• 자동차안전학회지
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• 제16권2호
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• pp.51-59
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• 2024

This study presents a method for health monitoring of rotating objects for mobility based on multiple recursive least squares(RLS) algorithms. The performance degradation of the rotating objects causes low handing / low driving performances and even fatal accidents. Therefore, health monitoring algorithm of rotating objects is one of the important technologies for mobility fail-safe and maintenance areas. In order for health monitoring of rotating objects, four recursive least squares algorithms with forgetting factor were designed in this study. The health monitoring algorithm proposed in this study consists of two steps such as uncertainty estimation and parameter changes estimation. In order to improve estimation accuracy, time delay function was applied to the estimated signals based on the first order differential equation and forgetting factors used for the RLS were reasonably tuned. The health monitoring algorithm was constructed in Matlab/Simulink environment and simulation-based performance evaluation was conducted using DC motor model. The evaluation results showed that the proposed algorithm estimates the actual parameter differences reasonably using velocity and current information.

• 한국지능시스템학회논문지
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• 제24권5호
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• pp.529-535
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• 2014

본 논문에서는 interval type-2 possibilistic fuzzy C-means(IT2PFCM) 클러스터링 방법에 multiple Gaussian kernels을 기반으로 한 possibilistic fuzzy C-means multiple kernels(PFCM-MK) 알고리즘을 결합하여 적응적인 하이브리드 클러스터링 방법인 multiple kernels interval type-2 possibilistic fuzzy C-means(IT2PFCM-MK) 방법을 제안 하였다. 일반적으로 possibilistic fuzzy C-means(PFCM) 알고리즘은 fuzzy C-means(FCM) 알고리즘의 단점인 노이즈 민감성 및 특이점 문제와 알고리즘 초기 클러스터의 Prototype에 따라 위치가 겹치는 문제를 해결하기 위해 제안 되었다. 하지만 이 방법 역시 퍼지화 파라미터 값에 따라 위와 같은 문제를 여전히 가지고 있기 때문에 이와 같은 문제를 보완하기 위해 interval type-2 퍼지 접근 방법을 이용 하는 interval type-2 possibilistic fuzzy C-means(IT2PFCM) 알고리즘을 제안 하였다. 또한 multiple kernels 함수를 interval type-2 possibilistic fuzzy C-means(IT2PFCM) 알고리즘에 적용하여 분류하기 복잡한 형태의 데이터와 노이즈가 있는 데이터에 대하여 보다 정확하고, 향상된 클러스터링을 수행할 수 있다.

• 비파괴검사학회지
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• 제11권2호
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• pp.22-31
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• 1991

Various types of ultrasonic techniques have been used for the estimation of compressive strength of concrete structures. However, conventional ultrasonic velocity method using only longitudial wave cannot be determined the compressive strength of concrete structures with accuracy. In this paper, by using the introduction of multiple parameter, e. g. velocity of shear wave, velocity of longitudinal wave, attenuation coefficient of shear wave, attenuation coefficient of longitudinal wave, combination condition, age and preservation method, multiple regression analysis method was applied to the determination of compressive strength of concrete structures. The experimental results show that velocity of shear wave can be estimated compressive strength of concrete with more accuracy compared with the velocity of longitudinal wave, accuracy of estimated error range of compressive strength of concrete structures can be enhanced within the range of ${\pm}$10% approximately.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.946-949
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• 2006

This study is a trial to make a design chart of sound insulation for multiple panel. Dilatational frequency, ${\Large f}_d$ becomes a key factor for optimal design since it acts like a turning point in sound insulation performance of such panels. Hence, in tuning the ${\Large f}_d$ optimally, elastic modulus of core material and thickness of the skin panel is designated to parameters. Based on these parameter, a design chart of sound insulation for multiple panel is made. Its applicability is proved by the case study of High noise reduction panel.

