Jettanasen, Chaiyan
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Jettanasen, Chaiyan
Alternative Name
Jettanasen, C.
Main Affiliation
Email
chaiyan.je@kmitl.ac.th
32 results
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Item type:Publication, Design and application of daylight-based lighting controller on LED luminaire(2020-05-01); ; ; Patcharoen, TheerasakThis paper proposes a control circuit for Light Emitting Diode (LED) luminaire based on the amount of daylight. This system uses both natural daylight and artificial light from a luminaire to reach the desired illuminance on a working plane. The control circuit operates by receiving an illuminance value from a light sensor and comparing it with a set value in the microcontroller before limiting the light intensity of LED by sending a control Pulse Width Modulation (PWM) signal to the LED driver. In the experimental setup, the controller and a physical model of a working space were built in order to field test the LED daylight control circuit. Electrical characteristics such as voltage, current, and power were measured with a power meter. Illumination characteristics in terms of illuminance on a working plane were determined using a lux meter. The results from actual field tests revealed that this LED control circuit was able to reduce the power consumption of the lighting system while keeping the illuminance on the working plane at the standard value. Both simulation results and the actual experiment of the control circuit proved that it could be used to improve the energy efficiency of both newly installed and retrofitted lighting systems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Evaluation Study on Electric Appliance Characteristics and Load Patterns in Residential Buildings(2026-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Transient Analysis to Distinguish Mechanically Switched Capacitors Using Discrete Wavelet Transform and Artificial Intelligence(2026-01-01); ; Capacitor banks are widely used in modern power systems for reactive power compensation and voltage regulation. However, switching operations of mechanically switched capacitors (MSCs) can generate transient phenomena, such as inrush currents, which may resemble fault currents and lead to misoperation of protection systems. Therefore, accurate detection and classification of transient events are essential for reliable system operation. This study proposes a hybrid approach for transient signal analysis and classification by integrating the discrete wavelet transform (DWT) with artificial intelligence (AI) techniques, including probabilistic neural networks and fuzzy inference systems (FIS). The DWT performs time–frequency analysis to extract multi-scale wavelet features from three-phase current signals. The proposed method enables both discrimination between inrush and fault currents and multi-class classification of transient events among six capacitor switching conditions, namely base case, pre-insertion resistor, pre-insertion inductor, current limiting reactor, 6% reactor, and synchronous closing. The methodology is validated using PSCAD/EMTDC simulations under isolated and back-to-back capacitor switching scenarios. The results demonstrate that the proposed DWT–AI approach achieves high classification accuracy exceeding 95%, outperforming conventional methods based on DWT alone and DWT combined with FIS. Furthermore, the proposed method improves protection system performance by reducing false tripping caused by transient inrush currents, while maintaining reliable fault detection capability. The findings confirm that integrating time–frequency signal processing with AI-based classification provides an effective and practical solution for transient event discrimination in MSC capacitor bank systems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development of micro-mobility based on piezoelectric energy harvesting for smart city applications(2020-04-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The conducted emission attenuation of micro-inverters for nanogrid systems(2020-01-01); Road lighting systems require a significant amount of electric energy. To compensate for the utilized energy, the concept of a nanogrid road lighting system is presented. A solar panel is installed on the top of a lighting pole to generate electric power. In this research, a photovoltaic simulator (PV simulator), which is used to simulate solar behavior such as current, voltage, and power based on temperature and solar irradiance levels, is employed to replace a solar panel. In the nanogrid system, grid-connected and stand-alone micro-inverters are employed to convert the electric power. The inverters comprise switching devices that can generate electromagnetic interference (EMI) when operating, which is harmful to the grid system and the electrical equipment. In general, EMI has been studied and reduced in electrical appliances, which only receive electric power. However, for the nanogrid system, which supplies electricity to the grid system, there is less study on the EMI topic because the usage is still not widespread. In the future, the nanogrid system will be widely used delivering high power directly into the electrical grid system. Therefore, the study and attenuation of EMI in the nanogrid system are very promising. Conducted emission (CE) is one form of EMI that flows through a cable connecting several appliances in the frequency range of 150 kHz to 30 MHz. CE of grid-connected and stand-alone micro-inverters have high levels in the low-frequency range between 150 kHz-5 MHz and then decreases steadily. CE attenuation is important for this inverter in a solar power system. This research studies the effect of CE mitigation on the nanogrid system. The result is compared with the Comite International Special des Perturbations Radio (CISPR) 14-1 standard. Finally, the passive EMI filter can reduce CE and meets the CISPR 14-1 standard. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design of a Personal Mobility Device for Elderly Users(2021-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Distributed Temperature Sensing and Real Time Thermal Rating for Underground Power Cable Monitoring(2025-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design and Simulation of Removable Pavement Edge Climbing Electric Wheelchair for Elderly and Disabled Users(2023-06-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Approach for Voltage Drop Improvement in Distribution Line Using High-Voltage Capacitor Bank(2025-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exploration of Sign Language Recognition Methods Based on Improved YOLOv5s(2025-03-01); Chiradeja, PathomthatGesture is a natural and intuitive means of interpersonal communication. Sign language recognition has become a hot topic in scientific research, holding significant importance and research value in fields such as deep learning, human–computer interaction, and pattern recognition. The sign language recognition process needs to ensure real-time performance and ease of deployment. Based on these two requirements, this paper proposes an improved YOLOv5s-based sign language recognition algorithm. Firstly, the lightweight concept from ShuffleNetV2 was applied to achieve lightweight characteristics and improve the model’s deployability. The specific improvements are as follows: The algorithm achieved model size reduction by removing the Focus layer, using the ShuffleNetv2 algorithm, and then channel pruning YOLOv5 at the head of the neck layer. All the convolutional layers and the cross-stage partial bottleneck layer with three convolutional layers in the backbone network were replaced with ShuffleBlock, the spatial pyramid pooling layer and a subsequent cross-stage partial bottleneck layer structure with three convolutional layers were removed, and the cross-stage partial bottleneck layer module with three convolutional layers in the detection header section was replaced with a depth-separable convolutional module. Experimental results show that the parameters of the improved YOLOv5 algorithm decreased from 7.2 M to 0.72 M, and the inference speed decreased from 3.3 ms to 1.1 ms.