• Bulletin of the Korean Mathematical Society
• /
• v.61 no.3
• /
• pp.699-715
• /
• 2024

We study the Riemann problem for the pressureless Euler system with the source term depending on the time. By means of the variable substitution, two kinds of Riemann solutions including deltashock and vacuum are constructed. The generalized Rankine-Hugoniot relation and entropy condition of the delta-shock are clarified. Because of the source term, the Riemann solutions are non-self-similar. Moreover, we propose a time-dependent viscous system to show all of the existence, uniqueness and stability of solutions involving the delta-shock by the vanishing viscosity method.

• Journal of applied mathematics & informatics
• /
• v.42 no.2
• /
• pp.333-351
• /
• 2024

In this paper, the concept of Pythagorean fuzzy sets are used to describe in semigroups. Then, some characterizations of regular (resp., intra-regular) semigroups by means of Pythagorean fuzzy left (resp., right) ideals and Pythagorean fuzzy (resp., generalized) bi-ideals of semigroups are investigated. Furthermore, the class of both regular and intra-regular semigroups by the properties of many kinds of their Pythagorean fuzzy ideals also being studied.

• The Korean Journal of Applied Statistics
• /
• v.2 no.1
• /
• pp.18-34
• /
• 1989

The problem of simultaneously estimating the pairwise differences of means of four independent normal populations with equal variances is considered. A statistical computing procedure involving a trivariate t density constructs the exact confidence intervals with simultaneous co verage probability equal to $1-\alpha$. For equal sample sizes, the new procedure is the same as the Tukey studentized range procedure. With unequal sample sizes, in the sense of efficiency for confidence interval lengths and experimentwise error rates, the procedure is superior to the various generalized Tukey procedures.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.19 no.37
• /
• pp.41-51
• /
• 1996

This paper evaluates the clustering performance of a neural network and a statistical method. Algorithms which are used in this paper are the GLVQ(Generalized Learning vector Quantization) for a neural method and the k-means algorithm fer a statistical clustering method. For comparison of two methods, we calculate the Rand's c statistics. As a result, the mean of c value obtained with the GLVQ is higher than that obtained with the k-means algorithm, while standard deviation of c value is lower. Experimental data sets were the Fisher's IRIS data and patterns extracted from handwritten numerals.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1145-1150
• /
• 1993

In this paper, two methods of fuzzy modeling are prsented to describe the input-output relationship effectively based on relation characteristics utilizing simplified reasoning and neuro-fuzzy reasoning. The methods of modeling by the simplified reasoning and the neuro-fuzzy reasoning are used when the input-output relation of a system is 'crisp' and 'fuzzy', respectively. The structure and the parameter identification in the modeling method by the simplified reasoning are carried out by means of FCM clustering and the proposed GA hybrid scheme, respectively. The structure and the parameter identification in the modeling method by the neuro-fuzzy reasoning are carried out by means of GA and BP algorithm, respectively. The feasibility of the proposed methods are evaluated through simulation.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.10 no.2
• /
• pp.191-202
• /
• 2006

Computer programs for a structure design help the optimum design that considers each condition. however, the findings can not explain accurately a behavior of the real-living structure because each condition of a structure is simplified and generalized. The smart structure is introduced to overcome these problems, and we can understand a behavior of the real-living structure by means of Health Monitoring System. In this study, we compare a behavior by means of the existing structure design with a behavior of the living structure by means of an experiment. As a result, we examine adequacy of a measuring system and developing possibility in the future.

