• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.471-479
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• 2018

Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.25-53
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• 2009

The purpose of this paper is to analysis sectoral patterns of technological innovation in Korean manufacturing sector. Pavitt(1984) put forward a well-known taxonomy that industries three groups of industries characterized by markedly different innovative modes, namely science-based, production-intensive and supplier-dominated industries. Using Pavitt's taxonomy as a framework, we try to explain similarities and differences among sectors in the sources and impact of innovations. Based on a sample of 2,371 firms in manufacturing industry, this paper investigated its relevance to explain the sources and directions of innovative activities in Korean industries. Empirical study shows that in supplier dominated firms most process innovations come from suppliers of equipment and materials. In science-based firms product innovation is produced internally, based on the rapid development of the underlying sciences in the universities and research institutes. It also shows that production-intensive firms have a positive association between innovativeness and customer collaboration. This explanation has implications for our understanding of the sources and directions of technical changes, the formation of technological advantages at the level of both region and country.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.3
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• pp.17-27
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• 2010

This study aimed to identify the problems of college students in reading the literature and on the basis of the identified problems, to suggest the approaches to solve the problems. To this end, time required for reading passages, reading patterns, understanding, memory and reading habits and attitudes were analyzed with the freshmen in A university. In accordance with the analysis results, 58% of subjects was good and 42% was not sufficient on the basis of the averages in Scholastic Aptitude Test. Second, 77% of subjects had the good patterns but 23% showed certain problems in reading patterns. Third, 69% and 67% of subjects illustrated good results in the analysis on understanding and memory, respectively. However, 31% and 33% were evaluated as being on the general level or requiring efforts in the analysis on understanding and memory, respectively. Next, according to the analysis on reading habits and attitudes, 77% had no problems but 23% required improvement. For solving the problems identified through the analysis, it is recommended to develop the scientific and standardized evaluation tools for evaluating the reading ability of college students. Second, it is necessary to evaluate the reading ability, habit and attitude during the screening process for admission or after admission. Finally, it is required to operate the Fundamental Academic Ability Learning Center(tentative name) to improve the ability of students who show the insufficient results in evaluation.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.63-70
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• 2021

Generating a super-resolution meteological data by using a high-resolution deep neural network can provide precise research and useful real-life services. We propose a new technique of generating improved training data for super-resolution deep neural networks. To generate high-resolution meteorological data with domain specific knowledge, Lambert conformal conic projection and objective analysis were applied based on observation data and ERA5 reanalysis field data of specialized institutions. As a result, temperature and humidity analysis data based on domain specific knowledge showed improved RMSE by up to 42% and 46%, respectively. Next, a super-resolution generative adversarial network (SRGAN) which is one of the aritifial intelligence techniques was used to automate the manual data generation technique using damain specific techniques as described above. Experiments were conducted to generate high-resolution data with 1 km resolution from global model data with 10 km resolution. Finally, the results generated with SRGAN have a higher resoltuion than the global model input data, and showed a similar analysis pattern to the manually generated high-resolution analysis data, but also showed a smooth boundary.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.77-87
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• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• The Journal of the Korea Contents Association
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• v.19 no.8
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• pp.23-34
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• 2019

The space before modern times is fixed and closed, then that after modern times is flexible and open. Based on the concept of space, the modern art exhibition hall gradually shifts from the interior space of a building to the outdoor space, giving birth to the concept of outdoor art pavilion. Based on this background, we analyzed and learned about the latest deformable art pavilions, focused on the investigation of its space performance characteristics and the case analysis, fully understood the design principles of deformable pavilions, and proposed the basic design directions and strategies for future research. Firstly, through learning the theories of transformable space, the characteristics and concept range of deformable space are understood. Secondly, based on the preliminary research and analysis of art pavilions, the performance characteristics are summarized. Thirdly, the pattern of deformable space and the method of reflecting the characteristics of deformable space are investigated based on cases. After summarizing the case analysis, we identified the differences of different art pavilions between deformable modes and space characteristics, and analyzed the causes. This work provides a basis for distinguishing the transformation patterns of the deformable space, and reveals the changes in space concepts and the expansion of space meaning in future architectural space design.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.1-6
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• 2019

Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.121-129
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• 2021

In this study, we try to minimize the tariff risk by constructing a hazardous cargo screening model by applying Association Rule Mining, one of the data mining techniques. For this, the risk level between supply chains is calculated using the Apriori Algorithm, which is an association analysis algorithm, using the big data of the import declaration form of the Korea Customs Service(KCS). We perform data preprocessing and association rule mining to generate a model to be used in screening the supply chain. In the preprocessing process, we extract the attributes required for rule generation from the import declaration data after the error removing process. Then, we generate the rules by using the extracted attributes as inputs to the Apriori algorithm. The generated association rule model is loaded in the KCS screening system. When the import declaration which should be checked is received, the screening system refers to the model and returns the confidence value based on the supply chain information on the import declaration data. The result will be used to determine whether to check the import case. The 5-fold cross-validation of 16.6% precision and 33.8% recall showed that import declaration data for 2 years and 6 months were divided into learning data and test data. This is a result that is about 3.4 times higher in precision and 1.5 times higher in recall than frequency-based methods. This confirms that the proposed method is an effective way to reduce tariff risks.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.461-469
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• 2020

In this paper, we propose a PCA-based feature feedback method for extracting important areas of handwritten numeric data sets and face data sets. A PCA-based feature feedback method is proposed by extending the previous LDA-based feature feedback method. In the proposed method, the data is reduced to important feature dimensions by applying the PCA technique, one of the dimension reduction machine learning algorithms. Through the weights derived during the dimensional reduction process, the important points of data in each reduced dimensional axis are identified. Each dimension axis has a different weight in the total data according to the size of the eigenvalue of the axis. Accordingly, a weight proportional to the size of the eigenvalues of each dimension axis is given, and an operation process is performed to add important points of data in each dimension axis. The critical area of the data is calculated by applying a threshold to the data obtained through the calculation process. After that, induces reverse mapping to the original data in the important area of the derived data, and selects the important area in the original data space. The results of the experiment on the MNIST dataset are checked, and the effectiveness and possibility of the pattern recognition method based on PCA-based feature feedback are verified by comparing the results with the existing LDA-based feature feedback method.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.81-93
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• 2022

The stock investing is one of the most popular investment techniques. However, since it is not easy to obtain a return through actual investment, various strategies have been devised and tried in the past to obtain an effective and stable return. Among them, the volatility breakout strategy identifies a strong uptrend that exceeds a certain level on a daily basis as a breakout signal, follows the uptrend, and quickly earns daily returns. It is one of the popular investment strategies that are widely used to realize profits. However, it is difficult to predict stock prices by understanding the price trend pattern of stocks. In this paper, we propose a method of buying and selling stocks by predicting the return in trading based on the volatility breakout strategy using a bi-directional long short-term memory deep neural network that can realize a return in a short period of time. As a result of the experiment assuming actual trading on the test data with the learned model, it can be seen that the results outperform both the return and stability compared to the existing closing price prediction model using the long-short-term memory deep neural network model.