• Journal of Korea Water Resources Association
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• v.49 no.7
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• pp.607-614
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• 2016

The well-timed water management is required to reduce drought damages. It is also necessary to induce residents in drought-affected areas to save water. Information on future storage is important in managing water resources based on the current and future states of drought. This study employed a kernel function to develop a probabilistic model for predicting dam storage considering inflow uncertainty. This study also investigated the application of the proposed probabilistic model during the extreme drought. This model can predict a probability of temporal variation of storage. Moreover, the model can be used to make a long-term plan since it can identify a temporal change of storage and estimate a required reserving volume of water to achieve the target storage.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.1-8
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• 2007

The Mobile Ad-hoc network does environmental information which an individual collects in nodes which are many as the kernel of the USN technology based on the radio communication. And it is the latest network description delivering critical data to the destination location desiring through a multi-hop. Recently, the Ad-hoc network relative technique development and service are activated. But the security function implementation including an authentication and encoding about the transmitted packets, and etc, is wirelessly the insufficient situation on the Ad-hoc network. This paper provides the security service of key exchange, key management. entity authentication, data enciphering, and etc on the Mobile Ad-hoc network. It implements with the Ad-hoc network security management server system design which processes the security protocol specialized in the Ad-hoc network and which it manages.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.163-171
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• 2008

In this paper, we present an adaptive parametric cubic convolution technique in order to enlarge the low resolution image to the high resolution image. The proposed method consists of two steps. During the first interpolation step, we acquire adaptive parameters in introducing a new cost-function to reflect frequency properties. And, the second interpolation step performs cubic convolution by applying the parameters obtained from the first step. The enhanced interpolation kernel using adaptive parameters produces output image better than the conventional one using a fixed parameter. Experimental results show that the proposed method can not only provides the performances of $0.5{\sim}4dB$ improvements in terms of PSNR, but also exhibit better edge preservation ability and original image similarity than conventional methods in the enlarged images.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.993-1000
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• 2016

Semi-supervised learning makes it easy to use an unlabeled data in the supervised learning such as classification. Applying the semi-supervised learning on the regression analysis, we propose two methods for a better regression function estimation. The proposed methods have been assumed different marginal densities of independent variables and different smoothing parameters in unlabeled and labeled data. We shows that the overfitted pilot estimator should be used to achieve the fastest convergence rate and unlabeled data may help to improve the convergence rate with well estimated smoothing parameters. We also find the conditions of smoothing parameters to achieve optimal convergence rate.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.525-534
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• 2017

The open set recognition method should be used for the cases that the classes of test data are not known completely in the training phase. So it is required to include two processes of classification and the validation test. This kind of research is very necessary for commercialization of face recognition modules, but few domestic researches results about it have been published. In this paper, we propose an open set face recognition method that includes two sequential validation phases. In the first phase, with dummy classes we perform classification based on sparse representation. Here, when the test data is classified into a dummy class, we conclude that the data is invalid. If the data is classified into one of the regular training classes, for second validation test we extract four features and apply them for the proposed decision function. In experiments, we proposed a simulation method for open set recognition and showed that the proposed validation test outperform SCI of the well-known validation method

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.251-264
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• 2008

In this study, a nonhomogeneous markov model which is able to simulate hourly rainfall series is developed for estimating reliable hydrologic variables. The proposed approach is applied to simulate hourly rainfall series in Korea. The simulated rainfall is used to estimate the design rainfall and flood in the watershed, and compared to observations in terms of reproducing underlying distributions of the data to assure model's validation. The model shows that the simulated rainfall series reproduce a similar statistical attribute with observations, and expecially maximum value is gradually increased as number of simulation increase. Therefore, with the proposed approach, the non-homogeneous markov model can be used to estimate variables for the purpose of design of hydraulic structures and analyze uncertainties associated with rainfall input in the hydrologic models.

• Environmental and Resource Economics Review
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• v.28 no.4
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• pp.467-495
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• 2019

Uncertainty of renewable energy such as photovoltaic(PV) power is detrimental to the flexibility of the power system. Therefore, precise prediction of PV power generation is important to make the power system stable. The purpose of this study is to forecast PV power generation using meteorological data including particulate matter(PM). In this study, PV power generation is predicted by support vector machine using RBF kernel function based on machine learning. Comparing the forecasting performances by including or excluding PM variable in predictor variables, we find that the forecasting model considering PM is better. Forecasting models considering PM variable show error reduction of 1.43%, 3.60%, and 3.88% in forecasting power generation between 6am~8pm, between 12pm~2pm, and at 1pm, respectively. Especially, the accuracy of the forecasting model including PM variable is increased in daytime when PV power generation is high.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.557-567
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• 2018

There are currently various file encryption solutions for encrypting and storing files on disk. However, the existing file encryption solutions handle encryption and decryption all at once by file or directory. In this paper, we propose a system call supporting partial encryption function of the file. The user sets the encryption information with the system call interface at a portion where encryption of the file data is desired. And then the user writes file data, the data is encrypted and stored. Also if the user sets decryption information and reads the file data, the necessary part is decrypted by applying the set information. For the proposed system call, It consists of inspection module, management module, encryption module, decryption module, and HMAC module as per required system call. And it was implemented on the Linux environment. Also the operation of implemented system call was verified on the development board, and the performance was analyzed by measuring performance speed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.6
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• pp.89-98
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• 2007

We have come a long way in the information age. Thanks to the advancement of such technologies as the internet, we have discovered new ways to convey information on a broader scope. However, negative aspects exist as is with anything else. These may include invasion of privacy over the web, or identity theft over the internet. What is more alarming is that malwares so called 'maliciouscodes' are rapidly spreading. Its intent is very destructive which can result in hacking, phishing and as aforementioned, one of the most disturbing problems on the net, invasion of privacy. This thesis describes the technology of how you can effectively analyze and detect these kind of malicious codes. We propose sequencial hybrid analysis for API calls that are hooked inside user-mode and kernel-level of Windows. This research explains how we can cope with malicious code more efficiently by abstracting malicious function signature and hiding attribute.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1424-1436
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• 2020

Although huge progress has been made in current image segmentation work, there are still no efficient segmentation strategies for tree image which is taken from natural environment and contains complex background. To improve those problems, we propose a method for tree image segmentation combining adaptive mean shifting with image abstraction. Our approach perform better than others because it focuses mainly on the background of image and characteristics of the tree itself. First, we abstract the original tree image using bilateral filtering and image pyramid from multiple perspectives, which can reduce the influence of the background and tree canopy gaps on clustering. Spatial location and gray scale features are obtained by step detection and the insertion rule method, respectively. Bandwidths calculated by spatial location and gray scale features are then used to determine the size of the Gaussian kernel function and in the mean shift clustering. Furthermore, the flood fill method is employed to fill the results of clustering and highlight the region of interest. To prove the effectiveness of tree image abstractions on image clustering, we compared different abstraction levels and achieved the optimal clustering results. For our algorithm, the average segmentation accuracy (SA), over-segmentation rate (OR), and under-segmentation rate (UR) of the crown are 91.21%, 3.54%, and 9.85%, respectively. The average values of the trunk are 92.78%, 8.16%, and 7.93%, respectively. Comparing the results of our method experimentally with other popular tree image segmentation methods, our segmentation method get rid of human interaction and shows higher SA. Meanwhile, this work shows a promising application prospect on visual reconstruction and factors measurement of tree.