• The Journal of Engineering Geology
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• v.15 no.4 s.42
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• pp.463-473
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• 2005

Because of using the same rating ranges for every rock types the RMR or the Q-system could not usually consider local geological characteristics They also could not present sufficiently the engineering anisotropy of rocks. The canonical correlation analysis was carried out with 3 kinds of face mapping data obtained from granite, sedimentary rock and phyllite in order to clarify a discrepancy between rock types. According to analysis results, as a type of rocks changes, RM factors have different influences on the total rating of RMR.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• Tunnel and Underground Space
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• v.13 no.4
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• pp.279-286
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• 2003

Many trials to set up the correlation between the rock mass classification and the earth resistivity have been carried out to design tunnel support type based on the interpreted electrical resistivity acquired by surface electrical survey. But it is hard to find reports on the comparison of the real rock support type determined during the excavation with the electrical resistivity by the inversion of the survey data acquired before the tunneling. In this study, the rock mass classification based on the face mapping data and the resistivity inversion data are investigated to see if it is possible to design reliably the rock support type based on the surface electrical survey. To get the quantitative correlation, rock engineering indices such as RCR(rock condition rating), N(Rock mass number), Q-system and RMR(rock mass rating) are calculated. Since resistivity data has low resolution, Kriging method as a post processing technique which minimizes the estimated variance is used to improve resolution. The result of correlation analysis shows that the 2D electrical resistivity survey is appropriate to see the general trend of the geology in the sense of rock type, though there might be some local area where these two factors do not coincide. But the correlation between the result of 3D survey and the rock mass classification turns out to be very high, and then 3D electrical resistivity survey can make it possible to set up more reliable rock support type.

• Journal of KSNVE
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• v.7 no.2
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• pp.255-260
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• 1997

This paper describes 3-dimensional volume array of 4 microphones including a reference microphone which is capable of imaging wideband noise source position in 2-dimensional image plane. The cross correlation function and corresponding imaging function between a reference microphone and other microphone, are derived as a function of noise source position. The magnitude of the imaging function gives noise source mapping in image plane. Since the image plane is selective from a rectangular and a cylindrical plane, noise source position information such as range and bearing relative to the array is identified very much easily. Simulation results for typical source configurations confirms the applicability of the proposed array in noise control field.

• Journal of the Korean Geographical Society
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• v.43 no.6
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• pp.1002-1015
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• 2008

We investigate the potential of digital elevation model and multivariate geostatistical kriging in mapping of temperature and rainfall based on sparse weather station observations. By using elevation data which have reasonable correlation with temperature and rainfall, and are exhaustively sampled in the study area, we try to generate spatial distributions of temperature and rainfall which well reflect topographic effects and have less smoothing effects. To illustrate the applicability of this approach, we carried out a case study of Jeju island using observation data acquired in January, April, August, and October, 2005. From the case study results, accounting for elevation via colocated cokriging could reflect detailed topographic characteristics in the study area with less smoothing effects. Colocated cokriging also showed much improved prediction capability, compared to that of traditional univariate ordinary kriging. According to the increase of the magnitude of correlation between temperature or rainfall and elevation, much improved prediction capability could be obtained. The decrease of relative nugget effects also resulted in the improvement of prediction capability.

• Sleep Medicine and Psychophysiology
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• v.2 no.2
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• pp.165-170
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• 1995

Augmenting-reducing evoked potentials(AREP) were studied in 38 college students to explore the topographic distribution between AR slope and personality. The Zuckerman Seeking Scale(SSS) and Eysenck Personality Questionnaire(EPQ) assessed personality. There was a significant positive correlation between AR slope and Extraversion-Introversion(E) in the frontocentral area ; the right posterior area showed a significant negative correlation with E. The Thrill and Adventure Seeking(TAS) subscale showed a significant negative correlation with slope in the right posterior temporal area. The average slope map of all subjects revealed a distribution showing more augmenting in frontocentral areas and more reducing in posterior areas.

