• 제목/요약/키워드: State Classification

검색결과 934건 처리시간 0.028초

Use of DNA Methylation for Cancer Detection and Molecular Classification

  • Zhu, Jingde;Yao, Xuebiao
    • BMB Reports
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    • 제40권2호
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    • pp.135-141
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    • 2007
  • Conjugation of the methyl group at the fifth carbon of cytosines within the palindromic dinucleotide 5'-CpG-3' sequence (DNA methylation) is the best studied epigenetic mechanism, which acts together with other epigenetic entities: histone modification, chromatin remodeling and microRNAs to shape the chromatin structure of DNA according to its functional state. The cancer genome is frequently characterized by hypermethylation of specific genes concurrently with an overall decrease in the level of 5-methyl cytosine, the pathological implication of which to the cancerous state has been well established. While the latest genome-wide technologies have been applied to classify and interpret the epigenetic layer of gene regulation in the physiological and disease states, the epigenetic testing has also been seriously explored in clinical practice for early detection, refining tumor staging and predicting disease recurrence. This critique reviews the latest research findings on the use of DNA methylation in cancer diagnosis, prognosis and staging/classification.

발전소에서의 SOx 공정 모사, 모니터링 및 패턴 분류 (SOx Process Simulation, Monitoring, and Pattern Classification in a Power Plant)

  • 최상욱;유창규;이인범
    • 제어로봇시스템학회논문지
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    • 제8권10호
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    • pp.827-832
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    • 2002
  • We propose a prediction method of the pollutant and a synchronous classification of the current state of SOx emission in the power plant. We use the auto-regressive with exogeneous (ARX) model as a predictor of SOx emission and use a radial basis function network (RBFN) as a pattem classifier. The ARX modeling scheme is implemented using recursive least squares (RLS) method to update the model parameters adaptively. The capability of SOx emission monitoring is utilized with the application of the RBFN classifier. Experimental results show that the ARX model can predict the SOx emission concentration well and ARX modeling parameters can be a good feature for the state monitoring. in addition, its validity has been verified through the power spectrum analysis. Consequently, the RBFN classifier in combination with ARX model is shown to be quite adequate for monitoring the state of SOx emission.

SVM을 이용하여 HMM과 심잡음 점수를 결합한 심음 기반 심장질환 분류기 (Heart Sound-Based Cardiac Disorder Classifiers Using an SVM to Combine HMM and Murmur Scores)

  • 곽철;권오욱
    • 한국음향학회지
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    • 제30권3호
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    • pp.149-157
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    • 2011
  • 본 논문은 support vector machine (SVM)을 사용하여 은닉 마코프 모델 (HMM)과 심잡음 존재 정보를 결합한 새로운 심장질환 분류 방법을 제안한다. 켑스트럼 특징과 HMM 비터비 (Viterbi) 알고리듬을 이용하여 입력 신호를 모든 심장질환 모델에 대하여 상태 단위로 분할하여 상태별로 로그우도 (점수)를 계산한다. 심잡음 신호의 시간적 위치 특성을 이용하기 위하여 입력신호를 두 개의 부대역으로 나누고 부대역별로 프레임 단위의 심잡음 점수를 계산한 다음, 비터비 알고리듬으로부터 구한 상태 분할 정보를 이용하여 상태단위의 심잡음 점수를 구한다. SVM은 모든 심장질환 종류에 대한 상태 단위의 HMM과 심잡음 점수를 입력으로 하여 최종적으로 심장질환을 판정한다. 심장질환 분류 실험결과, 제안한 방법은 기존의 켑스트럼 특징과 HMM 분류기를 이용한 방법에 비하여 20.4 %의 상대적 개선율을 보여준다.

소음인(少陰人) 병증(病證) 분류체계와 표준증후 연구 (The Research on the Classification of Soeumin Symptomatology and the Standardized Symptom)

  • 송은영;박병주;송안나;이의주;고병희;이준희
    • 사상체질의학회지
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    • 제23권4호
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    • pp.429-444
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    • 2011
  • 1. Objective This study is aimed to present the effective classification of Soeumin symptomatology and the standardized signs for classification which can be applied for KCD, ICD and the insurance codification system. 2. Methods 1) Differentiate Soeumin symptomatology based on exterior-interior patterns, favorable-unfavorable patterns, and mild-severe-dangerous-urgent patterns. 2) Investigate the standard signs and symptoms to claasify Soeumin symptomatology based on exterior-interior patterns, favorable-unfavorable patterns, and mild-severe-dangerous-urgent patterns. 3. Results and Conclusions 1) The diagnosis criteria for Soeumin exterior-interior disease is based upon signs & symptoms of cold/heat, condition of stool, state of digestive system(such as digestion and appetite)among others. 2) The diagnosis criteria for Soeumin favorable-unfavorable disease is generally based upon whether the vital force of the spleen is damaged or not. More specifically, for the exterior disease, whether or not sweating is present. For the interior disease, whether or not dry mouth, body ache(a main symptom of the exterior state), and anxiousness are present. 3) For the Soeumin Wool-gwang disease, the diagnosis criteria of mild-severe disease is whether or not chills is present and the degree of body fever. 4) For Soeumin Mang-yang disease, the diagnosis criteria of dangerous-urgent disease is whether or not chills is, the degree of sweating and urine condition. 5) For the Soeumin Greater-Yin disease, Abdominal-pain bowel irritability pattern and Epigastric discomfort pattern are early state signs, Jaundice pattern is mild-state sign, edema & Greater-Yang disease Yin-toxin pattern are terminal state signs. 6) For the Soeumin interior disease, Abdominal-pain bowel irritability pattern and Epigastric discomfort pattern are of the dangerous state pattern, Jang-gual and Exuberant-Yin-repelling-Yang pattern are of the urgent state patterns.

