• Bulletin of the Korean Mathematical Society
• /
• v.61 no.3
• /
• pp.837-848
• /
• 2024

It is shown in [1] that the Cauchy problem for the Fornberg-Whitham equation is well-posed in C¹(ℝ) and the data-to-solution map is Hölder continuous from C^α to C([0, T]; C^α) with α ∈ [0, 1). In this short paper, we further show that the data-to-solution map of the Fornberg-Whitham equation is not uniformly continuous on the initial data in C¹(ℝ).

• Journal of Information Technology Applications and Management
• /
• v.12 no.4
• /
• pp.25-31
• /
• 2005

We know that the Hurst Exponent (HE) is a real number in [0, 1] which denotes randomness of time series. in this research, we suggest non-linear analysis of human biological signals through HE. The feasibility of human biological signals using inductive incitement provides Some diagnosis for active treatment. In our experiment, we measured the heartbeat through the MCG, 29 healthy and 34 abnormal subjects ostensibly. The raw data of acupuncture incitement are supported by opinions of gross examination and pathological diagnosis. The mean values of HE are 0.345, 0.755 and 0.805 for the periods of before, during and after acupuncture treatment, respectively in case of abnormal subjects. On the other hand, the mean values, 0.808, 0.797 and 0.785 are for normal cases, correspondingly. From this data, we show that HE is very significant in abnormal controls according to an acupuncture incitement, and the incitement effect is evidently extracted in abnormal subjects. But, in normal subjects, the incitement effect is meaningless.

• International Journal of Computer Science & Network Security
• /
• v.23 no.4
• /
• pp.69-78
• /
• 2023

With the global rise of digital data, the uncontrolled quantity of data is susceptible to cyber warfare or cyber attacks. Therefore, it is necessary to improve cyber security systems. This research studies the behavior of malicious acts and uses Higuchi Fractal Dimension (HFD), which is a non-linear mathematical method to examine the intricacy of the behavior of these malicious acts and anomalies within the cyber physical system. The HFD algorithm was tested successfully using synthetic time series network data and validated on real-time network data, producing accurate results. It was found that the highest fractal dimension value was computed from the DoS attack time series data. Furthermore, the difference in the HFD values between the DoS attack data and the normal traffic data was the highest. The malicious network data and the non-malicious network data were successfully classified using the Receiver Operating Characteristics (ROC) method in conjunction with a scaling stationary index that helps to boost the ROC technique in classifying normal and malicious traffic. Hence, the suggested methodology may be utilized to rapidly detect the existence of abnormalities in traffic with the aim of further using other methods of cyber-attack detection.

• Proceedings of the Korean Statistical Society Conference
• /
• 2005.11a
• /
• pp.181-186
• /
• 2005

Many data sets obtained from surveys or medical trials often include missing observations. When these data sets are analyzed, it is general to use only complete cases. However, it is possible to have big biases or involve inefficiency. In this paper, we consider a method for estimating parameters in logistic linear models involving non-ignorable missing data mechanism. A binomial response and normal exploratory model for the missing data are used. We fit the model using the EM algorithm. The E-step is derived by Metropolis-hastings algorithm to generate a sample for missing data and Monte-carlo technique, and the M-step is by Newton-Raphson to maximize likelihood function. Asymptotic variances of the MLE's are derived and the standard error and estimates of parameters are compared.

• Journal of Korean Academy of Nursing
• /
• v.29 no.6
• /
• pp.1392-1402
• /
• 1999

This study was carried out to find out stress-eating relationship in obese high school girls and to investigate the factors related to stress-induced eating. The conceptual framework used in this study was individual difference model. The research method was methodological triangulation. The data of the study were collected from purposively sampled 309 normal high school girls and 314 obese high school girls in S city. 15 volunteers, obese high school girls, enrolled in this qualitative research. Quantitative data was collected from May 6 to June 10, 1997 through questionnaires about stress and stress-related eating changes and from June 23 to August 26, 1997, qualitative data was collected. The results of the study were as follows : 1. Obese high school girls were unaffected by stress(t=-1.84, p=0.0662). 2. Through quantitative analysis, obese people divided into two groups in their response to stress. One group was composed of stress- eater. The other group was composed of non- stress eater. 3. Disinhibition(t=-3.1275, p=0.0019), cognitive restrain (t=-3.1597, p=0.0017), hunger(t=-3.5878, p=0.0004) were significantly different between stress-eaters and non-stress eaters. 4. According to the interview, 5 subjects of obese girls were stress eaters, and 10 subjects were non-stress eaters. Through qualitative research, the related factors of eating were eating attitude & behavior, stimuli situations on eating, and personality. In stress-eater group, they constantly went on a diet, however, they were prompted to eat when an uneasy feeling such as anxiety, depression, annoyance developed. Their personality were entirely optimistic. Whereas non-stress eater group had no interest in diet and didn't appear to have psychological factors to stimulate eating in stressful situations. Their personality was not only optimistic but also keenly characteristic. 5. To compare obese-normal high school girls on the effect of stress in eating. Normal weigh high school girls decreased their eating when stressed(t= -13.62, p=0.0001). In conclusion, this study suggests that there are two different groups in obese high school girls in regards to eating responses on stressful situations. As a result of these finding, clinical and school nurses can detect the stress-eaters who need stress management intervention, and can apply appropriate management program according to the individual needs.

