• Title/Summary/Keyword: 단계별추출

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A Study on the Detection of Interfacial Defect to Boundary Surface in Semiconductor Package by Ultrasonic Signal Processing (초음파 신호처리에 의한 반도체 패키지의 접합경계면 결함 검출에 관한 연구)

  • Kim, Jae-Yeol;Hong, Won;Han, Jae-Ho
    • Journal of the Korean Society for Nondestructive Testing
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    • v.19 no.5
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    • pp.369-377
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    • 1999
  • Recently, it is gradually raised necessity that thickness of thin film is measured accuracy and managed in industrial circles and medical world. Ultrasonic signal processing method is likely to become a very powerful method for NDE method of detection of microdefects and thickness measurement of thin film below the limit of ultrasonic distance resolution in the opaque materials, provides useful information that cannot be obtained by a conventional measuring system. In the present research. considering a thin film below the limit of ultrasonic distance resolution sandwiched between three substances as acoustical analysis model, demonstrated the usefulness of ultrasonic signal processing technique using information of ultrasonic frequency for NDE of measurements of thin film thickness. Accordingly, for the detection of delamination between the junction condition of boundary microdefect of thin film sandwiched between three substances the results from digital image processing.

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Development of machine learning framework to inverse-track a contaminant source of hazardous chemicals in rivers (하천에 유입된 유해화학물질의 역추적을 위한 기계학습 프레임워크 개발)

  • Kwon, Siyoon;Seo, Il Won
    • Proceedings of the Korea Water Resources Association Conference
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    • 2020.06a
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    • pp.112-112
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    • 2020
  • 하천에서 유해화학물질 유입 사고 발생 시 수환경 피해를 최소화하기 위해 신속한 초기 대응이 필요하다. 따라서, 본 연구에서는 수환경 화학사고 대응 시스템 구축을 위해 하천 실시간 모니터링 지점에서 관측된 유해화학물질의 농도 자료를 이용하여 발생원의 유입 지점과 유입량을 역추적하는 프레임워크를 개발하였다. 본 연구에서 제시하는 프레임워크는 첫 번째로 하천 저장대 모형(Transient Storage Zone Model; TSM)과 HEC-RAS 모형을 이용하여 다양한 유량의 수리 조건에서 화학사고 시나리오를 생성하는 단계, 두번째로 생성된 시나리오의 유입 지점과 유입량에 대한 시간-농도 곡선 (BreakThrough Curve; BTC)을 21개의 곡선특징 (BTC feature)으로 추출하는 단계, 최종적으로 재귀적 특징 선택법(Recursive Feature Elimination; RFE)을 이용하여 의사결정나무 모형, 랜덤포레스트 모형, Xgboost 모형, 선형 서포트 벡터 머신, 커널 서포트 벡터 머신 그리고 Ridge 모형에 대한 모형별 주요 특징을 학습하고 성능을 비교하여 각각 유입 위치와 유입 질량 예측에 대한 최적 모형 및 특징 조합을 제시하는 단계로 구축하였다. 또한, 현장 적용성 제고를 위해 시간-농도 곡선을 2가지 경우 (Whole BTC와 Fractured BTC)로 가정하여 기계학습 모형을 학습시켜 모의결과를 비교하였다. 제시된 프레임워크의 검증을 위해서 낙동강 지류인 감천에 적용하여 모형을 구축하고 시나리오 자료 기반 검증과 Rhodamine WT를 이용한 추적자 실험자료를 이용한 검증을 수행하였다. 기계학습 모형들의 비교 검증 결과, 각 모형은 가중항 기반과 불순도 감소량 기반 특징 중요도 산출 방식에 따라 주요 특징이 상이하게 산출되었으며, 전체 시간-농도 곡선 (WBTC)과 부분 시간-농도 곡선 (FBTC)별 최적 모형도 다르게 산출되었다. 유입 위치 정확도 및 유입 질량 예측에 대한 R2는 대부분의 모형이 90% 이상의 우수한 결과를 나타냈다.

