• 한국지능시스템학회논문지
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• 제10권6호
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• pp.616-628
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• 2000

In this paper, we present the modeling of the ozone prediction system using Neuro-Fuzzy approaches. The mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, the modeling of ozone prediction system has many problems and the results of prediction is not a good performance so far. The Dynamic Polynomial Neural Network(DPNN) which employs a typical algorithm of GMDH(Group Method of Data Handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system. The structure of the final model is compact and the computation speed to produce an output is faster than other modeling methods. In addition to DPNN, this paper also includes a Fuzzy Logic Method for modeling of ozone prediction system. The results of each modeling method and the performance of ozone prediction are presented. The proposed method shows that the prediction to the ozone concentration based upon Neuro-Fuzzy approaches gives us a good performance for ozone prediction in high and low ozone concentration with the ability of superior data approximation and self organization.

• Journal of Information Processing Systems
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• 제19권1호
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• pp.130-138
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• 2023

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.191-194
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• 2001

The ozone forecasting systems have many problems because the mechanism of the ozone concentration is highly complex, nonlinear, and nonstationary. Also, the results of prediction are not a good performance so far, especially in the high-level ozone concentration. This paper describes the modeling method of the ozone prediction system using neuro-fuzzy approaches and fuzzy clustering. The dynamic polynomial neural network (DPNN) based upon a typical algorithm of GMDH (group method of data handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system.

• 소성∙가공
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• 제7권3호
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• pp.197-206
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• 1998

This paper presents the prediction of dynamic recrystallization behavior during hot forging of Inconel 718. Another experiment of pancake forging was also carried out to examine the recrystallization ration dynamically recrystallizaed grain size, and grain growth in the forging. In experiments cylindrical billets were forged by two operations with variations of forging temperature, reduction ration of deformation. and preheating process at each forging step. Also the finite element program, developed here for the prediction using the metallurgical models was used for the analysis of to Inconel 718 upsetting and the results were compared with experimental ones.

• 대한임베디드공학회논문지
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• 제16권6호
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• pp.323-330
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• 2021

Visual object tracking is a challenging area of study in the field of computer vision due to many difficult problems, including a fast variation of target shape, occlusion, and arbitrary ground truth object designation. In this paper, we focus on the reinforced feature of the dynamic search area to get better performance than conventional discriminative model prediction trackers on the condition when the accuracy deteriorates since low feature discrimination. We propose a reinforced input feature method shown like the spotlight effect on the dynamic search area of the target tracking. This method can be used to improve performances for deep learning based discriminative model prediction tracker, also various types of trackers which are used to infer the center of the target based on the visual object tracking. The proposed method shows the improved tracking performance than the baseline trackers, achieving a relative gain of 38% quantitative improvement from 0.433 to 0.601 F-score at the visual object tracking evaluation.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2011년 춘계학술대회논문집
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• pp.428-433
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• 2011

In this paper, a prediction method for dynamic stability derivatives is studied using steady state simulations in rotational coordinates. The simulations require the extension of a standard CFD formulations based on inertial coordinate. A new CFD code based on the method are developed. Flows induced by steady circular motions of airfoils with a constant pitch rate are simulated with the code. From the numerical simulations, the pitch rate derivatives are obtained at various Mach numbers, and the results are compared with other numerical results. The numerical simulations show that the new code are capable of predicting dynamic stability derivatives.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.368-368
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• 2000

In this paper, we present forecasting ozone concentration with decision support system. Since the mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, modeling of ozone prediction system has many problems and results of prediction are not good performance so far. Forecasting ozone concentration with decision support system is acquired to information from human knowledge and experiment data. Fuzzy clustering method uses the acquisition and dynamic polynomial neural network gives us a good performance for ozone prediction with ability of superior data approximation and self-organization.

• 한국지반공학회논문집
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• 제26권8호
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• pp.59-66
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• 2010

본 연구에서는 동다짐 공법을 적용할 시 개량심도와 진동영향 예측을 유한요소해석을 통해 그 적용성을 규명하였다. 동다짐 공법이 적용된 기존 현장 조건에 따라 지반을 모델링하였으며, 동적하중 모델은 rigid body force를 적용하였다. 개량심도는 심도별 최대 연직 입자 가속도로 예측을 하여 기존 경험식과 비교 분석하였으며, 진동영향은 진동원으로부터 거리별 연직 최대 입자 속도를 도출하여 기존 경험식에 의한 값과 비교 분석하였다. 수치해석 결과, 개량심도의 경우 기존의 경험식과 비슷한 양상을 띄고 있으며, 진동영향의 경우는 기존 식과 특정 구간에서 차이를 보이고 있으나, 어느 정도 소요이격거리를 예측 할 수 있었다. 이들 해석결과는 경험적인 방법과 더불어 동다짐 설계의 신뢰도 향상을 위한 기초적인 자료가 될 것으로 보인다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.219-224
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• 2004

There have been many researches on machine tool vibration and chatter to obtain assessment procedure and more productivity. In this paper chatter limit is predicted on a universal machining center which used a parallel mechanism. The prediction method uses the combination of structural dynamic characteristics and cutting dynamics. So the dynamic characieristics were obtained by vibration experiments. We showed the unstable cutting conditions, and from them we could plot the unstable borderlines.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2010년 춘계학술대회논문집
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• pp.358-361
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• 2010

A Reynolds-Averaged Navier-Stokes (RANS) code with transition prediction model is developed and the computational results on an oscillating airfoil are compared with the experimental data for OA209 airfoil. An approximated eN method that can predict transition onset points and the length of transition region is directly applied to the RANS code. The hysteresis loop in dynamic stall is compared for the computational results using transition prediction and fully turbulent models with the experimental data. Results with transition prediction show more correlation with the experimental data than the fully turbulent computation.