• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.834-841
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• 1996

Egg production in Korea is becoming automated with a large scale farm. Although many operations in egg production have been and cracks are regraded as a critical problem. A computer vision system was built to generate images of a single , stationary egg. This system includes a CCD camera, a frame grabber board, a personal computer (IBM PC AT 486) and an incandescent back lighting system. Image processing algorithms were developed to inspect egg shell and to sort eggs. Those values of both gray level and area of dark spots in the egg image were used as criteria to detect holes in egg and those values of both area and roundness of dark spots in the egg and those values of both area and roundness of dark spots in the egg image were used to detect cracks in egg. Fro a sample of 300 eggs. this system was able to correctly analyze an egg for the presence of a defect 97.5% of the time. The weights of eggs were found to be linear to both the projected area and the perimeter of eggs v ewed from above. Those two values were used as criteria to sort eggs. Accuracy in grading was found to be 96.7% as compared with results from weight by electronic scale.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.636-639
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• 2002

This article is focused on the research for the system design that isolate noxious data from internet for juveniles Normally, by motivating this software which was designed to isolate noxious data, harmful data was deleted or graded But these normal process contains a lot of complexity, for example, essential continual upgrade, grading mistake, etc. So, to solve these fallacy, word-weighting process, where several harmful words which can be optained in internet site are discriminance and weighted, is utilized by using AC machine. At the result, the isolation rate of harmful site rose up to 90%, which means this process is greatly efficient.

• Journal of Drive and Control
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• v.18 no.2
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• pp.20-31
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• 2021

An excavator is a construction machine that can perform various tasks such as trenching, piping, excavating, slope cutting, grading, and rock demolishing. In the 2010s, unmanned construction equipment using ICT technology was continuously developed. In this paper, the path design process was studied to implement the output data of the decision stage, and the path design algorithm was developed. For example, the output data of the decision stage were terrain data around the excavator, excavator mechanism information, excavator hydraulic information, the position and posture of the bucket at key points, the speed of the desired bucket path, and the required excavation volume. The result of the path design was the movement of the hydraulic cylinder, boom arm, bucket, and bucket edge. The core functions of the path design algorithm are the function of avoiding impact during the excavation process, the function to calculate the excavation depth that satisfies the required excavation volume, and the function that allows the bucket to pass through the main points of the excavation process while maintaining the speed of the desired path. In particular, in the process of developing the last function, the node tracking method expressed in the path design table was newly developed. The path design algorithm was verified as this path design satisfied the JCMAS H02 requirement.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1486-1495
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• 2020

The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.

• 한국노년학
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• v.40 no.2
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• pp.325-340
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• 2020

Early diagnosis and intervention of dementia is critical to minimize future risk and cost for patients and their families. The purpose of this study was to develop and validate Figure-Copy Test(FCT), as a new dementia screening test, that can measure neurological damage and cognitive impairment, and then to examine whether the grading precesses for screening can be automated through machine learning procedure by using FCT imag es. For this end, FCT, Korean version of MMSE for Dementia Screening (MMSE-DS) and Clock Drawing Test were administrated to a total of 270 participants from normal and damaged elderly groups. Results demonstrated that FCT scores showed high internal constancy and significant correlation coefficients with the other two test scores. Discriminant analyses showed that the accuracy of classification for the normal and damag ed g roups using FCT were 90.8% and 77.1%, respectively, and these were relatively higher than the other two tests. Importantly, we identified that the participants whose MMSE-DS scores were higher than the cutoff but showed lower scores in FCT were successfully screened out through clinical diagnosis. Finally, machine learning using the FCT image data showed an accuracy of 73.70%. In conclusion, our results suggest that FCT, a newly developed drawing test, can be easily implemented for efficient dementia screening.

• The Journal of Korean Academy of Prosthodontics
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• v.38 no.3
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• pp.336-347
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• 2000

Statement of problem. Luster loss in esthetic anterior ceromer restoration can occur and can be related with rough surface texture. Understanding durability of surface finishing methods like polishing and surface coating have critical importance. Purpose. This study evaluated the effect of tooth brushing and thermal cycling on surface luster of 3 ceromer systems (Artglass, Targis, Sculpture) treated with different surface finishing methods. Material and methods. Seventy-two samples were prepared: 12 for control group Z100, 12 for Artglass, 24 for Targis, and 24 for Sculpture. Half of the Targis and Sculpture were polished according to the manufacturer's recommendation. The rest of the samples were coated with staining and glazing solution for Targis and Sculpture, respectively. All specimens were subjected to 10,000 cycles between $5^{\circ}C\;and\;55^{\circ}C$ with 30 seconds dwell time. Tooth brushing abrasion tests were performed in a customized tooth brushing machine with 500g back and forth for 20,000 cycle. Luster comparisons were based on grading after direct observation, and light reflection area was measured with Image analysis software. Results. All materials showed an decrease in luster grade after thermal cycling and tooth brushing. The post-tooth brushing results revealed that the glazed Sculpture had greater mean luster grade than did any other groups. While, the stained Targis group showed greatest changes after tooth brushing (p < 0.05), polished Targis and Sculpture did not show significant changes. However, glazed Sculpture showed discretely fallen out glaze resin. Conclusion. From the results of this study, all of the ceromer specimens were much glossy than control composite group after tooth brushing. coatings used for Targis and Sculpture had not durability for long term use.

