• 대한공업교육학회지
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• v.40 no.2
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• pp.92-110
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• 2015

The purpose of this study is to provide necessary information to understand characteristics of vocational high school students and to enhance academic engagement through social support of teachers, leading to help research of teaching and learning strategy. A survey was conducted on 990 engineering major students attending 11 vocational high schools in Seoul metropolitan, Chungcheong, Jeolla, Kyeongsang and Kangwon regions. A questionnaire consists of measurement tools for the academic engagement (21 questions) and the social support of teachers (25 questions). The findings of this study are as follows: First, it is found that the level of students' academic engagement was high. But it appears that the students showed low engagement of emotion compared with that of behavior and cognition. There was no level difference according to gender, but there was a considerable difference according to a school year. The first year students' level of engagement was higher than the second and the third year students' in terms of cognition and emotion. Second, it shows that the level of the teachers' social support was normal, which was in the order of appraisal support, instrumental support, informational support, and emotional support. Especially, the level of appraisal support and instrumental support was most. Third, there were correlation and explanation between students' academic engagement and teachers' social support. Moreover, the result that teachers' emotional support has high correlation and explanation in qualitative terms of academic engagement support the importance. Therefore, it is concluded that the social support of teachers can make an positive influence on improving the academic engagement of students and provide students with adaptability and satisfaction with their school life, which may give students a positive effect in emotional development, self-formation, and complement.

• Journal of Elementary Mathematics Education in Korea
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• v.21 no.1
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• pp.23-47
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• 2017

The purpose of this study is to analyze the types of errors that may occur in the four arithmetic operations of the fractions after classified according to the level of academic achievement for sixth-grade elementary school student who Learning of the four arithmetic operations of the fountain has been completed. The study was proceed to get the information how change teaching content and method in accordance with the level of academic achievement by looking at the types of errors that can occur in the four arithmetic operations of the fractions. The test paper for checking the type of errors caused by calculation of fractional was developed and gave it to students to test. And we saw the result by error rate and correct rate of fraction that is displayed in accordance with the level of academic achievement. We investigated the characteristics of the type of error in the calculation of the arithmetic operations of fractional that is displayed in accordance with the level of academic achievement. First, in the addition of the fractions, all levels of students showing the highest error rate in the calculation error. Specially, error rate in the calculation of different denominator was higher than the error rate in the calculation of same denominator Second, in the subtraction of the fractions, the high level of students have the highest rate in the calculation error and middle and low level of students have the highest rate in the conceptual error. Third, in the multiplication of the fractions, the high and middle level of students have the highest rate in the calculation error and low level of students have the highest rate in the a reciprocal error. Fourth, in the division of the fractions, all levels of students have the highest r rate in the calculation error.

• The Journal of Bigdata
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• v.3 no.2
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• pp.19-33
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• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.1015-1026
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• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.403-410
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• 2020

In addition to the research on noise removal and super-resolution using the data restoration (Output result) function of Autoencoder, research on the performance improvement of clustering using the dimension reduction function of autoencoder are actively being conducted. The clustering function and data restoration function using Autoencoder have common points that both improve performance through the same learning. Based on these characteristics, this study conducted an experiment to see if the autoencoder model designed to have excellent data recovery performance is superior in clustering performance. Skip connection technique was used to design autoencoder with excellent data recovery performance. The output result performance and clustering performance of both autoencoder model with Skip connection and model without Skip connection were shown as graph and visual extract. The output result performance was increased, but the clustering performance was decreased. This result indicates that the neural network models such as autoencoders are not sure that each layer has learned the characteristics of the data well if the output result is good. Lastly, the performance degradation of clustering was compensated by using both latent code and skip connection. This study is a prior study to solve the Hanja Unicode problem by clustering.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.13-21
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• 2020

The Android author identification study can be interpreted as a method for revealing the source in a narrow range, but if viewed in a wide range, it can be interpreted as a study to gain insight to identify similar works through known works. The problem found in the Android author identification study is that it is an important code on the Android system, but it is difficult to find the important feature of the author due to the meaningless codes. Due to this, legitimate codes or behaviors were also incorrectly defined as malicious codes. To solve this, we introduced the concept of survival network to solve the problem by removing the features found in various Android apps and surviving unique features defined by authors. We conducted an experiment comparing the proposed framework with a previous study. From the results of experiments on 440 authors' identified apps, we obtained a classification accuracy of up to 92.10%, and showed a difference of up to 3.47% from the previous study. It used a small amount of learning data, but because it used unique features without duplicate features for each author, it was considered that there was a difference from previous studies. In addition, even in comparative experiments with previous studies according to the feature definition method, the same accuracy can be shown with a small number of features, and this can be seen that continuously overlapping meaningless features can be managed through the concept of a survival network.

