• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.5
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• pp.93-99
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• 2017

A model was developed using Artificial Neural Networks (ANNs) based on Principal Component Analysis (PCA), to accurately predict the air humidity inside an experimental greenhouse located in Daegu (latitude $35.53^{\circ}N$, longitude $128.36^{\circ}E$, and altitude 48 m), South Korea. The weather parameters, air temperature, relative humidity, solar radiation, and carbon dioxide inside and outside the greenhouse were monitored and measured by mounted sensors. Through the PCA of the data samples, three main components were used as the input data, and the measured inside humidity was used as the output data for the ALYUDA forecaster software of the ANN model. The Nash-Sutcliff Model Efficiency Coefficient (NSE) was used to analyze the difference between the experimental and the simulated results, in order to determine the predictive power of the ANN software. The results obtained revealed the variables that affect the inside air humidity through a sensitivity analysis graph. The measured humidity agreed well with the predicted humidity, which signifies that the model has a very high accuracy and can be used for predictions based on the computed $R^2$ and NSE values for the training and validation samples.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.146-154
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• 2019

An improved principal component analysis (PCA) method is applied for sensor fault detection and isolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducing methods are combined with general PCA method to improve the model performance in practice. In data pre-processing, singular points and random fluctuations in the original data are eliminated with various techniques respectively. In fault detecting, a statistics-based method is proposed to reduce the false alarms of $T^2$ and Q statistics. Finally, the effects of the proposed data pre-processing and false alarm reducing techniques are evaluated with sensor measurements from a real NPP. They are proved to be greatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile various sensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulation results show that the proposed PCA model presents favorable performance on the FDI of sensors no matter with major or small failures.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1960-1961
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• 2011

얼굴 인식에서 가장 많이 사용되고 있는 PCA(Principal Component Analysis)는 고차원의 얼굴 데이터를 낮은 차원으로 표현할 수 있다는 장점이 있다. LDA(Linear Discriminant Analysis)는 서로 다른 데이터를 잘 분리할 수 있으며, 얼굴 인식에서 우수한 성능을 보인다. 본 연구에서는 서로의 장점을 결합하여 PCA와 LDA를 혼합, 적용하였다. 고차원의 얼굴데이터를 PCA로 차원 축소한 후 LDA를 이용해 더욱 효과적인 분류가 되어 얼굴 인식률을 향상시킨다. 인식 모듈로는 pRBFNN(Polynomial Based Radial Basis Function Neural Networks) 모델을 구축하여 고차원 패턴인식 문제에 대한 해결책을 제시하고자 한다. 그리고 제안된 패턴분류기는 얼굴 데이터를 사용하여 성능을 확인한다.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.109-116
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• 2005

In this paper, we investigate the visual and quantitative analysis at the same time with an electronic tongue(e-tongue) system using an array of ISE(ion-selective electrode). We apply the FCM(fuzzy c-means) algorithm combined with PCA(principal component analysis), which can be reduced multi-dimensional data to third-dimensional data, to classify data patterns detected by E-Tongue system. The proposed technique can be designed to solve the cluster centers and membership grade of patterns combined with the output results obtained by PCA method. According to the proposed technique, the membership grade of unknown pattern, which does not shown previously can be determined and analyzed visually. Conclusionally, the relationship between the standard patterns and unknown pattern can be easily analyzed. Throughout the experimental trials, the proposed technique has been confirmed using developed E-Tongue system.

• Annual Conference of KIPS
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• 2020.05a
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• pp.69-71
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• 2020

Principal Component Analysis (PCA) is an effective data analysis technique which is commonly used for fault detection on collected data of Wireless Sensor Networks (WSN), However, applying PCA on the whole data make the detection performance low. In this paper, we propose Joint PCA and Adaptive Threshold for Fault Detection (JPATAD). Experimental results on a real dataset show a remarkably higher performance of JPATAD comparing to conventional PCA model in detection of noise which is a popular fault in collected data of sensors.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.383-392
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• 2000

Since late 1970, methods of influence or sensitivity analysis for detecting influential observations have been studied not only in regression and related methods but also in various multivariate methods. If results of multivariate analyses sometimes depend heavily on a small number of observations, we should be very careful to draw a conclusion. Similar phenomena may also occur in the case of incomplete data. In this research we try to study such influential observations in multivariate statistical analysis of incomplete data. Case of principal component analysis is studied with a numerical example.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.133-137
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• 2005

