• Agricultural and Biosystems Engineering
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• v.4 no.1
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• pp.8-15
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• 2003

In Korea, quality evaluation of dried oak mushrooms are done first by classifying them into more than 10 different categories based on the state of opening of the cap, surface pattern, and colors. And mushrooms of each category are further classified into 3 or 4 groups based on its shape and size, resulting into total 30 to 40 different grades. Quality evaluation and sorting based on the external visual features are usually done manually. Since visual features of mushroom affecting quality grades are distributed over the entire surface of the mushroom, both front (cap) and back (stem and gill) surfaces should be inspected thoroughly. In fact, it is almost impossible for human to inspect every mushroom, especially when they are fed continuously via conveyor. In this paper, considering real time on-line system implementation, image processing algorithms utilizing artificial neural network have been developed for the quality grading of a mushroom. The neural network based image processing utilized the raw gray value image of fed mushrooms captured by the camera without any complex image processing such as feature enhancement and extraction to identify the feeding state and to grade the quality of a mushroom. Developed algorithms were implemented to the prototype on-line grading and sorting system. The prototype was developed to simplify the system requirement and the overall mechanism. The system was composed of automatic devices for mushroom feeding and handling, a set of computer vision system with lighting chamber, one chip microprocessor based controller, and pneumatic actuators. The proposed grading scheme was tested using the prototype. Network training for the feeding state recognition and grading was done using static images. 200 samples (20 grade levels and 10 per each grade) were used for training. 300 samples (20 grade levels and 15 per each grade) were used to validate the trained network. By changing orientation of each sample, 600 data sets were made for the test and the trained network showed around 91 % of the grading accuracy. Though image processing itself required approximately less than 0.3 second depending on a mushroom, because of the actuating device and control response, average 0.6 to 0.7 second was required for grading and sorting of a mushroom resulting into the processing capability of 5,000/hr to 6,000/hr.

• Journal of Haehwa Medicine
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• v.15 no.1
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• pp.87-95
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• 2006

Objective : We suggested the clinical effect of Rainbow therapy on Bell's palsy. Methods : 20 Bell's palsy patients were divided into two groups. One group(A group) was treated by acupunture and the other group(B group) was treated by acupunture and Rainbow therapy. The effect of these treatments was evaluated by Yanagihara's unweighted grading system and House-Brackmann grading system. Results and Conclusions : In Yanagihara's unweighted grading system After 1 week and 2weeks treatment, group B marked more higher than group A in treatment outcome. We discovered that it is significant differences between two groups. After 3 weeks treatment, group B marked more higher than group A in treatment outcome but it is not significant differences between two groups. In House-Brackmann's facial nerve grading system, After 1 week treatment, group B marked more higher than group A in treatment outcome. We discovered that it is significant differences between two groups. After 2 weeks and 3 weeks treatment, group A marked more higher than group A in treatment outcome but it is not significant differences between two groups.

• Journal of Acupuncture Research
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• v.33 no.3
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• pp.117-127
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• 2016

Objectives : This study was performed to observe the effect of complex therapy, including electro-acupuncture and magnetic-acupuncture, on peripheral facial nerve palsy. Methods : Nine patients with peripheral facial nerve palsy were treated with acupuncture using electrical and magnetic stimulation. Acupoints in the face were stimulated with an electromagnetic field, as widely and as evenly as possible. To evaluate the effects before and after treatment we used Yanagihara's unweighted grading system, House-Brackmann scale, and Sunnybrook facial grading system and image once a week. Results : After treatment, the scores of Yanagihara's unweighted grading system, House-Brackmann scale and Sunnybrook facial grading system each improved (p-value < 0.05). Conclusion : Complex therapy using electro-acupuncture and magnetic-acupuncture might be an effective treatment to improve symptoms of peripheral facial nerve palsy. Further randomized-controlled trials are required to verify the efficacy and results of this study.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.393-410
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• 2021

The Korean Long-Term Care Insurance (K-LTCI) provides financial support for long-term care service to people who need various types of assistance with daily activities. As the number of elderly people in Korea is expected to increase in the future, the demand for long-term care insurance would also increase over time. Projection of future expenditure on K-LTCI depends on the number of beneficiaries within the grading system of K-LTCI based on the test scores of applicants. This study investigated the suitability of mixture distributions to the model K-LTCI score distribution using recent empirical data on K-LTCI, provided by the National Health Insurance Service (NHIS). Based on the developed mixture models, the number of beneficiaries in each grade and its variability under the current grading system were estimated by simulation. It was observed that a mixture model is suitable for K-LTCI score distribution and may prove useful in devising a funding plan for K-LTCI benefit payment and investigating the effects of any possible revision in the K-LTCI grading system.

