• Title/Summary/Keyword: Automatic Data Extraction

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A Feature Vector Extraction Method For the Automatic Classification of Power Quality Disturbances (전력 외란 자동 식별을 위한 특징 벡터 추출 기법)

  • Lee, Chul-Ho;Lee, Jae-Sang;Cho, Kwan-Young;Chung, Ji-Hyun;Nam, Sang-Won
    • Proceedings of the KIEE Conference
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    • 1996.11a
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    • pp.404-406
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    • 1996
  • The objective of this paper is to present a new feature-vector extraction method for the automatic detection and classification of power quality(PQ) disturbances, where FFT, DWT(Discrete Wavelet Transform), and data compression are utilized to extract an appropriate feature vector. In particular, the proposed classifier consists of three parts: i.e., (i) automatic detection of PQ disturbances, where the wavelet transform and signal power estimation method are utilized to detect each disturbance, (ii) feature vector extraction from the detected disturbance, and (iii) automatic classification, where Multi-Layer Perceptron(MLP) is used to classify each disturbance from the corresponding extracted feature vector. To demonstrate the performance and applicability of the proposed classification algorithm, some test results obtained by analyzing 7-class power quality disturbances generated by the EMTP are also provided.

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An Ontology-based Knowledge Management System - Integrated System of Web Information Extraction and Structuring Knowledge -

  • Mima, Hideki;Matsushima, Katsumori
    • Proceedings of the CALSEC Conference
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    • 2005.03a
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    • pp.55-61
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    • 2005
  • We will introduce a new web-based knowledge management system in progress, in which XML-based web information extraction and our structuring knowledge technologies are combined using ontology-based natural language processing. Our aim is to provide efficient access to heterogeneous information on the web, enabling users to use a wide range of textual and non textual resources, such as newspapers and databases, effortlessly to accelerate knowledge acquisition from such knowledge sources. In order to achieve the efficient knowledge management, we propose at first an XML-based Web information extraction which contains a sophisticated control language to extract data from Web pages. With using standard XML Technologies in the system, our approach can make extracting information easy because of a) detaching rules from processing, b) restricting target for processing, c) Interactive operations for developing extracting rules. Then we propose a structuring knowledge system which includes, 1) automatic term recognition, 2) domain oriented automatic term clustering, 3) similarity-based document retrieval, 4) real-time document clustering, and 5) visualization. The system supports integrating different types of databases (textual and non textual) and retrieving different types of information simultaneously. Through further explanation to the specification and the implementation technique of the system, we will demonstrate how the system can accelerate knowledge acquisition on the Web even for novice users of the field.

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Facial Features Extraction for Sasang Constitution Classification (사상채질 분류를 위한 안면부내 특징 요소 추출)

  • Bae, Na-Yeong;An, Taek-Won;Jo, Dong-Uk;Lee, Hwa-Seop
    • Journal of Sasang Constitutional Medicine
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    • v.17 no.2
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    • pp.46-51
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    • 2005
  • 1. Objectives The purpose of this study is to objectify the diagnosis of Sasang Constitution. Using the methods of this study, it will improve to classificate Sasang Constitution. 2. Methods 1) Automatic feature extraction of human frontal faces for Sasang Constitution classification. 2) Color feature extraction of human frontal faces (1)Erosion filtering (skin-white, the other-black) (2) Median median 3. Results and Conclusions Observing a person's shape has been the major method for Sasang Constitution classification, which usually has been dependent upon doctor's intuition as of these days. We are developing an automatic system which provides objective basic data for Sasang Constitution classification. For this, in this paper, firstly, the signal processing techniques are applied to automatic feature extraction of human frontal faces for Sasang Constitution classification. The experiment is conducted to verify the effectiveness of the proposed system.

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Self-Evolving Expert Systems based on Fuzzy Neural Network and RDB Inference Engine

  • Kim, Jin-Sung
    • Journal of Intelligence and Information Systems
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    • v.9 no.2
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    • pp.19-38
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    • 2003
  • In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

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Automatic Building Extraction Using LIDAR Data

  • Cho, Woo-Sug;Jwa, Yoon-Seok
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.1137-1139
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    • 2003
  • This paper proposed a practical method for building detection and extraction using airborne laser scanning data. The proposed method consists mainly of two processes: low and high level processes. The major distinction from the previous approaches is that we introduce a concept of pseudogrid (or binning) into raw laser scanning data to avoid the loss of information and accuracy due to interpolation as well as to define the adjacency of neighboring laser point data and to speed up the processing time. The approach begins with pseudo-grid generation, noise removal, segmentation, grouping for building detection, linearization and simplification of building boundary , and building extraction in 3D vector format. To achieve the efficient processing, each step changes the domain of input data such as point and pseudo-grid accordingly. The experimental results shows that the proposed method is promising.

