• Journal of Korean Society for Quality Management
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• v.35 no.1
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• pp.113-123
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• 2007

This paper considers the development of e-learning training program for the six sigma green belt. Comparative studies of existing e-learning programs are performed and a new one is proposed. A catapult simulator is developed and the automatic grading function which immediately computes the result of the catapult simulation and gives feedback to the trainees is presented. An illustrative example is also given.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.267-274
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• 2014

Even though computer based college lecture management system was developed long ago and has been used ever since, developing perfect lecture management is not simple. The main objective of this system is to develop appropriate online lecture supportive management program suitable for college lectures. This system [ADLEMS] mainly focuses on the management of college database lectures, exams, and grades. This system supports management and grading of attendance, reports, quizzes, mid-term, and final exams. Exam management categorizes into multiple choice questions, essay questions, short answer questions, and SQL. Especially for SQL, division analysis was applied when developing grading system for more effective grade management. For essay questions and short answer questions, manual [hand] grading method was used. Every student can verify the grading process in person to alleviate the problems occurring during the grading process.

• 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.

• Journal of Biosystems Engineering
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• v.20 no.1
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• pp.95-103
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• 1995

A series of study has been conducted to develop a multi-purpose fruit grader using a black ＆ white image processing system equipped with a 550 nm interference filter. A device and high performance algorithms were developed for sizing and color grading of Fuji apple in the previous study. In this study an emphasis was put on finding correlations between weights of several kinds of fruits and their area fractions(AF), and on compensating the blurring effect upon sizing and color grading by conveying speed of fruit. Also, the effect of orientation and direction of fruit on conveyor during image forming was analyzed to identify any difficulty (or utilizing an automatic fruit feeder. The results are summarized as follows. 1. The correlation coefficients(r) between the weights of fruits and their image sizes were 0.984~0.996 for apples, 0.983~0.990 for peachs, 0.995 for tomato, 0.986 for sweet persimmon and 0.970~0.993 for pears. 2. It was possible to grade fruits by color with the area weighted mean gray values(AWMGV) based on the mean gray valves of direct image and the compensated values of reflected image of a fruit, and also possible to sort fruits by size with AF. Accuracies in sizing and color grading ranged over 81.0% ~95.0% and 82.0% ~89.7% respectively as compared with results from sizing by electronic weight scale and grading by expert. 3. The blurring effect on the sizing and color grading depending on conveying speed was identified and regression equations were derived. 4. It was found that errors in sizing and coloring grading due to the change in direction and orientation of Fuji apple on the conveyor were not significant as far as the stem end of apple keeping upward.

• 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.

• 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 Dairy Science and Biotechnology
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• v.24 no.1
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• pp.1-7
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• 2006

This experiment was performed to identify the improvement of raw milk quality after introducing raw milk grading system(1993, June). The purpose of this experiment was to investigate chemical component and microbiological quality of raw milk in jeju. This experiment made it possible to spread high standard of quality of raw milk or milk product including yoghurt, ice cream etc., and to provide dairy industry information for the construction of Jeju international free city master plan. As a result, automatic milking system is improved a lot after introducing raw milk grading system and sustained good condition compared with other provinces. High ratio was shown dairy farm in jeju for pre-milking, pre-cooling system equipment and self laboring. Otherwise, the ratio of dairy farm doing test of mastitis is low. The ratio of first grade distribution in Jeju is 80.64%, which means that was improved before introducing raw milk grading system. The number of somatic cells found in summer more than that of other seasons in raw milk. However, these data is a little higher than the nation wide data medium. Also, general components, annual lipid ratio is 3.90% that improved compared with before introducing raw milk grading system. These data showed low in summer and similar to nation wide.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1999.12a
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• pp.615-625
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• 1999

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.204-204
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• 2004

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.106.5-106
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• 2001

A method for automatically selecting the electroencephalogram(EEG) segments was developed by grading each segment based on organization of EEG, vigilance level, open versus closed eyes condition and the artifact contamination.