• The KIPS Transactions:PartC
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• v.17C no.1
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• pp.109-118
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• 2010

Proxy mobile IPv6 (PMIPv6) protocol is a network-based mobility management protocol to support mobility for IPv6 nodes without host involvement. In PMIPv6, the Mobile Access Gateway (MAG) incurs a high signaling cost to update the location of a mobile node to the remote Local Mobility Anchor (LMA) if it moves frequently. This increases network overhead on the LMA, wastes network resources, and lengthens the delay time. Therefore, we propose a new mobility management scheme for minimizing signaling cost using the pointer forwarding. Our proposal can reduce signaling costs by registration with the neighbor MAG instead of the remote LMA using the pointer forwarding. The cost analysis using imbedded Markov chain presented in this paper shows that our proposal can achieve performance superior that of PMIPv6 scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.683-701
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• 2012

IEEE 802.11 standard has achieved huge success in the past decade and is still under development to provide higher physical data rate and better quality of service (QoS). An important problem for the development and optimization of IEEE 802.11 networks is the modeling of the MAC layer channel access protocol. Although there are already many theoretic analysis for the 802.11 MAC protocol in the literature, most of the models focus on the saturated traffic and assume infinite buffer at the MAC layer. In this paper we develop a unified analytical model for IEEE 802.11 MAC protocol in ad hoc networks. The impacts of channel access parameters, traffic rate and buffer size at the MAC layer are modeled with the assistance of a generalized Markov chain and an M/G/1/K queue model. The performance of throughput, packet delivery delay and dropping probability can be achieved. Extensive simulations show the analytical model is highly accurate. From the analytical model it is shown that for practical buffer configuration (e.g. buffer size larger than one), we can maximize the total throughput and reduce the packet blocking probability (due to limited buffer size) and the average queuing delay to zero by effectively controlling the offered load. The average MAC layer service delay as well as its standard deviation, is also much lower than that in saturated conditions and has an upper bound. It is also observed that the optimal load is very close to the maximum achievable throughput regardless of the number of stations or buffer size. Moreover, the model is scalable for performance analysis of 802.11e in unsaturated conditions and 802.11 ad hoc networks with heterogenous traffic flows.

• Journal of Environmental Science International
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• v.19 no.1
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• pp.115-124
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• 2010

Urban watershed can be found in the visible changes in technology, the most realistic satellite images is to use the data. Satellite image data on the indicators for progress on the nature of the change of land use is consistent and repetitive information, regular observation makes possible the detailed analysis of space-time. These remote sensing techniques and the type of course and, by using the time series history, the past, the dynamic model and the randomized prediction methodology for the conversion process if the city and river basin cooperation of the space changes effectively will be able to extrapolate. For each of the main changes in river flow, depending on the area of urbanization as determined according to reproduce the duration of the relationship between the urbanization of the area and runoff can be represented as a linear polynomial expression was, if a linear expression in the two fast slew rate of 0.858 to 0.861 showed up, and fast slew rate of 0.934 to 0.974 for the polynomial are reported. Change of land use changes in the watershed of the flow is one of the most affecting elements. Therefore, changes in land use of the correct classification of rivers is a more accurate calculation of the amount of the floodgate. In particular, using the Landsat images through the image of the land use category, land use past data and calculated using the Markov Chain model and predict the future land use plan in the water control project will be used for large likely.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.757-766
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• 2014

To compensate signal loss due to the channel blockage in satellite communication link, we propose a cooperative communication scheme for OTM(On-The-Move) terminal in next generation satellite communication systems. The proposed scheme configures cooperation subnet with adjacent OTM terminal with the help of ground communication equipment. Shared data is spread by orthogonal spreading code, then the spread sequences are transmitted simultaneously. The receiver combines the power of received signals by EGC(Equal gain combining). The OTM terminal blockage channel is modeled by 2-state Markov chain. We evaluate the bit error rate according to the blockage channel of the channel state for the performance analysis of the proposed scheme. As a result, the proposed scheme shows better BER performance than traditional scheme with the help of subset members. In particular, the proposed scheme shows superior performance as the channel block probability is higher. However, as the number of subset members is increasing, there is a constraint because of the higher multiple access interference.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.35-41
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• 2022

