This paper transforms fuzzy number into clear number using the centroid method, thus we can research the traditional linear regression model which is transformed from the fuzzy linear regression model. The model’s in...This paper transforms fuzzy number into clear number using the centroid method, thus we can research the traditional linear regression model which is transformed from the fuzzy linear regression model. The model’s input and output are fuzzy numbers, and the regression coefficients are clear numbers. This paper considers the parameter estimation and impact analysis based on data deletion. Through the study of example and comparison with other models, it can be concluded that the model in this paper is applied easily and better.展开更多
In this paper we developed a fuzzy inventory model for deteriorating items with time dependent demand rate. Shortages are allowed and completely backlogged. The backlogging rate of unsatisfied demand is assumed to be ...In this paper we developed a fuzzy inventory model for deteriorating items with time dependent demand rate. Shortages are allowed and completely backlogged. The backlogging rate of unsatisfied demand is assumed to be a decreasing exponential function of waiting time. The demand rate, deterioration rate and backlogging rate are assumed as a triangular fuzzy numbers. The purpose of our study is to defuzzify the total profit function by signed distance method and centroid method. Further a numerical example is also given to demonstrate the developed crisp and fuzzy models. A sensitivity analysis is also given to show the effect of change of the parameters.展开更多
Accurate fingertip detection is critical for translating hand gestures into actionable commands in vision-based human‒computer interaction(HCI)systems.However,challenges such as complex backgrounds,dynamic hand postur...Accurate fingertip detection is critical for translating hand gestures into actionable commands in vision-based human‒computer interaction(HCI)systems.However,challenges such as complex backgrounds,dynamic hand postures,and real-time processing constraints hinder reliable detection.This paper introduces a robust framework integrating three key innovations:(1)an adaptive Gaussian mixture model(GMM)enhanced with neighborhood pixel connectivity for precise motion extraction;(2)a weighted YCbCr color-space shadow removal algorithm to eliminate false positives;and(3)a centroid distance method refined with circularity constraints for accurate fingertip localization.Extensive experiments demonstrate a recognition accuracy of 97.26%across diverse scenarios,including varying illuminations,occlusions,and hand rotations.The algorithm processes each frame in 23.43 ms on average,satisfying real-time requirements.Comparative evaluations against state-of-the-art methods reveal significant improvements in precision(8.3%),recall(6.1%),and F-measure(7.8%).This work advances HCI applications such as virtual keyboards,gesture-controlled interfaces,and augmented reality systems.展开更多
文摘This paper transforms fuzzy number into clear number using the centroid method, thus we can research the traditional linear regression model which is transformed from the fuzzy linear regression model. The model’s input and output are fuzzy numbers, and the regression coefficients are clear numbers. This paper considers the parameter estimation and impact analysis based on data deletion. Through the study of example and comparison with other models, it can be concluded that the model in this paper is applied easily and better.
文摘In this paper we developed a fuzzy inventory model for deteriorating items with time dependent demand rate. Shortages are allowed and completely backlogged. The backlogging rate of unsatisfied demand is assumed to be a decreasing exponential function of waiting time. The demand rate, deterioration rate and backlogging rate are assumed as a triangular fuzzy numbers. The purpose of our study is to defuzzify the total profit function by signed distance method and centroid method. Further a numerical example is also given to demonstrate the developed crisp and fuzzy models. A sensitivity analysis is also given to show the effect of change of the parameters.
基金funded by the Jiaying University Research Start-Up Fund(grant number 323E0431).
文摘Accurate fingertip detection is critical for translating hand gestures into actionable commands in vision-based human‒computer interaction(HCI)systems.However,challenges such as complex backgrounds,dynamic hand postures,and real-time processing constraints hinder reliable detection.This paper introduces a robust framework integrating three key innovations:(1)an adaptive Gaussian mixture model(GMM)enhanced with neighborhood pixel connectivity for precise motion extraction;(2)a weighted YCbCr color-space shadow removal algorithm to eliminate false positives;and(3)a centroid distance method refined with circularity constraints for accurate fingertip localization.Extensive experiments demonstrate a recognition accuracy of 97.26%across diverse scenarios,including varying illuminations,occlusions,and hand rotations.The algorithm processes each frame in 23.43 ms on average,satisfying real-time requirements.Comparative evaluations against state-of-the-art methods reveal significant improvements in precision(8.3%),recall(6.1%),and F-measure(7.8%).This work advances HCI applications such as virtual keyboards,gesture-controlled interfaces,and augmented reality systems.
