• Title/Summary/Keyword: model for classification system

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A Study on the Model for Construction Records Classification System (건설기록물 분류체계 모형에 관한 연구)

  • Park, Yong-Boo;Kim, Tae-Soo
    • Journal of the Korean Society for information Management
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    • v.28 no.3
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    • pp.83-101
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    • 2011
  • The international standards, ISO 15489 and Family Code, recommend using functional classification method both in public and private organizations. In this study made a comparative analysis of the details of classification systems through case studies on records classification systems of a total of seven comprehensive construction companies in Korea including three large corporations and four small and medium-size businesses. Findings of this study suggester the direction of developing construction records classification system and its methodology. By summarizing classification standards derived from these case studies, key construction records classification standards were presented.

Performance Evaluation of Car Model Recognition System Using HOG and Artificial Neural Network (HOG와 인공신경망을 이용한 자동차 모델 인식 시스템 성능 분석)

  • Park, Ki-Wan;Bang, Ji-Sung;Kim, Byeong-Man
    • Journal of Korea Society of Industrial Information Systems
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    • v.21 no.5
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    • pp.1-10
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    • 2016
  • In this paper, a car model recognition system using image processing and machine learning is proposed and it's performance is also evaluated. The system recognizes the front of car because the front of car is different for every car model and manufacturer, and difficult to remodel. The proposed method extracts HOG features from training data set, then builds classification model by the HOG features. If user takes photo of the front of car, then HOG features are extracted from the photo image and are used to determine the model of car based on the trained classification model. Experimental results show a high average recognition rate of 98%.

Improving the Records Classification System Based on the Business Reference Model (BRM) Through an Analysis of Legislative Classification System Types (법령 기반 분류체계의 유형 분석을 통한 BRM 기반 기록분류 개선 방안 연구)

  • Ziyoung Park
    • Journal of Korean Society of Archives and Records Management
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    • v.24 no.2
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    • pp.139-163
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    • 2024
  • This study aims to analyze classification systems used in the public sector, collected based on legislation, and to improve the classification system for public records. From the Korean Law Information Center, 375 legislative clauses were searched, revealing about 80 classification systems. These systems were initially divided into lists, tables, and hierarchical classifications. Six types of classification system uses were proposed after combining three management types and two system functions. Among these models, classification systems used for core operations in public agencies often had the same entity as both developer and user. While systems adopted from other institutions were often modified as needed, they were predominantly used for reference tasks rather than core operations. However, in records management, crucial tasks such as record classification and disposal commonly use unmodified classification system items developed and managed by other agencies. Consequently, this study proposes that structural improvements are necessary for the record classification system. It suggests developing dedicated classification systems to support core functions or modifying existing systems and also applying records management disposal standards and guidelines to other relevant legislative provisions.

Classification Method of Congestion Change Type for Efficient Traffic Management (효율적인 교통관리를 위한 혼잡상황변화 유형 분류기법 개발)

  • Shim, Sangwoo;Lee, Hwanpil;Lee, Kyujin;Choi, Keechoo
    • International Journal of Highway Engineering
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    • v.16 no.4
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    • pp.127-134
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    • 2014
  • PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.

Cluster-based Linear Projection and %ixture of Experts Model for ATR System (자동 목표물 인식 시스템을 위한 클러스터 기반 투영기법과 혼합 전문가 구조)

  • 신호철;최재철;이진성;조주현;김성대
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.40 no.3
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    • pp.203-216
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    • 2003
  • In this paper a new feature extraction and target classification method is proposed for the recognition part of FLIR(Forwar Looking Infrared)-image-based ATR system. Proposed feature extraction method is "cluster(=set of classes)-based"version of previous fisherfaces method that is known by its robustness to illumination changes in face recognition. Expecially introduced class clustering and cluster-based projection method maximizes the performance of fisherfaces method. Proposed target image classification method is based on the mixture of experts model which consists of RBF-type experts and MLP-type gating networks. Mixture of experts model is well-suited with ATR system because it should recognizee various targets in complexed feature space by variously mixed conditions. In proposed classification method, one expert takes charge of one cluster and the separated structure with experts reduces the complexity of feature space and achieves more accurate local discrimination between classes. Proposed feature extraction and classification method showed distinguished performances in recognition test with customized. FLIR-vehicle-image database. Expecially robustness to pixelwise sensor noise and un-wanted intensity variations was verified by simulation.

