• 정보교육학회논문지
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• 제25권6호
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• pp.927-934
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• 2021

인공지능은 우리의 삶에 점차 많은 부분을 차지하고 있으며, 발전하는 속도도 빨라지고 있다. 학생들의 컴퓨팅 사고력을 인공지능이 학습하는 방법대로 길러주는 것을 ACT(AI based Computational Thinking)라고 한다. ACT 중 패턴 인식은 문제를 효율적으로 해결하기 위해 필수적인 요소이다. 패턴 분석은 패턴 인식 과정의 일부로 볼 수 있다. 실제로 넷플릭스의 개인 맞춤 영화 추천, 반복된 증상을 분석하여 코로나 바이러스로 명명하는 것 등이 모두 패턴 분석의 결과이다. 패턴인식을 포함한 ACT의 중요성이 부각되는 것에 반면, 유치원과 초등학교 저학년을 대상으로 한 소프트웨어 교육은 국외에 비해 많이 부족한 실정이다. 따라서 본 연구에서는 유치원 학생들을 대상으로 하여 패턴 분석을 통한 인공지능 기반 컴퓨팅 사고력 계발을 위한 교재를 설계하고 개발하였다.

• 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.

• 한국컴퓨터정보학회논문지
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• 제21권5호
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• pp.167-175
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• 2016

The volatile component analysis of 82 commercialized liquors(44 samples of single malt whisky, 20 samples of blended whisky and 18 samples of brandy) was carried out by gas chromatography after liquid-liquid extraction with dichloromethane. Pattern recognition techniques such as principle component analysis(PCA), cluster analysis(CA), linear discriminant analysis(LDA) and partial least square discriminant analysis(PLSDA) were applied for the discrimination of different liquor categories. Classification rules were validated by considering sensitivity and specificity of each class. Both techniques, LDA and PLSDA, gave 100% sensitivity and specificity for all of the categories. These results suggested that the common characteristics and identities as typification of whiskies and brandys was founded by using multivariate data analysis method.

• 전기학회논문지
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• 제63권6호
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• pp.797-803
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• 2014

In this paper, 3D face recognition system is designed by using polynomial based on RBFNNs. In case of 2D face recognition, the recognition performance reduced by the external environmental factors such as illumination and facial pose. In order to compensate for these shortcomings of 2D face recognition, 3D face recognition. In the preprocessing part, according to the change of each position angle the obtained 3D face image shapes are changed into front image shapes through pose compensation. the depth data of face image shape by using Multiple Point Signature is extracted. Overall face depth information is obtained by using two or more reference points. The direct use of the extracted data an high-dimensional data leads to the deterioration of learning speed as well as recognition performance. We exploit principle component analysis(PCA) algorithm to conduct the dimension reduction of high-dimensional data. Parameter optimization is carried out with the aid of PSO for effective training and recognition. The proposed pattern classifier is experimented with and evaluated by using dataset obtained in IC & CI Lab.

• Natural Product Sciences
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• 제24권3호
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• pp.164-170
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• 2018

Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.

• 센서학회지
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• 제17권6호
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• pp.406-413
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• 2008

We have been implemented simple statistical pattern recognition methods for harmful gases classification using gas sensors array fabricated by MEMS (Micro Electro Mechanical System) technology. The performance of pattern recognition method as a gas classifier is highly dependent on the choice of pre-processing techniques for sensor and sensors array signals and optimal classification algorithms among the various classification techniques. We carried out pre-processing for each sensor's signal as well as sensors array signals to extract features for each gas. We adapted simple statistical pattern recognition algorithms, which were PCA (Principal Component Analysis) for visualization of patterns clustering and MLR (Multi-Linear Regression) for real-time system implementation, to classify harmful gases. Experimental results of adapted pattern recognition methods with pre-processing techniques have been shown good clustering performance and expected easy implementation for real-time sensing system.

• 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.

• 디지털산업정보학회논문지
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• 제8권2호
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• pp.31-40
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• 2012

Face recognition refers to the process of identifying individuals based on their facial features. It has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous consumer applications, such as access control, surveillance, security, credit-card verification, and criminal identification. However, illumination variation on face generally cause performance degradation of face recognition systems under practical environments. Thus, this paper proposes an novel face recognition system using a fusion approach based on local binary pattern and two-dimensional principal component analysis. To minimize illumination effects, the face image undergoes the local binary pattern operation, and the resultant image are divided into two sub-images. Then, two-dimensional principal component analysis algorithm is separately applied to each sub-images. The individual scores obtained from two sub-images are integrated using a weighted-summation rule, and the fused-score is utilized to classify the unknown user. The performance evaluation of the proposed system was performed using the Yale B database and CMU-PIE database, and the proposed method shows the better recognition results in comparison with existing face recognition techniques.

• Journal of Multimedia Information System
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• 제5권4호
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• pp.221-228
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• 2018

Skin-color information is not sufficient for palmprint segmentation in complex scenes, including mobile environments. Traditional active shape model (ASM) combines gray information and shape information, but its performance is not good in complex scenes. An improved ASM method is developed for palmprint segmentation, in which Perux method normalizes the shape of the palm. Then the shape model of the palm is calculated with principal component analysis. Finally, the color likelihood degree is used to replace the gray information for target fitting. The improved ASM method reduces the complexity, while improves the accuracy and robustness.