• Journal of the Optical Society of Korea
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• v.20 no.1
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• pp.204-211
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• 2016

Oil pollution seriously endangers the biological environment and human health. Due to the diversity of oils and the complexity of oil composition, it is of great significance to identify the oil contaminants. The 3-D fluorescence spectrum combined with a second order correction algorithm was adopted to measure an oil mixture with overlapped fluorescence spectra. The self-weighted alternating trilinear decomposition (SWATLD) is a kind of second order correction, which has developed rapidly in recent years. Micellar solutions of #0 diesel, #93 gasoline and ordinary kerosene in different concentrations were made up. The 3-D fluorescence spectra of the mixed oil solutions were measured by a FLS920 fluorescence spectrometer. The SWATLD algorithm was applied to decompose the spectrum data. The predict concentration and recovery rate obtained by the experiment show that the SWATLD algorithm has advantages of insensitivity to component number and high resolution for mixed oils.

• Korean Journal of Food Science and Technology
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• v.37 no.6
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• pp.1048-1064
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• 2005

Detection of specific compounds influencing food flavor quality is not easy. Electronic nose, comprised of electronic chemical sensors with partial specificity and appropriate pattern recognition system, is capable of recognizing simple and complex volatiles. It provides fast analysis with simple and straightforward results and is best suited for quality control and process monitoring of flavor in food industry. This review examines application of electronic nose in food analysis with brief explanation of its principle. Characteristics of different sensors and sensor drift. and solutions to related problems are reviewed. Applications of electronic nose in food industry include monitoring of fermentation process and lipid oxidation, prediction of shelf life, identification of irradiated volatile compounds, discrimination of food material origin, and quality control of food and processing by principal component analysis and neural network analysis. Electronic nose could be useful for quality control in food industry when correlating analytical instrumental data with sensory evaluation results.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.339-355
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• 2015

Corrosion of prestressed concrete structures is one of the main challenges that engineers face today. In response to this national need, this paper presents the results of a long-term project that aims at developing a structural health monitoring (SHM) technology for the nondestructive evaluation of prestressed structures. In this paper, the use of permanently installed low profile piezoelectric transducers (PZT) is proposed in order to record the acoustic emissions (AE) along the length of the strand. The results of an accelerated corrosion test are presented and k-means clustering is applied via principal component analysis (PCA) of AE features to provide an accurate diagnosis of the strand health. The proposed approach shows good correlation between acoustic emissions features and strand failure. Moreover, a clustering technique for the identification of false alarms is proposed.

• Smart Structures and Systems
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• v.15 no.3
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• pp.751-767
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• 2015

A new procedure is proposed for assessing probabilistic condition of structures considering effect of measured data uncertainty. In this procedure, multiple Finite Element (FE) models are identified by using weighting vectors that represent the uncertainty conditions of measured data. The distribution of structural parameters is analysed using a Principal Component Analysis (PCA) in relation to uncertainty conditions, and the identified models are classified into groups according to their similarity by using a K-means method. The condition of a structure is then assessed probabilistically using FE models in the classified groups, each of which represents specific uncertainty condition of measured data. Yeondae bridge, a steel-box girder expressway bridge in Korea, is used as an illustrative example. Probabilistic condition of the bridge is evaluated by the distribution of load rating factors obtained using multiple FE models. The numerical example shows that the proposed method can quantify uncertainty of measured data and subsequently evaluate efficiently the probabilistic condition of bridges.

• Journal of Construction Engineering and Project Management
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• v.7 no.4
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• pp.20-28
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• 2017

In almost every construction project, delay is an inevitable yet controllable phenomenon. The Indian construction industry encounters an enormous amount of delays in projects. Delay affects both time and money in the forms of schedule and cost overruns, respectively. Due to impressive and dynamic growth in the Indian construction sector, planned efforts are essential to limit these undesirable delays. On account of the surge in the rate of residential building construction, the task of identification and analysis of the delays in residential projects in India has been attempted by the authors. A questionnaire survey was conducted involving 100 stakeholders. Further analysis included an Importance Index to rank the identified delays, Principle Component Analysis for advanced statistical analysis, and Correlation Analysis to check the extent of agreement amongst stakeholders. Conclusions drawn with reference to the analysed data eventually reflected finance-related issues, as well as labour related problems as the dominating causes of delays. The aim of the research is to provide insight to the construction stakeholders and researchers, on an international scale, with the obtained results.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.2
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• pp.177-183
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• 2001

This paper proposes an efficient method for recognizing the fingerprint using the extracted features by adaptive principal component analysis(PCA). The adaptive PCA is implemented by a single-layer neural network for extracting the linear features of fingerprint data. And, the extracted data are transformed into binary data for reducing storage space and transmission time. The proposed method has been applied to recognize the 100 fingerprint data. The simulation results show that the recognitions are all successful and capable of about ${\pm}8^{\circ}$ rotated data.

