Pavarangkoon, Praphan

Preferred name

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School of Information Technology

praphan.pa@kmitl.ac.th

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Scale-Based Slicer Placement in Elastic Optical Networks
(2023-01-01)
Kitsuwan, Nattapong
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Pavarangkoon, Praphan
This paper proposes a scale-based slicer placement scheme to reduce bandwidth blocking rate (BBR) in elastic optical networks (EONs). In EONs, a slicing-stitching technology is adopted to overcome a spectrum slot allocation rule that the spectrum slots of the same request must be consecutive on the same link. This technology is done by slicers to split a spectrum band into multiple sub-spectrum components. In EONs with limited slicers, determining equally the number of slicers on every node may not result in a low BBR since the traffic volume on each node is not the same. We investigate and determine the number of slicers needed for each node as a scale-based policy. The simulation results show that our scheme achieves a BBR reduction of 91%, compared to the conventional scheme.
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Large L-shape Fit Spectrum Allocation for Elastic Optical Network with Spectrum Slicing
(2021-01-13)
Akaki, Kaito
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Pavarangkoon, Praphan
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Kitsuwan, Nattapong
A slicing and stitching technology has been invented to relax the consecutive constraint of spectrum slot allocation in elastic optical network (EON). This technology splits a spectrum band into several signal bands, called optical components, by making a copy of the original spectrum band and filtering out an unwanted signal on each spectrum band. The remaining optical components are injected into a transmission channel. At the destination, the optical components are recovered by using phase preserving wavelength conversion. Therefore, a request is able to allocate to dis-consecutive groups of slots. A conventional spectrum allocation scheme with this technology adopts slicing devices, called spectrum slicers, at only a source node. There is a problem of allocation patterns due to lack of flexibility since slicing at intermediate nodes is not considered. In this paper, we propose a spectrum allocation scheme considering slicers at both source node and intermediate nodes. Performance of the proposed scheme is evaluated by a computer simulation. The results show that the proposed scheme with 20 slicers reduces 45% of request blocking probability compared to the conventional scheme in COST239 topology when the traffic is 300 Erlang.
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Prediction of Medication-Related Osteonecrosis of the Jaw in Patients Receiving Antiresorptive Therapy Using Machine Learning Models
(2025-03-01)
Warin, Kritsasith
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Lochanachit, Sirasit
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Pavarangkoon, Praphan
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Techapanurak, Engkarat
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Somyanonthanakul, Rachasak
Background: Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication associated with the use of antiresorptive agents, impacting patient quality of life and treatment outcomes. Predictive modeling may aid in a better understanding of MRONJ development. Purpose: The study aimed to evaluate machine learning (ML)–based models for predicting MRONJ in patients receiving antiresorptive therapy. Study Design, Setting, Sample: This retrospective in silico study analyzed electronic medical records from Thammasat University Hospital, covering the period from January 2012 to December 2022. The sample included subjects receiving antiresorptive therapy, excluding those with a history of radiation therapy or metastatic jaw disease. Predictor Variables: The primary predictor variable was the predicted probability of MRONJ development from the ML models. Outcome Variables: The outcome variable was MRONJ status coded as present or absent based on chart review. Covariates: Covariates included demographic data, MRONJ occurrence, location and staging of MRONJ, comorbidities, diseases related to antiresorptive agents, types of antiresorptive agents, therapy duration, concurrent medications, blood calcium levels, and dental factors. Analyses: Model performance was assessed via accuracy, sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve. Additionally, univariate and multivariate Cox regression analyses were conducted to identify factors significantly associated with MRONJ development. P ≤ .05 was statistically significant. Results: The study analyzed data from 5,305 subjects with a mean age of 75 ± 11.1 years, predominantly female. MRONJ was observed in 81 cases (1.5%), with a median time to development of 33 months (interquartile range = 3). Among the 6 models tested, the best-performing model had an accuracy of 0.95 and an area under the receiver operating characteristic curve of 0.89-0.90. Significant predictors identified through Cox regression included metabolic syndrome (hazard ratio = 14.064, 95% confidence interval = 1.111-178.067, P = .041) and patients receiving intravenous pamidronate (hazard ratio = 5.932, 95% confidence interval = 1.755-20.051, P = .004), indicating their association with MRONJ development. Conclusions and Relevance: ML-based predictive and time-to-event models effectively predict MRONJ risk, aiding in the strategic prevention and management for patients undergoing antiresorptive therapy.
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International Deployment of Visual IoT for Disaster Mitigation
(2023-01-01)
Murata, Ken T.
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Kikuta, Kazutaka