Design and Implementation of an Automated Fruit Quality Classification System

  • Choi, Han Suk
    • Smart Media Journal
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    • v.7 no.4
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    • pp.37-43
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    • 2018
  • Most of fruit quality classification has been done by time consuming, inaccurate and intensive manual labor. This study proposed an automated fruit grading system based on appearances and internal flavors. In this study, image processing technique and a weight checker were used to measure the value of appearance features and the near infrared spectroscopy analysis method was used to estimate the value of internal flavors. Additionally, I suggested 8x8x5x5 ANN based fruit quality classifier model to grade fruits quality. The proposed automated fruit quality classification system is expected to be very beneficial for many farms where heavy manual labor is usually needed for fruit quality classification.

Various Quality Fingerprint Classification Using the Optimal Stochastic Models (최적화된 확률 모델을 이용한 다양한 품질의 지문분류)

  • Jung, Hye-Wuk;Lee, Jee-Hyong
    • Journal of the Korea Society for Simulation
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    • v.19 no.1
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    • pp.143-151
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    • 2010
  • Fingerprint classification is a step to increase the efficiency of an 1:N fingerprint recognition system and plays a role to reduce the matching time of fingerprint and to increase accuracy of recognition. It is difficult to classify fingerprints, because the ridge pattern of each fingerprint class has an overlapping characteristic with more than one class, fingerprint images may include a lot of noise and an input condition is an exceptional case. In this paper, we propose a novel approach to design a stochastic model and to accomplish fingerprint classification using a directional characteristic of fingerprints for an effective classification of various qualities. We compute the directional value by searching a fingerprint ridge pixel by pixel and extract a directional characteristic by merging a computed directional value by fixed pixels unit. The modified Markov model of each fingerprint class is generated using Markov model which is a stochastic information extraction and a recognition method by extracted directional characteristic. The weight list of classification model of each class is decided by analyzing the state transition matrixes of the generated Markov model of each class and the optimized value which improves the performance of fingerprint classification using GA (Genetic Algorithm) is estimated. The performance of the optimized classification model by GA is superior to the model before the optimization by the experiment result of applying the fingerprint database of various qualities to the optimized model by GA. And the proposed method effectively achieved fingerprint classification to exceptional input conditions because this approach is independent of the existence and nonexistence of singular points by the result of analyzing the fingerprint database which is used to the experiments.

A Study on Classification System of Urban Facilities Management Service Model in u-City (u-City 도시시설물관리 서비스모델 분류체계 연구)

  • Kim, Tae-Hoon;Nam, Sang-Kwan;Choi, Hyun-Sang
    • Journal of Korean Society for Geospatial Information Science
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    • v.17 no.4
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    • pp.81-86
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    • 2009
  • This research is a part of the Intelligent Urban Facility Management project of the Korean Land Spatialization Group (KGSL). First, this study started from the investigation of existing u-City service model in order to drive essential components and considerations for the urban facilities management system. Considering the driven conclusions, this study finally proposed the new classification system of urban facilities management service model and the adequate application method in u-City.

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Family System Model and Adolescent Adjustment - The Olson Circumplex and Beavers Systems Models - (가족체계모델과 청소년의 적응)

  • 전귀연
    • Korean Journal of Human Ecology
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    • v.2 no.1
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    • pp.38-51
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    • 1999
  • The purpose of this study was to test the validity of Olson Circumplex Model and Beavers Systems Model related to adolescent adjustment. The 830 subjects were selected from the second grade of middle and high schools and adolescents of Juvenile Judge in the city of Taegu. The survey instruments were FACESIII, SFIII, State-Trait Anxiety Inventory, Depression Scale, and Delinquency Scale. Factor Analysis, Cronbach's ${\alpha}$. MANOVA, Scheff'e test were conducted for the data analysis. The major findings of this study were as follows: 1) Family system classification method on Olson Circumplex Model was partially useful in evaluating anxiety, depression, and delinquency of adolescent. 2) Family system classification method on Beavers Systems Model was partially useful in evaluating anxiety and depression of adolescent. (Korean J Human Ecology 2(1) : 38~51, 1999)

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Sasang Constitution Classification System by Morphological Feature Extraction of Facial Images

  • Lee, Hye-Lim;Cho, Jin-Soo
    • Journal of the Korea Society of Computer and Information
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    • v.20 no.8
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    • pp.15-21
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    • 2015
  • This study proposed a Sasang constitution classification system that can increase the objectivity and reliability of Sasang constitution diagnosis using the image of frontal face, in order to solve problems in the subjective classification of Sasang constitution based on Sasang constitution specialists' experiences. For classification, characteristics indicating the shapes of the eyes, nose, mouth and chin were defined, and such characteristics were extracted using the morphological statistic analysis of face images. Then, Sasang constitution was classified through a SVM (Support Vector Machine) classifier using the extracted characteristics as its input, and according to the results of experiment, the proposed system showed a correct recognition rate of 93.33%. Different from existing systems that designate characteristic points directly, this system showed a high correct recognition rate and therefore it is expected to be useful as a more objective Sasang constitution classification system.