• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2008년도 춘계학술발표대회
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• pp.231-234
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• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in load demand data. The main aim of our work is to forecast customers' contract information from capacity of daily power consumption patterns. According to the result, we try to evaluate the contract information's suitability. The proposed our approach consists of three stages: (i) data preprocessing: noise or outlier is detected and removed (ii) cluster analysis: SOMs clustering is used to create load patterns and the representative load profiles and (iii) classification: we applied the K-NNs classifier in order to predict the customers' contract information base on power consumption patterns. According to the our proposed methodology, power load measured from AMR(automatic meter reading) system, as well as customer indexes, were used as inputs. The output was the classification of representative load profiles (or classes). Lastly, in order to evaluate KNN classification technique, the proposed methodology was applied on a set of high voltage customers of the Korea power system and the results of our experiments was presented.

• 한국비블리아학회지
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• 제7권1호
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• pp.193-230
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• 1994

The systematic classification of database is much debated issue currently in telecommunication industry. Nevertheless, the attempt to build the systematic model is nowadays nowhere to be found. The purpose of this study is to gain a general overview relating to this subject and to make out a draft for the development of standard model. Relating th the study for the databases classification, it was classified from the 9 points of view： manufacturer, subject, processed form (level), (re)presented form, language, completion state and updating cycle, retrieval method, communication media, and use.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.609-615
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• 1998

In this paper, we propose efficient total recognition system of handwritten and printed numerals for enhancing the classification time. The proposed system consist two step neuroclassifier: Printed numerals classifier and Handwritten numerals classifier. The performance of the propose classifier was tested on 5000 handwritten numerals database of NIST and 100 printed numerals database. In case of handwritten classifier, the overall classification times were 11 second. And in case of proposed system, the overall classification times were reduced by...

• 반도체디스플레이기술학회지
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• 제10권3호
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• pp.75-81
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• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

• Asia pacific journal of information systems
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• 제17권1호
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• pp.33-58
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• 2007

Quiet often, an organization tries to grapple with inconsistent and partial information to generate relevant information to support decision making and action. As such, an organization scans the environment interprets scanned data, executes actions, and learns from feedback of actions, which boils down to computational interpretations and learning in terms of machine learning, statistics, and database. The ExOM proposed in this paper is geared to facilitate such knowledge discovery found in large databases in a most flexible manner. It supports a broad range of learning and classification styles and integrates them with traditional database functions. The learning and classification components of the ExOM are tightly integrated so that learning and classification of objects is less burdensome to ordinary users. A brief sketch of a strategy as to the expressiveness of terminological language is followed by a description of prototype implementation of the learning and classification components of the ExOM.

• 한국콘텐츠학회논문지
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• 제17권9호
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• pp.132-144
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• 2017

대용량 지문 데이터베이스를 사용하는 지문인식 시스템에서 처리 속도와 정확성을 높이기 위해서는 지문을 클래스별로 카테고리화하는 지문분류 기술을 사용해야 한다. 지문분류 방법은 지문 융선으로부터 특징을 추출하고 지문 융선의 흐름과 형상에 따라 정의되어 있는 클래스를 기준으로 학습 및 추론 기법을 이용하여 분류한다. 기존에는 종이에 회전 날인하여 습득된 NIST 데이터베이스를 이용한 연구가 많이 수행되었지만, 지문인식 입력 센서를 이용한 자동화된 시스템이 보편화됨에 따라 FVC에서 공개한 지문 데이터와 같이 센서로부터 입력된 지문 이미지를 이용한 연구가 증가하고 있으며, 최근에는 딥러닝을 이용한 지문분류 방법이 제안되고 있다. 본 논문에서는 지문분류를 위한 특징 추출 및 분류 기술의 동향을 살펴보고 분류성능을 비교한다. 또한 센서 기반 지문 이미지의 다양한 품질을 고려한 지문분류 기술 연구의 필요성에 대하여 정리하고, 딥러닝 기술을 적용한 지문분류 방법을 분석해 봄으로써 지속적으로 사용이 증가되고 있는 대용량 지문 데이터베이스의 분류 기술 연구에 대한 성능향상에 보탬이 되고자 한다.

