• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.689-690
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• 2021

본 논문에서는 다양한 환경에서 수집된 서로 다른 시계열 데이터를 통합하여 분석 활용하기 위해 추가로 생성해야 할 시계열 데이터의 메타 정보를 정의하고 이를 기반하여 새로운 통합 데이터를 생성하는 방법을 소개한다. 시계열 데이터는 표준화된 기술 방법이 없고 다양한 소스에서 생성되기 때문에 이를 통합하고 활용할 경우 그 기준이 없기 때문에 전문적 지식이 없다면 처리에 어려움을 겪는다. 그러므로 서로 다른 특성의 데이터를 새로운 기준에 의거하여 통합하는 것을 목적으로 필요한 메타 정보를 정의하고 이를 기준으로 데이터를 재가공할 수 있도록 하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.45-48
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• 2022

IoT 기술의 발전 및 확산으로 다양한 도메인에서 서로 다른 특성의 시계열 데이터가 수집되고 있다. 이에 따라 단일 목적으로 수집된 시계열 데이터만 아니라, 다른 목적으로 수집된 시계열 데이터들 또한 통합하여 분석활용하려는 수요 또한 높아지고 있다. 본 논문은 파편화된 시계열 데이터들을 선택하여 통합한 후 딥러닝 모델을 생성하고 활용할 수 있는 해시함수 기반 학습 모델 관리 플랫폼을 설계하고 구현하였다. 특정되지 않은 데이터들을 기반하여 모델을 학습하고 활용할 경우 생성 모델이 개별적으로 어떤 데이터로 어떻게 생성되었는지 기술되어야 향후 활용에 용이하다. 특히 시계열 데이터의 경우 학습 데이터의 시간 정보에 의존적일 수밖에 없으므로 해당 정보의 관리도 필요하다. 본 논문에서는 이러한 문제를 해결하기 위해 해시 함수를 이용해서 생성된 모델을 계층적으로 저장하여 원하는 모델을 쉽게 검색하고 활용할 수 있도록 하였다.

• Journal of Information Technology Applications and Management
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• v.14 no.4
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• pp.43-59
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• 2007

The purpose of the study was to examine the effectiveness of Neuro Feedback training by observing the pre and post brainwave measurement results of about 50 (experimental group 25. comparative group 25) subjects who have shown psychological difficulties in studying. attention deficit, and personalities. The study took place at Neuro Feedback training Center B. in between the months of July 2006 and May 2007. The methodology involved in the study included the Coloring Analysis Program of the Brain Quotient Test. As the brain waves are adjusted by timeseries linear analysis. the brain function quotients can reflect the functional states of the brain. Through the test, three parameters relaxation, attention and concentration-were initially measured for one minute each and the lowest parameter out of the three was selected as the training mode or improvement target. The training took place two or three times a week. for about 40 to 60 minutes per session. Because the clients have come to the training center at different times. the researcher sampled the results of only those who had attended more than 30 training sessions. The tool used to measure the psychological reaction was POMS (Profile of Mood State). while the tool used to measure the emotional and attention-deficit characteristics was the Amen Clinic ADD Type questionnaire. Hypothesis testing included t-test. The result of the study showed the Theta: SMR ratio of (left)p = .013. (right) p = .019. The result also confirmed the differences of both ATQ(left) p = .011. (right)p = .030 and SQ(left) p = .017. (right) p = .022. The result confirmed of emotional p = .000. attention-deficit characteristics p = .000. The result of the study suggest Neuro Feedback technique's possibility in positively affecting the subjects' mental state and attention-deficit characteristics.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.13-15
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• 2012

최근 다양한 서비스가 등장하면서 폭발적으로 증가하는 데이터로 인해 이를 처리하고 분석하기 위한 대용량 처리 및 분석 시스템이 제안되고 있다. 본 논문에서는 이러한 시스템에서 효율적인 데이터 큐브관리 방법을 제안한다. 실험을 통해 제안한 방법이 대용량 시계열 데이터 처리 및 분석 시스템에서 중요한 질의 처리 시간을 단축시키는 것을 보였다.

• Korean Journal of Social Welfare Studies
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• no.37
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• pp.117-144
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• 2008

This is a timeseries study on the riving forces behind the changes of Korean welfare state. There are a few previous studies on the determinants of korean welfare state. These previous studies have some limitations in terms of reliability of the data source and validity of the statistical method used. Using the Comparative Social Policy Data-set(CSPD), we try to overcome the limitation of these previous studies. And adapting the time series regression, we examine the hypotheses about the changes of korean welfare state. In this study, four dependent variables are examined: the ratio of public social welfare expenditure to the GDP(WELGDP), the ratio of public social welfare expenditure to the government budget(WELGOV), the ratio of social expenditure to the GDP(SOCX), social welfare expenditure per capita. And independent variables were selected based on the theoretical background on the changes of welfare state. The results of this study as follows: First, the variables based on structural functionalism (industrialization) are the major driving forces behind the changes of korean welfare state since 1960s. Second, the effect of unemployment variable may be reasonably interpreted as reflecting the residual characteristics of korean welfare state. Third, the politics of the left based on power resource theory should be restrictedly interpreted. Ultimately, korean welfare state is still at rudimentary stage where the theory of industrialization is well applied as a driving forces behind the changes of welfare state.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.2
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• pp.138-145
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• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.399-402
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• 2022

