• Journal of The Korean Association of Information Education
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• v.22 no.1
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• pp.151-158
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• 2018

The purpose of this study is to develop a system for predicting students' collaborative problem solving ability based on the ICT factors of PISA 2015 that affect collaborative problem solving ability. The PISA 2015 computer-based collaborative problem-solving capability evaluation included 5,581 students in Korea. As a research method, correlation analysis was used to select meaningful variables. And the collaborative problem solving ability prediction model was created by using the deep learning method. As a result of the model generation, we were able to predict collaborative problem solving ability with about 95% accuracy for the test data set. Based on this model, a collaborative problem solving ability prediction system was designed and implemented. This research is expected to provide a new perspective on applying big data and artificial intelligence in decision making for ICT input and use in education.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.269-279
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• 2020

Traffic estimation mainly involves surveying equipment such as automatic vehicle classification, vehicle detection system, toll collection system, and personnel surveys through CCTV (Closed Circuit TeleVision), but this requires a lot of manpower and cost. In this study, we proposed a method of estimating traffic volume using deep learning and stereo CCTV to overcome the limitation of not detecting the entire vehicle in case of single CCTV. COCO (Common Objects in Context) dataset was used to train deep learning models to detect vehicles, and each vehicle was detected in left and right CCTV images in real time. Then, the vehicle that could not be detected from each image was additionally detected by using affine transformation to improve the accuracy of traffic volume. Experiments were conducted separately for the normal road environment and the case of weather conditions with fog. In the normal road environment, vehicle detection improved by 6.75% and 5.92% in left and right images, respectively, than in a single CCTV image. In addition, in the foggy road environment, vehicle detection was improved by 10.79% and 12.88% in the left and right images, respectively.

• Journal of the HCI Society of Korea
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• v.13 no.3
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• pp.65-73
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• 2018

Game balance settings are crucial to game design. Game balancing must take into account a large amount of numerical values, configuration data, and the relationship between elements. Once released and served, a game - even for a balanced game - often requires calibration according to the game player's preference. To achieve sustainability, game balance needs adjustment while allowing for small changes. In fact, from the producers' standpoint, game balance issue is a critical success factor in game production. Therefore, they often invest much time and capital in game design. However, if such a costly game cannot provide players with an appropriate level of difficulty, the game is more likely to fail. On the contrary, if the game successfully identifies the game players' propensity and performs self-balancing to provide appropriate difficulty levels, this will significantly reduce the likelihood of game failure, while at the same time increasing the lifecycle of the game. Accordingly, if a novel technology for game balancing is developed using artificial intelligence (AI) that offers personalized, intelligent, and customized service to individual game players, it would bring significant changes to the game production system.

• Journal of the Korea Convergence Society
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• v.8 no.9
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• pp.17-23
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• 2017

The National Competency Standards (NCS) is a standard that allows korea to efficiently organize the training of national talents by systematically classifying the knowledge, skills, and attitudes necessary for the job of industry groups. Ontology is a discipline that allows the abstract information in the human concept to be expressed in a form that enables computing to be done. There is a need to formalize the knowledge management by converting the NCS system currently stored in the simple DB into an ontology. This study design and implement NCS ontology for the task of "Application SW Engineering" among vast NCS jobs, enabling intelligent knowledge management and inference search of the job. In addition, it provides consistency with the formalization specification of the learning contents structure of the competency unit elements of the job, and provides the basis for extension to the whole NCS job ontology.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.717-724
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• 2020

Due to the important role played by emotion in human interaction, affective computing is dedicated in trying to understand and regulate emotion through human-aware artificial intelligence. By understanding, emotion mental diseases such as depression, autism, attention deficit hyperactivity disorder, and game addiction will be better managed as they are all associated with emotion. Various studies for emotion recognition have been conducted to solve these problems. In applying machine learning for the emotion recognition, the efforts to reduce the complexity of the algorithm and improve the accuracy are required. In this paper, we investigate emotion Electroencephalogram (EEG) feature reduction and classification using Stack AutoEncoder (SAE) and Long-Short-Term-Memory/Recurrent Neural Networks (LSTM/RNN) classification respectively. The proposed method reduced the complexity of the model and significantly enhance the performance of the classifiers.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.12
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• pp.297-304
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• 2019

