目的探讨由美国国家医学中心和贝克曼研究所研制的癌症患者照顾者生活质量量表(Quality of LifeFamily Version,QOLScale-FAMILY)用于中国喉癌患者照顾者生活质量测量的可能性,为喉癌患者照顾者生活质量的评定提供一个量化工具。方法通...目的探讨由美国国家医学中心和贝克曼研究所研制的癌症患者照顾者生活质量量表(Quality of LifeFamily Version,QOLScale-FAMILY)用于中国喉癌患者照顾者生活质量测量的可能性,为喉癌患者照顾者生活质量的评定提供一个量化工具。方法通过对QOLScale-FAMILY量表的翻译、回译、文化调试制定出中文版的QOL Scale-FAMILY量表,并采用信访、电话访谈、面对面问卷访谈的方法,对100例喉癌患者照顾者(主要是患者配偶和子女)的生活质量进行测定,考核QOL Scale-FAMILY的可行性以及信度与效度。结果中文版的QOL Scale-FAMILY具有良好的内部一致性,总量表的Cronbach′sα系数为0.794,除了精神健康维度α=0.649外,其他3个维度满足群组比较的要求;重测信度γ=0.841。因子分析产生的4个公共因子与理论结构基本一致,结构效度的累积方差贡献率为60.2%。结论QOLScale-FAMILY中文版具有较好的信度和效度,对部分条目进行调整后,可以应用于喉癌患者照顾者生活质量的测定。展开更多
It is obvious that the change trend of our government expenditure scale declined constantly .The expenditure proportion of science, education, culture and hygiene increased fast, reflected the situation that the finan...It is obvious that the change trend of our government expenditure scale declined constantly .The expenditure proportion of science, education, culture and hygiene increased fast, reflected the situation that the finance of our country was transformed into “public finance” from “building type finance” gradually.The expenditure proportion of the local is higher than central authorities. This is disadvantageous for our country’s economy development and society stability.展开更多
Stable local feature detection is a fundamental component of many stereo vision problems such as 3-D reconstruction, object localization, and object tracking. A robust method for extracting scale-invariant feature poi...Stable local feature detection is a fundamental component of many stereo vision problems such as 3-D reconstruction, object localization, and object tracking. A robust method for extracting scale-invariant feature points is presented. First, the Harris corners in three-level pyramid are extracted. Then, the points detected at the highest level of the pyramid are correctly propagated to the lower level by pyramid based scale invariant （PBSI） method. The corners detected repeatedly in different levels are chosen as final feature points. Finally, the characteristic scale is obtained based on maximum entropy method. The experimental results show that the algorithm has low computation cost, strong antinoise capability, and excellent performance in the presence of significant scale changes.展开更多
On the basis of scale invariant feature transform（SIFT） descriptors,a novel kind of local invariants based on SIFT sequence scale（SIFT-SS） is proposed and applied to target classification.First of all,the merits o...On the basis of scale invariant feature transform（SIFT） descriptors,a novel kind of local invariants based on SIFT sequence scale（SIFT-SS） is proposed and applied to target classification.First of all,the merits of using an SIFT algorithm for target classification are discussed.Secondly,the scales of SIFT descriptors are sorted by descending as SIFT-SS,which is sent to a support vector machine（SVM） with radial based function（RBF） kernel in order to train SVM classifier,which will be used for achieving target classification.Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants（AMI） and multi-scale auto-convolution（MSA） in some complex situations,such as the situation with the existence of noises and occlusions.Moreover,the computational time of SIFT-SS is shorter than MSA and longer than AMI.展开更多
Local diversity AdaBoost support vector machine（LDAB-SVM） is proposed for large scale dataset classification problems.The training dataset is split into several blocks firstly, and some models based on these dataset...Local diversity AdaBoost support vector machine（LDAB-SVM） is proposed for large scale dataset classification problems.The training dataset is split into several blocks firstly, and some models based on these dataset blocks are built.In order to obtain a better performance, AdaBoost is used in each model building.In the boosting iteration step, the component learners which have higher diversity and accuracy are collected via the kernel parameters adjusting.Then the local models via voting method are integrated.The experimental study shows that LDAB-SVM can deal with large scale dataset efficiently without reducing the performance of the classifier.展开更多
文摘It is obvious that the change trend of our government expenditure scale declined constantly .The expenditure proportion of science, education, culture and hygiene increased fast, reflected the situation that the finance of our country was transformed into “public finance” from “building type finance” gradually.The expenditure proportion of the local is higher than central authorities. This is disadvantageous for our country’s economy development and society stability.
基金supported by the Development Program of China and the National Science Foundation Project (60475024)National High Technology Research (2006AA09Z203)
文摘Stable local feature detection is a fundamental component of many stereo vision problems such as 3-D reconstruction, object localization, and object tracking. A robust method for extracting scale-invariant feature points is presented. First, the Harris corners in three-level pyramid are extracted. Then, the points detected at the highest level of the pyramid are correctly propagated to the lower level by pyramid based scale invariant （PBSI） method. The corners detected repeatedly in different levels are chosen as final feature points. Finally, the characteristic scale is obtained based on maximum entropy method. The experimental results show that the algorithm has low computation cost, strong antinoise capability, and excellent performance in the presence of significant scale changes.
基金supported by the National High Technology Research and Development Program (863 Program) (2010AA7080302)
文摘On the basis of scale invariant feature transform（SIFT） descriptors,a novel kind of local invariants based on SIFT sequence scale（SIFT-SS） is proposed and applied to target classification.First of all,the merits of using an SIFT algorithm for target classification are discussed.Secondly,the scales of SIFT descriptors are sorted by descending as SIFT-SS,which is sent to a support vector machine（SVM） with radial based function（RBF） kernel in order to train SVM classifier,which will be used for achieving target classification.Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants（AMI） and multi-scale auto-convolution（MSA） in some complex situations,such as the situation with the existence of noises and occlusions.Moreover,the computational time of SIFT-SS is shorter than MSA and longer than AMI.
基金supported by the National Natural Science Foundation of China (60603098)
文摘Local diversity AdaBoost support vector machine（LDAB-SVM） is proposed for large scale dataset classification problems.The training dataset is split into several blocks firstly, and some models based on these dataset blocks are built.In order to obtain a better performance, AdaBoost is used in each model building.In the boosting iteration step, the component learners which have higher diversity and accuracy are collected via the kernel parameters adjusting.Then the local models via voting method are integrated.The experimental study shows that LDAB-SVM can deal with large scale dataset efficiently without reducing the performance of the classifier.
