• Analytical Science and Technology
• /
• v.30 no.5
• /
• pp.234-239
• /
• 2017

Identifying the components of residues that are not completely burned at the sites of fires site can provide valuable information for tracing the causes of fires. In order to clarify the types of plastic combustion residues found at the scenes of fires, we studied the residue formed after the combustion of polyethylene (PE) and acrylonitrile butadiene styrene (ABS). Plastic samples were burned at 200, 300, 350, 400, and $500^{\circ}C$ for 3 min using a cone calorimeter, and the changes in weight and combustion products were observed. The powder products obtained by lyophilization and pulverization of the combustion products obtained at each temperature were analyzed by a Fourier transform-near infrared (FT-NIR) spectrometer. When the PE samples were burned, the weight did not change up to $350^{\circ}C$, however a significant change in the weight could be measured above $400^{\circ}C$. The principal component analysis (PCA) of the FT-NIR spectra of the PE and ABS samples obtained at each temperature confirmed that the combustion residues at each temperature were PE and ABS, respectively. Therefore, the types of unburned plastics found at the sites of fires can be confirmed rapidly by near infrared spectroscopy.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.47 no.4
• /
• pp.49-55
• /
• 2010

Cast shadow caused by an illumination condition can produce troublesome effects for face recognition system using reflectance image. Consequently, we need to separate cast shadow area from feature area for improvement of recognition accuracy. A Bilateral filter smooths image while preserving edges, by means of a nonlinear combination of nearby pixel values. Processing such characteristics, this method is suited to our purpose in illumination estimation process based on Retinex. Therefore, in this paper, we propose a new illumination normalization method based on the Bilateral filter in face images. The proposed method produces a reflectance image that is preserved relatively exact cast shadow area, because coefficient of filter is designed to multiply proximity and discontinuity of pixels in input image. Performance of our method is measured by a recognition accuracy of principle component analysis(PCA) and evaluated to compare with other conventional illumination normalization methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2002.11a
• /
• pp.452-456
• /
• 2002

The sensor based control system has some sensor fault while operating in the field. In this paper, a sensor fault detection and reconstruction system for a sun tracking controller has been researched by using polynomial regression and principle component analysis approach. The developed sun tracking system controls tow actuators with sensor based mechanism as on-line control and sun orbit information as off-line control, alternatively. To show the validity of the developed system, several experiments were illustrated.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.11 no.3
• /
• pp.261-270
• /
• 2017

Bad sitting postures are known to cause for a variety of diseases or physical deformation. However, it is not easy to fit right sitting posture for long periods of time. Therefore, methods of distinguishing and inducing good sitting posture have been constantly proposed. Proposed methods were image processing, using pressure sensor attached to the chair, and using the IMU (Internal Measurement Unit). The method of using IMU has advantages of simple hardware configuration and free of various constraints in measurement. In this paper, we researched on distinguishing sitting postures with a small amount of data using just one IMU. Feature extraction method was used to find data which contribution is the least for classification. Machine learning algorithms were used to find the best position to classify and we found best machine learning algorithm. Used feature extraction method was PCA(Principal Component Analysis). Used Machine learning models were five : SVM(Support Vector Machine), KNN(K Nearest Neighbor), K-means (K-means Algorithm) GMM (Gaussian Mixture Model), and HMM (Hidden Marcov Model). As a result of research, back neck is suitable position for classification because classification rate of it was highest in every model. It was confirmed that Yaw data which is one of the IMU data has the smallest contribution to classification rate using PCA and there was no changes in classification rate after removal it. SVM, KNN are suitable for classification because their classification rate are higher than the others.

