• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1725-1730
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• 2017

The acceleration sensor and the gyroscopic sensor are used as representative sensors to detect repetitive motion and have been used to analyze various sporting components. However, both sensors have problems with noise sensitivity and accumulation of errors. There have been attempts to use two sensors together to overcome hardware problems. The complementary filter has shown successful results in mitigating the problems of both sensors by minimizing the disadvantages of accelerometer and gyroscope sensors and maximizing their advantages. In this paper, we proposed a modified method using neural network to reduce variable. The neural network is an algorithm that can precisely measure even in unexpected environments or situations by pre-learning the number of various cases. The proposed method applies a Neural Network by dividing the repetitive motion into three sections, the first, the middle and the end. As a result, the recognition rate is 96.35%, 98.77%, 96.92% and the accuracy is 97.18%.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.161-168
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• 2021

The corporate environment is undergoing many changes as the transition to a knowledge-based economy accelerates. Many changes are taking place in China, including the strategy of Chinese manufacturer 2025. It has no role in the manufacturing plant and is striving to lead the industry based on advanced technology. Therefore, the purpose of this research is to understand one's own mechanism as a result of technological innovation of Chinese companies. Therefore, in this study, based on the previous study, in the Delphi survey, eight factors were finally derived, and the eight factors were surveyed by practitioners of Chinese companies about their own mechanism. As a result of analysis, the importance of one's mechanism based on the industry as a whole is patent, design registration, lead time, confidentiality, complementary manufacturing, complementary sales and services, design complexity, learning curve effect / economies of scale. In turn, its importance appeared. The results of this study may help corporate practitioners develop their intellectual property strategic plans through their own mechanisms that are tailored to their company.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.707-722
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• 2021

The need for Maker Education has received attention as an educational environment for cultivating the active and creative ability that can solve new problems in this era, and it is applied in various educational fields. Many of them use Design Thinking as a stage of maker activities. However, the educational value of each concept has not been magnified, since maker programs are designed by simply borrowing steps without considering the similar but different features of them. Therefore, this study developed a model of Maker Education utilizing Design Thinking based on complementary relationships. To this end, formative research methodology was conducted by the following procedures, developing a draft, conducting a formative evaluation, and completing the final model. As a result, the stages of Maker Education were visualized and detailed activities and instructing strategies in each step by reflecting the features of Maker Education, the autonomy of the learner and producing visible outputs using various tools and materials, and Design Thinking, the specific process of solving problems and enabling social participation.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.997-1004
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• 2011

A fault detection algorithm of a charge and discharge system to ensure the safe use of hybrid electric vehicle is proposed in this paper. This algorithm can be used as a complementary way to existing fault detection technique for a charge and discharge system. The proposed algorithm uses a SVDD technique, which additionally utilizes two methods for learning a large amount of data; one is to incrementally learn a large amount of data, the other one is to remove the data that does not affect the next learning using a new data reduction technique. Removal of data is selected by using lines connecting support vectors. In the proposed method, the data processing speed is drastically improved and the storage space used is remarkably reduced than the conventional methods using the SVDD technique only. A battery data and speed data of a commercial hybrid electrical vehicle are utilized in this study. A fault boundary is produced via SVDD techniques using the input and output in normal operation of the system without using mathematical modeling. A fault detection simulation is performed using both an artificial fault data and the obtained fault boundary via SVDD techniques. In the fault detection simulation, fault detection time via proposed algorithm is compared with that of the peak-peak method. Also the proposed algorithm is revealed to detect fault in the region where conventional peak-peak method is never able to do.

• Journal of Science Education
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• v.47 no.1
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• pp.37-51
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• 2023

This study explored the factors influencing major satisfaction and academic satisfaction of science education major students at the College of Education using machine learning models, random forest, gradient boosting model, and SHAP. Analysis results showed that the performance of the gradient boosting model was better than that of the random forest, but the difference was not large. Factors influencing major satisfaction include 'satisfaction with science teachers in high school corresponding to the subject of one's major', 'motivation for teaching job', and 'age'. Through the SHAP value, the influence of variables was identified, and the results were derived for the group as a whole and for individual analysis. The comprehensive and individual results could be complementary with each other. Based on the research results, implications for ways to support pre-service science teachers' major and academic satisfaction were proposed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.305-307
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• 2021

Alzheimer's Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other's weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.

