• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.6
• /
• pp.183-190
• /
• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• Therapeutic Science for Rehabilitation
• /
• v.10 no.1
• /
• pp.53-61
• /
• 2021

Introduction : OMFT is a therapeutic technique based on sensorimotor, motor control and motor learning, and its major goal is to improve oral motor function. The oral motor conceptual hierarchical development is divided into 5 steps: 1) sensorimotor, 2) movement integration, 3) structural movement, 4) functional oral motor, and 5) comprehensive oral motor. Discussion : The OMFT consists of 3 techniques, 10 categories, and 50 sub-item. 1) Warming up technique: 2 categories, 12 sub-item, warming up by sensory awareness and adaptation, therapy situation adaptation, neck movement; 2) Key point technique: 7 categories, 30 sub-item, oral motor facilitation and increasing chewing skill by direct stroke of oral structures such as the face, lips, cheeks, gum, jaws, and tongue; 3) Application technique: 1 category, 8 sub-item, facilitate food intake and swallowing. Conclusion : The goal of this article is to introduce 3 techniques, 50 sub-item of OMFT, as a comprehensive oral motor therapy method, for application to clients. This article provides information that will help oral motor specialists in treating clients with oral motor problems more effectively and professionally.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.11
• /
• pp.439-448
• /
• 2021

Driver negligence accounts for the largest proportion of the causes of traffic accidents, and research to detect them is continuously being conducted. This paper proposes a method to accurately detect a distracted driver and localize the most characteristic parts of the driver. The proposed method hierarchically constructs a CNN basic model that classifies 10 classes based on CAM in order to detect driver distration and 4 subclass models for detailed classification of classes having a confusing or common feature area in this model. The classification result output from each model can be considered as a new feature indicating the degree of matching with the CNN feature maps, and the accuracy of classification is improved by horizontally combining and learning them. In addition, by combining the heat map results reflecting the classification results of the basic and detailed classification models, the characteristic areas of attention in the image are found. The proposed method obtained an accuracy of 95.14% in an experiment using the State Farm data set, which is 2.94% higher than the 92.2%, which is the highest accuracy among the results using this data set. Also, it was confirmed by the experiment that more meaningful and accurate attention areas were found than the results of the attention area found when only the basic model was used.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.323-338
• /
• 2023

TV advertising with deep analysis of watching pattern of audiences is important to set-top box audience targeting. Applying session-based recommendation model(SBR) to internet commercial, or recommendation based on searching history of user showed its effectiveness in previous studies, but applying SBR to the TV advertising was difficult in South Korea due to data unavailabilities. Also, traditional SBR has limitations for dealing with user preferences, especially in data with user identification information. To tackle with these problems, we first obtain set-top box data from three major broadcasting companies in South Korea(SKB, KT, LGU+) through collaboration with Korea Broadcast Advertising Corporation(KOBACO), and this data contains of watching sequence of 4,847 anonymized users for 6 month respectively. Second, we develop personalized session-based recommendation model to deal with hierarchical data of user-session-item. Experiments conducted on set-top box audience dataset and two other public dataset for validation. In result, our proposed model outperformed baseline model in some criteria.

• Journal of Soil and Groundwater Environment
• /
• v.28 no.6
• /
• pp.71-89
• /
• 2023

A groundwater potential map (GPM) was built for the Nakdonggang River Basin based on ten variables, including hydrogeologic unit, fault-line density, depth to groundwater, distance to surface water, lineament density, slope, stream drainage density, soil drainage, land cover, and annual rainfall. To integrate the thematic layers for GPM, the criteria were first weighted using the Analytic Hierarchical Process (AHP) and then overlaid using the Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) model. Finally, the groundwater potential was categorized into five classes (very high (VH), high (H), moderate (M), low (L), very low (VL)) and verified by examining the specific capacity of individual wells on each class. The wells in the area categorized as VH showed the highest median specific capacity (5.2 m³/day/m), while the wells with specific capacity < 1.39 m³/day/m were distributed in the areas categorized as L or VL. The accuracy of GPM generated in the work looked acceptable, although the specific capacity data were not enough to verify GPM in the studied large watershed. To create GPMs for the determination of high-yield well locations, the resolution and reliability of thematic maps should be improved. Criterion values for groundwater potential should be established when machine learning or statistical models are used in the GPM evaluation process.

• Journal of Korean Home Economics Education Association
• /
• v.31 no.1
• /
• pp.1-19
• /
• 2019

The purposes of this study were to concretely reveal the effect of the parents' child-rearing attitude perceived by adolescents and the school life adaptation on the problem behavior, and to verify the moderating effect of the ego-resilience on the relationship between the parents' child-rearing attitude and the school life adaptation and the adolescent problem behavior. This study analyzed a total of 2,107 students in the first year of high school, which was the 4th year data(2013) of Korea Children Youth Panel Survey(KCYPS) 2010. The reliability, descriptive statistics, t-test, and hierarchical regression analysis were conducted using SPSS WIN 22.0. The results were as follows. First, the effect of the parents' child-rearing attitude(supervision, affection, reasonable explanation, excessive interference, excessive expectation, and inconsistency), school life adaptation(relationship with teacher, relationship with friend, school regulation, and learning activity), and ego-resilience on the adolescent problem behavior was analyzed. As a result, the relationship with friend(-) had the highest influence on the adolescent problem behavior, followed by learning activity(-), inconsistency(+), ego-resilience(-), excessive interference(+), and supervision(-). However, the remaining sub-variables did not have any significant influence on the adolescent problem behavior. Second, the moderating effect of the ego-resilience on the relationship among the parents' child-rearing attitude, adaptation to school life, and adolescent problem behavior. The ego-resilience was found to moderate the effects of parents' positive child-rearing attitude, interpersonal relationships, and school adaptation on the adolescent problem behavior. However, the moderating effect was not significant for the effect of negative child-rearing attitude on the adolescent problem behavior. Therefore, various ego-resilience enhancement programs need to be developed and researched as a part of the safety education through the home economics class.

• Journal of The Korean Association For Science Education
• /
• v.38 no.6
• /
• pp.765-780
• /
• 2018

The purpose of this study is to synthesize the theoretical perspectives, research methods, and research results of teachers' professional vision by reviewing and analyzing previous research papers and to suggest implications for science teacher education and research. Three databases were used to search peer reviewed journal articles published between 1997-2017, which include 'teachers' and 'professional vision' explicitly in abstracts and empirical studies only. 21 articles in total were analyzed and review results are as follows. First, researchers regarded professional vision as a new concept of teacher professionalism. Previous research viewed professional vision as integrated structure of teachers' knowledge or ability activated at specific moment. Second, the analytical framework of professional vision included two aspects; 'selective attention' and 'reasoning'. Several aspects of lessons or the desirable teaching and learning factors are suggested as the subcategories of selective attention. Hierarchical levels or independent reasoning ability factors are suggested as the subcategories of reasoning process. Third, research on teachers' professional vision focused more on middle school teachers than elementary teachers and on various subject areas. Most studies used video clips and more cases of using videos of non-participants were found. In case of measurement of professional vision, most quantitative scoring methods were whether the responses of experts and teachers on video clips were consistent. Last, most studies examined or assessed teachers' professional vision. It is reported that in-service teachers' professional vision was evaluated higher than novice teachers' and using video clips were effective to examine and improve teachers' professional vision.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.1-17
• /
• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.