Numsomran, Arjin

Preferred name

Numsomran, Arjin

Alternative Name

Numsomran, A.

Main Affiliation

School of Engineering

arjin.nu@kmitl.ac.th

Now showing 1 - 10 of 14

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Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems
(2023-01-01)
Chaoraingern, Jutarut
;
Tipsuwanporn, Vittaya
;
Numsomran, Arjin
BCG’s implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artificial intelligence. Utilizing a low-cost Arducam Pico4ML embedded device and TinyML, a plastic waste classifying system prototype is developed for plastic bottle segregation. The grayscale image datasets of PET, HDPE plastic bottles, and unknown objects are adjusted in the image pre-processing state and utilized to create trained models using MobileNetV2 convolutional-based neural network algorithms. Effective feature extraction and model training are performed on the Edge Impulse platform, and the trained model is exported to an embedded device using the optimized compiler. A further RS485 Modbus communication protocol feature enables integration with a programmable logic controller (PLC). The validation results of the trained model indicate a classification performance of 100% accuracy. Based on the average precision results, it is notable that the trained model can recognize the most common waste with an average accuracy of over 90%. The minimum classification rate of the MobileNetV2 quantized model is 249 milliseconds. It is also implemented in low-cost embedded devices for real-time plastic waste classification using fewer processing resources (185.4K ROM and 88K RAM). The findings exhibit sequential contributions that satisfy the criteria for classifying plastic bottles and the machine’s integration capacity. These outcomes are anticipated to foster social shifts in behavior and enhance public awareness about plastic waste management.
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Improving the efficiency of water management system in biomass power plant using cyber-physical cloud computing
(2018-08-01)
Kungwalrut, Pranai
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Kongratana, Viriya
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Trisuwannawat, Thanit
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Tipsuwanporn, Vittaya
;
Numsomran, Arjin
Optimized water management strategies are among the most crucial concerns in a biomass power plant. Especially during the times of water scarcity, an improper water supply planning and operation result in a detrimental power generation or shut down processes. This paper aims to propose the cyber-physical cloud computing (CPCC), a mechanism of analysis and control physical process using cloud-based framework, in order to improve the efficiency of water management system in a biomass power plant. In this study, a 9.9 MW biomass power plant is implemented to investigate the performance of CPCC. The architecture of the proposed system consists of three physical tiers. The first tier is a tier of physical devices included with pressure, flow, pH sensors, water pumps and valves that are responsible for detecting the physical data and interacting with the water production process. The second tier is an edge computing device which functions as a controller, embedded server, data storage, gateway and switch to manage all operational tasks in an intranet area. The third tier is a cloud computing system which enables big data applications such as online monitoring and visualization of process operation, adaptive filtration fouling control, consuming water and total water cost analysis. The results validate the effectiveness of the proposed system as the ability of an adaptive fouling control system to adjust backwash scheduling and chemical dosing so that achieving the target of purified water quality even during the fluctuation of raw water qualities. Subsequently, the water treatment system can achieve the capability for optimal total water cost operations under a target permeate flow rate and the percent of the recovery. Significantly, the data analytics in the CPCC reflect current operational requirements of water under local climate conditions, likewise contribute to the practical solution for sustainable water resource planning in the energy production.
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Development of load control and management system
(2002-01-01)
Tipsuwanporn, V.
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Srisuwan, K.
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Kulpanich, S.
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Suesut, T.
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Numsomran, A.
This paper presents the conservation of electrical energy in building with the technique to develop load control and management system in order to adjust load factor of system. Using the limiting and controlling the maximum demand, we can acquire the prediction and compare it to the maximum demand set point. This system consist computer which works as controller, processor and database unit and works with digital power meter inform of multidrop network by serial communication via RS-485. The control system use PLC to control load via serial communication RS-485. The data of measurement such as voltage, current, power, power factor, energy, etc., can be saved as database and analysis. The load factor adjustment by limiting and controlling maximum demand can reduce the cost of electric consumption and energy generation.
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Optimization parameters of rotary positioner controller using CDM
(2009-11-01)
Meemongkol, A.
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Tipsuwanporn, V.
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Numsomran, A.