데이터베이스 분류 표준화를 위한 기초연구 (A Pilot Study on the Standard Model for the Classification of Database)

  • 고영만
    • 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.

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Condition assessment of stay cables through enhanced time series classification using a deep learning approach

  • Zhang, Zhiming;Yan, Jin;Li, Liangding;Pan, Hong;Dong, Chuanzhi
    • Smart Structures and Systems
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    • 제29권1호
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    • pp.105-116
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    • 2022
  • Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

Classification of Behavioral Lexicon and Definition of Upper, Lower Body Structures in Animation Character

  • Hongsik Pak;Suhyeon Choi;Taegu Lee
    • International Journal of Internet, Broadcasting and Communication
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    • 제15권3호
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    • pp.103-117
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    • 2023
  • This study focuses on the behavioural lexical classification for extracting animation character actions and the analysis of the character's upper and lower body movements. The behaviour and state of characters in the animation industry are crucial, and digital technology is enhancing the industry's value. However, research on animation motion application technology and behavioural lexical classification is still lacking. Therefore, this study aims to classify the predicates enabling animation motion, differentiate the upper and lower body movements of characters, and apply the behavioural lexicon's motion data. The necessity of this research lies in the potential contributions of advanced character motion technology to various industrial fields, and the use of the behavioural lexicon to elucidate and repurpose character motion. The research method applies a grammatical, behavioural, and semantic predicate classification and behavioural motion analysis based on the character's upper and lower body movements.

Classification Index and Grade Levels for Energy Efficiency Classification of Agricultural Dryers in Korea

  • Shin, Chang Seop;Park, Jin Geun;Kim, Kyeong Uk
    • Journal of Biosystems Engineering
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    • 제39권2호
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    • pp.96-100
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    • 2014
  • Purpose: The objective of this study was to develop a classification index and the grade levels for a five-grade energy efficiency classification of agricultural dryers in Korea. Methods: The classification index and the grade levels were determined by using the performance test data published by the FACT over the last eight years to reflect a state of the art technology for agricultural dryers in Korea. The five grades were designed to have the classified dryers distributed normally over the grades with 15% for the $1^{st}$ grade, 20% for the $2^{nd}$ grade, 30% for the $3^{rd}$ grade, 20% for the $4^{th}$ grade and 15% for the $5^{th}$ grade. Results: The classification index was defined as the total amount of fuel and electrical energy consumed per 1% of the wet basis moisture content evaporated from a unit mass of grain or agricultural crops during the drying process: 1 MT of paddy rice for grain dryers and 1 kg of red pepper for agricultural crop dryers as the standard mass. Conclusions: The grade levels for the five-grade energy efficiency classification of grain dryers, kerosene dryers, and electric dryers were proposed in terms of the classification index value.

Estimation of Leaf Wetness Duration Using An Empirical Model

  • Kim, Kwang-Soo;S.Elwynn Taylor;Mark L.Gleason;Kenneth J.Koehler
    • 한국농림기상학회:학술대회논문집
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    • 한국농림기상학회 2001년도 춘계 학술발표논문집
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    • pp.93-96
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    • 2001
  • Estimation of leaf wetness duration (LWD) facilitates assessment of the likelihood of outbreaks of many crop diseases. Models that estimate LWD may be more convenient and grower-friendly than measuring it with wetness sensors. Empirical models utilizing statistical procedures such as CART (Classification and Regression Tree; Gleason et al., 1994) have estimated LWD with accuracy comparable to that of electronic sensors.(omitted)

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Measurements of Impervious Surfaces - per-pixel, sub-pixel, and object-oriented classification -

  • Kang, Min Jo;Mesev, Victor;Kim, Won Kyung
    • 대한원격탐사학회지
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    • 제31권4호
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    • pp.303-319
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    • 2015
  • The objectives of this paper are to measure surface imperviousness using three different classification methods: per-pixel, sub-pixel, and object-oriented classification. They are tested on high-spatial resolution QuickBird data at 2.4 meters (four spectral bands and three principal component bands) as well as a medium-spatial resolution Landsat TM image at 30 meters. To measure impervious surfaces, we selected 30 sample sites with different land uses and residential densities across image representing the city of Phoenix, Arizona, USA. For per-pixel an unsupervised classification is first conducted to provide prior knowledge on the possible candidate spectral classes, and then a supervised classification is performed using the maximum-likelihood rule. For sub-pixel classification, a Linear Spectral Mixture Analysis (LSMA) is used to disentangle land cover information from mixed pixels. For object-oriented classification several different sets of scale parameters and expert decision rules are implemented, including a nearest neighbor classifier. The results from these three methods show that the object-oriented approach (accuracy of 91%) provides more accurate results than those achieved by per-pixel algorithm (accuracy of 67% and 83% using Landsat TM and QuickBird, respectively). It is also clear that sub-pixel algorithm gives more accurate results (accuracy of 87%) in case of intensive and dense urban areas using medium-resolution imagery.