• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.278-287
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• 2019

Spatial information extraction is to retrieve static and dynamic aspects in natural language text by explicitly marking spatial elements and their relational words. This paper proposes a deep learning approach for spatial information extraction for Korean language using a two-step bidirectional LSTM-CRF ensemble model. The integrated model of spatial element extraction and spatial relation attribute extraction is proposed too. An experiment with the Korean SpaceBank demonstrates the better efficiency of the proposed deep learning model than that of the previous CRF model, also showing that the proposed ensemble model performed better than the single model.

• Korean Chemical Engineering Research
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• v.55 no.4
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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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• v.13 no.4
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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.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.7
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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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• v.16 no.3
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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.

• Proceedings of the KSRS Conference
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• 2003.11a
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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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• v.50 no.3
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• pp.545-550
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• 2012

A 17-run Box-Behnken design (BBD) was used to optimize the extraction conditions of astaxanthin from shrimp waste. Three factors such as ratio of ethanol to raw material, extraction temperature ($^{\circ}C$) and extraction time (min) were investigated. The adjusted coefficient of determination ($R^2{_{adj}}$) for the model was 0.9218, and the probability value (p=0.0003) demonstrated a high significance for the regression model. The optimum extraction conditions were found to be: optimized ratio of ethanol to raw material 29.7, extraction temperature $49.5^{\circ}C$ and extraction time 59.9 min. Under these conditions, the mean extraction yield of astaxanthin was $17.80{\mu}g/g$, which was in good agreement with the predicted model value. Under these conditions, validation experiments were done and the mean extraction yield of astaxanthin was $17.77{\mu}g/g$, which is in good agreement with the predicted model value.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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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.

• The korean journal of orthodontics
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• v.52 no.2
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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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• v.28 no.1
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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.