Houngkamhang, Nongluck

Preferred name

Main Affiliation

Integrated Innovative Technology

nongluck.ho@kmitl.ac.th

Now showing 1 - 10 of 34

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Frame-Level Accident Recognition via Detection Confidence Aggregation: A Cross-Domain Validation Framework for Thai Roadway Surveillance
(2026-07-01)
Gabbualoy, Somprasonk
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Phasukkit, Pattarapong
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Houngkamhang, Nongluck
Real-time roadway surveillance now leans hard on automated detection. How a model trained in one geographic context actually behaves on another, though, is still underexplored for Southeast Asian deployments. We answer that question for Thai roadway closed-circuit television with a cross-domain validation framework. A YOLOv11n (Ultralytics v8.2.0; Ultralytics, Los Angeles, CA, USA) detector trained with focal loss feeds a confidence-aggregation step that turns per-detection scores into a per-frame accident score, and we put four aggregation operators head-to-head. Reliability comes from DeLong variance estimation paired with non-parametric bootstrap on 1245 Thai frames that carry 23 positive accident events. Under maximum-class aggregation the proposed configuration reaches a frame-level AUROC of 0.959 ± 0.020 across three random seeds. Under top-K aggregation it reaches 0.965 ± 0.018. Per-seed DeLong 95 percent intervals exclude chance performance throughout. We also evaluate three baseline configurations: YOLOv5su comes in at 0.738, YOLOv8n at 0.868, and a Chiang Mai-tuned YOLOv11n variant at 0.918. The architectural progression seen on standard benchmarks therefore carries cleanly into the cross-domain setting. The same Chiang Mai-tuned variant reached an in-domain mAP50 of 0.952 yet only 0.918 cross-region AUROC on a separate Thai region, which is a quiet but clear signal that geographic proximity within a country does not on its own remove distributional shift. Bounding-box localisation appears as a secondary diagnostic because the operational target here is frame-level alerting rather than pixel-precise annotation. Edge deployment optimisation falls outside the present scope. What the work leaves behind is a reproducible baseline and a statistical protocol that follow-up Southeast Asian roadway-safety research can build on.
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A simple and high -performance immobilization technique of membrane protein from crude cell lysate sample for a membrane-based immunoassay application
(2023-01-01)
Khemthongcharoen, Numfon
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Uawithya, Panapat
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Yookong, Nutthapon
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Chanasakulniyom, Mayuree
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Jeamsaksiri, Wutthinan
Membrane proteins are difficult to be extracted and to be coated on the substrate of the immunoassay reaction chamber because of their hydrophobicity. Traditional method to prepare membrane protein sample requires many steps of protein extraction and purification that may lead to protein structure deformation and protein dysfunction. This work proposes a simple technique to prepare and immobilize the membrane protein suspended in an unprocessed crude cell lysate sample. Membrane fractions in crude cell lysate were incorporated with the large unilamellar vesicle (LUV) that was mainly composed of POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) before coating in the polystyrene plate by passive adsorption technique. Immunofluorescence staining and the Enzyme-Linked Immunosorbent Assay (ELISA) examination of a strictly conformation-dependent integral membrane protein, Myelin Oligodendrocyte Glycoprotein (MOG), demonstrate that LUV incorporated cell lysate sample obviously promotes MOG protein immobilization in the microplate well. With LUV incorporation, the dose–response curve of the MOG transfected cell lysate coating plate can be 2–9 times differentiated from that of the untransfected cell lysate coating plate. The LUV incorporated MOG transfected cell lysate can be efficiently coated in the microplate without carbonate/bicarbonate coating buffer assistance.
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Rapid detection of cypermethrin by using surface-enhanced raman scattering technique
(2020-01-01)
Leung, Wipawanee
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Limwichean, Saksorn
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Nuntawong, Noppadon
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Eiamchai, Pitak
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Kalasung, Sukon
