• 제어로봇시스템학회논문지
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• 제11권4호
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• pp.304-313
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• 2005

The robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, robot need to recognize his position and direction for intelligent performance in an unknown environment. And the mobile robots may navigate by means of a number of monitoring systems such as the sonar-sensing system or the visual-sensing system. Notice that in the conventional fusion schemes, the measurement is dependent on the current data sets only. Therefore, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this research, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the accurate measurement. As a general approach of sensor fusion, a UT -Based Sensor Fusion(UTSF) scheme using Unscented Transformation(UT) is proposed for either joint or disjoint data structure and applied to the landmark identification for mobile robot navigation. Theoretical basis is illustrated by examples and the effectiveness is proved through the simulations and experiments. The newly proposed, UT-Based UTSF scheme is applied to the navigation of a mobile robot in an unstructured environment as well as structured environment, and its performance is verified by the computer simulation and the experiment.

• Transactions on Control, Automation and Systems Engineering
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• 제3권1호
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• pp.43-50
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• 2001

In this paper, we propose the design procedure of advance Polynomial Neural Networks(PNN) architecture for optimal model identification of complex and nonlinear system. The proposed PNN architecture is presented as the generic and advanced type. The essence of the design procedure dwells on the Group Method of Data Handling(GMDH). PNN is a flexible neural architecture whose structure is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. In this sense, PNN is a self-organizing network. With the aid of three representative numerical examples, compari-sons show that the proposed advanced PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and generalization capabilities of model is evaluated and also discussed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2856-2858
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• 1999

In this paper, the FPNN(Fuzzy Polynomial Neural Networks) algorithm with multi-layer fuzzy inference structure is proposed for the model identification of a complex nonlinear system. The FPNN structure is generated from the mutual combination of PNN (Polynomial Neural Network) structure and fuzzy inference method. The PNN extended from the GMDH(Group Method of Data Handling) uses several types of polynomials such as linear, quadratic and modifled quadratic besides the biquadratic polynomial used in the GMDH. In the fuzzy inference method, simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used Each node of the FPNN is defined as a fuzzy rule and its structure is a kind of fuzzy-neural networks. Gas furnace data used to evaluate the performance of our proposed model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2519-2521
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• 2003

In this paper, we propose Self-Organizing Fuzzy Polynomial Neural Networks(SOFPNN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. It is shown that this network exhibits a dynamic structure as the number of its layers as well as the number of nodes in each layer of the SOFPNN are not predetermined (as this is the case in a popular topology of a multilayer perceptron). As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership function are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SOFPNN architectures, that is, the basic and modified one with both the generic and the advanced type. The superiority and effectiveness of the proposed SOFPNN architecture is demonstrated through nonlinear function numerical example.

• Geomechanics and Engineering
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• 제13권6호
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• pp.997-1010
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• 2017

The black box model is a relatively new option for nonlinear dynamic system identification. It can be used for prediction problems just based on analyzing the input and output data without considering the changes of the internal structure. In this paper, a black box model was presented to solve unconstrained overlying strata movement problems in coal mine production. Based on the black box theory, the overlying strata regional system was viewed as a "black box", and the black box model on overburden strata movement was established. Then, the rock mechanical properties and the mining thickness and mined-out section area were selected as the subject and object respectively, and the influences of coal mining on the overburden regional system were discussed. Finally, a corrected method for height prediction of the fractured zone was obtained. According to actual mine geological conditions, the measured geological data were introduced into the black box model of overlying strata movement for height calculation, and the fractured zone height was determined as 40.36 m, which was comparable to the actual height value (43.91 m) of the fractured zone detected by Double-block Leak Hunting in Drill. By comparing the calculation result and actual surface subsidence value, it can be concluded that the proposed model is adaptable for height prediction of the fractured zone.

• Wind and Structures
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• 제19권4호
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• pp.353-370
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• 2014

A new method is proposed in this study for estimating the damping ratio of a super tall building under strong wind loads with short-time measured acceleration signals. This method incorporates two main steps. Firstly, the power spectral density of wind-induced acceleration response is obtained by the wavelet transform, then the dynamic characteristics including the natural frequency and damping ratio for the first vibration mode are estimated by a nonlinear regression analysis on the power spectral density. A numerical simulation illustrated that the damping ratios identified by the wavelet spectrum are superior in precision and stability to those values obtained from Welch's periodogram spectrum. To verify the efficiency of the proposed method, wind-induced acceleration responses of the Guangzhou West Tower (GZWT) measured in the field during Typhoon Usagi, which affected this building on September 22, 2013, were used. The damping ratios identified varied from 0.38% to 0.61% in direction 1 and from 0.22% to 0.59% in direction 2. This information is expected to be of considerable interest and practical use for engineers and researchers involved in the wind-resistant design of super-tall buildings.

