• Journal of Applied Reliability
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• v.7 no.1
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• pp.31-43
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• 2007

The function of O-ring seals is to prevent leakage during the service life of the components in which they are installed. The life prediction of O-ring is very important at various industry fields. Generally, to evaluated the long-term performance of O-ring in severe environments has applied a life prediction technique based on accelerated life test (ALT). In this work, Accelerated thermal aging test(l20, 130, 140, $150^{\circ}C$) of O-ring was applied for life prediction of O-ring. The property changes after thermal aging test was measured using TGA, DSC, FT - IR, Video Microscope and SEM. Shape parameter and life prediction were obtained using MINITAB program.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.425-426
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• 2016

In this paper, we proposed an algorithm based on movement prediction using Markov chain in delay tolerant networks(DTNs). The existing prediction algorithms require additional information such as a node's schedule and connectivity between nodes. However, network reliability is lowered when additional information is unknown. To solve this problem, we proposed an algorithm for predicting a movement path of the node by using Markov chain. The proposed algorithm maps speed and direction for a node into state, and predict movement path of the node using transition probability matrix generated by Markov chain. As the result, proposed algorithm show that the proposed algorithms has competitive delivery ratio but with less average latency.

• Computers and Concrete
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• v.11 no.4
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• pp.337-350
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• 2013

The sensitivity of compressive strength of no-slump concrete to its ingredient materials and proportions, necessitate the use of robust models to guarantee both estimation and generalization features. It was known that the problem of compressive strength prediction owes high degree of complexity and uncertainty due to the variable nature of materials, workmanship quality, etc. Moreover, using the chemical and mineral additives, superimposes the problem's complexity. Traditionally this property of concrete is predicted by conventional linear or nonlinear regression models. In general, these models comprise lower accuracy and in most cases they fail to meet the extrapolation accuracy and generalization requirements. Recently, artificial intelligence-based robust systems have been successfully implemented in this area. In this regard, this paper aims to investigate the use of optimized support vector machine (SVM) to predict the compressive strength of no-slump concrete and compare with optimized neural network (ANN). The results showed that after optimization process, both models are applicable for prediction purposes with similar high-qualities of estimation and generalization norms; however, it was indicated that optimization and modeling with SVM is very rapid than ANN models.

• Journal of Biosystems Engineering
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• v.14 no.3
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• pp.158-167
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• 1989

This study was conducted to investigate the applicability of true model theory in a powered lugged wheel-soil system and to examine the possibility of using principles of similitude in investigating the dimensions of soil parameters pertinent to a powered lugged wheel-soil system concerning the sinkage prediction. The following conclusions were derived from the study; 1) The sinkage of prototype wheels proved to be predicted by those of the model wheels for the range of the dynamic weight tested. 2) A conditional equation which can be used for the prediction of sinkage of prototype by model test was derived as $n_f=n{_\ell}{^{-b}}$. The range of the numerical value of b, which is the exponent on the length dimension of the soil property ${\alpha}$, was found to be -1.48~-2.54. 3) Considering a relatively wide variation of b values, it was concluded that there are several soil properties which are pertinent to the powered lugged-wheel soil system concerning the sinkage prediction.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.11-17
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• 2019

In recent years, damage and loss of life and property have been occurred frequently due to swelling waves in the East Sea. Swelling waves are not easy to predict because they are caused by various factors. In this research, we build a model for predicting the swelling waves occurrence in the East Coast of Korea using machine learning technique. We collect historical data of unloading interruption in the Pohang Port, and collect air pressure, wind speed, direction, water temperature data of the offshore Pohang Port. We select important variables for prediction, and test various machine learning prediction algorithms. As a result, tide level, water temperature, and air pressure were selected, and Random Forest model produced best performance. We confirm that Random Forest model shows best performance and it produces 88.86% of accuracy

• Journal of Biosystems Engineering
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• v.14 no.2
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• pp.67-79
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• 1989

