• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3105-3105
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• 2001

Ingests and fecal contamination on a poultry carcass is a food safety hazard due to potential microbiological contamination. A visible/near-infrared (NIR) spectrometer was used to discriminate among pure ingesta and fecal material, breast skin contaminated with ingesta or fecal material and uncontaminated breast skin. Birds were fed isocaloric diets formulated with either maize, mile, or wheat and soybean meal for protein requirements. Following completion of the feeding period (14 days), the birds were humanely processed and eviscerated to obtain ingests from the crop or proventriculus and feces from the duodenum, ceca, and colon portion of the digestive tract. Pure feces and ingesta, breast skin, and contaminated breast skin were scanned from 400 to 2500 nm and analyzed from 400 to 900 nm. Principal component analysis (PCA) of reflectance spectra was used to discriminate between contaminates and uncontaminated breast skin. Results indicate that visible (400 to 760 nm) and NIR 760-900 nm spectra can detect contaminates. From PCA analysis, key wavelengths were identified for discrimination of uncontaminated skin from contaminates based the evaluation of loadings weights.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.454-462
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• 2014

Retention of possible churning customer is one of the most important issues in customer relationship management, so companies try to predict churn customers using their large-scale high-dimensional data. This study focuses on dealing with large data sets by reducing the dimensionality. By using six different dimension reduction methods-Principal Component Analysis (PCA), factor analysis (FA), locally linear embedding (LLE), local tangent space alignment (LTSA), locally preserving projections (LPP), and deep auto-encoder-our experiments apply each dimension reduction method to the training data, build a classification model using the mapped data and then measure the performance using hit rate to compare the dimension reduction methods. In the result, PCA shows good performance despite its simplicity, and the deep auto-encoder gives the best overall performance. These results can be explained by the characteristics of the churn prediction data that is highly correlated and overlapped over the classes. We also proposed a simple out-of-sample extension method for the nonlinear dimension reduction methods, LLE and LTSA, utilizing the characteristic of the data.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1009-1015
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• 2009

Handwriting verification is a technique of distinguishing the same person's handwriting specimen from imitations with any two or more texts using one's handwriting individuality. This paper suggests an effective verification method for the handwritten signature or text on the off-line environment using pattern recognition technology. The core processes of the method which has been researched in this paper are extraction of letter area, extraction of features employing structural characteristics of handwritten text, feature analysis employing DTW(Dynamic Time Warping) algorithm and PCA(Principal Component Analysis). The experimental results show a superior performance of the suggested method.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.393-398
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• 2007

In this paper, we propose the development of Artificial Hippocampus Algorithm(AHA) which remodels a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 4 steps system (EC, DG CA3, and CA1) and improve speed of teaming by addition of modulator to long-term memory teaming. In hippocampus system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labeled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CA1 region, convergence of connection weight which is used long-term memory is learned fast a by neural network which is applied modulator. To measure performance of Artificial Hippocampus Algorithm, PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) are applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by AHA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.107-116
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• 2004

The advent of microarray technologies gives an opportunity to moni tor the expression of ten thousands of genes, simultaneously. Such microarray data can be deteriorated by experimental errors and image artifacts, which generate non-negligible outliers that are estimated by 15% of typical microarray data. Thus, it is an important issue to detect and correct the se faulty probes prior to high-level data analysis such as classification or clustering. In this paper, we propose a systematic procedure for the detection of faulty probes and its proper correction in Genechip array based on multivariate statistical approaches. Principal component analysis (PCA), one of the most widely used multivariate statistical approaches, has been applied to construct a statistical correlation model with 20 pairs of probes for each gene. And, the faulty probes are identified by inspecting the squared prediction error (SPE) of each probe from the PCA model. Then, the outlying probes are reconstructed by the iterative optimization approach minimizing SPE. We used the public data presented from the gene chip project of human fibroblast cell. Through the application study, the proposed approach showed good performance for probe correction without removing faulty probes, which may be desirable in the viewpoint of the maximum use of data information.

