Nunak, Navaphattra
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Nunak, Navaphattra
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Nunak, N.
Nunak, Navapattra
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navaphattra.nu@kmitl.ac.th
12 results
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Item type:Publication, Optimal Design and Installation of Solar Rooftop for Thai government building(2025-01-01); ; This research presents an approach for installing grid-connected solar cells on the rooftops of a prototype building in Thailand to offset electricity consumption, aligning with TOU electricity pricing. It includes design methods, energy-saving calculations, and illustrates the savings from solar cell installation over a one-year period, without accounting for solar cell deterioration or system loss charges. The study compares the concept of installing solar cells on the rooftop based on the building's electricity usage characteristics, distinguishing between installing solar cells across the entire rooftop area and according to the building's electricity usage behavior during daytime and working days, ensuring minimal electricity remains while still being sufficient for use. Furthermore, it calculates the comparison before and after installation to provide information for rooftop installation on the prototype building. The research results indicate that installing solar cells across the entire rooftop area can save electricity costs up to 550,000 THB per year. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Estimating the Power Losses in Induction Motor using Temperature Monitoring(2025-01-01); ; The paper proposes the estimation of power losses in electrical machines using a measurement technique based on the temperature of the electrical machine and the heat transfer equation. To demonstrate this concept, the heat diffusivity of the induction motor can be illustrated through thermal images. This research addresses the impact of supply voltage unbalance on power loss in induction motors. The system comprises A contact temperature measuring circuit utilizing the IC LM 35DZ as a temperature sensor and signal converter, paired with a computer equipped with a National Instruments PCI 6014 data acquisition card. The experiment was conducted by adjusting the phase unbalance to 10%, 20%, and 50% to observe temperature changes. This allows for a direct temperature in the induction motor. The primary advantage of this method is that the machines do not need to stop or be removed from their operational process to measure their losses during preliminary maintenance. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An investigation of oil residue on surface by infrared thermography(2020-01-01); ; This work aims to propose a visual inspection, infrared thermography technique, of oil residual mass on stainless steel surface (SS) (hydrophilic surface representative) and polytetrafluoroethylene (PTFE) surface (hydrophobic surface representative). An improved understanding of the oil fouling characteristic is a key point to develop this technique. The effect of surface roughness on the oil contact angle, oil residual mass, and the resulting average temperature of the residues on SS and PTFE surface was studied. For the infrared thermography technique, the mass of oil adhered to each interface using heating at a temperature of 80<sup>0</sup>C for 10 minutes. SS AISI 304 plaques with the average surface roughness of 0.4, 0.8, and 3.2 µm and PTFE with that of 0.4 and 0.8 µm were examined. All samples were snapped top view using a thermal image camera. The average temperatures were obtained from the color spectrum of the thermal images. It could be summarized that the proposed measurement is possible to detect the accumulation of oil on the SS whereas it was not clearly different that on the PTFE surface. A greater oil residual mass on both hydrophilic and hydrophobic surfaces is a result of an increase in surface roughness and sequential wettability from the contact angle as expected. Moreover, each liquid-solid interfacial material has its specific surface characteristic. Finally, each liquid-solid interfacial material has its specific surface characteristic and the new and used oil-SS interface could be detected by infrared thermography technique. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, DEVELOPMENT OF OBJECT DETECTION AND CLASSIFICATION WITH YOLOV4 FOR SIMILAR AND STRUCTURAL DEFORMED FISH(2022-03-31); ; Food scarcity is an issue of concern due to the continued growth of the human population and the threat of global warming and climate change. Increasing food production is expected to meet the challenges of food needs that will continue to increase in the future. Automation is one of the solutions to increase food productivity, including in the aquaculture industry, where fish recognition is essential to support it. This paper presents fish recognition using YOLO version 4 (YOLOv4) on the «Fish-Pak» dataset, which contains six species of identical and structurally damaged fish, both of which are characteristics of fish processed in the aquaculture industry. Data augmentation was generated to meet the validation criteria and improve the data balance between classes. For fish images on a conveyor, flip, rotation, and translation augmentation techniques are