• Journal of Korea Multimedia Society
• /
• v.13 no.10
• /
• pp.1494-1505
• /
• 2010

A variety of sounds take place in medium and small-sized manufactories producing many kinds of parts in a small quantity with one press. We developed the identification system for the quality of parts using HTM(Hierarchical Temporal Memory)-based sound recognition. HTM is the theory that the operation principle of human brain's neocortex is applied to computer, suggested by Jeff Hopkins. This theory memorizes temporal and spatial patterns hierarchically about the real world, which is known for its cognitive power superior to the previous recognition technologies in many cases. By applying the HTM model to the sound recognition, we developed the identification system for the quality of molding parts. In order to verify its performance we recorded the various sounds at the moment of producing parts in the real factory, constructed the HTM network of sound, and then identified the quality of parts by repeating learning and training. It reveals that this system gets an excellent and accurate results at the noisy factory.

• The Journal of the Korea Contents Association
• /
• v.14 no.9
• /
• pp.634-638
• /
• 2014

The purpose of the study is to analyse students' academic achievement by their university major field and changes of grade. The subject was entering students of C medical graduate school in 2008. We divided entering students' major fields into two fields that are natural science and non-natural science filed. We analysed academic achievements of entering students by grades, curriculum from 2008 to 2011. And We analysed to find out whether there are differences in academic achievements by grades, curriculum of each major fields. There were no significant statistical differences in academic achievements by grade, curriculum of the two different university major fields. Futhermore, as a results of analysis on level(high, medium, low) distribution and differences of academic achievements by grade, curriculum of the two different university major fields, there were no statistically meaningful results. There is the need to keep entrance selection systems that open the possibility of selecting the students with other academic background. And there is the need to change general awareness assuming that there are differences in academic achievements by university major fields. We need to guide students with belief of their learning possibility.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.3
• /
• pp.322-327
• /
• 2007

Researchers in the field of psychology used Electroencephalographic (EEG) to record activities of human brain lot many years. As technology develope, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study emotion recognition method which uses one of physiological signals and gestures in the existing research. In this paper, we use together physiological signals and gestures for emotion recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both physiological signals and gestures gets high recognition rates better than using physiological signals or gestures. Both physiological signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on a reinforcement learning.

• Journal of The Korean Association of Information Education
• /
• v.13 no.1
• /
• pp.69-76
• /
• 2009

This paper proposes a teaching plan of programming language class for university of education students amusingly with LEGO Mindstorms NXT robot. The goal of class is not fragmentary knowledge acquirement but problem-solving of maze. This robot communicates with GUI named NXT-G installed in computer via USB. GUI is not text-based but icon-based programming tool. This paper designs a semester with 3 steps such as beginner, intermediate, high-rank. Beginner step is consists of learning of basic functions such as GUI usage and several sensors of robot. Intermediate step is consists of solving of maze problem with low complexity. High-rank step is consists of solving maze problem with medium and high complexity. All maze problem-solving have 3 process with algorithm, flowchart, and programming with stack.

• Journal of Digital Convergence
• /
• v.18 no.4
• /
• pp.315-323
• /
• 2020

This study is a similar experimental study before and after non-equivalence control to examine the effect of fundamental nursing practice education applying reciprocal peer tutoring on confidence in performance, core nursing skills, and practice satisfaction of nursing students. Data collected by 83 people sophomore P university nursing students, the study period is from May 17 to 11 March 2019. As a results, the fundamental nursing practice education applied with the reciprocal peer tutoring method had an effect on the confidence in performance and core nursing skills of 'medium' degree of difficulty, and had a positive effect on the satisfaction of practice. In the future, this study suggests that the method of reciprocal peer teaching is extended to various practical majors and the effects of core nursing skills are tested.

