• Proceedings of the KIEE Conference
• /
• 1991.11a
• /
• pp.349-352
• /
• 1991

Fuzzy modelling is a useful method when the variation of plant dynamics is large. In the fuzzy modelling by parameter identification, a new method is proposed in the part of premise parameters identification and in expanding MISO system into MIMO system. Using the proposed method, a fuzzy model of the drum boiler of the thermal power plant can be derived. In addition, feedwater control of the drum by fuzzy controller using the fuzzy model, is simulated.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1994.06a
• /
• pp.758-763
• /
• 1994

The work discusses the problems of modelling of the process of acoustic signal generation in machines. We have pointed out that in the task of minimizing of both moise and vibration, the key problem is identification of sources and paths of propagation, both in terms of their location and of definition of their characteristic features. Properly conducted identification makes possible the use of relatively simple mathematical models and this fact is particularly important for a broad application of the proposed methods in practice.

• Smart Structures and Systems
• /
• v.20 no.2
• /
• pp.127-137
• /
• 2017

Identification of damping characteristics is of significant importance for dynamic response analysis and condition assessment of structural systems. Damping is associated with the behavior of the energy dissipation mechanism. Identification of damping ratios based on the sensitivity of dynamic responses and the model updating technique is investigated with numerical and experimental investigations. The effectiveness and performance of using the sensitivity-based model updating method and vibration monitoring data for damping ratios identification are investigated. Numerical studies on a three-dimensional truss bridge model are conducted to verify the effectiveness of the proposed approach. Measurement noise effect and the initial finite element modelling errors are considered. The results demonstrate that the damping ratio identification with the proposed approach is not sensitive to the noise effect but could be affected significantly by the modelling errors. Experimental studies on a steel planar frame structure are conducted. The robustness and performance of the proposed damping identification approach are investigated with real measured vibration data. The results demonstrate that the proposed approach has a decent and reliable performance to identify the damping ratios.

• Proceedings of the KSR Conference
• /
• 2009.05a
• /
• pp.1069-1077
• /
• 2009

For external noise prediction of railway vehicles, sophisticated individual source modelling as well as appropriate noise propagation model from the sources is necessary to ensure the accuracy of the predicted results and contributions of each equipment to the overall noise levels. Accurate and reasonable identification procedures of sound sources of equipment including source strength, directivity and positions installed in the train play an important role in a prediction model, since it is not easy to establish a simple model for the sources with a single rule due to the complexity of source characteristics of equipment in size and directivity pattern. This paper guidelines practical considerations for identification of noise sources in railway vehicles including typical source characteristics of several sub-systems that emits noise to the environment, particularly for electric multiple unit(EMU), and verify effectiveness of assumptions used in the modelling of equipment by measurement with a simple part. The predicted external noise level of a complete train using Exnoise, which was developed by Hyundai-Rotem and has been verified in the a lot of field-tests, incorporating source modelling considered in this paper shows close correlation with the measured ones.

• Journal of Navigation and Port Research
• /
• v.28 no.5
• /
• pp.435-441
• /
• 2004

This paper deals with modelling and identification of a shipboard stabilized satellite antenna system using the optimal neural network structure. It is difficult for shipboard satellite antenna system to control and identification because of their approximating ability of nonlinear function So it is important to design the neural network with optimal structure for minimum error and fast response time. In this paper, a neural network structure using genetic algorithm is optimized And genetic algorithm is also used for identifying a shipboard satellite antenna system It is noticed that the optimal neural network structure actually describes the real movement of ship well. Through practical test, the optimal neural network structure is shown to be effective for modelling the shipboard satellite antenna system.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.83-86
• /
• 2005

