• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.527-540
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• 2020

While analyzing data, researching outliers, which are out of the main tendency, is as important as researching data that follow the general tendency. In this study we discuss the influence function for outlier discrimination. We derive sample influence functions of sample mean, sample variance, and sample standard deviation, which were not directly derived in previous research. The results enable us to mathematically examine the relationship between the empirical influence function and sample influence function. We can also consider a method to approximate the sample influence function by the empirical influence function. Also, the validity of the relationship between the approximated sample influence function and the empirical influence function is also verified by the simulation of random sampled data in normal distribution. As the result of a simulation, both the relationship between the two influence functions, sample and empirical, and the method of approximating the sample influence function through the emperical influence function were verified. This research has significance in proposing a method that reduces errors in the approximation of the empirical influence function and in proposing an effective and practical method that proceeds from previous research that approximates the sample influence function directly through empirical influence function by constant revision.

• Journal of Korean Society of Transportation
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• v.34 no.2
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• pp.158-167
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• 2016

This study evaluates potential sources of errors in k-NN(k-nearest neighbor) algorithm such as procedures, variables, and input data. Previous research has been thoroughly reviewed for understanding fundamentals of k-NN algorithm that has been widely used for short-term traffic states prediction. The framework of this algorithm commonly includes historical data smoothing, pattern database, similarity measure, k-value, and prediction horizon. The outcomes of this study suggests that: i) historical data smoothing is recommended to reduce random noise of measured traffic data; ii) the historical database should contain traffic state information on both normal and event conditions; and iii) trial and error method can improve the prediction accuracy by better searching for the optimum input time series and k-value. The study results also demonstrates that predicted error increases with the duration of prediction horizon and rapidly changing traffic states.

• Science of Emotion and Sensibility
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• v.8 no.3
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• pp.221-229
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• 2005

This study aimed to understand the sex differences in cognitive behaviors in website design and demonstrate a practical basis for utilizing these differences into more user-centered design concept. Especially, we focused on the sex-different preference according to the information architecture of website navigation. First, We investigated general differences between men and women in cognitive behaviors through various literature studies. According to our investigation, men's cognitive works generally tend to follow a regular sequence and proceed step by step. On the other hand, women's cognitive style is generally characterized by random generation and simultaneous progress. To examine that these differences can be found in use of website navigation, we made an experiment in website design. We designed several test websites that have same contents but different style of navigation structure. A similar number of men and women were chosen for this test and they implemented given tasks. During the test, participants reported their preference on each websites and their implementing time and number of errors were collected. Based on the analysis of test data, it was possible to conclude that male participants' preference for the navigation with a narrow and deep information structure is relatively higher than female participants' preference for the same navigation, On the other hand, female participants have a preference of the navigation with a broad and swallow information structure. The result of study showed that there is a close correlation between the sex differences in preference of navigation types and the general sex differences in cognitive behavior. This finding can be used as a basis for designing the website navigation in which sex differences are reflected.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.143-148
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• 2020

In the case of a system that detects and controls the phase of an alternating voltage, the analog control method compensates the phase offset part by filtering for the detected phase and applies it to the control. However, in the digital control method, precise control cannot be achieved due to an error between the operating frequency of the microprocessor or the microcontroller and the input phase time when controlled using such phase detection. In general, when the method used is a certain time, the accumulated error is compensated and adjusted at random. To solve this problem, a method of detecting a zero point in real time and compensating for the operating frequency of the microprocessor is needed. Therefore, the research to be performed in this paper to reduce these errors and apply them to precise digital control is as follows. 1) Research on how to implement Zero Crossing Detection algorithm through simulation modeling to compensate the zero point to match the operating frequency through detection. 2) A study on the method of detecting zero points in real time through the Zero Crossing Detection design using a microcontroller and compensating for the operating frequency of the microprocessor. 3) A study on the estimation of the rotor position of BLDC motors using the Zero Crossing Detection circuit.

• Geophysics and Geophysical Exploration
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• v.10 no.4
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• pp.241-251
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• 2007

The observed spectra from Odaesan earthquake were fitted to a point-source spectral model to evaluate the source spectrum and spatial features of the modelling error. The source spectrum was calculated by removing from the observed spectra the path and site dependent responses (Yun, 2007) that were previously revealed through an inversion process applied to a large accumulated spectral dataset. The stress drop parameter of one-corner Brune's ${\omega}^2$ source model fitted to the estimated source spectrum was well predicted by the scaling relation between magnitude and stress drop developed by Yun et al. (2006). In particular, the estimated spectrum was quite comparable to the two-corner source model that was empirically developed for recent moderate earthquakes occurring around the Korean Peninsula, which indicates that Odaesan earthquake is one of typical moderate earthquakes representative of Korean Peninsula. Other features of the observed spectra from Odaesan earthquake were also evaluated based on the commonly treated random error between the observed data and the estimated point-source spectral model. Radiation pattern of the error according to azimuth angle was found to be similar to the theoretical estimate. It was also observed that the spatial distribution of the errors was correlated with the geological map and the $Q_0$ map which are indicatives of seismic boundaries.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.1
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• pp.9-18
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• 2018

