• Proceedings of the Korea Information Processing Society Conference
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• 2001.10b
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• pp.1217-1220
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• 2001

우편물의 자동구분은 우편물을 OVIS(OCR-Video coding Integrated System)에 자동으로 공급하고 우편물의 수취인 주소영역을 카메라를 이용하여 획득한 다음 우편번호를 인식하여 바코드로 변환하여 인쇄하게 되고, 이 우편물은 LSM(Letter Sorting Machine)으로 보내져 BCR(Bar Code Reader)에 의해 인쇄된 바코드를 판독하여 행선지별로 구분하는 과정을 거친다. 주소의 번지 이하 부분은 배달원의 수작업에 의해 최종 배달지점 순서대로 정렬한 다음 배달하게 된다. 이 부분의 작업에 소요되는 시간은 배달원 일일 평균 4 시간에 달하며 원가절감 대상으로 지적되고 있다. 이 부분을 자동화하여 우편물 처리시간을 단축하고 생산성을 향상하는 방안이 대두되고 있으며, 이를 해결하기 위해 번지 부분까지 OCR을 이용, 인식하여 우편번호 및 순로 데이터 베이스에서 인식결과에 해당하는 코드를 추출하여 해당 구분 칸으로 우편물을 분류하는 방식을 택하면 집배원이 우편물을 배달하는 순로까지 자동으로 정렬할 수 있게 된다. 본 논문은 수취인 주소영역의 주소부분을 자동판독하기 위한 시스템을 개발한 내용을 다루고자 한다.

• 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.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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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 the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1601-1607
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• 2010

This paper presents FMEA (Failure Mode Effect Analysis) for maintenance of mail sorting machine which is for automatic sorting of mail. We suggest the update method of regular diagnosis item and period for maintenance of mail sorting machine using the risk priority number which is calculated by severity, occurrence, and detection of failure mode of mail sorting machine, and shows FMEA adoption example of letter sorting machine. This paper also describes the current maintenance system and status of mail sorting machine in the domestic postal logistics environment, and FMEA adoption step. The proposed maintenance using FMEA will be adapted for more easy and efficiency maintenance of mail sorting machine.

• Journal of Biosystems Engineering
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• v.32 no.2 s.121
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• pp.130-135
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• 2007

An on-line real time grading and sorting system for dried oak mushrooms was developed for on-site application. Quality grades of the mushrooms were determined according to an industrial specification. Three dimensional visual quality features were used for the grading. A progressive color computer vision system with white LED illumination was implemented to develop an algorithm to extract external quality patterns of the dried oak mushrooms. Cap (top) and gil (stem) surface images were acquired sequentially and side image was obtained using mirror. Algorithms for extracting size, roundness, pattern and color of the cap, thickness, color of the gil and amount of rolled edge of the dried mushroom were developed. Utilizing those quality factors normal and abnormal ones were classified and normal mushrooms were further classified into 30 different grades. The sorting device was developed using microprocessor controlled electro-pneumatic system with stainless buckets. Grading accuracy was around 97% and processing time was 0.4 s in average.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.2
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• pp.146-155
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• 2008

To improve the performance of tiled display system, two parallel tiled display algorithms are proposed by combining the sort-first and the sort-last sorting methods. In the proposed algorithms, the view frustum culling is employed along with the OpenGL display list for the sort-first sorting, and the pre-detection sort-last sparse sorting method is used for sort-last sorting. Through the benchmarking tests, the performances of two proposed algorithms are investigated. Based on the observations, it is suggested how to select an optimal algorithm among the two proposed parallel tiled display algorithms for the given visualization model.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.373-382
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• 2023

The task of sorting and excluding blemished apples and others that lack commercial appeal is currently performed manually by human eye sorting, which not only causes musculoskeletal disorders in workers but also requires a significant amount of time and labor. In this study, an automated apple-sorting machine was developed to prevent musculoskeletal disorders in apple production workers and to streamline the process of sorting blemished and non-marketable apples from the better quality fruit. The apple-sorting machine is composed of an arm-rest, a main body, and a height-adjustable part, and uses object detection through a machine learning technology called 'You Only Look Once (YOLO)' to sort the apples. The machine was initially trained using apple image data, RoboFlow, and Google Colab, and the resulting images were analyzed using Jetson Nano. An algorithm was developed to link the Jetson Nano outputs and the conveyor belt to classify the analyzed apple images. This apple-sorting machine can immediately sort and exclude apples with surface defects, thereby reducing the time needed to sort the fruit and, accordingly, achieving cuts in labor costs. Furthermore, the apple-sorting machine can produce uniform quality sorting with a high level of accuracy compared with the subjective judgment of manual sorting by eye. This is expected to improve the productivity of apple growing operations and increase profitability.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.705-713
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• 2006

For the analysis of multiunit extracellular neural signals as multiple spike trains, neural spike sorting is essential. Existing algorithms for the spike sorting have been unsatisfactory when the signal-to-noise ratio(SNR) is low, especially for implementation of fully-automated systems. We present a novel method that shows satisfactory performance even under low SNR, and compare its performance with a recent method based on principal component analysis(PCA) and fuzzy c-means(FCM) clustering algorithm. Our system consists of a spike detector that shows high performance under low SNR, a feature extractor that utilizes projection pursuit based on negentropy maximization, and an unsupervised classifier based on Gaussian mixture model. It is shown that the proposed feature extractor gives better performance compared to the PCA, and the proposed combination of spike detector, feature extraction, and unsupervised classification yields much better performance than the PCA-FCM, in that the realization of fully-automated unsupervised spike sorting becomes more feasible.

• Resources Recycling
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• v.26 no.6
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• pp.73-83
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• 2017

Used small household appliances have a wide variety of product types and component materials, and contain high percentage of black plastics. However, they are not being recycled efficiently as conventional sensors such as near-infrared ray (NIR), etc. are not able to detect black plastic by types. In the present study, an automatic sorting system was developed based on laser-induced breakdown spectroscopy (LIBS) to promote the recycling of waste plastics. The system we developed mainly consists of sample feeder, automatic position recognition system, LIBS device, separator and control unit. By applying laser pulse on the target sample, characteristic spectral data can be obtained and analyzed by using CCD detectors. The obtained data was then treated by using a classifier, which was developed based on artificial intelligent algorithm. The separation tests on waste plastics also were carried out by using a lab-scale automatic sorting system and the test results will be discussed. The classification rate of the radial basis neural network (RBFNNs) classifier developed in this study was about > 97%. The recognition rate of the black plastic by types with the automatic sorting system was more than 94.0% and the sorting efficiency was more than 80.0%. Automatic sorting system based on LIBS technology is in its infant stage and it has a high potential for utilization in and outside Korea due to its excellent economic efficiency.

• 전자공학회논문지 IE
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• v.48 no.1
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• pp.16-24
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• 2011

In this paper, we developed a auto sorting system for T type welding nut using a vision inspector. We used edge and thread detection with histogram of image which is captured by machine vision camera. We also used a binary morphology operation for a detection of spot. As a result we performed numeric inspection of 0.1mm accuracy. This is impossible in old sorting system and inspector with naked eye. Also, we reduced the manufacturing unit cost to 25% and improved a production efficiency to 330%.