• Korean Journal of Remote Sensing
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• v.18 no.3
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• pp.141-146
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• 2002

Chlorophyll-a concentration maps of Yongdam reservoir in September and October, 2001 were produced using Landsat ETM imagery and the in-situ water quality measurement data. In-situ water samples were collected on 16th September and 18th October during the satellite overpass. The correlations between the DN values of the imagery and the values of chlorophyll-a concentration were analyzed. The visible bands(band 1, 2, 3) and the near infrared band(band 4) data of September image showed the correlation coefficient values higher than 0.9. The October image showed correlation coefficient values of about 0.7 due to the low variations of chlorophyll-a concentration. Regression models between the DN values of the Landsat ETM image and the chlorophyll-a concentration have been developed for each image. The developed regression models were then applied to each image, and finally the chlorophyll-a distribution maps of Yongdam reservoir were produced. The produced maps showed the spatial distribution of the chlorophyll-a in Yongdam reservoir in a synoptic way so that the tropic state could be easily monitored and analysed in the spatial domain.

• Fisheries and Aquatic Sciences
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• v.26 no.4
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• pp.282-293
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• 2023

Nile perch and Nile tilapia are major commercial species in Uganda, and thus require continuous production. However, their production is impacted by anthropogenic activities such as fishing in breeding habitats. The aim of this study was to identify and characterize Nile perch and Nile tilapia fish breeding habitats on Lake Kyoga. Water quality, lake bottom, fish and vegetation type samples were collected from 20 sites in April of 2021 and 2022. Key informant interviews were conducted with experienced fishermen at five fish landing sites. The water quality parameters indicated significant difference within the sites using analysis of variance. Sandy and muddy bottom types were equally spread at 40% each by use of a pie chart. Fish gonads showed no significant difference among the 20 sites. Bivariate correlation analysis of the vegetation types indicated a strong negative correlation with Nile perch while Nile tilapia had a positive correlation. Principal component analysis of the water quality, fish gonads and habitat vegetation components cumulatively contributed 82.5% in characterizing a fish breeding habitat. Four sites for Nile perch and four sites for Nile tilapia were characterized as breeding sites on Lake Kyoga and are recommended for mapping and gazettement as breeding habitats for sustainable fisheries management.

• Tunnel and Underground Space
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• v.27 no.4
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• pp.185-194
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• 2017

This technical paper is a study on the unique displacements of Shotcrete Lining at the mined tunnel during excavation period through deep consideration with real time data from monitoring instrumentations correlation with the numerical analysis to identify the rock stresses and the rock spring points at the working face of the Conventional tunnelling by the Drill and Blast, based on the geological face mapping results of the project NS2, Transmission cable tunnel project in Singapore. The created geometry of numerical model was prepared to the real mined tunnel construction site including, vertical shaft, construction adit, tunnel junction area, and 2 enlargement caverns. The convergence measurements by the monitoring instrumentation were performed during the tunnel excavation and shaft sinking construction stages to guarantee the safety of complicated underground structures.

• International Journal of Highway Engineering
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• v.19 no.2
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• pp.35-44
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• 2017

PURPOSES: This study suggests a specific methodology for the prediction of road surface temperature using vehicular ambient temperature sensors. In addition, four kind of models is developed based on machine learning algorithms. METHODS : Thermal Mapping System is employed to collect road surface and vehicular ambient temperature data on the defined survey route in 2015 and 2016 year, respectively. For modelling, all types of collected temperature data should be classified into response and predictor before applying a machine learning tool such as MATLAB. In this study, collected road surface temperature are considered as response while vehicular ambient temperatures defied as predictor. Through data learning using machine learning tool, models were developed and finally compared predicted and actual temperature based on average absolute error. RESULTS : According to comparison results, model enables to estimate actual road surface temperature variation pattern along the roads very well. Model III is slightly better than the rest of models in terms of estimation performance. CONCLUSIONS : When correlation between response and predictor is high, when plenty of historical data exists, and when a lot of predictors are available, estimation performance of would be much better.