Wyniki wyszukiwania - BazTech

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Temperature and Humidity Data Evaluation of Tight Sportswear During Motion Based on Intelligent Modeling

Cheng Pengpeng, Wang Jianping, Zeng Xianyi, Bruniaux Pascal, Chen Daoling

Fibres & Textiles in Eastern Europe

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2023

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Vol. 31, no. 3

1--8

EN

A neural network structure of Long Short Term Memory (LSTM) is proposed which could be used to predict the temperaturę and humidity of other key parts from the temperature and humidity data of some parts of the human body when wearing tight sportswear, so as to realize the temperature and humidity data prediction of all key points of the human body. The temperaturę and humidity of different people wearing tights were collected by DHT sensors. The experimental results show that the LSTM neural network structure proposed has higher prediction accuracy than other algorithms, and the model evaluates the feasibility of temperature and humidity data of tights in a state of motion, which facilitates the study of dynamic thermal and humid comfort and reduces the time cost of analyzing the temperature and humidity distribution and changing the law during human movement. It will effectively promote the study of temperature and humidity changes when people wear sports tights, provide theoretical reference for the study of human skin temperature in the field of sports medicine, and provide practical guidance for the application of human skin temperature changes in sports clothing production, diagnosis and prevention of sports injuries.

2

Intelligent Prediction Model of the Thermal and Moisture Comfort of the Skin-Tight Garment

Cheng Pengpeng, Wang Jianping, Zeng Xianyi, Bruniaux Pascal, Chen Daoling

Fibres & Textiles in Eastern Europe

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2022

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Vol. 30, no. 1 (151)

50--58

EN

In order to improve the efficiency and accuracy of predicting the thermal and moisture comfort of skin-tight clothing (also called skin-tight underwear), principal component analysis (PCA) is used to reduce the dimensions of related variables and eliminate the multicollinearity relationship among variables. Then, the optimized variables are used as the input parameters of the coupled intelligent model of the genetic algorithm (GA) and back propagation (BP) neural network, and the thermal and moisture comfort of different tights (tight tops and tight trousers) under different sports conditions is analysed. At the same time, in order to verify the superiority of the genetic algorithm and BP neural network intelligent model, the prediction results of GA-BP, PCA-BP and BP are compared with this model. The results show that principal component analysis (PCA) improves the accuracy and adaptability of the GA-BP neural network in predicting thermal and humidity comfort. The forecasting effect of the PCA-GA-BP neural network is obviously better than that of the GA-BP, PCA-BP, BP model, which can accurately predict the thermal and moisture comfort of tight-fitting sportswear. The model has better forecasting accuracy and a simpler structure.

3

A New Consumer Profile Definition Method Based on Fuzzy Technology and Fuzzy AHP

Zhang Junjie, Zeng Xianyi, Dong Min, Yuan Hua

Autex Research Journal

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2022

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Vol. 22, no. 2

208--216

EN

The research in this paper aims to set up a new consumer profile definition method based on fuzzy technology and fuzzy AHP. The result of the study could be applied to garment recommendation systems for a special consumer. Consumer profiles are chosen as research objects. The fuzzy technology and fuzzy AHP are applied in this research, which aims to provide a new method of using fuzzy technology and fuzzy AHP to define consumer profiles. We define tall–short and fat–thin by fuzzy technology and set up the weights of consumer profile by fuzzy AHP methods. The fuzzy technology and fuzzy AHP are applied for building consumer profiles that can be used for a consumer-oriented intelligent garment recommendation system.