanzhen Liu ◽  
Ming Zhou ◽  
Ruixia Li

Purpose This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately. Design/methodology/approach The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform. Findings Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better. Originality/value The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.


1994 ◽  
Vol 03 (02) ◽  
pp. 157-185 ◽  
Author(s):  
DERSHUNG YANG ◽  
LARRY A. RENDELL ◽  
JULIE L. WEBSTER ◽  
DORIS S. SHAW ◽  
JAMES H. GARRETT

A new neural network called AUGURS is designed to assist a user of a Computer-Aided Design system in utilizing standard graphic symbols. With AUGURS, the CAD user can avoid searching for standard symbols in a large library and rely on AUGURS to automatically retrieve those symbols resembling the user’s drawing. More specifically, AUGURS inputs a bitmap image normalized with respect to location, size, and orientation, and outputs a list of standard symbols ranked by its assessment of the similarity between the symbol and the input image. Only the top ranked symbols are presented to the user for selection. AUGURS encodes geometric knowledge into its network structure and carefully balances its discriminant power and noise tolerance. The encoded knowledge enables AUGURS to learn reasonably well despite the limited number of training examples, the most serious challenge for the CAD domain. We have compared AUGURS with the Zipcode Net, a traditional layered feed-forward network with an unconstrained structure, and a network that inputs either Zernike or pseudo-Zernike moments. The experimental results conclude that AUGURS can achieve the best recognition performance among all networks being compared with reasonable recognition and learning efficiency.


2017 ◽  
Vol 14 (2) ◽  
pp. 155-158 ◽  
Author(s):  
Guimei Wang ◽  
Yong Shuo Zhang ◽  
Lijie Yang ◽  
Shuai Zhang

Purpose This paper aims to optimize the weighing control system and compensate weighing error for weighing control system of coal mine paste-filling weighing control system. Design/methodology/approach The process of the paste-filling weighing control system is analyzed and the mathematical model of the paste-filling material weight is established. Then, the back-propagation (BP) neural network is used to optimize the control system and compensate the weighing error. Findings Without the BP neural network, the weighing error of the paste-filling control system is more than 3 per cent, whereas after optimization with the BP neural network, the weighing error is less than 1 per cent. With the simulation results, it is seen that the weighing error of the paste-filling control system decreases and the accuracy of the weighing control system improves and optimizes. Originality/value The method can be further used to improve the control precision of the coal mine paste-filling system.


2014 ◽  
Vol 971-973 ◽  
pp. 1316-1320 ◽  
Author(s):  
Yan Zhou ◽  
Ping Yang ◽  
Si Yu Wang

Based on kansei engineering theory, with mobile phone model as the research object, on the speciation analysis of deconstruction, study of the correlation between form design elements and subjective feeling. To obtain mobile phone simulation modeling samples by the concept of orthogonal test. Through collecting a large number of perceptual image vocabulary, the multiple scale method, class cluster method was applied to typical filtered emotional vocabulary, as the shape of subjective evaluation metrics. Using the mobile phone modeling sample obtained from the screened and representative emotional semantic difference experiment on the emotional reactions of vocabulary, get the corresponding relation between mobile phones form design elements and subjective evaluation value. Quantitative fuzzy uncertainty of emotional problem, and using kansei engineering theory to improve product that cares consumers emotional has become the direction of product design.


Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1722
Author(s):  
Qianwen Fu ◽  
Jian Lv ◽  
Shihao Tang ◽  
Qingsheng Xie

To effectively organize design elements in virtual reality (VR) scene design and provide evaluation methods for the design process, we built a user image space cognitive model. This involved perceptual engineering methods and optimization of the VR interface. First, we studied the coupling of user cognition and design features in the VR system via the Kansei Engineering (KE) method. The quantitative theory I and KE model regression analysis were used to analyze the design elements of the VR system’s human–computer interaction interface. Combined with the complex network method, we summarized the relationship between design features and analyzed the important design features that affect users’ perceptual imagery. Then, based on the characteristics of machine learning, we used a convolutional neural network (CNN) to predict and analyze the user’s perceptual imagery in the VR system, to provide assistance for the design optimization of the VR system design. Finally, we verified the validity and feasibility of the solution by combining it with the human–machine interface design of the VR system. We conducted a feasibility analysis of the KE model, in which the similarity between the multivariate regression analysis of the VR intention space and the experimental test was approximately 97% and the error was very small; thus, the VR intention space model was well correlated. The Mean Square Error (MSE) of the convolutional neural network (CNN) prediction model was calculated with a measured value of 0.0074, and the MSE value was less than 0.01. The results show that this method can improve the effectiveness and feasibility of the design scheme. Designers use important design feature elements to assist in VR system optimization design and use CNN machine learning methods to predict user image values in VR systems and improve the design efficiency. Facing the same design task requirements in VR system interfaces, the traditional design scheme was compared with the scheme optimized by this method. The results showed that the design scheme optimized by this method better fits the user’s perceptual imagery index, and thus the user’s task operation experience was better.