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A Study of the Characteristics of Different Coffee Beans by Roasting and Extracting Condition (종류별 커피의 볶음 및 추출조건에 따른 품질 특성에 관한 연구)

  • Kim, Ha-Kyung;Hwang, Seong-Yun;Yoon, Soo-Bong;Chun, Dug-Sang;Kong, Suk-Kil;Kang, Kun-Og
    • The Korean Journal of Food And Nutrition
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    • v.20 no.1
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    • pp.14-19
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    • 2007
  • Caffeine is one of the most well known stimulants which can potentially increase mental performance, release fatigue and decrease depression. Green beans from different soils and climates contain different levels of caffeine, and as well as extracted coffee with different roasting and extracting methods. An investigation looking at pH, acidity, extractable solid and caffeine contents was assessed according to roasting and extracting conditions of various coffee beans. Brazilian coffee beans did not show much variation in pH with respect to roasting and extracting temperature, however, acidity increased in low roasting and extracting temperatures. This was however most prominently observed in Ethiopian and Indonesian coffee beans. The large expansion of coffee bean cells renders them highly porous to the passage of water, consequently extracted solids were found to increase with increasing temperature. This was especially apparent in Columbian coffee which had the highest extracted solids. The amount of caffeine extracted from coffee beans also increased with the higher temperature extraction. The Indonesian and Vietnam robusta coffee varieties showed the highest caffeine content.

Region Extraction of License Plates in Noise Environment Using YUV Color Space Convert (YUV컬러 공간변환에 의한 잡음환경의 차량번호판 영역추출)

  • Kim Jae-Nam;Choi Tae-Il;Kim Byung-Ki
    • The KIPS Transactions:PartD
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    • v.13D no.1 s.104
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    • pp.125-132
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    • 2006
  • The existing recognition system of license plates cannot get the satisfactory result in noise environments. The purpose of this paper is to propose an algorithm that can recognize the region of license plates accurately in a noise environment. The algorithm is formulated by reorganizing the U- and V-channels of YUV color space as YUV is insensitive to light and carries less data than RGB color information. The region of license plates has been extracted by the geometric characteristics, sizes, and places of labeling images. The proposed algorithm was found to improve the process of extracting the region of license plates in various noise environments.

Extraction of Smocking in Elevator Using Robust Scene Change Detection Method (강건한 장면 전환 검출 기법을 이용한 엘리베이터 내의 흡연 추출)

  • Lee, Kang-Ho;Shin, Seong-Yoon;Rhee, Yang-Won
    • Journal of the Korea Society of Computer and Information
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    • v.18 no.10
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    • pp.89-95
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    • 2013
  • Smoking in elevators is a criminal offense that is included in a misdemeanor. Because of that smoking in elevators can be very critical for our growing children and weak women. In this paper, we would like to extract criminals doing this criminal offense to smoke in elevators. Extraction method detect difference value using modified color-X2-test and it was normalized. Next, we find frames that has occurred scene change in successive frames using the four-step algorithm of scene change detection. Finally, we present the method of smoking image retrieval and extraction in stored large amount of video. In the experiment, we show process and number of scene change detection, and the number of video searched per retrieval time. The extracted smoking video is to submit as evidence for the police or court.

Change of Fragrant Components by Flowering Stages in Lilium Oriental Hybrid 'Casa Blanca' (오리엔탈 나리 '카사블랑카'의 개화단계별 향기성분 변화)

  • Rho, A Ran;Pak, Chun Ho
    • Horticultural Science & Technology
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    • v.19 no.2
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    • pp.163-169
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    • 2001
  • This experiment was conducted to find out suitable extraction time of available fragrant component in Lilium Oriental Hybrida 'Casa Blanca' based on qualitative and quantitative variation of its fragrant component in its flowering stages. The content of essential oil in its flowering stages increased with the progress of flowering stages except flower bud stage and it had the most oil content in its stage after full bloom. The analysis of the essential oil in Lilium Oriental Hybrida 'Casa Blanca' in its flowering stages shows that its main constituents were farnesol (18.96%), benzyl salicylate (13.81%), butyl-hydroxy toluene (12.87%), geranyl linalool isomer (7.46%), isoeugenol (7.17%) in its each stage. Benzenoids had much content at half bloom stage and full bloom stage while fatty acid derivatives had much content at initial flowering stage and after flowering stage. Most benzenoids such as butyl-hydroxy toluene and isoeugenol, which are some of main constituents of Lilium Oriental Hybrida 'Casa Blanca' had strong antioxidant effect. So they were expected to be used as antioxidant agents for food, feed, vegetable oil. The content of monoterpene compounds like geranyl linalool isomer increased at its later stage. Sesquiterpene such as farnesol, which is main component of lily existed only at full bloom stage. Therefore farnesol and geranyl linalool isomer of Lilium Oriental Hybrida 'Casa Blanca' are expected to be used for important spices with good fragrance. The suitable extraction time for the usable main constituents of Lilium Oriental Hybrida 'Casa Blanca' is as follows; farnesol and benzyl salicylate: at full bloom stage, butyl-hydroxy toluene: at half bloom stage, geranyl linalool isomer and isoeugenol: at stage after full bloom. Finally there was variation in essential oil components and contents of Lilium Oriental Hybrida 'Casa Blanca' in its flowering stages and they are expected to be used usefully for flavor industry, food industry, aromatherapy, when they are extracted at their suitable extraction time.

