scholarly journals A Novel Exercise Thermophysiology Comfort Prediction Model with Fuzzy Logic

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Nan Jia ◽  
Liang Yu ◽  
KaiXing Yang ◽  
RuoMei Wang ◽  
XiaoNan Luo ◽  
...  
Keyword(s):  
Prediction Model ◽  
Human Body ◽  
Fuzzy Inference ◽  
Exercise Program ◽  
Prediction Method ◽  
Inference System ◽  
Physiological Properties ◽  
Improve Health Status ◽  
Regulatory Model ◽  
Improve Health

Participation in a regular exercise program can improve health status and contribute to an increase in life expectancy. However, exercise accidents like dehydration, exertional heatstroke, syncope, and even sudden death exist. If these accidents can be analyzed or predicted before they happen, it will be beneficial to alleviate or avoid uncomfortable or unacceptable human disease. Therefore, an exercise thermophysiology comfort prediction model is needed. In this paper, coupling the thermal interactions among human body, clothing, and environment (HCE) as well as the human body physiological properties, a human thermophysiology regulatory model is designed to enhance the human thermophysiology simulation in the HCE system. Some important thermal and physiological performances can be simulated. According to the simulation results, a human exercise thermophysiology comfort prediction method based on fuzzy inference system is proposed. The experiment results show that there is the same prediction trend between the experiment result and simulation result about thermophysiology comfort. At last, a mobile application platform for human exercise comfort prediction is designed and implemented.

Surface Roughness Prediction in Precision Surface Grinding of Nano-Ceramic Coating Based on Improved ANFIS

2010 ◽  
Vol 44-47 ◽  
pp. 2293-2298 ◽  
Author(s):  
Xiong Hua Guo ◽  
Mao Fu Liu ◽  
Chang Rong Zhao
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Ceramic Coating ◽  
Fuzzy Inference ◽  
Prediction Method ◽  
Surface Grinding ◽  
Precision Grinding ◽  
Anfis Model ◽  
Inference System ◽  
Roughness Prediction

For improving surface integrity and machining quality after precision grinding of the parts of nano-ceramic coating, and investigating its prediction technique of surface roughness, the prediction model of surface roughness in precision surface grinding of nano-ceramic coating based on adaptive network-based fuzzy inference system (ANFIS) was proposed in this paper. Then, the proposed prediction model was improved by hybrid Taguchi genetic algorithm (HTGA). At last, by comparative analysis of prediction results from traditional BP neural network model, simple ANFIS model and improved ANFIS model, the effectiveness of the proposed model was verified using grinding parameters and measured surface roughness in grinding tests as training and testing samples. It showed that the prediction accuracy of the improved ANFIS model proposed in this paper was higher, and it was an effective prediction method of surface roughness in precision grinding of nano-ceramic coating.

Thermal Error Prediction of the Spindle Using Adaptive Neuro-Fuzzy Inference System

2015 ◽  
Vol 789-790 ◽  
pp. 263-267
Author(s):  
Yan Lei Li ◽  
Ming Yan Wang ◽  
You Min Hu ◽  
Bo Wu
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Thermal Error ◽  
Temperature Data ◽  
Current Sensor ◽  
Error Prediction ◽  
Inference System ◽  
Neuro Fuzzy

This paper proposes a new method to predict the spindle deformation based on temperature data. The method introduces ANFIS (adaptive neuro-fuzzy inference system). For building the predictive model, we first extract temperature data from sensors in the spindle, and then they are used as the inputs to train ANFIS. To evaluate the performance of the prediction, an experiment is implemented. Three Pt-100 thermal resistances is used to monitor the spindle temperature, and an inductive current sensor is used to obtain the spindle deformation. The experimental results display that our prediction model can better predict the spindle deformation and improve the performance of the spindle.

scholarly journals Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
İsmail Kıyak ◽  
Gökhan Gökmen ◽  
Gökhan Koçyiğit
Keyword(s):  
Fuzzy Inference ◽  
Prediction Method ◽  
Operating Conditions ◽  
Lifetime Prediction ◽  
Statistical Characteristics ◽  
Ambient Conditions ◽  
Inference System ◽  
Initial Task ◽  
Neuro Fuzzy ◽  
A Cell

Predicting the lifetime of a LED lighting system is important for the implementation of design specifications and comparative analysis of the financial competition of various illuminating systems. Most lifetime information published by LED manufacturers and standardization organizations is limited to certain temperature and current values. However, as a result of different working and ambient conditions throughout the whole operating period, significant differences in lifetimes can be observed. In this article, an advanced method of lifetime prediction is proposed considering the initial task areas and the statistical characteristics of the study values obtained in the accelerated fragmentation test. This study proposes a new method to predict the lifetime of COB LED using an artificial intelligence approach and LM-80 data. Accordingly, a database with 6000 hours of LM-80 data was created using the Neuro-Fuzzy (ANFIS) algorithm, and a highly accurate lifetime prediction method was developed. This method reveals an approximate similarity of 99.8506% with the benchmark lifetime. The proposed methodology may provide a useful guideline to lifetime predictions of LED-related products which can also be adapted to different operating conditions in a shorter time compared to conventional methods. At the same time, this method can be used in the life prediction of nanosensors and can be produced with the 3D technique.

