• Journal of the Korean Wood Science and Technology
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• v.43 no.1
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• pp.25-35
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• 2015

The goal of this study was to review the visual grading and allowable stress determination methodologies for the domestic softwood. Previous studies used different grading (KFRI 1995-27, KFRI 2000-39, KFRI 2007-3, KFRI 2009-1) and allowable stress determination methodologies (ASTM D 245, KS F 2152, JAS 1990). The results of the visual grading were different by each researcher. Compared to the $1^{st}$ grade proportion from the previous studies using the previous specification on visual grading (KFRI 1995-2007), a higher $1^{st}$ grade proportion was found from the studies using the current specification (KFRI 2009). Compared to the allowable stress values from the small clear sample, the higher allowable stress values from the structural size were found. The results indicated that the strength reduction factor used in small clear sample was too conservative for the different grades. To obtain consistent results for the grade, it is required to have experts in visual grading and authorized organizations. An official standard methodology for the allowable stress value determination needs to be defined for the reliable stress value.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1386-1394
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• 2022

As consumption of high-quality fruits increases and sales and packaging units become smaller, the demand for automatic fruit grading systems is increasing. Compared to other crops, the quality of fruit is determined by visual characteristics such as shape, color, and scratches, rather than just physical size and weight. Accordingly, this study presents a CNN model that can effectively extract and classify the visual features of fruits and a perceptron that classifies fruits using physical features, and proposes a stacking ensemble model that can effectively combine the classification results of these two neural networks. The experiments with AI Hub public data show that the stacking ensemble model is effective for grading fruits. However, the ensemble model does not always improve the performance of classifying all the fruit grading. So, it is necessary to adapt the model according to the kind of fruit.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.284-293
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• 2023

Objective: To validate a simplified ordinal scoring method, referred to as modified length-based grading, for assessing coronary artery calcium (CAC) severity on non-electrocardiogram (ECG)-gated chest computed tomography (CT). Materials and Methods: This retrospective study enrolled 120 patients (mean age ± standard deviation [SD], 63.1 ± 14.5 years; male, 64) who underwent both non-ECG-gated chest CT and ECG-gated cardiac CT between January 2011 and December 2021. Six radiologists independently assessed CAC severity on chest CT using two scoring methods (visual assessment and modified length-based grading) and categorized the results as none, mild, moderate, or severe. The CAC category on cardiac CT assessed using the Agatston score was used as the reference standard. Agreement among the six observers for CAC category classification was assessed using Fleiss kappa statistics. Agreement between CAC categories on chest CT obtained using either method and the Agatston score categories on cardiac CT was assessed using Cohen's kappa. The time taken to evaluate CAC grading was compared between the observers and two grading methods. Results: For differentiation of the four CAC categories, interobserver agreement was moderate for visual assessment (Fleiss kappa, 0.553 [95% confidence interval {CI}: 0.496-0.610]) and good for modified length-based grading (Fleiss kappa, 0.695 [95% CI: 0.636-0.754]). The modified length-based grading demonstrated better agreement with the reference standard categorization with cardiac CT than visual assessment (Cohen's kappa, 0.565 [95% CI: 0.511-0.619 for visual assessment vs. 0.695 [95% CI: 0.638-0.752] for modified length-based grading). The overall time for evaluating CAC grading was slightly shorter in visual assessment (mean ± SD, 41.8 ± 38.9 s) than in modified length-based grading (43.5 ± 33.2 s) (P < 0.001). Conclusion: The modified length-based grading worked well for evaluating CAC on non-ECG-gated chest CT with better interobserver agreement and agreement with cardiac CT than visual assessment.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1243-1253
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• 1993