Cypermethrin is a toxic pesticide in the pyrethroid group. A Surface Enhanced Raman Scattering (SERS) based sensor has been developed to achieve simple pesticide sensing. In this work, rapid detection of cypermethrin by using the handheld Raman spectroscopy coupled with SERS substrate was demonstrated. SERS-active silver nanorods substrate was used to enhance Raman signals of test samples. The effect of exposure time and drop volume of sample was studied for cypermethrin measurement. The results found that the silver nanorods substrate can be used to measure cypermethrin in the range of 10-6 to 10-3 M with a handheld Raman spectrometer. Furthermore, the Raman signal of cypermethrin was confirmed by measuring solid cypermethrin with the standard Raman spectrometer. SERS substrate was competent to detect cypermethrin with a limit of detection (LOD) of 10-6 M.
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Gold nanorod-optical fiber for sensing biomolecular interaction
(2020-01-01)
Chueaiarrom, Channarong
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Loedsapchinda, Naruenard
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Puttharugsa, Chokchai
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Pipatpanukul, Chinnawut
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Srikhirin, Toemsak
The optical fiber deposits with gold nanorods was successfully accomplished as a biosensor device which sensing to the refractive index change on the surrounding surface. The principle is based on the localized surface plasmon resonance (LSPR) occurs on the metal surface of gold nanorods (AuNRs). The gold nanorods were coated on the glass core fiber surface through the covalent bond. The uncladded area fiber optic was incubated in 5% of (3-Mercaptopropyl) trimethoxysilane (MPTMS) in ethanol for overnight to alter the thiol group on the surface. The gold nanorods with aspect ratio ⁓2.035 was deposited on thiol surface by incubation the optical fiber in gold nanorod solution for 6 hr. The sensor provides the sensitivity of 4.94×104 a.u./RIU. Moreover, antibody for blood grouping was immobilized on the AuNRs-optical fiber to detect the red blood cells. The AuNRs-optical fiber can detect A antigen on the immobilized anti-A surface with Δ%T of 0.98 and can detect B-antigen on the immobilized anti-B surface with Δ%T of 1.78. This work possesses possibility of a simple AuNRs-optical fiber to detect the biomolecular interaction. The authors would like to thank Thailand Center of Excellence in Physics for financial support.
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Polydimethylsiloxane (PDMS) microfluidic modifications for cell-based immunofluorescence assay
(2021-01-01)
Khemthongcharoen, Numfon
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Uawithya, Panapat
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Chanasakulniyom, Mayuree
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Yasawong, Montri
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Jeamsaksiri, Wutthinan
Polydimethylsiloxane (PDMS) is a hydrophobic elastomer commonly used for microfluidic fabrication. PDMS has to be modified to improve its hydrophilicity and thus inhibits non-specific protein adsorption. This work evaluates the modification materials for the development of microfluidic cell-based immunofluorescence (IF) assay. In cell-based IF assay, PDMS is modified not just to inhibit the adsorption of non-specific florescent-conjugated protein that causes the elevation of background signal, but also to firmly support cell adhesion for subsequent immunostaining procedure. PDMS materials modified by three regular modification materials consisting of an extracellular matrix (poly-L-lysine; PLL), a hydrophilic polymer (polyvinyl alcohol; PVA) and a non- ionic surfactant (pluronic F127) were compared with each other based on hydrophilicity improvement, minimization of non-specific background signal, and enhancement of human embryonic kidney (HEK) cell adhesion. Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) analysis confirms that all modification materials are successfully immobilized on the PDMS surfaces. Due to its antifouling mechanism, pluronic modification greatly improves the hydrophilicity of the PDMS and inhibits non-specific protein adsorption. Even though the hydrophilicity and non-specific protein adsorption resistivity of the PDMS modified with PLL did not significantly differ from those of the unmodified PDMS, PLL modification obviously promotes HEK cell adhesion. Negative control and Myelin Oligodendrocyte Glycoprotein (MOG) expressing HEK cells were immobilized in microfluidics for IF assay evaluation. Results demonstrate that positive MOG expressing cells can be selectively stained by anti-MOG IgG antibody within 1 h at room temperature. Microfluidic platforms also enhance immobilized cell distribution, which compatibly supports single-cell analysis technique.
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Intelligent Battery Management System for Electric Vehicles: AI-Driven Voltage Cell Prediction Using GRU and K-Means Clustering
(2025-01-01)
Pahaisuk, Narawit
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Pourbunthidkul, Supavee