This study was conducted to investigate the applicability of true model theory for pull prediction in a powered lugged wheel-soil system and to examine the possibility of using principles of similitude in investigating the dimensions of soil parameters pertinent to a powered lugged wheel-soil system concerning the pull prediction. The following conclusions were derived from the study; 1) The pull of prototype wheels proved to be predicted by those of the model wheels for the range of the dynamic weight tested. The pull curves of models and prototype were respectively very similar in the shape. From this basic knowledge, it was enabled to apply the similitude theory to the performance prediction of the true model. 2) A conditional equation which can be used for the prediction of pull of prototype by model test was derived as follows. $n_f=n_1^{-b}$ where $n_f$ : force scale = $w/w_m$ $n_1$ : length scale = ${\ell}/{\ell}_m$ b : exponent on the length dimension of the soil property ${\alpha}$ The range of the numerical value of b, which was determined by the least square method, was found to be -2.0~-2.6. 3) Considering a relatively wide variation of b values in the pull prediction, b is considered to be a function of many variales. Thus it was concluded that there are several soil properties which are pertinent to the powered lugged-wheel-soil system concerning the pull prediction, and these soil properties may have the different effects on the pull of model and protytype wheels, to give the different dimension on the soil parameters.

• Journal of the Korean housing association
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• v.17 no.3
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• pp.61-69
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• 2006

Vertical vibrations on the slab of buildings are affected by types of vibration sources, transfer paths, and the material property and the size of members. Among these parameters, the vibration sources and the transfer path can not be controlled, but the property and the size of members can be controlled in the phase of design the members. In this study, the vibration responses according to the property and size of members were obtained by using a prediction program based on dynamic-stiffness matrix. Three parameters which are not usually considered as major factors for architecral planning were selected fur these analyses. They are the strength of materials, the thickness of wall and the thickness of slab. The ground vibration source located near a building was used as vibration input data in the analyses. This study has its originality on presenting appropriate property and size of structural members in order to reduce vertical vibration of slab in shear-wall structures. Analysing the results from the vibration estimation program according to the variations of parameters, the appropriate ratio among the sizes of structural members were proposed. From these results, the vibration level on the slab which is not constructed yet would be predicted and the vibration peak level can be reduced or shifted into the desirable frequency range. Therefore, the vertical vibration could be controlled in the phase of designing buildings.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.183-189
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• 2013

This paper proposes a novel lossless image compression scheme composed of direction-adaptive prediction and context-based entropy coding. In the prediction stage, we analyze the directional property with respect to the current coding pixel and select an appropriate prediction pixel. In order to further reduce the prediction error, we propose a prediction error compensation technique based on the context model defined by the activities and directional properties of neighboring pixels. The proposed scheme applies a context-based Golomb-Rice coding as the entropy coding since the coding efficiency can be improved by using the conditional entropy from the viewpoint of the information theory. Experimental results indicate that the proposed lossless image compression scheme outperforms the low complexity and high efficient JPEG-LS in terms of the coding efficiency by 1.3% on average for various test images, specifically for the images with a remarkable direction the proposed scheme shows better results.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.273-286
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• 2021

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen's formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA's) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts' newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA's) meso-scale forecasting data. We analyzed the accuracy of each model's results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.5 no.1
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• pp.7-12
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• 2006

Recently, the fractal interpolation methods have been widely introduced and used to estimate and analyze various theoretical and experimental data. Because of the chaotic behaviors of dynamic cutting force data, some method for end-milling force analysis must be used. The fractal analysis used in this paper is fractal linear interpolation and fractal dimension. Also, several methods for computing fractal dimensions have been used in which the fractal dimension of the typical dynamic end-milling force was calculated according to number of data points that are generally lower than 200 data points sampled. This fractal analysis shows a possible prediction of end-milling force that has some dynamic chatter property or stationary property in endmilling operation.