• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.4
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• pp.1045-1050
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• 1998

Classification techniques are often an importand component of intelligent systems and are use for both deta preprocessing and decision making. In the design of a classification system, the labled samples must be given to provide a priori information for the classification. Moreover, the number of classes to be categorized must be known a priori information, called OFCAM. In OFCAM, an unsupervised by OFCAM, the database of a classification system, called PCSDB, is constructed. Then, PCSDB can be effectively used in the decision process of the system.

• Educational Technology International
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• v.9 no.1
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• pp.49-77
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• 2008

As clearly suggested in the case of Wikipedia, collective intelligence is predicted to develop into the most important platform of knowledge and information in the future society. But it just remains at the level of activities for group projects in the present frame of education and so it doesn't lead to creating collective intelligence. This study looks into an 'information repository model of collective intelligence' that makes it possible to deliver an education process a priori of Shared Knowledge Reservoir to "Junior Digital Nomad", who is definitely and will be in existence, and that further enables them to be active there in reality. Based on this storage model, it suggests a practicable web system model; Junior Maphistory e-encyclopedia, which is appropriately consistent with the features of Web 2.0 and can grow into a general historical and geographical information service.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.149-168
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• 2016

The Active Appearance Model (AAM) is a class of deformable models, which, in the segmentation process, integrates the priori knowledge on the shape and the texture and deformation of the structures studied. This model in its sequential form is computationally intensive and operates on large data sets. This paper presents another framework to implement the standard version of the AAM model. We suggest a distributed and parallel approach justified by the characteristics of the model and their potentialities. We introduce a schema for the representation of the overall model and we study of operations that can be parallelized. This approach is intended to exploit the benefits build in the area of advanced image processing.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6A
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• pp.481-487
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• 2005

In order to increase the autonomous navigation capability of a mobile robot, it is very crucial to develop a method for recognizing a priori known environmental characteristics. This paper proposes an ultrasonic sensor based real-time method for recognizing a priori known indoor environmental characteristics like a wall and corner. The ultrasonic sensor consists of an ultrasonic transmitter and two ultrasonic receivers placed symmetrically about the transmitter. Unlike previous methods the information obtained from the sensor is processed in real-time by extended Kalman filter to be able to correct the position and orientation of robot with respect to known environmental characteristics.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.3
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• pp.4-19
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• 1991

In this paper, we propose a fuzzy neural network pattern classifier utilizing fuzzy information. This system works without any a priori information about the number of clusters or cluster centers. It classifies each input according to the distance between the weights and the normalized input using Bezdek's [1] fuzzy membership value equation. This model returns the correct membership value for each input vector and find several cluster centers. Some experimental studies of comparison with other algorithms will be presented for sample data sets.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.235-249
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• 2004

This paper proposes a method for estimating isotropic out-of-focus blur and a fully digital auto-focusing based on a priori estimate set of PSFs. The proposed algorithm for identifying the isotropic PSF is performed by approximating an isotropic blur to a novel discrete PSF model and estimating the PSF model coefficients from degraded edges. After acquiring the set of PSFs by proposed PSF estimation algorithm the proposed fully digital auto-focusing system can restore out-of-focused images by two steps: i) selecting an optimal PSF and ii) restoring the out-of-focused image by digital image restoration.

• Geophysics and Geophysical Exploration
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• v.16 no.1
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• pp.27-35
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• 2013

In this study, we developed the trial product on 3D magnetic inversion tentatively named 'KMag3D'. Also, we briefly introduced its own function and graphic user interface on which especially focused through the development in the form of user manual. KMag3D is consisted of two fundamental frame for the 3D magnetic inversion. First, algebraic reconstruction technique was selected as a 3D inversion algorithm instead of least square method conventionally used in various magnetic inversion. By comparison, it was turned out that algebraic reconstruction algorithm was more effective and economic than that of least squares in aspect of both computation time and memory. Second, for the effective determination of the 3D initial and a-priori information model required in the execution of our algorithm, we proposed the practical technique based on the assemblage of 2D forward modeling and inversion results for individual user-selected 2D profiles. And in succession, initial and a-priori information model were constructed by appropriate interpolation along the strke direction. From this, we concluded that our technique is both suitable and very practical for the application of 3D magentic inversion problem.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.176-176
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• 2018

This study presents a regional, probabilistic framework for estimating streamflow via spatial scaling in the Great Lakes basin, which is the largest lake system in the world. The framework follows a two-fold strategy including (1) a quadratic-programming based optimization model a priori to explore the model structure, and (2) a time-varying hierarchical Bayesian model based on insights found in the optimization model. The proposed model is developed to explore three innovations in hierarchical modeling for reconstructing historical streamflow at ungaged sites: (1) information of physical characteristics is utilized in spatial scaling, (2) a time-varying approach is introduced based on climate information, and (3) heteroscedasticity in residual errors is considered to improve streamflow predictive distributions. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with four simpler nested formulations and the optimization model to confirm specific hypotheses embedded in the full model structure. The nested models assume a similar hierarchical Bayesian structure to our proposed model with their own set of simplifications and omissions. Results suggest that each of three innovations improve historical out-of-sample streamflow reconstructions although these improvements vary corrsponding to each innovation. Finally, we conclude with a discussion of possible model improvements considered by additional model structure and covariates.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.3
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• pp.166-175
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• 1998

When we make a speaker adapted model using MAPE (maximum a posteriori estimation), the adapted model has one mixture in each state. This is because we cannot estimate a number of a priori distribution from a speaker-independent model in each state. If the model is represented by one mixture in each state, it is not well adadpted to specific speaker because it is difficult to represent various speech informationof the speaker with one mixture. In this paper, we suggest the method using several mixtures to well represent various speech information of the speaker in each state. But, because speaker-specific training dat is not sufficient, this method can't be used in every state. So, we make the number of mixtures in each state variable in proportion to the number of frames and to the determinant ofthe variance matrix in the state. Using the proposed method, we reduced the error rate than methods using one branch in each state.

• Speech Sciences
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• v.11 no.4
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• pp.75-88
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• 2004

In this paper, we propose a new approach to sequential linear regression adaptation of continuous density hidden Markov models (CDHMMs) based on transformation space model (TSM). The proposed TSM which characterizes the a priori knowledge of the training speakers associated with maximum likelihood linear regression (MLLR) matrix parameters is effectively described in terms of the latent variable models. The TSM provides various sources of information such as the correlation information, the prior distribution, and the prior knowledge of the regression parameters that are very useful for rapid adaptation. The quasi-Bayes (QB) estimation algorithm is formulated to incrementally update the hyperparameters of the TSM and regression matrices simultaneously. Experimental results showed that the proposed TSM approach is better than that of the conventional quasi-Bayes linear regression (QBLR) algorithm for a small amount of adaptation data.