• 융합정보논문지
• /
• 제10권7호
• /
• pp.14-19
• /
• 2020

확장성 보고서 언어인 XML기술을 회계보고 영역에 응용한 인터넷 표준인 XBRL에 기초한 회계정보처리 모형을 제안하고자 한다. 기업마다 문서의 특성이 상이하기에 의사결정자에게 유용한 정보를 제공하여야 한다는 회계의 목적에 비추어 그 중요성이 크다. 본 연구는 X-Hive 데이터베이스 내에 XBRL로 저장된 XML 계층구조를 기반으로 하는 데이터 마이닝 모형을 제안하고자 한다. 데이터마이닝 분석은 연관규칙으로 실험되었고 XBRL을 기반으로 DC-Apriori 데이터마이닝 방법을 Apriori알고리즘과 X쿼리를 결합하여 제안한다. 마지막으로 제안 모형의 타당성과 유효성에 대해서는 실험을 통해 검증하였다.

• 방송공학회논문지
• /
• 제28권3호
• /
• pp.275-284
• /
• 2023

유전자 발현 데이터는 질병의 예후 예측, 약물 반응성 예측 등 질병에 대한 이해와 정밀 의료 실현을 위한 연구들에 활용될 수 있지만 충분한 양의 데이터를 수집하는 데 많은 비용적 문제가 있다. 본 논문에서는 Conditional VAE에 기반한 유전자 발현 데이터 생성 모델을 제안하였다. 이전 연구인 WGAN-GP기반의 유전자 발현 생성 모델과 정형 데이터 생성 모델인 CTGAN, TVAE와 비교하여 본 논문의 Conditional VAE기반 모델이 생물학적, 통계학적으로 더 유의미한 합성 데이터를 생성할 수 있음을 보였다.

• 한국초등과학교육학회지:초등과학교육
• /
• 제38권3호
• /
• pp.375-386
• /
• 2019

This study explores how elementary school students construct food pyramid prediction models using scientific reasoning. Thirty small groups of sixth-grade students in the Kyoungki province (n=138) participated in this study; each small group constructed a food pyramid prediction model based on scientific reasoning, utilizing prior knowledge on topics such as biotic and abiotic factors, food chains, food webs, and food pyramid concepts. To understand the scientific reasoning applied by the students during the modeling process, three forms of qualitative data were collected and analyzed: each small group's discourse, their representation, and the researcher's field notes. Based on this data, the researcher categorized the students' model patterns into three categories and identified how the students used scientific reasoning in their model patterns. The study found that the model patterns consisted of the population number variation model, the biological and abiotic factors change model, and the equilibrium model. In the population number variation model, students used phenomenon-based reasoning and relation-based reasoning to predict variations in the number of producers and consumers. In the biotic and abiotic factors change model, students used relation-based reasoning to predict the effects on producers and consumers as well as on decomposers and abiotic factors. In the equilibrium model, students predicted that "the food pyramid would reach equilibrium," using relation-based reasoning and model-based reasoning. This study demonstrates that elementary school students can systematically elaborate on complicated ecology concepts using scientific reasoning and modeling processes.

• 한국측량학회지
• /
• 제38권2호
• /
• pp.153-163
• /
• 2020

After launching the GOCE (Gravity Field and Steady-State Ocean Circulation Explorer) which obtains high-frequency gravity signal using a gravity gradiometer, many research institutes are concentrating on the development of GGM (Global Geopotential Model) based on GOCE data and evaluating its precision. The precision of some GGMs was also evaluated in Korea. However, some studies dealt with GGMs constructed based on initial GOCE data or others applied a part of GNSS (Global Navigation Satellite System) / Leveling data on UCPs (Unified Control Points) for the precision evaluation. Now, GGMs which have a higher degree than EGM2008 (Earth Gravitational Model 2008) are available and UCPs were fully established at the end of 2019. Thus, EIGEN-6C4 (European Improved Gravity Field of the Earth by New techniques - 6C4), GECO (GOCE and EGM2008 Combined model), XGM2016 (Experimental Gravity Field Model 2016), SGG-UGM-1, XGM2019e_2159 were collected with EGM2008, and their precisions were assessed based on the GNSS/Leveling data on UCPs. Among GGMs, it was found that XGM2019e_2159 showed the minimum difference compared to a total of 5,313 points of GNSS/Leveling data. It is about a 1.5cm and 0.6cm level of improvement compare to EGM2008 and EIGEN-6C4. Especially, the local biases in the northern part of Gyeonggi-do, Jeju island shown in the EGM2008 was removed, so that both mean and standard deviation of the difference of XGM2019e_2159 to the GNSS/Leveling are homogeneous regardless of region (mountainous or plain area). NGA (National Geospatial-Intelligence Agency) is currently in progress in developing EGM2020 and XGM2019e_2159 is the experimentally published model of EGM2020. Therefore, it is expected that the improved GGM will be available shortly so that it is necessary to verify the precision of new GGMs consistently.

