• Journal of the Economic Geographical Society of Korea
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• v.22 no.3
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• pp.351-365
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• 2019

This study aims to predict the changes in population distribution in Korea by considering spatio-temporal characteristics of major migration reasons. For the purpose, we analyze the spatio-temporal characteristics of each major migration reason(such as job, family, housing, and education) and estimate the transition probability, respectively. By appling Markov chain model processes with the ChapmanKolmogorov equation based on the transition probability, we predict the changes in the population distribution for the next six years. As the results, we found that there were differences of population changes by regions, while there were geographic movements into metropolitan areas and cities in general. The methodologies and the results presented in this study can be utilized for the provision of customized planning policies. In the long run, it can be used as a basis for planning and enforcing regionally tailored policies that strengthen inflow factors and improve outflow factors based on the trends of population inflow and outflow by region by movement factors as well as identify the patterns of population inflow and outflow in each region and predict future population volatility.

• Journal of Applied Reliability
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• v.18 no.1
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• pp.20-32
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• 2018

Purpose: The purpose of this study is to set up an anomaly detection criteria for sensor data coming from a motorcycle. Methods: Five sensor values for accelerator pedal, engine rpm, transmission rpm, gear and speed are obtained every 0.02 second from a motorcycle. Exploratory data analysis is used to find any pattern in the data. Traditional process control methods such as X control chart and time series models are fitted to find any anomaly behavior in the data. Finally unsupervised learning algorithm such as k-means clustering is used to find any anomaly spot in the sensor data. Results: According to exploratory data analysis, the distribution of accelerator pedal sensor values is very much skewed to the left. The motorcycle seemed to have been driven in a city at speed less than 45 kilometers per hour. Traditional process control charts such as X control chart fail due to severe autocorrelation in each sensor data. However, ARIMA model found three abnormal points where they are beyond 2 sigma limits in the control chart. We applied a copula based Markov chain to perform statistical process control for correlated observations. Copula based Markov model found anomaly behavior in the similar places as ARIMA model. In an unsupervised learning algorithm, large sensor values get subdivided into two, three, and four disjoint regions. So extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior in the sensor values. Conclusion: Exploratory data analysis is useful to find any pattern in the sensor data. Process control chart using ARIMA and Joe's copula based Markov model also give warnings near similar places in the data. Unsupervised learning algorithm shows us that the extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.225-233
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• 2011

Stock market prices, economic indices, trends and changes of social phenomena, etc. are categorized as time series data. Research on time series data has been prevalent for a while as it could not only lead to valuable representation of data but also provide future trends as well as changes in direction. We take a conventional model based approach, known as Markov chain modeling for the prediction on stock market prices. To improve prediction accuracy, we apply Markov modeling over carefully selected intervals of training data to fit the trend under consideration to the model. Another method we take is to apply clustering to data and build models of the resultant clusters. We confirmed that clustered models are better off in predicting, however, with the loss of prediction rate.

• 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.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.226-228
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• 2005

This paper deals with power system stabilization problem using a network control system in which the control is applied through a communication channel in feedback form. Analysis and synthesis issues are investigated by modeling the packet delivery characteristics of the network as a Bernoulli random variable, which is described by a two state Markov chain. This model assumption yields an overall system which is described by a discrete-time Markov jump linear system. These employ the norm to measure the performance of the system, and they compute the norm via a necessary and sufficient matrix inequality condition.

• ETRI Journal
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• v.19 no.1
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• pp.13-25
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• 1997

In this paper, we propose a new queuing model, MMDP/MMDP/1/K, for an asynchronous transfer mode(ATM) multiplexer with multiple quality of service(QoS) variable bit rate (VBR) traffic in broadband-integrated services digital network (B-ISDN). We use the Markov Modulated Deterministic Process(MMDP) to approximate the actual arrival process and another MMDP for service process Using queuing analysis, we derive a formula for the cell loss probability of the ATM multiplexer in terms of the limiting probabilities of a Markov chain. The cell loss probability can be used for connection admission control in ATM multiplexer and the calculation of equivalent bandwidth for arrival traffic, The major advantages of this approach are simplicity in analysis, accuracy of analysis by comparison of simulation, and numerical stability.

