• 한국대기환경학회지
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• 제6권1호
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• pp.1-10
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• 1990

The purpose of the study was to analyze air quality in underground shopping centers using pattern recognition methods. In order to perform this, the concentraion of air pollutants such as $CO, NO_2, NO_x, SO_2$, and particulate matters was measured at the 11 different shopping centers in Seoul metropolitan area and the total of 47 samples were obtained at random based on the size of shopping centers. To introduce a new concept of the "average concentration" for the indoor air quality analyses, the various multivariate statistical analyses have been studied. Thus, a cluster analysis was applied to separate the samples into pseudo-patterns and a disjoint principal component analysis was used to generate homogeneous patterns after removing outliers from the pseudo-patterns. The 6 homogeneous patterns were then obtained as follows:the first pattern was a group of clean sites;the second a group of sites having high dust concentration;the third a group of sites having high dust and $NO_x$ concentration;the fourth a group of sites having low dust and $SO_2$ concentraion and high CO concentration;the fifth a group of sites having high $NO_2 and SO_2$ concentration;and the final a group of miscellaneous sites. Thus, the average concentration could be estimated for each pattern.h pattern.

• 한국철도학회논문집
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• 제4권3호
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• pp.79-86
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• 2001

The signal pattern recognition method by acoustic emission signal is applied to detect and classify the defects of a journal bearing in a power plant. AE signals of main defects such as overheating, wear and corrosion are obtained from a small scale model. To detect and classify the defects, AE signal pattern recognition program is developed. As the classification methods, the wavelet transformation analysis, the frequency domain analysis and time domain analysis are used. Among three analyses, the wavelet transformation analysis is most effective to detect and classify the defects of the journal bearing..

• 말소리와 음성과학
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• 제14권2호
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• pp.11-17
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• 2022

English vowels play an important role in verbal communication. However, Korean students tend to experience difficulty pronouncing a certain set of vowels despite extensive education in English. The aim of this study is to apply speech recognition software to evaluate Korean students' pronunciation of English vowels in minimal pair words and then to examine acoustic characteristics of the pairs in order to check their pronunciation problems. Thirty female Korean college students participated in the recording. Speech recognition rates were obtained to examine which English vowels were correctly pronounced. To compare and verify the recognition results, such acoustic analyses as the first and second formant trajectories and durations were also collected using Praat. The results showed an overall recognition rate of 54.7%. Some students incorrectly switched the tense and lax counterparts and produced the same vowel sounds for qualitatively different English vowels. From the acoustic analyses of the vowel formant trajectories, some of these vowel pairs were almost overlapped or exhibited slight acoustic differences at the majority of the measurement points. On the other hand, statistical analyses on the first formant trajectories of the three vowel pairs revealed significant differences throughout the measurement points, a finding that requires further investigation. Durational comparisons revealed a consistent pattern among the vowel pairs. The author concludes that speech recognition and analysis software can be useful to diagnose pronunciation problems of English-language learners.

• Bulletin of the Korean Chemical Society
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• 제30권1호
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• pp.137-144
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• 2009

In this study, quantitative and pattern recognition analyses for the quality evaluation of Herba Epimedii using HPLC was developed. For quantitative analysis, five major bioactive constituents, hyperin, epimedin A, epimedin B, epimedin C, and icariin were determined. Analysis was carried out on Capcell pak $C_{18}$ column ($250{\time}4.6$ mm, 5 ${\mu}m$) with a mobile phase of mixture of acetonitrile and 0.1% formic acid, using UV detection at 270 nm. The linear behavior was observed over the investigated concentration range (2-50 ${\mu}g/mL;\;r_2\;>$ 0.99) for all analytes. The intraand inter-day precisions were lower than 4.3% (as a relative standard deviation, RSD) and accuracies between 95.1% and 104.4%. The HPLC analytical method for pattern recognition analysis was validated by repeated analysis of one reference sample. The RSD of intra- and inter-day variation of relative retention time (RRT) and relative peak area (RPA) of the 12 selected common peaks were below 0.8% and 4.7%, respectively. The developed methods were applied to analysis of twenty Herba Epimedii extract samples. Contents of hyperin, epimedin A, epimedin B, epimedin C, and icariin were calculated to be 0$\sim$0.79, 0.69$\sim$1.91, 0.93$\sim$9.58, 0.65$\sim$3.05, and 2.43$\sim$11.8 mg/g dried plant. Principal component analysis (PCA) showed that most samples were clustered together with the reference samples but several apart from the main cluster in the PC score plot, indicating differences in overall chemical composition between two clusters. The present study suggests that quantitative determination of marker compounds combined with pattern-recognition method can provide a comprehensive approach for the quality assessment of herbal medicines.

