• Journal of Korean Society of Transportation
• /
• v.28 no.1
• /
• pp.125-134
• /
• 2010

It is occurred bus arrival time errors when a bus arrives at a bus stop because of a variety of traffic condition such as traffic signal cycle, the time to get on and off a bus, a bus-only lane and so on. In this paper, bus delay time which is occurred as the result of traffic condition was estimated with Markov Chain process and bus arrival time at each bus stop was predicted with it. As the result of the study, it is confirmed to improve accuracy than the method of bus arrival time prediction with existing method (weighed moving average method) in case predicting bus arrival time using 7 by 7 and 9 by 9 matrixes.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.10d
• /
• pp.782-785
• /
• 2006

셀 내의 모바일 노드들의 수가 많고 셀 간의 이동이 빈번한 고속 모바일 네트워크에서는 기존 802.11 프로토콜로는 좋은 성능을 보장할 수 없다. 셀 내에 새로 진입한 노드들은 네트워크에 참여하기 위해 자신의 존재 여부를 알려야 한다. 802.11 표준에서는 이러한 선행되어야 할 작업을 스캐닝, 인증, 결합의 3가지 단계로 규정한다. 이 등록 작업은 셀 내의 다른 데이터 패킷을 보내려는 노드들과의 경쟁을 통해 이루어진다. 그러므로 셀 내의 노드 수가 많을 경우 기본적인 통신을 위해 선행되어야 할 등록 작업이 지연될 수 있다. 802.11 표준에서는 DCF 방식을 기본 매체 접근 프로토콜로 한다. DCF는 BEB (Binary Exponential Backoff) 알고리즘을 기반으로 한다. BEB 알고리즘의 여러 문제점[5]으로 이를 대체할 알고리즘이 연구되어왔으며, 그룹화를 통해 경쟁하는 노드의 수를 줄이는 방법도 고려되었다. 본 논문에서는 802.11의 성능 평가를 위한 모델링에 Markov chain을 이용한 논문[1]을 기반으로 하나의 노드가 등록 작업에 소요하는 평균 시간을 해석적으로 계산하였다. 셀 내의 전체 노드 수에 증가함에 따라 등록 시간을 계산하고, 직접 시뮬레이션을 통해 수식으로 얻어진 결과와 비교하였다. 또한 그룹화를 시뮬레이션 하여 전체 노드 수에 따라서 적절한 그룹 수의 조정이 그룹화하지 않았을 경우보다 더 나은 성능을 보여줄 수 있다는 것을 보였다.

• Journal of Sensor Science and Technology
• /
• v.26 no.4
• /
• pp.286-291
• /
• 2017

With regards to heading estimation using gyroscope and magnetometer signals, magnetic disturbance added in the magnetometer signals is a main degradation factor in the estimation accuracy. Although there are a number of existing mechanisms that may properly compensate for the magnetic disturbances, they are designed to react only to the magnetic disturbances, but not to the time derivative of disturbances. Note that the sensors may experience abrupt changes in the magnetic disturbances, particularly for ambulatory applications. This paper proposes a magnetic disturbance model-embedded heading estimation filter for time-varying magnetic environments. The proposed magnetic disturbance model is based on a first-order Markov chain with a conditional switching technique depending on the time derivative of disturbances. Once a high amount of derivative is detected, the corrupted magnetometer signals are discarded to protect the filter from them. In our experimental results, the averaged heading error of tests was $1.46^{\circ}$, while that of the original approach without switching was $5.75^{\circ}$.

• Korean Management Science Review
• /
• v.10 no.1
• /
• pp.139-156
• /
• 1993

Various ATM switches have been proposed since Asynchronous Transfer Mode (ATM) was recognized as appropriate for implementing broadband integrated services digital network (BISDN). An ATM switching network may be evaluated on two sides : traffic performances (maximum throughput, delay, and packet loss probability, etc.) and structural features (complexity, i.e. the number of switch elements necessary to construct the same size switching network, maintenance, modularity, and fault tolerance, etc.). ATM switching networks proposed to date tend to show the contrary characteristics between structural features and traffic performance. The Knockout Switch, which is well known as one of ATM switches, shows a good traffic performance but it needs so many switch elements and buffers. In this paper, we propose an input and output buffered Knockout Switch for the purpose of reducing the number of switch elements and buffers of the existing Knockout Switch. We analyze the traffic performance and the structural features of the proposed switching architecture through a discrete time Markov chain and compare with those of the existing Knockout Switch. It was found that the proposed architecture could reduce more than 40 percent of switch elements and more than 30 percent of buffers under a given requirement of cell loss probability of the switch.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.36 no.1
• /
• pp.63-72
• /
• 1994

The objective of the study is to develop weather generators for daily rainfall and small pan evaporation and to test the applicability with recorded data. Daily rainfall forecasting model(DRFM) was developed that uses a first order Markov chain to describe rainfall seque- nces and applies an incomplete Gamma function to predict the amount of precipitation. Daily evaporation forecasting model(DEFM) that adopts a normal distribution function to generate the evaporation for dry and wet days was also formulated. DRFM and DEFM were tested with twenty year weather data from eleven stations using Chi-square and Kolmogorov and Smirnov goodness of fit tests. The test results showed that the generated sequences of rainfall occurrence, amount of rainfall, and pan evaporation were statistically fit to recorded data from eleven, seven, and seven stations at the 5% level of significance. Generated rainfall data from DRFM were very close in frequency distri- bution patterns to records for stations all over the country. Pan evaporation for rainy days generated were less accurate than that for dry days. And the proposed models may be used as tools to provide many mathematical models with long-term daily rainfall and small pan evaporation data. An example is an irrigation scheduling model, which will be further detailed in the paper.

