• 한국재난정보학회 논문집
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• 제14권1호
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• pp.28-35
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• 2018

본 연구에서는 재난현장 또는 일상에서 발생할 수 있는 다수사상자의 중증도 분류를 신속하고 정확하게 수행하기 위한 시스템을 설계하여 구현하였으며, 중증도 분류 알고리즘의 정확도뿐만 아니라 사용자 편의성 등 현장의 요구사항을 적극 반영하였다. 개발된 e-Triage System은 IoT개념을 활용하여 다양한 중증도 분류 알고리즘을 적용하였으며, 기존의 중증도 분류표의 단점을 극복하기 위하여 NFC 모듈 등 전자적 요소를 반영한 e-Triage Tag를 구현하였다. 앱으로 구현된 중증도 분류 알고리즘을 사용하여 신속하고 정확한 환자의 평가가 가능함을 입증하였고, 시인성을 위해 전자 중증도 분류 결과를 4가지 LED램프로 표출하였으며, 2차 분류를 통해 RTS 점수를 FND(Flexible Numeric Display)로 표출하였다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2001년도 추계학술대회 논문집
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• pp.265-268
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• 2001

As growing the electronic commerce there are significant changes in the products/services catalog into the on-line environment. Advertent of e-catalog business opportunity for their own product/services enlarges the market volume and there are diverse methods for the presentation of its product/services. A method for the presentation of product/services features one uses identification and classification system. This study constructs a classification system and database layout for the product/services classification system as a part of e-catalog system. We consider the specific method for the GDAS-based dataset and UNSPSC classification system in the distribution industry.

• 한국철도학회논문집
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• 제9권4호
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• pp.335-342
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• 2006

Disaster management system should be built for minimizing damage factor that affects to construction facility from natural disaster. It could be classified by three categories such as disaster prevention, damage survey and recovery phases. For an integrated disaster management system, a disaster information classification system(DICS) is necessary for the reasonable disaster information management. This study suggests an integrated DICS that includes disaster type classification, facility type classification and information type classification for disaster management service. The applicability of suggested DICS is verified by railway facility and the research result could be used as a basic information system for national disaster management system.

• 한국문헌정보학회지
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• 제23권
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• pp.207-261
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• 1992

The purpose of this study is (1) to design and test a database which can be automatically classified, and (2) to generate automatic classification number by processing the keywords in titles using the code combination method of Colon Classification(CC) as well as an automatic recognition of subjects in order to develop an automatic classification system (Auto BC System) based on CC which can be applied to any research library. To conduct this study, 1,510 words in the fields of agricultrue and medicine were selected, analized in terms of [P], [M], [E], [S], [T] employed in CC, and included in a database for classification. For the above-mentioned subject fields, the principle of an automatic classification was specified in order to generate automatic classification codes as well as to perform an automatic subject recognition of the titles included. Whenever necessary, editing, deleting, appending and reindexing of a database can be made in this automatic classification system. Appendix 1 shows the result of the automatic classification of books in the fields of agriculture and medicine. The results of the study are summarized below. 1. The classification number for the title of a book can be automatically generated by using the facet principles of Colon Classification. 2. The automatic subject recognition of a book is achieved by designing a database making use of a globe-principle, and by specifying the subject field for each word. 3. The automatic subject-recognition of input data is achieved by measuring the number of searched words by each subject field. 4. The combination of classification numbers is achieved by flowcharting of classification formular of each subject field. 5. The efficient control of classification numbers is achieved by designing control codes on the database for classification. 6. The automatic classification by means of Auto BC has been proved to be successful in the research library concentrating on a Single field. The general library may have some problem in employing this system. The automatic classification through Auto BC has the following advantages: 1. Speed of the classification process can be improve. 2. The revision or updating of classification schemes can be facilitated. 3. Multiple concepts can be expressed in a single classification code. 4. The consistency of classification can be achieved with the classification formular rather than the classifier's subjective judgement. 5. A user's retrieving process can be made after combining the classification numbers through keywords relating to the material to be searched. 6. The materials can be classified by a librarian without subject backgrounds. 7. The large body of materials can be quickly classified by means of a machine processing. 8. This automatic classification is expected to make a good contribution to design of the total system for library operations. 9. The information flow among libraries can be promoted owing to the use of the same program for the automatic classification.

• Journal of information and communication convergence engineering
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• 제17권1호
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• pp.41-48
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• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.