• ETRI Journal
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• v.18 no.3
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• pp.127-145
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• 1996

A new analytic framework based on a linear algebra approach is proposed for examining the performance of a direct sequence spread spectrum (DS/SS) slotted ALOHA wireless communication network systems with delay capture. The discrete-time Markov chain model has been introduced to account for the effect of randomized time of arrival (TOA) at the central receiver and determine the evolution of the finite population network performance in a single-hop environment. The proposed linear algebra approach applied to the given Markov problem requires only computing the eigenvector ${\prod}$ of the state transition matrix and then normalizing it to have the sum of its entries equal to 1. MATLAB computation results show that systems employing discrete TOA randomization and delay capture significantly improves throughput-delay performance and the employed analysis approach is quite easily and staightforwardly applicable to the current analysis problem.

• Journal of KIISE:Information Networking
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• v.31 no.2
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• pp.188-195
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• 2004

We have analyzed the performance of TCP in the CDMA mobile communications systems with the adaptive modulation and coding(AMC). The wireless channel using AMC is characterized with not high error rate but highly varying bandwidth. Due to time-varying bandwidth, timeout events of TCP occurs more frequently, which leads to the throughput degradation. The analysis model is composed of the two parts. In the first part, we divide TCP packet stream into ‘packet groups’and derive the probability distribution of the wireless transmission time of each Packet group that reflects the time varying characteristics of AMC. In the second part, we formulate embedded Markov chain by making use of the results of the first part to model TCP timer mechanism and wireless transmission. Since our system model is characterized by the forward link high speed data transmission using AMC, the results reported in this paper can be used as a guideline for the design and operation of HSDPA, 1xEV-DO, and 1xEV-DV.

• Economic Analysis
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• v.17 no.2
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• pp.27-55
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• 2011

Although many theoretical studies have tried to explain the volatility in financial markets using models of herd behavior, there have been few empirical studies on dynamic herding due to the technical difficulty of detecting herd behavior with time-series data. Thus, this paper theoretically extends a continuous beliefs system belonging to an agent based economic model by introducing a term representing agents'mutual dependence into each agent's utility function and derives a SV(stochastic volatility)-type econometric model. From this model the time-varying herding parameters are efficiently estimated by a Markov chain Monte Carlo method. Using monthly data of KOSPI and DOW, this paper provides some empirical evidences for stronger herding in the Korean stock market than in the U.S. stock market, and further stronger herding after the global financial crisis than before it. More interesting finding is that time-varying herd behavior has weak autocorrelation and the global financial crisis may increase its volatility significantly.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.665-673
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• 2008

This study is to assess the future potential climate and land use change impact on streamflow and stream water quality of the study watershed using the established model parameters (I). The CCCma (Canadian Centre for Climate Modelling and Analysis) CGCM2 (Canadian Global Coupled Model) based on IPCC SRES (Special Report Emission Scenarios) A2 and B2 scenarios were adopted for future climate condition, and the data were downscaled by Stochastic Spatio-Temporal Random Cascade Model technique. The future land use condition was predicted by using modified CA-Markov (Cellular Automata-Markov chain) technique with the past time series of Landsat satellite images. The model was applied for the future extreme precipitation cases of around 2030, 2060 and 2090. The predicted results showed that the runoff ratio increased 8% based on the 2005 precipitation (1160.1 mm) and runoff ratio (65%). Accordingly the Sediment, T-N and T-P also increased 120%, 16% and 10% respectively for the case of 50% precipitation increase. This research has the meaning in providing the methodological procedures for the evaluation of future potential climate and land use changes on watershed hydrology and stream water quality. This model result are expected to plan in advance for healthy and sustainable watershed management and countermeasures of climate change.