• The Journal of the Convergence on Culture Technology
• /
• v.6 no.4
• /
• pp.165-170
• /
• 2020

In this study, after applying VR 360-degree video contents for intravenous infusion education, basic data on whether VR 360-degree video can be applied as educational content in the future is prepared by grasping the empathy and flow of nursing students in graduating grades. The VR 360 degree intravenous infusion educational content was developed in four-step process of planning, production, modification and completion. The design of this study was descriptive research, and the study period was from November 9 to November 22, 2019. The subjects of this study were 4th grade nursing students at a university, totaling 64 students. Nursing students watched VR 360 degree intravenous infusion educational content using HMD(head mounted display) under the safety management of the researcher. As a result of the study, the empathy of nursing students was 5.32±0.88 points and the flow was 6.02±0.84 points out of 7-point scale. The VR 360 degree intravenous infusion educational content developed in this study can be used as an educational medium in subjects and comparative departments, and it is necessary to specifically develop and verify teaching and learning methods in future studies.

• Industry Promotion Research
• /
• v.1 no.2
• /
• pp.63-69
• /
• 2016

This study analyzed the effect of Chung-nam province small manufaturing firm male workers' participation in training on perceptions of effectiveness. The study results showed that Off-the-job training satisfaction significantly affected satisfaction of training system and helpfulness of self-development. It means that workers considered lecture or e-learning method is better than on-the-job training, so it is suggested that small manufacturing businesses should establish more organized training system for on-the-job training because workers' perception of satisfaction and effectiveness of OJT was lower than Off-JT. This study provided implications for verifying the effectiveness depend on the type of training and presenting important points to enhance workers' satisfaction of education and training.

• The Journal of the Acoustical Society of Korea
• /
• v.38 no.1
• /
• pp.23-29
• /
• 2019

In this paper, a deep learning-based detection and classification using multi-band frequency signals is presented for detecting earthquakes prevalent in Korea. Based on an analysis of the previous earthquakes in Korea, it is observed that multi-band signals are appropriate for classifying earthquake signals. Therefore, in this paper, we propose a deep CNN (Convolutional Neural Network) using multi-band signals as training data. The proposed algorithm extracts the multi-band signals (Low/Medium/High frequency) by applying band pass filters to mel-spectrum of earthquake signals. Then, we construct three CNN architecture pipelines for extracting features and classifying the earthquake signals by a late fusion of the three CNNs. We validate effectiveness of the proposed method by performing various experiments for classifying the domestic earthquake signals detected in 2018.

• The Mathematical Education
• /
• v.58 no.1
• /
• pp.55-77
• /
• 2019

Textbooks play a very important role as a medium for implementing curriculum in the school. This study aims to analyze tasks for mathematical competencies in the high school 'mathematics' textbooks based on the 2015 revised mathematics curriculum emphasizing competencies. And our study is based on the following two research question. 1. What is the relationship between core competencies and mathematical competencies? 2. What is the distribution of competencies of tasks for mathematical competencies presented in the textbooks? 3. How does the tasks for mathematical competencies reflect the meaning of the mathematical competencies? For this study, the tasks, marked mathematical competencies, were analyzed by elements of each mathematical competencies based on those concept proposed by basic research for the development of the latest mathematics curriculum. The implications of the study are as follows. First, it is necessary to make efforts to strengthen the connection with core competencies while making the most of characteristics of subject(mathematics). Second, it needs to refine the textbook authorization standards, and it should be utilized as an opportunity to improve the textbook. Third, in order to realize competencies-centered education in the school, there should be development of teaching and learning materials that can be used directly.

• Journal of the Korea Convergence Society
• /
• v.11 no.12
• /
• pp.23-30
• /
• 2020

In this study, the relationship between natural gas (NG) data and gas-related environmental elements was performed using machine learning algorithms to predict the level of gas leakage risk without directly measuring gas leakage data. The study was based on open data provided by the server using the IoT-based remote control Picarro gas sensor specification. The naturel gas leaks into the air, it is a big problem for air pollution, environment and the health. The proposed method is multivariate outlier removing method based Random Forest (RF) classification for predicting risk of NG leak. After, unsupervised k-means clustering, the experimental dataset has done imbalanced data. Therefore, we focusing our proposed models can predict medium and high risk so best. In this case, we compared the receiver operating characteristic (ROC) curve, accuracy, area under the ROC curve (AUC), and mean standard error (MSE) for each classification model. As a result of our experiments, the evaluation measurements include accuracy, area under the ROC curve (AUC), and MSE; 99.71%, 99.57%, and 0.0016 for MOL_RF respectively.