scholarly journals Neural network-based thermal comfort prediction for the elderly

2021 ◽  
Vol 237 ◽  
pp. 02022
Author(s):  
JinJin Zhang ◽  
Hong Liu ◽  
YuXin Wu ◽  
Shan Zhou ◽  
MengJia Liu
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Skin Temperature ◽  
Thermal Sensation ◽  
The Elderly ◽  
Learning Technology ◽  
Local Skin ◽  
Human Thermal Comfort ◽  
Network Algorithm ◽  
Neural Network Algorithm

Machine learning technology has become a hot topic and is being applied in many fields. However, in the prediction of thermal sensation in the elderly, there is not enough research on the neural network to predict the effect of human thermal comfort. In this paper, two neural network algorithms were used to predict the thermal expectation of the elderly, and the accuracy of the two algorithms was compared to find a suitable neural network algorithm to predict human thermal comfort. The dataset was collected from the laboratory study and included 10 local skin temperatures of the subjects, thermal perception voted at three temperatures (28/30/32°C), different wind speeds, and two forms of wind. Thirteen subjects with an average age of 63.5 years old were recruited for the subjective survey. These subjects sat for long periods of summer working conditions, wore uniform thermal resistance clothing, and collected votes on thermal sensation, as well as skin temperature. The results showed that the prediction accuracy of the two algorithms was related to the added influence factors, and the RBF neural network algorithm was the most accurate in predicting thermal sensation of the elderly. The main influencing factors were average skin temperature, wind speed and body fat rate.

Predicting indoor thermal sensation for the elderly in welfare centres in Korea using local skin temperatures

2016 ◽  
Vol 26 (8) ◽  
pp. 1155-1167 ◽  
Author(s):  
Chihye Bae ◽  
Hyunjung Lee ◽  
Chungyoon Chun
Keyword(s):  
Field Study ◽  
Skin Temperature ◽  
Elderly People ◽  
Thermal Sensation ◽  
The Elderly ◽  
The Body ◽  
Elderly Subjects ◽  
Climate Chamber ◽  
Local Skin ◽  
Skin Temperatures

This study aims to develop a method to predict thermal sensation in elderly people. To identify the point on the body where skin temperature can best predict thermal sensation in elderly people aged 65 or older and develop a thermal comfort measurement model that can replace the psychological scale, experiments were conducted in a stainless steel wall finish climate chamber and at the seven senior welfare centres in Korea. The results of the climate chamber experiment with 30 healthy elderly people (15 males, 15 females) showed that there was a correlation between thermal sensation and local skin temperature on the back of the hand, the upper arm, the top of the foot and the cheek. This developed thermal sensation prediction model was then applied in a field study at senior welfare centres to verify whether the model could be applied to a large number of elderly subjects in different locations. The field study with 294 elderly people (111 males, 183 females) shows that cheek and back of the hand skin temperatures were useful in predicting thermal sensation in the elderly, and predicted thermal sensation based on the skin temperature of the cheek had the strongest correlation with thermal sensation among the participants.

scholarly journals Effect of Landscape Microclimates on Thermal Comfort and Physiological Wellbeing

2019 ◽  
Vol 11 (19) ◽  
pp. 5387 ◽  
Author(s):  
Binyi Liu ◽  
Zefeng Lian ◽  
Robert D. Brown
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Thermal Comfort ◽  
Skin Temperature ◽  
Skin Conductance ◽  
Thermal Sensation ◽  
Global Climate ◽  
Evidence Base ◽  
Climate Conditions ◽  
Human Thermal Comfort

Global climate change and intensifying heat islands have reduced human thermal comfort and health in urban outdoor environments. However, there has been little research that has focused on how microclimates affect human thermal comfort, both psychologically and physiologically. We investigated the effect of a range of landscape microclimates on human thermal comfort and health using questionnaires and physiological measurements, including skin temperature, skin conductance, and heart rate variability, and compared the results with the effect of prevailing climate conditions in open spaces. We observed that in landscape microclimates, thermal sensation votes significantly decreased from 1.18 ± 0.66 (warm–hot) to 0.23 ± 0.61 (neutral–slightly warm), and thermal comfort increased from 1.18 ± 0.66 (uncomfortable–neutral) to 0.23 ± 0.61 (neutral–comfortable). In the landscape microclimates, skin temperature and skin conductance decreased 0.3 ± 0.8 °C and 0.6 ± 1.0 μs, respectively, while in the control, these two parameters increased by 0.5 ± 0.9 °C and 0.2 ± 0.7 μs, respectively. Further, in landscape microclimates, subject heart rate variability increased significantly. These results suggest landscape microclimates improve human thermal comfort and health, both psychologically and physiologically. These findings can provide an evidence base that will assist urban planners in designing urban environments for the health and wellbeing of residents.

scholarly journals Compensated Fuzzy Neural Network-Based Music Teaching Ability Assessment Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xu Chen
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Teaching Practice ◽  
Assessment Model ◽  
Learning Technology ◽  
Music Teaching ◽  
Network Algorithm ◽  
Teaching Ability ◽  
Fuzzy Neural ◽  
Neural Network Algorithm

College is the main place to carry out music teaching, and it is important to assess the music teaching ability in college effectively. Based on this, this paper firstly analyzes the necessity of music teaching ability assessment and briefly summarizes the application of neural network and deep learning technology in music teaching ability assessment and secondly designs an assessment model based on compensated fuzzy neural network algorithm and analyzes the accuracy of the model, finds out the causes of forming abnormal output by analysing the general dimensional conditions of the algorithm of the model, and proposes corresponding correction. Finally, the reliability and feasibility of the music teaching ability assessment model were experimentally verified by combining with teaching practice. The research results confirm the feasibility of the compensated fuzzy neural network algorithm in music teaching ability assessment, which has important reference significance for improving the quality of music teaching in colleges and universities.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

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