• International Journal of Advanced Culture Technology
• /
• 제10권1호
• /
• pp.274-283
• /
• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• 한국도로학회논문집
• /
• 제18권1호
• /
• pp.91-97
• /
• 2016

PURPOSES : The objective of this paper is to develop a pavement performance model based on the Bayesian algorithm, and compare the measured and predicted performance data. METHODS : In this paper, several pavement types such as SMA (stone mastic asphalt), PSMA (polymer-modified stone mastic asphalt), PMA (polymer-modified asphalt), SBS (styrene-butadiene-styrene) modified asphalt, and DGA (dense-graded asphalt) are modeled in terms of the performance evaluation of pavement structures, using the Bayesian algorithm. RESULTS : From case studies related to the performance model development, the statistical parameters of the mean value and standard deviation can be obtained through the Bayesian algorithm, using the initial performance data of two different pavement cases. Furthermore, an accurate performance model can be developed, based on the comparison between the measured and predicted performance data. CONCLUSIONS : Based on the results of the case studies, it is concluded that the determined coefficients of the nonlinear performance models can be used to accurately predict the long-term performance behaviors of DGA and modified asphalt concrete pavements. In addition, the developed models were evaluated through comparison studies between the initial measurement and prediction data, as well as between the final measurement and prediction data. In the model development, the initial measured data were used.

• 스마트미디어저널
• /
• 제13권4호
• /
• pp.9-15
• /
• 2024

Tree-based algorithms have been the dominant methods used build a prediction model for tabular data. This also includes personal credit data. However, they are limited to compatibility with categorical and numerical data only, and also do not capture information of the relationship between other features. In this work, we proposed an ensemble model using the Transformer architecture that includes text features and harness the self-attention mechanism to tackle the feature relationships limitation. We describe a text formatter module, that converts the original tabular data into sentence data that is fed into FinBERT along with other text features. Furthermore, we employed FT-Transformer that train with the original tabular data. We evaluate this multi-modal approach with two popular tree-based algorithms known as, Random Forest and Extreme Gradient Boosting, XGBoost and TabTransformer. Our proposed method shows superior Default Recall, F1 score and AUC results across two public data sets. Our results are significant for financial institutions to reduce the risk of financial loss regarding defaulters.

• 한국화재소방학회논문지
• /
• 제17권2호
• /
• pp.50-55
• /
• 2003

The lower flash points for the binary system, n-butanol+n-decane, were measured by Pensky-Martens closed cup tester. The experimental results showed the minimum in the flash point versus composition curve. The experimental data were compared with the values calculated by the reduced model under an ideal solution assumption and the flash point-prediction models based on the Van Laar and Wilson equations. The predictive curve based upon the reduced model deviated form the experimental data for this system. The experimental results were in good agreement with the predictive curves, which use the Van Laar and Wilson equations to estimate activity coefficients. However, the predictive curve of the flash point prediction model based on the Willson equation described the experimentally-derived data more effectively than that of the flash point prediction model based on the Van Laar equation.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2002년도 추계학술대회 논문집
• /
• pp.1088-1092
• /
• 2002

The purpose of this investigation was to study the kinematics of joints between foot segments based on computer graphic-based model during the stance phase of walking. In the model, ail joints were assumed to act as monocentric, single degree of freedom hinge joints. The motion of foot was captured by a video collection system using four cameras. The model fitted in an individual subject was simulated with this motion data. The kinematic data of tarsometatarsal joints and metatarso-phalangeal joint were quantitatively similar to the previous data. Therefore, our method using the computer graphic-based model is considered useful.

• 한국전자거래학회지
• /
• 제17권3호
• /
• pp.43-60
• /
• 2012

본 논문은 개인정보의 법적 및 기술적 특성을 고려한 라이프 사이클 모델들을 각각 검토했다. 그리고, 이를 토대로 국내 IT 기업에 적합한 '개인정보의 동의 관리 기반 모델'을 제안했다. 본 논문에서 제시한 모델은 기존의 모델이 간과하고 있던 '동의'와 '관리' 요소를 모델에 적극 반영했다는 특징이 있다. 본 모델의 타당성은 2가지 방식으로 검증했다. 첫째, IT 기업의 개인정보 라이프 사이클 구성 요소를 파악 후 모델별로 적용하여 '동의 관리' 모델의 우수성을 검증했다. 둘째, 개인정보의 라이프 사이클 전체 프로세스에 '동의'와 '관리' 내용이 포함됨을 입증했다. 본 연구 결과를 활용하면 IT 기업이 개인정보 활용 현황을 분석하고 보호 체계를 마련하는데 기여할 수 있을 것으로 기대된다.