• 복식
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• 제18권
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• pp.307-319
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• 1992

The Purpose of this study Gerber company AM-300 system of the automatic system of Producing the original form the automatic system of Producing the original form of "Jogori(a Korean Jacket)' and Grading by the usage of computers and find out its efficiency. In the result, the auther has found out the following facts and became confident on the facts; The AM-300 program of the automatic system enabled to produce the original form of 'Jogori' and Grading fitting in a short time and definately, and which indicated that the automatically producing system of the original form of 'Jorgori' and Granding is efficient. Even in the aspect of education, it has been acknowledged that there is necessity of using computers, the accumulation of techincs and technology based on traditions by cultivating professional designers, and computerization so that the composition of 'Hanbok' (Korean clothes) should be rational and scientific. In addition, advertisement and education on the traditionalism and superiority of 'Hanblk' are indispensable and absolutely necessary. Also, to succeed folk costumes rightly, the usage of computers is thought to be a way to effectiveness. So far in the study, only the automatic system of producing the original form of 'Jogori' and Grading through computers is emphasized on, however in the future, such an automatic system should be continuously supplemented, studied on and developed even in other various fields such as in pattern making, design, products planning, etc..

• 한국ITS학회 논문지
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• 제16권5호
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• pp.109-120
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• 2017

자동차 운전면허 시험에서 객관적인 평가가 중요하다. 특히 도로 주행시험은 실제 도로에서 운전능력, 규칙준수, 상황판단능력 등을 종합적으로 시험하는 것이다. 이를 위하여 본 논문에서는 GPS와 센서 데이터 및 기기조작 정보를 활용하여 운전면허 시험의 자동채점시스템을 제안한다. 자동채점 시스템은 차량탑재장치, 채점용단말, 데이터 제어장치, 데이터 저장 및 처리를 위한 서버로 구성되어 있다. 차량탑재장치는 차량에 설치된 센서 데이터를 수집한다. 채점 단말은 차량탑재장치로부터 받은 데이터를 활용하여 기준에 따라 자동채점 한다. 또한 이동 중에 GPS 오차로 인하여 차량이 도로를 벗어나 표현되므로 지도매칭 기법과 경로이탈 및 복귀 알고리즘을 적용하였다. 이 시스템은 기존 시험 채점 방식과 달리 자동 채점이 가능하며, 시험 결과에서 정확한 차량위치 표시와 GPS 음영지역을 벗어났을 때 10초 이내로 경로 복귀를 하였다. 이 시스템은 도로 주행 교육에도 활용될 수 있을 것으로 기대된다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.607-614
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• 1996

A computer vision based automatic intelligent sorting system for dried oak mushrooms has been developed. The developed system was composed of automatic devices for mushroom feeding and handling, two sets of computer vision system for grading , and computer with digital I/O board for PLC interface, and pneumatic actuators for the system control. Considering the efficiency of grading process and the real time on-line system implementation, grading was done sequentially at two consecutive independent stages using the captured image of either side. At the first stage, four grades of high quality categories were determined from the cap surface images and at the second stage 8 grades of medium and low quality categories were determined from the gill side images. The previously developed neuro-net based mushroom grading algorithm which allowed real time on-line processing was implemented and tested. Developed system revealed successful performance of sorting capability of approximate y 5, 000 mushrooms/hr per each line i.e. average 0.75 sec/mushroom with the grading accuracy of more than 88%.

• Journal of Biosystems Engineering
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• 제26권2호
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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.

• 한국멀티미디어학회논문지
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• 제25권10호
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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.

• Journal of Biosystems Engineering
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• 제23권6호
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• pp.607-614
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• 1998

An automatic grading algorithm was developed to replace the manual trading of white ginseng. The algorithm consists of three consecutive stages, (a) image acquisition and preprocessing, (b) mathematical feature extraction, and (c) grade decision using artificial neural network. Mathematical features such as area ratio, mean and standard deviation of graylevel, skewness of graylevel histogram, and the number of run segment are extracted from five equally divided parts of ginseng. An artificial neural network model was used to classify white ginsengs into three categories. The performance of the algorithm was evaluated using 120 ginseng samples and the rate of successful classification was 74%.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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• pp.1230-1242
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• 1993

In a case of mushroom (Lentinus Edodes L.) , visual features are crucial for grading and the quantitative evaluation of the growth state. The extracted quantitative visual features can be used as a performance index for the drying process control or used for the automatic sorting and grading task. First, primary external features of the front and back sides of mushroom were analyzed. And computer vision based algorithm were developed for the extraction and measurement of those features. An automatic thresholding algorithm , which is the combined type of the window extension and maximum depth finding was developed. Freeman's chain coding was modified by gradually expanding the mask size from 3X3 to 9X9 to preserve the boundary connectivity. According to the side of mushroom determined from the automatic recognition algorithm size thickness, overall shape, and skin texture such as pattern, color (lightness) ,membrane state, and crack were quantified and measured. A portion of t e stalk was also identified and automatically removed , while reconstructing a new boundary using the Overhauser curve formulation . Algorithms applied and developed were coded using MS_C language Ver, 6.0, PC VISION Plus library functions, and VGA graphic function as a menu driven way.

• 수산해양기술연구
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• 제56권1호
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• pp.55-60
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• 2020

In this study, the authors introduce a newly developed flatfish grading system. Owing to the features of flatfish with and wide body, the general types of grading system are not easy to apply for it. Furthermore, the flatfish to be graded is alive such that the existing measurement and grading systems cannot be used for it as well. This study gives a solution for measuring and grading the flatfish with high speed and good accuracy. For this object, the authors developed flatfish measurement and grading system. This system consist of the feeding, conveying, measurement part and sorting part. Especially, the measurement part is made by vision based measuring technique which satisfies the given specification. The result from the experiment shows that the developed system is applicable for measuring and grading the flatfish sizes in variety.

• 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.