• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.4
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• pp.7-14
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• 2010

Stable-state behaviors of asynchronous sequential machines represented as finite state machines can be corrected by feedback control schemes. In this paper, we propose a state feedback control scheme for input/state asynchronous machines with uncertain transitions. The considered asynchronous machine is deterministic, but its state transition function is partially known due to model uncertainty or inner logic errors. The control objective is to compensate the behavior of the closed-loop system so that it matches a sub-behavior of a prescribed model despite uncertain transitions. Furthermore, during the execution of corrective action, the controller reflects the exact knowledge of transitions into the next step, i.e., the range of the behavior of the closed-loop system can be enlarged through learning. The design procedure for the proposed controller is described in a case study.

• The Mathematical Education
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• v.26 no.2
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• pp.55-61
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• 1988

이 논문에서는 CS-Semistratifiable 공간보다 더 일반화된 공간 Cn-Semistratifiable을 정의 하며 그에 따른 여러가지 성질들을 조사하였다. 위상 공간(X, $\tau$)에 대하여 $\alpha$$\times$$\tau$에서 X의 폐집합족으로의 함수 S가 존재하여 다음 조건들을 만족할 때 공간X는 Cn-Semistratifiable over $\alpha$라 정의한다. a) 임의의 개집합 U에 대하여 U=U{S($\beta$, U) : $\beta$<$\alpha$} b) U, V가 X의 개집합이고 U⊂CV이면 모든 $\beta$<$\alpha$에 대하여 S($\beta$, V)⊂S($\beta$, V)이다. c) 만약 ${\gamma}$<$\beta$<$\alpha$ 이라면 임의의 개집합 U에 대하여 S(${\gamma}$, U)⊂S($\beta$, U)이다. d) X의 수렴하는 net $X_{\beta}$$\longrightarrow$X와 X를 품는 임의의 개집합 U에 대하여 적당한 $\beta$<$\alpha$가 존재하여 X$\in$S($\beta$. U)이고 { $X_{\beta}$}는 S($\beta$, U)안에 eventual하게 들어간다. 위의 정의에 의하여 다음과 같은 성질들이 증명되었다. 1 . Strstifiable over $\alpha$$\longrightarrow$cn-semistratifiable over$\longrightarrow$semistratifiable over $\alpha$ 2, 어떤 공간이 cn-Semistratifiable over $\alpha$이기 위한 필요충분 조건은 그것이 linearly cushioned cn-pairnet를 갖는 것이다. 3. cn-semistratifiable over $\alpha$의 부분공간 역시 cn-semistratifiabie over $\alpha$ 하다. 4. on-semistratifiable over $\alpha$의 유한개의 적공간 역시 cn-semistratifiabie over $\alpha$한다. 5. 폐 cn-semistratifiable over $\alpha$ 부분공간들의 합공간 역시 on-semistrbtifiable over $\alpha$ 하다. 6. 폐연속 net-cevering 함수에 의하여 cn-semistratifiable over $\alpha$ 성질이 보존된다.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.199-211
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• 2019

This paper is concerned with issues in the finite mixture of regression modeling as well as the simultaneous selection of the number of mixing components and relevant predictors. We propose a penalized likelihood method for both mixture components and regression coefficients that enable the simultaneous identification of significant variables and the determination of important mixture components in mixture of regression models. To avoid over-fitting and bias problems, we applied smoothly clipped absolute deviation (SCAD) penalties on the logarithm of component probabilities suggested by Huang et al. (Statistical Sinica, 27, 147-169, 2013) as well as several well-known penalty functions for coefficients in regression models. Simulation studies reveal that our method is satisfactory with well-known penalties such as SCAD, MCP, and adaptive lasso.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.27-37
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• 2011

The IDS/IPS(Intrusion Detection/Prevention System) has been widely deployed to protect the internal network against internet attacks. Reducing the packet inspection time is one of the most important challenges of improving the performance of the IDS/IPS. Since the IDS/IPS needs to match multiple patterns for the incoming traffic, we may have to apply the multiple pattern matching schemes, some of which use finite automata, while the others use the shift table. In this paper, we first show that the performance of those schemes would degrade with various kinds of pattern sets and payload, and then propose a hybrid multiple pattern matching scheme which combines those two schemes. The proposed scheme is organized to guarantee an appropriate level of performance in any cases. The experimental results using real traffic show that the time required to do multiple pattern matching could be reduced effectively.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.217-223
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• 2022

