• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1326-1328
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• 2003

An elliptical basis function (EBF) network is proposed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and uses the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture -density distributions in the feature space, the proposed network not only possesses the advantage of the RBF mechanism but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is faster in training, more accurate, and simpler in structure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7C
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• pp.931-936
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• 2004

GBFCM(DM), Gradient-based Fuzzy c-means with Divergence Measure, for efficient clustering of GPDF(Gaussian Probability Density Function) in MPEG VBR video data modeling is proposed in this paper. The proposed GBFCM(DM) is based on GBFCM( Gradient-based Fuzzy c-means) with the Divergence for its distance measure. In this paper, sets of real-time MPEG VBR Video traffic data are considered. Each of 12 frames MPEG VBR Video data are first transformed to 12-dimensional data for modeling and the transformed 12-dimensional data are Pass through the proposed GBFCM(DM) for classification. The GBFCM(DM) is compared with conventional FCM and GBFCM algorithms. The results show that the GBFCM(DM) gives 5∼15% improvement in False Alarm Rate over conventional algorithms such as FCM and GBFCM.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1119-1137
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• 2017

This study assessed the water provisioning and climate mitigation ecosystem services of the urban forest in Seoul and Gyeonggi-do. The ecosystem service assessment is conducted based on natural function, natural function and population, and natural function and the beneficiary of the ecosystem service. Then, the impact of climate change on ecosystem services is analyzed to figure out the sensitivity of the impact on the beneficiary when the natural function of forest destroys under climate change. Gyeonggi-do has higher function-based water provisioning ecosystem service than Seoul. And population-based water provisioning ecosystem service appears to be higher in the densely populated area. On the other hand, beneficiary-based water provisioning ecosystem service by applying both natural water supply function and beneficiary distribution appears different with the result of population-based water provisioning service assessment. In other words, regions with high beneficiary population show higher ecosystem service than those with a low beneficiary population even though they have the same water storage function. In addition, climate change has a negative impact on the water provisioning ecosystem service. Under climate change, water provisioning service is expected to decrease by 26%. For climate mitigation service, regions close to the forest seem to have a low temperature, which indicates their high climate mitigation service. The center of the city with high beneficiary population shows high beneficiary-based ecosystem service. The climate change impacts the forest growth to decrease which affect the beneficiary-based climate mitigation ecosystem service to decrease by 33%. From this study, we conclude that beneficiary-based function and ecosystem service assessment is needed as well as the supply-based classification of forest function suggested by Korea Forest Service. In addition, we suggest that not only supply-based function classification and ecosystem service assessment but also beneficiary-based function classification and ecosystem service assessment is needed for managing the urban forest, which has been destroyed by climate change. This will contribute to revaluing cases where a forest with low natural function but high beneficiary-based ecosystem service, which is not considered under the current forest function-based assessment system. Moreover, this could assist in developing a suitable management plan for the urban forest.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.545-557
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• 2001

In this paper we propose a hierarchical classification algorithm based on rough membership function which can reason a new object approximately. We use the fuzzy reasoning method that substitutes fuzzy membership value for linguistic uncertainty and reason approximately based on the composition of membership values of conditional sttributes Here we use the rough membership function instead of the fuzzy membership function It can reduce the process that the fuzzy algorithm using fuzzy membership function produces fuzzy rules In addition, we transform the information system to the understandable minimal decision information system In order to do we, study the discretization of continuous valued attributes and propose the discretization algorithm based on the rough membership function and the entropy of the information theory The test shows a good partition that produce the smaller decision system We experimented the IRIS data etc. using our proposed algorithm The experimental results with IRIS data shows 96%~98% rate of classification.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.187-194
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• 1999

Case-Based Reasoning(CBR) systems support ill structured decision-making. The measure of the success of a CBR system depends on its ability to retrieve the most relevant previous cases in support of the solution of a new case. One of the methodologies widely used in existing CBR systems to retrieve previous cases is that of the Nearest Neighbor(NN) matching function. The NN matching function is based on assumptions of the independence of attributes in previous case and the availability of rules and procedures for matching.(omitted)

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.114-122
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• 2000

This paper proposes a classification method using an amorphous Prototype to minimize classification error caused by such fixed-Prototype-based methods as Fuzzy C-Means, Nearest Neighborring Classification, FMMCNN, and Fuzzy-ART. For this method, a new fuzzy neural network is introduced, in which a convex polytope is generated or adaptively reshaped to classify the given datum into a proper group. Thus, this method contains a function to classify sequential data set. To show the validity of this method, various numerical experiments including comparison results with FMMCNN are presented

• Journal of Korean Society for Quality Management
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• v.23 no.3
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• pp.126-139
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• 1995

We describe a formal approach to the construction of optimal classification rule for the two-group normal classification with equal population mean problem. Based on the utility function of Bernardo, we suggest a balanced design for the classification and construct the optimal rule under the balanced design condition. The rule is characterized by a constrained minimization of total risk of misclassification, the constraint of which is constructed by the process of equation between expected utilities of the two group conditional densities. The efficacy of the suggested rule is examined through numerical studies. This indicates that, in case little is known about the relative population sizes, dramatic gains in accuracy of classification result can be achieved.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.381-392
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• 2003

Multi-temporal approaches using sequential data acquired over multiple years are essential for satisfactory discrimination between many land-cover classes whose signatures exhibit seasonal trends. At any particular time, the response of several classes may be indistinguishable. A harmonic model that can represent seasonal variability is characterized by four components: mean level, frequency, phase and amplitude. The trigonometric components of the harmonic function inherently contain temporal information about changes in land-cover characteristics. Using the estimates which are obtained from sequential images through spectral analysis, seasonal periodicity can be incorporates into multi-temporal classification. The Normalized Difference Vegetation Index (NDVI) was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula for 1996 ～ 2000 using a dynamic technique. Land-cover types were then classified both with the estimated harmonic components using an unsupervised classification approach based on a hierarchical clustering algorithm. The results of the classification using the harmonic components show that the new approach is potentially very effective for identifying land-cover types by the analysis of its multi-temporal behavior.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.47-53
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• 2001

In this paper, we compare the classification performances of both ensemble and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. In view of the unknown relationship between input and output function, we use a Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: When the level of the variance is medium, Bagging & Parameter Combining performs worse than Logistic Regression, Variable Selection Bagging and Clustering. However, classification performances of Logistic Regression, Variable Selection Bagging, Bagging and Clustering are not significantly different when the variance of input data is either small or large. When there is strong correlation in input variables, Variable Selection Bagging outperforms both Logistic Regression and Parameter combining. In general, Parameter Combining algorithm appears to be the worst at our disappointment.

• Fashion & Textile Research Journal
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• v.26 no.1
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• pp.35-43
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• 2024

This study aimed to objectively classify the lateral torso posture types and functions of older women. We used 3D body scan data of 119 women aged 70-85 years from the 6th SizeKorea project. First, we defined three torso axes to represent the lateral torso posture types: posterior waist-back, back-cervical, and whole torso axes. Next, we asked experts to select one of four lateral torso posture types-stooped, straight, leaning back, and swayback postures-by looking at the lateral photographic data of 119 older women. To identify the axis that best represented each lateral torso posture type, a discriminant analysis was conducted using the angle of each of the three torso axes as an independent variable and an expert's visual classification as a dependent variable. Based on the analysis, the whole torso and backcervical axis angles were selected as variables for judging lateral torso posture types. Subsequently, we developed a classification function to determine which of the four lateral torso posture types of a particular participant was applicable for a new individual. The method developed in this study is significant in that it enables the objective classification of the lateral torso postures types of older women.