• Bulletin of the Korean Chemical Society
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• 제31권11호
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• pp.3371-3381
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• 2010

In this study, quantitative and pattern recognition analysis for the quality evaluation of Magnoliae Flos using HPLC/UV was developed. For quantitative analysis, eleven major bioactive lignan compounds were determined. The separation conditions employed for HPLC/UV were optimized using ODS $C_{18}$ column ($250{\times}4.6\;mm$, $5\;{\mu}m$) with isocratic elution of acetonitrile and water with 1% acetic acid as the mobile phase at a flow rate of 1.0 mL/min and a detection wavelength of 278 nm. These methods were fully validated with respect to the linearity, accuracy, precision, recovery, and robustness. The HPLC/UV method was applied successfully to the quantification of eleven major compounds in the extract of Magnoliae Flos. The HPLC analytical method for pattern recognition analysis was validated by repeated analysis of twenty one reference samples corresponding to seven different species of Magnoliae Flos and nine samples purchased from market. The results indicate that the established HPLC/UV method is suitable for the quantitative analysis and quality control of multi-components in Magnoliae Flos.

• Bulletin of the Korean Chemical Society
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• 제32권1호
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• pp.239-246
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• 2011

In this study, quantitative and pattern recognition analysis for the quality evaluation of Cimicifugae Rhizoma using HPLC/UV was developed. For quantitative analysis, three major bioactive phenolic compounds were determined. The separation conditions employed for HPLC/UV were optimized using ODS $C_{18}$ column ($250{\times}4.6mm$, $5{\mu}M$) with isocratic elution of acetonitrile and water with 0.1% phosphoric acid as the mobile phase at a flow rate of 1.0 mL/min and a detection wavelength of 323 nm. These methods were fully validated with respect to the linearity, accuracy, precision, recovery, and robustness. The HPLC/UV method was applied successfully to the quantification of three major compounds in the extract of Cimicifugae Rhizoma. The HPLC analytical method for pattern recognition analysis was validated by repeated analysis of twelve reference samples corresponding to five different species of Cimicifugae Rhizoma and seventeen samples purchased from markets. The results indicate that the established HPLC/UV method is suitable for the quantitative analysis and quality control of multi-components in Cimicifugae Rhizoma.

• Bulletin of the Korean Chemical Society
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• 제35권7호
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• pp.1956-1964
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• 2014

To provide the scientific corroboration of the traditional uses of Akebia quinata (Thunb.) Decne., a detailed analytical examination of A. quinata stems was carried out using a reversed-phase high performance liquid chromatography (RP-HPLC) method coupled to photodiode array detector (PDA) for the simultaneous determination of four phenolic substances; cuneataside D (1), 2-(3,4-dihydroxyphenyl)ethyl-O-${\beta}$-D-glucopyranoside (2), 3-caffeoylquinic acid (3) and calceolarioside B (4). Particular attention was focused on the main compound, 3-caffeoylquinic acid (3), which has a range of biological functions. In addition, 2-(3,4-dihydroxyphenyl)ethyl-O-${\beta}$-D-glucopyranoside (2) was considered as a discernible marker of A. quinata from its easy confuse plants. The contents of compounds 2 and 3 ranged from 0.72 to 2.68 mg/g and from 1.66 to 5.64 mg/g, respectively. The validation data indicated that this HPLC/PDA assay was used successfully to quantify the four phenolic compounds in A. quinata from different locations using relatively simple conditions and procedures. The pattern-recognition analysis data from 53 samples classified them into two groups, allowing discrimination between A. quinata and comparable herbs. The results suggest that the established HPLC/PDA method is suitable for quantitation and pattern-recognition analyses for a quality evaluation of this medicinal herb.

