• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.20-29
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• 2006

Modeling urban climate caused by land use conversion is critical for human welfare and sustainable development, but has hampered because detailed information on urban characteristics is hard to obtain. With the advantage of satellite observations and the new statistical boundary system, this paper measures the economic and environmental effects of green area loss due to land use conversion in urban areas. To perform this purpose, data were collected from the various sources basic statistical unit data from the National Statistical Office, digital maps from the National Geographic Information Institute, satellite images, and field surveys when necessary. All data (maps and attributes) are built into the geographic information system (GIS). This paper also utilizes Landsat TM 5 imagery of Daegu city to derive vegetation index and to measure average surface temperature. The satellite data were examined using standard image processing software, ERDAS IMAGINE, and the results of the digital processing were presented with ARCVIEW(v.3.3). SAS package was used to perform statistical analyses. This study presents that there exists a strong relationship between land use change and climatic change as well as land price change. Based on results of the analysis, this paper suggests that planners should implement effective tools and policies of urban growth management to detect environmental quality and to make right decisions on policies concerning smart urban growth.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.33-40
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• 2003

Data mining plays an important role in a knowledge discovery process and various algorithms of data mining can be selected for the specific purpose. Most of traditional hierachical clustering methode are suitable for processing small data sets, so they difficulties in handling large data sets because of limited resources and insufficient efficiency. In this study we propose a hybrid neural networks clustering technique, called PPC for Pre-Post Clustering that can be applied to large data sets and find unknown patterns. PPC combinds an artificial intelligence method, SOM and a statistical method, hierarchical clustering technique, and clusters data through two processes. In pre-clustering process, PPC digests large data sets using SOM. Then in post-clustering, PPC measures Similarity values according to cohesive distances which show inner features, and adjacent distances which show external distances between clusters. At last PPC clusters large data sets using the simularity values. Experiment with UCI repository data showed that PPC had better cohensive values than the other clustering techniques.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.82-95
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• 2019

In this study, we analyzed the beam time series of ocean reverberation which was conducted in the eastsouthern region of East Sea, Korea during the August, 2015. The reverberation data was gathered by moving research vessel towing LFM (Linear Frequency Modulation) source and triplet receiver array. After signal processing, we analyzed the variation of ocean reverberation level according to the seafloor bathymetry, source/receiver depth and sound speed profile. In addition, we used the normalized data by using cell averaging algorithm and identified the statistical characteristics of seafloor scatterer by using moment estimation method and estimated shape parameter. Also, we analyzed the coincidence of data with Rayleigh and K-distribution probability by Kolmogorov-Smirnov test. The results show that there is range dependency of reverberation according to the bathymetry and also that the time delay and the intensity level change depend on the depths of source and receiver. In addition, we observed that statistical characteristics of similar Rayleigh probability distribution in the ocean reverberation.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2954-2972
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• 1998

It is expected that the transport of compressed video will become a significant part of total network traffic because of the widespread introduction of multimedial services such as VOD(video on demand). Accordingly, VBR(variable bit-rate) encoded video will be widely used, due to its advantages in statistical multiplexing gain and consistent vido quality. Since the transport of video traffic requires larger bandwidth than that of voice and data, the characterization of video source and traffic modeling is very important for the design of proper resource allocation scheme in ATM networks. Suitable statistical source models are also required to analyze performance metrics such as packet loss, delay and jitter. In this paper, we analyzed and described on the characterization and traffic modeling of MPEG video sources. The models are broadly classified into two categories; i.e., statistical models and deterministic models. In statistical models, the models are categorized into five groups: AR(autoregressive), Markov, composite Marko and AR, TES, and selfsimilar models. In deterministic models, the models are categorized into $({\sigma},\;{\rho}$, parameterized model, D-BIND, and Empirical Envelopes models. Each model was analyzed for its characteristics along with corresponding advantages and shortcomings, and we made comparisons on the complexity of each model.

