• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.695-708
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• 2022

Recently, several studies have been conducted using state space model. In this study, a dynamic linear model with state space model form is applied to stock data. The monthly returns for 135 Korean stocks are fitted to a dynamic linear model, to obtain an estimate of the time-varying 𝛽-coefficient time-series. The model formula used for the return is a capital asset pricing model formula explained in economics. In particular, the transition equation of the state space model form is appropriately modified to satisfy the assumptions of the error term. k-shape clustering is performed to classify the 135 estimated 𝛽 time-series into several groups. As a result of the clustering, four clusters are obtained, each consisting of approximately 30 stocks. It is found that the distribution is different for each group, so that it is well grouped to have its own characteristics. In addition, a common pattern is observed for each group, which could be interpreted appropriately.

• Journal of the Korea Society for Simulation
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• v.25 no.3
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• pp.1-14
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• 2016

This paper proposes four general wireless gateway types, which are distinguished by their own processing ways to connect between a wireless sub-network of things and the mobile internet that links mobile network to internet step by step. In this paper, we also design general processing procedures of these four types using the state transition model. Gateways of each types were developed on the basis of the resulted state transition models and their performances were evaluated through several tests, analyzed, and compared each other. As the results of our evaluation, compared with the other types, the type, which combines both of a low-power Sleep-interrupt way and polling ways for receiving data or responses in all the waiting states of a gateway, shows the best performance in all of data transmission real-timeliness, data loss and energy consumption.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.107-116
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• 2006

We propose a planning algorithm to automatically generate a robust behavior plan (RBP) with which mobile robots can achieve their task goal from any initial states under dynamically changing environments. For this, task description space (TDS) is formulated, where a redundant task configuration space and simulation model of physical space are employed. Successful task episodes are collected, where $A^*$ algorithm is employed. Interesting TDS state vectors are extracted, where occurrence frequency is used. Clusters of TDS state vectors are found by using state transition tuples and features of state transition tuples. From these operations, characteristics of successfully performed tasks by a simulator are abstracted and generalized. Then, a robust behavior plan is constructed as an ordered tree structure, where nodes of the tree are represented by attentive TDS state vector of each cluster. The validity of our method is tested by real robot's experimentation for a box-pushing-into-a-goal task.

• Journal of the military operations research society of Korea
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• v.11 no.1
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• pp.111-129
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• 1985

Markov chains with large transition probability matrices occur in many applications such as manpowr models. Under certain conditions the state space of a stationary discrete parameter finite Markov chain may be partitioned into subsets, each of which may be treated as a single state of a smaller chain that retains the Markov property. Such a chain is said to be 'lumpable' and the resulting lumped chain is a special case of more general functions of Markov chains. There are several reasons why one might wish to lump. First, there may be analytical benefits, including relative simplicity of the reduced model and development of a new model which inherits known or assumed strong properties of the original model (the Markov property). Second, there may be statistical benefits, such as increased robustness of the smaller chain as well as improved estimates of transition probabilities. Finally, the identification of lumps may provide new insights about the process under investigation.

• Journal of the Korean Chemical Society
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• v.29 no.1
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• pp.15-22
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• 1985

Kinetic studies of nucleophilic substitution reaction of substituted benzoyl cyanides and benzoyl chlorides with pyridines were conducted at 25$^{\circ}C$ in pure acetone solvent. Results showed that (ⅰ) magnitudes of $_{\rho}_S$, $_{\rho}_N$ and ${\beta}$ associated with a change of substituent in the nucleophile indicate relatively advanced bond-formation in the transition state, (ⅱ) the potential energy surface model is able to predict the reaction mechanism, but it is unable to predict the transition state variation to a more product-like transition state, where bond-formation is much more progressed than bond breaking, upon changing the leaving group to that with better leaving ability (ⅲ) the quantum mechanical model predicted the product-like transition state and slightly better leaving ability of CN- as compared with Cl-.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.196-202
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• 2024

The ability to understand given environments and plan a sequence of actions leading to goal state is crucial for personal service robots. With recent advancements in deep learning, numerous studies have proposed methods for state representation in planning. However, previous works lack explicit information about relationships between objects when the state observation is converted to a single visual embedding containing all state information. In this paper, we introduce graph-based state representation that incorporates both object and relationship features. To leverage these advantages in addressing the task planning problem, we propose a Graph Neural Network (GNN)-based subgoal prediction model. This model can extract rich information about object and their interconnected relationships from given state graph. Moreover, a search-based algorithm is integrated with pre-trained subgoal prediction model and state transition module to explore diverse states and find proper sequence of subgoals. The proposed method is trained with synthetic task dataset collected in simulation environment, demonstrating a higher success rate with fewer additional searches compared to baseline methods.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.150-155
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• 1997

Modelling and Simulation of the activation process for the myocardium is meaningful to understand special excitation conduction system in the heart and to study cardiac functions. In this paper, we propose two dimensional cellular automata model for the activation process of the myocardium and simulated by means of discrete time and discrete event algorithm. In the model, cells are classified into anatomically similar characteristic parts of heart; SA node, internodal tracks, AV node, His bundle, bundle branch and four layers of the ventricular muscle, each of which has a set of cells with preassigned properties, that is, activation time, refractory duration and conduction time between neighbor cell. Each cell in this model has state variables to represent the state of the cell and has some simple state transition rules to change values of state variables executed by state transition function. Simulation results are as follows. First, simulation of the normal and abnormal activation process for the myocardium has been done with discrete time and discrete event formalism. Next, we show that the simulation results of discrete time and discrete event cell space model is the same. Finally, we compare the simulation time of discrete event myocardium model with discrete time myocardium models and show that the discrete event myocardium model spends much less simulation time than discrete time myocardium model and conclude the discrete event simulation method Is excellent in the simulation time aspect if the interval deviation of event time is large.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.364-364
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• 2000

Timed Petri Net(TPN) is one of methods to model and to analyze Discrete Event Dynamic Systems(DEDSs) with real time values. It has two time values, earliest firing time ($\alpha$$_{i}$) and latest firing time ($\beta$$_{I}$) for the each transition. A transition of TPN is fired at arbitrary time of time interval ($\alpha$$_{I}$, $\beta$$_{i}$). Uncertainty of firing time gives difficulty to analyze and estimate a modeled system. In this paper, we proposed the Fuzzy Transition Timed Petri Net(FTTPN) with fuzzy theory to determine the optimal transition time (${\gamma}$$_{i}$). The transition firing time (${\gamma}$$_{i}$) of FTTPN is determined from fuzzy controller which is modeled with information of state transition. Each of the traffic signal controllers are modeled using the proposed method and timed petri net. And its Performance is evaluated by simulation of traffic signal controller. controller.

• Bulletin of the Korean Chemical Society
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• v.15 no.8
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• pp.627-631
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• 1994

For the Schlogl model with the first order transition under the influence of the multiplicative noise singular at the unstable steady state, the effects of the parameters on the stationary probability distributions obtained by the Ito and Stratonovich methods are discussed and compared in detail.

• Bulletin of the Korean Chemical Society
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• v.15 no.8
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• pp.631-636
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• 1994

For the Schlogl model with the second order transition under the influence of the multiplicative noise singular at the unstable steady state, the detailed discussions are presented for various kinds of stochastic phenomena, suchas the effects of parameters on stationary probability distribution, noise-induced phase transitions and escape rate.