• Title/Summary/Keyword: Markov transition probability

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Predicting PM2.5 Concentrations Using Artificial Neural Networks and Markov Chain, a Case Study Karaj City

  • Asadollahfardi, Gholamreza;Zangooei, Hossein;Aria, Shiva Homayoun
    • Asian Journal of Atmospheric Environment
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    • v.10 no.2
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    • pp.67-79
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    • 2016
  • The forecasting of air pollution is an important and popular topic in environmental engineering. Due to health impacts caused by unacceptable particulate matter (PM) levels, it has become one of the greatest concerns in metropolitan cities like Karaj City in Iran. In this study, the concentration of $PM_{2.5}$ was predicted by applying a multilayer percepteron (MLP) neural network, a radial basis function (RBF) neural network and a Markov chain model. Two months of hourly data including temperature, NO, $NO_2$, $NO_x$, CO, $SO_2$ and $PM_{10}$ were used as inputs to the artificial neural networks. From 1,488 data, 1,300 of data was used to train the models and the rest of the data were applied to test the models. The results of using artificial neural networks indicated that the models performed well in predicting $PM_{2.5}$ concentrations. The application of a Markov chain described the probable occurrences of unhealthy hours. The MLP neural network with two hidden layers including 19 neurons in the first layer and 16 neurons in the second layer provided the best results. The coefficient of determination ($R^2$), Index of Agreement (IA) and Efficiency (E) between the observed and the predicted data using an MLP neural network were 0.92, 0.93 and 0.981, respectively. In the MLP neural network, the MBE was 0.0546 which indicates the adequacy of the model. In the RBF neural network, increasing the number of neurons to 1,488 caused the RMSE to decline from 7.88 to 0.00 and caused $R^2$ to reach 0.93. In the Markov chain model the absolute error was 0.014 which indicated an acceptable accuracy and precision. We concluded the probability of occurrence state duration and transition of $PM_{2.5}$ pollution is predictable using a Markov chain method.

A Study on the Simulation of Daily Precipitation Using Multivariate Kernel Density Estimation (다변량 핵밀도 추정법을 이용한 일강수량 모의에 대한 연구)

  • Cha, Young-Il;Moon, Young-Il
    • Journal of Korea Water Resources Association
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    • v.38 no.8 s.157
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    • pp.595-604
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    • 2005
  • Precipitation simulation for making the data size larger is an important task for hydrologic analysis. The simulation can be divided into two major categories which are the parametric and nonparametric methods. Also, precipitation simulation depends on time intervals such as daily or hourly rainfall simulations. So far, Markov model is the most favored method for daily precipitation simulation. However, most models are consist of state transition probability by using the homogeneous Markov chain model. In order to make a state vector, the small size of data brings difficulties, and also the assumption of homogeneousness among the state vector in a month causes problems. In other words, the process of daily precipitation mechanism is nonstationary. In order to overcome these problems, this paper focused on the nonparametric method by using uni-variate and multi-variate when simulating a precipitation instead of currently used parametric method.

Predictive Location Management Strategy Using Two Directional Consecutive LAs in a Cellular Network (이동 통신망에서 방향성을 지닌 2개의 연속적 위치영역을 이용한 예측 위치 관리 전략)

  • Chang, I.K.;Hong, J.S.;Kim, J.P.;Lie, C.H.
    • Journal of the Korean Operations Research and Management Science Society
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    • v.33 no.3
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    • pp.43-58
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    • 2008
  • In this paper, we have presented a dynamic, predictive location update scheme that takes into account each user's mobility patterns. A user's past movement history is used to create two-dimensional transition probability matrix which makes use of two directional consecutive location areas. A mobile terminal utilizes the transition probability to develop a predictive path which consists of several predictive nodes and then the location update is saved as long as a mobile user follows the predictive path. Using continuous-time Markov chain, cost functions of location update and paging are derived and it is shown that the number of predictive nodes can be determined optimally. To evaluate the proposed scheme, simulations are designed and the numerical analysis is carried out. The numerical analysis features user's mobility patterns and regularity, call arrival rates, and cost ratio of location update to paging. Results show that the proposed scheme gives lower total location management cost, compared to the other location update schemes.