• Journal of Elementary Mathematics Education in Korea
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• v.23 no.1
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• pp.75-92
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• 2019

The 2015 revised mathematics curriculum encourages students to use graphs in newspapers and the Internet as materials when teaching graphs, and to experience a series of statistical problem-solving processes that collect, classify, organize, graph and interpret data. The graphs that the students learn through traditional textbooks were a single type of graphs. In particular, the graphs of the 5th and 6th grade groups were only increased in numbers, but the basic concepts were repeated in the 3rd and 4th grades. Fortunately, from the 2009 revision curriculum, it is possible to select the graph suitable for the situation while comparing the characteristics of some graphs. In most cases, the graphs used in the real world are presented in the form of a compounded infographics. The purpose of this study is to analyze and analyze the manifestations of information processing competence elements emphasized in the 2015 revised curriculum through the statistical project class using the informal graphic in the fifth grade of elementary school. And we suggested a concrete class plan.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.778-804
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• 2017

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

• Journal of the Korean Home Economics Association
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• v.47 no.1
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• pp.105-113
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• 2009

The purpose of this study was to explore differences in social information processing according to children's sex and types of aggression in response to instrumental and relational provocation factors. Two hundred and fifty-one 4, 5, and 6 graders were selected from an elementary school in Seoul. To evaluate their social information processing, the Intent Attributions and Feelings of Distress(Crick, 1995; Fitzgerald & Asher, 1987) and Response Decision Instrument(Crick & Werner, 1998) were revised and analyzed. A peer-nomination measure(Crick, 1995; Crick & Grotpeter, 1995) was used to select aggressive groups. Data were subjected to descriptive statistical analysis and multivariate [2(sex: M, F)${\times}$3(type of aggression: overt, relational, overt and relational aggression)] analysis of variance. Findings revealed that children's social information processing patterns were different according to sex and type of aggression. Also aggressive children responded differently in their social information processing according to instrumental and relational provocation factors. Implications of these findings for the role of gender, aggression type, and provocation type are discussed in order to better understanding of children's social information processing.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.161-168
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• 2020

Predicting outcome of the sports enables teams to establish their strategy by analyzing variables that affect overall game flow and wins and losses. Many studies have been conducted on the prediction of the outcome of sports events through statistical techniques and machine learning techniques. Predictive performance is the most important in a game prediction model. However, statistical and machine learning models show different optimal performance depending on the characteristics of the data used for learning. In this paper, we propose a new ensemble model to predict English Premier League soccer games using statistical models and the machine learning models which showed good performance in predicting the results of the soccer games and this model is possible to select a model that performs best when predicting the data even if the data are different. The proposed ensemble model predicts game results by learning the final prediction model with the game prediction results of each single model and the actual game results. Experimental results for the proposed model show higher performance than the single models.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.118-127
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• 2020

The paper introduces trends and methodological challenges of quantitative Korean text analysis by using the case studies of academic and news media articles on "migration" and "immigration" within the periods of 2017-2019. The quantitative text analysis based on natural language processing technology (NLP) and this became an essential tool for social science. It is a part of data science that converts documents into structured data and performs hypothesis discovery and verification as the data and visualize data. Furthermore, we examed the commonly applied social scientific statistical models of quantitative text analysis by using Natural Language Processing (NLP) with R programming and Quanteda.

• 한국해양학회지
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• v.22 no.3
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• pp.143-152
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• 1987

The digital data were obtained using Kennedy 9000 magnetic tape deck which was connected to the SMS960 side scan sonar during the field operations. The data of three consecutive survey tracks near Seongsan-po, Cheju were used for the development of this study. The softwares were mainly written in Fortran-77 using VAX 11/780 MINI-COMPUTER (CPU Memory; 4MB). The established mapping system consists of the pretreatment and the digital processing of seafloor image data. The pretreatment was necessary because the raw digital data format of the field magnetic tapes was not compatible to the VAX system. Therefore the raw data were read by the personal computer using the Assembler language and the data format was converted to IBM compatible, and next data were communicated to the VAX system. The digital processing includes geometrical correction for slant range, statistical analysis and cartography of the seafloor image. The sound speed in the water column was assumed 1,500 m/sec for the slant range correction and the moving average method was used for the signal trace smoothing. Histograms and cumulative curves were established for the statistical analysis, that was purposed to classify the backscattering strength from the sea-bottom. The seafloor image was displayed on the color screen of the TEKTRONIX 4113B terminal. According to the brief interpretation of the result image map, rocky and sedimentary bottoms were very well discriminated. Also it was shown that the backscattered acoustic pressurecorrelateswith the grain size and sorting of surface sediments.