Nunak, Navaphattra

Preferred name

Nunak, Navaphattra

Alternative Name

Nunak, N.

Nunak, Navapattra

Main Affiliation

School of Engineering

navaphattra.nu@kmitl.ac.th

Now showing 1 - 10 of 30

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Mass-volume-area properties of frozen Skipjack tuna
(2012-05-01)
Pornchaloempong, Pimpen
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Sirisomboon, Panmanas
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Nunak, Navapattra
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 (R2 = 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.
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A CFD study on wall shear stress of falling water film on food contact surface
(2018-08-14)
Suthanarak, Nawaporn
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Nunak, Navaphattra
This paper proposes a CFD model to simulate the wall shear stress of the falling water film on the hydrophilic surface (aluminum) of the inclined flat plate in the range of 30°, 60° and 90° at a flow rate of 100, 200 and 300 L/h. Wall shear stress simulated from the CFD using SST k-ω with the VOF model was compared with the real data obtained from the experiment. It was found that both inclined angle and water flow rate had an effect to the wall shear stress and the CFD with the proposed model had the potential to predict it with the maximum averaged error of 2.9%.
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Measuring geometric mean diameter of fruits and vegetables using light sectioning method
(2009-01-01)
Nunak, Navaphattra
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Suesut, Taweepol
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.
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Effects of heat transfer surface temperature on liquid egg yolk fouling
(2024-01-01)
Sritham, Eakasit
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Nunak, Navaphattra
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Suthanupaphwut, Worapanya
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Somlitsopak, Badin
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Chaishome, Jedsada
This study was aimed at investigating the effects of different surface temperatures (60-80°C) on the formation of egg yolk deposits on heat transfer surface. Experimental data from the fouling period were fitted with zero- and first-order reaction models and the reaction kinetics of fouling was obtained using the Arrhenius equation. Egg yolk fouling curves exhibited an asymptotic pattern showing only fouling and post-fouling periods. The fouling resistance at transition point increased with the increasing surface temperature. The zero-order reaction model was well describing the reaction rate of egg yolk fouling. The obtained activation energy of 85.47 kJ/mol was less than that for thermal denaturation of egg yolk proteins. The fouling process of egg yolk was mainly controlled by the deposition reaction.
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Effect of alkaline treatment on decomposition temperature of fibres from empty oil palm bunch
(2019-01-01)
Chaishome, Jedsada
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Nunak, Navaphattra
—This article reports an investigation of the decomposition temperature of fibres from empty oil palm bunch to achieve improved understanding of the processing temperature limitation of this fibre as reinforcement for thermoplastic composite manufacturing. The effect of alkaline treatments on their chemical decomposition and sample weight changes were performed, using fourier transform infrared spectrometry (FTIR) and thermogravimetry (TG) techniques. The results show that the hemicellulose removal of fibres after treatment is a factor that provides an advantage on the thermal stability of the fibres. Both initial decomposition temperature (T<inf>0</inf>) and maximum decomposition temperature (T<inf>max</inf>) are minor increase. This could reduce the detrimental effect of thermal degradation on the single fibre performance and leads to shift its consolidation temperature ceiling from below 221oC (untreated fibres) to 243oC (5% NaOH treated fibres) during composite consolidation.
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Fish Detection and Classification for Automatic Sorting System with an Optimized YOLO Algorithm
(2023-03-01)
Kuswantori, Ari
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Suesut, Taweepol
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Tangsrirat, Worapong
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Schleining, Gerhard
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Nunak, Navaphattra
Featured Application: In the future, the application of this study is very feasible and very close to being implemented for the auto-sorting system for various fish or other objects, in the fish industry or other industries, with deep learning and machine vision technology. Automatic fish recognition using deep learning and computer or machine vision is a key part of making the fish industry more productive through automation. An automatic sorting system will help to tackle the challenges of increasing food demand and the threat of food scarcity in the future due to the continuing growth of the world population and the impact of global warming and climate change. As far as the authors know, there has been no published work so far to detect and classify moving fish for the fish culture industry, especially for automatic sorting purposes based on the fish species using deep learning and machine vision. This paper proposes an approach based on the recognition algorithm YOLOv4, optimized with a unique labeling technique. The proposed method was tested with videos of real fish running on a conveyor, which were put randomly in position and order at a speed of 505.08 m/h and could obtain an accuracy of 98.15%. This study with a simple but effective method is expected to be a guide for automatically detecting, classifying, and sorting fish.
2
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Automatic Fish Classification Using Lanczos Resampling and Deep Learning
(2025-09-01)
Kuswantori, Ari
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Suesut, Taweepol
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Suthanupaphwut, Worapanya
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Tangsrirat, Worapong
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Nunak, Navaphattra
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%.
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Simple and Effective Techniques for Automatic Fish Species Classification Using Image Processing and Deep Learning
(2025-01-01)
Kuswantori, Ari
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Nunak, Navaphattra
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Suthanupaphwut, Worapanya
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Schleining, Gerhard
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Tangsrirat, Worapong
The advancement of automation in the fish industry, a critical segment of the food sector, has become increasingly relevant in light of the growing global population and the impacts of climate change and global warming. Enhancing productivity through automation is essential to mitigate the looming threat of food scarcity. In this context, automatic fish classification using computer vision has garnered significant attention, with various studies exploring both complex and simple approaches. While complex methods have shown promising results, simpler approaches often fall short in performance. This study proposes a simple yet effective method that highlights key distinguishing features of fish—namely, body shape and scale patterns—for species classification. The Lanczos resampling technique is employed to crop, resize, and focus on the features, enabling a lightweight deep learning model to effectively learn and classify fish species. With the right conceptual framework, appropriate feature extraction techniques, and an efficient deep learning architecture, the proposed method addresses the classification challenge in a straightforward yet effective manner. Experimental evaluations using the Fish-Pak dataset, comprising six aquaculture fish species, and the KMITL Fish dataset, containing eight species, demonstrate the effectiveness of the method, achieving accuracy rates of 97.16% and 98.59%, respectively.
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A Fast and Simple Machine Vision Framework for Approximating the Volume of Axi-Symmetric Objects Using Shadow Ray Casting
(2024-01-01)
Sukprasertchai, Siwakorn
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Suesut, Taweepol
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Nunak, Navaphattra
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.
3
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Inspection of Heat Seal Packing Bag Integrity Using Thermal Images With YOLO Algorithm
(2025-01-01)
Chaishome, Jedsada
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Khannakum, Wirat
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Nunak, Navaphattra
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Suesut, Taweepol
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.
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Nunak, Navaphattra

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