• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.569-577
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• 2018

In this paper, with the aid of information which is included within data, preprocessing algorithm-based black plastic classifier is designed. The slope and area of spectrum obtained by using laser induced breakdown spectroscopy(LIBS) are analyzed for each material and its ensuing information is applied as the input data of the proposed classifier. The slope is represented by the rate of change of wavelength and intensity. Also, the area is calculated by the wavelength of the spectrum peak where the material property of chemical elements such as carbon and hydrogen appears. Using informations such as slope and area, input data of the proposed classifier is constructed. In the preprocessing part of the classifier, Principal Component Analysis(PCA) and fuzzy transform are used for dimensional reduction from high dimensional input variables to low dimensional input variables. Characteristic analysis of the materials as well as the processing speed of the classifier is improved. In the condition part, FCM clustering is applied and linear function is used as connection weight in the conclusion part. By means of Particle Swarm Optimization(PSO), parameters such as the number of clusters, fuzzification coefficient and the number of input variables are optimized. To demonstrate the superiority of classification performance, classification rate is compared by using WEKA 3.8 data mining software which contains various classifiers such as Naivebayes, SVM and Multilayer perceptron.

• Korean journal of food and cookery science
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• v.18 no.4
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• pp.448-454
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• 2002

This study was performed to develop Cnidium officinale-flavored oils. Cnidium officinale is one of the Korean aromatic medicinal plants. The flavor patterns of Cnidium officinale-flavored oils during storage were detected by using an electronic nose with 6 metal oxide sensors, and a principal component analysis (PCA) was carried out. The overall acceptability of flavor and the masking effects on fetid smell of beef of Cnidium officinale-flavored oils were investigated by sensory evaluation. In COI-flavored oil, flavor patterns between the storage samples for 1 week and 16 weeks could be distinguished. And in CO II-flavored oil, flavor patterns between the samples stored for 1 week and 8 weeks and the flavor patterns between the samples stored for 1 week and 16 weeks in CS I-flavored oil could be distinguished. In CS II-flavored oil, flavor patterns of the samples stored for 1, 4, and 8 weeks also could be distinguished. Fetid smell in beef was significantly reduced by the addition of COI- and CS II-flavored oils. As the storage time increased, overall acceptability of Cnidium officinale-flavored oil decreased, indicating that Cnidium officinale-flavored oils were most preferred at 8 weeks of storage.

• KIEE International Transactions on Power Engineering
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• v.4A no.1
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• pp.33-41
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• 2004

Knowledge of partial discharge (PD) is important to accurately diagnose and predict the condition of insulation. The PD phenomenon is highly complex and seems to be random in its occurrence. This paper indicates the possible use of chaos theory for the recognition and distinction concerning PD signals. Chaos refers to a state where the predictive abilities of a systems future are lost and the system is rendered aperiodic. The analysis of PD using deterministic chaos comprises of the study of the basic system dynamics of the PD phenomenon. This involves the construction of the PD attractor in state space. The simulation results show that the variance of an orthogonal axis in an attractor of chaos theory increases according to the magnitude and the number of PDs. However, it is difficult to clearly identify the characteristics of the PDs. Thus, we calculated the magnitude on an orthogonal axis in an attractor using singular value decomposition (SVD) and principal component analysis (PCA) to extract the numerical characteristics. In this paper, we proposed the condition monitoring method for gas insulated switchgear (GIS) using SVD for efficient calculation of the variance. Thousands of simulations have proven the accuracy and effectiveness of the proposed algorithm.

• Journal of Integrative Natural Science
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• v.8 no.2
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• pp.117-127
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• 2015

Lentil (Lens culinaris Medik.) is one of the important legumes and cheaper source of protein in Bangladesh that displays great biological diversity. Isozyme, one of the most important protein markers to detect genetic polymorphism in lentil, whereas we considered thirteen-isozyme in six varieties viz., BARI masur-1, BARI masur-2, BARI masur-3, BARI masur-4, BARI masur-5 and BARI masur-6. The highest polymorphism was found in tyrosinase isozyme system. UPGMA analysis revealed that the highest similarity between BARI masur-5 and BARI masur-6 whereas, the highest genetic distance between BARI masur-1 and BARI masur-5 reflecting higher intervarietal variation. Principal component analysis (PCA) also revealed the similar results that of unweighted pair group method with arithmetic mean (UPGMA). The first, second and third PCs contributed 81.58%, 11.19% and 4.94% variation respectively, with cumulative variation of the first three PCs was 75.45%. Consequently, Isozyme could clearly assed the genetic diversity at intervarietal levels and these two varieties can be considered as valuable gene resources for future breeding and conservation programs.

