• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.61-74
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• 2022

Recently, youth unemployment, especially the unemployment problem of university graduates, has emerged as a social problem. Unemployment of university graduates is both a pan-national issue and a university-level issue, and each university is making many efforts to increase the employment rate of graduates. In this study, we present a model that predicts employment availability of D-university graduates by utilizing Machine Learning. The variables used were analyzed using up to 138 personal information, admission information, bachelor's information, etc., but in order to reflect them in the future curriculum, only the data after admission works effectively, so by department / student. The proposal was limited to the recommended ability to improve the separate employment rate. In other words, since admission grades are indicators that cannot be improved due to individual efforts after enrollment, they were used to improve the degree of prediction of employment rate. In this research, we implemented a employment prediction model through analysis of the core ability of D-University, which reflects the university's philosophy, goals, human resources awards, etc., and machined the impact of the introduction of a new core ability prediction model on actual employment. Use learning to evaluate. Carried out. It is significant to establish a basis for improving the employment rate by applying the results of future research to the establishment of curriculums by department and guidance for student careers.

• Korean Journal of Radiology
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• v.24 no.11
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• pp.1151-1163
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• 2023

Objective: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model. Materials and Methods: A convolutional neural network was trained to predict age according to the bone development shown on a hand radiograph (bone age) using 21036 hand radiographs of Korean children and adolescents without known bone development-affecting diseases/conditions obtained between 1998 and 2019 (median age [interquartile range {IQR}], 9 [7-12] years; male:female, 11794:9242) and their chronological ages as labels (Korean model). We constructed 2 separate external datasets consisting of Korean children and adolescents with healthy bone development (Institution 1: n = 343; median age [IQR], 10 [4-15] years; male: female, 183:160; Institution 2: n = 321; median age [IQR], 9 [5-14] years; male: female, 164:157) to test the model performance. The mean absolute error (MAE), root mean square error (RMSE), and proportions of bone age predictions within 6, 12, 18, and 24 months of the reference age (chronological age) were compared between the Korean model and a commercial model (VUNO Med-BoneAge version 1.1; VUNO) trained with Greulich-Pyle-based age as the label (GP-based model). Results: Compared with the GP-based model, the Korean model showed a lower RMSE (11.2 vs. 13.8 months; P = 0.004) and MAE (8.2 vs. 10.5 months; P = 0.002), a higher proportion of bone age predictions within 18 months of chronological age (88.3% vs. 82.2%; P = 0.031) for Institution 1, and a lower MAE (9.5 vs. 11.0 months; P = 0.022) and higher proportion of bone age predictions within 6 months (44.5% vs. 36.4%; P = 0.044) for Institution 2. Conclusion: The Korean model trained using the chronological ages of Korean children and adolescents without known bone development-affecting diseases/conditions as labels performed better in bone age assessment than the GP-based model in the Korean pediatric population. Further validation is required to confirm its accuracy.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.178-181
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• 2005

Support vector machines is a powerful learning algorithm and attempt to separate belonging to two given sets in N-dimensional real space by a nonlinear surface, often only implicitly dened by a kernel function. We described breast tissue images analyses using texture features from Haar wavelet transformed images to classify breast lesion of ductal organ Benign, DCIS and CA. The approach for creating a classifier is composed of 2 steps: feature extraction and classification. Therefore, in the feature extraction step, we extracted texture features from wavelet transformed images with $10{\times}$ magnification. In the classification step, we created four classifiers from each image of extracted features using SVM(Support Vector Machines). In this study, we conclude that the best classifier in histological sections of breast tissue in the texture features from second-level wavelet transformed images used in Polynomial function.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2346-2351
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• 2011

This paper presents the intelligent external noise analysis method for nonlinear maneuvering target. After recognizing maneuvering pattern of the target by the proposed method, we track the state of the target. The external noise can be divided into mere noise and acceleration using only the measurement. divided noise passes through the filtering step and acceleration is punched into dynamic model to compensate expected states. The acceleration is the most deterministic factor to the maneuvering. By dividing, approximating, and compensating the acceleration, we can reduce the tracking error effectively. We use the fuzzy c-means (FCM) clustering as the method to divide external noise. FCM can separate the acceleration from the noise without criteria. It makes the criteria with the data made by measurement at every sampling time. So it can show the adaptive tracking result. The proposed method proceeds the tracking target simultaneously with the learning process. Thus it can apply to the online system. The proposed method shows the remarkable tracking result on the linear and nonlinear maneuvering. Finally, some examples are provided to show the feasibility of the proposed algorithm.