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A Study on the Satisfactions of Tourism Information DB Services in HiTEL (HiTEL의 관광정보 DB서비스 정보만족도에 관한 연구)

  • 박희석
    • The Journal of Information Technology and Database
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    • v.1 no.2
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    • pp.71-102
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    • 1994
  • 현대는 고도 정보화사회(information society)로서 DB의 가치는 점점 증가하고 전반적인 DB 서비스는 일반적으로 많이 발전하여 우수한 정보 서비스가 제공되고 있다. 그러나 관광정보 DB서비스의 가치는 아직까지 거의 인식되지 않고 있는 실정이다. 따라서 본 연구에서는 초보단계에 있는 관광정보 DB서비스의 만족도를 측정함으로써 국내 관광정보 DB서비스의 수준을 파악하고 관광정보제공자에게 개선해야 될 문제점을 제시하고자 한다. 그러므로 관광정보 DB서비스의 개념을 정립하고 관광정보 DB서비스의 현황을 고찰하고자 Bailey와 Pearson의 연구에서 제시한 39가지 요인 중에서 관광정보 DB서비스와 관련된 요인을 추출하고 의미를 재정의하여 HiTEL의 이용자를 대상으로 첫째, 정보서비스의 메뉴별 선호도 둘째, 관광정보 DB서비스의 만족도 셋째, 관광정보 DB서비스의 각 메뉴별 선호도와 만족도 넷째, 이용자 특성별로 각 메뉴의 선호도를 비교분석 다섯째, 관광정보 DB서비스의 만족요인을 분석하였다. 관광정보 DB서비스의 전반적인 만족도는 평균치(4.0)보다 저조한 3.85로서 저조하게 나타나고 있다. 즉, 관광정보의 이해도(4.55)는 평균보다 다소 좋은 반면, 다른 관광정보의 만족도는 평균치 보다 저조하다. 특히 관광정보의 연결사정(3.68)과 관광정보의 제공속도(3.44)가 아주 낮게 나타났다. 한편 HiTEL이 제공하는 관광정보 DB서비스의 각 메뉴 중에서 가장 자주 이용되는 메뉴는 알림나라(40.2%)이며, 아시아나항공(22.5%), 농협일렉트로뱅크(13.7%), 대한항공(9.8%), 위너스 OK서비스(6.9%), MEET/매경SCOUT(6.9%)의 순으로 관광정보는 전반적으로 골고루 이용되고 있다. 또한, 메뉴별 관광정보 DB서비스의 만족도가 평균만족도보다 낮은 것은 알림나라(3.48), 농협일렉트로뱅크(3.84), 대한항공(3.90), 위너스OK서비스(3.95)로 나타났다. 그리고 각 메뉴별 이용자의 계층별 선호도는 첫째, 남성은 알림나라(40%), 아시아나 항공(23.16%), 농협일렉트로뱅크(12.63%)를 많이 이용하나, 여성은 알림나라(42.86%), 농협일렉트로뱅크(28.57%)를 주로 선호하는 것으로 나타났다. 둘째, 연령별 선호도는 나이가 많을수록 단순 관광안내정보인 알림나라, 농협일렉트로뱅크보다는 보다 항공관련 정보를 더욱 선호하는 것으로 나타났다. 셋째, 직업별 선호도는 학생과 사무직에 종사하는 사람이 가장 많은 메뉴를 이용하며, 전문직, 관리직, 기술직은 대체적으로 항공관련정보를 더 선호하는 것으로 분석되었다. 넷째, 학력별 선호도는 고학력자일수록 많은 메뉴를 이용하고 있으며, 다섯째, 가입시기별 선호도는 가입한 시간이 길어짐에 따라 항공관련정보를 선호하는 것으로 나타났다.

