An XGBoost based evaluation methodology of product color emotion design

Ding, Ming; Zhao, Lingying; Pei, Huining; Song, Meijia

doi:10.1299/jamdsm.2021jamdsm0075

Cited by 15 publications

(7 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with QTTI, BPNN is modelled with a nonlinear architecture and gradient descent, ofering high compatibility and operational convenience. Ding et al combines QTTI and BPNN in a colour matching study of exercise bikes, revealing the diferences between linear and nonlinear methods and giving logical colour matching recommendations [18]. Özcan et al uses both QTTI and BPNN to build a product design database and proposes a decision-making system based on technique for order preference by similarity to an ideal solution (TOPSIS) and grey relational analysis to assist design decisions [19].…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

“…Te transfer function combination of the input layer and the hidden layer is fnally determined as "tansig" function and "purelin" function, and the heuristic algorithm is determined as algorithm [18], as shown in Figure 9(a). Te determined 30 samples are used for training, 4 samples as validation samples, and the input layer data are normalized by equation (7).…”

Section: Te Establishment Of Prediction Model Based On Bpnnmentioning

confidence: 99%

See 1 more Smart Citation

Multisensory Design of Electric Shavers Based on Kansei Engineering and Artificial Neural Networks

Lin

Woo

Luo

et al. 2023

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The market scale of electric shavers in China has reached ¥ 26.3 billion in 2021. Consumers currently place an increasing emphasis on the Kansei image conveyed by products rather than just concerning with functional satisfaction. To meet consumers’ expectations, the emotional message conveyed by product design is essential under multisensory channels. This research first collected 230 electric shavers samples and 135 pairs of consumers’ Kansei words, then reduced them into 34 representative samples using multidimensional scale and clustering analysis, with 4 groups of representative Kansei words selected via the expert group. Moreover, consumers’ Kansei images were evaluated via questionnaire using the semantic differential scales, with 416 valid samples acquired in total. Meanwhile, design elements of the samples (including item and category) were classified by ways of morphological analysis and audio software. At last, the prediction models of the electric shavers were established between the overall design elements and user’s Kansei evaluation under the multisensory channel of visual model and auditory audio taking advantage of Quantification Theory Type I , back propagation neural network, and genetic algorithm-based BPNN. The proposed models can provide defined design indexes and references in multisensory design, facilitating designers to design in a logical and scientific manner rather than designing as per experience.

show abstract

Section: Back Propagation Neural Networkmentioning

confidence: 99%

Section: Te Establishment Of Prediction Model Based On Bpnnmentioning

confidence: 99%

Multisensory Design of Electric Shavers Based on Kansei Engineering and Artificial Neural Networks

Lin

Woo

Luo

et al. 2023

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…It is a tree boosting technique that is scalable in nature. It provides an easy way to prevent over-fitting problems [44]. It can be very helpful while using a sparse and low sample size dataset.…”

Section: Xgboost Exclusive Gradient Boosting Technique Hasmentioning

confidence: 99%

Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid

et al. 2022

View full text Add to dashboard Cite

The stability of the power grid is concernment due to the high demand and supply to smart cities, homes, factories, and so on. Different machine learning (ML) and deep learning (DL) models can be used to tackle the problem of stability prediction for the energy grid. This study elaborates on the necessity of IoT technology to make energy grid networks smart. Different prediction models, namely, logistic regression, naïve Bayes, decision tree, support vector machine, random forest, XGBoost, k-nearest neighbor, and optimized artificial neural network (ANN), have been applied on openly available smart energy grid datasets to predict their stability. The present article uses metrics such as accuracy, precision, recall, f1-score, and ROC curve to compare different predictive models. Data augmentation and feature scaling have been applied to the dataset to get better results. The augmented dataset provides better results as compared with the normal dataset. This study concludes that the deep learning predictive model ANN optimized with Adam optimizer provides better results than other predictive models. The ANN model provides 97.27% accuracy, 96.79% precision, 95.67% recall, and 96.22% F1 score.

show abstract

“…For example, even colors extracted from a single image can have hundreds of color combinations. At this time, it is necessary to establish an decision model between consumers' emotional preferences and product colors 17 , 25 , 26 .…”

Section: Introductionmentioning

confidence: 99%

An eye- tracking technology and MLP-based color matching design method

Hua

2023

Sci Rep

View full text Add to dashboard Cite

Images are a significant source of inspiration for designers to carry out the color design. However, the absence of animated images in the product color design can create confusion for designers. To translate the colours of the animated images into product colours, this work used eye-tracking technology to aid colour extraction and the multilayer perceptron neural network (MLP) algorithm to train a product colour decision model to filter the best product colour schemes. Firstly, eye tracking technology is used to collect the distribution of hotspots of the subject while viewing the animated images. Based on the distribution of eye-tracking hotspots, the most interesting animated colours were extracted. Then, the MLP is applied to train a colour decision model for children's shopping cart products, and the colour decision model is used to filter the optimal solution for the product colour, and finally the colour design is completed from the animated colour to the three-colour children's shopping cart product. Experimental results show that the color extraction based on the eye-tracking technology and the color scheme screening based on the intelligent algorithm can realize the effective conversion from animated image colors to product colors. This work proposes a color scheme design method from animations to products, which further expands the image color sources in product color design and can accurately find the color scheme that matches the animated image and the product.

show abstract

An XGBoost based evaluation methodology of product color emotion design

Cited by 15 publications

References 43 publications

Multisensory Design of Electric Shavers Based on Kansei Engineering and Artificial Neural Networks

Multisensory Design of Electric Shavers Based on Kansei Engineering and Artificial Neural Networks

Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid

An eye- tracking technology and MLP-based color matching design method

Contact Info

Product

Resources

About