• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.212-217
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• 1992

In this paper, a two ports machine(TPM) model for discrete event dynamic systems(DEDS) is proposed. The proposed model is a finite state machine which has two inputs and two outputs. Inputs and outputs have two components, events and informations. TPM is different from other state machine models, since TPM has symmetric input and output. This symmetry enables the block diagram representation of the DEDS with TPM blocks, summing points, multiplying points, branch points, and connections. The graphical representation of DEDS is analogous to that of control system theory. TPM has a matrix representation of its transition and information map. This matrix representation simplifies the analysis of the DEDS.

• Proceedings of the Korean Society of Potoscience Conference
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• 2006.06a
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• pp.32-32
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• 2006

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2171-2179
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• 2009

In this paper, we propose a novel Image Encryption using MLCA (Maximum Length Cellular Automata) and CAT (Cellular Automata Transform). Firstly, we use the Wolfram rule matrix to generate MLCA state transition matrix T. Then the state transition matrix T changes pixel value of original image according to pixel position. Next, we obtain Gateway Values to generate 2D CAT basis function. Lastly, the basis function encrypts the MLCA encrypted image into cellular automata space. The experimental results and security analysis show that the proposed method guarantees better security and non-lossy encryption.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1105-1112
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• 2020

One-dimensional 3-neighborhood Cellular Automata (CA)-based pseudo-random number generators are widely applied in generating test patterns to evaluate system performance and generating key sequence generators in cryptographic systems. In this paper, in order to design a CA-based key sequence generator that can generate more complex and confusing sequences, we study a one-dimensional symmetric 5-neighborhood CA that expands to five neighbors affecting the state transition of each cell. In particular, we propose an n-cell one-dimensional symmetric 5-neighborhood CA synthesis algorithm using the algebraic method that uses the Krylov matrix and the one-dimensional 90/150 CA synthesis algorithm proposed by Cho et al. [6].

• Bulletin of the Korean Chemical Society
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• v.34 no.11
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• pp.3274-3278
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• 2013

$CF_2Cl{\cdots}Cl$, an elusive photo-isomer of $CF_2Cl_2$, has been observed in matrix IR spectra from the precursors exposed to radiation from laser ablation of transition-metals. Other plausible products, $CFCl_2{\cdots}F$ and $FClC{\cdots}F-Cl$ are not detected due to their considerably higher energies. Parallel to its previously reported analogues, the C-X bonds are considerably stronger than those of the reactant, and particularly the Cl atom that is weakly bound to the residual Cl atom forms an unusually strong carbon-halogen bond. NBO analysis reveals that the C-Cl bond is a true double bond, and the weak $Cl{\cdots}Cl$ bond is largely ionic, $F_2C=Cl^{\delta+}{\cdots}Cl^{\delta-}$. IRC computation reproduces smooth inter-conversion between the reactant and product, and the transition state is energetically close to the product, consistent with its prompt disappearance in the early stage of photolysis.

• Journal of IKEEE
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• v.13 no.2
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• pp.159-166
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• 2009

So far, many researches have been done to retrieve music information using static classification descriptors such as genre and mood. Since static classification descriptors are based on diverse content-based musical features, they are effective in retrieving similar music in terms of such features. However, human emotion or mood transition triggered by music enables more effective and sophisticated query in music retrieval. So far, few works have been done to evaluate the effect of human mood transition by music. Using formal representation of such mood transitions, we can provide personalized service more effectively in the new applications such as music recommendation. In this paper, we first propose our Emotion State Transition Model (ESTM) for describing human mood transition by music and then describe a music classification and recommendation scheme based on the ESTM. In the experiment, diverse content-based features were extracted from music clips, dimensionally reduced by NMF (Non-negative Matrix Factorization, and classified by SVM (Support Vector Machine). In the performance analysis, we achieved average accuracy 67.54% and maximum accuracy 87.78%.

• Journal of Korean Institute of Industrial Engineers
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• v.8 no.2
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• pp.15-21
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• 1982

This paper offers an alternative to the common probability generating function approach to the solution of steady state equations when a Markovian queue has a multivariate state space. Identifying states and substates and grouping them into vectors appropriately, we formulate a two - dimensional Markovian queue as a Markov chain. Solving the resulting matrix equations the transition point steady state probabilities (SSPs) are obtained. These are then converted into arbitrary time SSPs. The procedure uses only probabilistic arguments and thus avoids a large and cumbersome state space which often poses difficulties in the solution of steady state equations. For the purpose of numerical illustration of the approach we solve a Markovian queue with one server and two classes of customers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.549-555
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• 2000

We propose an approximate algorithm to analyze the queuing system with n bursty and heterogeneous arrival processes. Each input process is modeled by Interrupted Bernoulli Process(IBP). We approximate N arrival processes by a single state variable and subsequently simplify the transition probability matrix of the Markov chain associated with these N arrival processes. Using this single state variable of arrival processes, we describe the state of the queuing system and analyze the system numerically with the reduced transition probability matrix. We compute the queue length distribution, the delay distribution, and the loss probability. Comparisons with simulation data show that the approximation algorithm has a good accuracy.

• Journal of the Korean Vacuum Society
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• v.12 no.S1
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• pp.100-103
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• 2003

The magnetoresistance (MR) of $La_{1-\chi}S_{\chi}CoO_{3-\delta}$ films prepared by pulsed-laser deposition were investigated in order to clarify the magnetotransport properties around the metal-insulator transition. For the films in the metallic state ($\chi$ > 0.25), the MR(T) manifests a small peak at the Curie temperature due to the spin-disorder scattering. The transition of the film into the insulating state ($\chi\;\leq$ 0.25) is accompanied by an essential growth of the MR and results in a significant increase in the MR(T) with decreasing temperature, due to a phase separation into the ferromagnetic-metal clusters and the insulating matrix.

• Journal of Wind Energy
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• v.14 no.1
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• pp.37-43
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• 2023

In this study, synthetic time series wind data was generated numerically using a second-order Markov chain. One year of wind data in 2020 measured by the AWS on Wido Island was used to investigate the statistics for measured wind data. Both the transition probability matrix and the cumulative transition probability matrix for annual hourly mean wind speed were obtained through statistical analysis. Probability density distribution along the wind speed and autocorrelation according to time were compared with the first- and the second-order Markov chains with various lengths of time series wind data. Probability density distributions for measured wind data and synthetic wind data using the first- and the second-order Markov chains were also compared to each other. For the case of the second-order Markov chain, some improvement of the autocorrelation was verified. It turns out that the autocorrelation converges to zero according to increasing the wind speed when the data size is sufficiently large. The generation of artificial wind data is expected to be useful as input data for virtual digital twin wind turbines.