• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.542-549
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• 2004

The compressive strength of concrete is commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network (PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value. Adaptive probabilistic neural network (APNN) was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment algorithm. The conventional PNN and APNN were applied to predict the compressive strength of concrete using actual test data of a concrete company. APNN showed better results than the conventional PNN in predicting the compressive strength of concrete.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.10
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• pp.2495-2500
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• 1994

A method of prediction for the fatigue life of surface crack, that is, initial cracks grow and penetrate through the thickness, was presented in the previous study of the author. Effects of parameters such as the initial crack length, material factors, etc., for the life were discussed. In this paper, the probabilistic distribution of the life is calculated. Effects of the distribution of parameters for the distribution of life were also discussed.

• The Journal of Korea Robotics Society
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• v.5 no.3
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• pp.262-269
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• 2010

This paper presents a probabilistic head tracking method, mainly applicable to face recognition and human robot interaction, which can robustly track human head against various variations such as pose/scale change, illumination change, and background clutters. Compared to conventional particle filter based approaches, the proposed method can effectively track a human head by regularizing the sample space and sequentially weighting multiple visual cues, in the prediction and observation stages, respectively. Experimental results show the robustness of the proposed method, and it is worthy to be mentioned that some proposed probabilistic framework could be easily applied to other object tracking problems.

• Journal of the Korean Society of Safety
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• v.21 no.1 s.73
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• pp.28-34
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• 2006

A procedure of estimating failure probability is demonstrated for a pressurized pipe of CrMo steel used at $538^{\circ}C$. Probabilistic fracture mechanics were employed considering variations of pressure loading, material properties and geometry. Probability density functions of major material variables were determined by statistical analyses of implemented data obtained by previous experiments. Distributions of the major variables were reflected in Monte Carlo simulation and failure probability as a function of operating time was determined. The creep crack growth life assessed by conventional deterministic approach was shown to be conservative compared with those obtained by probabilistic one. Sensitivity analysis for each input variable was also conducted to understand the most influencing variables to the residual life analysis. Internal pressure, creep crack growth coefficient and creep coefficient were more sensitive to failure probability than other variables.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3145-3162
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• 2023

As an important technology of the internetwork, wireless sensor network technique plays an important role in indoor localization. Non-line-of-sight (NLOS) problem has a large effect on indoor location accuracy. A location algorithm based on improved particle filter and directional probabilistic data association (IPF-DPDA) for WSN is proposed to solve NLOS issue in this paper. Firstly, the improved particle filter is proposed to reduce error of measuring distance. Then the hypothesis test is used to detect whether measurements are in LOS situations or NLOS situations for N different groups. When there are measurements in the validation gate, the corresponding association probabilities are applied to weight retained position estimate to gain final location estimation. We have improved the traditional data association and added directional information on the original basis. If the validation gate has no measured value, we make use of the Kalman prediction value to renew. Finally, simulation and experimental results show that compared with existing methods, the IPF-DPDA performance better.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.3
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• pp.257-272
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• 2016

It is usually not an easy task to counter-measure on time and appropriately when confronting with troubles in mechanized tunnelling job-sites because of the limitation of available spaces to perform those actions with the existence of disk cutter, cutter head, chamber and other various apparatus in Tunnel Boring Machine (TBM). So, it is important to predict the ground condition ahead of a tunnel face during tunnel excavation. Efforts have been made to utilize geophysical methods such as elastic wave survey, electromagnetic wave survey, electrical resistivity survey, etc for predicting the ground condition ahead of the TBM tunnel face. Each prediction method among these geophysical methods has its own advantage and disadvantage. Therefore, it might be needed to apply several geophysical methods rather than just one to predict the ground condition ahead of the tunnel face in the complex and/or mixed grounds since those methods will compensate among others. The problem is that each prediction method will give us different answer on the predicted ground condition; how to combine different solutions into a most reasonable and representative predicted value might be important. Therefore, in this study, we proposed a methodology how to systematically combine each prediction method utilizing probabilistic analysis as well as analytic hierarchy process. The proposed methods is applied to a virtual job site to confirm the applicability of the model to predict the ground condition ahead of the tunnel face in the mechanized tunnelling.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.4
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• pp.27-36
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• 2019