This paper presents a method for point-based 3D building reconstruction using LiDAR data and digital map. The proposed method consists of three processes: extraction of building roof points, identification of roof types, and 3D building reconstruction. After extracting points inside the polygon of building, the ground surface, wall and tree points among the extracted points are removed through the filtering process. The filtered points are then fitted into the flat plane using ODR(Orthogonal Distance Regression). If the fitting error is within the predefined threshold, the surface is classified as a flat roof. Otherwise, the surface is fitted and classified into a gable or arch roof through RMSE analysis. Based on the roof types identified in automated fashion, the 3D building reconstruction is performed. Experimental results showed that the proposed method classified successfully three different types of roof and that the fusion of LiDAR data and digital map could be a feasible method of modelling 3D building reconstruction.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.3
• /
• pp.65-71
• /
• 1997

With the emergence of neural network, there is a revived interest in identification of nonlinear systems. So in this paper, to identify unknown nonlinear systems dynamically we propose DPNN(Dynamic Polynomial Neural Network) using GMDH (Group Method of Data Handling) algorithm. The dynamic system identification using GMDH consists of applying a set of inputloutput data to train the network by dynamically computing the necessary coeffici1:nt sets. Then, MRAC(Mode1 Reference Adaptive Control) is designed to control nonlinear systems using DPNN. In the result, we can see that the modelling and control using DPNN work well by computer simulation.

• Journal of Advanced Marine Engineering and Technology
• /
• v.40 no.10
• /
• pp.935-941
• /
• 2016

Predicting and evaluating ship manoeuvring characteristics are very important not only for the design stage, but also for the existing vessels. There are several ways to predict ship's manoeuvrability and most of them are highly connected with the estimation of hydrodynamic coefficients. This paper presents a new estimation method using the system identification with mathematical algorithms for estimating hydrodynamic coefficient in the ship's mathematical model. Specifically a double ended ferry which equips four azimuth propulsion systems were chosen as benchmark ship and a set of benchmark data which is generated in the fast time simulation software was provided to conduct mathematical optimization process. Also the initial values for the optimization were borrowed from the empirical regression formulas of the simulation software of Rheinmetall Defence ship simulator. Therefore the newly suggested mathematical optimization algorithm gave a successful result for estimation hydrodynamic coefficients. Proper optimization conditions of the objective function and constraints were also verified during the study.

• International journal of advanced smart convergence
• /
• v.4 no.1
• /
• pp.45-53
• /
• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.3112-3112
• /
• 2001

Near-Infrared Spectroscopy (NIRS) was investigated aiming at the identification of falsified drugs. The identification is based on comparison of the NIR spectrum of a sample with a typical spectra of an authentic drug using multivariate modelling and classification algorithms (PCA/SIMCA). Two spectrophotometers (Brimrose - Luminar 2000 and 2030), based on acoustic-optical filter (AOTF) technology, sharing the same controlling computer, software (Brimrose - Snap 2.03) and the data acquisition electronics, were employed. The Luminar 2000 scans the range 850 1800 nm and was employed for transmitance/absorbance measurements of liquids with a transflectance optical bundle probe with total optical path of 5 mm and a circular area of 0.5 $\textrm{cm}^2$. Model 2030 scans the rage 1100 2400 nm and was employed for reflectance measurement of solids drugs. 300 spectra, acquired in about 20 s, were averaged for each sample. Chemometric treatment of the spectral data, modelling and classification were performed by using the Unscrambler 7.5 software (CAMO Norway). This package provides the Principal Component Analysis (PCA) and SIMCA algorithms, used for modelling and classification, respectively. Initially, NIRS was evaluated for spectrum acquisition of various drugs, selected in order to accomplish the diversity of physico-chemical characteristics found among commercial products. Parameters which could affect the spectra of a given drug (especially if presented as solid tablets) were investigated and the results showed that the first derivative can minimize spectral changes associated with tablet geometry, physical differences in their faces and position in relation to the probe beam. The effect of ambient humidity and temperature were also investigated. The first factor needs to be controlled for model construction because the ambient humidity can cause spectral alterations that should cause the wrong classification of a real drug if the factor is not considered by the model.