In this paper, we investigate visual and quantitative analysis of different tastes in the liquids using multi-array chemical sensor (MACS) based on the ion-selective electrodes (ISEs), which is so called the electronic tongue (E-Tongue) system. We apply the Fuzzy C-means (FCM) algorithm combined with Principal Component Analysis (PCA), which can be used to reduce multi-dimensional data to two- or three-dimensional data, to classify visually data patterns detected by E-Tongue system. The proposed technique can be determined the cluster centers and membership grade of patterns through the unsupervised way. The membership grade of an unknown pattern, which does not shown previously, can be visually and analytically determined. Throughout the experimental trails, the E-tongue system combined with the proposed algorithms is demonstrated robust performance for visual and quantitative analysis for different tastes in the liquids.

• Journal of Microbiology and Biotechnology
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• v.21 no.11
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• pp.1127-1142
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• 2011

The aim of this investigation was to select effective Pseudomonas sp. strains that can enhance the productivity of soybean-wheat cropping systems in Vertisols of Central India. Out of 13 strains of Pseudomonas species tested in vitro, only five strains displayed plant growth-promoting (PGP) properties. All the strains significantly increased soil enzyme activities, except acid phosphatase, total system productivity, and nutrient uptake in field evaluation; soil nutrient status was not significantly influenced. Available data indicated that six strains were better than the others. Principal component analysis (PCA) coupled cluster analysis of yield and nutrient data separated these strains into five distinct clusters with only two effective strains, GRP3 and HHRE81 in cluster IV. In spite of single cluster formation by strains GRP3 and HHRE81, they were diverse owing to greater intracluster distance (4.42) between each other. These results suggest that the GRP3 and HHRE81 strains may be used to increase the productivity efficiency of soybean-wheat cropping systems in Vertisols of Central India. Moreover, the PCA coupled cluster analysis tool may help in the selection of other such strains.

• The Journal of Advanced Prosthodontics
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• v.16 no.2
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• pp.67-76
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• 2024

PURPOSE. This study aims to assess and predict lifespan of dental prostheses using newly developed Korean Association of Prosthodontics (KAP) criteria through a large-scale, multi-institutional survey. MATERIALS AND METHODS. Survey was conducted including 16 institutions. Cox proportional hazards model and principal component analysis (PCA) were used to find out relevant factors and predict life expectancy. RESULTS. 1,703 fixed and 815 removable prostheses data were collected and evaluated. Statistically significant factors in fixed prosthesis failure were plaque index and material type, with a median survival of 10 to 18 years and 14 to 20 years each. In removable prosthesis, factors were national health insurance coverage, antagonist type, and prosthesis type (complete or partial denture), with median survival of 10 to 13 years, 11 to 14 years, and 10 to 15 years each. For still-usable prostheses, PCA analysis predicted an additional 3 years in fixed and 4.8 years in removable prosthesis. CONCLUSION. Life expectancy of a prosthesis differed significantly by factors mostly controllable either by dentist or a patient. Overall life expectancy was shown to be longer than previous research.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.3
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• pp.311-317
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• 2015

Phenotypic characterization and body biometric in 13 traits (height at withers, body length, chest girth, paunch girth, ear length, tail length, length of tail up to switch, face length, face width, horn length, circumference of horn at base, distances between pin bone and hip bone) were recorded in 233 adult Gojri buffaloes from Punjab and Himachal Pradesh states of India. Traits were analysed by using varimax rotated principal component analysis (PCA) with Kaiser Normalization to explain body conformation. PCA revealed four components which explained about 70.9% of the total variation. First component described the general body conformation and explained 31.5% of total variation. It was represented by significant positive high loading of height at wither, body length, heart girth, face length and face width. The communality ranged from 0.83 (hip bone distance) to 0.45 (horn length) and unique factors ranged from 0.16 to 0.55 for all these 13 different biometric traits. Present study suggests that first principal component can be used in the evaluation and comparison of body conformation in buffaloes and thus provides an opportunity to distinguish between early and late maturing to adult, based on a small group of biometric traits to explain body conformation in adult buffaloes.