Khawne, Amnach

Preferred name

Khawne, Amnach

Main Affiliation

School of Engineering

amnach.kh@kmitl.ac.th

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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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Ultrasonic echo image adaptive watermarking using the just-noticeable difference estimation
(2009-12-01)
Khawne, Amnach
;
Hamamoto, Kazuhiko
;
Chitsobhuk, Orachat
Most of the image watermarking methods, using the properties of the human visual system (HVS), have been proposed in literature. The component of the visual threshold is usually related to either the spatial contrast sensitivity function (CSF) or the visual masking. Especially on the contrast masking, most methods have not mention to the effect near to the edge region. Since the HVS is sensitive what happens on the edge area. This paper proposes ultrasound image watermarking using the visual threshold corresponding to the HVS in which the coefficients in a DCT-block have been classified based on the texture, edge, and plain area. This classification method enables not only useful for imperceptibility when the watermark is insert into an image but also achievable a robustness of watermark detection. A comparison of the proposed method with other methods has been carried out which shown that the proposed method robusts to blockwise memoryless manipulations, and also robust against noise addition.
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Performance Characterisation and Predictive Modelling of WireGuard VPN on Kubernetes: Efficient Resource Management Towards Auto-scaling
(2026-01-01)
Sridee, Pachara Threerapat
;
Khawne, Amnach
This study investigates WireGuard Virtual Private Network (WG VPN) performance on Kubernetes (K8s), analysing resource use and developing regression models. Metrics (Throughput, CPU, Memory, Jitter) were analysed across vCPU and physical core allocations. We found that over-provisioning vCPUs degraded performance due to increased overhead, with optimal throughput achieved, especially with two vCPUs and two physical cores. Among these optimal matched configurations, one demonstrated superior cost-effectiveness (high throughput/core, low CPU) and minimal jitter, which is vital for real-time applications. Pod-level CPU lacked traffic correlation due to the host kernel reliance on WGVPN, making Node-level Horizontal Scaling more suitable than Pod-level Vertical. Regression models for Node-level CPU (best: Linear Regression, with MAE decreasing by 16.74% compared to Multi-Layer Perceptron Model) and Traffic Demands (best: Instance-Based k-Nearest Neighbours, with MAE decreasing by 9.02% compared to Multi-Layer Perceptron Model) were developed. Findings offer insights for optimising WGVPN on K8s and future auto-scaling.
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Image watermarking based on adaptive models of human visual perception
(2010-01-01)
Khawne, Amnach
;
Hamamoto, Kazuhiko
;
Chitsobhuk, Orachat
This paper proposes a digital image watermarking based on adaptive models of human visual perception. The algorithm exploits the local activities estimated from wavelet coefficients of each subband to adaptively control the luminance masking. The adaptive luminance is thus delicately combined with the contrast masking and edge detection and adopted as a visibility threshold. With the proposed combination of adaptive visual sensitivity parameters, the proposed perceptual model can be more appropriate to the different characteristics of various images. The weighting function is chosen such that the fidelity, imperceptibility and robustness could be preserved without making any perceptual difference to the image quality. © 2010 The Institute of Electrical Engineers of Japan.
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Optimum watermark detection of ultrasonic echo medical images
(2015-03-01)
Khawne, Amnach
;
Attachoo, Boonwat
;
Hamamoto, Kazuhiko
To prevent unauthorized access to a person's medical images, it is widely acknowledged that the security features of confidentiality, availability and integrity should be in place. This paper proposes the watermarking of ultrasonic echo images together with optimal watermark detection, in which pseudorandom noise is added to the images for integrity. The optimum watermark detection is the integration of the generalized Gaussian distribution (ρ-GGD) and the Cauchy distribution. The results show that the proposed method gives good detection performance. The proposed method not merely achieves optimum detection using the Rao test but also leads to the highest detection probability with JPEG2000 compression. Compared with other detection methods, our proposed method exhibits better watermark detection performance even when the watermark-to-document ratio (WDR) is -50 dB.

Khawne, Amnach

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