• Journal of Digital Convergence
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• v.10 no.11
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• pp.427-432
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• 2012

The article herein suggested a compound repository and a descriptive method to develop a compound knowledge process. A data target saved in a compound knowledge repository suggested in this article includes all compound knowledge meta data and digital resources, which can be divided into the three following factors according to the purpose: user roles, functional elements, and service ranges. The three factors are basic components to describe abstract models of repository. In this article, meta data of compound knowledge are defined by being classified into the two factors. A component stands for the property about a main agent, activity unit or resource that use and create knowledge, and a context presents the context in which knowledge object are included. An agent of the compound knowledge process performs classification, registration, and pattern information management of composite knowledge, and serves as data flow and processing between compound knowledge repository and user. The agent of the compound knowledge process consists of the following functions: warning to inform data search and extraction, data collection and output for data exchange in an distributed environment, storage and registration for data, request and transmission to call for physical material wanted after search of meta data. In this article, the construction of a compound knowledge repository for recommendation system to be developed can serve a role to enhance learning productivity through real-time visualization of timely knowledge by presenting well-put various contents to users in the field of industry to occur work and learning at the same time.

• Journal of The Korean Association For Science Education
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• v.32 no.7
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• pp.1124-1138
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• 2012

This study investigates Korean middle school students' perceptions as global citizens (i.e. ecological worldview, social and moral compassion, and socioscientific accountability) of Socioscientific Issues (SSI). We developed questionnaires that consisted of 20 Likert-type items to gauge their preceptions of the three different SSI contexts (i.e. nuclear power generation, bio-technology, climate change), and administered them to 225 9th grade students in Seoul. The results revealed that participants showed relatively high scores for ecological worldview but scored low on social and moral compassion across the SSI contexts. In addition, participants presented much higher scores for ecological worldview and socioscientific accountability regarding the issues of climate change. The participant responses indicated that they perceived more inter-connectedness with the environment and felt the responsibility of promoting sustainable development more to prevent further devastation in the context of climate change compared to nuclear power generation or biotechnology.

• Journal of The Korean Association For Science Education
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• v.37 no.4
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• pp.625-636
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• 2017

The purpose of this study is to compare the differences in the operation form of science academy, science high school, and ordinary high school. We tried to find what kinds of variables differ in operation form based on the perception of the beneficiaries. For this purpose, 288 beneficiaries were surveyed. The groups were divided into three groups according to the high school they graduated from and then the differences among the groups were compared. The results are as follows. First, as a result of comparing the operation form according to high school using the most similar system design, it was confirmed that there are differences in curriculum and teacher variables. Second, as the result of analyzing the perception of curriculum and teacher professionalism based on the education experience of the beneficiary, the satisfaction for science academy was higher, but the satisfaction for science in ordinary high school was lower. Third, the key variables showing the differences in the operation forms between science academy and science high school were the learning contents, learning process and learning environment. The results of this study are expected to be used as basic data for future improvement of gifted education curriculum.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2461-2469
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• 1997

Target detection and recognition problems, in which neural networks are widely used, require translation invariant and real-time processing in addition to the requirements that general pattern recognition problems need. This paper presents a novel architecture that meets the requirements and explains effective methodology to train the network. The proposed neural network is an architectural extension of the shared-weight neural network that is composed of the feature extraction stage followed by the pattern recognition stage. Its feature extraction stage performs correlational operation on the input with a weight kernel, and the entire neural network can be considered a nonlinear correlation filter. Therefore, the output of the proposed neural network is correlational plane with peak values at the location of the target. The architecture of this neural network is suitable for implementing with parallel or distributed computers, and this fact allows the application to the problems which require realtime processing. Net training methodology to overcome the problem caused by unbalance of the number of targets and non-targets is also introduced. To verify the performance, the proposed network is applied to detection and recognition problem of a specific automobile driving around in a parking lot. The results show no false alarms and fast processing enough to track a target that moves as fast as about 190 km per hour.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.905-922
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• 2021

Cluster analysis is one of unsupervised learning techniques used for discovering clusters when there is no prior knowledge of group membership. K-means, one of the commonly used cluster analysis techniques, may fail when the number of variables becomes large. In such high-dimensional cases, it is common to perform tandem analysis, K-means cluster analysis after reducing the number of variables using dimension reduction methods. However, there is no guarantee that the reduced dimension reveals the cluster structure properly. Principal component analysis may mask the structure of clusters, especially when there are large variances for variables that are not related to cluster structure. To overcome this, techniques that perform dimension reduction and cluster analysis simultaneously have been suggested. This study proposes probabilistic reduced K-means, the transition of reduced K-means (De Soete and Caroll, 1994) into a probabilistic framework. Simulation shows that the proposed method performs better than tandem clustering or clustering without any dimension reduction. When the number of the variables is larger than the number of samples in each cluster, probabilistic reduced K-means show better formation of clusters than non-probabilistic reduced K-means. In the application to a real data set, it revealed similar or better cluster structure compared to other methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.411-420
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• 2018