• Journal of Biosystems Engineering
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• v.37 no.3
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• pp.166-176
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• 2012

Purpose: This study was conducted to investigate the potential of interactance mode of NIR spectroscopy technology for the estimation of soluble solids content (SSC) and firmness of muskmelons. Methods: Melon samples were taken from local greenhouses in three different harvesting seasons (experiments 1, 2, and 3). The fruit attributes were measured at the 6 points on an equator of each sample where the spectral data were collected. The prediction models were developed using the original spectral data and the spectral data sets preprocessed by 20 methods. The performance of the models was compared. Results: In the prediction of SSC, the highest coefficient of determination ($R_{cv}{^2}$) values of the cross-validation was 0.755 (standard error of prediction, SEP=$0.89^{\circ}Brix$) with the preprocessing of normalization with range in experiment 1. The highest coefficient of determination in the robustness tests, $R_{rt}{^2}$=0.650 (SEP=$1.03^{\circ}Brix$), was found when the best model of experiment 3 was evaluated with the data set of experiment 2. The best $R_{cv}{^2}$ for the prediction of firmness was 0.715 (SEP=3.63 N) when no preprocessing was applied in experiment 1. The highest $R_{rt}{^2}$ was 0.404 (SEP=5.30 N) when the best model of experiment 3 was applied to the data set of experiment 1. Conclusions: From the test results, it can be concluded that the interactance mode of VIS/NIR spectroscopy technology has a great potential to measure SSC and firmness of thick-skinned muskmelons.

• Journal of the Korean Wood Science and Technology
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• v.25 no.1
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• pp.8-14
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• 1997

Stress wave velocity, wave impedance, and stress wave elasticity of small, clear bending specimens of five domestic softwoods (Pinus densiflora, Pinus koraiensis, Chamaecyparis obtusa, Cryptomeria japonica, and Larix leptolepis) and four tropical hardwoods(Kempas, Malas, Taun, and Terminalia) were correlated with static bending modulus of elasticity(MOE) and modulus of rupture(MOR). The degree of correlation between stress wave parameters and static bending properties was dependent on wood species tested. Stress wave elasticity and wave impedance were better predictors for static bending properties than stress wave velocity for each species individually and for softwood or hardwood species taken as a group, even though elasticity and impedance were nearly equally correlated with static bending properties apparently. Based upon the correlation coefficient between stress wave parameters and static properties, stress wave elasticity and wave impedance were found as stress wave parameters which can be used for the purpose of the reliable and successful prediction of bending properties. The degree of correlation between static MOE and MOR was also different according to wood species tested. Static MOE was nearly as well correlated with MOR as was stress wave elasticity. The results of this research are encouraging and can be considered as a basis for further work using full-size lumber. From the results of this study, it was concluded that stress wave measurements could provide useful predictions of static bending properties and was a feasible method for machine stress grading of domestic softwoods and tropical hardwoods tested in this study.

• Food Science of Animal Resources
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• v.41 no.4
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• pp.563-588
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• 2021

Increasing meat demand in terms of both quality and quantity in conjunction with feeding a growing population has resulted in regulatory agencies imposing stringent guidelines on meat quality and safety. Objective and accurate rapid non-destructive detection methods and evaluation techniques based on artificial intelligence have become the research hotspot in recent years and have been widely applied in the meat industry. Therefore, this review surveyed the key technologies of non-destructive detection for meat quality, mainly including ultrasonic technology, machine (computer) vision technology, near-infrared spectroscopy technology, hyperspectral technology, Raman spectra technology, and electronic nose/tongue. The technical characteristics and evaluation methods were compared and analyzed; the practical applications of non-destructive detection technologies in meat quality assessment were explored; and the current challenges and future research directions were discussed. The literature presented in this review clearly demonstrate that previous research on non-destructive technologies are of great significance to ensure consumers' urgent demand for high-quality meat by promoting automatic, real-time inspection and quality control in meat production. In the near future, with ever-growing application requirements and research developments, it is a trend to integrate such systems to provide effective solutions for various grain quality evaluation applications.

• Food Science of Animal Resources
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• v.38 no.5
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• pp.1109-1119
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• 2018

In this paper, we report the development of a nondestructive prediction model for lean meat percentage (LMP) in Korean pig carcasses and in the major cuts using a machine vision technique. A popular vision system in the meat industry, the VCS2000 was installed in a modern Korean slaughterhouse, and the images of half carcasses were captured using three cameras from 175 selected pork carcasses (86 castrated males and 89 females). The imaged carcasses were divided into calibration (n=135) and validation (n=39) sets and a multilinear regression (MLR) analysis was utilized to develop the prediction equation from the calibration set. The efficiency of the prediction equation was then evaluated by an independent validation set. We found that the prediction equation - developed to estimate LMP in whole carcasses based on six variables - was characterized by a coefficient of determination ($R^2_v$) value of 0.77 (root-mean square error [RMSEV] of 2.12%). In addition, the predicted LMP values for the major cuts: ham, belly, and shoulder exhibited $R^2_v$ values${\geq}0.8$ (0.73 for loin parts) with low RMSEV values. However, lower accuracy ($R^2_v=0.67$) was achieved for tenderloin cuts. These results indicate that the LMP in Korean pig carcasses and major cuts can be predicted successfully using the VCS2000-based prediction equation developed here. The ultimate advantages of this technique are compatibility and speed, as the VCS2000 imaging system can be installed in any slaughterhouse with minor modifications to facilitate the on-line and real-time prediction of LMP in pig carcasses.