• Journal of environmental and Sanitary engineering
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• v.22 no.3
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• pp.65-76
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• 2007

Values and attitude towards the environment begin to form in elementary school. Thus, environmental education is effective to promote children's sensibility on the environment, to increase their interest and concern on it, and to make them have friendly attitudes towards it. As a measure of such education, experiential learning activities are being emphasized, where children can see, feel and experience for themselves in a familiar environment surrounding them. Based on the results of this research, the following proposals can be made for environmental education necessary for elementary school children. i) the contents of environmental education should be selected and organized according to grades. Also, schedule should be secured to provide environmental education in certain time. ii) program should be developed to fit into local characteristics and academic level, providing connective and consistent environmental education. iii) activities for environmental education in elementary school can be effective only if connective guidances are provided among school, home and local community. iv) the recycling and separate collection activity program used in this research was limited to 3rd graders in small-size rural schools. Additional research may be necessary to see how long their attitudes last according to different grades.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.4
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• pp.293-299
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• 2004

This paper is about the extraction of basis function for ECG signal processing. In the first step, it is assumed that ECG signal consists of linearly mixed independent source signals. 12 channel ECG signals, which were sampled at 600sps, were used and the basis function, which can separate and detect source signals - QRS complex, P and T waves, - was found by applying the fast fixed point algorithm, which is one of learning algorithms in independent component analysis(ICA). The possibilities of significant point detection and classification of normal and abnormal ECG, using the basis function, were suggested. Finally, the proposed method showed that it could overcome the difficulty in separating specific frequency in ECG signal processing by wavelet transform. And, it was found that independent component analysis(ICA) could be applied to ECG signal processing for detection of significant points and classification of abnormal beats.

• Proceedings of the KSPS conference
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• 2005.04a
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• pp.59-62
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• 2005

One's mother tongue can have an effect on learning a foreign language, especially on pronunciation. Investigating and comparing English vowels /${\varepsilon}$/ and /${\ae}$/, and their- supposedly- corresponding vowels in Korean /에/ and /애/, this study addresses the following questions: Can Koreans distinguish /에/ and /애/? Can they distinguish /${\varepsilon}$/ and /${\ae}$/ in English? And what is the relationship between the Korean vowels and the English vowels? That is, is the conventional correspondence of /에/-/${\varepsilon}$/ and /애/-/${\ae}$/ appropriate? The results showed that most Korean students distinguish neither Korean /에/ and /애/, nor English /${\varepsilon}$/ and /${\ae}$/. While not distinguishable within a language, Korean /에/ and /애/ still form a separate group from English /${\varepsilon}$/ and /${\ae}$/. Therefore the correspondence between /에/-/${\varepsilon}$/ and /애/-/${\ae}$/ is not appropriate. Strategies for teaching English pronunciation should be designed accordingly.

• English Language & Literature Teaching
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• v.12 no.2
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• pp.61-85
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• 2006

The purpose of the present study is to compare Korean EFL learners' speech corpus (i.e. oral productions) with their composition corpus (i.e. written productions). Four college students participated in the study. The composition corpus was collected through a writing assignment, and the speech corpus was gathered by audio-taping their oral presentations. The results of the data analysis indicate that (i) As for error frequency, young adult low-intermediate Korean EFL learners showed high frequency in determiners (mostly, indefinite articles), vocabulary (mostly, semantic errors), and prepositions. The frequency order did not show much difference between the speech corpus and the composition corpus; and (ii) When comparing the oral productions with the written productions, there were not many differences between them in terms of the contents, a style (i.e., colloquial vs. literary), vocabulary selection, and error types and frequency. Therefore, it is assumed that the proficiency in oral presentation of EFL learners at this learning stage heavily depends on how much/how well they are able to write. In other words, EFL learners' writing and speaking skills are closely co-related. It implies that the teacher does not need to separate teaching how to speak from teaching how to write. The teacher may use the same methods or strategies to help the learners improve their English speaking and writing skills. Furthermore, it will be more effective to teach writing before speaking since they have more opportunities to write than speak in the EFL contexts.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.134-143
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• 2017

Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2310-2325
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• 2016

In this paper, we provide a trajectory-generation algorithm that can identify pedestrians in real time. Typically, the contours for the extraction of pedestrians from the foreground of images are not clear due to factors including brightness and shade; furthermore, pedestrians move in different directions and interact with each other. These issues mean that the identification of pedestrians and the generation of trajectories are somewhat difficult. We propose a new method for trajectory generation regarding multiple pedestrians. The first stage of the method distinguishes between those pedestrian-blob situations that need to be merged and those that require splitting, followed by the use of trained decision trees to separate the pedestrians. The second stage generates the trajectories of each pedestrian by using the point-correspondence method; however, we introduce a new point-correspondence algorithm for which the A* search method has been modified. By using fuzzy membership functions, a heuristic evaluation of the correspondence between the blobs was also conducted. The proposed method was implemented and tested with the PETS 2009 dataset to show an effective multiple-pedestrian-tracking capability in a pedestrian-interaction environment.