• Journal of Wetlands Research
• /
• v.17 no.3
• /
• pp.251-263
• /
• 2015

This study suggested a novel approach of estimating the optimal probability density function (OPDF) of the annual maximum rainfall time series (AMRT) combining the L-moment ratio diagram and the geographical information system. This study also reported several interesting geographical characteristics of the AMRT in Korea. To achieve this purpose, this study determined the OPDF of the AMRT with the duration of 1-, 3-, 6-, 12-, and 24-hours using the method of L-moment ratio diagram for each of the 67 rain gages in Korea. Then, a map with the Thiessen polygons of the 67 rain gages colored differently according the different type of the OPDF, was produced to analyze the spatial trend of the OPDF. In addition, this study produced the color maps which show the fitness of a given probability density function to represent the AMRT. The study found that (1) both L-skewness and L-kurtosis of the AMRT have clear geographical trends, which means that the extreme rainfall events are highly influenced by geography; (2) the impact of the altitude on these two rainfall statistics is greater for the mountaneous region than for the non-mountaneous region. In the mountaneous region, the areas with higher altitude are more likely to experience the less-frequent and strong rainfall events than the areas with lower altitude; (3) The most representative OPDFs of Korea except for the Southern edge are Generalized Extreme Value distribution and the Generalized Logistic distribution. The AMRT of southern edge of Korea was best represented by the Generalized Pareto distribution.

• The KIPS Transactions:PartD
• /
• v.14D no.1 s.111
• /
• pp.21-34
• /
• 2007

According to recent rapid price drop and capacity growth of main memory, the number of applications on main memory databases is dramatically increasing. Cache miss, which means a phenomenon that the data required by CPU is not resident in cache and is accessed from main memory, is one of the major causes of performance degradation of main memory databases. Several cache-conscious trees have been proposed for reducing cache miss and making the most use of cache in main memory databases. Since each cache-conscious tree has its own unique features, more than one cache-conscious tree can be used in a single application depending on the application's requirement. Moreover, if there is no existing cache-conscious tree that satisfies the application's requirement, we should implement a new cache-conscious tree only for the application's sake. In this paper, we propose the cache-conscious generalized search tree (CC-GiST). The CC-GiST is an extension of the disk-based generalized search tree (GiST) [HNP95] to be tache-conscious, and provides the entire common features and algorithms in the existing cache-conscious trees including pointer compression and key compression techniques. For implementing a cache-conscious tree based on the CC-GiST proposed in this paper, one should implement only a few functions specific to the cache-conscious tree. We show how to implement the most representative cache-conscious trees such as the CSB+-tree, the pkB-tree, and the CR-tree based on the CC-GiST. The CC-GiST eliminates the troublesomeness caused by managing mire than one cache-conscious tree in an application, and provides a framework for efficiently implementing arbitrary cache-conscious trees with new features.

• The Transactions of the Korea Information Processing Society
• /
• v.2 no.6
• /
• pp.995-1001
• /
• 1995

The structure of a typical pattern recognition consists a pre-processing, a feature extraction(algorithm) and classification or recognition. In classification, when widely varying patterns exist in same category, we need the clustering which organize the similar patterns. Clustering algorithm is two approaches. Firs, statistical approaches which are k-means, ISODATA algorithm. Second, neural network approach which is T. Kohonen's LVQ(Learning Vector Quantization). Nikhil R. Palet al proposed the GLVQ(Generalized LVQ, 1993). This paper suggest the efficient feature extraction methods of handwritten numerals in GLVQ clustering network. We use the handwritten numeral data from 21's authors(ie, 200 patterns) and compare the proportion of misclassified patterns for each feature extraction methods. As results, when we use the projection combination method, the classification ratio is 98.5%.

• Journal of Korea Multimedia Society
• /
• v.9 no.3
• /
• pp.369-376
• /
• 2006

In this paper, we propose an improved fuzzy RBF network which dynamically adjusts the rate of learning by applying the Delta-bar-Delta algorithm in order to improve the learning performance of fuzzy RBF networks. The proposed learning algorithm, which combines the fuzzy C-Means algorithm with the generalized delta learning method, improves its learning performance by dynamically adjusting the rate of learning. The adjustment of the learning rate is achieved by self-generating middle-layered nodes and by applying the Delta-bar-Delta algorithm to the generalized delta learning method for the learning of middle and output layers. To evaluate the learning performance of the proposed RBF network, we used 40 identifiers extracted from a container image as the training data. Our experimental results show that the proposed method consumes less training time and improves the convergence of teaming, compared to the conventional ART2-based RBF network and fuzzy RBF network.