• Journal of The Korean Association For Science Education
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• v.41 no.2
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• pp.155-169
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• 2021

This study aims at exploring the features of science teaching orientation (STO) and its relationships with other PCK (pedagogical content knowledge) components. To do this, based on the definition of STO by Friedrichsen, Driel, & Abell(2011) and PCK model by Magnusson, Krajcik, & Borko(1999), we observed one experienced elementary teacher's science lessons for 21 lesson hours (10 hours of 'Motion of Objects' and 11 hours of 'Light and Lens') and carried out qualitative analyses of the data obtained from lessons observation, teacher interviews, and CoRe (content representation) responses. We analyzed the teacher's three aspects of STO (i.e. beliefs about the goals and purpose of science teaching, beliefs about the nature of science, and beliefs about science teaching and learning) which can converge into an overall STO of 'inquiry'. And these aspects of STO appear to interact differently with four PCK components (i.e. curriculum knowledge, learner knowledge, instructional knowledge, and assessment knowledge) depending on the topic of the lesson. It is hoped that this in-depth understanding of the features of STO and its relationship with other PCK components would provide useful information on how to monitor and improve STO and PCK of elementary teachers.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.4
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• pp.1-18
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• 2021

Today, record management has become more important in management as records generated from administrative work and data production have increased significantly, and the development of information and communication technology, the working environment, and the size and various functions of the government have expanded. It is explained as an example in connection with the concept of public records with the characteristics of big data and big data characteristics. Social, Technological, Economical, Environmental and Political (STEEP) analysis was conducted to examine such areas according to the big data generation environment. The appropriateness and necessity of applying big data technology in the field of public record management were identified, and the top priority applicable framework for public record management work was schematized, and business implications were presented. First, a new organization, additional research, and attempts are needed to apply big data analysis technology to public record management procedures and standards and to record management experts. Second, it is necessary to train record management specialists with "big data analysis qualifications" related to integrated thinking so that unstructured and hidden patterns can be found in a large amount of data. Third, after self-learning by combining big data technology and artificial intelligence in the field of public records, the context should be analyzed, and the social phenomena and environment of public institutions should be analyzed and predicted.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.189-198
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• 2023

As crimes frequently occur on the street, the spread of CCTV is increasing. However, due to the shortcomings of passively operated CCTV, the need for intelligent CCTV is attracting attention. Due to the heavy system of such intelligent CCTV, high-performance devices are required, which has a problem in that it is expensive to replace the general CCTV. To solve this problem, an intelligent CCTV system that recognizes low-quality images and operates even on devices with low performance is required. Therefore, this paper proposes a Saying CCTV system that can detect threats in real time by using the AWS cloud platform to lighten the system and convert images into text. Based on the data extracted using YOLO v4 and OpenPose, it is implemented to determine the risk object, threat behavior, and threat situation, and calculate the risk using machine learning. Through this, the system can be operated anytime and anywhere as long as the network is connected, and the system can be used even with devices with minimal performance for video shooting and image upload. Furthermore, it is possible to quickly prevent crime by automating meaningful statistics on crime by analyzing the video and using the data stored as text.

• Journal of The Korean Association For Science Education
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• v.28 no.6
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• pp.685-695
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• 2008

An analysis on the Inquiry unit of Science 10 textbooks was conducted in terms of nature of science (NOS). The subject of the analysis was instructional objectives, activities and sentences in the unit of ten Science 10 textbooks. Contents of the instructional objectives could be grouped into nature of science, nature of scientists, scientific methods, and Science-Technology-Society. The concrete nature of scientific knowledge (SK) and constructing scientific theory or model, however, were not found in the objectives. The total number of activities in the Inquiry unit was 38. Seventeen out of them were presented without any supplemental or introductory materials, and 21 activities were provided with information followed by questions, discussions or investigations. For the most activities, any clear statements about NOS elements and desired/informed views of NOS were not made. The sentences of the Inquiry units were mixed up with constructivist and inductive views on NOS. The definition of science tended to be described based on the inductive view. And the generation of SK tended to be described as discovering regularities in natural phenomena rather than constructing theories. For science teachers who want to teach NOS effectively, stating clear learning objectives and elements of NOS and presenting reading materials with relevant views on nature of science were necessary.