• 한국문화재보존과학회:학술대회논문집
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• 2004.10a
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• pp.131-134
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• 2004

A lot of foreign objects(ceramics, pottery, bronze, glassware and coins, etc.) have been found in the archaeological sites in Korea. These foreign objects are the evidences of the cultural exchanges of that time, whose scale and routes are an important part of the studies in ancient history. So it is crucial to accumulate basic reference information such as the raw materials and the production method of these objects through scientific researches, along with archeological researches. These scientific research materials provide a basis for finding the importing route and the origin of these objects. Besides, we can find out extraordinary and distinctive production technique by comparison with tile domestic objects. This article reports the result of an analysis, performing on 36 samples of the glassware fragments excavated from the South Tomb of the Hwangnamdaechong, to verify their components and note peculiar features. We have analyzed the major and minor components of 10 elements, and then by using these data examined the differences in the composition of components, varying with the origin and color of glassware. We used the PCA(principal component analysis) as the statistical method to classify the sample in order to find out how the samples formed groups.

• Journal of Distribution Science
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• v.14 no.9
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• pp.65-72
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• 2016

Purpose - This study is to examine the effect of Korean Wave (Hallyu) towards consumer purchase intention of Korean Product in Indonesia. In addition, this study also investigates the image of Korea whether it can give an impact on Indonesian consumers' intention to purchase Korean Products. Research design, data, and methodology - A total of 227 respondents from Indonesian consumers were collected using online surveys. The results from this survey were analyzed using principal component analysis (PCA) to identify each of the factors. Multiple regression and process analysis (designed by Andrew F. Hayes) were conducted to test the hypotheses. Results - This research found that 'Korean Wave', 'Ethnocentrism', and 'Country-of-Origin Image' significantly affected consumer purchase intention towards Korean Products in Indonesia, while 'Country Image' on the purchase intention was not significant affected the purchase intention. Conclusions - Study findings provide useful information for business practitioners and government to develop and maintain the use of Korean Wave in the business and marketing fields. By only referring to the image of country, it will be difficult for the consumers to decide whether they want to purchase the products or not. In other words, the favorable image of Korea, usually represented by high level of industrialization and economy, is more likely to be enhanced by favorable image of product and Korean cultural wave.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.552-555
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• 2018

This study developed a gesture recognition method using Kinect sensor and proposed a fighting action control interface. To extract the pattern features of a gesture, it used a method of extracting them in consideration of a body rate based on the shoulders, rather than of absolute positions. Although the same gesture is made, the positional coordinates of each joint caught by Kinect sensor can be different depending on a length and direction of the arm. Therefore, this study applied principal component analysis in order for gesture modeling and analysis. The method helps to reduce the effects of data errors and bring about dimensional contraction effect. In addition, this study proposed a modified matching algorithm to reduce motion restrictions of gesture recognition system.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.9-15
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• 2011

In this paper, we design and implement of gate management system by face recognition using smart phone. We investigate various algorithms for face recognition on smart phones. First step in any face recognition system is face detection. We investigated algorithms like color segmentation, template matching etc. for face detection, and Eigen & Fisher face for face recognition. The algorithms have been first profiled in MATLAB and then implemented on the Android phone. While implementing the algorithms, we made a tradeoff between accuracy and computational complexity of the algorithm mainly because we are implementing the face recognition system on a smart phone with limited hardware capabilities.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.722-727
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• 2007

As the world construction market is rearranged by the level of technological capability, recently the technological capability in construction industry is developing rapidly. The important of measuring and analyzing technological capability in construction industry is gaining more and more emphasis. It enables to grasp the past and present situation of construction industry as well as to foresee changes in the future. However the concept of technological capability cannot be identified easily, as well as it is hard to compare that capability of construction industry among different countries. Although there have been numerous studies conducted on the technological capability of construction industry, most of the studies were in formsof surveys of specialists or industry professionals which lacked objectivity and sound basis for data. This study will be focused on investigating the methodology in exploiting and measuring surface of the earth and developing indicator and process to understand technological capability in construction industry through quantitative and statistical analysis. Then it will verify them through a case study.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1614-1622
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• 2018

The research related to fault diagnosis in permanent magnet synchronous motors (PMSMs) has attracted considerable attention in recent years because various faults such as permanent magnet demagnetization and short-circuited turns can occur and result in unexpected failure of motor related system. Several conventional current and back electromotive force (BEMF) analysis techniques were proposed to detect certain faults in PMSMs; however, they generally deal with a single fault only. On the contrary, cases of multiple faults are common in PMSMs. We propose a fault diagnosis method for PMSMs with single and multiple combined faults. Our method uses three phase BEMF voltages based on the fast Fourier transform (FFT), support vector machine(SVM), and visualization tools for identifying fault types and severities in PMSMs. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) are used to visualize the high-dimensional data into two-dimensional space. Experimental results show good visualization performance and high classification accuracy to identify fault types and severities for single and multiple faults in PMSMs.