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Feature Vector Extraction and Automatic Classification for Transient SONAR Signals using Wavelet Theory and Neural Networks (Wavelet 이론과 신경회로망을 이용한 천이 수중 신호의 특징벡타 추출 및 자동 식별)

  • Yang, Seung-Chul;Nam, Sang-Won;Jung, Yong-Min;Cho, Yong-Soo;Oh, Won-Tcheon
    • The Journal of the Acoustical Society of Korea
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    • v.14 no.3
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    • pp.71-81
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    • 1995
  • In this paper, feature vector extraction methods and classification algorithms for the automatic classification of transient signals in underwater are discussed. A feature vector extraction method using wavelet transform, which shows good performance with small number of coefficients, is proposed and compared with the existing classical methods. For the automatic classification, artificial neural networks such as multilayer perceptron (MLP), radial basis function (RBF), and MLP-Class are utilized, where those neural networks as well as extracted feature vectors are combined to improve the performance and reliability of the proposed algorithm. It is confirmed by computer simulation with Traco's standard transient data set I and simulated data that the proposed feature vector extraction method and classification algorithm perform well, assuming that the energy of a given transient signal is sufficiently larger than that of a ambient noise, that there are the finite number of noise sources, and that there does not exist noise sources more than two simultaneously.

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Comparison of Performance Factors for Automatic Classification of Records Utilizing Metadata (메타데이터를 활용한 기록물 자동분류 성능 요소 비교)

  • Young Bum Gim;Woo Kwon Chang
    • Journal of the Korean Society for information Management
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    • v.40 no.3
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    • pp.99-118
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    • 2023
  • The objective of this study is to identify performance factors in the automatic classification of records by utilizing metadata that contains the contextual information of records. For this study, we collected 97,064 records of original textual information from Korean central administrative agencies in 2022. Various classification algorithms, data selection methods, and feature extraction techniques are applied and compared with the intent to discern the optimal performance-inducing technique. The study results demonstrated that among classification algorithms, Random Forest displayed higher performance, and among feature extraction techniques, the TF method proved to be the most effective. The minimum data quantity of unit tasks had a minimal influence on performance, and the addition of features positively affected performance, while their removal had a discernible negative impact.

Landmark Extraction for 3D Human Body Scan Data Using Markerless Matching (마커 없는 매칭을 활용한 3 차원 인체 스캔 데이터의 기준점 추출)

  • Yoon, Dong-Wook;Heo, Nam-Bin;Ko, Hyeong-Seok
    • 한국HCI학회:학술대회논문집
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    • 2009.02a
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    • pp.163-167
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    • 2009
  • 3D human body scan technique is known to be practically useful in industrial field as the technique becomes more precise and cheaper. Landmark extraction is essential for full utilization of the scan data. In this paper, we suggest an algorithm for automatic landmark extraction. For this purpose, we perform markerless matching to the target data using PCA analysis and quasi-Newton optimization. Landmarks are extracted from the topology of resulting body.

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Feature Extraction Algorithm for Underwater Transient Signal Using Cepstral Coefficients Based on Wavelet Packet (웨이브렛 패킷 기반 캡스트럼 계수를 이용한 수중 천이신호 특징 추출 알고리즘)

  • Kim, Juho;Paeng, Dong-Guk;Lee, Chong Hyun;Lee, Seung Woo
    • Journal of Ocean Engineering and Technology
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    • v.28 no.6
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    • pp.552-559
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    • 2014
  • In general, the number of underwater transient signals is very limited for research on automatic recognition. Data-dependent feature extraction is one of the most effective methods in this case. Therefore, we suggest WPCC (Wavelet packet ceptsral coefficient) as a feature extraction method. A wavelet packet best tree for each data set is formed using an entropy-based cost function. Then, every terminal node of the best trees is counted to build a common wavelet best tree. It corresponds to flexible and non-uniform filter bank reflecting characteristics for the data set. A GMM (Gaussian mixture model) is used to classify five classes of underwater transient data sets. The error rate of the WPCC is compared using MFCC (Mel-frequency ceptsral coefficients). The error rates of WPCC-db20, db40, and MFCC are 0.4%, 0%, and 0.4%, respectively, when the training data consist of six out of the nine pieces of data in each class. However, WPCC-db20 and db40 show rates of 2.98% and 1.20%, respectively, while MFCC shows a rate of 7.14% when the training data consists of only three pieces. This shows that WPCC is less sensitive to the number of training data pieces than MFCC. Thus, it could be a more appropriate method for underwater transient recognition. These results may be helpful to develop an automatic recognition system for an underwater transient signal.

A Modified Iterative N-FINDR Algorithm for Fully Automatic Extraction of Endmembers from Hyperspectral Imagery (초분광 영상의 endmember 자동 추출을 위한 수정된 Iterative N-FINDR 기법 개발)

  • Kim, Kwang-Eun
    • Korean Journal of Remote Sensing
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    • v.27 no.5
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    • pp.565-572
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    • 2011
  • A modified iterative N-FINDR algorithm is developed for fully automatic extraction of endmembers from hyperspectral image data. This algorithm exploits the advantages of iterative NFINDR technique and Iterative Error analysis technique. The experiments using a simulated hyperspectral image data shows that the optimum number of endmembers can be automatically decided. The extracted endmembers and finally generated abundance fraction maps show the potentialities of the proposed algorithm. More studies are needed for verification of the applicability of the algorithm to the real hyperspectral image data where the absence of pure pixels is common.