The authors present optimization parameters of rotary positioner controller in hard disk drive servo track writing process using coefficient diagram method; CDM. Due to estimation parameters in PI Positioning Control System by expected ratio method cannot meet the required specification of response effectively, we suggest coefficient diagram method for defining controller parameters under the requirement of the system. Finally, the simulation results show that our proposed method can improve the problem in tuning parameter of rotary positioner controller. It is satisfied specification of performance of control system. Furthermore, it is very convenient as a fast adjustment damping ratio as well as a high speed response.
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Design of PI controller using MRAC techniques for couple-tanks process
(2009-11-01)
Boonsrimuang, P.
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Numsomran, A.
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Kangwanrat, S.
The typical coupled-tanks process that is TITO plant has the difficulty in controller design because changing of system dynamics and interacting of process. This paper presents design methodology of auto-adjustable PI controller using MRAC technique. The proposed method can adjust the controller parameters in response to changes in plant and disturbance real time by referring to the reference model that specifies properties of the desired control system.
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Embedded Sensor Data Fusion and TinyML for Real-Time Remaining Useful Life Estimation of UAV Li Polymer Batteries
(2025-06-01)
Chaoraingern, Jutarut
;
Numsomran, Arjin
The accurate real-time estimation of the remaining useful life (RUL) of lithium-polymer (LiPo) batteries is a critical enabler for ensuring the safety, reliability, and operational efficiency of unmanned aerial vehicles (UAVs). Nevertheless, achieving such prognostics on resource-constrained embedded platforms remains a considerable technical challenge. This study proposes an end-to-end TinyML-based framework that integrates embedded sensor data fusion with an optimized feedforward neural network (FFNN) model for efficient RUL estimation under strict hardware limitations. The system collects voltage, discharge time, and capacity measurements through a lightweight data fusion pipeline and leverages the Edge Impulse platform with the EON™Compiler for model optimization. The trained model is deployed on a dual-core ARM Cortex-M0+ Raspberry Pi RP2040 microcontroller, communicating wirelessly with a LabVIEW-based visualization system for real-time monitoring. Experimental validation on an 80-gram UAV equipped with a 1100 mAh LiPo battery demonstrates a mean absolute error (MAE) of 3.46 cycles and a root mean squared error (RMSE) of 3.75 cycles. Model testing results show an overall accuracy of (Formula presented.), with a mean squared error (MSE) of 55.68, a mean absolute error (MAE) of 5.38, and a variance score of 0.99, indicating strong regression precision and robustness. Furthermore, the quantized (int8) version of the model achieves an inference latency of 2 ms, with memory utilization of only 1.2 KB RAM and 11 KB flash, confirming its suitability for real-time deployment on resource-constrained embedded devices. Overall, the proposed framework effectively demonstrates the feasibility of combining embedded sensor data fusion and TinyML to enable accurate, low-latency, and resource-efficient real-time RUL estimation for UAV battery health management.
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Efficient fractional order reference model of adaptive controller design for multi-input multi-output thermal system
(2020-01-01)
Chaoraingern, Jutarut
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Tipsuwanporn, Vittaya
;
Numsomran, Arjin
The diverse control techniques have been combined with fractional calculus to enhance the control system performance. This paper presents an efficient fractional-order model reference adaptive controller (FOMRAC) design, which aims to demonstrate the solution for temperature reference tracking and cross-coupling rejection in the multi-input multi-output thermal system as well as cognizing of power consumption saving constraints. The mathematical modeling, nonlinear dynamic characteristic details, and system identification of the thermal system are described while the fractional-order controller combined with a model reference adaptive control (FOMRAC) based on MIT rule is developed so that to create the nonlinear adaptive mechanism which enables the excellent performance to control the multi-input multi-output thermal system. Likewise, a decoupling compensator is constructed to remunerate the effect of the cross-coupling interaction. The validation of the proposed control scheme is performed through the Matlab simulation and the experiment on the multi-input multi-output thermal system. The results illustrated the FOMRAC technique in which the controller's adjustable parameters can provide efficiency stability and performance to minimize the settling time and percent overshoot of the control system response. Besides, the analysis of the power consumption in the control system is addressed to reinforce the useful ability of the proposed method compared with the integral-order model reference adaptive controller (IOMRAC) and the traditional PID controller. The results revealed that the proposed FOMRAC technique exhibited much better than other methods because of the effective optimization of adaptive gain mechanism and fractional-order operators.
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ALIVE: An Agentic Longitudinal IDE-IPA Value Evaluation Framework for Innovation-Driven Enterprise Assessment
(2026-01-01)
Chaoraingern, Jutarut
