• 한국공간구조학회논문집
• /
• 제4권2호
• /
• pp.73-80
• /
• 2004

본 논문에서는 비선형 파라메트릭 사영필터를 이용한 2차원 트러스 구조물의 손상 검출에 대한 연구를 제시한다. 역문제의 해석은 최근 많은 관심을 끌고 있으며, 역문제 해석법으로서 필터이론을 사용한 접근법이 많은 관심을 받고 있다. 특히 칼만 필터는 신호 통신, 시스템 제어 등의 많은 분야에서 적용되어 왔으며 그 유효성이 입증되었다. 본 논문에서는 비선형 파라메트릭 사영필터를 2차원 트러스 구조물의 손상추정에 적용하고 손상된 구조물의 고유 진동수과 고유 모드를 관측 데이터로 채택하여 손상부재의 위치와 손상정도를 추정한다. 마지막으로 수치해석 예를 통하여 제안된 해석법의 유효성을 밝힌다.

• Asian Pacific Journal of Cancer Prevention
• /
• 제13권4호
• /
• pp.1533-1537
• /
• 2012

In general, breast cancer is the most common malignancy among women in developed as well as some developing countries, often being the second leading cause of cancer mortality after lung cancer. Using a parametric log-logistic model to consider the effects of prognostic factors, the present study focused on the 5-year survival of women with the diagnosis of breast cancer in Southern Iran. A total of 1,148 women who were diagnosed with primary invasive breast cancer from January 2001 to January 2005 were included and divided into three prognosis groups: poor, medium, and good. The survival times as well as the hazard rates of the three different groups were compared. The log-logistic model was employed as the best parametric model which could explain survival times. The hazard rates of the poor and the medium prognosis groups were respectively 13 and 3 times greater than in the good prognosis group. Also, the difference between the overall survival rates of the poor and the medium prognosis groups was highly significant in comparison to the good prognosis group. Use of the parametric log-logistic model - also a proportional odds model - allowed assessment of the natural process of the disease based on hazard and identification of trends.

• Journal of Electrical Engineering and Technology
• /
• 제8권5호
• /
• pp.975-983
• /
• 2013

This study proposes a low order dynamic model of a building system in order to predict thermal behavior within a building and its energy consumption. The building system includes a thermally well-insulated room and an electric heater. It is modeled by a second order lumped RC thermal network based on the thermal-electrical analogy. In order to identify unknown parameters of the model, an experimental procedure is firstly detailed. Then, the different linear parametric models (ARMA, ARX, ARMAX, BJ, and OE models) are recalled. The parameters of the parametric models are obtained by the least square approach. The obtained parameters are interpreted to the parameters of the physically based model in accordance with their relationship. Afterwards, the obtained models are implemented in Matlab/Simulink(R) and are evaluated by the mean of the sum of absolute error (MAE) and the mean of the sum of square error (MSE) with the variable of indoor temperature of the room. Quantities of electrical energy and converted thermal energy are also compared. This study will permit a further study on Model Predictive Control adapting to the proposed model in order to reduce energy consumption of the building.

• Smart Structures and Systems
• /
• 제4권1호
• /
• pp.47-66
• /
• 2008

The present work utilizes system identification technique for health monitoring of shear building, wherein Parametric State Space modeling has been adopted. The method requires input excitation to the structure and also output acceleration responses of both undamaged and damaged structure obtained from numerically simulated model. Modal parameters like eigen frequencies and eigen vectors have been extracted from the State Space model after introducing appropriate transformation. Least square technique has been utilized for the evaluation of the stiffness matrix after having obtained the modal matrix for the entire structure. Highly accurate values of stiffness of the structure could be evaluated corresponding to both the undamaged as well as damaged state of a structure, while considering noise in the simulated output response analogous to real time scenario. The damaged floor could also be located very conveniently and accurately by this adopted strategy. This method of damage detection can be applied in case of output acceleration responses recorded by sensors from the actual structure. Further, in case of even limited availability of sensors along the height of a multi-storeyed building, the methodology could yield very accurate information related to structural stiffness.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
• /
• pp.678-680
• /
• 2004