scholarly journals Neuro-Fuzzy Dynamic Position Prediction for Autonomous Work-Class ROV Docking

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 693
Author(s):  
Petar Trslić ◽  
Edin Omerdic ◽  
Gerard Dooly ◽  
Daniel Toal
Keyword(s):  
Fuzzy Inference ◽  
North Atlantic Ocean ◽  
Prediction Method ◽  
Hydrodynamic Drag ◽  
Remotely Operated Vehicle ◽  
Inference System ◽  
Drag Forces ◽  
Neuro Fuzzy ◽  
Heave Motion ◽  
Work Class

This paper presents a docking station heave motion prediction method for dynamic remotely operated vehicle (ROV) docking, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Due to the limited power onboard the subsea vehicle, high hydrodynamic drag forces, and inertia, work-class ROVs are often unable to match the heave motion of a docking station suspended from a surface vessel. Therefore, the docking relies entirely on the experience of the ROV pilot to estimate heave motion, and on human-in-the-loop ROV control. However, such an approach is not available for autonomous docking. To address this problem, an ANFIS-based method for prediction of a docking station heave motion is proposed and presented. The performance of the network was evaluated on real-world reference trajectories recorded during offshore trials in the North Atlantic Ocean during January 2019. The hardware used during the trials included a work-class ROV with a cage type TMS, deployed using an A-frame launch and recovery system.

Experimental Measurement of Erosive Wear and Development of Prediction Model Using Adaptive Nero Fuzzy Inference System

2017 ◽  
Author(s):  
Milind S. Patil ◽  
Shyamkumar D. Kalpande ◽  
Sanjay P. Shekhawat ◽  
Eknath R. Deore ◽  
Chandrashekhar D. Mohod
Keyword(s):  
Prediction Model ◽  
Experimental Measurement ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Erosive Wear ◽  
Inference System

scholarly journals Fuzzy Approach in Rail Track Degradation Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Mostafa Karimpour ◽  
Lalith Hitihamillage ◽  
Najwa Elkhoury ◽  
Sara Moridpour ◽  
Reyhaneh Hesami
Keyword(s):  
Prediction Model ◽  
Human Error ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Model Development ◽  
Rail Transport ◽  
Maintenance Cost ◽  
Inference System ◽  
Rail Track

Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work. With the restrictions on financial support, the rail transport authorities are in pursuit of improved modern methods, which can provide a precise prediction of rail maintenance timeframe. The expectation from such a method is to develop models to minimise the human error that is strongly related to manual prediction. Such models will help rail transport authorities in understanding how the track degradation occurs at different conditions (e.g., rail type, rail profile) over time. They need a well-structured technique to identify the precise time when rail tracks fail to minimise the maintenance cost/time. The rail track characteristics that have been collected over the years will be used in developing a degradation prediction model for rail tracks. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use the data in prediction model development. An accurate model can play a key role in the estimation of the long-term behaviour of rail tracks. Accurate models can increase the efficiency of maintenance activities and decrease the cost of maintenance in long-term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curves and straight sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model. The results from the developed model show that it is capable of predicting the gauge values with R2 of 0.6 and 0.78 for curves and straights, respectively.

scholarly journals Predicting Real-Time Crash Risk for Urban Expressways in China

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Miaomiao Liu ◽  
Yongsheng Chen
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Fuzzy Inference ◽  
Risk Prediction Model ◽  
Training Data ◽  
Sensor Data ◽  
Crash Risk ◽  
Inference System

We developed a real-time crash risk prediction model for urban expressways in China in this study. About two-year crash data and their matching traffic sensor data from the Beijing section of Jingha expressway were utilized for this research. The traffic data in six 5-minute intervals between 0 and 30 minutes prior to crash occurrence was extracted, respectively. To obtain the appropriate data training period, the data (in each 5-minute interval) during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined. Then we proposed a new real-time crash risk prediction model using decision tree method and adaptive neural network fuzzy inference system (ANFIS). By comparing several real-time crash risk prediction methods, it was found that our proposed method had higher precision than others. And the training error and testing error were minimum (0.280 and 0.291, resp.) when the data during 0 to 30 minutes prior to crash occurrence was collected and the decision tree-ANFIS method was applied to train and establish the real-time crash risk prediction model. The prediction accuracy of the crash occurrence could reach 65% when 0.60 was considered as the crash prediction threshold.

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