Nunak, Navaphattra
Loading...
Preferred name
Nunak, Navaphattra
Alternative Name
Nunak, N.
Nunak, Navapattra
Main Affiliation
Email
navaphattra.nu@kmitl.ac.th
13 results
Now showing 1 - 10 of 13
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Similarity model for estimating the error of clamp-on ultrasonic flowmeter: Flow in water supply piping system(2017-01-01); ; This study was aimed at presenting the similarity model for the estimation of error of a clamp-on, transit-time ultrasonic flow measurement. Dimensional analysis was based on the Buckingham Pi’s theorem. The groups of independent parameters that were taken into account for the analysis included pipe characteristic, fluid characteristic, and meter installation setting. Experimental testing section was fabricated using PVC pipes with diameters of 1-in and 2-in and 45º PVC elbows. Flow velocity was fixed at 0.5 m/s. The upstream and downstream distances were in the ranges of 2D-20D and 2D-10D, respectively. It was found that the upstream and downstream distances greatly affected the accuracy of the measurement. Larger relative errors were found from the measurement on the smaller pipe. With the installation of ultrasonic transducers according to the recommended value by FCI, the error obtained with the measurements on 1-in and 2-in diameter pipes were, respectively, 7% and 0.35%. The acceptable measurement ranges of upstream distance for 1-in and 2-in diameter pipes were 16D-20D and 6D-20D, respectively. The measurements on a 2-in diameter pipe with a downstream distance in a range of 4D-10D was acceptable. For the 1-in diameter pipe, any downstream distance less than 10D resulted in unacceptable error. The accuracy of measurement was more sensitive to the change of downstream distance than the change of upstream distance. The applicable range of the prototype prediction equation was greatly affected by the flow model. The equation obtained with a 1-in diameter flow model could only be used to predict a 2-in diameter prototype within a range of 18D-20D. Increasing the size a flow model could greatly broaden the applicable range the applicable range of the prediction equation. A 2-in diameter flow model could be used to predict a prototype upto 150 in. - 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, Mass-volume-area properties of frozen Skipjack tuna(2012-05-01); ; This study investigates the physical properties of Skipjack tuna (Katsuwonus pelamis), including weight, size, volume, apparent density, surface and projected area, and the correlation between these parameters. An analysis of the physical characteristics of the Skipjack tuna suggests that its shape can be broken down into three main sections: the head, the center, and the tail. The central section of Skipjack tuna can be considered a barrel shape with an elliptical cross section, whereas the head and tail can be considered cones with elliptical bases. Linear regression analyses between fish weight and other geometric properties revealed a significant correlation (R<sup>2</sup> = 0.74-0.97) to volume, projected area (side and top view), length, width (measured at the thickest part), and perimeter (measured at the thickest part). The results from this systematic analysis could be used in the design of new processes and equipment for Skipjack tuna processing. © 2012 Copyright Taylor and Francis Group, LLC. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Measuring geometric mean diameter of fruits and vegetables using light sectioning method(2009-01-01); This paper proposes a new technique to measure the geometric mean diameter (GMD) of selected fruits and vegetables calculated from a three-dimensional (3D) image by computer vision system (CVS). From a single view of the image data a linear laser light projects onto the top of the sample through the center in order to mark the measurement points. The planar metrology and the measurement between planes are employed to calculate the width and height of the samples. Homography transformation and cross ratio are the mathematical parameter applied to calibrate the image data to real world distance (in metric system). GMD of sample can be calculated from a single view of the image with this technique. The percentage of error of GMD obtained from CVS compared with GMD measurements using vernier calipers is approximately 0.03-5.14 depending on the shape of the objects. However, it can be concluded that this technique is worthwhile for measuring GMD of symmetrical objects. - 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); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fish Detection and Classification for Automatic Sorting System with an Optimized YOLO Algorithm(2023-03-01); ;