• 한국콘텐츠학회논문지
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• 제19권11호
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• pp.278-287
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• 2019

공간 정보 추출은 자연어 텍스트에 있는 정적 및 동적인 공간 정보를 공간 개체와 그들 사이의 관계로 명확히 표시하여 추출하는 것을 말한다. 이 논문은 2단계 양방향 LSTM-CRF 앙상블 모델을 사용하여 한국어 공간 정보를 추출할 수 있는 심층 학습 방법을 제안한다. 또한 공간 개체 추출과 공간 관계 속성 추출을 통합한 모델을 소개한다. 한국어 공간정보 말뭉치(Korean SpaceBank)를 사용하여 실험한 결과 제안한 심층학습 방법이 기존의 CRF 모델보다 우수함을 보였으며, 특히 제안한 앙상블 모델이 단일 모델보다 더 우수한 성능을 보였다.

• Korean Chemical Engineering Research
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• 제55권4호
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• pp.574-577
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• 2017

In Indonesia, vetiver oil is one commodity that plays an important role in the country's foreign exchange earnings. Currently, the extraction of essential oil from vetiver still uses conventional methods. Therefore, the aim of this study was to know and verify the kinetics and mechanism of microwave hydrodistillation of vetiver based on two models. In this study, microwave hydrodistillation was used to extract essential oils from vetiver. The extraction was carried out in nine extraction cycles of 20 min to 3 hours. The rate constant, the equilibrium extraction capacity, and the initial extraction rate were calculated using the two models. Kinetics of oil extraction from vetiver by microwave hydrodistillation proved that the extraction process was based on the second-order extraction model. The second-order model was satisfactorily applied, with high coefficients of correlation ($R^2=0.9427$), showing that it well described the process.

• Industrial Engineering and Management Systems
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• 제13권4호
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• pp.432-441
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• 2014

As the business model is receiving considerable attention these days, the ability to collect business model related information has become essential requirement for a company. The annual report is one of the most important external documents which contain crucial information about the company's business model. By investigating business descriptions and their future strategies within the annual report, we can easily analyze a company's business model. However, given the sheer volume of the data, which is usually over a hundred pages, it is not practical to depend only on manual extraction. The purpose of this study is to complement the manual extraction process by using text mining techniques. In this study, the text mining technique is applied in business model concept extraction and business model evolution analysis. By concept, we mean the overview of a company's business model within a specific year, and, by evolution, we mean temporal changes in the business model concept over time. The efficiency and effectiveness of our methodology is illustrated by a case example of three companies in the US video rental industry.

• 전자공학회논문지D
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• 제35D권7호
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• pp.48-55
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• 1998

We propose a new static current source model and parameter extraction method for AlGaAs/GaAs HBT. The proposed model has 9 parameters describing internal currents and are experessed with the physically meaningful parameters.The proposed parameter extraction method uses the measured dC IV curves and does not need the gummel plt data and any optimization process. the constructed model based on the proposed method predicts the measured data well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.771-791
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• 2022

Protein-protein interaction (PPI) extraction from original text is important for revealing the molecular mechanism of biological processes. With the rapid growth of biomedical literature, manually extracting PPI has become more time-consuming and laborious. Therefore, the automatic PPI extraction from the raw literature through natural language processing technology has attracted the attention of the majority of researchers. We propose a PPI extraction model based on the large pre-trained language model and adversarial training. It enhances the learning of semantic and syntactic features using BioBERT pre-trained weights, which are built on large-scale domain corpora, and adversarial perturbations are applied to the embedding layer to improve the robustness of the model. Experimental results showed that the proposed model achieved the highest F1 scores (83.93% and 90.31%) on two corpora with large sample sizes, namely, AIMed and BioInfer, respectively, compared with the previous method. It also achieved comparable performance on three corpora with small sample sizes, namely, HPRD50, IEPA, and LLL.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.859-861
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• 2003

In order to present a space in details, it is indispensable to acquire 3D shape and texture simultaneously from the same platform. 3D shape is acquired by Laser Scanner as point cloud data, and texture is acquired by CCD sensor. Positioning data is acquired by IMU (Inertial Measurement Unit). All the sensors and equipments are assembled on a hand-trolley. In this research, a method of integrating the 3D shape and texture for automated construction of Digital Surface Model is developed. This Digital Surface Model is applied for efficient feature extraction. More detailed extraction is possible , because 3D Digital Surface Model has both 3D shape and texture information.

• Korean Chemical Engineering Research
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• 제50권3호
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• pp.545-550
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• 2012

최적의 추출조건을 찾는데 매우 유용한 방법인 반응 표면 분석법(RSM, response surface methodology)을 사용하여 새우껍질로부터 astaxanthin 추출조건을 최적화하였다. 추출조건은 용매 에탄올과 추출물질의 비율, 추출온도($^{\circ}C$), 추출시간(min)의 세가지 독립변수를 설정하여 BBD (Box-Behnken design) 방법을 이용하였다. 이 BBD 모델링은 0.9218의 $R^2{_{adj}}$값과 0.0003의 확률 값 p 값으로 회귀 모델에 대한 신뢰도를 입증하였다. RSM 분석을 통해 찾아낸 새우껍질로부터 astaxanthin의 최적 추출조건은 에탄올 용매비 1:29.7, 추출온도 $49.5^{\circ}C$, 추출시간 59.9 분이고, 이 때 astaxanthin 추출량은 $17.80{\mu}g/g$으로 예측하였다. 최적 수율로 예측된 결과는 각각의 조건에 따른 실험을 통해 그 예측의 정확도를 확인하였으며 $17.77{\mu}g/g$으로 예측조건과 비슷한 결과를 보였다.

• 제어로봇시스템학회논문지
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• 제15권4호
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• pp.433-438
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• 2009

In this paper, we propose the robust features extraction method of human by using the skeleton model and hybrid silhouette model. The proposed feature extraction method is divided by hands, shoulder line and elbow region extraction. We use the peer's color information to find the position of hands and propose the circle detection method to extract the shoulder line and elbow. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• 대한치과교정학회지
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• 제52권2호
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• pp.102-111
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• 2022

Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.

• ETRI Journal
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• 제28권1호
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• pp.115-118
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• 2006

In this letter, a simple model parameter extraction methodology for an on-chip spiral inductor is proposed based on a wide-band inductor model that incorporates parallel inductance and resistance to model skin and proximity effects, and capacitance to model the decrease in series resistance above the frequency near the peak quality factor. The wide-band inductor model does not require any frequency dependent elements, and model parameters can be extracted directly from the measured data with some curve fitting. The validity of the proposed model and parameter extraction methodology are verified with various size inductors fabricated using $0.18\;{\mu}m$ CMOS technology.