• 한국자동차공학회논문집
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• 제11권5호
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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.

• 콘크리트학회논문집
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• 제17권6호
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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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• 제15권4호
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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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• 제17권8호
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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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• 제89권3호
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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.

• 한국수자원학회논문집
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• 제44권10호
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• pp.843-851
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• 2011

수문학 분야에서 중장기 유출량 예측은 입력변수의 불확실성 등으로 인하여 확률론적 방법을 사용하는 것이 바람직한 것으로 알려져 왔다. 본 연구에서는 금강유역을 대상으로 구성된 바 있는 RRFS-ESP 시스템에 PDF-ratio 방법을 기반으로한 사전처리기능을 장착하여 보다 효율적인 중장기 예측시스템으로의 확장을 시도하여 보았다. 이를 위하여 기상청에서 제공하는 확률기상정보를 이용하여 가중치를 산정하고 이를 기반으로 시나리오별 예측확률을 갱신하였다. 예측결과에 대하여 각 기법별 예측점수를 산정하여 본 결과 우선 ESP 기법에 의한 예측점수의 평균이 초보예측 점수를 상회하여 본 연구에서 구성한 RRFS-ESP 시스템의 적용성을 확인할 수 있었다. 또한 확률기상전망을 이용하여 갱신한 유입량 시나리오의 예측점수가 ESP 기법에 의한 예측점수를 상회하고 있음을 확인할 수 있어 ESP 기법에 의한 예측결과를 확률기상전망을 이용하여 갱신할 경우 예측 정확도를 보다 개선시킬 수 있음을 확인할 수 있었다.

• International Journal of Control, Automation, and Systems
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• 제3권4호
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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.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2005년도 창립60주년 기념 추계 학술대회
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• pp.144.1-144.1
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• 2005

• 한국정보과학회논문지:시스템및이론
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• 제35권2호
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• pp.75-83
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• 2008

Joye와 연구자들은 기존의 조합 소수 판단 검사에서 trial division 과정을 제거한 새로운 소수 생성 알고리즘 (이하 JPV 알고리즘)을 제시하였으며, 이 알고리즘이 기존의 조합 소수 생성 알고리즘에 비해 $30{\sim}40%$ 정도 빠르다고 주장하였다. 하지만 이 비교는 전체 수행시간이 아닌 Fermat 검사의 호출 횟수만을 비교한 것으로 정확한 비교와는 거리가 있다. 기존의 조합 소수 생성 알고리즘에 대해 이론적인 수행시간 예측 방법이 있음에도 불구하고 두 알고리즘의 전체 수행시간을 비교할 수 없었던 이유는 JPV 알고리즘에 대한 이론적인 수행 시간 예측 모델이 없었기 때문이다. 본 논문에서는 먼저 JPV 알고리즘을 확률적으로 분석하여 수행시간 예측 모델을 제시하고, 이 모델을 이용하여 JPV 알고리즘과 기존의 조차 소수 생성 알고리즘의 전체 수행시간을 비교한다. 이 모델을 이용하여 펜티엄4 시스템에서 512비트 소수의 생성 시간을 예측해 본 결과 Fermat 검사의 호출 횟수를 이용한 비교와는 달리 JPV 알고리즘이 기존의 조합 소수 생성 알고리즘보다 느리다는 결론을 얻었다. 이러한 이론적인 분석을 통한 비교는 실제 동일한 환경에서 실험을 통해서 검증되었다. 또한, 본 논문에서는 JPV 알고리즘의 성능 개선 방법을 제시한다. 이 방법을 사용하여 JPV 알고리즘을 개선하면 동일한 공간을 사용할 경우에 JPV 알고리즘이 기존의 조합 소수 생성 알고리즘과 비슷한 성능을 보인다.

• 대기
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• 제30권2호
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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.