• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.237-245
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• 2001

Automated essay grading has been proposed for over thirty years. Only recently have practical implementations been constructed and tested. This paper investigated the role of the nearest-neighbour algorithm within the information retrieval as a way of grading the essay automatically called Automated Essay Grading System. It intended to offer teachers an individualized assistance in grading the student\`s essay. The system involved several processes, which are the indexing, the structuring of the model answer and the grade processing. The indexing process comprised the document indexing and query processing which are mainly used for representing the documents and the query. Structuring the model answer is actually preparing the marking scheme and the grade processing is the process of assessing the essay. To test the effectiveness of the developed algorithms, the algorithms are tested against the History text in Malay. The result showed that th information retrieval and the nearest-neighbour algorithm are practical combination that offer acceptable performance for grading the essay.

• Journal of Biosystems Engineering
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• v.22 no.1
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• pp.30-40
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• 1997

A computer vision system was built to automatically grade the leaf tobacco. A color image processing algorithm was developed to extract shape, color and texture features. An improved back propagation algorithm in an artificial neural network was applied to grade the Burley type dried leaf tobacco. The success rate of grading in three-grade classification(1, 3, 5) was higher than the rate of grading in six-grade classification(1, 2, 3, 4, 5, off), on the average success rate of both the twenty-five local pixel-set and the sixteen local pixel-set. And, the average grading success rate using both shape and color features was higher than the rate using shape, color and texture features. Thus, the texture feature obtained by the spatial gray level dependence method was found not to be important in grading leaf tobacco. Grading according to the shape, color and texture features obtained by machine vision system seemed to be inadequate for replacing manual grading of Burely type dried leaf tobacco.

• 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 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.23 no.6
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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%.

• 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.

• 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.

• Computers and Concrete
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• v.10 no.1
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• pp.19-33
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• 2012

The performance of a concrete is significantly influenced by its mixture proportion and the coating thickness on aggregate surface. The concrete in this study is designed by estimating the blending ratio of aggregate using a densified mixture design algorithm (DMDA) based on an ideal grading curve and estimating the paste volume as the sum of the amount of paste needed to provide an assigned coating paste thickness. So as to obtain appropriate concrete amount, and thus can accurately estimate the property of concrete. Deduction of this mix design formula is simple and easy understanding, and meanwhile to obtain result is fast. This estimation model of mix design is expected to reward to industry and effectively upgrade concrete quality.

• Journal of Biosystems Engineering
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• v.20 no.4
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• pp.376-382
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• 1995

This study was intended to develop more reliable fruit sorting algorithm regardless of the feeding positions of fruits by using the neural network in which various information could be included as input data. Specific objectives of this study were to select proper input units in the neural network by investigating the features of input image, to analyze the sorting accuracy of the algorithm depending on the feeding positions of Fuji apple and to evaluate the performance of the algorithm for practical usage. the average value in color grading accuracy was 90%. Based on the computing time required for color grading, the maximum sorting capacity was estimated to approximately 10, 800 apples per hours. Finally, it is concluded that the neuro-net based color sorting algorithm developed in this study has feasibility for practical usage.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.11
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• pp.1153-1160
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• 2013

SMISMO valve configuration is now starting to draw attention of the researchers of the construction equipment industry to increase the fuel efficiency of their equipment like excavators and wheel-loaders. An open-loop control strategy for grading operation of hydraulic excavator systems using SMISMO valve configuration is investigated in this paper. Tabor's algorithm for 1 d.o.f. SMISMO system under the assumption of quasi-static operation is revealed as not adequate for multi d.o.f. system with large moment of inertia even though the motion of the system is slow. New parameters are proposed in this paper. It modifies Tabor's open-loop control strategy for the grading operation of hydraulic excavators using SMISMO valve configuration. A simulation-based parameter tuning method is also proposed. It uses GA (Genetic Algorithm) to find the best parameter values. Simulation study for a practical hydraulic excavator shows the validity of the proposed open-loop control strategy.