• 대한의용생체공학회:의공학회지
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• 제39권6호
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• pp.268-277
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• 2018

With drastic change in the market and technology of medical devices, a comparative analysis is necessary in advanced systems internationally in order to prepare domestically applicable plans for improvement in classification and differential grade management for items of medical devices. This research examines and analyzes the differences of definition and legal systems of medical devices among Korea, United States, EU, Japan and China, and investigates classification and grading system of each country to identify disadvantages of classification and grading structures for medical device in Korea. This research suggests ways to supplement the disadvantages of domestic classification and grading system of medical devices, and elicits differential management plans for medical devices.

• 지능정보연구
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• 제1권1호
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• pp.61-72
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• 1995

Dynamic and explicit ordering of strategies is a key process in modeling knowledge-level problem-solving behavior. This paper addressed the important problem of howl to make the scheduler more knowledge-intensive in a way that facilitates the acquisition, integration, and maintenance of the scheduler control knowledge. The solution a, pp.oach described in this paper involved formulating the scheduler task as a heuristic classification problem, and then implementing it as a classification expert system. By doing this, the wide spectrum of known methods of acquiring, refining, and maintaining the knowledge of a classification expert system are a, pp.icable to the scheduler control knowledge. One important innovation of this research is that of recursive heuristic classification : this paper demonstrates that it is possible to formulate and solve a key subcomponent of heuristic classification as heuristic classification problem. Another key innovation is the creation of a method of dynamic heuristic classification : the classification alternatives that are selected among are dynamically generated in real-time and then evidence is gathered for and aginst these alternatives. In contrast, the normal model of heuristic classification is that of structured selection between a set of preenumerated fixed alternatives.

• 한국비블리아학회지
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• 제33권2호
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• pp.55-73
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• 2022

본 연구는 국내 분류체계의 사례를 조사하고 학술표준분류체계와 비교·분석하여 향후 개선 방향성을 도출하고자 국내 각 분야에서 운영되는 분류체계의 사례를 살펴보았다. 이를 바탕으로 제시하는 학술표준분류체계의 향후 개선 방향성은 다음과 같다. 첫째, 학술표준분류체계의 지속적 발전을 위해서 법률로서 분류체계의 운영을 명확하게 보장하는 것이 필요한 것으로 보인다. 둘째, 범용성 넓은 분류체계의 제작으로 학문연구 시 국내외 자료 수집 및 비교를 원활하게 할 수 있도록 현안과 세계적 범용성을 모두 충족하는 포괄적 분류원칙으로 개선해 나가야 한다. 셋째, 학술표준분류체계의 명확한 개정주기 선정이 필요하며, 방대한 분야에 걸친 학문 분야를 반영하기 위해서는 5년 주기로 개정을 진행하는 것이 적절한 것으로 보인다. 현재 이와 같은 국내 분류체계에 관한 연구가 부족한 실정으로 향후 이와 같은 조사가 지속적으로 이루어져 국내 분류체계에 대한 지속적 관심과 연구가 필요하다.

• 정보기술과데이타베이스저널
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• 제2권2호
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• pp.71-85
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• 1995

Many research results conducted by neural network researchers have claimed that the classification accuracy of neural networks is superior to, or at least equal to that of conventional methods. However, in series of neural network classifications, it was found that the classification accuracy strongly depends on the characteristics of training data set. Even though there are many research reports that the classification accuracy of neural networks can be different, depending on the composition and architecture of the networks, training algorithm, and test data set, very few research addressed the problem of classification accuracy when the basic assumption of data monotonicity is violated, In this research, development project of automated credit evaluation system is described. The finding was that arrangement of training data is critical to successful implementation of neural training to maintain monotonicity of the data set, for enhancing classification accuracy of neural networks.

• 한국임상약학회지
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• 제22권3호
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• pp.260-267
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• 2012

The objective of this study was to investigate drug classification system in Korea and other developed countries. Laws and regulations of Korea regarding the system were retrieved from sources posted in Ministry of Government Legislation. We also reviewed previous research reports performed as part of government's effort to reform the system The system in the foreign countries was retrieved from the official homepage operated by each country's government. There have been two research funded by Korean government, which strongly suggested that the system should be reformed. However, we found that the system was never reformed and still effective. Drug classification system in US and most western countries consists of two categories, i.e., prescription drugs and non-prescription drugs except UK, which classifies into three categories: Prescription Only Medicines, Pharmacy Medicines, and General Sales List Medicines. Interestingly, in Japan, non-prescription drugs are further classified into three groups: Group 1, 2, and 3. Recently, Ministry of Health and Welfare (MOHW) in Korea proposed a plan to reclassify all the approved drugs according to purportedly rational and scientific criteria. However, the plan does not include reform of the existing laws and regulations, which appears that it is just one-time action rather than a sustainable administration backed up by law. Therefore, it is recommended that Korean MOHW take appropriate action on laws and regulations with regard to the system to meet global harmonization standard.