appropriate. YOLOv4 was applied to the whole fish body and then combined with several techniques to determine the impact on the accuracy of the results. These techniques include landmarking, subclassing, adding scale data, adding head data, and class elimination. Performance for each model was evaluated with a confusion matrix, and analysis of the impact of the combination of these techniques was also reviewed. From the experimental test results, the accuracy of YOLOv4 for the whole fish body is only 43.01 %. The result rose to 72.65 % with the landmarking technique, then rose to 76.64 % with the subclassing technique, and finally rose to 77.42 % by adding scale data. The accuracy did not improve to 76.47 % by adding head data, and the accuracy rose to 98.75 % with the class elimination technique. The final result was excellent and acceptable. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Monitoring of Food Fouling in A Plate Heat Exchanger Using Heat Flux Sensor(2025-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Inspection of Heat Seal Packing Bag Integrity Using Thermal Images With YOLO Algorithm(2025-01-01); ; This paper presents an inspection of heat seal packing bag integrity using thermal imaging with a deep learning technique. The performance was evaluated by comparing the object detection rates obtained from the YOLOv4-Tiny Algorithm and the Convolutional Neural Network (CNN) technique. Two sets of completely sealed and failure-sealed packaging bags were prepared for the training (100 bags) and testing (100 bags) of the models. Some sample bags containing tomato sauce inserted between the seals represent a failure-sealed condition. The integrity of the heat seal packaging bag was analyzed by examining the differences in color shade patterns of the thermal images. As a result, the YOLOv4-Tiny model achieved a detection accuracy of 98.80%, significantly outperforming the CNN technique, which detected only 78.40%, while using the same dataset; additionally, the time required for detection and training was faster. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effects of heat transfer surface temperature on liquid egg yolk fouling(2024-01-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Fast and Simple Machine Vision Framework for Approximating the Volume of Axi-Symmetric Objects Using Shadow Ray Casting(2024-01-01); The volume measurement using machine vision system is contactless techniques that play an important role in industries now a day. Basically, three-dimensional reconstruction is required to determine a depth using a special lighting system or multiple cameras. This increases the complexity of the measurement system. A fast and simple machine vision framework called RayVol for estimating the volume of axisymmetric objects in near real-time using a single camera and simple illumination is presented. The RayVol framework employs a shadow casting method to reconstruct the 3D shape of the object by tracing rays from the object’s shadow pixels to the light source location. The result of this technique shows a significant accuracy improvement from the area-projection method. A virtual slice representing the cross-section of an object is reconstructed using a cubic spline approximation from baseline points derived from the boundary pixels of the object image and a shadow casting method. The volume estimation was calculated by restricted integration using the Riemann sum estimation algorithm, and the closed area of the virtual slices was calculated using the shoestring algorithm. Mangoes were used as a case study of the RayVol framework. The volume estimation provides the correlation coefficient of 0.9849 between the developed system and the water replacement method. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automatic Fish Classification Using Lanczos Resampling and Deep Learning(2025-09-01); ; The development of automation in the fish industry, a vital sector of the food industry, is a highly relevant and essential topic. This development is essential for boosting output and mitigating the risk of future food shortages brought on by the world’s population expansion. Automatic fish classification using computer vision has been widely developed in fish industry automation, and a lot of research on that topic has been published. However, while some research has produced promising results using complex methods, others have applied simpler approaches with less satisfactory outcomes. This study suggests a straightforward but efficient technique for differentiating between fish species by concentrating on their main characteristics, such as body form and scale patterns. To effectively support these image capturing properties, the Lanczos re-sampling technique is used in this study. Additionally, our basic deep learning model can correctly learn and identify fish species thanks to a fish picture categorization engine created using Google Teachable Machine. Utilizing the Fish-Pak dataset, a popular fish picture dataset frequently used in studies on fish species classification, the suggested approach successfully overcomes the difficulty and attains a high accuracy rate of 97.16%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Simple and Effective Techniques for Automatic Fish Species Classification Using Image Processing and Deep Learning(2025-01-01);