• The Mathematical Education
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• v.62 no.4
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• pp.491-513
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• 2023

High achievement in mathematics is a very complex process in which various factors such as cognitive factors, affective factors, and social and environmental factors work respectively and complementary. A number of previous studies conducted so far have shown that there are certain factors affecting math learning and these factors have positive or negative effects on it. However, these studies were conducted with limited variables and it was not possible to present a comprehensive analysis of what would be necessary to get good achievements in mathematics learning. Therefore, in this study, we analyzed the process of experience of students who experienced success in mathematics learning using the analysis method of the grounded theory. In addition, the collected data was analyzed to explain the process of leading to the successful experience in mathematics learning. As a result of the analysis, it was revealed that students form their identity as successful learners through the processes of 'new phase stage', 'experience accumulation stage', 'stand-up stage', and 'maintenance effort stage'. Through this study, we were able to get implications for what actions are needed to experience success in math learning by looking at the process of the experience what interviewees have gone through.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.41-47
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• 2024

When there were disparities in performance between models trained in the time and frequency domains, even after conducting an ensemble, we observed that the performance of the ensemble was compromised due to imbalances in the individual model performances. Therefore, this paper proposes a leakage detection technique to enhance the accuracy of pipeline leakage detection through a step-wise learning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-step learning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucial features from the provided data in each domain. In Stage 2, the pre-trained models were utilized by removing their respective classifiers. Subsequently, the features from both domains were fused, and a new classifier was added for retraining. The proposed transfer learning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequency domains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverse information. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.

• The Mathematical Education
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• v.39 no.2
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• pp.151-166
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• 2000

The purpose of this study is to understand what two models of SOLO taxonomy and van Hiele theory suggest and find out what relation there is between the category system of the SOLO taxonomy and the thinking level of the van Hiele theory. The van Hiele theory describes in line of ranking level so that it may increase the teaching effects by putting together a class, which takes into consideration the students thoughts. The SOLO taxonomy focused on the response mode of the students rather than the thinking level or the developmental stage of them to pursuit the method that can describe the students understanding in depth quality-wise. Although the SOLO taxonomy and the van Hiele model seem to have different form and character from outside in terms of their goals, a closer examination reveals that the two stances have much in common and that the models are complementary. Although the van Hiele placed more focus on the thoughts, because the conclusion was based on the students responses, the van Hiele theory can be interpreted within the structure identified in the SOLO model. In this study, we have tried to understand how the response structure form the SOLO taxonomy and the thinking level of the van Hiele theory are related, based on the studies of Pegg and Davery1998). If you briefly look at them, there are following corresponding relation between the SOLO taxonomy and the van Hiele theory. a) The relational level(R) in iconic moe is van Hiele level 1. b) The multisturctural level(M$_2$) in the second cycle of concrete-symbolic mode is van Hiel level 2. c) The relation level(R$_2$) in the second cycle of concrete-symbolic mode is van Hiele level 3. d) The unistructural level(U$_2$) in the second cycle of formal mode is van Hiele level 4. e) The postformal mode is van Hiele levle 5. Though it would be difficult to conclude that these correspondences were perfectly done, if you look at their relation, you can see that the learning process of the students were not carried out uniformly. Therefore, by studying the students response structure, using the SOLO taxonomy, and identifying the learning cycle and understand the geometrical concept more in depth.

• Knowledge Management Research
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• v.17 no.2
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• pp.27-50
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• 2016

Korean manufacturing industry have recently faced the catch-up of China in the mass commodity product, such as automotive, display, and smart phone in terms of market as well as technology. Accordingly, discussion on the importance of achieving catch-up in complex product systems (CoPS) has been increasing as a new innovation engine for the industry. In order to achieve successful catch-up of CoPS, we explored emerging technologies of CoPS, which are featured by the characteristics of radical novelty, relatively fast growth and self-sustaining, through the study of emerging technologies of gas turbine for power generation. We found that emerging technologies of the gas turbine are technologies for combustion nozzle and composition of electrical machine for increasing power efficiency, washing technology for particulate matter, cast and material processing technology for enhancing durability from fatigue, cooling technologies from extremely high temperature, interconnection operation technology between renewable energy and the gas turbine for flexibility in power generation, and big data technology for remote monitoring and diagnosis of the gas turbine. We also found that those emerging technologies resulted in technological progress of the gas turbine by converging with other conventional technologies in the gas turbine. It indicates that emerging technologies in CoPS can be appeared on various technological knowledge fields and have complementary relationship with conventional technologies for technology progress of CoPS. It also implies that latecomers need to pursue integrated learning that includes emerging technologies as well as conventional technologies rather than independent learning related to emerging technologies for successful catch-up of CoPS. Our findings provide an important initial theoretical ground for investigating the emerging technologies and their characteristics in CoPS as well as recognizing knowledge management strategy for successful catch-up of latecomers. Our findings also contribute to the policy development of the CoPS from the perspective of innovation strategy and knowledge management.