To deduce a new color difference formula based on CIE 1997 Color Appearance Model(CIECAM97s), a color space J a 1 b 1 is first constructed with color appearance descriptors J,a,b in CIECAM97s. The new f...To deduce a new color difference formula based on CIE 1997 Color Appearance Model(CIECAM97s), a color space J a 1 b 1 is first constructed with color appearance descriptors J,a,b in CIECAM97s. The new formula is then deduced in the space and named CDF CIECAM97s. The factors for lightness, chroma and hue correction in the formula are derived by linear regression according to BFD? CP data sets. It is found by statistical analysis that CDF CIECAM97s is in closer accordance with the visual assessments when compared with CMC(1∶1), CIE94 and CIE L *a *b * color difference formulae. Based on color appearance model, the new color difference formula can be used to predict color difference perception in a varity of different viewing conditions.展开更多
A method was proposed to evaluate the real-time reliability for a single product based on damaged measurement degradation data.Most researches on degradation analysis often assumed that the measurement process did not...A method was proposed to evaluate the real-time reliability for a single product based on damaged measurement degradation data.Most researches on degradation analysis often assumed that the measurement process did not have any impact on the product's performance.However,in some cases,the measurement process may exert extra stress on products being measured.To obtain trustful results in such a situation,a new degradation model was derived.Then,by fusing the prior information of product and its own on-line degradation data,the real-time reliability was evaluated on the basis of Bayesian formula.To make the proposed method more practical,a procedure based on expectation maximization (EM) algorithm was presented to estimate the unknown parameters.Finally,the performance of the proposed method was illustrated by a simulation study.The results show that ignoring the influence of the damaged measurement process can lead to biased evaluation results,if the damaged measurement process is involved.展开更多
A new parallel architecture for quantified boolean formula(QBF)solving was proposed,and the prediction model based on machine learning technology was proposed for how sharing knowledge affects the solving performance ...A new parallel architecture for quantified boolean formula(QBF)solving was proposed,and the prediction model based on machine learning technology was proposed for how sharing knowledge affects the solving performance in QBF parallel solving system,and the experimental evaluation scheme was also designed.It shows that the characterization factor of clause and cube influence the solving performance markedly in our experiment.At the same time,the heuristic machine learning algorithm was applied,support vector machine was chosen to predict the performance of QBF parallel solving system based on clause sharing and cube sharing.The relative error of accuracy for prediction can be controlled in a reasonable range of 20%30%.The results show the important and complex role that knowledge sharing plays in any modern parallel solver.It shows that the parallel solver with machine learning reduces the quantity of knowledge sharing about 30%and saving computational resource but does not reduce the performance of solving system.展开更多
文摘To deduce a new color difference formula based on CIE 1997 Color Appearance Model(CIECAM97s), a color space J a 1 b 1 is first constructed with color appearance descriptors J,a,b in CIECAM97s. The new formula is then deduced in the space and named CDF CIECAM97s. The factors for lightness, chroma and hue correction in the formula are derived by linear regression according to BFD? CP data sets. It is found by statistical analysis that CDF CIECAM97s is in closer accordance with the visual assessments when compared with CMC(1∶1), CIE94 and CIE L *a *b * color difference formulae. Based on color appearance model, the new color difference formula can be used to predict color difference perception in a varity of different viewing conditions.
基金Project(60904002)supported by the National Natural Science Foundation of China
文摘A method was proposed to evaluate the real-time reliability for a single product based on damaged measurement degradation data.Most researches on degradation analysis often assumed that the measurement process did not have any impact on the product's performance.However,in some cases,the measurement process may exert extra stress on products being measured.To obtain trustful results in such a situation,a new degradation model was derived.Then,by fusing the prior information of product and its own on-line degradation data,the real-time reliability was evaluated on the basis of Bayesian formula.To make the proposed method more practical,a procedure based on expectation maximization (EM) algorithm was presented to estimate the unknown parameters.Finally,the performance of the proposed method was illustrated by a simulation study.The results show that ignoring the influence of the damaged measurement process can lead to biased evaluation results,if the damaged measurement process is involved.
基金Project(61171141)supported by the National Natural Science Foundation of China
文摘A new parallel architecture for quantified boolean formula(QBF)solving was proposed,and the prediction model based on machine learning technology was proposed for how sharing knowledge affects the solving performance in QBF parallel solving system,and the experimental evaluation scheme was also designed.It shows that the characterization factor of clause and cube influence the solving performance markedly in our experiment.At the same time,the heuristic machine learning algorithm was applied,support vector machine was chosen to predict the performance of QBF parallel solving system based on clause sharing and cube sharing.The relative error of accuracy for prediction can be controlled in a reasonable range of 20%30%.The results show the important and complex role that knowledge sharing plays in any modern parallel solver.It shows that the parallel solver with machine learning reduces the quantity of knowledge sharing about 30%and saving computational resource but does not reduce the performance of solving system.