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Phasukkit, Pattarapong
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Houngkamhang, Nongluck
The advancement of Electric Vehicles (EVs) requires intelligent Battery Management Systems (BMS) for accurate voltage prediction, ensuring battery reliability, longevity, and efficiency. Traditional BMS architectures rely on rule-based monitoring, which lacks predictive capabilities for early fault detection and proactive maintenance. This study presents an AI-driven BMS framework, integrating K-Means clustering and Gated Recurrent Unit (GRU) networks to enhance real-time voltage forecasting. Unlike conventional approaches that focus solely on clustering or deep learning, this research combines both methodologies to create a robust predictive system. K-Means clustering segments battery voltage data into operational groups, improving predictive accuracy by organizing similar voltage behaviors. GRU networks then capture sequential voltage dependencies, enabling precise voltage fluctuation predictions. Experimental validation was conducted using voltage data from 120 lithium-ion battery cells, recorded at 20 km/h under three load conditions (Load 0, Load 10, and Load 20) over a 5-minute duration. The findings demonstrate that K-Means clustering effectively categorizes battery voltage states, while GRU-based forecasting achieves high accuracy, reinforcing the practicality of AI-powered predictive maintenance. This study advances AI-driven BMS frameworks by demonstrating the integration of clustering and deep learning for voltage prediction, providing a foundation for real-time battery diagnostics and predictive analytics.
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Gold-nanoparticle-based fiber optic sensor for sensing the refractive index of environmental solutions
(2018-08-01)
Houngkamhang, Nongluck
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Charoensuwan, Sittan
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Sonthipakdee, Onanong
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Nawattanapaiboon, Kawin
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Somboonkaew, Armote
Here an optical fiber modified with gold nanoparticles was successfully fabricated to sense the refractive index of a chemical solution surrounding its surface, with the potential of utilizing this sensor as a biosensing device. Gold nanoparticles, with an average diameter ~20 nm, were synthesized via the citrate reduction method and used to functionalize the glass core of an optical fiber. The sensing principle, which is based on localized surface plasmon resonance, requires the metal gold nanoparticles to be exposed to both the incident and absorbing lights, with the potential detection capabilities determined from the observed light intensity measurement. The optical fiber was uncladded at its center to expose the glass core fiber, and gold nanoparticles were immobilized on the exposed surface using a silane coupling agent. The sensitivity of the gold-nanoparticle-modified optical fibers in measuring the refractive index changes of a solution was compared for unclad lengths ranging between 1 and 2 cm. The attenuation of light depended on both the refractive index of solution and the length of the unclad fiber. The results showed that the fiber sensitivity increased as the unclad length increased due to greater amount of gold on the surface. Antibody-A, which has a specific binding to antigen-A, was then covalently linked to the fiber surface via an amide bond to demonstrate the potential bio-sensing platform of this sensor. The antibody-A functionalized optical fiber was used to detect the red blood cell samples in groups A, B, and O, where it effectively detected both the specific and non-specific binding signals. This fiber optic biosensor therefore provides a low-cost and simple fabrication setup that has potential field applications. Moreover, this setup could potentially be applied to detect other types of whole cell samples.
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Extended-gate field effect transistor (EGFET) for carbaryl pesticide detection based on enzyme inhibition assay
(2017-01-01)
Sasipongpana, Supanat
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Rayanasukha, Yossawat
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Prichanont, Seeroong
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Thanachayanont, Chanchana
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Porntheeraphat, Supanit
