• Agricultural and Biosystems Engineering
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• 제4권1호
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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.

• 혜화의학회지
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• 제15권1호
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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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• 제33권3호
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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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• 제28권4호
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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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• 제39권3호
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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.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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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.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 추계종합학술대회
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• pp.363-366
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• 2001

현재 육안에 의한 피혁의 등급 판정 과정은 장시간 시 피로에 의한 일관성 결여로 인해 판정 결과에 대한 신뢰성을 주지 못한다. 따라서 피혁의 품질을 결정하기 위한 객관적인 지표와 이를 기준으로 등급 판정 과정의 자동화가 필요하다. 본 논문에서 적용된 피혁 자동 선별 시스템은 피혁에 대한 정보를 취득하는 과정과 이들 정보로부터 등급을 판정하는 과정으로 구성된다. 피혁의 품질은 조밀도와 결함의 종류 및 분포도와 같은 피혁 정보에 의해 결정된다. 본 논문에서는 디지털 카메라에 의해 획득된 흑백 영상으로부터 피혁의 조밀도 및 결함에 대한 정보를 추출하는 알고리즘을 제안한다. 조밀도에 대한 정보는 원 영상을 주파수 영역으로 변환한 후 나타나는 퓨리에 스펙트럼 분포의 특징 값들에 의해서 추출된다. 그리고 결함에 대한 정보는 전처리 과정을 거친 영상으로부터 경계선 검출 후 검색 윈도우를 사용하여 윈도우에 해당하는 픽셀들의 통계적 수치에 의해서 검출된다. 피혁 전체에 대한 정보들은 피혁의 등급을 판정하는 지표로 사용되며 실제 머신 비젼 시스템에 적용된다.

• Journal of Biosystems Engineering
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• 제31권6호
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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.

• 한국의류학회지
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• 제43권6호
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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.

• 컴퓨터교육학회논문지
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• 제20권5호
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• pp.79-88
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• 2017

질높은 주관식 문제 채점을 위해서는 답변 속에 들어있는 단어간의 구문의미적 관계를 분석하는 구문 의미 분석이 필요하다. 그러나 구문의미 분석의 결과인 구문의미트리는 단어간의 구조적 의미 관계를 내포하고 있어 단어의 나열인 일차원적인 벡터의 유사도 계산을 적용할 수가 없다. 본 연구는 단어의 지식과 함께 단어와 단어간의 구조적 의미 관계를 내포하는 구문의미트리를 비교하는 비교 시스템에 대한 연구를 한다. 본 연구에서는 구문의미트리 비교를 위해 유사성 계산 원칙을 제안하고 실험을 통해 검증하였다. 본 구문의미트리 비교 시스템은 구문의미분석의 결과를 비교할 수 있게 하여 주관식 문제 채점에 도움을 줄 것이고 문서 유사도 영역에도 활용할 수 있을 것이다.