• Proceedings of the KSRS Conference
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• v.2
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• pp.647-650
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• 2006

Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery was acquired over the Little Miami River Watershed (1756 square miles) in Ohio, U.S.A., which is one of the largest hyperspectral image acquisition. For the development of a 4m-resolution land cover dataset, a hierarchical approach was employed using two different classification algorithms: 'Image Object Segmentation' for level-1 and 'Spectral Angle Mapper' for level-2. This classification scheme was developed to overcome the spectral inseparability of urban and rural features and to deal with radiometric distortions due to cross-track illumination. The land cover class members were lentic, lotic, forest, corn, soybean, wheat, dry herbaceous, grass, urban barren, rural barren, urban/built, and unclassified. The final phase of processing was completed after an extensive Quality Assurance and Quality Control (QA/QC) phase. With respect to the eleven land cover class members, the overall accuracy with a total of 902 reference points was 83.9% at 4m resolution. The dataset is available for public research, and applications of this product will represent an improvement over more commonly utilized data of coarser spatial resolution such as National Land Cover Data (NLCD).

• The Journal of Society for e-Business Studies
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• v.7 no.1
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• pp.153-166
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• 2002

Nowadays an information exchange on Bn such as B2B electronic commerce is spreading. Therefore the systematic and stable management mechanism for storing the exchanged information is needed. For this goal there are many research activities for connection between XML application and relational database. But because XML data have hierarchical structures and relational database can store only flat-structured data, we need to store XML data in object-relational database that support hierarchical structure. Accordingly the modeling methodology for storing XML data in object-relational database is needed. In order to build good quality application systems, modeling is an important first step. In 1997, the OMG adopted the UML as its standard modeling language. Since industry has warmly embraced UML its popularity should become more important in the future. So a design methodology based on UML is need to develop efficiently XML applications. In this paper, we propose a unified design methodology for In applications based on object-relational database using In. To this goal, first we introduce a XML modeling methodology to design W3C XML schema using UML and second we propose data modeling methodology for object-relational database schema to store efficiently XML data in object-relational databases.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.263-270
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• 2009

Noise band removal is a crucial step before spectral matching since the noise bands can distort the typical shape of spectral reflectance, leading to degradation on the matching results. This paper proposes a statistical noise band removal method for hyperspectral data using the correlation coefficient between two bands. The correlation coefficient measures the strength and direction of a linear relationship between two random variables. Considering each band of the hyperspectral data as a random variable, the correlation between two signal bands is high; existence of a noisy band will produce a low correlation due to ill-correlativeness and undirected ness. The unsupervised k-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID in order to evaluate the validation of the proposed method. This paper also proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures. The experimental results conducted on a 228-band hyperspectral data show that while the SAM measure is rather resistant, the performance of SID measure is more sensitive to noise.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.27-33
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• 2008

The clustering of gene expression data is used to analyze the results of microarray studies. This clustering is one of the frequently used methods in understanding degrees of biological change and gene expression. In biological research, MCL algorithm is an algorithm that clusters nodes within a graph, and is quick and efficient. We have modified the existing MCL algorithm and applied it to microarray data. In applying the MCL algorithm we put forth a simulation that adjusts two factors, namely inflation and diagonal tent and converted them by making use of Markov matrix. Furthermore, in order to distinguish class more clearly in the modified MCL algorithm we took the average of each row and used it as a threshold. Therefore, the improved algorithm can increase accuracy better than the existing ones. In other words, in the actual experiment, it showed an average of 70% accuracy when compared with an existing class. We also compared the MCL algorithm with the self-organizing map(SOM) clustering, K-means clustering and hierarchical clustering (HC) algorithms. And the result showed that it showed better results than ones derived from hierarchical clustering and K-means method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.331-338
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• 1992

The application of Connectionist expert system using neural network to fault diagnosis of power system is presented and compared with rule-based expert system. Also, the merits of Connectionist model using neural network is presented. In this paper, the neural network for fault diagnosis is hierarchically composed by 3 neural network classes. The whole power system is divided into subsystems, the neural networks (Class II) which take charge of each subsystem and the neural network (Class III) which connects subsystems are composed. Every section of power system is classified into one of the typical sections which can be applied with same diagnosis rules, as line-section, bus-section, transformer-section. For each typical section, only one neural network (Class I) is composed. As the proposed model has hierarchical structure, the great reduction of learning structure is achieved. With parallel distributed processing, we show the possibility of on-line fault diagnosis.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.543-553
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• 2020

