• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.2
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• pp.274-279
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• 2009

H.264 suggests applying a 2-D 6-tap wiener filter to realize the interpolation for half-pixel positions, followed by a bilinear interpolation to get the data of quarter-pixels precision. This method is comparatively simpler; however, it only considers the affection of 4-connection neighborhood ignoring the influence that comes from the changing rate between respective neighborhoods. As a result, it has the characteristics of a Low-pass filter under the risk of losing high-frequency weights. The Cubic interpolation uses the gray-values within the larger regions of points to be sampled for interpolation. Nevertheless, the cubic interpolation is more complicated and computational. We give a deep analysis on the features while applying both bilinear and cubic interpolation in H.264 presenting a proper selection of interpolation algorithm with respect to specific distribution of gray-value in a certain grand block. The experiments point out that load far motion searching and interpolation are reduced when promoting the precision of interpolation simultaneously.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.6
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• pp.1409-1415
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• 2010

In this paper we proposed the watermarking technique using the markspace which is selected by tree-structure between the subbands in the wavelet domain and feature information in the spatial domain. The watermarking candidate region in the wavelet domain is obtained by the markspace selection algorithm divides the highest frequency subband to several segments and calculates theirs energy and the averages value of the total energy of the subband. Also the markspace of the spatial domain is obtained by the boundary information of a image. The final markspace is selected by the markspaces of the wavelet and spatial domain. The watermark is embedded into the selected markspace using the random addresses by LFSR. Finally the watermarking image is generated using the inverse wavelet transform. The proposed watermarking algorithm shows the robustness against the attacks such as JPEG, blurring, sharpening, and gaussian noise.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.2
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• pp.104-112
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• 2002

In this paper, it is suggested that the selection method of parameter of Power System Stabilizer(PSS) with robustness in low frequency oscillation for Thyristor Controlled Series Capacitor(TCSC) using Geletic Algorithm(GA). A TCSC meddle consists of a stories capacitor and a parallel path with a thyristor valve and a series inductor. Also in in parallel, as is typical with series capacitor applications, is a metal-oxide varistor(MOV) for overvoltage protection. The proposed PSS parameters are optimized using GA in order to maintain optimal operation of TCSC which is expected to be applied in transmission system to achieve a number of benefits under the various operating conditions. In order to verify the robustness of the proposed method, we considered the dynamic response of angular velocity deviation and terminal voltage deviation under a power fluctuation and rotor angle variation.

• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.692-702
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• 2006

In this study, a weighted fuzzy min-max (WFMM) neural network model for pattern classification is proposed. The model has a modified structure of FMM neural network in which the weight concept is added to represent the frequency factor of feature values in a learning data set. First we present in this paper a new activation function of the network which is defined as a hyperbox membership function. Then we introduce a new learning algorithm for the model that consists of three kinds of processes: hyperbox creation/expansion, hyperbox overlap test, and hyperbox contraction. A weight adaptation rule considering the frequency factors is defined for the learning process. Finally we describe a feature analysis technique using the proposed model. Four kinds of relevance factors among feature values, feature types, hyperboxes and patterns classes are proposed to analyze relative importance of each feature in a given problem. Two types of practical applications, Fisher's Iris data and Cleveland medical data, have been used for the experiments. Through the experimental results, the effectiveness of the proposed method is discussed.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.41-55
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• 2021

To detect thunderstorms occurring in Korea, National Meteorological Satellite Center (NMSC) also introduced the rapid-development thunderstorm (RDT) algorithm developed by EUMETSAT. At NMCS, the H-RDT (HR) based on the Himawari-8 satellite and the K-RDT (KR) which combines the GK2A convection initiation output with the RDT were developed. In this study, we optimized the KR (KU) to improve the detection level of thunderstorms occurring in Korea. For this, we used all available data, such as GK2A/AMI, RADAR, lightning, and numerical model data from the recent two years (2019-2020). The machine learning of logistic regression and stepwise variable selection was used to optimize the KU algorithms. For considering the developing stages and duration time of thunderstorms, and data availability of GK2A/AMI, a total of 72 types of detection algorithms were developed. The level of detection of the KR, HR, and KU was evaluated qualitatively and quantitatively using lightning and RADAR data. Visual inspection using the lightning and RADAR data showed that all three algorithms detect thunderstorms that occurred in Korea well. However, the level of detection differs according to the lightning frequency and day/night, and the higher the frequency of lightning, the higher the detection level is. And the level of detection is generally higher at night than day. The quantitative verification of KU using lightning (RADAR) data showed that POD and FAR are 0.70 (0.34) and 0.57 (0.04), respectively. The verification results showed that the detection level of KU is slightly better than that of KR and HR.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.11-17
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• 2022

