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    Predictive quality assurance of a linear accelerator based on the machine performance check application using statistical process control and ARIMA forecast modeling
    (2020-08-01)
    Puyati, Wayo
    ;
    Khawne, Amnach
    ;
    Barnes, Michael
    ;
    Zwan, Benjamin
    ;
    Greer, Peter
    Purpose: A predictive linac quality assurance system based on the output of the Machine Performance Check (MPC) application was developed using statistical process control and autoregressive integrated moving average forecast modeling. The aim of this study is to demonstrate the feasibility of predictive quality assurance based on MPC tests that allow proactive preventative maintenance procedures to be carried out to better ensure optimal linac performance and minimize downtime. Method and Materials: Daily MPC data were acquired for a total of 490 measurements. The initial 85% of data were used in prediction model learning with the autoregressive integrated moving average technique and in calculating upper and lower control limits for statistical process control analysis. The remaining 15% of data were used in testing the accuracy of the predictions of the proposed system. Two types of prediction were studied, namely, one-step-ahead values for predicting the next day's quality assurance results and six-step-ahead values for predicting up to a week ahead. Results that fall within the upper and lower control limits indicate a normal stage of machine performance, while the tolerance, determined from AAPM TG-142, is the clinically required performance. The gap between the control limits and the clinical tolerances (as the warning stage) provides a window of opportunity for rectifying linac performance issues before they become clinically significant. The accuracy of the predictive model was tested using the root-mean-square error, absolute error, and average accuracy rate for all MPC test parameters. Results: The accuracy of the predictive model is considered high (average root-mean-square error and absolute error for all parameters of less than 0.05). The average accuracy rate for indicating the normal/warning stages was higher than 85.00%. Conclusion: Predictive quality assurance with the MPC will allow preventative maintenance, which could lead to improved linac performance and a reduction in unscheduled linac downtime.
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    Efficiency improvement for unconstrained face recognition by weightening probability values of modular PCA and Wavelet PCA
    (2008-05-29)
    Puyati, Wayo
    ;
    Walairacht, Aranya
    Principal Component Analysis (PCA) is a well-known classical appearance-base method in face recognition. In the previous works, the preprocessing process significantly improved the recognition rate. Modular PCA and Wavelet PCA are the preprocessing processes of PCA, which increase the recognition rate of the original PCA. Modular PCA is suitable for the highvaried face database, while Wavelet PCA for the low-varied face database. In this paper, we propose the preprocessing method which combines between Modular PCA and Wavelet PCA with the weightening probability values. The experiments are compared among our propose method, Modular PCA, Wavelet PCA and original PCA with face database from Yale, ORL and UMIST. The experimental results show that the recognition rate of our method is higher compared to the other methods and also support variety of face database.
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    PCA in wavelet domain for face recognition
    (2006-11-17)
    Puyati, Wayo
    ;
    Walairacht, Somsak
    ;
    Walairacht, Aranya
    In this paper, the preprocessing process aimed to reduce size of input image by using wavelet transform before transformed image is sent to the process of PCA for recognition. We used ORL Face Databases from AT&T Laboratories Cambridge in the experiments. The results show that the 4<sup>th</sup> Order Symlets level 2 and level 3 improve the accuracy rate of recognition when compare among Haar wavelets, the 4<sup>th</sup> Order Daubechies wavelets, and Biorthogonal wavelets (orthogonal 6.8). In the case of overall processing time for training, the length of filter of wavelet is directly effect the time consuming. Since LL subband of wavelet decomposition becomes the input for PCA, the memory usage can be greatly reduced.