• International Journal of Contents
• /
• v.15 no.1
• /
• pp.39-51
• /
• 2019

Spontaneous speech provides rich information defining the linguistic characteristics of individuals. As such, computational analysis of speech would enhance the efficiency involved in evaluating patients' speech. This study aims to provide a method to differentiate the persons with and without aphasia based on language usage. Ten aphasic patients and their counterpart normal controls participated, and they were all tasked to describe a set of given words. Their utterances were linguistically processed and compared to each other. Computational analyses from PCA (Principle Component Analysis) to machine learning were conducted to select the relevant linguistic features, and consequently to classify the two groups based on the features selected. It was found that functional words, not content words, were the main differentiator of the two groups. The most viable discriminators were demonstratives, function words, sentence final endings, and postpositions. The machine learning classification model was found to be quite accurate (90%), and to impressively be stable. This study is noteworthy as it is the first attempt that uses computational analysis to characterize the word usage patterns in Korean aphasic patients, thereby discriminating from the normal group.

• Korean journal of applied entomology
• /
• v.35 no.2
• /
• pp.104-113
• /
• 1996

In order to clarify the interspecific variation of Astiphrom jezoense, a morphometric analysis was performed for seven populations using PCA (principle component analysis) and discriminant analysis. As a result of PCA, 25 quantitative characters are grouped into four factors. The characteristics on legs are especially important components both in male and in female. Morphometric analysis indicate that considerable morphological gap is correlated with geographical habitat. Important discriminant characters are MOD (maximum ocellar distance), Fn (first tergite length) in female and MSL (malar space length) in male.

• Journal of Sensor Science and Technology
• /
• v.8 no.1
• /
• pp.63-68
• /
• 1999

We proposed a method of eye pattern detection in the 2-D image which was obtained by CCD video camera. To detect face region and eye pattern, we proposed pattern search network and batch SVD algorithm which had the statistical equivalence of PCA. We also used HMM to improve the accuracy of detection. As a result, we acknowledged that the proposed algorithm was superior to PCA pattern detection algorithm in computational cost and accuracy of defection. Furthermore, we evaluated that the proposed algorithm was possible in real-time face pattern detection with 2 frame images per second.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.11B
• /
• pp.1339-1345
• /
• 2011

Red tide is a natural phenomenon that change sea color by harmful algal blooms. There have been many studies on red tide due to increasing of red tide damage. However, to automatically classify the red tide algae is not enough. Recognition of red tide algae is difficult because they do not have matching center features for recognizing algae image object. Previously studies are used a few type of red tide algae for classification. In this paper, we proposed the red tide algae recognition method using PCA and roundness of image objects.

• Korean Journal of Environmental Biology
• /
• v.22 no.3
• /
• pp.446-455
• /
• 2004

The ichthyofauna and community structure in Hongcheon river, Korea, was investigated from April to October 2002. During the surveyed period, 52 species belonging 11 families were collected. There were 23 Korean endemic species (44.23%), including Rhodeus uyekii, Acheilognathus signifer, A. yamatsutae, Coreoleuciscus splendidus, Koreocobitis rotundicaudata and Silurus microdorsalis. Dominant species were Zacco platypus (20.38%), and subdominant species were Z. temmincki (19.62%). Also, Rhynchocypris oxycephalus (8.45%), Pungtungia herzi (8.01%), C. splendidus (6.63%) were numerous. Of the 6 introduced fishes in Hongcheon river Carassius cuvieri, Lepomis macrochirus and Micropterus salmoides were originated from foreign countries but Anguilla japonica, Gymnogobius urotaenia, Rhinogobius giurinus were introduced from other native river systems. According to the fish distribution, the fish community of Hongcheon river was divided into 4 groups by principle component analysis (PCA).

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.4
• /
• pp.703-708
• /
• 2022

K-means clustering is a representative clustering technique. However, there is a limitation in not being able to integrate the performance evaluation scale and the method of determining the minimum number of clusters. In this paper, a method for numerically determining the minimum number of clusters is introduced. The explained variance is presented as an integrated measure. We propose that the k-means clustering method should be performed in the subspace of the PCA in order to simultaneously satisfy the minimum number of clusters and the threshold of the explained variance. It aims to present an explanation in principle why principal component analysis and k-means clustering are sequentially performed in pattern recognition and machine learning.