Visual features of a mushroom(Lentinus Edodes L) are critical in sorting and grading as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. Though actions involved in human grading looks simple, a decision making undereath the simple action comes form the results of the complex neural processing of the visual image. And processing details involved in the visual recognition of the human brain has not been fully investigated yet. Recently, however, an artificial neural network has drawn a great attention because of its functional capability as a partial substitute of the human brain. Since most agricultural products are not uniquely defined in its physical properties and do not have a well defined job structure, a research of the neuro-net based human like information processing toward the agricultural product and processing are widely open and promising. In this pape , neuro-net based grading and sorting system was developed for a mushroom . A computer vision system was utilized for extracting and quantifying the qualitative visual features of sampled mushrooms. The extracted visual features and their corresponding grades were used as input/output pairs for training the neural network and the trained results of the network were presented . The computer vision system used is composed of the IBM PC compatible 386DX, ITEX PFG frame grabber, B/W CCD camera , VGA color graphic monitor , and image output RGB monitor.

• Journal of the Korean Wood Science and Technology
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• v.38 no.6
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• pp.470-479
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• 2010

In this study, the visual grading based on the visual characteristics and structural timber bending test were conducted for domestic yellow poplar dimension lumber. Structural performance of domestic yellow poplar dimension lumber was conducted through the evaluation of strength and stiffness. Visual grading rule of yellow poplar dimension lumber did not exist in Korea. Visual grading of yellow poplar dimension lumber was performed according to the NSLB (Northern Softwood Lumber Bureau) standard grading rules including several hardwood dimension lumber. The allowable bending stress was calculated from the results of a visual grading. Compared with NDS (National Design Specification), the yellow poplar dimension lumber showed enough strength for structural uses. In addition, the visual grading was performed according to the KFRI (Korea Forest Research Institute) grading rule to calculated allowable bending stress and to evaluated the feasibility. The yellow poplar was classified into the pine groups by the KFRI criteria regulated by specific gravity. Allowable bending stress based on weibull distribution had became highly than KFRI criteria, as No. 1 (10.0 MPa), No. 2 (7.4 MPa) and No. 3 (4.1 MPa). And the availability of yellow poplar dimension lumber for structural uses had been confirmed. The Modulus of Elasticity (MOE) of domestic yellow poplar dimension lumber had not met the NDS and KFRI criteria. However, for the use of domestic yellow poplar, average values of MOE which obtained through this test were suggested as design value for domestic yellow poplar. Design values were supposed No. 1, 2 (9,000 MPa) and No. 3 (8,000 MPa).

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.773-779
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• 1996

Dried oak mushroom have complex and various visual features. Grading and sorting of dried oak mushrooms has been done by the human expert. Though actions involved in human grading looked simple, a decision making underneath the simple action comes from the result of the complex neural processing of the visual image. Through processing details involved in human visual recognition has not been fully investigated yet, it might say human can recognize objects via one of three ways such as extracting specific features or just image itself without extracting those features or in a combined manner. In most cases, extracting some special quantitative features from the camera image requires complex algorithms and processing of the gray level image requires the heavy computing load. This fact can be worse especially in dealing with nonuniform, irregular and fuzzy shaped agricultural products, resulting in poor performance because of the sensitiveness to the crisp criteria or specific ules set up by algorithms. Also restriction of the real time processing often forces to use binary segmentation but in that case some important information of the object can be lost. In this paper, the neuro net based real time recognition algorithm was proposed without extracting any visual feature but using only the directly captured raw gray images. Specially formated adaptable size of grids was proposed for the network input. The compensation of illumination was also done to accomodate the variable lighting environment. The proposed grading scheme showed very successful results.

• Journal of Bio-Environment Control
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• v.3 no.1
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• pp.42-51
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• 1994

Visual features of a mushromm(Lentinus Edodes L.) are critical in sorting and grading as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. Though actions involved in human grading look simple, it decision making underneath the simple action comes from the result of the complex neural processing of visual image. Recently, an artificial neural network has drawn a great attention because of its functional capability as a partial substitute of the human brain. Since most agricultural products are not uniquely defined in its physical properties and do not have a well defined job structure, the neuro -net based computer visual information processing is the promising approach toward the automation in the agricultural field. In this paper, first, the neuro - net based classification of simple geometric primitives were done and the generalization property of the network was tested for degraded primitives. And then the neuro-net based grading system was developed for a mushroom. A computer vision system was utilized for extracting and quantifying the qualitative visual features of sampled mushrooms. The extracted visual features of sampled mushrooms and their corresponding grades were used as input/output pairs for training the neural network. The grading performance of the trained network for the mushrooms graded previously by the expert were also presented.