Data mining is the task of accessing useful information from a large capacity of data. It can also be referred to as searching for correlations that can provide clues about the future in large data warehouses by using computer algorithms. It has been used in the tourism field for marketing, analysis, and business improvement purposes. This study aims to analyze the tourist profile in Turkey through data mining methods. The reason relies behind the selection of Turkey is the fact that Turkey welcomes millions of tourist every year which can be a role model for other touristic countries. In this study, an anonymous and large-scale data set was used under the law on the protection of personal data. The dataset was taken from a leading tourism company that is still active in Turkey. By using the k-means clustering algorithm on this data, key parameters of profiles were obtained and people were clustered into groups according to their characteristics. According to the outcomes, distinguishing characteristics are gathered under three main titles. These are the age of the tourists, the frequency of their vacations and the period between the reservation and the vacation itself. The results obtained show that the frequency of tourist vacations, the time between bookings and vacations, and age are the most important and characteristic parameters for a tourist's profile. Finally, planning future investments, events and campaign packages can make tourism companies more competitive and improve quality of service. For both businesses and tourists, it is advantageous to prepare individual events and offers for the three major groups of tourists.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.206-216
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• 2013

The emotion plays a critical role in human's daily life including learning, action, decision and communication. In this paper, emotion discrimination classifier is designed to reduce system complexity through reduced selection of dominant features from biosignals. The photoplethysmography(PPG), skin temperature, skin conductance, fontal and parietal electroencephalography(EEG) signals were measured during 4 types of movie watching associated with the induction of neutral, sad, fear joy emotions. The genetic algorithm with support vector machine(SVM) based fitness function was designed to determine dominant features among 24 parameters extracted from measured biosignals. It shows maximum classification accuracy of 96.4%, which is 17% higher than that of SVM alone. The minimum error features selected are the mean and NN50 of heart rate variability from PPG signal, the mean of PPG induced pulse transit time, the mean of skin resistance, and ${\delta}$ and ${\beta}$ frequency band powers of parietal EEG. The combination of parietal EEG, PPG, and skin resistance is recommendable in high accuracy instrumentation, while the combinational use of PPG and skin conductance(79% accuracy) is affordable in simplified instrumentation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.6C
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• pp.648-658
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• 2003

As the usage of image/video contents increase, a security problem for the payed image data or the ones requiring confidentiality is raised. This paper proposed an image encryption methodology to hide the image information. The target data of it is the result from quantization in wavelet domain. This method encrypts only part of the image data rather than the whole data of the original image, in which three types of data selection methodologies were involved. First, by using the fact that the wavelet transform decomposes the original image into frequency sub-bands, only some of the frequency sub-bands were included in encryption to make the resulting image unrecognizable. In the data to represent each pixel, only MSBs were taken for encryption. Finally, pixels to be encrypted in a specific sub-band were selected randomly by using LFSR(Linear Feedback Shift Register). Part of the key for encryption was used for the seed value of LFSR and in selecting the parallel output bits of the LFSR for random selection so that the strength of encryption algorithm increased. The experiments have been performed with the proposed methods implemented in software for about 500 images, from which the result showed that only about 1/1000 amount of data to the original image can obtain the encryption effect not to recognize the original image. Consequently, we are sure that the proposed are efficient image encryption methods to acquire the high encryption effect with small amount of encryption. Also, in this paper, several encryption scheme according to the selection of the sub-bands and the number of bits from LFSR outputs for pixel selection have been proposed, and it has been shown that there exits a relation of trade-off between the execution time and the effect of the encryption. It means that the proposed methods can be selectively used according to the application areas. Also, because the proposed methods are performed in the application layer, they are expected to be a good solution for the end-to-end security problem, which is appearing as one of the important problems in the networks with both wired and wireless sections.

• Journal of Sensor Science and Technology
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• v.12 no.6
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• pp.289-297
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• 2003

In this paper, we propose accurate classification method of an EEG signals during a mental tasks. In the experimental task, subjects achieved through the process of responding to visual stimulus, understanding the given problem, controlling hand motions, and select a key. To recognize the subjects' selection time, we analyzed with 4 types feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, $\theta$, $\gamma$ waves. From the analysed features, we construct specific rules for each subject meta rules including common factors in all subjects. In this system, the architecture of the neural network is a three layered feedforward networks with one hidden layer which implements the error back propagation learning algorithm. Applying the algorithms to 4 subjects show 87% classification success rates. In this paper, the proposed detection method can be a basic technology for brain-computer-interface by combining with discrimination methods.

• Journal of Satellite, Information and Communications
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• v.6 no.2
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• pp.72-77
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• 2011

Spectrum sensing is a critical functionality of CR network; it allow secondary user to detect spectral holes and to opportunistically use under-utilized frequency bands without causing harmful interference to primary use. Recently, wideband service has been increase for processing abundance of data traffic. So CR network needs a realizable implementation design of spectrum sensing for wideband. To get high resolution performance of wideband sensing must precede algorithm processing for reliability signal detection. By the way, the performance of spectrum sensing can be degraded due to fading and shadowing. In order to overcome this problem, we propose system model of wideband sensing scheme on energy detected collaborative technique. we divide wideband into narrowbands and use narrowbands to detect signal excepting some narrowbands including bad channel through the CSI. And we simulate and analyze in terms of detection probability with various SNR.