In many cases the systems are so complex that it is not possible to obtain reasonable models using physical laws. Also a model based on physical laws contains a number of unknown parameters even if the structure is derived from physical laws. These problems can be solved by system identification. In this paper, plate activity vibration is selected as an example for system identification. The transfer functions of this system is derived by using ARMAX based on input/output data through experiment.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
• /
• pp.372-377
• /
• 1993

In this paper, we investigate a set-membership identification approach to the quantification of an upper bound of model uncertainty in frequency domain, which is required in the H$_{\infty}$ robust control system design. First we formulate this problem as a set-membership identification of a nominal model error in the presence f unknown noise input with unknown bound, while the ordinary set-membership approaches assume that an upper bound of the uncertain input is known. For this purpose, the proposed algorithm includes the estimation of the bound of the uncertain input. thus the proposed method can obtain the hard bound of the model error in frequency domain as well as a parametric lower-order nominal model. Finally numerical simulation results are shown to confirm the validity of the presented algorithm..

• 동력기계공학회지
• /
• 제2권2호
• /
• pp.73-80
• /
• 1998

The identification of drilling joint dynamics which consists of drilling and structural dynamics and the on-line time series detection of malfunction process is substantial not only for the investigation of the static and dynamic characteristics but also for the analytic realization of diagnostic and control systems in drilling. Therefore, We have discussed on the comparative assessment of two recursive time series modeling algorithms that can represent the drilling operation and detect the abnormal geometric behaviors in precision roundshape machining such as turning, drilling and boring in precision diemaking. For this purpose, simulation and experimental work were performed to show the malfunctional behaviors for drilling operation. For this purpose, a new two recursive approach (Recursive Extended Instrument Variable Method : REIVM, Recursive Least Square Method : RLSM) may be adopted for the on-line system identification and monitoring of a malfunction behavior of drilling process, such as chipping, wear, chatter and hole lobe waviness.

• Smart Structures and Systems
• /
• 제17권3호
• /
• pp.431-444
• /
• 2016

This paper was written in the context of a benchmark study promoted by The Hong Kong Polytechnic University using data samples collected in an instrumented cable-stayed bridge. The main goal of the benchmark test was to study the identification of the bridge modes of vibration under different wind conditions. In this contribution, the tools developed at ViBest/FEUP for automated data processing of setups collected by dynamic monitoring systems are presented and applied to the data made available in the context of the benchmark study. The applied tools are based on parametric output only modal identification methods combined with clustering algorithms. The obtained results demonstrate that the proposed algorithms succeeded to automatically identify the modes with relevant contribution for the bridge response under different wind conditions.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2001년도 하계학술대회 논문집 D
• /
• pp.2305-2307
• /
• 2001

Generally, The modeling method for the mathematical model is mdeled by using the physical laws and the system identification. In this paper, The arago's disk system of the operating principle of induction motors is selected as an example for identification. The system transfer function is derived from input/output data through experiment. Model is estimated by using ARX, ARMAX, BJ, OE model structure and compared each other.

• 품질경영학회지
• /
• 제39권2호
• /
• pp.234-243
• /
• 2011

Multivariate control charts are widely used to monitor the performance of a multivariate process over time to maintain control of the process. Although existing multivariate control charts provide control limits to monitor the process and detect any extraordinary events, it is a challenge to identify the causes of an out-of-control alarm when the number of process variables is large. Several fault identification methods have been developed to address this issue. However, these methods require a normality assumption of the process data. In the present study, we propose a bootstrapped-based $T^2$ decomposition technique that does not require any distributional assumption. A simulation study was conducted to examine the properties of the proposed fault identification method under various scenarios and compare it with the existing parametric $T^2$ decomposition method. The simulation results showed that the proposed method produced better results than the existing one, especially in nonnormal situations.