• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.33 no.5
• /
• pp.508-513
• /
• 2009

Friction is an important factor for precise position tracking control of servo systems. Servo systems with highly nonlinear friction are sensitive to the variation of operating condition. To overcome this problem, we use the LuGre friction model which can consider dynamic characteristics of friction. The LuGre friction model is used as a feed-forward compensator to improve tracking performance of servo systems. The parameters of the LuGre friction model are identified through experiments. The experimental result shows that the tracking performance of servo systems with higherly nonlinear friction can be improved by using feed-forward friction compensation.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2001.11b
• /
• pp.46-51
• /
• 2001

For Fast Real-time Recognition of Nonlinear Error Data, a new Neural Network algorithm which recognized the map in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion, In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of map, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear map information is processed,

• Environmental Engineering Research
• /
• v.14 no.2
• /
• pp.102-110
• /
• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.26 no.1
• /
• pp.141-149
• /
• 2002

A numerical simulation is performed for the cooling heat transfer of a heated cylindrical pedestal by an axisymmetric jet impingement. Based on the k- $\varepsilon$- f$\sub$${\mu}$/ model of Park et at., the linear and nonlinear stress-strain relations are extended. The Reynolds number based on the jet diameter(D) is fixed at Re$\sub$D/ = 23000. The local heat transfer coefficients are compared with available experimental data. The predictions by k- $\varepsilon$-f$\sub$${\mu}$/ model are in good agreement with the experiments, whereas the standard 7- f model does not properly resolve the flow structures.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.7 no.5
• /
• pp.106-112
• /
• 1998

Experience and experiments show that in many cases the buckling limit is reached at a much smaller load level than is predicted by linear buckling analysis. In this paper, it is considered linear and nonlinear of plane vehicle structure and estimates design sensitivity of the cross sectional area that is composed plane vehicle structure and performs optimal design. It compares linear vehicle structure with nonlinear vehicle structure for optima design result that is selected constraint condition of buckling load.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.9 no.6
• /
• pp.57-64
• /
• 2001

This study presents an self tuning regulator(STR) to improve the air-fuel ratio control of performance of gasoline engine. The STR is designed based on the nonlinear dynamic engine model, and the performance of the STR is evaluated through the simulation and experiments. The STR shows better performance than a conventional PI controller in terms of the response time and disturbance rejection. Since the STR has less calculation load than the complex nonlinear controller, this algorithm can be easily applied to on-board engine controller.

• Journal of Korean Society for Quality Management
• /
• v.28 no.2
• /
• pp.135-146
• /
• 2000

This study has adapted a genetic algorithm for an optimal design for the first time. The models using a simulation are the nonlinear models. Using an genetic algorithm in D-optimal, it is more efficient than previous algorithms to get an object function. Not like other algorithms, without any troublesome restrictions about the initial solution, not falling into a local optimal solution, it's the most suitable algorithm. Also if we use it without any adding experiments, we can use it to find optimal design of experimental condition efficiently.

• Proceedings of the IEEK Conference
• /
• 2002.06d
• /
• pp.255-258
• /
• 2002

Recently, Digital TV viewer have been replacing cathode ray tubes (CRT) with Plasma display panel(PDP). But the chromaticity of the primaries are dependent on RGB input signals. And the colorimetry of PDP changes with gray scale and has a poor performance in color reproduction. In this paper we propose the enhanced algorithm of color reproduction considering nonlinear gamut mapping algorithm. In order to test performance of this algorithm we use the sample colors. As a result of experiments, it was confirmed that the color difference of the digital PDP using the proposed algorithm was considerably reduced.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.45 no.1
• /
• pp.20-27
• /
• 2008

In this paper, a nonlinear classifier of a feed-forward neural network is implemented on an FPGA chip. The feedforward neural network is implemented in hardware for fast parallel processing. After off line training of neural network, weight values are saved and used to perform forward propagation of neural processing. As an example, AND and XOR digital logic classification is conducted in off line, and then weight values are used in neural network. Experiments are conducted successfully and confirmed that the FPGA neural network hardware works well.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.57 no.2
• /
• pp.96-101
• /
• 2008

The field of load modeling has attracted the attention since it plays an important role for improving the accuracy of stability analysis and power flow estimation. Also, load modeling is an essential factor in the simulation and evaluation of power system performance. However, conventional load modeling techniques have some limitations with respect to accuracy for nonlinear and composite loads. Thus, precision load modeling technique and reasonable application method is needed for more accurate power system analysis. In this paper, we develop an intelligent load modeling method based. on neural network and application techniques for power system. The proposed method makes it possible to effectively estimate the load model for nonlinear models as well as linear models. Reasonable application method is also proposed for stability analysis. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented.