• Journal of the Korean Applied Science and Technology
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• v.34 no.2
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• pp.410-417
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• 2017

The physical properties of perilla leaves cultivated in Geumsan province were analyzed according storage conditions. The a/b values of perilla leaves increased with increasing storage period. Electronic nose composed of 12 different metal oxide sensors was used to differentiate flavors of perilla leaves. Sensitivities(delta $R_{gas}/R_{air}$) of sensors from electronic nose were obtained by principal compound analysis(PCA). Proportion of the first principal component was 93.07% at $25^{\circ}C$ and 97.81% at $4^{\circ}C$, respectively. In our result, flavor patterns of perilla leaves can be differentiated according to the storage temperature.

• Journal of Information Technology Services
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• v.6 no.3
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• pp.241-249
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• 2007

This paper describes a real-time face tracking system using effective detector and Kalman filter. In the proposed system, an image is separated into a background and an object using a real-time updated face color for effective face detection. The face features are extracted using the five types of simple Haar-like features. The extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. The moving face is traced with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. The proposed system sets up an initial skin color and updates a region of a skin color through a moving skin color in a real time. It is possible to remove a background which has a similar color with a skin through updating a skin color in a real time. Also, as reducing a potential-face region using a skin color, the performance is increased up to 50% when comparing to the case of extracting features from a whole region.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.609-616
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• 2017

In this paper, we propose an algorithm to obtain more stable and high recognition rate by using left and right rotation information of input image in order to obtain a stable recognition rate in face recognition. The proposed algorithm uses the facial image as the input information in the web camera environment to reduce the size of the image and normalize the information about the brightness and color to obtain the improved recognition rate. We apply Principal Component Analysis (PCA) to the detected candidate regions to obtain feature vectors and classify faces. Also, In order to reduce the error rate range of the recognition rate, a set of data with the left and right $45^{\circ}$ rotation information is constructed considering the directionality of the input face image, and each feature vector is obtained with PCA. In order to obtain a stable recognition rate with the obtained feature vector, it is after scattered in the eigenspace and the final face is recognized by comparing euclidean distant distances to each feature. The PCA-based feature vector is low-dimensional data, but there is no problem in expressing the face, and the recognition speed can be fast because of the small amount of calculation. The method proposed in this paper can improve the safety and accuracy of recognition and recognition rate faster than other algorithms, and can be used for real-time recognition system.

• Journal of Plant Biotechnology
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• v.42 no.3
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• pp.257-264
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• 2015

To determine whether metabolite fingerprinting for whole cell extracts based on Fourier transform infrared (FT-IR) spectroscopy can be used to discriminate and compare metabolic equivalence, standard medicinal parts from four medicinal plants (Cynanchum wilfordii Hemsley, Atractylodes japonica Koidz, Polygonum multiflorum Thunberg and Astragalus membranaceus Bunge) and their in vitro-produced adventitious roots were analyzed by FT-IR spectroscopy. The principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) from the FT-IR spectral data showed that the whole metabolic pattern from Cynanchum wilfordii was highly similar to Astragalus membranaceus. However, Atractylodes japonica and Polygonum multiflorum showed significantly different metabolic patterns. Furthermore, adventitious roots from Cynanchum wilfordii and Astragalus membranaceus also showed similar metabolic patterns compared to their standard medicinal parts. These results clearly show that mass proliferation of adventitious roots may be applied to aquire novel supply of standard medicinal parts from medicinal plants. However, the whole metabolic pattern from adventitious roots of Atractylodes japonica and Polygonum multiflorum were not similar to their standard medicinal parts. Furthermore, FT-IR spectroscopy combined with multivariate analyses established in this study may be applied as an alternative tool to discriminate the whole metabolic equivalence from several standard medicinal parts. Thus, we suggest that these metabolic discrimination systems may be applied for metabolic standardization of herbal medicinal resources.

• Journal of Bio-Environment Control
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• v.30 no.1
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• pp.19-26
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• 2021

In this study, analysis was performed to identify three greenhouses located in the same area using principal component analysis (PCA) and linear discrimination analysis (LDA). The environmental data in the greenhouse were from 3 farms in the same area, and the values collected at 1 hour intervals for a total of 4 weeks from April 1 to April 28 were used. Before analyzing the data, it was pre-processed to normalize the data, and the analysis was performed by dividing it into 80% of the training data and 20% of the test data. As a result of PCA and LDA analysis, it was found that PCA classification accuracy was 57.51% and LDA classification was 67.06%, indicating that it can be classified by greenhouse. Based on the farmhouse data classified in advance, the data of the new environment can be classified into specific groups to determine the tendency of the data. Such data is judged to be a way to increase the utilization of data by facilitating identification.

• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.91-105
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• 1999

Spatial data integration using multiple geo-based data sets has been regarded as one of the primary GIS application issues. As for this issue, several integration schemes have been developed as the perspectives of mathematical geology or geo-mathematics. However, research-based approaches for statistical/quantitative assessments between integrated layer and input layers are not fully considered yet. Related to this niche point, in this study, spatial data integration using multiple geoscientific data sets by known integration algorithms was primarily performed. For spatial integration by using raster-based GIS functionality, geological, geochemical, geophysical data sets, DEM-driven data sets and remotely sensed imagery data sets from the Ogdong area were utilized for geological thematic mapping related by mineral potential mapping. In addition, statistical/quantitative information extraction with respective to relationships among used data sets and/or between each data set and integrated layer was carried out, with the scope of multiple data fusion and schematic statistical assessment methodology. As for the spatial integration scheme, certainty factor (CF) estimation and principal component analysis (PCA) were applied. However, this study was not aimed at direct comparison of both methodologies; whereas, for the statistical/quantitative assessment between integrated layer and input layers, some statistical methodologies based on contingency table were focused. Especially, for the bias reduction, jackknife technique was also applied in PCA-based spatial integration. Through the statistic analyses with respect to the integration information in this case study, new information for relationships of integrated layer and input layers was extracted. In addition, influence effects of input data sets with respect to integrated layer were assessed. This kind of approach provides a decision-making information in the viewpoint of GIS and is also exploratory data analysis in conjunction with GIS and geoscientific application, especially handing spatial integration or data fusion with complex variable data sets.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.11-14
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• 2007

인간과 컴퓨터간의 상호교류 하는데 있어서 감정 인식은 필수라 하겠다. 그래서 본 논문에서는 음성 신호 및 얼굴 영상을 BL(Bayesian Learning)과 PCA(Principal Component Analysis)에 적용하여 5가지 감정 (Normal, Happy, Sad, Anger, Surprise) 으로 패턴 분류하였다. 그리고 각각 신호의 단점을 보완하고 인식률을 높이기 위해 결정 융합 방법과 특징 융합 방법을 이용하여 감정융합을 실행하였다. 결정 융합 방법은 각각 인식 시스템을 통해 얻어진 인식 결과 값을 퍼지 소속 함수에 적용하여 감정 융합하였으며, 특정 융합 방법은 SFS(Sequential Forward Selection)특정 선택 방법을 통해 우수한 특정들을 선택한 후 MLP(Multi Layer Perceptron) 기반 신경망(Neural Networks)에 적용하여 감정 융합을 실행하였다.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.222-225
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• 2000

Human frequently communicate non-linguistic information with gesture. So, we must develop efficient and fast gesture recognition algorithms for more natural human-computer interaction. However, it is difficult to recognize gesture automatically because human's body is three dimensional object with very complex structure. In this paper, we suggest a method which is able to detect key frames and frame changes, and to classify image sequence into some gesture groups. Gesture is classifiable according to moving part of body. First, we detect some frames that motion areas are changed abruptly and save those frames as key frames, and then use the frames to classify sequences. We symbolize each image of classified sequence using Principal Component Analysis(PCA) and clustering algorithm since it is better to use fewer components for representation of gestures. Symbols are used as the input symbols for the Hidden Markov Model(HMM) and recognized as a gesture with probability calculation.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1189-1192
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• 2005

본 논문에서는 스케치 연산자를 적용하여 견실한 얼굴인식 방법을 제안한다. 제안된 방법은 인식 대상의 중요한 특성인 에지(edge), 벨리(valley) 및 질감(texture) 성분을 효과적으로 표현하기 위한 방법으로써, BDIP(block difference of inverse probabilities)를 사용하여 얼굴의 특징을 스케치 영상과 같이 나타내는 얼굴 영상을 획득한다. 그리고, BDIP 처리된 얼굴 영상은 입력 데이터의 차원 축소 및 얼굴 특징 벡터의 추출을 위해 PCA(Principal Component Analysis)를 수행한 후, Nearest Neighbor 분류기를 통해 인식을 수행한다. 제안된 방법의 성능을 평가하기 위하여, 일반적으로 많이 사용되는 HE(Histogram equalization)을 사용한 얼굴 인식 방법과의 비교를 수행한다. 실험결과, 본 논문에서 제안한 방법이 고유값이 적은 경우에 가장 높은 인식률을 나타내는 것을 알 수 있었다.