• The Korean Journal of Ecology
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• v.23 no.6
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• pp.439-444
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• 2000

Forest vegetation in Mt. Jangan County Park, Jeonlabuk-Do, Korea, was investigated by classification and ordination methods. By the cluster analysis (classification) method, nine groups were recognized as follows : Quercus serrata community, Quercus serrata- Carpinus laxiflora community, Cornus controversa community, Fraxinus mandshurica community, Carpinus laxiflora community, Quereus variabilis community, Quercus mongolica - Sasa borealis community. Quercus mongolica - Symplocos chinensis for. pilosa community and Quercus mongolica - Rhododendron schlippenbachii community. These groups showed differences in species composition and environmental characteristics, but Quercus mongolica - Sasa borealis community, Quercus mongolica - Symplocos chinensis for. pilosa community and Quercus mongolica - Rhododendron schlippenbachii community among them showed very similar floristic composition to each other. The interrelationship between the floristic composition of the vegetation and environmental factors was analysed by principal component analysis (PCA). Quercus mongolica community was distributed at a high altitude (900～1200 m above sea level). Fraxinus mandshurica community and Cornus controversa community were differentiated from the other communities with high contents of soil moisture and pH. On the other hand, Carpinus laxiflora community and Quercus variabilis community were distributed at places with adequate levels of soil moisture, soil organic matter. and at low altitude. In this study, the altitude and soil moisture were the main factors determining the forest vegetation. They were strongly correlated with the dominant compositional gradient at the localities examined.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5417-5421
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• 2014

Objectives: The identification of cutaneous T cell lymphoma (CTCL) biomarkers may serve as a predictor of disease progression and treatment response. The aim of this study was to map potential biomarkers in CTCL plasma. Design and Methods: Plasma metabolic perturbations between CTCL cases and healthy individuals were investigated using metabolomics and ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS). Results: Principal component analysis (PCA) of the spectra showed clear metabolic changes between the two groups. Thirty six potential biomarkers associated with CTCL were found. Conclusions: Based on PCA, several biomarkers were determined and further identified by LC/MS/MS analysis. All of these could be potential early markers of CTCL. In addition, we established that heparin as a nticoagulant has better pre-treatment results than EDTA with the UHPLC-QTOF/MS appraoch.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.39-47
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• 2015

The centrality is calculated to describe the importance of a node in a graph and ranking is given according to the centrality for each node. There are many centrality measures and we use degree centrality, closeness centrality, eigenvector centrality, and betweenness centrality. In this paper, we analyze the effect of an additional edge of a graph on centrality and ranking through experimental computations. It is found that the effect of an additional edge on centrality and ranking of the nodes in the graph is different according to the graph structure using PCA. The results can be used for define the graph characteristics.

• Korean Journal of Materials Research
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• v.34 no.5
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• pp.235-241
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• 2024

Gas identification techniques using pattern recognition methods were developed from four micro-electronic gas sensors for noxious gas mixture analysis. The target gases for the air quality monitoring inside vehicles were two exhaust gases, carbon monoxide (CO) and nitrogen oxides (NO_x), and two odor gases, ammonia (NH₃) and formaldehyde (HCHO). Four MEMS gas sensors with sensing materials of Pd-SnO₂ for CO, In₂O₃ for NO_X, Ru-WO₃ for NH₃, and hybridized SnO₂-ZnO material for HCHO were fabricated. In six binary mixed gas systems with oxidizing and reducing gases, the gas sensing behaviors and the sensor responses of these methods were examined for the discrimination of gas species. The gas sensitivity data was extracted and their patterns were determined using principal component analysis (PCA) techniques. The PCA plot results showed good separation among the mixed gas systems, suggesting that the gas mixture tests for noxious gases and their mixtures could be well classified and discriminated changes.

• Food Science and Preservation
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• v.31 no.3
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• pp.408-422
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• 2024

This study analyzed the factors that influence menu choices through eye-tracking and questionnaires in menu design. Demographic data of subjects coincided with choosing a menu and eye-tracking data. Hot Crispy Chicken Burger is the most popular menu. The study found that regardless of the selected menu, the menu name (35.5 seconds), price (21.6 seconds), and image (16.0 seconds) were viewed the longest, followed by country of origin (8.81 seconds), calories (4.6 seconds), and special indications (p<0.05). The menu name and image were checked more frequently, while calorie information was checked less often. As a result of analyzing various factors that influence menu selection through, Consumer experience and image greatly influenced menu choices. Therefore, if you want to receive a menu selection, it is considered effective to make good use of the menu name and image. In results of principal component analysis (PCA) by gender showed. Men had the longest price in the fixation duration. But, for females, there was a significant difference in gaze fixation when they took the exam, with menu names and special indications being important selection criteria. Since the results show that selection criteria and information acquisition methods differ depending on gender, this research is thought to be able to suggest directions for menu design.