• Journal of Biosystems Engineering
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• v.39 no.3
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• pp.174-183
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• 2014

Purpose: A robust, efficient auto-grading computer vision system for meat carcasses is in high demand by researchers all over the world. In this paper, we discuss our study, in which we developed a system to speed up line processing and provide reliable results for pork grading, comparing the results of our algorithms with visual human subjectivity measurements. Methods: We differentiated fat and lean using an entropic correlation algorithm. We also developed a self-designed robust segmentation algorithm that successfully segmented several porkcut samples; this algorithm can help to eliminate the current issues associated with autothresholding. Results: In this study, we carefully considered the key step of autoextracting lean tissue. We introduced a self-proposed scheme and implemented it in over 200 pork-cut samples. The accuracy and computation time were acceptable, showing excellent potential for use in online commercial systems. Conclusions: This paper summarizes the main results reported in recent application studies, which include modifying and smoothing the lean area of pork-cut sections of commercial fresh pork by human experts for an auto-grading process. The developed algorithms were implemented in a prototype mobile processing unit, which can be implemented at the pork processing site.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.363-366
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• 2001

The present process of grading leather quality by the rare eyes is not reliable. Because inconsistency of grading due to eyes strain for long time can cause incorrect result of grading. Therefore it is necessary to automate the process of grading quality of leather based on objective standard for it. In this paper, leather automatic classification system consists of the process obtaining the information of leather and the process grading the quality of leather from the information. Leather is graded by its information such as texture density, types and distribution of defects. This paper proposes the algorithm which sorts out leather information like texture density and defects from the gray-level images obtained by digital camera. The density information is sorted out by the distribution value of Fourier spectrum which comes out after original image is converted to the image in frequency domain. And the defect information is obtained by the statistics of pixels which is relevant to Window using searching Window after sort out boundary lines from preprocessed images. The information for entire leather is used as standard of grading leather quality, and the proposed algorithm is practically applied to machine vision system.

• Journal of Biosystems Engineering
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• v.31 no.6 s.119
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• pp.514-523
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• 2006

Production of green pepper has been increased due to customer's preference and a projected ten-year boom in the industry in Korea. This study was carried out to develop an automatic grading and sorting system for green pepper using machine vision. The system consisted of a feeding mechanism, segregation section, an image inspection chamber, image processing section, system control section, grading section, and discharging section. Green peppers were separated and transported using a bowl feeder with a vibrator and a belt conveyor, respectively. Images were taken using color CCD cameras and a color frame grabber. An on-line grading algorithm was developed using Visual C/C++. The green peppers could be graded into four classes by activating air nozzles located at the discharging section. Length and curvature of each green pepper were measured while removing a stem of it. The first derivative of thickness profile was used to remove a stem area of segmented image of the pepper. While pepper is moving at 0.45 m/s, the accuracy of grading sorting for large, medium and small pepper are 86.0%, 81.3% and 90.6% respectively. Sorting performance was 121 kg/hour, and about five times better than manual sorting. The developed system was also economically feasible to grade and sort green peppers showing the cost about 40% lower than that of manual operations.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.6
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• pp.877-890
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• 2019

This study investigated the location of grading lines and grading amount defined by 17 women's wear brands for a pants item by target age groups (20-30, 30-40, and 40-50). This study utilized 6th Size Korea data and a script-based on a 3D scan measurement program to analyze the increase rate of body size in order to suggest a grading deviation distribution ratio for the pants using regression analysis. This study found that most brands appropriately divided grading amount at front thigh girth and back hip girth into the side and center by 1:1. Most brands divided the grading amount at the front hip girth into the side and center by 1:1; however, the ratio found from Size Korea is 0.8:1.2 for the 20-30 age group, 0.7:1.3 for the 30-40 age group, and 0.6:1.4 for the 40-50 age group. Regarding the back thigh girth, the brands targeting 20-30s, 30-40s, and 40-50s respectively assigned the grading amount into the side and center by 1:1, 1:1.2, and 1:1.3. However, the ratio found from Size Korea is 1.4:0.6 for the 20-30 age group, 1.7:0.3 for the 30-40 age group, and 1.3:0.7 for the 40-50 age group. The results can be utilized in improving the grading system of the pants item.

• The Journal of Korean Association of Computer Education
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• v.20 no.5
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• pp.79-88
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• 2017

To upgrade the subjective question grading, we need the syntactic-semantic analysis to analyze syntatic-semantic relation between words in answering. However, since the syntactic-semantic tree has structural and semantic relation between words, we can not apply the method calculating the similarity between vectors. This paper suggests the comparing system with syntactic-semantic tree which has structural and semantic relation between words. In this thesis, we suggest similarity calculation principles for comparing the trees and verify the principles through experiments. This system will help the subjective question grading by comparing the trees and be utilized in distinguishing similar documents.