DSpace-CRIS

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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
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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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Blockchain as a Notarization System for Military Data Sharing
(2025-01-01)
Chamnikul, Chatchawan
;
Khawne, Amnach
This paper presents a blockchain-based notarization system designed to secure and authenticate military data sharing. In the context of national defense, where the integrity and confidentiality of sensitive military data are paramount, traditional centralized systems often fail to meet the required security standards. Our proposed system leverages a permissioned blockchain platform, specifically Hyperledger Fabric, combined with smart contracts to create an immutable and transparent ledger. This ensures that once military data is recorded, it cannot be altered or tampered with, thereby enhancing data integrity and auditability. The system is further integrated with existing military information infrastructures through secure APIs and middleware, enabling seamless data exchange. Experimental evaluations conducted in a simulated environment demonstrate that the system achieves high transaction throughput, low latency, and robust error management. Although challenges remain in scalability and fine-grained access control, the results confirm that blockchain technology has the potential to significantly improve the security and transparency of military data sharing. Future work will focus on addressing these challenges and refining the system for real-world deployment in defense operations.
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Improving Water Salinity Forecasting in Bang Pakong River with Attention Mechanism
(2024-12-02)
Saksopit, Thongthai
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Khawne, Amnach
Seawater intrusion in the Bang Pakong River estuary poses a significant threat to freshwater resources used for agriculture, municipal consumption, and industrial applications. Accurate prediction of salinity fluctuations is crucial for effective water management strategies. This study proposes an enhanced univariate salinity prediction method utilizing a Long Short-Term Memory (LSTM) model augmented with an Attention Mechanism. The Attention Mechanism empowers the LSTM to selectively focus on crucial information within extended historical salinity data sequences. The optimal input sequence length for the model is determined through a training process, aiming for the most accurate predictions. Here, the model forecasts salinity values 24 hours ahead and is evaluated against actual measurements. Performance metrics demonstrate that the Attention-LSTM model achieves the lowest error (MAE: 0.007834, MSE: 0.000094, RMSE: 0.009697, MAPE: 0.048736) and the highest accuracy (R<sup>2</sup>: 0.782927) at an input sequence length of 504 hours. These findings highlight the potential of the Attention-LSTM model for improved salinity prediction in the Bang Pakong River estuary, aiding water resource management strategies.
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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
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Khawne, Amnach
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Barnes, Michael
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Zwan, Benjamin
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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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