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Numsomran, Arjin
Agentic artificial intelligence architectures have demonstrated transformative potential for automating complex, multi-dimensional evaluation tasks; however, existing deployments remain predominantly stateless, incapable of accumulating institutional knowledge across evaluation cycles and therefore structurally unfit for longitudinal domains such as Impact Pathway Assessment and Social Return on Investment scoring, which inherently demand temporal coherence and precedent-informed judgment. This article presents ALIVE: an Agentic Longitudinal IDE-IPA Value Evaluation Framework, a sixth-generation (L6) multi-agent architecture addressing this limitation through the PACT Loop: Perceive normalizes inputs and retrieves episodic priors from ChromaDB; Analyze scores 18 dimensions in parallel via a shared rubric loaded once across all agents; Converge applies an auto-calibrating halting rule terminating on score stability, target achievement, or budget exhaustion; and Transfer extracts lessons via idempotent atom derivation, matches peer patterns, and broadcasts learnings, with longitudinal state in PostgreSQL and ChromaDB ensuring each cycle improves the next. Four agents (Funder, Company, Researcher, and Society), each instantiated as a role-specific skill context, are orchestrated by Claude via 21 stateless MCP tools on Railway.app without server-side API key. A self-evaluation loop scores each response against a structured rubric, triggering retry until a quality-gate is satisfied. ALIVE operationalizes the IDE-IPA Analyzer-Pro V2.0, a 100-point, 18-dimension rubric spanning Standard IDE Assessment (Part A), Research-Specific Assessment (Part B), Impact Pathway Logic (Part C), and SROI Assessment (Part D). Validation on thirty synthetic proposals across six industry domains against a three-expert panel demonstrates ICC<inf>21</inf>=0.922 (≥ 0.80 good-agreement threshold), funding decision accuracy of 86.7%, and sub-five-minute processing per proposal, establishing ALIVE as a scalable, self-improving infrastructure for longitudinal impact pathway assessment in research funding administration.
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PID controller design for following control of hard disk drive by characteristic ratio assignment method
(2009-11-01)
Chaoraingern, J.
;
Trisuwannawat, T.
;
Numsomran, A.
The author present PID controller design for following control of hard disk drive by characteristic ratio assignment method. The study in this paper concerns design of a PID controller which sufficiently robust to the disturbances and plant perturbations on following control of hard disk drive. Characteristic Ratio Assignment (CRA) is shown to be an efficient control technique to serve this requirement. The controller design by CRA is based on the choice of the coefficients of the characteristic polynomial of the closed loop system according to the convenient performance criteria such as equivalent time constant and ration of characteristic coefficient. Hence, in this study, CRA method is applied in PID controller design for following control of hard disk drive. Matlab simulation results shown that CRA design is fairly stable and robust whilst giving the convenience in controller's parameters adjustment.
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Adaptive fractional order PIλDμ control for multi-configuration tank process (MCTP) toolbox
(2018-10-01)
Numsomran, Arjin
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Trisuwannawat, Thanit
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Tipsuwanporn, Vittaya
This paper presents an adaptive fractional order PIλDμ control for multi-configuration tank process (MCTP) toolbox which aims at demonstrating the problem of reference tracking and cross coupling rejection in multi-input-multi-output system. Moreover, we investigate the cases where the system is in the mode of minimum phase and non-minimum phase configuration. Besides providing theoretical control system analysis and design, we develop the multi-configuration tank process software toolbox for providing the non-linear functions of dynamic models of multi-configuration tank process which is the advantage tool for investigating the performances of the controllers. The software toolbox is developed from discrete state P-file S-function which is operated within the MATLAB environment for providing many non-linear functions of dynamic models of multi-configuration tank process such as multi-input multi-output quadruple tank full-interacting process, multi-input multi-output quadruple tank process, multi-input single-output triple tank interacting process, multi-input multi-output coupled tank interacting process. All actual process attributes are encapsulated in S-function as the input parameters, thus the multi-tank function block can be simply adjusted by specifying the physical properties of the tank system. The study explains about the mathematical model of multi-configuration tank process, nonlinear dynamic characteristic, minimum phase and non-minimum phase configuration, software toolbox features and also describes the design of the adaptive fractional order PIλDμ controller including the performance validation. The results have been illustrated that the proposed controller design scheme can provide the sufficient effectiveness in the performance, stability and robustness. Furthermore, these tests reinforce the usefulness of MCTP toolbox as a complete simulation tool for users to perform an engineering research of multi-configuration tank control system analysis and design, moreover, it contains very useful for validating the control algorithm of multi-input-multi-output system.

Numsomran, Arjin

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