Heavy snow damage can be prevented in advance with an appropriate security system. To develop the security system, we developed a model that predicts snow depth after a few hours when the snow depth is observed, and utilized it to calculate a failure probability with various types of greenhouses and observed snow depth data. We compared the Markov chain model and Bayesian long short-term memory models with varying input data. Markov chain model showed the worst performance, and the models that used only past snow depth data outperformed the models that used other weather data with snow depth (temperature, humidity, wind speed). Also, the models that utilized 1-hour past data outperformed the models that utilized 3-hour data and 6-hour data. Finally, the Bayesian LSTM model that uses 1-hour snow depth data was selected to predict snow depth. We compared the selected model and the shifting method, which uses present data as future data without prediction, and the model outperformed the shifting method when predicting data after 11-24 hours.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.189-196
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• 2010

It is never an easy task to physically randomize the sequence of cards. For instance, US 1970 draft lottery resulted in a social turmoil since the outcome sequence of 366 birthday numbers showed a significant relationship with the input order (Wikipedia, "Draft Lottery 1969", Retrieved 2009/05/01). We are motivated by Laplace's 1825 book titled Philosophical Essay on Probabilities that says "Suppose that the numbers 1, 2, ..., 100 are placed, according to their natural ordering, in an urn, and suppose further that, after having shaken the urn, to shuffle the numbers, one draws one number. It is clear that if the shuffling has been properly done, each number will have the same chance of being drawn. But if we fear that there are small differences between them depending on the order in which the numbers were put into the urn, we can decrease these differences considerably by placing these numbers in a second urn in the order in which they are drawn from the first urn, and then shaking the second urn to shuffle the numbers. These differences, already imperceptible in the second urn, would be diminished more and more by using a third urn, a fourth urn, &c." (translated by Andrew 1. Dale, 1995, Springer. pp. 35-36). Laplace foresaw what would happen to us in 150 years later, and, even more, suggested the possible tool to handle the problem. But he did omit the detailed arguments for the solution. Thus we would like to write the supplement in modern terms for Laplace in this research note. We formulate the problem with a lottery box model, to which Markov chain theory can be applied. By applying Markov chains repeatedly, one expects the uniform distribution on k states as stationary distribution. Additionally, we show that the probability of even-number of successes in binomial distribution with trials and the success probability $\theta$ approaches to 0.5, as n increases to infinity. Our theory is illustrated to the cases of truncated geometric distribution and the US 1970 draft lottery.

• Science of Emotion and Sensibility
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• v.24 no.1
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• pp.59-72
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• 2021

Concepts and categories offer the basis for inference pertaining to unobserved features. Prior research on category-based induction that used blank properties has suggested that similarity between categories and features explains feature inference (Rips, 1975; Osherson et al., 1990). However, it was shown by later research that prior knowledge had a large influence on category-based inference and cases were reported where similarity effects completely disappeared. Thus, this study tested category-based feature inference when features are connected in a causal chain and proposed a feature inference model that predicts participants' inference ratings. Each participant learned a category with four features connected in a causal chain and then performed feature inference tasks for an unobserved feature in various exemplars of the category. The results revealed nonindependence, that is, the features not only linked directly to the target feature but also to those screened-off by other feature nodes and affected feature inference (a violation of the causal Markov condition). Feature inference model of causal model theory (Sloman, 2005) explained nonindependence by predicting the effects of directly linked features and indirectly related features. Indirect features equally affected participants' inference regardless of causal distance, and the model predicted smaller effects regarding causally distant features.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.813-826
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• 2015

This research deals with an estimation method for kinetic reaction model. The kinetic reaction model is a model to explain spread or changing process based on interaction between species on the Biochemical area. This model can be applied to a model for disease spreading as well as a model for system Biology. In the search, we assumed that the spread of species is stochastic and we construct the reaction model based on stochastic movement. We utilized Gillespie algorithm in order to construct likelihood function. We introduced a Bayesian estimation method using Markov chain Monte Carlo methods that produces more stable results. We applied the Bayesian estimation method to the Lotka-Volterra model and gene transcription model and had more stable estimation results.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.161-175
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• 2001

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. Hierarchical models including selection models are introduced and shown to be useful in such Bayesian meta-analysis. Semiparametric hierarchical models are proposed using the Dirichlet process prior. These rich class of models combine the information of independent studies, allowing investigation of variability both between and within studies, and weight function. Here we investigate sensitivity of results to unobserved studies by considering a hierachical selection model with including unknown weight function and use Markov chain Monte Carlo methods to develop inference for the parameters of interest. Using Bayesian method, this model is used on a meta-analysis of twelve studies comparing the effectiveness of two different types of flouride, in preventing cavities. Clinical informative prior is assumed. Summaries and plots of model parameters are analyzed to address questions of interest.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.