• 간호행정학회지
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• 제9권4호
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• pp.651-661
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• 2003

Purpose: This is the descriptive research project of which purpose is to acquire the practice, research, and educational data by establishing the database after confirming, classifying, and relating the nursing diagnosis, nursing intervention, and nursing outcome of Breast cancer patients by using the Yoo Hyung-sook's(2001) related 3N database model as the tool. Method : The Nursing Data occurring on Breast cancer patients nursing process was mapped to nursing diagnosis of NANDA, nursing interventions of NIC, nursing outcomes of NOC the 3N database linkage database which is related with the nursing process that was developed by using Yoo Hyung-sook's(2001). Result : 1. The nursing diagnosis were totally 505, and 26 articles of the nursing diagnosis were applied among 149 nursing diagnosis classification systems. 2. As for the nursing intervention, 250 articles(5l.4%) of nursing intervention were applied among 486 nursing intervention classification systems. 3. Regarding the nursing outcome, 28 articles(1l.2%l of the nursing outcome were applied among 250 nursing outcome classification systems. Conclusion: The result of this research in which the relating among the nursing diagnosis, nursing intervention, and nursing outcome of Breast cancer patients by using 3N nursing database was established is thought to be applied in the research and practice as well as to be utilized in the lecture or practice of the nursing process.

• 한국컴퓨터정보학회논문지
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• 제24권2호
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• pp.127-137
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• 2019

In this study, we have developed a calculation system for BIM-based quantities, 4D process, and 5D construction costs, by integrating object shape attributes and the standard classification system which consist of Cost Breakdown System(CBS), Object Breakdown System(OBS) and Work Breakdown System(WBS) in order to use for the 4 dimensional process control of roads and rivers. First, a new BIM library database connected with the BIM library shape objects was built according to the CBS/OBS/WBS standard classification system of the civil engineering field, and a integrated database system of BIM-based quantities, process(4D), and construction costs(5D) for roads and rivers was constructed. Nextly, the process classification system and the cost classification system were automatically disassembled to the BIM objects consisting of the Revit-family style elements. Finally, we added functions for automatically generating four dimensional activities and generating a automatic cost statement according to the combination of WBS and CBS classification system The ultimate goal of this study was to extend the integrated quantities, process(4D), and construction costs(5D) system for new roads and rivers, enabling the integrated use of process(4D) and construction costs(5D) in the design and construction stage, based on the tasks described above.

• Genomics & Informatics
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• 제16권4호
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• pp.22.1-22.5
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• 2018

In 1966, the International Classification of Viruses (ICNV) was established to standardize the naming of viruses. In 1975, the organization was renamed "International Committee on Taxonomy of Viruses (ICTV)," by which it is still known today. The primary virus classification provided by ICTV in 1971 was for viruses infecting vertebrates, which includes 19 genera, 2 families, and 24 unclassified groups. Presently, the 10th virus taxonomy has been published. However, the early classification of viruses was based on clinical results "in vivo" and "in vitro," as well as on the shape of the Phenotype virus. Due to the development of next-generation sequencing and the accompanying bioinformatics analysis pipelines, a reconstruction of the classification system has been proposed. At a meeting held in Boston, USA between June 9-11, 2016, there was even an in-depth discussion regarding the classification of viruses using metagenomic data. One suggested activity that arose from the meeting was that viral taxonomy should be reconstructed, based on genotype and bioinformatics analysis "in silico." This article describes our efforts to achieve this goal by construction of a web-based system and the extension of an associated database, based on ICTV taxonomy. This virus taxonomy web system was designed specifically to extend the virus taxonomy up to strain and isolation, which was then connected with the NCBI database to facilitate searches for specific viral genes; there are also links to journals provided by the EMBL RESTful API that improves accessibility for academic groups.

• 방사선산업학회지
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• 제8권3호
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• pp.155-159
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• 2014

RI-Biomics is a new concept that combines radioisotopes (RI) and Biomics. For efficient collection of information, establishment of database for technical information system and its application to the system, there is an increasing need for constructing the standard classification system of technical information by its systematical classification. In this paper, we have summarized the development process of the standard classification system of technical information in the field of RI-Biomics and its application to the system. Constructing the draft version for the standard classification system of technical information was based on that standard classification one in national science and technology in Korea. The final classification system was then derived through the reconstruction and the feedback process based on the consultation from the 7 experts. These results were applied to the database of technical information system after transforming as standard code. Thus, the standard classification system were composed of 5 large classifications and 20 small classifications, and those classification are expected to establish the foundation of information system by achieving the circular structure of collection-analysis-application of information.