The technological advances such as communication, sensor, and artificial intelligence lead smart factory construction. Smart factory aims at efficient process control by utilizing data from the existing automation process and intelligence technology such as machine learning. As a result of constructing smart factory, productivity increases, but costs increase. Therefore, small companies try to make a step-by-step transition from existing process to smart factory. In this paper, we have proposed a remote control system that support data collection, monitoring, and control for manufacturing equipment to support the construction of CNC cutting machine based small-scale smart factory. We have proposed the structure and design of the proposed system and efficient sensing data transmission scheme. To check the feasibility, the system was implemented for CNC cutting machine and functionality verification was performed. For performance evaluation, the web page access time was measured. The results means that the implemented system is available level.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.49-54
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• 2003

In this paper, we propose a controller with the self-organizing neural network compensator for compensating PID controller's response. PID controller has simple design method but needs a lot of trials and errors to determine coefficients. A neural network control method does not have optimal structure as the parameters are pre-specified by designers. In this paper, to solve this problem, we use a self-organizing neural network which has Back Propagation Network algorithm using a Gaussian Potential Function as an activation function of hidden layer nodes for compensating PID controller's output. Self-Organizing Neural Network's learning is proceeded by Gaussian Function's Mean, Variance and number which are automatically adjusted. As the results of simulation through the second order plant, we confirmed that the proposed controller get a good response compare with a PID controller. And we implemented the of controller performance hydraulic servo motor system using the DSP processor. Then we observed an experimental results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.235-244
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• 2016

Automotive symbols are more widely needed for new, convenient driving devices in automobiles. Good automotive symbols should be detectable, identifiable at first glance, easily learned, recognizable, and produce quick responses after practice. In this paper, a methodology for developing and evaluating automotive symbols is suggested. It includes multiple tests, such as comprehension, perceptual quality, appropriateness, and integrated evaluation. 28 symbols were tested and evaluated by the suggested methodology for convenient driving systems, such as a lane departure warning system (LDWS), cruise control (CCS), and a collision warning system (CWS). Most of the KS R ISO 2575 symbols had higher scores of comprehension, perceptual quality, and appropriateness, but the sunroof and camera symbols had lower scores. Standard symbols with several new functions should be developed. This methodology could be useful for developing and evaluating automotive symbols.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.331-337
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• 2021

The adaptation of smartfarm technology that converges ICT is increasing productivity and competitiveness in the agriculture. Technologies have been developed that enable environmental monitoring through various sensors and automatic control of the cultivation environment, and researches are underway to advance smartfarm technology using data generated from smartfarms. In this paper, an environmental control method to reduce the energy consumption of a smartfarm by using the environment and control data of the smartfarm is proposed. It was confirmed that energy consumption could be reduced compared to an independent environmental control method by creating an environmental prediction model using accumulated environmental data and selecting a control method to minimize energy consumption in a given situation by considering multiple environmental factors. In the future, research is needed to obtain higher energy efficiency through the advancement of the predictive model and the improvement of the complex control algorithms.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.49-57
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• 2021

With the advent of the era of the fourth industry, more and more scientific techniques are being used to solve decision-making problems. In particular, big data analysis technology is developing as it becomes easier to collect numerical data. Therefore, in this study, in order to overcome the limitations of qualitatively analyzing investment trends, the association of various products was analyzed using associated analysis techniques. For the experiment, two experimental periods were divided based on the COVID-19 economic crisis, and sales information from individuals, institutions, and foreign investors was collected, and related analysis algorithms were implemented through r software. As a result of the experiment, institutions and foreigners recently invested in the KOSPI and KOSDAQ markets and bought futures and products such as ETF. Individuals purchased ETN and ETF products together, which is presumed to be the result of the recent great interest in sector investment. In addition, after COVID-19, all investors tended to be passive in investing in high-risk products of futures and options. This paper is thought to be a useful reference for product sales and product design in the financial field.