| 1. | A processing method of gross error in intelligent electronic measuring system
系统中粗大误差的处理方法 |
| 2. | Reliability qualification test for electronic measuring instruments
一种智能电子测量系统中粗大误差的处理方法 |
| 3. | Gross error procesing technology in intelligence measuring system with high precision
高精度智能测量系统中粗大误差的处理技术 |
| 4. | Data processing is performed by pc ， which includes detecting rough error by quartile method base on first order differential , adopting three points algorithm to eliminate deviations of rack displacement and installation , applying minimum containing area method to evaluate the sectional plane radius of roller and realizing it by genetic algorithm , adopting akima method to fit curve of roll profile
主要的数据处理工作在上位机进行，采用基于差分法的分位数算法剔除粗大误差；采用三点式测量原理消除探头的安装及运动所产生的误差；采用最小包容区域法评定轧辊半径，并应用遗传算法实现最小包容区域法的寻优计算；采用阿克玛插值方法进行辊型的曲线拟合。 |
| 5. | An isolated point " s statistic excluding method is proposed in this paper to eliminate crassitude error in clouding data , which include plenty of oddity data . the method based on the distance between two neighbour points can eliminate the data beyond normal distribution . a error limitation of angle and chordal highness method is used to filtrate clouding point
针对大量含奇异点的数据点云，本文提出了剔除粗大误差的孤立点统计排异法，该方法根据对相邻点距离的统计，剔除在正态分布以外的点；对大量数据的精减，利用角度和弦高的最大允许偏差法进行点云精减。 |
| 6. | Moreover , the fuzzy clustering discriminate analysis method for distinguishing and eliminating the gross error of the measurement sample is established . the gross error of the practical measured data is distinguished by use of the method prove the established the gross error distinguishing model practical to the measurement system
建立了测量样本粗大误差判别和剔除的模糊聚类判别方法，通过对实际测量数据的粗大误差判别，证明了所建立的粗大误差判别模型对测量系统的实用性。 |
| 7. | In order to improve test precision , techniques such as auto - gain control , proper sampling rate select and dithering should be applied into the data acquisition process , and data pre - processing techniques should be used to eliminate data with careless error and correct data with system error
为了提高测试精度，在数据采集中，可以采用量程自动切换、合理的采样频率以及加扰技术；对采集的数据，需要进行预处理，主要包括剔除含有粗大误差的数据和消除系统误差等。 |
| 8. | This dissertation focuses on the application of data fusion in two - phase flow regime identification . following is the main contribution of the dissertation . 1 ) based on quartile and first order differential , a new outlier detection algorithm is presented , experiments show that the method combining with low pass filter can remove gross error and unwanted frequency components
本文的主要工作与创新点在于： 1 )将分位数方法与一阶差分法相结合，提出了一种基于一阶差分的粗差剔除方法，该方法与低通滤波器的结合可以有效去除两相流压力波动信号中的粗大误差以及信号频带以外的频率成分。 |