An extended - gate filed effect transistor (EGFET) is a device which can be applied to chemical sensing devices. In this study, EGFET was constructed by using an indium tin oxide (ITO) glass substrate. The small piece ITO with sensing area around 3x3 mm2 was used as carbaryl pesticide sensor based on enzyme inhibition assay. The prepared ITO-EGFET sensors were test with pH solutions to obtain sensitivity compared with commercial ion sensitive field effect transistor (ISFET). Preparation of ITO substrate for enzyme inhibition assay by dropped acetylcolinesterase enzyme at 0.5 Unit which were trapped in an agarose gel matrix onto substrates. This prepared ITO acts as extended gate sensing membrane for EGFET. The amounts of drop volume of enzyme were varied to obtain the highest signal for reaction with acetylcholine substrate. Cabaryl can inhibit enzyme function resulting in the decreasing signal of interaction between enzyme and acetylcholine substrate. This ITO-EGFET can be used to detect carbaryl in concentration rage 0.001 - 1 mM by using the ITO-EGFET without enzyme as control condition. This work shows possibility of ITO-EGFET for application in pesticide detection with an easy substrate preparation, low cost and can be applied in field used with others pesticides.
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Enhancement of Bacterial Anti−Adhesion Properties on Robust PDMS Micro−Structure Using a Simple Flame Treatment Method
(2022-02-01)
Houngkamhang, Nongluck
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Chaisawat, Ploymanee
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Joksathit, Waisaree
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Samart, Sutichai
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Chutipaijit, Sutee
Biofilm−associated infections caused by an accumulation of micro−organisms and pathogens significantly impact the environment, health risks, and the global economy. Currently, a non−biocide−releasing superhydrophobic surface is a potential solution for antibacterial purposes. This research demonstrated a well−designed robust polydimethylsiloxane (PDMS) micro−structure and a flame treatment process with improved hydrophobicity and bacterial anti−adhesion proper-ties. After the flame treatment at 700 ± 20 °C for 15 s, unique flower−petal re−entrant nano−structures were formed on pillars (PIL−F, width: 1.87 ± 0.30 μm, height: 7.76 ± 0.13 μm, aspect ratio (A.R.): 4.14) and circular rings with eight stripe supporters (C−RESS−F, width: 0.50 ± 0.04 μm, height: 3.55 ± 0.11 μm, A.R.: 7.10) PDMS micro−patterns. The water contact angle (WCA) and ethylene glycol contact angle (EGCA) of flame−treated flat−PDMS (FLT−F), PIL–F, and C–RESS−F patterns were (133.9 ± 3.8°, 128.6 ± 5.3°), (156.1 ± 1.5°, 151.5 ± 2.1°), and (146.3 ± 3.5°, 150.7 ± 1.8°), respectively. The Escherichia coli adhesion on the C−RESS−F micro−pattern with hydrophobicity and superoleophobicity was 42.6%, 31.8%, and 2.9% less than FLT−F, PIL−F, and Teflon surfaces. Therefore, the flame−treated C−RESS−F pattern is one of the promising bacterial anti−adhesion micro−structures in practical utilization for various applications.
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An Enhanced Cascaded Deep Learning Framework for Multi-Cell Voltage Forecasting and State of Charge Estimation in Electric Vehicle Batteries Using LSTM Networks
(2025-06-01)
Pourbunthidkul, Supavee
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Pahaisuk, Narawit
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Laon, Popphon
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Houngkamhang, Nongluck
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Phasukkit, Pattarapong
Enhanced Battery Management Systems (BMS) are essential for improving operational efficacy and safety within Electric Vehicles (EVs), especially in tropical climates where traditional systems encounter considerable performance constraints. This research introduces a novel two-tiered deep learning framework that utilizes a two-stage Long Short-Term Memory (LSTM) framework for precise prediction of battery voltage and SoC. The first tier employs LSTM-1 forecasts individual cell voltages across a full-scale 120-cell Lithium Iron Phosphate (LFP) battery pack using multivariate time-series data, including voltage history, vehicle speed, current, temperature, and load metrics, derived from dynamometer testing. Experiments simulate real-world urban driving, with speeds from 6 km/h to 40 km/h and load variations of 0, 10, and 20%. The second tier uses LSTM-2 for SoC estimation, designed to handle temperature-dependent voltage fluctuations in high-temperature environments. This cascade design allows the system to capture complex temporal and inter-cell dependencies, making it especially effective under high-temperature and variable-load environments. Empirical validation demonstrates a 15% improvement in SoC estimation accuracy over traditional methods under real-world driving conditions. This study marks the first deep learning-based BMS optimization validated in tropical climates, setting a new benchmark for EV battery management in similar regions. The framework’s performance enhances EV reliability, supporting the growing electric mobility sector.

Houngkamhang, Nongluck

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