• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 춘계학술대회논문집
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• pp.501-509
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• 2003

A method of identifying structural parameters such as stiffness and damping coefficients at interfacial points of vibro-acoustic systems is suggested using an optimization technique. To identify the parameters using a numerical optimization algorithm, cost functions are defined. The cost function should be zero at the correct parameter values. To minimize the cost functions using an optimization technique, a design sensitivity analysis procedure is developed in the framework of the multi-domain FRF-based substructuring method. As a numerical example, a ladder-like structure problem is introduced. With known parameter values and different initial guesses of the parameters, convergence characteristics to the exact value are compared for the three cost functions. Investigating the contours of the cost functions, we find the first cost function has the largest convergent region to the correct value. As another practical problem, stiffnesses of engine mounts and bushings in a passenger car are identified. The numerical examples show that the proposed method is efficient and accurate even when applied to realistic problems.

• Journal of Advanced Marine Engineering and Technology
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• 제22권5호
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• pp.652-659
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• 1998

The present study is aimed to develop a new PIV operating software through optimization of vector tracking identification including versatile pre-processings and post-processing techniques. And the result exhibits an improved version corresponding various input and output multimedia compared to previous commercial software developed by other makers. An upgraded identification method called grey-level cross correlation coefficient method by direct calculation is suggested and related user-friendly pop-up menu are also represented. Post-processings comprising turbulence statistics are also introduced with graphic output functions.

• 대한기계학회논문집A
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• 제31권11호
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• pp.1100-1107
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• 2007

In this study, a method to characterize material properties of adhesive that is used in a layered plates bonding process is developed by combined evaluation of experiment, simulation and optimization technique. A small bonded specimens of rectangular plate are prepared to this end, and put into a thermal loading conditions. $Moir{\acute{e}}$ interferomety is used to measure submicron displacements occurred during the process. The elevated temperature is chosen as control factors. FE analysis with constant values for the adhesive materials is also carried out to simulate the experiment. Significant differences are observed from the two results, in which the simulation predicts the monotonic increase of the bending displacement whereas the measurement shows decrease of the displacement at above $75^{\circ}C$. In order to minimize the difference of the two, material parameters of the adhesive at a number of different temperatures are posed as unknowns to be determined, and optimization is conducted. As a result, optimum material parameters are found that excellently matches the simulation and experiment, which are decreased with respect to the temperature.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 추계학술대회 논문집
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• pp.565-566
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• 2007

• 한국식품영양학회지
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• 제1권2호
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• pp.31-36
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• 1988

An adaptive on-line optimization method has been applied to test the ability to maximize the cellular productivity of a continuous methylotroph culture system which was simulated by a variable yield Monod-type model. Optimum dilution rate and productivity were successively obtained and maintained at all times by the algorithm that utilizes steepest descent technique as optimization method and recursive least-square method with forgetting factor as dynamic model identification.

• International Journal of Control, Automation, and Systems
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• 제1권2호
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• 해양환경안전학회지
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• 제23권7호
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• pp.933-940
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• 2017

선박의 다양한 흘수 및 트림 조건은 조종성능 추정을 위한 중요한 요소 중 하나이다. 본 논문에서는 세 종류의 흘수 및 트림 조건에서의 해상 시운전 자료를 바탕으로 하여 선체 유체력 미계수를 추정하였다. 시스템 식별법(system identification)의 하나인 수학적 최적화(mathematical optimization method) 및 Rheinmetall Defense사의 선박 운동 모델을 적용한 fast time 시뮬레이션 소프트웨어를 이용하여 시운전 항적데이터 및 관련 시뮬레이션 자료를 이용하여 선체 유체력 미계수를 추정하였다. 최적화 된 계수를 적용한 시뮬레이션 결과는 기존 계수 추정식을 사용한 시뮬레이션 결과와 대비하여 해상 시운전 계측 결과와 유사함을 보여주었으며 추가로 진행된 2차 검증 결과에서도 상대적으로 높은 유사함을 확인하였다.

• 대한임베디드공학회논문지
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• 제19권1호
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• pp.19-25
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• 2024

This paper proposes an artificial intelligence-based personal information area object detection optimization method in an embedded system to de-identify personal information in video data. As an object detection optimization method, first, in order to increase the detection rate for personal information areas when detecting objects, a gyro sensor is used to collect the shooting angle of the image data when acquiring the image, and the image data is converted into a horizontal image through the collected shooting angle. Based on this, each learning model was created according to changes in the size of the image resolution of the learning data and changes in the learning method of the learning engine, and the effectiveness of the optimal learning model was selected and evaluated through an experimental method. As a de-identification method, a shuffling-based masking method was used, and double-key-based encryption of the masking information was used to prevent restoration by others. In order to reuse the original image, the original image could be restored through a security key. Through this, we were able to secure security for high personal information areas and improve usability through original image restoration. The research results of this paper are expected to contribute to industrial use of data without personal information leakage and to reducing the cost of personal information protection in industrial fields using video through de-identification of personal information areas included in video data.

• 대한음성학회지:말소리
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• 제62호
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• pp.149-161
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• 2007

In this paper we proposed a modified reliability function for improving bimodal speaker identification(BSI) performance. The convectional reliability function, used by N. Fox[1], is extended by introducing an optimization factor. We evaluated the proposed method in BSI domain. A BSI system was implemented based on GMM and it was tested using VidTIMIT database. Through speaker identification experiments we verified the usefulness of our proposed method. The experiments showed the improved performance, i.e., the reduction of error rate by 39%.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.45.6-45
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• 2002

In this paper, a novel optimization algorithm which searches for the local minima of a given cost function is proposed using the familiar property of a binary string, and is applied to the parametric identification of a continuous-time state equation by the estimation of system parameters as well as initial state values. A simple electrical circuit severs as an example, whose precise identification results show the superiority of the proposed algorithm.