Rassoul Noorossana, M. Nikoo,
Volume 26, Issue 2 (7-2015)
Abstract
In many manufacturing processes, the quality of a product is characterized by a non-linear relationship between a dependent variable and one or more independent variables. Using nonlinear regression for monitoring nonlinear profiles have been proposed in the literature of profile monitoring which is faced with two problems 1) the distribution of regression coefficients in small samples is unknown and 2) with the increasing complexity of process, regression parameters will increase and thereby the efficiency of control charts decreases. In this paper, wavelet transform is used in Phase II for monitoring nonlinear profiles. In wavelets transform, two parameters specify the smoothing level, the first one is threshold and the second one is decomposition level of regression function form. First, using the adjusted coefficient of determination, decomposition level is specified and then process performance is monitored using the mean of wavelet coefficients and profile variance. The efficiency of the proposed control charts based on the average run length (ARL) criterion for real data is compared with the existing control charts for monitoring nonlinear profiles in Phase II
![\"AWT](\"./files/site1/images/2(2).png\")
Sudheer Babu Punuri,
Volume 31, Issue 3 (9-2020)
Abstract
With the ever-increasing request for speed and the increasing number of Cyber Attacks are having fast and accurate skill to provide verification that is convenient, rapid and exact. Even though possible that it is very difficult to fool Image Recognition Skill in this makes it helpful in serving preclude fraud. In this paper, we propose a model for pixel wise operations, which is needed for identification of a location point. The computer vision is not limited to pixel wise operations. It can be complex and far more complex than image processing. Initially, we take the unstructured Image Segmentation with the help of K-Means Clustering Algorithm is used. Once complete the preprocessing step then extracts the segmented image from the surveillance cameras to identify the expressions and vehicle images. In the raw image from the surveillance camera is the image of a person and vehicle is to classify with the help DWT. Further, the images of the appearances stood also taken with phenomenon called Smart Selfie Click (SSC). These two features are extracted in-order to identify whether the vehicle should be permitted into the campus or not. Thus, verification is possible. These two images are nothing but reliable object extracted for location identification.