• International Journal of Computer Science & Network Security
• /
• v.22 no.12
• /
• pp.51-56
• /
• 2022

In space based constellation network, users are allowed to enter or leave the network arbitrarily. Hence, the number, identities and transmitted data of active users vary with time and have considerable impacts on the receiver's performance. The so-called problem of multiuser detection means identifying the identity of each active user and detecting the data transmitted by each active user. Traditional methods assume that the number of active users is equal to the maximum number of users that the network can hold. The model of traditional methods are simple and the performance are suboptimal. In this paper a Maximum A Posteriori Probability (MAP) based multiuser detection method is proposed. The proposed method models the activity state of users as Markov chain and transforms multiuser detection into searching optimal path in grid map with BCJR algorithm. Simulation results indicate that the proposed method obtains 2.6dB and 1dB E_b/N₀ gains respectively when activity detection error rate and symbol error rate reach 10^-3, comparing with reference methods.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.123-123
• /
• 2020

The present study is aimed to correcting radar-based mean areal precipitation forecasts to improve urban flood predictions and uncertainty analysis of water levels contributed at each stage in the process. For this reason, a long short-term memory (LSTM) network is used to reproduce three-hour mean areal precipitation (MAP) forecasts from the quantitative precipitation forecasts (QPFs) of the McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE). The Gangnam urban catchment located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 24 heavy rainfall events, 22 grid points from the MAPLE system and the observed MAP values estimated from five ground rain gauges of KMA Automatic Weather System. The corrected MAP forecasts were input into the developed coupled 1D/2D model to predict water levels and relevant inundation areas. The results indicate the viability of the proposed framework for generating three-hour MAP forecasts and urban flooding predictions. For the analysis uncertainty contributions of the source related to the process, the Bayesian Markov Chain Monte Carlo (MCMC) using delayed rejection and adaptive metropolis algorithm is applied. For this purpose, the uncertainty contributions of the stages such as QPE input, QPF MAP source LSTM-corrected source, and MAP input and the coupled model is discussed.

• The Bulletin of The Korean Astronomical Society
• /
• v.44 no.2
• /
• pp.75.4-75.4
• /
• 2019

We present a quantitative analysis of the relationship between the gas dynamics and star formation history of DDO 210 which is an irregular dwarf galaxy in the local Universe. We perform profile analysis of an high-resolution neutral hydrogen (HI) data cube of the galaxy taken with the large Very Large Array (VLA) survey, LITTLE THINGS using newly developed algorithm based on a Bayesian Markov Chain Monte Carlo (MCMC) technique. The complex HI structure and kinematics of the galaxy are decomposed into multiple kinematic components in a quantitative way like 1) bulk motions which are most likely to follow the underlying circular rotation of the disk, 2) non-circular motions deviating from the bulk motions, and 3) kinematically cold and warm components with narrower and wider velocity dispersion. The decomposed kinematic components are then spatially correlated with the distribution of stellar populations obtained from the color-magnitude diagram (CMD) fitting method. The cold and warm gas components show negative and positive correlations between their velocity dispersions and the surface star formation rates of the populations with ages of < 40 Myr and 100~400 Myr, respectively. The cold gas is most likely to be associated with the young stellar populations. Then the stellar feedback of the young populations could influence the warm gas. The age difference between the populations which show the correlations indicates the time delay of the stellar feedback.

• The Bulletin of The Korean Astronomical Society
• /
• v.45 no.1
• /
• pp.48.4-49
• /
• 2020

We reconstruct the expansion history of the universe using type Ia supernovae (SN Ia) in a manner independent of any cosmological model assumptions. To do so, we implement a nonparametric iterative smoothing method on the Joint Light-curve Analysis (JLA) data while exploring the SN Ia light-curve hyperparameter space by Markov Chain Monte Carlo (MCMC) sampling. We test to see how the posteriors of these hyperparameters depend on cosmology, whether using different dark energy models or reconstructions shift these posteriors. Our constraints on the SN Ia light-curve hyperparameters from our model-independent analysis are very consistent with the constraints from using different parameterizations of the equation of state of dark energy, namely the flat ΛCDM cosmology, the Chevallier-Polarski-Linder model, and the Phenomenologically Emergent Dark Energy (PEDE) model. This implies that the distance moduli constructed from the JLA data are mostly independent of the cosmological models. We also studied that the possibility the light-curve parameters evolve with redshift and our results show consistency with no evolution. The reconstructed expansion history of the universe and dark energy properties also seem to be in good agreement with the expectations of the standard ΛCDM model. However, our results also indicate that the data still allow for considerable flexibility in the expansion history of the universe. This work is published in ApJ.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.1
• /
• pp.64-88
• /
• 2024

Security and reliability are the utmost importance facts in intelligent networked vehicles. Stochastic Petri Net and Z (SPZN) as an excellent formal verification tool for modeling concurrent systems, can effectively handles concurrent operations within a system, establishes relationships among components, and conducts verification and reasoning to ensure the system's safety and reliability in practical applications. However, the application of a system with numerous nodes to Petri Net often leads to the issue of state explosion. To tackle these challenges, a refinement and abstraction method based on SPZN is proposed in this paper. This approach can not only refine and abstract the Stochastic Petri Net but also establish a corresponding relationship with the Z language. In determining the implementation rate of transitions in Stochastic Petri Net, we employ the interval average and weighted average method, which significantly reduces the time and space complexity compared to alternative techniques and is suitable for expert systems at various levels. This reduction facilitates subsequent comprehensive system analysis and module analysis. Furthermore, by analyzing the properties of Markov Chain isomorphism in the case study, recommendations for minimizing system risks in the application of intelligent parking within the intelligent networked vehicle system can be put forward.