A Design and Implementation of Reliability Analyzer for Embedded Software using Markov Chain Model and Unit Testing (내장형 소프트웨어 마르코프 체인 모델과 단위 테스트를 이용한 내장형 소프트웨어 신뢰도 분석 도구의 설계와 구현)

  • Kwak, Dong-Gyu;Yoo, Chae-Woo;Choi, Jae-Young
    • Journal of the Korea Society of Computer and Information
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    • v.16 no.12
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    • pp.1-10
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    • 2011
  • As requirements of embedded system get complicated, the tool for analyzing the reliability of embedded software is being needed. A probabilistic modeling is used as the way of analyzing the reliability of a software and to apply it to embedded software controlling multiple devices. So, it is necessary to specialize that to embedded software. Also, existing reliability analyzers should measure the transition probability of each condition in different ways and doesn't consider reusing the model once used. In this paper, we suggest a reliability analyzer for embedded software using embedded software Markov chin model and a unit testing tool. Embedded software Markov chain model is model specializing Markov chain model which is used for analyzing reliability to an embedded software. And a unit testing tool has host-target structure which is appropriate to development environment of embedded software. This tool can analyze the reliability more easily than existing tool by automatically measuring the transition probability between units for analyzing reliability from the result of unit testing. It can also directly apply the test result updated by unit testing tool by representing software model as a XML oriented document and has the advantage that many developers can access easily using the web oriented interface and SVN store. In this paper, we show reliability analyzing of a example by so doing show usefulness of reliability analyzer.

A Study on the Stationary State of Military Pension using Markov Chains (마코프 체인을 이용한 군인연금 안정상태에 관한 연구)

  • Bae, Young-Min
    • Journal of Digital Convergence
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    • v.19 no.2
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    • pp.61-69
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    • 2021
  • The military pension deficit is increasing due to an increase in the average life expectancy and pension option rate, and a significant reason for this is estimated to be a continued increase in the number of military pension recipients. In terms of the soundness of military pension finances, this paper uses the Markov chain model to validate the stability of the military group, suggesting the direction of future military pension system in terms of the ratio of pension receipts to employees, and verifying the feasibility of the method applied through verification. Through this paper, we have confirmed that the initial 45,270 military personnel converge to 43,141 after a certain period of time and reach a stable state, which is expected to help us to estimate the long term size of military pension recipients to confirm the direction of national financial support. Military man who are eligible for pensions for more than 20 years have a relatively low rate of turnover or retirement compared to ordinary private groups, making it easier to define their status and simplify state transition probabilities. Therefore, it is expected that the sustainability of the military pension will be confirmed from a long term perspective by viewing the military group as a system and applying it to the Markov chain model by checking the probability of transfer of status such as promotion, maintaining the current grade, and retirement during the period.

A Study on the Change of Occurrence Characteristics of Daily Seoul Rainfall using Markov Chain (마코프 연쇄를 이용한 서울지점 일강우의 발생특성 변화 연구)

  • Hwang, Seok-Hwan;Kim, Joong-Hoon;Yoo, Chul-Sang;Jung, Sung-Won;Joo, Jin-Gul
    • Journal of Korea Water Resources Association
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    • v.42 no.9
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    • pp.747-758
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    • 2009
  • In this study, long-term variabilities of rainfall-occurrence characteristics are analyzed using rainfall data at Seoul, which is the longest data record existing in world. first, the accuracy of Chukwooki data set (CWK) are evaluated in view of rainfall-occurrence probability by analyzing the transition probabilities and occurrence characteristics based on Markov chain. And long-term inter-monthly variabilities of transition probabilities are analyzed using two dimensional LOWESS regression. From the results of analyzed transition probabilities and occurrence characteristics, it is different that rainfall-occurrence characteristics between CWK and modern rain gage data set (MRG) for original rainfall data sets (M00). For characteristics of rainfall series, occurrences probabilities of rainfall are increased and durations of each rainfall are shorter than past. And from the results of analyzing the long-term inter-monthly variabilities of transition probabilities, in case of M20, lengths of dry spells between CWK and MRG are not different significantly and lengths of wet spells are decreased persistently after A.D. 1830. Especially, decreasing trend for lengths of wet spells at recent september are appeared significantly. These results are considered with increasing trend of recent rainfall, it is concluded that recent frequencies and intensities of rainfall are increasing.