Recently, the use of image stitching technology has been increasing as the number of contents based on virtual reality increases. Image Stitching is a method for matching multiple images to produce a high resolution image and a wide field of view image. The image stitching is used in various fields beyond the limitation of images generated from one camera. Image Stitching detects feature points and corresponding points to match multiple images, and calculates the homography among images using the RANSAC algorithm. Generally, corresponding points are needed for calculating conversion relation. However, the corresponding points include various types of noise that can be caused by false assumptions or errors about the conversion relationship. This noise is an obstacle to accurately predict the conversion relation. Therefore, RANSAC algorithm is used to construct an accurate conversion relationship from the outliers that interfere with the prediction of the model parameters because matching methods can usually occur incorrect correspondence points. In this paper, we propose an algorithm that extracts more accurate inliers and computes accurate transformation relations by using correspondence point relation information used in RANSAC algorithm. The correspondence point relation information uses distance ratio between corresponding points used in image matching. This paper aims to reduce the processing time while maintaining the same performance as RANSAC.

• Korea journal of population studies
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• v.31 no.1
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• pp.99-125
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• 2008

This article aimed to clear that the systematic bias according to the length of work time exists between estimated work time by self-reported questions used mainly in measuring and calculating the length of work time because of strong points of easy in measuring and so on and diary work time by time-diary method used because of the strong point that can measure more accurate lifetime by recording various activities of respondents during 24 hours in the stream. As the result that analyze the data from Lifetime Use Survey in 2004, the result like the contradiction came that the tendency of overstating work time is rising according as estimated work time increases via estimated work time, whereas the tendency of understating work time is rising according as diary work time increases via diary work time. The reason that the opposite results come despite the data from the same survey is that random errors act in the opposite directions by regression to the mean. Therefore, we cannot emphasize that a man working long hours tends to exaggerate his work hours by the result via estimated work time. That is, the fact that the systematic bias by the increase of work time does not exist is confirmed, and therefore, it is also impossible to raise questions about the accuracy of the data through estimated work time by self-reported questions from the evidence of the existence of that bias.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.155-164
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• 2021

Many studies have been conducted on developing automatic plant identification algorithms using machine learning to various plant features, such as leaves and flowers. Unlike other plant characteristics, barks show only little change regardless of the season and are maintained for a long period. Nevertheless, barks show a complex shape with a large variation depending on the environment, and there are insufficient materials that can be utilized to train algorithms. Here, in addition to the previously published bark image dataset, BarkNet v.1.0, images of barks were collected, and a dataset consisting of 53 tree species that can be easily observed in Korea was presented. A convolutional neural network (CNN) was trained and tested on the dataset, and the factors that interfere with the model's performance were identified. For CNN architecture, VGG-16 and 19 were utilized. As a result, VGG-16 achieved 90.41% and VGG-19 achieved 92.62% accuracy. When tested on new tree images that do not exist in the original dataset but belong to the same genus or family, it was confirmed that more than 80% of cases were successfully identified as the same genus or family. Meanwhile, it was found that the model tended to misclassify when there were distracting features in the image, including leaves, mosses, and knots. In these cases, we propose that random cropping and classification by majority votes are valid for improving possible errors in training and inferences.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.1
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• pp.43-51
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• 2009

In 1998, Kocher et al. introduced Differential Power Attack on block ciphers. This attack allows to extract secret key used in cryptographic primitives even if these are executed inside tamper-resistant devices such as smart card. At FSE 2003 and 2004, Akkar and Goubin presented several masking methods, randomizing the first few and last few($3{\sim}4$) rounds of the cipher with independent random masks at each round and thereby disabling power attacks on subsequent inner rounds, to protect iterated block ciphers such as DES against Differential Power Attack. Since then, Handschuh and Preneel have shown how to attack Akkar's masking method using Differential Cryptanalysis. This paper presents how to combine Truncated Differential Cryptanalysis and Power Attack to extract the secret key from intermediate unmasked values and shows how much more efficient our attacks are implemented than the Handschuh-Preneel method in term of reducing the number of required plaintexts, even if some errors of Hamming weights occur when they are measured.

• Economic Analysis
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• v.27 no.1
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• pp.67-90
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• 2021

This study found an interesting fact that the nonlinear relationship structure between volatility and trading volume changed before and after the COVID-19 pandemic according to empirical analysis using Bitcoin (BTC) market data that sensitively reflects investors' trading behavior. That is, their relationship appeared positive (+) in a stable market state before COVID-19 pandemic, as in theory based on the information flow paradigm. In a state under severe market stress due to COVID-19 pandemic, however, their dependence structure changed and even negative (-). This can be seen as a consequence of increased market stress caused by COVID-19 pandemics from a behavioral economics perspective, resulting in structural changes in the asset market and a significant impact on the nonlinear dependence of volatility and trading volume (in particular, their dependence at extreme quantiles). Hence, it should be recognized that in addition to information flows, psychological phenomena such as behavioral biases or herd behavior, which are closely related to market stress, can be a key in changing their dependence structure. For empirical analysis, this study performs a test of Ross (2015) for detecting a structural change, and proposes a Copula Regression Quantiles (CRQ) approach that can identify their nonlinear relationship structure and the asymmetric dependence in their distribution tails without the assumption of i.i.d. random variable. In addition, it was confirmed that when the relationship between their extreme values was analyzed by linear models, incorrect results could be derived due to model specification errors.