• Journal of Information Technology Services
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• v.5 no.1
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• pp.177-190
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• 2006

Since their first inception a few decades ago, software components have received much attention mainly due to their alleged benefits of quality and productivity improvement. Despite this, it is yet to be agreed upon what and how components should be designed. This paper aims to bridge the gap by proposing a collaborative process where the voice of the customer is captured and documented by employing the event and entity models. These requirement elements (WHAT) are cross-tabulated in three relation matrices in accordance with the weights provided by the business users. The requirements are fed into the algorithm invented by the authors to optimize the component grouping (HOW). This collaborative process has been successfully validated at an enterprise wide software development project. The process was effective to help the users more actively involved in the design of the system and made the whole process faster and more adaptive to the changes.

• Journal of Wellness
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• v.6 no.1
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• pp.51-61
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• 2011

The purpose of this study was to identify the component factors of professional sport team brand equity and to empirically examine factors composed of it's brand equity. The subjects of this study was the group that spectator professional sport game. For those sheet, judged to be insincere and to be unsuitable for the purpose of this study, and were missing questions excluded. SPSS 13.0 for window statistics package and AMOS 5.0 for window statistics package were used for data analysis. The goodness of the model was confirmed by data analysis and then the hypotheses testing were conducted. The findings are as follows: Firstly, brand associations(rivalry, commitment, play, socialization, success, history) have a significant effect on brand loyalty. But brand mark didn't have a significant effect on brand loyalty. Secondly, brand awareness(identification, internalization) have a significant effect on brand loyalty.

• The Korean Journal of Food And Nutrition
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• v.25 no.4
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• pp.930-940
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• 2012

A comparison of essential oils composition of Aster tataricus L. (gaemichwi), Ligularia fischeri (gomchwi), Solidago virga-aurea var. asiatica Nakai (miyeokchwi), and Aster scaber (chamchwi) was performed by gas chromatography and mass spectrometry for the identification of volatile flavor characteristics in chwi-namuls. The essential oils were extracted by the hydro distillation extraction method. One hundred volatile flavor components were identified from gaemichwi essential oil. ${\alpha}$-Pinene (11.5%) was the most abundant compound, followed by myrcene (8.9%) and ${\beta}$-pinene (7.5%). Ninety-one volatile flavor components were identified from the essential oil of gomchwi. Aromadendrene (14.8%) was the most abundant component, followed by ${\beta}$-caryophyllene (7.6%) and 1-methyl-4-(1-methylethylidene)-cyclohexene (7.3%). Ninety-five volatile flavor constituents were detected in the essential oil of miyeokchwi, moreover, spathulenol (15.7%) was the most abundant component. Ninety-six volatile flavor constituents were detected in the essential oil of chamchwi. Epi-bicyclosesquiphellandrene (21.9%) was the most abundant component, followed by ${\beta}$-caryophyllene (9.5%) and ${\delta}$-terpinene (8.9%). The essential oil composition of gaemichwi was characterized by a higher contents of pinenes. The essential oil composition of gomchwi can be easily distinguished by the percentage of aromadendrene. Spathulenol and epi-bicyclosesquiphellandrene were regarded as the characteristic odorants of miyeokchwi and chamchwi, respectively.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.1
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• pp.51-63
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• 1997

Total suspended particulate matters in the ambient air were analyzed for eight chemical elements (Ca, Co, Cu, Fe, Mn, Pb, Si, and Zn) using an x-ray fluorescence spectrometry (XRF) at the Kyung Hee University - Suwon Campus during 1989 to 1994. To use these data as basis for source identification study, membership of each sample was selected to represent one of the well defined sample groups. The data sets consisting of 83 objects and 8 variables were initially separated into two groups, fine (d$_{p}$<3.3 ${\mu}{\textrm}{m}$) and coarse particle groups (d$_{p}$>3.3 ${\mu}{\textrm}{m}$). A hierarchical clustering method was examined to obtain possible member of homogeneous sample classes for each of the two groups by transforming raw data and by applying various distances. A disjoint principal component analysis was then used to define homogeneous sample classes after deleting outliers. Each of five homogeneous sample classes was determined for the fine and the coarse particle group, respectively. The data were properly classified via an application of logarithmic transformation and Euclidean distance concept. After determining homogeneous classes, correlation coefficients among eight chemical variables within all the homogeneous classes for calculated and meteorological variables (temperature. relative humidity, wind speed, wind direction, and precipitation) were examined as well to intensively interpret environmental factors influencing the characteristics of each class for each group. According to our analysis, we found that each class had its own distinct seasonal pattern that was affected most sensitively by wind direction.ion.