• 한국공작기계학회논문집
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• 제11권4호
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• pp.89-96
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• 2002

This paper aims to develop an intelligent model for predicting top-bead width for the robotic GMA(Gas Metal Arc) welding process using BP(Back-propagation) neural network and multiple regression analysis. Firstly, based on experimental data, the basic factors affecting top-bead width are identified. Then BP neural network model and multiple regression models of top-bead width are established. The modeling methods and procedure are explained. The developed models are then verified by data obtained from the additional experiment and the predictive behaviors of the two kind of models are compared and analysed. Finally the modeling methods, predictive behaviors md the advantages of each models are discussed.

• 전력전자학회논문지
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• 제6권6호
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• pp.489-497
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• 2001

병렬로 구성되 UPS 사이에 파라미터 불일치에 따른 순환전류와 전압리플이 발생되어지는데, 이들은 전체 UPS 시스템의 고장 및 신뢰성 저하를 유발한다. 본 논문에서는 이러한 문제점들은 Double 위상동동기기와 능동 다중인 터페이스 리액터를 사용하여 해결하였다. 또한 ADSP21061을 사용하여 제어기를 디지털적으로 구현하였다.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.109-117
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• 2017

In this article, a control chart based on multiple dependent (or deferred) state sampling for the gamma distributed quality characteristic is proposed using the gamma to normal transformation. The proposed control chart has two pairs of control limits, which can be determined by considering the in-control average run length (ARL). The shift in the scale parameter of a gamma distribution is considered and the out-of-control ARL is evaluated. The performance of the proposed chart has been shown for different levels of the parameters of the proposed control chart. It is also shown that the proposed chart is better than the Shewhart chart in terms of ARLs. A case study with a real data has been included for the practical usage of the proposed scheme.

• ETRI Journal
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• 제28권6호
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• pp.761-769
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• 2006

In this paper, we address the problem of closely spaced source localization using sensor array processing. In particular, the performance efficiency (measured in terms of the root mean square error) of the unconditional maximum likelihood (UML) algorithm for estimating the direction of arrival (DOA) of near-field sources is evaluated. Four parameters are considered in this evaluation: angular separation among sources, signal-to-noise ratio (SNR), number of snapshots, and number of sources (multiple sources). Simulations are conducted to illustrate the UML performance to compute the DOA of sources in the near-field. Finally, results are also presented that compare the performance of the UML DOA estimator with the existing multiple signal classification approach. The results show the capability of the UML estimator for estimating the DOA when the angular separation is taken into account as a critical parameter. These results are consistent in both low SNR and multiple-source scenarios.

• 한국전기전자재료학회논문지
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• 제14권3호
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• pp.241-245
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• 2001

In this paper, we present parameter optimization technique for GaAs/AlGaAs multiple quantum well avalanche photodiodes used for image capture mechanism in high-definition system. Even under flawless environment in semiconductor manufacturing process, random variation in process parameters can bring the fluctuation to device performance. The precise modeling for this variation is thus required for accurate prediction of device performance. The precise modeling for this variation is thus required for accurate prediction of device performance. This paper will first use experimental design and neural networks to model the nonlinear relationship between device process parameters and device performance parameters. The derived model was then put into genetic algorithms to acquire optimized device process parameters. From the optimized technique, we can predict device performance before high-volume manufacturign, and also increase production efficiency.

• Communications for Statistical Applications and Methods
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• 제9권2호
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• pp.521-531
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• 2002

Various diagnostic techniques for identifying influential observations are mostly based on the deletion of a single observation. While such techniques can satisfactorily identify influential observations in many cases, they will not always be successful because of some mask effect. It is necessary, therefore, to develop techniques that examine the potentially influential effects of a subset of observations. The partial regression plots can be used to examine an influential observation for a single parameter in multiple linear regression. However, it is often desirable to detect influential observations for a subset of regression parameters when interest centers on a selected subset of independent variables. Thus, we propose a diagnostic measure which deals with detecting influential observations on a subset of regression parameters. In this paper, we propose a measure M, which can be effectively used for the detection of influential observations on a subset of regression parameters in multiple linear regression. An illustrated example is given to show how we can use the new measure M to identify influential observations on a subset of regression parameters.