• Current Optics and Photonics
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• 제5권5호
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• pp.524-531
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• 2021

In an integral-imaging microscopy (IIM) system, a microlens array (MLA) is the primary optical element; however, surface errors impede the resolution of a raw image's details. Calibration is a major concern with regard to incorrect projection of the light rays. A ray-tracing-based calibration method for an IIM camera is proposed, to address four errors: MLA decentering, rotational, translational, and subimage-scaling errors. All of these parameters are evaluated using the reference image obtained from the ray-traced white image. The areas and center points of the microlens are estimated using an "8-connected" and a "center-of-gravity" method respectively. The proposed approach significantly improves the rectified-image quality and nonlinear image brightness for an IIM system. Numerical and optical experiments on multiple real objects demonstrate the robustness and effectiveness of our proposed method, which achieves on average a 35% improvement in brightness for an IIM raw image.

• 한국지진공학회논문집
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• 제25권3호
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• pp.93-102
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• 2021

The dynamic characterization of a three-story auxiliary building in a nuclear power plant (NPP) constructed with a monolithic reinforced concrete shear wall is investigated in this study. The shear wall is subjected to a joint-research, round-robin analysis organized by the Korea Atomic Energy Research Institute, South Korea, to predict seismic responses of that auxiliary building in NPP through a shake table test. Five different intensity measures of the base excitation are applied to the shaking table test to get the acceleration responses from the different building locations for one horizontal direction (front-back). Simultaneously to understand the global damage scenario of the structure, a frequency search test is conducted after each excitation. The primary motivation of this study is to develop a nonlinear numerical model considering the multi-layered shell element and compare it with the test result to validate through the modal parameter identification and floor responses. In addition, the acceleration amplification factor is evaluated to judge the dynamic behavior of the shear wall with the existing standard, thus providing theoretical support for engineering practice.

• Structural Engineering and Mechanics
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• 제71권6호
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• pp.683-697
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• 2019

The reinforced concrete (RC) structures usually suffer large residual displacements under strong motions. The large residual displacements may substantially reduce the anti-seismic capacity of structures during the aftershock and increase the difficulty and cost of structural repair after an earthquake. To reduce the adverse residual displacement, several self-centering energy dissipation braces (SCEBs) have been proposed to be installed to the RC structures. To investigate the seismic responses of the RC structures with SCEBs under the earthquake excitation, an extended Bouc-Wen model with degradation and self-centering effects is developed in this study. The extended model realized by MATLAB/Simulink program is able to capture the hysteretic characteristics of the RC structures with SCEBs, such as the energy dissipation and the degradation, especially the self-centering effect. The predicted hysteretic behavior of the RC structures with SCEBs based on the extended model, which used the unscented Kalman filter (UKF) for parameter identification, is compared with the experimental results. Comparison results show that the predicted hysteretic curves can be in good agreement with the experimental results. The nonlinear dynamic analyses using the extended model are then carried out to explore the seismic performance of the RC structures with SCEBs. The analysis results demonstrate that the SCEB can effectively reduce the residual displacements of the RC structures, but slightly increase the acceleration.

• 전력전자학회논문지
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• 제26권5호
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• pp.364-371
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• 2021

A magnetic flux saturation model of Synchronous Reluctance Motors (SynRMs) and a parameter estimation method are proposed at standstill. The proposed magnetic flux model includes the nonlinear relationship between the current and the magnetic flux for self-saturation and cross-saturation. Voltage is injected at standstill to estimate the magnetic flux saturation model. Voltages are injected into the d-axis and q-axis to obtain data on self-saturation. Subsequently, voltages are simultaneously injected into the d-q axis to obtain data on cross-saturation. On the basis of the measured current and the calculated magnetic flux, the parameters of the proposed model are estimated using the least square method (LSM). Simulation and experiment were performed on a 1.5-kW SynRM to verify the proposed method. The proposed model can be used to create a high-efficiency operation table, a sensorless algorithm, and a current controller to improve the control performance of a motor.