2020 ◽  
Vol 32 (3) ◽  
pp. 173-180
Author(s):  
Min Wu ◽  
Bailin Lv

Purpose Viscosity is an important basic physical property of liquid solders. However, because of the very complex nonlinear relationship between the viscosity of the liquid ternary Sn-based lead-free solder and its determinants, a theoretical model for the viscosity of the liquid Sn-based solder alloy has not been proposed. This paper aims to address the viscosity issues that must be considered when developing new lead-free solders. Design/methodology/approach A BP neural network model was established to predict the viscosity of the liquid alloy and the predicted values were compared with the corresponding experimental data in the literature data. At the same time, the BP neural network model is compared with the existing theoretical model. In addition, a mathematical model for estimating the melt viscosity of ternary tin-based lead-free solders was constructed using a polynomial fitting method. Findings A reasonable BP neural network model was established to predict the melt viscosity of ternary tin-based lead-free solders. The viscosity prediction of the BP neural network agrees well with the experimental results. Compared to the Seetharaman and the Moelwyn–Hughes models, the BP neural network model can predict the viscosity of liquid alloys without the need to calculate the relevant thermodynamic parameters. In addition, a simple equation for estimating the melt viscosity of a ternary tin-based lead-free solder has been proposed. Originality/value The study identified nine factors that affect the melt viscosity of ternary tin-based lead-free solders and used these factors as input parameters for BP neural network models. The BP neural network model is more convenient because it does not require the calculation of relevant thermodynamic parameters. In addition, a mathematical model for estimating the viscosity of a ternary Sn-based lead-free solder alloy has been proposed. The overall research shows that the BP neural network model can be well applied to the theoretical study of the viscosity of liquid solder alloys. Using a constructed BP neural network to predict the viscosity of a lead-free solder melt helps to study the liquid physical properties of lead-free solders that are widely used in electronic information.


Author(s):  
Yu Yan ◽  
Wei Jiang ◽  
An Zhang ◽  
Qiao Min Li ◽  
Hong Jun Li ◽  
...  

Purpose This study aims to the three major problems of low cleaning efficiency, high labor intensity and difficult to evaluate the cleaning effect for manual insulators cleaning in ultra high voltage (UHV) converter station, the purpose of this paper is to propose a basic configuration of UHV vertical insulator cleaning robot with multi-freedom-degree mechanical arm system on mobile airborne platform and its innovation cleaning operation motion planning. Design/methodology/approach The main factors affecting the insulators cleaning effect in the operation process have been analyzed. Because of the complex coupling relationship between the influencing factors and the insulators cleaning effect, it is difficult to establish its analytical mathematical model. Combining the non-linear mapping and approximation characteristics of back propagation (BP) neural network, the insulator cleaning effect evaluation can be abstracted as a non-linear approximation process from actual cleaning effect to ideal cleaning effect. An evaluation method of robot insulator cleaning effect based on BP neural network has been proposed. Findings Through the BP neural network training, the robot cleaning control parameters can be obtained and used in the robot online operation control, so that the better cleaning effect can be also obtained. Finally, a physical prototype of UHV vertical insulator cleaning robot has been developed, and the effectiveness and engineering practicability of the proposed robot configuration, cleaning effect evaluation method are all verified by simulation experiments and field operation experiments. At the same time, this method has the remarkable characteristics of sound versatility, strong adaptability, easy expansion and popularization. Originality/value An UHV vertical insulator cleaning robot operation system platform with multi-arm system on airborne platform has been proposed. Through the coordinated movement of the manipulator each joint, the manipulator can be positioned to the insulator strings, and the insulator can be cleaned by two pairs high-pressure nozzles located at the double manipulator. The influence factors of robot insulator cleaning effect have been analyzed. The BP neural network model of insulator cleaning effect evaluation has been established. The evaluation method of robot insulator cleaning effect based on BP neural network has also been proposed, and the corresponding evaluation result can be obtained through the network training. Through the system integration design, the robot physical prototype has been developed. For the evaluation of other operation effects of power system, the validity and engineering practicability of the robot mechanism, motion planning and the method for evaluating the effect of robot insulator cleaning have been verified by simulation and field operation experiments.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaohong Lu ◽  
Yu Zhou ◽  
Jinhui Qiao ◽  
Yihan Luan ◽  
Yongquan Wang

Purpose The purpose of this paper is to analyze the measurement error of a three-dimensional coordinate measurement system based on dual-position-sensitive detector (PSD) under different background light. Design/methodology/approach The mind evolutionary algorithm (MEA)-back propagation (BP) neural network is used to predict the three-dimensional coordinates of the points, and the influence of the background light on the measurement accuracy of the three-dimensional coordinates based on PSD is obtained. Findings The influence of the background light on the measurement accuracy of the system is quantitatively calculated. The background light has a significant influence on the prediction accuracy of the three-dimensional coordinate measurement system. The optical method, electrical method and photoelectric compensation method are proposed to improve the measurement accuracy. Originality/value BP neural network based on MEA is applied to the coordinate prediction of the three-dimensional coordinate measurement system based on dual-PSD, and the influence of background light on the measurement accuracy is quantitatively analyzed.


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