Absorption linewidth inversion with wavelength modulation spectroscopy

For absorption linewidth inversion with wavelength modulation spectroscopy（WMS）, an optimized WMS spectral line fitting method was demonstrated to infer absorption linewidth effectively, and the analytical expressions for relationships between Lorentzian linewidth and the separations of first harmonic peak-to-valley and second harmonic zero-crossing were deduced. The transition of CO_2 centered at 4991.25 cm^（-1） was used to verify the optimized spectral fitting method and the analytical expressions. Results showed that the optimized spectra fitting method was able to infer absorption accurately and compute more than 10 times faster than the commonly used numerical fitting procedure. The second harmonic zero-crossing separation method calculated an even 6 orders faster than the spectra fitting without losing any accuracy for Lorentzian dominated cases. Additionally, linewidth calculated through second harmonic zero-crossing was preferred for much smaller error than the first harmonic peak-to-valley separation method. The presented analytical expressions can also be used in on-line optical sensing applications, electron paramagnetic resonance, and further theoretical characterization of absorption lineshape.

Influence of the Fitted Straight Line for Confidence Bands Algorithm in Q-Q Plots

Confidence bands in a Normal Q-Q Plot allow us to detect non-normality of a data set rigorously, and in such a way that the conclusion does not depend on the subjectivity of the observer of the graph. In the construction of the graph, it is usual to fit a straight line to the plotted points, which serves both to check the hypothesis of normality (linear configuration of the plotted points) and to produce estimates of the parameters of the distribution. We can opt for dif-ferent types of lines. In this paper, we study the influence of five types of fitted straight lines in a Normal Q-Q Plot used for construction the confidence bands based on the exact distribution of the order statistics.

Fortified Financial Forecasting Models Based on Non-Linear Searching Approaches

The paper＇s aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data （i.e., secular trends, cyclical variations, seasonal effects, and stochastic variations）, they believe the best forecasting model is the one which realistically considers the underlying causal factors in a situational relationship and therefore has the best ＂track records＂ in generating data. Paper＇s models can be adjusted for variations in related a time series which processes a great deal of randomness, to improve the accuracy of the financial forecasts. Because of Na＇fve forecasting models are based on an extrapolation of past values for future. These models may be adjusted for seasonal, secular, and cyclical trends in related data. When a data series processes a great deal of randomness, smoothing techniques, such as moving averages and exponential smoothing, may improve the accuracy of the financial forecasts. But neither Na＇fve models nor smoothing techniques are capable of identifying major future changes in the direction of a situational data series. Hereby, nonlinear techniques, like direct and sequential search approaches, overcome those shortcomings can be used. The methodology which we have used is based on inferential analysis. To build the models to identify the major future changes in the direction of a situational data series, a comparative model building is applied. Hereby, the paper suggests using some of the nonlinear techniques, like direct and sequential search approaches, to reduce the technical shortcomings. The final result of the paper is to manipulate, to prepare, and to integrate heuristic non-linear searching methods to serve calculating adjusted factors to produce the best forecast data.

双参数车辆行驶偏离提醒与预警策略研究

为提高车辆行驶偏离测量与干预的准确性,提出双参数偏离测量与干预策略。借助VBAI进行图像处理,利用最大类间方差法求解出不同环境下最佳二值化分割阈值。设计灰度采集线获得车道边缘点,借助Fit Line算法拟合边缘点完成车道线识别。根据预警模型对相对航偏角与像素距离比进行分析,将提醒或预警的干预结果显示在界面上。经道路实测,报警准确率在97.7%以上,处理速度高于1/42 s/frame。系统对提高行驶安全性,降低偏离引起的交通事故有着实际的应用价值。

Automatic Measurement of Two Dimensional Object

Some core techniques related with automatic measurement of two dimensional object are discussed. Two kinds of corner point detecting methods are given. Least square fitting is used for edge line fitting. Region selection for accurate measurement is proposed. Combined with image recognition, location and macro recording file, automatic measurement of two dimensional object is realized.

Improvement of Location Algorithm in Wireless Networks

In order to improve the accuracy of wireless network positioning,the triangulation method of wireless network positioning technology is proposed,which is based on the linear least square fitting method.It makes the observed value and the fitting value very close,effectively solves the problem of significant contradiction between the fitting result and the observed value in the principle of least square method,and can realize the accurate measurement of geographic information by wireless network positioning technology.