• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• 한국산학기술학회논문지
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• 제14권4호
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• pp.1683-1689
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• 2013

협상과정을 Markov 확률과정으로 전제하고 확률과정의 상태별 이행확률의 크기가 협상참가자의 사적인 정보에 의하여 결정된다고 가정한다. 예를 들어, 판매자와 구매자의 가격협상의 예에서 협상상대방의 특징-예를 들어, 유보가격-에 대한 정보가 사적인 정보일 때 협상참가자 모두가 수용가능한 가격을 찾는 과정이 이행확률의 크기에 영향을 받고, 결과적으로 협상지연의 정도를 결정함을 보인다. 또한 협상의 참가자가 모두 교대로 제안을 하는 제안과 대응제안의 방법의 협상에서보다 협상참가자중 어느 한 쪽의 일방에서 제안을 하는 방법의 협상에서 협상지연이 더욱 길어짐을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4436-4452
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• 2015

The IEEE 802.15.6 standard is introduced to satisfy all the requirements for monitoring systems operating in, on, or around the human body. In this paper, analytical models are developed for evaluating the performance of the IEEE 802.15.6 CSMA/CA-based medium access control protocol for wireless body area networks (WBAN) under unsaturation condition. We employ a three-dimensional Markov chain to model the backoff procedure, and an M/G/1/K queuing system to describe the packet queues in the buffer. The throughput and delay performances of WBAN operating in the beacon mode are analyzed in heterogeneous network comprised of different user priorities. Simulation results are included to demonstrate the accuracy of the proposed analytical model.

• 한국위성정보통신학회논문지
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• 제5권2호
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• pp.87-91
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• 2010

IEEE 802.15.4과 IEEE 802.15.7은 대표적인 저속 무선망 및 가시광 WPAN 표준이다. 이 표준의 MAC 프로토콜들은 비컨 모드에서 GTS를 이용하여 실시간 응용 프로그램에 대한 QoS 보장 트래픽 플로우를 지원할 수 있다. 그러나 최적으로 할당하는 방법은 아직 명확하게 해결되지 않았다. IEEE 802.15.4 MAC에 관한 현재의 분석 모델은 주로 포화 트래픽 또는 non-bursty 불포화 트래픽 상황이란 가정 하에 개발되었다. 이러한 가정은 bursty 멀티미디어 트래픽의 특성을 반영하지 못한다. 이 논문에서는 burst Markov 변조On-Off 도착 트래픽을 사용하여 GTS 할당을 위한 새로운 분석 모델을 제안한다.

• ETRI Journal
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• 제28권3호
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• pp.301-310
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• 2006

In mobile communications, a class of variable-complexity algorithms for convolutional decoding known as sequential decoding algorithms is of interest since they have a computational time that could vary with changing channel conditions. The Fano algorithm is one well-known version of a sequential decoding algorithm. Since the decoding time of a Fano decoder follows the Pareto distribution, which is a heavy-tailed distribution parameterized by the channel signal-to-noise ratio (SNR), buffers are required to absorb the variable decoding delays of Fano decoders. Furthermore, since the decoding time drawn by a certain Pareto distribution can become unbounded, a maximum limit is often employed by a practical decoder to limit the worst-case decoding time. In this paper, we investigate the relations between buffer occupancy, decoding time, and channel conditions in a system where the Fano decoder is not allowed to run with unbounded decoding time. A timeout limit is thus imposed so that the decoding will be terminated if the decoding time reaches the limit. We use discrete-time semi-Markov models to describe such a Fano decoding system with timeout limits. Our queuing analysis provides expressions characterizing the average buffer occupancy as a function of channel conditions and timeout limits. Both numerical and simulation results are provided to validate the analytical results.

• ETRI Journal
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• 제36권4호
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• pp.662-672
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• 2014

Human action recognition is used in areas such as surveillance, entertainment, and healthcare. This paper proposes a system to recognize both single and continuous human actions from monocular video sequences, based on 3D human modeling and cyclic hidden Markov models (CHMMs). First, for each frame in a monocular video sequence, the 3D coordinates of joints belonging to a human object, through actions of multiple cycles, are extracted using 3D human modeling techniques. The 3D coordinates are then converted into a set of geometrical relational features (GRFs) for dimensionality reduction and discrimination increase. For further dimensionality reduction, k-means clustering is applied to the GRFs to generate clustered feature vectors. These vectors are used to train CHMMs separately for different types of actions, based on the Baum-Welch re-estimation algorithm. For recognition of continuous actions that are concatenated from several distinct types of actions, a designed graphical model is used to systematically concatenate different separately trained CHMMs. The experimental results show the effective performance of our proposed system in both single and continuous action recognition problems.