본 논문에서는 불완전한 시계열 데이터를 활용하기 전 데이터를 선별하여 활용하는 방법을 소개한다. 시계열 데이터의 품질은 수집 네트워크와 수집 기기의 시간적 변화와 같은 가변적 상황에 의존적이므로 불규칙적으로 이상 혹은 누락 데이터가 발생한다. 이때 에러를 포함하였다는 이유로 일괄적으로 데이터를 제거하여 활용하지 않거나, 혹은 누락 데이터의 구간을 조건 없이 복원하여 활용한다면 원하지 않는 결과를 초래할 수 있다. 제안하는 방법은 시계열 데이터의 구간에 대한 누락 데이터의 통계적 정보를 축출하고 이에 기반하여 활용 목적과 활용 가능한 품질의 기준에 부합하지 않는다면 활용 불가능한 데이터라고 판별하고 미리 분석 등의 데이터 활용 시 자동 제외하는 구조를 제안하고 실험하였다. 제안하는 방법은 활용 목적과 상황에 적응적으로 누락 값을 포함하는 데이터의 빠른 활용 판단이 가능하며 보다 나은 분석 결과를 얻을 수 있다.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.875-883
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• 2021

In view of integration and development, compliance with regulations on information disclosure has important implications for users. Corporate social responsibility disclosure (CSRD) is an increasing concern of the community and society. CSRD always poses many challenges for the profitability of banks. The article uses the ordinary least square method to examine this relationship and employs timeseries data of five years from 18 Vietnamese listed banks from 2015 to 2019. The analysis is informed by Jensen and Meckling's Agency theory, Freeman's Stakeholder theory, and Dowling and Pfeffer's Legitimacy theory. The study results show that, with the CSRD dependent variable, return on assets (ROA) and net interest margin (NIM) have an opposite influence, but return on equity (ROE) has no effect on CSRD, while on the profitability dependent variable, CSRD has a different influence from ROA, ROE, and NIM. To enhance the relationship between CSRD and profitability, Vietnamese listed banks need to comply with CSRD as well as demonstrate responsibility to the community and society. Managers need to have clear development policies and strategies to ensure both profitability and responsibility regarding social and community activities. The State Securities Commission of Vietnam should enforce strict sanctions, conduct inspection, and complete evaluation criteria for Vietnamese listed banks.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.6
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• pp.1004-1022
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• 2022

This study seeks to investigate K-fashion's external image by examining the trends in global media reporting. It applies Dynamic Topic Modeling (DTM), which captures the evolution of topics in a sequentially organized corpus of documents, and consists of text preprocessing, the determination of the number of topics, and a timeseries analysis of the probability distribution of words within topics. The data set comprised 551 online media articles on 'Korean fashion' or 'K-fashion' published on Google News between 2010 and 2021. The analysis identifies seven topics: 'brand look and style,' 'lifestyle,' 'traditional style,' 'Seoul Fashion Week (SFW) event,' 'model size,' 'K-pop,' and 'fashion market,' as well as annual topic proportion trends. It also explores annual word changes within the topic and indicates increasing and decreasing word patterns. In most topics, the probability distribution of the word 'brand' is confirmed to be on the increase, while 'digital,' 'platform,' and 'virtual' have been newly created in the 'SFW event' topic. Moreover, this study confirms the transition of each K-fashion topic over the past 12 years, along with various factors related to Hallyu content, traditional culture, government support, and digital technology innovation.

• ETRI Journal
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• v.46 no.5
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• pp.915-927
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• 2024

We obtain the air quality index (AQI) for a descriptive system aimed to communicate pollution risks to the population. The AQI is calculated based on major air pollutants including O₃, CO, SO₂, NO, NO₂, benzene, and particulate matter PM2.5 that should be continuously balanced in clean air. Air pollution is a major limitation for urbanization and population growth in developing countries. Hence, automated AQI prediction by a deep learning method applied to time series may be advantageous. We use a seasonal autoregressive integrated moving average (SARIMA) model for predicting values reflecting past trends considered as seasonal patterns. In addition, a transductive long short-term memory (TLSTM) model learns dependencies through recurring memory blocks, thus learning long-term dependencies for AQI prediction. Further, the TLSTM increases the accuracy close to test points, which constitute a validation group. AQI prediction results confirm that the proposed SARIMA-TLSTM model achieves a higher accuracy (93%) than an existing convolutional neural network (87.98%), least absolute shrinkage and selection operator model (78%), and generative adversarial network (89.4%).