• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.840-840
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• 2003

Operational Modal Analysis (OMA) is a technique for identification of modal parameters by measurement of only the system's response. On many lightweight structures, such as load-speaker cones and disk drive read/write heads, is impossible or impractical to measure the input forces. Another characteristic of lightweight structure is their sensitivity to mass loading from sensors. The Scanning Laser Doppler Vibrometry(SLDV) allows response measurements to be taken without mass loading. One disadvantage of OMA testing compared to tradition input output modal testing is the OMA mode shapes are un-scaled. This means that the mode shape obtained from an OMA test can not used for analytical structural modification studies. However, the un-scaled mode shapes from an OMA test can be used to update a Finite Element Model (FEM). The updated FEM can then be used to analytically predict the effect of structural modifications. This paper will present the results of an OMA test performed on a simple plate and motor in operating conditions. The un-scaled mode shapes from this test will be used to update a FEM model of the system. The updated FEM model will be then be used to predict the effect of attaching a mass to the plate. The shapes predicted by the FEM for the modified system will be compared to a second OMA test on the modified system

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1108-1110
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• 2007

The resonant converters cause the high voltage stress according to the input voltage, which increases the conduction loss in converter power switches. The topology of LLC half bridge resonant converter provides ZVS characteristic and also the stress of voltage and current is smaller than that of the general resonant converters. So we can expect the higher efficiency. In this paper, the LLC resonant converter is designed for slim adapter. In the adapter design, we should consider the weight, the size and overheat of the adapter. Thus the optimal design of transformer is the most important facts. Some parameters should be considered in order to get the highest efficiency. The LLC resonant converter input is 390VDC Link voltage of PFC and the output has 16VDC/90W ratings. The efficiency measured is about up to 93%.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.204-210
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• 2005

This paper presents online identification algorithm for TSK fuzzy model. The proposed algorithm identify structure of premise part by using distance, and obtain the parameters of the piecewise linear function consisting consequent part by using recursive least square. Only input space was considered in Most researches on structure identification, but input and output space is considered in the proposed algorithm. By doing so, outliers are excluded in clustering effectively. The existing other algorithm has disadvantage that it is sensitive to noise by using data itself as cluster centers. The proposed algorithm is non-sensitive to noise not by using data itself as cluster centers. Model can be obtained through one pass and it is not needed to memorize many data in the proposed algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.43-48
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• 2002

This paper discusses the application of fuzzy-ARTMAP neural network to compensate the nonlinearity of satellite communication channel. The fuzzy-ARTMAP is the class of ART(adaptive resonance theory) architectures designed fur supervised loaming. It has capabilities not fecund in other neural network approaches, that includes a small number of parameters, no requirements fur the choice of initial weights, automatic increase of hidden units, and capability of adding new data without retraining previously trained data. By a match tracking process with vigilance parameter, fuzzy-ARTMAP neural network achieves a minimax teaming rule that minimizes predictive error and maximizes generalization. Thus, the system automatically leans a minimal number of recognition categories, or hidden units, to meet accuracy criteria. As a input-converting process for implementing fuzzy-ARTMAP equalizer, the sigmoid function is chosen to convert actual channel output to the proper input values of fuzzy-ARTMAP. Simulation studies are performed over satellite nonlinear channels. QPSK signals with Gaussian noise are generated at random from Volterra model. The performance of proposed fuzzy-ARTMAP equalizer is compared with MLP equalizer.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.3
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• pp.10-17
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• 2016

This paper presents a laboratory validation of a new approach for Finite Element Model Updating(FEMU) on short-span bridges by combining ambient vibration measurements with static load input-deflection output measurements. The conventional FEMU approach based on modal parameters requires the assumption on the system mass matrix for the eigen-value analysis. The proposed approach doesn't require the assumption and even provides a way to update the mass matrix. The proposed approach consists of two steps: 1) updating the stiffness matrix using the static input-deflection output measurements, and 2) updating the mass matrix using a few lower natural frequencies. For a validation of the proposed approach, Young's modulus of the laboratory model was updated by the proposed approach and compared with the value obtained from strain-stress tests in a Universal Testing Machine. Result of the conventional FEMU was also compared with the result of the proposed approach. It was found that proposed approach successfully estimated the Young's modulus and the mass density reasonably while the conventional FEMU showed a large error when used with higher-modes. In addition, the FE modeling error was discussed.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.1
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• pp.47-52
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• 2004

This paper presents a CMOS optical receiver design featuring wide input dynamic range and low bit error rate suitable for FTTH application. We achieved 60dB input dynamic range for up to 100Mbps by controlling the PMOS feedback resistance of transimpedance preamplifier according to its output signal level. Auto-bias circuit is designed in current mirror configuration to minimize duty error. Circuit simulation has been performed using 2-poly, 3-metal, 0.6um CMOS process parameters. The designed receiver consumes less than 130mW at 100Mbps with 5V power supply.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.372-372
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• 2000

In this paper, we propose Neuro Fuzzy Polynomial Networks(NFPN) based on Polynomial Neural Network(PNN) and Neuro-Fuzzy(NF) for model identification of complex and nonlinear systems. The proposed NFPN is generated from the mutually combined structure of both NF and PNN. The one and the other are considered as the premise part and consequence part of NFPN structure respectively. As the premise part of NFPN, NF uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. As the consequence part of NFPN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. NFPN is available effectively for multi-input variables and high-order polynomial according to the combination of NF with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. In order to evaluate the performance of proposed models, we use the nonlinear function. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1729-1738
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• 2014

The offline verification of state of charge estimation, power estimation, fault diagnosis and emergency control of battery management system (BMS) is one of the key technologies in the field of electric vehicle battery system. It is difficult to test and verify the battery management system software in the early stage, especially for algorithms such as system state estimation, emergency control and so on. This article carried out the virtual environment modeling for verification of battery management system. According to the input/output parameters of battery management system, virtual environment is determined to run the battery management system. With the integration of the developed BMS model and the external model, the virtual environment model has been established for battery management system in the vehicle's working environment. Through the virtual environment model, the effectiveness of software algorithm of BMS was verified, such as battery state parameters estimation, power estimation, fault diagnosis, charge and discharge management, etc.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.8
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• pp.40-51
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• 1996

This paper describes some research works of computer-aided design of blanking and piercing progressive die for stator and rotor parts. An approach to the system is based on knowledge based rules. The deveolped system is composed of six modules such as main program, input and shape treatment, production feasibility check, strip layout, die layout and drawing edit module. Using this system, design parameters ( geometric shapes, die and punch dimensions and dimensions of tool elements) are determined and output is gen- erated in graphic from. Knowledges for tool design are extracted from the plasticity theories, handbooks, relevant references and empirical know-hows of experts in blkanking companies. The developed system provides powerful capabilities for process planning and die design of stator and rotor parts.