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Nagatsuma, Tsutomu
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Imanaka, Hideo
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Pavarangkoon, Praphan
This paper proposes a novel methodology that utilizes a newly developed visual Internet of Things (IoT) system for resilient natural disaster mitigation. This system enables the detection of disasters through remote control functions integrated with visual IoT sensors and artificial intelligence (AI)-based image processing of images captured by these sensors. The system is designed using commercial off-the-shelf (COTS) components to reduce installation costs, making it feasible for deployment worldwide, including in developing countries. In 2023, the proposal for this system was presented to the ITUD with the aim of widespread implementation in developing nations. The paper introduces innovative methodologies for visual IoT in international disaster mitigation, accompanied by use cases and detailed technological insights.
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Spectrum allocation scheme considering spectrum slicing in elastic optical networks
(2021-07-01)
Kitsuwan, Nattapong
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Akaki, Kaito
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Pavarangkoon, Praphan
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Nag, Avishek
Recent advances in physical layer optics have made slicing of optical bands into multiple subbands with different bandwidths possible. Due to this development, spectrum allocation has become easier in elastic optical networks (EONs). More specifically, owing to the slicing technology, the defragmentation problem in EONs can be addressed easily, i.e., more demands can be fit into empty spectrum slots by breaking the demands as needed using the slicing technology. This paper proposes a spectrum allocation scheme considering the slicing process at any nodes, e.g., source node and intermediate nodes, in EONs. Slicing-and-stitching technology is applied to break the contiguous-spectrum constraint in an EON so that spectrum fragmentation is reduced. While slicing a demand from one node to another, the following questions must be answered: (1) Which parts of the spectrum band should be sliced? (2) In which node(s) along the path of a demand should the slicings be done to reduce the bandwidth blocking rate? To answer the above questions, we formulate a mixed-integer linear programming (MILP) model that jointly addresses the above questions as well as minimizes the total number of slicers in a network. To measure the performance of the MILP, we used the bandwidth blocking ratio (BBR) of the network as a performance metric. Our results from the MILP show that introducing slicing at every node in the network improves the BBR by as much as 68% compared to a conventional case where slicing a demand is allowed only at the source node.
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Reducing Bandwidth Blocking Rate in Elastic Optical Networks Through Scale-Based Slicer Placement Strategy
(2023-01-01)
Kitsuwan, Nattapong
;
Pavarangkoon, Praphan
In this paper, we present a scale-based slicer placement strategy aimed at decreasing the bandwidth blocking rate (BBR) in elastic optical networks (EONs). EONs adopt slicing-and-stitching technology to bypass the requirement of consecutive spectrum slots on the same link for a single request. This is achieved through the use of slicers to split the requested spectrum band into multiple sub-spectrum components. However, a uniform distribution of slicers across all nodes may not guarantee low BBR due to varying traffic volumes on each node. Our proposed scale-based policy determines the necessary number of slicers for each node based on our investigation. Simulation results demonstrate that our strategy leads to a 91% reduction in BBR compared to conventional methods.
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Evaluation and Optimization of LLM and RAG Components for a Post-Operative Oral Surgery Consultation Chatbot
(2025-01-01)
Lochanachit, Sirasit
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Bunlaue, Patcharamon
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Kaewmuneechoke, Chanapat
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Wilairatanaporn, Nopasorn
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Trachoo, Vorapat
The increasing demand for dental services highlights the need for efficient post-operative oral surgery consultations. Many patients experience anxiety due to limited knowledge of oral care and treatment. This study introduces a chatbot prototype integrating Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to provide accurate, context-aware responses. The research evaluates various LLMs, embedding models, and chunking techniques to enhance chatbot performance. The multilingual-e5-large embedding model excelled in retrieval tasks due to its multilingual training, instruction tuning, and contrastive pre-training, ensuring high retrieval precision. The Hybrid Chunking method was selected for its ability to segment text contextually, combining Markdown-based, token-based, and semantic segmentation for optimal chunk relevance. The Llama3.3 (70B) model was chosen for its superior fluency, relevance, and ability to handle complex dependencies. The results demonstrate that combining the multilingual-e5-large embedding model, Hybrid Chunking technique, and Llama3.3 (70B) model improves retrieval precision, response accuracy, and relevance, enhancing patient care and operational effectiveness of dental staffs.
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YOLO-augment strategy with diffusion-based inpainting for enhanced traffic sign detection
(2026-01-01)
Sub-r-pa, Chayanon
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Pavarangkoon, Praphan
