目的比较激光扫描共聚焦显微镜测量中央区和涡状区角膜上皮基底神经丛结果的一致性,从而筛选出更加合理可靠的检查部位。方法前瞻性研究。2018年11月至2019年2月募集年龄在20~40岁的健康志愿者30人作为研究对象,所有受试者经过筛查排除...目的比较激光扫描共聚焦显微镜测量中央区和涡状区角膜上皮基底神经丛结果的一致性,从而筛选出更加合理可靠的检查部位。方法前瞻性研究。2018年11月至2019年2月募集年龄在20~40岁的健康志愿者30人作为研究对象,所有受试者经过筛查排除影响神经功能的全身和局部病变后纳入本研究。入组受试者均选择右眼为受检眼,由2名经验丰富的医师完成3次激光扫描共聚焦显微镜检查。首先于受试当天由操作者A和操作者B各完成1次检查,1周后再由操作者A重复检查1次。所得图像用Adobe Photoshop图像处理软件拼接后,再用Neuron J图像分析软件进行定量分析,得到神经纤维长度(nerve fiber length,NFL)(mm·mm^-2)。然后分别根据涡状区和中央区NFL值绘制Bland-Altman散点图,并计算重复性系数(coefficient of repeatability,CoR)和95%一致性区间(limit of agreement,LOA),用于评估两个部位观察者内和观察者间一致性。结果所有受检眼均可于角膜中央偏鼻下方查见涡状结构,重复检查获得的涡状区图像显示角膜神经形态稳定,中央区图像显示角膜神经形态变异较大。涡状区一致性分析观察者内CoR值为6.6%,观察者间CoR值为6.1%,一致性很高;而中央区一致性分析观察者内CoR值为44.1%,观察者间CoR值为31.8%,一致性较差。Bland-Altman散点图涡状区观察者内和观察者间测量差值均聚集于“0”刻度线附近,且95%LOA较窄,均在平均NFL的1个标准差的区间内,一致性良好;中央区观察者内和观察者间测量差值分布较分散,95%LOA较宽,约为两次检查平均NFL标准差的2~3倍,一致性较差。结论涡状结构在健康人角膜上皮基底神经丛普遍存在,其形态特殊,位置固定,在图像采集时能够作为明确的角膜标记物;涡状区图像重复测量一致性良好,可以应用于角膜上皮基底神经丛的定量分析。展开更多
电压互感器测量准确性直接影响电网的安全稳定和经济运行。针对一次电压波动下高维随机矩阵评估方法无法可靠评估电压互感器误差状态的问题,提出一种基于差分间距自适应的差分处理(differential processing of adaptive differential in...电压互感器测量准确性直接影响电网的安全稳定和经济运行。针对一次电压波动下高维随机矩阵评估方法无法可靠评估电压互感器误差状态的问题,提出一种基于差分间距自适应的差分处理(differential processing of adaptive differential interval,DP-ADI)和同相测量一致性(measurement consistency of same phase,MCSP)的电压互感器误差状态评估方法。该方法按照时间序列理论,将一次电压波动分为由确定因素引起的非平稳波动与随机因素引起的微小缓慢平稳波动,同时提出了基于差分间距自适应的平稳化方法与基于同相测量一致性的差异性评价指标,分别削弱一次电压波动中的非平稳波动与微小缓慢平稳波动对状态评估的影响。仿真实验及现场应用结果表明:改进后的高维随机矩阵评估方法能够可靠地评估电压互感器0.1%的误差异常状态。展开更多
Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.T...Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.The degraded speech is firstly separated into three classes(unvoiced,voiced and silence),and then the consistency measurement between the degraded speech signal and the pre-trained reference model for each class is calculated and mapped to an objective speech quality score using data mining.Fuzzy Gaussian mixture model(GMM)is used to generate the artificial reference model trained on perceptual linear predictive(PLP)features.The mean opinion score(MOS)mapping methods including multivariate non-linear regression(MNLR),fuzzy neural network(FNN)and support vector regression(SVR)are designed and compared with the standard ITU-T P.563 method.Experimental results show that the assessment methods with data mining perform better than ITU-T P.563.Moreover,FNN and SVR are more efficient than MNLR,and FNN performs best with 14.50% increase in the correlation coefficient and 32.76% decrease in the root-mean-square MOS error.展开更多
Although either absolute speed or speed difference can be considered as a measure for speed consistency, few researches consider both in practice. The factor analysis method was introduced to extract an optimal number...Although either absolute speed or speed difference can be considered as a measure for speed consistency, few researches consider both in practice. The factor analysis method was introduced to extract an optimal number of factors from numerous original measures. The freeway diverging zone was divided into four elements, namely the upstream, the diverge area, the downstream and the exit ramp. Operating speeds together with individual vehicle speeds were collected at each element with radar guns. Following the factor analysis procedure, two factors, which explain 96.722% of the variance in the original data, were retained from the initial seven speed measures. According to the loadings after Varimax rotation, the two factors are clearly classified into two categories. The first category is named "speed scale" reflecting the absolute speed, and the other one is named "speed dispersion" interpreting speed discreteness. Then, the weighted score of speed consistency for each diverge area is given in terms of linear combination of the two retained factors. To facilitate the level classification of speed consistency, the weighted scores are normalized in the range of (0, 1.0). The criterion for speed consistency classification is given as 0≤F N <0.30, good consistency; 0.30≤F N <0.60, fair consistency; 0.60≤ F N ≤1.00, poor consistency. The validation by comparing with previously developed measures shows that the proposed measure is acceptable in evaluating speed consistency.展开更多