Subspace clustering for online data streams requires a large amount of memory resources as all subsets of data dimensions must be examined. In order to track the continuous change of clusters for a data stream in a finite memory space, in this paper, we propose a grid-based subspace clustering algorithm that effectively uses memory resources. Given an n-dimensional data stream, the distribution information of data items in data space is monitored by a grid-cell list. When the frequency of data items in the grid-cell list of the first level is high and it becomes a unit grid-cell, the grid-cell list of the next level is created as a child node in order to find clusters of all possible subspaces from the grid-cell. In this way, a maximum n-level grid-cell subspace tree is constructed, and a k-dimensional subspace cluster can be found at the k^th level of the subspace grid-cell tree. Through experiments, it was confirmed that the proposed method uses computing resources more efficiently by expanding only the dense space while maintaining the same accuracy as the existing method.

• Journal of Korean Society of Transportation
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• v.33 no.4
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• pp.379-392
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• 2015

Freight flow data in Korea has been collected for a variety of purposes by various organizations. However, since the representation and format of the data varies, it has not been substantially used for freight analyses and furthermore for freight policies. In order to increase the applicability of those data sets, it is required to bring them in a table and compare for finding the differences. Then, it is shown that the raw data can be aggregated by a particular criterion such as mode, origin and destination, and type commodity. This study aims to examine the freight data issue in terms of three different points of view. First, we investigated various freight volume data sets which are released by several organizations. Second, we tried to develop formulations for freight volume data. Third, we discussed how to apply the formulations to network models in which particular OR (Operations Research) techniques are used. The results emphasized that some data might be useless for modeling once they are aggregated. As a result of examining the freight volume data, this study found that 14 organizations share their data sets at various aggregation levels. This study is not an ordinary research article, which normally includes data analysis, because it seems to be impossible to conduct extensive case studies. The reason is that the data dealt in this study are diverse. Nevertheless, this study might guide the research direction in the freight transport research society in terms of data issue. Especially, it can be concluded that this study is a timely research because the governmemt has emphasized the importance of sharing data to public throughout 'government 3.0' for research purpose.

• Journal of Navigation and Port Research
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• v.35 no.10
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• pp.823-827
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• 2011

The present Common Structural Rules for double hull oil tanker is not included the residual strength, which is one of the functional requirements in design part of Goal-based new ship construction standards (GBS). The GBS will be enforced after July 1, 2016. The requirement related residual strength has the goal to build safe ship even if she has the specified damages due to marine accidents including collision and grounding. In order to assess the residual strength based on risk for structural damages according to GBS, tons of nonlinear FE analysis work taking into account various types of damage will be needed. The Smith's method, a kind of simplified method for the strength analysis is very useful for this purpose. In this paper, the residual strength assessments based on ultimate strength using Smith's method were carried out. The objected ship is VLCC with stranding damage in bottom structures. Also, the results were compared with that of nonlinear FE analysis using three cargo hold model.

• Journal of The Korean Association For Science Education
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• v.33 no.2
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• pp.229-248
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• 2013

The purpose of this study is to develop sequential concept tests (ConcepTest) for teachers' conceptual change on the straight propagation of light through in-service training of science teachers by peer instruction. We revised the ConcepTests for attaining the goal concept by implementing similar training courses for teachers three times and analyzing the results using both Hake gain and verbal protocol. The final form helped most teachers to reach the goal concept. While teachers are solving a given concept problem test, they had shown not only significant cognitive conflict to select one among candidate answers, but also used the concept obtained through the previous problem. The sequential ConcepTests developed in this study can be useful for training elementary and secondary teachers or pre-service teacher education.

• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.733-742
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• 2007

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Due to this reason, memory usage for data stream analysis should be confined finitely although new data elements are continuously generated in a data stream. To satisfy this requirement, data stream processing sacrifices the correctness of its analysis result by allowing some errors. The old distribution statistics are diminished by a predefined decay rate as time goes by, so that the effect of the obsolete information on the current result of clustering can be eliminated without maintaining any data element physically. This paper proposes a grid based clustering algorithm for a data stream. Given a set of initial grid cells, the dense range of a grid cell is recursively partitioned into a smaller cell based on the distribution statistics of data elements by a top down manner until the smallest cell, called a unit cell, is identified. Since only the distribution statistics of data elements are maintained by dynamically partitioned grid cells, the clusters of a data stream can be effectively found without maintaining the data elements physically. Furthermore, the memory usage of the proposed algorithm is adjusted adaptively to the size of confined memory space by flexibly resizing the size of a unit cell. As a result, the confined memory space can be fully utilized to generate the result of clustering as accurately as possible. The proposed algorithm is analyzed by a series of experiments to identify its various characteristics