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Huang, Su Wen
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Fan, Ming Zhong
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Chen, Rung Ching
Traffic sign datasets often suffer from data scarcity and class imbalance, which challenge the development of robust autonomous driving systems. This article proposes a novel dataset augmentation method that leverages Stable Diffusion inpainting to generate realistic synthetic traffic signs. The method fine-tunes a Stable Diffusion model and introduces an object-size-based crop (OSB-crop) technique with mask adjustments to ensure high-quality augmentations that maintain contextual consistency. Evaluations using the Fréchet Inception Distance (FID) show average scores of 195.85 for the DFG-T10 subset and 247.077 for the DFG-B10 subset, demonstrating the ability to produce realistic inpainted signs, particularly for more represented minority classes. Qualitative analyses further highlight seamless integration into real-world scenes, although challenges remain for extremely underrepresented classes and ensuring perfect visual fidelity. The benefits of this approach include its potential to enhance traffic sign datasets, address class imbalances, and improve the potential for training more reliable autonomous driving systems by providing more diverse and realistic training data. This study focuses on evaluating the quality of the generated data itself as a foundational step toward enhancing downstream detection models. However, limitations include the computational cost of fine-tuning and the difficulty in achieving high-quality inpainting for all underrepresented classes, especially those with poor initial data quality. This work lays a strong foundation for advancing dataset augmentation techniques for real-world applications.
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Heuristic Approaches for Cache Node Placement in Content-Centric Networking Under Maximum Link Traffic Minimization
(2026-01-01)
Pavarangkoon, Praphan
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Nakajima, Shohei
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Kitsuwan, Nattapong
This paper proposes heuristic approaches for cache node placement in Content-Centric Networking (CCN) with the objective of minimizing maximum link traffic. The work builds on an exact Integer Linear Programming (ILP) formulation from our earlier study, which jointly models routing and caching decisions but becomes computationally expensive for large-scale networks. To address this limitation, we develop scalable heuristic algorithms that approximate the ILP solution while requiring much lower computation time. We introduce four heuristic approaches, comprising two Linear Programming (LP)-based local search algorithms, 2Swap and GreedySwap, which exploit the fractional solution of the LP relaxation, and two proxy-based heuristics, Population-Weighted Closeness (PWC) and Population-Weighted Betweenness (PWB), which estimate cache utility using population-weighted centrality measures without solving any optimization model. Experimental evaluations on four network topologies show that the LP-based heuristics remain within 0-6% of the ILP optimum while reducing computation time by factors ranging from about 40× to more than 160×. Compared with the proxy-based heuristics, the LP-based heuristics consistently yield lower maximum link traffic across all settings. These results indicate that the proposed heuristic approaches provide effective and scalable solutions for cache node placement in practical CCN deployments.
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Elastic optical network with spectrum slicing for fragmented bandwidth allocation
(2020-09-01)
Kitsuwan, Nattapong
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Pavarangkoon, Praphan
;
Nag, Avishek
Elastic Optical Networks (EONs) allow the channel spacing and the spectral width of an optical signal to be dynamically adjusted and hence have become an important paradigm in managing the heterogeneous bandwidth demands of optical backbone networks. The entire available optical spectrum is divided into some spectrum slots which define the smallest granularity of bandwidth and optical signals with variable bandwidths can occupy different number of such slots. The constraints imposed by the physical layer of an EON require that the slots occupied by an optical signal from source to destination have to be consecutive and contiguous in terms of their relative position in the optical spectrum. Furthermore, the same spectrum slots need to be reserved throughout the entire optical signal's path from its source to destination. The above constraints make the routing and spectrum allocation (RSA) in EONs very challenging because unavailability of enough spectrum slots that together equals the spectral width of the optical signal associated with an end-to-end request, will result in blocking of the request. Recent developments in the physical layer technologies have made all-optical ‘slicing’ of a request possible and make the request to be ‘fit’ into multiple non-consecutive spectral slots in an EON. But these all-optical ‘slicers’ employ complex technologies and can be very costly to employ. In this paper, we propose a spectrum allocation scheme for an EON node architecture with these ‘slicers’ and we also formulate a modified RSA scheme for EONs employing slicers, both as a mixed-integer linear programming (MILP) model and a heuristic algorithm. Our main aim is to analyze the tradeoff between the number of slicers that can be used per node versus the spectrum utilization and bandwidth blocking rate. The numerical results show that the proposed scheme with slicers can significantly improve bandwidth blocking rate, compared to the conventional scheme without slicer.

Pavarangkoon, Praphan

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