文摘目的比较激光扫描共聚焦显微镜测量中央区和涡状区角膜上皮基底神经丛结果的一致性,从而筛选出更加合理可靠的检查部位。方法前瞻性研究。2018年11月至2019年2月募集年龄在20~40岁的健康志愿者30人作为研究对象,所有受试者经过筛查排除影响神经功能的全身和局部病变后纳入本研究。入组受试者均选择右眼为受检眼,由2名经验丰富的医师完成3次激光扫描共聚焦显微镜检查。首先于受试当天由操作者A和操作者B各完成1次检查,1周后再由操作者A重复检查1次。所得图像用Adobe Photoshop图像处理软件拼接后,再用Neuron J图像分析软件进行定量分析,得到神经纤维长度(nerve fiber length,NFL)(mm·mm^-2)。然后分别根据涡状区和中央区NFL值绘制Bland-Altman散点图,并计算重复性系数(coefficient of repeatability,CoR)和95%一致性区间(limit of agreement,LOA),用于评估两个部位观察者内和观察者间一致性。结果所有受检眼均可于角膜中央偏鼻下方查见涡状结构,重复检查获得的涡状区图像显示角膜神经形态稳定,中央区图像显示角膜神经形态变异较大。涡状区一致性分析观察者内CoR值为6.6%,观察者间CoR值为6.1%,一致性很高;而中央区一致性分析观察者内CoR值为44.1%,观察者间CoR值为31.8%,一致性较差。Bland-Altman散点图涡状区观察者内和观察者间测量差值均聚集于“0”刻度线附近,且95%LOA较窄,均在平均NFL的1个标准差的区间内,一致性良好;中央区观察者内和观察者间测量差值分布较分散,95%LOA较宽,约为两次检查平均NFL标准差的2~3倍,一致性较差。结论涡状结构在健康人角膜上皮基底神经丛普遍存在,其形态特殊,位置固定,在图像采集时能够作为明确的角膜标记物;涡状区图像重复测量一致性良好,可以应用于角膜上皮基底神经丛的定量分析。
文摘电压互感器测量准确性直接影响电网的安全稳定和经济运行。针对一次电压波动下高维随机矩阵评估方法无法可靠评估电压互感器误差状态的问题,提出一种基于差分间距自适应的差分处理(differential processing of adaptive differential interval,DP-ADI)和同相测量一致性(measurement consistency of same phase,MCSP)的电压互感器误差状态评估方法。该方法按照时间序列理论,将一次电压波动分为由确定因素引起的非平稳波动与随机因素引起的微小缓慢平稳波动,同时提出了基于差分间距自适应的平稳化方法与基于同相测量一致性的差异性评价指标,分别削弱一次电压波动中的非平稳波动与微小缓慢平稳波动对状态评估的影响。仿真实验及现场应用结果表明:改进后的高维随机矩阵评估方法能够可靠地评估电压互感器0.1%的误差异常状态。
基金Projects(61001188,1161140319)supported by the National Natural Science Foundation of ChinaProject(2012ZX03001034)supported by the National Science and Technology Major ProjectProject(YETP1202)supported by Beijing Higher Education Young Elite Teacher Project,China
文摘Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.The degraded speech is firstly separated into three classes(unvoiced,voiced and silence),and then the consistency measurement between the degraded speech signal and the pre-trained reference model for each class is calculated and mapped to an objective speech quality score using data mining.Fuzzy Gaussian mixture model(GMM)is used to generate the artificial reference model trained on perceptual linear predictive(PLP)features.The mean opinion score(MOS)mapping methods including multivariate non-linear regression(MNLR),fuzzy neural network(FNN)and support vector regression(SVR)are designed and compared with the standard ITU-T P.563 method.Experimental results show that the assessment methods with data mining perform better than ITU-T P.563.Moreover,FNN and SVR are more efficient than MNLR,and FNN performs best with 14.50% increase in the correlation coefficient and 32.76% decrease in the root-mean-square MOS error.
基金Project(2012CB725400) supported by the National Key Basic Research Program of ChinaProject(2012AA112304) supported by the National High Technology Research and Development Program of ChinaProject(2009BAG13A07-5) supported by National Science and Technology Plan of Action of China for Traffic Safety
文摘Although either absolute speed or speed difference can be considered as a measure for speed consistency, few researches consider both in practice. The factor analysis method was introduced to extract an optimal number of factors from numerous original measures. The freeway diverging zone was divided into four elements, namely the upstream, the diverge area, the downstream and the exit ramp. Operating speeds together with individual vehicle speeds were collected at each element with radar guns. Following the factor analysis procedure, two factors, which explain 96.722% of the variance in the original data, were retained from the initial seven speed measures. According to the loadings after Varimax rotation, the two factors are clearly classified into two categories. The first category is named "speed scale" reflecting the absolute speed, and the other one is named "speed dispersion" interpreting speed discreteness. Then, the weighted score of speed consistency for each diverge area is given in terms of linear combination of the two retained factors. To facilitate the level classification of speed consistency, the weighted scores are normalized in the range of (0, 1.0). The criterion for speed consistency classification is given as 0≤F N <0.30, good consistency; 0.30≤F N <0.60, fair consistency; 0.60≤ F N ≤1.00, poor consistency. The validation by comparing with previously developed measures shows that the proposed measure is acceptable in evaluating speed consistency.
