• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.5
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• pp.89-94
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• 2003

Rolling stock structures such as bogie frame and car body play an important role for the support of vehicle leading. In general, more than 25 years' durability is needed for them. A lot of study has been carried out for the prediction of the fatigue life of the bogie frame and car body in experimental and theoretical domains. One of the new methods is a probabilistic fatigue lift evaluation. The objective of this paper is to estimate the fatigue lift of the bogie frame of an electric car, which was developed by the Korea Railroad Research Institute (KRRI). We used two approaches. In the first approach probabilistic distribution of S-N curve and limit state function of the equivalent stress of the measured stress spectra are used. In the second approach, limit state function is also used. And load spectra measured by strain gauges are approximated by the two-parameter Weibull distribution. Other probabilistic variables are represented by log-normal and normal distributions. Finally, reliability index and structural integrity of the bogie frame are estimated.

• Journal of the Korea Concrete Institute
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• v.17 no.6 s.90
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• pp.1075-1084
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• 2005

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. Improved probabilistic neural network was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment (DDA) algorithm. The conventional PNN and the PNN with DDA algorithm(IPNN) were applied to predict the compressive strength of concrete using actual test data of two concrete companies. IPNN showed better results than the conventional PNN in predicting the compressive strength of concrete.

• Industrial Engineering and Management Systems
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• v.15 no.4
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• pp.324-334
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• 2016

Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2259-2277
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• 2023

We propose a probabilistic fish growth model for smart aquaculture systems equipped with IoT sensors that monitor the ecological environment. As IoT sensors permeate into smart aquaculture systems, environmental data such as oxygen level and temperature are collected frequently and automatically. However, there still exists data on fish weight, tank allocation, and other factors that are collected less frequently and manually by human workers due to technological limitations. Unlike sensor data, human-collected data are hard to obtain and are prone to poor quality due to missing data and reading errors. In a situation where different types of data are mixed, it becomes challenging to develop an effective fish growth model. This study explores the unique characteristics of such a combined environmental and weight dataset. To address these characteristics, we develop a preprocessing method and a probabilistic fish growth model using mixed data sampling (MIDAS) and overlapping mixtures of Gaussian processes (OMGP). We modify the OMGP to be applicable to prediction by setting a proper prior distribution that utilizes the characteristic that the ratio of fish groups does not significantly change as they grow. We conduct a numerical study using the eel dataset collected from a real smart aquaculture system, which reveals the promising performance of our model.

• Structural Engineering and Mechanics
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• v.89 no.3
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• pp.309-321
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• 2024

In this paper, the effect of semi-rigid connections on the stability bearing capacity of cross-bracings in steel tubular transmission towers is investigated. Herein, a prediction method based on the hybrid model which is a combination of particle swarm optimization (PSO) and backpropagation neural network (BPNN) is proposed to accurately predict the stability bearing capacity of cross-bracings with semi-rigid connections and to efficiently conduct its probabilistic assessment. Firstly, the establishment of the finite element (FE) model of cross-bracings with semi-rigid connections is developed on the basis of the development of the mechanical model. Then, a dataset of 7425 samples generated by the FE model is used to train and test the PSO-BPNN model, and the accuracy of the proposed method is evaluated. Finally, the probabilistic assessment for the stability bearing capacity of cross-bracings with semi-rigid connections is conducted based on the proposed method and the Monte Carlo simulation, in which the geometric and material properties including the outer diameter and thickness of cross-sections and the yield strength of steel are considered as random variables. The results indicate that the proposed method based on the PSO-BPNN model has high accuracy in predicting the stability bearing capacity of cross-bracings with semi-rigid connections. Meanwhile, the semi-rigid connections could enhance the stability bearing capacity of cross-bracings and the reliability of cross-bracings would significantly increase after considering semi-rigid connections.

• Journal of Korea Water Resources Association
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• v.44 no.10
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• pp.843-851
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• 2011

In hydrology, it is appropriate to use probabilistic method for forecasting mid/long term streamflow due to the uncertainty of input data. Through this study, it is expanded mid/long term forecasting system more effectively adding priory process function based on PDF-ratio method to the RRFS-ESP system for Guem River Basin. For implementing this purpose, weight is estimated using probabilistic weather forecasting information from KMA. Based on these results, ESP probability is updated per scenario. Through the estimated result per method, the average forecast score using ESP method is higher than that of naive forecasting and it confirmed that ESP method results in appropriate score for RRFS-ESP system. It is also shown that the score of ESP method applying revised inflow scenario using probabilistic weather forecasting is higher than that of ESP method. As a results, it will be improved the accuracy of forecasting using probabilistic weather forecasting.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.509-523
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• 2005

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving. In this article, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.144.1-144.1
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• 2005

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.2
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• pp.75-83
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• 2008

Joye et al. introduced a new prime generation algorithm (JPV algorithm hereafter), by removing the trial division from the previous combined prime generation algorithm (combined algorithm hereafter) and claimed that JPV algorithm is $30{\sim}40%$ faster than the combined algorithm. However, they only compared the number of Fermat-test calls, instead of comparing the total running times of two algorithms. The reason why the total running times could not be compared is that there was no probabilistic analysis on the running time of the JPV algorithm even though there was a probabilistic analysis for the combined algorithm. In this paper, we present a probabilistic analysis on the running time of the JPV algorithm. With this analytic model, we compare the running times of the JPV algorithm and the combined algorithm. Our model predicts that JPV algorithm is slower than the combined algorithm when a 512-bit prime is generated on a Pentium 4 system. Although our prediction is contrary to the previous prediction from comparing Fermat-test calls, our prediction corresponds to the experimental results more exactly. In addition, we propose a method to improve the JPV algorithm. With this method, the JPV algorithm can be comparable to the combined algorithm with the same space requirement.

• Atmosphere
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• v.30 no.2
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• pp.141-154
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• 2020

Seasonal forecast is growing in demand, as it provides valuable information for decision making and potential to reduce impact on weather events. This study examines how operational climate prediction systems can be reliable, producing the probability forecast in seasonal scale. A reliability diagram was used, which is a tool for the reliability by comparing probabilities with the corresponding observed frequency. It is proposed for a method grading scales of 1-5 based on the reliability diagram to quantify the reliability. Probabilities are derived from ensemble members using hindcast data. The analysis is focused on skill for 2 m temperature and precipitation from climate prediction systems in KMA, UKMO, and ECMWF, NCEP and JMA. Five categorizations are found depending on variables, seasons and regions. The probability forecast for 2 m temperature can be relied on while that for precipitation is reliable only in few regions. The probabilistic skill in KMA and UKMO is comparable with ECMWF, and the reliabilities tend to increase as the ensemble size and hindcast period increasing.