• Journal of Biosystems Engineering
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• v.26 no.2
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• pp.147-154
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• 2001

In Korea and Japan, dried oak mushrooms are classified into 12 to 16 different categories based on its external visual quality. And grading used to be done manually by the human expert and is limited to the randomly sampled oak mushrooms. Visual features of dried oak mushrooms dominate its quality and are distributed over both sides of the gill and the cap. The 2nd prototype computer vision based automatic grading and sorting system for dried oak mushrooms was developed based on the 1st prototype. Sorting function was improved and overall system for grading was simplified to one stage grading instead of two stage grading by inspecting both front and back sides of mushrooms. Neuro-net based side(gill or cap) recognition algorithm of the fed mushroom was adopted. Grading was performed with both images of gill and cap using neural network. A real time simultaneous discharge algorithm, which is good for objects randomly fed individually and for multi-objects located along a series of discharge buckets, was developed and implemented to the controller and the performance was verified. Two hundreds samples chosen from 10 samples per 20 grade categories were used to verify the performance of each unit such as feeding, reversing, grading, and discharging unites. Test results showed that success rates of one-line feeding, reversing, grading, and discharging functions were 93%, 95%, 94%, and 99% respectively. The developed prototype revealed successful performance such as the approximate sorting capability of 3,600 mushrooms/hr per each line i.e. average 1sec/mushroom. Considering processing time of approximate 0.2 sec for grading, it was desired to reduce time to reverse a mushroom to acquire the reversed surface image.

• Journal of the Korean Wood Science and Technology
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• v.39 no.1
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• pp.1-7
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• 2011

Recently, the visual grading rule of Korea Forest Research Institute (KFRI) was revised and it is necessary to investigate the distribution characteristics of visual graded lumber in accordance with the revised rule. Therefore, in this study, the distribution characteristics of bending strength was investigated with revised visual grading rule and changed prior rule, respectively. The size of specimens was $38{\times}140{\times}3,000$ (mm) and the species were $Larix$ $kaempferi$ and $Pinus$ $koraiensis$. The moisture content was under 18% and the specimens were tested in accordance with ASTM D-198. The number of No. 1 and 2 grades, suitable for structural lumber, was increased when the revised visual grading rule was applied. Moreover, the revised rule was more effective to distinguish sharply between No. 1 and 2 grades and below No. 3 grade. Meanwhile, the lower 5% exclusion limit and allowable stresses were generally decreased when revised visual grading rule had been applied. However, the announcement of Korea Forest Service, tested with small clear specimen, was much lower than the allowable stresses of this test, tested with structural lumber. Therefore, the revision of allowable design values should be considered for more exact use and effective structural design.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3121-3121
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• 2001

The factors involved in development of electronic grading systems for commodities such as grains and seeds include determination of the factors that influence the end-product utilization of the commodities, and the degree to which these can be predicted by electronic methods. The possibility of exchanging existing methods of grading by electronic methods has to be considered. The respective merits of techniques such as Digital Imaging and Near-infrared (NIR) spectroscopy have to be considered. Digital Imaging is a computerized version of visual inspection and grading, whereas NIR spectroscopy has the potential for grading on the basis of composition and functionality, Selection and evaluation of NIR instruments is an important factor, as are sampling and sample presentation to electronic instruments, and particularly the engineering involved in sample presentation. Sample assembly, and software for calibration development are described in the presentation. Finally the impact and implications of introduction of electronic grading are discussed with particular attention to marketing of the commodities.