• Journal of Information Management
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• v.40 no.4
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• pp.1-22
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• 2009

Large-scaled information extraction plays an important role in advanced information retrieval as well as question answering and summarization. Information extraction can be defined as a process of converting unstructured documents into formalized, tabular information, which consists of named-entity recognition, terminology extraction, coreference resolution and relation extraction. Since all the elementary technologies have been studied independently so far, it is not trivial to integrate all the necessary processes of information extraction due to the diversity of their input/output formation approaches and operating environments. As a result, it is difficult to handle scientific documents to extract both named-entities and technical terms at once. In this study, we define scientific as a set of 10 types of named entities and technical terminologies in a biomedical domain. in order to automatically extract these entities from scientific documents at once, we develop a framework for scientific core entity extraction which embraces all the pivotal language processors, named-entity recognizer, co-reference resolver and terminology extractor. Each module of the integrated system has been evaluated with various corpus as well as KEEC 2009. The system will be utilized for various information service areas such as information retrieval, question-answering(Q&A), document indexing, dictionary construction, and so on.

• Journal of the Korean Geotechnical Society
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• v.27 no.8
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• pp.63-71
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• 2011

In case of applying BIM method to the civil engineering of irregularly shaped structure, BIM method is recognized to have relatively high construction productivity. In this paper, we developed quantity calculation algorithms applying BIM method to NATM tunnel construction method and implemented BIM based 3D-BIM Modeling Quantity Calculation. The results showed that BIM-based method has high reliabilty in structure work in which errors occurred only in the range between 0.00% and -1.45%. On the other hand, BIM method applied to earth work showed great error range of -19.78% to 35.30%. So the benefit and applicability of BIM method in civil engineering were confirmed. In addition, routine method for the quantity of earth work has negligible error as in the case of structure work. But, rock type's quantity calculation showed significant errors so that the reliability of 2D-based volume calculation is problematic. It may thus be concluded that 3D-BIM is more reliable than the routine method in estimating the quantity of earth work. Considering the reliability and merits in the stage of its design, construction and maintenance levels, the application of BIM to civil engineering works is recommended.

• Korean Journal of Food Science and Technology
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• v.47 no.3
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• pp.321-327
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• 2015

The optimal condition for preparation of powdered calcium acetate (LCCA) which has high solubility, from calcined powder (LCCP) of the littleneck clam shell by response surface methodology (RSM) was examined. Increased molar ratio of LCCP led to reduced solubility, yield, color values, and overall quality. The critical values of multiple response optimization of independent variables were 2.57 M of acetic acid and 1.57 M of LCCP. The actual values (pH 7.0, 96.1% for solubility, and 220.9% for yield) under the optimized condition were similar to the predicted values. LCCA showed strong buffering capacity between pH 4.89 and 4.92 on addition of ~2 mL of 1 N HCl. The calcium content and solubility of LCCA were 21.9-23.0 g/100 g and 96.1-100.1%, respectively. The FT-IR and XRD patterns of LCCA were identified as calcium acetate monohydrate, and FESEM images revealed an irregular and rod-like microstructure.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.377-392
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• 2015

In today's data-driven society, we've been hearing a great deal about the power of Big Data over the last couple of years. At the same time, it has become the most important issue that the problems is caused by the data collection, management and utilization. Moreover, Big Data has a wide applications ranging from situation awareness, decision-making to the area to enable for the foreseeable future with man-made and analysis of data. It is necessary to process data into meaningful information given that the huge amount of structured and unstructured data being created in the private and the public sector, even in disaster management. This data should be public and private sector at the same time for the appropriate linkage analysis for effective disaster management. In this paper, we conducted a literature review and case study efficient Big Data to derive the revitalization of national disaster management. The study obtained data on the role and responsibility of the public sector and the private sector to leverage Big Data for promotion of national disaster management plan. Both public and private sectors should promote common development challenges related to the openness and sharing of Big Data, technology and expansion of infrastructure, legal and institutional maintenance. The implications of the finding were discussed.

• Korean Journal of Ecology and Environment
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• v.48 no.3
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• pp.195-202
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• 2015

We identified research trends for freshwater exotic species in South Korea using text mining methods in conjunction with bibliometric analysis. We searched scientific and common names of freshwater exotic species as searching keywords including 1 mammal species, 3 amphibian-reptile species, 11 fish species, 2 aquatic plant species. A total of 245 articles including research articles and abstracts of conference proceedings published by 56 academic societies and institutes were collected from scientific article databases. The search keywords used were the common names for the exotic species. The $20^{th}$ century (1900's) saw the number of articles increase; however, during the early $21^{st}$ century (2000's) the number of published articles decreased slowly. The number of articles focusing on physiological and embryological research was significantly greater than taxonomic and ecological studies. Rainbow trout and Nile tilapia were the main research topic, specifically physiological and embryological research associated with the aquaculture of these species. Ecological studies were only conducted on the distribution and effect of large-mouth bass and nutria. The ecological risk associated with freshwater exotic species has been expressed yet the scientific information might be insufficient to remove doubt about ecological issues as expressed by interested by individuals and policy makers due to bias in research topics with respect to freshwater exotic species. The research topics of freshwater exotic species would have to diversify to effectively manage freshwater exotic species.