The purpose of this study is to analyze the effects of subjective class consciousness on life satisfaction. This research aimed to not only analyze the relative explanatory power, but also the influence of satisfaction of life within the socioeconomic status where the elderly consider themselves to be an integral part. The elderly's satisfaction in life was analyzed in comparison with demographic characteristics such as gender and age. The correlations of objective socioeconomic characteristics such as income level and education level were also observed. For this purpose, the Korea Labor Panel 17th data (2014) was used to conduct a one-way batch distribution analysis and a hierarchical regression analysis. It was seen that there was a correlation in the Korean elderly in terms of class consciousness and life satisfaction. The elderly with a lower subjective class consciousness showed lower life satisfaction. The relative influences were stronger than the demographic and socioeconomic characteristics of the elderly, and the explanatory power was much higher than the objective income levels. These results show that the subjective perception of their socioeconomic status has a significant influence on the level of life satisfaction of the Korean elderly, independent of the objective income level.

• Journal of Biomedical Engineering Research
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• v.29 no.6
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• pp.466-476
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• 2008

The more people use ambulatory electrocardiogram(ECG) for arrhythmia detection, the more researchers report the automatic classification algorithms. Most of the previous studies don't consider the un-balanced data distribution. Even in patients, there are much more normal beats than abnormal beats among the data from 24 hours. To solve this problem, the hierarchical classification using 21 features was adopted for arrhythmia abnormal beat detection. The features include R-R intervals and data to describe the morphology of the wave. To validate the algorithm, 44 non-pacemaker recordings from physionet were used. The hierarchical classification model with 2 stages on domain knowledge was constructed. Using our suggested method, we could improve the performance in abnormal beat classification from the conventional multi-class classification method. In conclusion, the domain knowledge based hierarchical classification is useful to the ECG beat classification with unbalanced data distribution.

• Journal of Korean Society of Rural Planning
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• v.14 no.3
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• pp.63-73
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• 2008

The purpose of this study is to analyze the determinants of the digital divide at individual level and regional level in Korea, considering interaction between individual and the regional variables. Following results are obtained. First, individual level digital devide in the 16 different regions has been found in terms of Internet use, implying the needs for further analysis on impact of the regional factor in individual Internet use. Second, the result finds the impact of level-l individual variables, "gender, age, education, income and jobs" on digital divide, significantly at level 10% level. Third, the regional variables influencing the individual digital divide were not found at state level. However, regional factors might affect digital devide at county level. Study suggest some plans to reduce digital divide. First, the digital devide at individual level should be remedied by focusing on neglected class of people. Second, we need to approach the digital divide by analyzing in more detail, reflecting interactions of the regional variables and individual variables. Third, we should come up with a policy for mending the digital divide at regional level.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.249-256
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• 2024

Machine learning-based intrusion detection methodologies require a large amount of uniform learning data for each class to be classified, and have the problem of having to retrain the entire system when adding an attack type to be detected or classified. In this paper, we use feature learning and hierarchical classification methods to solve classification problems and data imbalance problems using relatively little training data, and propose an intrusion detection methodology that makes it easy to add new attack types. The feasibility of the proposed system was verified through experiments using KDD IDS data..

• Korean Journal of Childcare and Education
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• v.20 no.2
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• pp.83-103
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• 2024

Objective: This study aimed to investigate the effects of children's characteristics and childcare teachers' attributes on the frequency and level of imaginative play in two-year-old classrooms. Methods: The study involved 191 toddlers, their mothers, and 32 teachers from childcare centers. Toddler characteristics encompassed temperament along with demographic variables such as gender and age. Teacher' attributes related to play included playfulness, play-support belief, and interactions with toddlers. Data analysis was conducted using SPSS 22.0 and HLM 8.2 software, employing basic analysis, hierarchical linear analysis, and hierarchical regression analysis. Results: First, as toddlers' age increased, both the frequency and level of their imaginative play increased. Second, individual-level model analysis revealed a positive effect of toddlers' extroversion on the level of imaginative play. Third, the class-level model results indicated that teachers' emotions had a negative effect, whereas their encouragement positively influenced the level of imaginative play. Conclusion/Implications: The significance of this study lies in its utilization of a multilayered model analysis, which offers a more robust examination of variable influences by accounting for hierarchical data structures.