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Variation in bioactive principles and bioactive compounds of Rosa rugosa fruit during ripening (해당화 열매 성숙단계에 따른 생리활성 및 기능성 물질 변화 분석)

  • Kwak, Minjeong;Eom, Seung Hee;Gil, Jinsu;Kim, Ju-Sung;Hyun, Tae Kyung
    • Journal of Plant Biotechnology
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    • v.46 no.3
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    • pp.236-245
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    • 2019
  • Fruit ripening is a genetically programmed process involving a number of biochemical and physiological processes assisted by variations in gene expression and enzyme activities. This process generally affects the phytochemical profile and the bioactive principles in fruits and vegetables. To appraise the variation in bioactive principles of fruits from Rosa rugosa during its ripening process, we analyzed the changes in antioxidant and anti-elastase activities and polyphenolic compounds during the four ripening stages of fruits. Overall, an extract of unripe fruits contained the highest levels of total phenolic and flavonoid contents, radical scavenging activity, reducing power, oxygen radical antioxidant capacity, and elastase inhibitory activity, compared with the extracts of fruits at other stages of ripening. Additionally, we found that the reduction of flavonoid content occurs because of decreased transcriptional levels of genes involved in flavonoid biosynthesis pathway during the ripening process. Based on HPLC analysis, we found that the extract of unripe fruits contained the highest amount of myricetin, caffeic acid, chlorogenic acid, syringic acid, and p-coumaric acid and suggested that the antioxidant and anti-elastase activities of the extract obtained from stage 1, should be mediated by the presence of these compounds. Additionally, we analyzed the interaction sites and patterns between these compounds and elastase using the structure-based molecular docking approach, and suggested that chlorogenic acid strongly interacted with elastase. Together, these findings suggest that the maturity of fruits has profound effects on the pharmaceutical value of R. rugosa.

A Study on Leakage Detection Technique Using Transfer Learning-Based Feature Fusion (전이학습 기반 특징융합을 이용한 누출판별 기법 연구)

  • YuJin Han;Tae-Jin Park;Jonghyuk Lee;Ji-Hoon Bae
    • The Transactions of the Korea Information Processing Society
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    • v.13 no.2
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    • pp.41-47
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    • 2024
  • When there were disparities in performance between models trained in the time and frequency domains, even after conducting an ensemble, we observed that the performance of the ensemble was compromised due to imbalances in the individual model performances. Therefore, this paper proposes a leakage detection technique to enhance the accuracy of pipeline leakage detection through a step-wise learning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-step learning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucial features from the provided data in each domain. In Stage 2, the pre-trained models were utilized by removing their respective classifiers. Subsequently, the features from both domains were fused, and a new classifier was added for retraining. The proposed transfer learning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequency domains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverse information. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.

A Study On Handwritten Numeral Recognition Using Numeral Shape Grasp and Divided FSOM (숫자의 형태 이해와 분할된 FSOM을 이용한 필기 숫자 인식에 관한 연구)

  • 서석배;김대진;강대성
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.24 no.8B
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    • pp.1490-1499
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    • 1999
  • This paper proposes a new handwritten numeral recognition method using numeral shape grasps and FSOM (Fuzzy Self-Organizing Map). The proposed algorithm is based on the idea that numeral input data with similar shapes are classified into the same class. Shapes of numeral data are created using lines of external-contact and the class of numeral data is determined by template matching of the shapes. Each class of numeral data has FSOM and feature extraction method, respectively. In this paper, we divide the numeral database into the 16 classes. The divided FSOM model allows not only an independent learning phase of SOM but also step-by-step learning. Experiments using Concordia University handwritten numeral database proved that the proposed algorithm is effective to improve recognition accuracy.

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