• Science of Emotion and Sensibility
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• v.22 no.2
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• pp.49-58
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• 2019

In the era of the 4th Industrial Revolution, artificial intelligence (AI) has become one of the core technologies in terms of the business strategy among information technology companies. Both international and domestic major portal companies are launching AI search services. These AI search services utilize voice, images, and other unstructured data to provide different experiences from existing text-based search services. An unfamiliar experience is a factor that can hinder the usability of the service. Therefore, the usability testing of the AI search services is necessary. This study examines the usability of the AI search service on the Naver App 8.9.3 beta version by comparing it with the search services of the current Naver App and targets 30 people in their 20s and 30s, who have experience using Naver apps. The usability of Smart Lens, Smart Voice, Smart Around, and AiRS, which are the Naver App beta versions of their artificial intelligence search service, is evaluated and statistically significant usability changes are revealed. Smart Lens, Smart Voice, and Smart Around exhibited positive changes, whereas AiRS exhibited negative changes in terms of usability. This study evaluates the change in usability according to the application of the artificial intelligence search services and investigates the correlation between the evaluation factors. The obtained data are expected to be useful for the usability evaluation of services that use AI.

• The Journal of Bigdata
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• v.6 no.1
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• pp.63-81
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• 2021

The performance of natural language processing is rapidly improving due to the recent development and application of machine learning and deep learning technologies, and as a result, the field of application is expanding. In particular, as the demand for analysis on unstructured text data increases, interest in NLP(Natural Language Processing) is also increasing. However, due to the complexity and difficulty of the natural language preprocessing process and machine learning and deep learning theories, there are still high barriers to the use of natural language processing. In this paper, for an overall understanding of NLP, by examining the main fields of NLP that are currently being actively researched and the current state of major technologies centered on machine learning and deep learning, We want to provide a foundation to understand and utilize NLP more easily. Therefore, we investigated the change of NLP in AI(artificial intelligence) through the changes of the taxonomy of AI technology. The main areas of NLP which consists of language model, text classification, text generation, document summarization, question answering and machine translation were explained with state of the art deep learning models. In addition, major deep learning models utilized in NLP were explained, and data sets and evaluation measures for performance evaluation were summarized. We hope researchers who want to utilize NLP for various purposes in their field be able to understand the overall technical status and the main technologies of NLP through this paper.

• Journal of the Korea Institute of Building Construction
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• v.22 no.3
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• pp.281-291
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• 2022

This study attempts to use big data to determine the indicators necessary for a fire risk assessment of buildings. Because most of the causes affecting the fire risk of buildings are fixed as indicators considering only the building itself, previously only limited and subjective assessment has been performed. Therefore, if various internal and external indicators can be considered using big data, effective measures can be taken to reduce the fire risk of buildings. To collect the data necessary to determine indicators, a query language was first selected, and professional literature was collected in the form of unstructured data using a web crawling technique. To collect the words in the literature, pre-processing was performed such as user dictionary registration, duplicate literature, and stopwords. Then, through a review of previous research, words were classified into four components, and representative keywords related to risk were selected from each component. Risk-related indicators were collected through analysis of related words of representative keywords. By examining the indicators according to their selection criteria, 20 indicators could be determined. This research methodology indicates the applicability of big data analysis for establishing measures to reduce fire risk in buildings, and the determined risk indicators can be used as reference materials for assessment.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.1
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• pp.68-72
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• 2022

Angioimmunoblastic T-cell lymphoma (AITL) is a lymphoproliferative disorder of mature T follicular helper cells. Atypical lymphoid cells were observed in the bone marrow of an 80-year-old woman, and the flow cytometric determined immunophenotypes of B-cells were unusual, that is, CD19+, CD20-, and CD22- with lambda light chain restriction. Initially, we suspected BM involvement of B-cell lymphoma based on the presence of abnormal B-cells. However, the patient was diagnosed with AITL involving BM. A re-analysis of flow cytometric immunophenotyping revealed a minor, aberrant T-cell population, and the lambda light chain restriction observed by surface staining was considered non-specific binding. This case demonstrates B-cells in patients with EBV-positive T-cell lymphoma may exhibit immunophenotypes resembling those of plasma cells, and that proliferation of abnormal B-cells or plasma cells could also potentially mask underlying T-cell lymphoma. A more integrated approach is required for accurate diagnosis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.841-849
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• 2023

Construction projects have risks due to various factors such as construction delays and construction accidents. Based on these construction risks, the method of calculating the construction period of the construction project is mainly made by subjective judgment that relies on supervisor experience. In addition, unreasonable shortening construction to meet construction project schedules delayed by construction delays and construction disasters causes negative consequences such as poor construction, and economic losses are caused by the absence of infrastructure due to delayed schedules. Data-based scientific approaches and statistical analysis are needed to solve the risks of such construction projects. Data collected in actual construction projects is stored in unstructured text, so to apply data-based risks, data pre-processing involves a lot of manpower and cost, so basic data through a data classification model using text mining is required. Therefore, in this study, a document-based data generation classification model for risk management was developed through a data classification model based on SVM (Support Vector Machine) by collecting construction project documents and utilizing text mining. Through quantitative analysis through future research results, it is expected that risk management will be possible by being used as efficient and objective basic data for construction project process management.