• 펄프종이기술
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• 제35권5호
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• pp.46-52
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• 2003

In this work we developed the closed-loop model of a paper machine during grade change with the intention to provide a reliable dynamic model to be used in the model-based grade change control scheme. During the grade change, chemical and physical characteristics of paper process change with time. It is very difficult to represent these characteristics on-line by using physical process models. In this work, the wet circulation part and the drying section were considered as a single process and closed-loop identification technique was used to develop the grade change model. Comparison of the results of numerical simulations with mill operation data demonstrates the effectiveness of the model identified.

• 선박안전
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• 통권23호
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• pp.65-77
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• 2007

This study is about the survey technique and the development of measuring method about a wooden pattern of the small vessel made by CNC Machine. we will propose a survey method about processing grade, objectivity of survey, an external shape and a surface defect survey method, Furthermore, CMM(Coordinate Measuring Machine) and OMM(On Machine Measurement).

• 한국펄프종이공학회:학술대회논문집
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• 한국펄프종이공학회 2003년도 추계학술발표논문집
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• pp.291-305
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• 2003

In this work we developed the closed-loop model of a paper machine during grade change with the intention to provide a reliable dynamic model to be used in the model-based grade change control scheme. During the grade change, chemical and physical characteristics of paper process change with time. It is very difficult to represent these characteristics on-line by using physical process models. In this work, the wet circulation part and the drying section were considered as a single process and closed-loop identification technique was used to develop the grade change model. Comparison of the results of numerical simulations with plant operation data demonstrates the effectiveness of the model identified.

• 임산에너지
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• 제25권2호
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• pp.49-54
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• 2006

국산 침엽수재의 구조재 활용에 필요한 등급구분을 위해 소나무, 잣나무와 낙엽송 $2{\times}6"$ 부재에 대해 초음파를 이용한 비파괴 방법을 적용하였다. 정적 탄성계수와 초음파 시험에 의한 동적 탄성계수의 상관관계를 나타내는 k-factor는 소나무의 경우 1.0602, 잣나무 1.0013, 낙엽송 1.2320로 나타났다. k-factor를 적용할 경우 비파괴방법에 의한 동적 탄성계수 측정에 의해 침엽수 구조부재 등급구분이 가능할 것으로 판단되었다. 기계응력 등급구분에서 소나무는 E9 이상의 등급이 전체의 76%인 반면 잣나무는 E7 이상의 등급이 전체의 85%, 낙엽송은 E11 이상의 등급이 68% 분포하였다. 정적 탄성계수와 휨파괴계수(MOR)의 상관관계도 비교적 높게 나타나 이를 동적 탄성계수로부터 추정한 k-factor와 연계할 경우 초음파 비파괴 등급구분 방법에 의해 국산 침엽수재의 휨강도 성능평가가 가능할 것으로 판단되었다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.III
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• pp.569-577
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• 2000

The position of tobacco leaves is affluence to the quality. To evaluate its quality, sample leaves was collected according to the position of attachment. In Korea, the position was divided into four classes such as high, middle, low and inside positioned leaves. Until now, the grade of standard sample was determined by human expert from korea ginseng and tobacco company. Many research were done by the chemical and spectrum analysis using NIR and computer vision. The grade of tobacco leaves mainly classified into 5 grades according to the attached position and its chemical composition. In high and low positioned leaves shows a low level grade under grade 3. Generally, inside and medium positioned leaf has a high level grade. This is the basic research to develop a real time tobacco leaves grading system combined with portable NIR spectrum analysis system. However, this research just deals with position recognition and grading using the color machine vision. The RGB color information was converted to HSI image format and the sample was all investigated using the bundle of tobacco leaves. Quality grade and position recognition was performed through well known general error back propagation neural network. Finally, the relationship about attached leaf position and its grade was analyzed.

• 한국멀티미디어학회논문지
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• 제22권8호
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• pp.832-843
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• 2019

Prostate cancer is a high-risk with a high incidence and is a disease that occurs only in men. Accurate diagnosis of cancer is necessary as the incidence of cancer patients is increasing. Prostate cancer is also a disease that is difficult to predict progress, so it is necessary to predict in advance through prognosis. Therefore, in this paper, grade classification is attempted based on texture feature extraction. There are two main methods of classification: Uses One-way Analysis of Variance (ANOVA) to determine whether texture features are significant values, compares them with all texture features and then uses only one classification i.e. Benign versus. The second method consisted of more detailed classifications without using ANOVA for better analysis between different grades. Results of both these methods are compared and analyzed through the machine learning models such as Support Vector Machine and K-Nearest Neighbor. The accuracy of Benign versus Grade 4&5 using the second method with the best results was 90.0 percentage.

• Journal of information and communication convergence engineering
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• 제21권4호
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• pp.281-286
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• 2023

Diagnosing reading disorders involves complex procedures to evaluate complex cognitive processes. For an accurate diagnosis, a series of tests and evaluations by human experts are required. In this study, we propose a quantitative tool to diagnose reading disorders based on natural reading behaviors using minimal human input. The eye movements of the third- and fourth-grade students were recorded while they read a text at their own pace. Seven machine learning models were used to evaluate the gaze patterns of the words in the presented text and classify the students as normal or having a reading disorder. The accuracy of the machine learning-based diagnosis was measured using the diagnosis by human experts as the ground truth. The highest accuracy of 0.8 was achieved by the support vector machine and random forest classifiers. This result demonstrated that machine learning-based automated diagnosis could substitute for the traditional diagnosis of reading disorders and enable large-scale screening for students at an early age.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.60-66
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• 2004

Hardness value decreased about 3% for annealed specimens and increased about 60% for one hour aged specimens. But the values of the other specimens aged two hours or more showed almost the same. The yield strength was the highest about 1,800㎫ in 0.06%Nb specimen having twice as much as the base meta1 specimen. Also, the elongation was the highest in 0.03%Nb specimen showing the same as base metal specimen. The higher aging temperature and the longer aging time, the higher fatigue life. On the other hand, the 0.03%Nb specimen showed the highest fatigue life which increased about 12% more than base metal specime. 0.06%Nb specimen aged at 482$^{\circ}C$ for 8 hours simultaneously satisfied the 250 grade strength and 200 grade elongation having the most superior mechanical properties.

• Journal of Biosystems Engineering
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• 제22권1호
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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.

• 한국보육지원학회지
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• 제17권5호
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• pp.83-103
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• 2021

Objective: This study aimed to develop a longitudinal predictive model that identifies first-grade children who are at risk for ADHD and to investigate the factors that predict the probability of belonging to the at-risk group for ADHD by using machine learning. Methods: The data of 1,445 first-grade children from the 1st, 3rd, 6th, 7th, and 8th waves of the Korean Children's Panel were analyzed. The output factors were the at-risk and non-risk group for ADHD divided by the CBCL DSM-ADHD scale. Prenatal as well as developmental factors during infancy and early childhood were used as input factors. Results: The model that best classifies the at-risk and the non-risk group for ADHD was the LASSO model. The input factors which increased the probability of being in the at-risk group for ADHD were temperament of negative emotionality, communication abilities, gross motor skills, social competences, and academic readiness. Conclusion/Implications: The outcomes indicate that children who showed specific risk indicators during infancy and early childhood are likely to be classified as being at risk for ADHD when entering elementary schools. The results may enable parents and clinicians to identify children with ADHD early by observing early signs and thus provide interventions as early as possible.