An accurate trajectory prediction is a key to the safe and efficient operations of aircraft. One way to improve trajectory prediction accuracy is to develop a model for aircraft ground speed prediction. This paper proposes a generative model for posterior aircraft ground speed prediction. The proposed method fits the Gaussian Mixture Model(GMM) to historical data of aircraft speed, and then the model is used to generates probabilistic speed profile of the aircraft. The performances of the proposed method are demonstrated with real traffic data in Incheon Flight Information Region(FIR).

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.199-209
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• 2015

This paper presents a model predictive control (MPC) approach to control the steering angle in an autonomous vehicle. In designing a highly automated driving control algorithm, one of the research issues is to cope with probable risky situations for enhancement of safety. While human drivers maneuver the vehicle, they determine the appropriate steering angle and acceleration based on the predictable trajectories of surrounding vehicles. Likewise, it is required that the automated driving control algorithm should determine the desired steering angle and acceleration with the consideration of not only the current states of surrounding vehicles but also their predictable behaviors. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, we define a safe driving envelope with the consideration of probable risky behaviors among the predicted probable behaviors of surrounding vehicles over a finite prediction horizon. For the control of the vehicle while satisfying the safe driving envelope and system constraints over a finite prediction horizon, a MPC approach is used in this research. At each time step, MPC based controller computes the desired steering angle to keep the subject vehicle in the safe driving envelope over a finite prediction horizon. Simulation and experimental tests show the effectiveness of the proposed algorithm.

• Journal of the Earthquake Engineering Society of Korea
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• v.18 no.3
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• pp.151-160
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• 2014

Probabilistic tsunami hazard analysis (PTHA) is based on the approach of probabilistic seismic hazard analysis (PSHA) which is performed using various seismotectonic models and ground-motion prediction equations. The major difference between PTHA and PSHA is that PTHA requires the wave parameters of tsunami. The wave parameters can be estimated from tsunami propagation analysis. Therefore, a tsunami simulation analysis was conducted for the purpose of evaluating the wave parameters required for the PTHA of Uljin nuclear power plant (NPP) site. The tsunamigenic fault sources in the western part of Japan were chosen for the analysis. The wave heights for 80 rupture scenarios were numerically simulated. The synthetic tsunami waveforms were obtained around the Uljin NPP site. The results show that the wave heights are closely related with the location of the fault sources and the associated potential earthquake magnitudes. These wave parameters can be used as input data for the future PTHA study of the Uljin NPP site.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.4
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• pp.44-50
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• 2010

Structural vibration is a significant problem in many multi-part or multi-component assemblies. In aircraft industry, structures are composed of various fasteners, such as bolts, snap, hinge, weld or other fastener or connector (collectively "fasteners"). Due to these, prediction and design involving dynamic characteristics is quite complicated. However, the current state of the art does not provide an analytical tool to effectively predict structure's dynamic characteristics, because consideration of structural uncertainties (i.e. material properties, geometric tolerance, dimensional tolerance, environment and so on) is difficult and very small fasteners in the structure cause a huge amount of analysis time to predict dynamic characteristics using the FEM (finite element method). In this study, to resolve the current state of the art, a new approach is proposed using the FEM and probabilistic analysis. Firstly, equivalent elements are developed using simple element (e.g. bar, beam, mass) to replace fasteners' finite element model. Developed equivalent elements enable to explain static behavior and dynamic behavior of the structure. Secondly, probabilistic analysis is applied to evaluate the PDF (probability density function) of dynamic characteristics due to tolerance, material properties and so on. MCS (Monte-Carlo simulation) is employed for this. Proposed methodology offers efficiency of dynamic analysis and reality of the field as well. Simple plates joined by fasteners are taken as an example to illustrate the proposed method.