• Smart Structures and Systems
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• 제29권1호
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• Bulletin of the Korean Chemical Society
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• 제34권7호
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• pp.2081-2085
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• 2013

A quantitative method for determining levels of three bioactive compounds based on pattern recognition was developed and fully validated for the quality control of Alismatis Rhizoma (AR) by HPLC. Separation conditions were optimised using an Optimapak $C_{18}$ column ($250mm{\times}4.6mm$, 5 ${\mu}m$) with a mobile phase of acetonitrile and 0.1% aqueous phosphoric acid and detection wavelengths of 205 and 245 nm. Method validation yielded acceptable linearity ($r^2$ > 0.9998) and percent recovery (98.06%-101.71%). Limits of detection ranged from 0.08 to 0.15 ${\mu}g/mL$. Levels of the three bioactive compounds, alisol C acetate, alisol B, and alisol B acetate, in AR were 0.07-0.45, 0.38-10.32, and 1.13-8.59 mg/g dried weight, respectively. Pattern analyses based on these three compounds were able to differentiate Chinese and Korean samples accurately. The results demonstrate that alisol B and its acetate may be used as marker compounds for AR quality and can be regulated to no less than 0.36 and 1.29 mg/g of dried sample, respectively. The method described here is suitable for quantitative analyses and quality control of multiple components in AR.

• 디자인융복합연구
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• 제15권6호
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• pp.195-214
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• 2016

예측 분석은 패턴인식(Pattern recognition) 혹은 기계학습(Machine learning)으로 불리는 확률적 학습 알고리즘을 기반으로 하기 때문에 사용자가 분석 과정에 개입하여 더 많은 정보를 얻어내기 위해서는 높은 통계적 지식수준이 요구된다. 또한 사용자는 분석 결과외의 다른 정보를 확인 할 수 없고 데이터의 특성 변화와 데이터 하나하나의 특징을 파악하기 힘들다는 단점이 있다. 본 연구는 이러한 예측분석의 단점을 보완하고자 통계적인 데이터 분석 방법과 시각화 분석 방법을 결합하여 데이터 분석을 진행하였으며 통계적인 분석 방법만을 진행 할 경우 발생하는 단점을 보완하고 데이터에서 더 많은 정보를 도출해 내기 위한 방법론을 제시 하고자하였다. 이를 위해 본 연구는 영화 리뷰에서 추출한 감정 어휘가 독립변인이고 영화의 흥행 값이 종속변인인 데이터를 예제 데이터로 활용하여 진행하였다. 본 연구의 연구 방법론을 적용하였을 때의 이점은 다음과 같다. 첫째, 의사결정나무 분석에서 제시된 분할 기준이 적용될 때 마다 변하는 데이터의 패턴을 파악할 수 있다. 둘째, 제시된 최종 예측모형에 포함된 데이터들의 특성을 확인 할 수 있다. 본 연구의 시사점은 예측모형의 단점을 보완하고 데이터로부터 더 많은 정보를 추출하기 위해 통계적인 데이터 분석과 시각적인 데이터 분석을 결합하여 시행하였다는 것이다. 통계적인 분석 방법을 통해 각 변수의 관계를 파악하고 높은 예측 값을 가지는 모형을 도출하였으며, 시각화 분석에서는 인터랙션 기능을 제공함으로서 통계적으로 제시된 예측모형을 검증하고 더 다양한 정보를 도출 할 수 있게 하였다.