Unsaturated Throughput Analysis of IEEE 802.11 DCF under Imperfect Channel Sensing

  • Shin, Soo-Young
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.4
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    • pp.989-1005
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    • 2012
  • In this paper, throughput of IEEE 802.11 carrier-sense multiple access (CSMA) with collision-avoidance (CA) protocols in non-saturated traffic conditions is presented taking into account the impact of imperfect channel sensing. The imperfect channel sensing includes both missed-detection and false alarm and their impact on the utilization of IEEE 802.11 analyzed and expressed as a closed form. To include the imperfect channel sensing at the physical layer, we modified the state transition probabilities of well-known two state Markov process model. Simulation results closely match the theoretical expressions confirming the effectiveness of the proposed model. Based on both theoretical and simulated results, the choice of the best probability detection while maintaining probability of false alarm is less than 0.5 is a key factor for maximizing utilization of IEEE 802.11.

A Markov Chain Model for Population Distribution Prediction Considering Spatio-Temporal Characteristics by Migration Factors (이동요인별 시·공간적 인구이동 특성을 고려한 인구분포 예측: 마르코프 연쇄 모형을 활용하여)

  • Park, So Hyun;Lee, Keumsook
    • 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.

A Multi-stage Markov Process Model to Evaluate the Performance of Priority Queues in Discrete-Event Simulation: A Case Study with a War Game Model (이산사건 시뮬레이션에서의 우선순위 큐 성능분석을 위한 다단계 마코브 프로세스 모델: 창조 모델에 대한 사례연구)

  • Yim, Dong-Soon
    • Journal of the Korea Society for Simulation
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    • v.17 no.4
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    • pp.61-69
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    • 2008
  • In order to evaluate the performance of priority queues for future event list in discrete-event simulations, models representing patterns of enqueue and dequeue processes are required. The time complexities of diverse priority queue implementations can be compared using the performance models. This study aims at developing such performance models especially under the environment that a developed simulation model is used repeatedly for a long period. The developed performance model is based on multi-stage Markov process models; probabilistic patterns of enqueue and dequeue are considered by incorporating non-homogeneous transition probability. All necessary parameters in this performance model would be estimated by analyzing a results obtained by executing the simulation model. A case study with a war game simulation model shows how the parameters defined in muti-stage Markov process models are estimated.

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Modeling the Spatial Dynamics of Urban Green Spaces in Daegu with a CA-Markov Model (CA-Markov 모형을 이용한 대구시 녹지의 공간적 변화 모델링)

  • Seo, Hyun-Jin;Jun, Byong-Woon
    • Journal of the Korean Geographical Society
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    • v.52 no.1
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    • pp.123-141
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    • 2017
  • This study predicted urban green spaces for 2020 based on two scenarios keeping or freeing the green-belt in the Daegu metropolitan city using a hybrid Cellular Automata(CA)-Markov model and analyzed the spatial dynamics of urban green spaces between 2009 and 2020 using a land cover change detection technique and spatial metrics. Markov chain analysis was employed to derive the transition probability for projecting land cover change into the future for 2020 based on two land cover maps in 1998 and 2009 provided by the Ministry of Environment. Multi-criteria evaluation(MCE) was adopted to develop seven suitability maps which were empirically derived in relation to the six restriction factors underlying the land cover change between the years 1998 and 2009. A hybrid CA-Markov model was then implemented to predict the land cover change over an 11 year period to 2020 based on two scenarios keeping or freeing the green-belt. The projected land cover for 2009 was cross-validated with the actual land cover in 2009 using Kappa statistics. Results show that urban green spaces will be remarkably fragmented in the suburban areas such as Dalseong-gun, Seongseo, Ansim and Chilgok in the year 2020 if the Daegu metropolitan city keeps its urbanization at current pace and in case of keeping the green-belt. In case of freeing the green-belt, urban green spaces will be fragmented on the fringes of the green-belt. It is thus required to monitor urban green spaces systematically considering the spatial change patterns identified by this study for sustainably managing them in the Daegu metropolitan city in the near future.

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