• Journal of Korean Biological Nursing Science
• /
• v.14 no.4
• /
• pp.300-307
• /
• 2012

Purpose: The purpose of this research was to develop and prove the effectiveness of structured arm exercise, which was used to reduce Arteriovenous Fistula (AVF) and Arteriovenous graft (AVG) stricture of hemodialysis patients. Methods: Quasi-experimental research design with non-equivalent control group was applied. 26 Subjects were participated in this study. 12 of hemodialysis patients who do not have a normal range of Static Intra Access Pressure Vein (SIAPV) score in the last three months were assigned to the experimental group and 14 patients who have a normal range of SIAPV score in the last three months to the control group. To analyze the collecting data after structured arm exercise, non parametric method with the repeated measures ANOVA by the Friedman test and Wilcoxon Signed Ranks Test for post-hoc test was performed. Results: Unlike the experimental group after three months, the control group's SIAPV data went over the normal range. The experimental AVF group showed a difference in data after month 2 and month 3. - In AVG group, there were clear differences in each month of the test. Conclusion: This study proved that structured arm exercise therapy could be a simple and effective intervention. It is suggested to be actively utilized for hemodialysis patients.

• Journal of the Korean Statistical Society
• /
• v.7 no.1
• /
• pp.11-16
• /
• 1978

Bayesian procedures are in vogue to revise the parameter estimates of the forecasting model in the light of actual time series data. In this paper, we study the Bayes forecast for demand and the risk when (a) 'noise' and (b) mean demand rate in a constant process model have moderately non-normal probability distributions.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2006.04a
• /
• pp.469-473
• /
• 2006

In general, a classification process between ground data and non-ground data, which include building objects, is required prior to producing a DEM for a certain surface reconstruction from LiDAR data in which the DEM can be produced from the ground data, and certain objects like buildings can be reconstructed using non-ground data. Thus, an exact classification between ground and non-ground data from LiDAR data is the most important factor in the ground reconstruction process using LiDAR data. In particular, building objects can be largely used as digital maps, orthophotos, and urban planning regarding the object in the ground and become an essential to providing three dimensional information for certain urban areas. In this study, an entropy theory, which has been used as a standard of disorder or uncertainty for data used in the information theory, is used to apply a more objective and generalized method in the recognition and segmentation of buildings from raw LiDAR data. In particular, a method that directly uses the raw LiDAR data, which is a type of point shape vector data, without any changes, to a type of normal lattices was proposed, and the existing algorithm that segments LiDAR data into ground and non-ground data as a binarization manner was improved. In addition, this study proposes a generalized building extraction method that excludes precedent information for buildings and topographies and subsidiary materials, which have different data sources.

• Journal of Korean Institute of Industrial Engineers
• /
• v.39 no.2
• /
• pp.109-118
• /
• 2013

We propose a method for estimating coefficients of AR (autoregressive) model which named MLPAR (Maximum Likelihood of Pearson system for Auto-Regressive model). In the present method for estimating coefficients of AR model, there is an assumption that residual or error term of the model follows the normal distribution. In common cases, we can observe that the error of AR model does not follow the normal distribution. So the normal assumption will cause decreasing prediction accuracy of AR model. In the paper, we propose the MLPAR which does not assume the normal distribution of error term. The MLPAR estimates coefficients of auto-regressive model and distribution moments of residual by using pearson distribution system and maximum likelihood estimation. Comparing proposed method to auto-regressive model, results are shown to verify improved performance of the MLPAR in terms of prediction accuracy.

• Communications of the Korean Mathematical Society
• /
• v.10 no.3
• /
• pp.653-659
• /
• 1995

We prove that two essentially normal elements of a type $II_{\infty}$ factor von Neumann algebra are unitarily equivalent up to the compact ideal if and only if they have the identical essential spectrum and the same index data. Also we calculate the spectrum and essential spectrum of a non-unitary isometry of von Neumann algebra.