This study was conducted to identify the effects of interpersonal communication between health care providers after receiving supplementary education. The participants of this study were 433 health care providers who work at 29 general hospitals in Gwangju Metropolitan City and Jeollanamdo Province. Data were collected from June 8 to June 25, 2018 and evaluated by t-tests, dispersion analysis, correlation analysis and stepwise regression. The results were produced by investigating interpersonal communications according to socio-demographic and health-related characteristics including age, education level, bed size of the hospital at which the participant worked, job satisfaction, hospital location, personal health status, experience with health care management and experience with depression. There were significant differences in communication observed according to supplemental education awareness regarding age, bed size of hospital, occupation, wage, type of medical institution of employment, job satisfaction, work location, health status, health care education experience and chronic disease. There were positive correlations between supplemental education awareness in health workers and their interpersonal communication. The factors that had positive effects on interpersonal communication were level of education and health-related education experience, while age, hospital bed size and job dissatisfaction had negative effects. Finally, support environment, learning transfer and results were identified as sub-factors of supplemental education. Based on the results above, it was proposed that educational training to enhance results, provide a supportive environment and foster learning transfer be developed to increase communication between health workers and provide a safe health service for patients.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1711-1720
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• 2020

Particulate matter (PM) that has been artificially generated during the recent of rapid industrialization and urbanization moves and disperses according to weather conditions, and adversely affects the human skin and respiratory systems. The purpose of this study is to predict the PM10 concentration in Seoul using meteorological factors as input dataset for multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models, and compared and evaluated the performance of the models. First, the PM10 concentration data obtained at 39 air quality monitoring sites (AQMS) in Seoul were divided into training and validation dataset (8:2 ratio). The nine meteorological factors (mean, maximum, and minimum temperature, precipitation, average and maximum wind speed, wind direction, yellow dust, and relative humidity), obtained by the automatic weather system (AWS), were composed to input dataset of models. The coefficients of determination (R2) between the observed PM10 concentration and that predicted by the MLR, SVM, and RF models was 0.260, 0.772, and 0.793, respectively, and the RF model best predicted the PM10 concentration. Among the AQMS used for model validation, Gwanak-gu and Gangnam-daero AQMS are relatively close to AWS, and the SVM and RF models were highly accurate according to the model validations. The Jongno-gu AQMS is relatively far from the AWS, but since PM10 concentration for the two adjacent AQMS were used for model training, both models presented high accuracy. By contrast, Yongsan-gu AQMS was relatively far from AQMS and AWS, both models performed poorly.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.267-277
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• 2021

Both teachers and learners should participate in non-face-to-face class due to COVID-19. The non-face-to-face class has brought about many problems, where they made adequate preparations for such abrupt situation. This study attempted to understand and improve problems occurring during non-face-to-face midi class. The findings are as follows: First, there were differences in equipment available to contact and non-face-to-face class. Such a problem could be improved by using Reaper, DAW which can be installed and freely utilized without any functional limits, regardless of the types of operating systems. Second, latency could not be reduced, when the screen share function of Zoom was used, since it was impossible to select audio interface's drivers in DAW. This problem was improved by again receiving audio output as input and sending it, from the perspectives of teachers. In addition, learners who used the operating system of Windows and have no audio interfaces usually suffer from latency during practices. The latency can be reduced by installing Asio4all. Third, image degradation and screen disconnection phenomena occurred due to the lack of resource. Two computers were connected by using a capture board and the screen disconnection phenomena could be improved by distributing resources and maintaining high-resolution. The system for allowing non-face-to-face midi class could be successfully established, as one more computer was connected by using Vienna Ensemble Pro and more plug-ins were used by securing additional resources. Consequently, the problems of non-face-to-face midi class could be understood and improved.

• Journal of The Korean Association For Science Education
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• v.42 no.2
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• pp.215-226
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• 2022

This study analyzed the trends and causes of the decline in Korean students' positive experiences about science (PES). To do this, 4th to 10th grade students were sampled by grade at general elementary, middle, and high schools in Seoul, and then a questionnaire was administered to ask the students about their PES and the causes for their decline. The results of one-way ANOVA for Test for Indicators of Positive Experiences about Science (TIPES) revealed that there were no statistically significant differences according to grade and school level in the overall mean of TIPES scores. However, the results were slightly different for each sub-component. That is, in 'science academic emotion,' the mean of elementary school students was statistically significantly higher than that of middle school students. In addition, the mean of 4th graders was significantly higher than the mean of middle school 1st graders, middle school 3rd graders, and high school 1st graders, respectively. The mean of high school students was statistically significantly higher than that of middle school students in 'science-related career aspiration.' In the 'science-related self-concept', 'science learning motivation,' and 'science-related attitude,' the differences in scores according to grade and school level were not statistically significant. The main causes of the decline in each sub-components of PES were somewhat different depending on the school level. Based on these results, the ways to improve students' PES were sought according to grade and school level.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.69-75
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• 2024

This paper proposes an innovative cognitive IoT framework specifically designed for fog computing (FC) in the context of industrial Internet of Things (IIoT). The discourse in this paper is centered on the intricate design and functional architecture of the Cognitive IoT platform. A crucial feature of this platform is the integration of machine learning (ML) and artificial intelligence (AI), which enhances its operational flexibility and compatibility with a wide range of industrial applications. An exemplary application of this platform is highlighted through the Predictive Maintenance-as-a-Service (PdM-as-a-Service) model, which focuses on real-time monitoring of machine conditions. This model transcends traditional maintenance approaches by leveraging real-time data analytics for maintenance and management operations. Empirical results substantiate the platform's effectiveness within a fog computing milieu, thereby illustrating its transformative potential in the domain of industrial IoT applications. Furthermore, the paper delineates the inherent challenges and prospective research trajectories in the spheres of Cognitive IoT and Fog Computing within the ambit of Industrial Internet of Things (IIoT).