• 한국조사연구학회:학술대회논문집
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• 한국조사연구학회 2006년도 춘계학술대회 발표논문집
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• pp.131-159
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• 2006

This paper consider multinomial group testing which is concerned with classification each of N given units into one of k disjoint categories. In this paper, we propose exact Bayesian, approximate Bayesian, bootstrap methods for estimating individual category proportions using the multinomial group testing model proposed by Bar-Lev et al (2005). By the comparison of Mcan Squre Error (MSE), it is shown that the exact Bayesian method has a bettor efficiency and consistency than maximum likelihood method. We suggest an approximate Bayesian approach using Markov Chain Monte Carlo (MCMC) for posterior computation. We derive exact credible intervals based on the exact Bayesian estimators and present confidence intervals using the bootstrap and MCMC. These intervals arc shown to often have better coverage properties and similar mean lengths to maximum likelihood method already available. Furthermore the proposed models are illustrated using data from a HIV blooding test study throughout California, 2000.

• 한국음향학회지
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• 제16권2호
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• pp.49-57
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• 1997

본 논문에서는 대어휘 연속음성인식을 위한 확률 발음사전 모델에 대해서 제안하였다. 확률 발음 사전은 HMM과 같이 단위음소 상태의 Markov chain으로 이루어져 있으며, 각 음소 상태들은 음소들에 대한 확률 분포 함수로 표현된다. 확률 발음 사전의 생성은 음성자료와 음소 모델을 이용하여 음소 단위의 분할과 인식을 통해서 자동으로 생성되게 된다. 제안된 확률 발음 사전은 단어내 변이와 단어간 변이를 모두 효과적으로 표현할 수 있었으며, 인식 모델과 인식기의 특성을 반영함으로써 전체 인식 시스템의 성능을 보다 높일 수 있었다. 3000 단어 연속음성인식 실험 결과 확률 발음 사전을 사용함으로써 표준 발음 표기를 사용하는 인식 시스템에 비해 단어 오류율은 23.6%, 문장 오류율은 10% 정도를 감소시킬 수 있었다.

• Journal of Construction Engineering and Project Management
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• 제8권1호
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• pp.10-21
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• 2018

Many construction projects involve a plethora of safety-related problems that can cause loss of productivity, diminished revenue, time overruns, and legal challenges. Incorporating data collection and analytics methods can help overcome the root causes of many such problems. However, in a dynamic construction workplace collecting data from a large number of resources is not a trivial task and can be costly, while many contractors lack the motivation to incorporate technology in their activities. In this research, an Android-based mobile application, Preemptive Construction Site Safety (PCS2) is developed and tested for real-time location tracking, trajectory prediction, and prevention of potential collisions between workers and site hazards. PCS2 uses ubiquitous mobile technology (smartphones) for positional data collection, and a robust trajectory prediction technique that couples hidden Markov model (HMM) with risk-taking behavior modeling. The effectiveness of PCS2 is evaluated in field experiments where impending collisions are predicted and safety alerts are generated with enough lead time for the user. With further improvement in interface design and underlying mathematical models, PCS2 will have practical benefits in large scale multi-agent construction worksites by significantly reducing the likelihood of proximity-related accidents between workers and equipment.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2008년도 정기총회 및 학술발표대회
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• pp.723-726
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• 2008

It is not always easy to estimate the parameters in hydrologic models due to insufficient hydrologic data when hydraulic structures are designed or water resources plan are established, uncertainty analysis, therefore, are inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. The NWS-PC model is calibrated against observed daily runoff, and thirteen parameters in the model are optimized as well as posterior distributions associated with each parameter are derived. The Bayesian Markov Chain Monte Carlo shows a improved result in terms of statistical performance measures and graphical examination. The patterns of runoff can be influenced by various factors and the Bayesian approaches are capable of translating the uncertainties into parameter uncertainties. One could provide against an expected runoff event by utilizing information driven by Bayesian methods. Therefore, the rainfall-runoff analysis coupled with the uncertainty analysis can give us an insight in evaluating flood risk and dam size in a reasonable way.