• Transactions of the Korean Society of Automotive Engineers
• /
• v.5 no.4
• /
• pp.121-129
• /
• 1997

Gear train system test rig of power circulating type was fabricated, and systematic experiment for measuring dynamic characteristics of the planetary gear trains in the low speed region has been carried out using the test rig. The measured parameters are fillet strains of the sun gear and ring gear, carrier displacements, torques of the input and output shafts. The results are as follows : i) Even though the loading torque is constant, torque variation has been observed on the input and output hafts, ii) The variation of the torque has two frequency components, i.e. lower one of the input shaft rotation and higher one of the two teeth meshing, iii) The variation of the fillet strains shows the same tendency as that of the torque, iv) The loci of the carrier depend on the torque and rotational speed.

• The Korean Journal of Applied Statistics
• /
• v.25 no.4
• /
• pp.589-603
• /
• 2012

Mahalanobis-Taguchi system(MTS) is a statistical tool for classifying the normal group and abnormal group in multivariate data structures. In addition to the classification itself, the MTS uses a method for selecting variables useful for the classification. This method can be used efficiently especially when the abnormal group data are scattered without a specific directionality. When the feedback adjustment procedure through the measurements of the process output for controlling process input variables is not practically possible, the reset procedure can be an alternative one. This article proposes a reset procedure using the MTS. Moreover, a method for identifying input variables to reset is also proposed by the use of the contribution. The identification of the root-cause parameters using the existing dimension-reduced contribution tends to be difficult due to the variety of correlation relationships of multivariate data structures. However, it became possible to provide an improved decision when used together with the location-centered contribution and the individual-parameter contribution.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.10 no.6
• /
• pp.592-597
• /
• 2005

The push pull forward converter is suitable in a low output voltage, a high output current applications with wide input voltage ranges. All magnetic components including output inductor, transformer and input filter can be integrated into single EI/EE core. The integrated push pull forward converter is considered through the comparison of efficiency according to the circuit parameters. The Nicera company's 5M FEE18/8/10C and NC-2H FEI32/8/20 cores are used for the transformer. The integrated push pull forward converter ratings are of $36\~72V$ input and 3.3V/30A output. In case that NC-2H FEI32/8/20 core used in the converter, the efficiency is measured up to $83.5\%$ at the switching frequency 200 kHz and the 11A load. The efficiencies of $76.4\%$ at a full load and $82.95\%$ at a half load are measured.

• KIEE International Transaction on Systems and Control
• /
• v.2D no.2
• /
• pp.108-114
• /
• 2002

This paper describes the design of a robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by multilayered neural networks (MNNs) whose parameters are adjusted on-line, according to some adaptive laws far controlling the output of the nonlinear system, to track a given trajectory. The main contribution of this paper is a method for considering input constraint with a rigorous stability proof. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive MNN model. An overall control system guarantees that the tracking error converges at about zero and that all signals involved are uniformly bounded even in the presence of input saturation. Theoretical results are illustrated through a simulation example.

• The Journal of the Korea Contents Association
• /
• v.11 no.3
• /
• pp.48-55
• /
• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• Journal of Korean Society on Water Environment
• /
• v.26 no.1
• /
• pp.10-18
• /
• 2010

Water quality and the distribution of ammonia oxidizing bacteria were characterized in constructed wetland of Shihwa lake. Both physico-chemical parameters and fecal indicator microorganisms including total coliforms, E.coli, Enterococcus spp. were measured. In addition, denaturant gradient gel electrophoresis (DGGE) was carried out after PCR amplification of amoA gene from input, output, and wetland sites of the Banwol, Donghwa, and Samhwa stream in Shihwa lake area. Physico-chemical parameters were in proper range for typical nitrifying bacteria to grow and perform their biological activities. Average concentrations of fecal indicator microorganisms of wetland samples were lower than those of input sites. These results suggested that microbial water quality improved by the process of constructed wetland. According to phylogenetic information obtained from DGGE from study sites, distribution of nitrifying bacteria from each of input, output, and wetland were generally distinctive one another. In addition, distribution of nitrifying bacteria between Banwol and Donghwa streams showed higher similarity (52.6%) than this of Samhwa stream (15.2%). These results indicated that characteristics of ammonia oxidizing bacteria in Samhwa were unique in comparison with those of Banwol and Donghwa stream.

• Smart Structures and Systems
• /
• v.4 no.1
• /
• pp.47-66
• /
• 2008

The present work utilizes system identification technique for health monitoring of shear building, wherein Parametric State Space modeling has been adopted. The method requires input excitation to the structure and also output acceleration responses of both undamaged and damaged structure obtained from numerically simulated model. Modal parameters like eigen frequencies and eigen vectors have been extracted from the State Space model after introducing appropriate transformation. Least square technique has been utilized for the evaluation of the stiffness matrix after having obtained the modal matrix for the entire structure. Highly accurate values of stiffness of the structure could be evaluated corresponding to both the undamaged as well as damaged state of a structure, while considering noise in the simulated output response analogous to real time scenario. The damaged floor could also be located very conveniently and accurately by this adopted strategy. This method of damage detection can be applied in case of output acceleration responses recorded by sensors from the actual structure. Further, in case of even limited availability of sensors along the height of a multi-storeyed building, the methodology could yield very accurate information related to structural stiffness.

• Nuclear Engineering and Technology
• /
• v.27 no.3
• /
• pp.336-343
• /
• 1995

The Self Tuning Regulator(STR) method which is an approach of adaptive control theory, is ap-plied to design the fully automatic power controller of the nonlinear reactor model. The adaptive control represent a proper approach to design the suboptimal controller for nonlinear, time-varying stochastic systems. The control system is based on a third­order linear model with unknown, time-varying parameters. The updating of the parameter estimates is achieved by the recursive extended least square method with a variable forgetting factor. Based on the estimated parameters, the output (average coolant temperature) is predicted one-step ahead. And then, a weighted one-step ahead controller is designed so that the difference between the output and the desired output is minimized and the variation of the control rod position is small. Also, an integral action is added in order to remove the steady­state error. A nonlinear M plant model was used to simulate the proposed controller of reactor power which covers a wide operating range. From the simulation result, the performances of this controller for ramp input (increase or decrease) are proved to be successful. However, for step input this controller leaves something to be desired.

• Proceedings of the IEEK Conference
• /
• 2002.07a
• /
• pp.627-630
• /
• 2002

A methodology based on factorial design and Motto Carlo methods is developed and implemented for incorporating uncertainties within a multiphase subsurface flow and transport simulation system. Due to uncertainties in intrinsic permeability and longitudinal dispersivity, the predicted output is also uncertain based on the well-developed multiphase compositional simulator. The simulation results reveal that the uncertainties in input parameters pose considerable influences on the predicted output, and the mean and variance of permeability will have significant impacts on the modeling output. The proposed method offers an effective tool for evaluating uncertainty in multiphase flow simulation system.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.127.1-127
• /
• 2001

In this paper we propose an input/output model based fault diagnosis method to detect and isolate single faults in the robot arm control system. The proposed algorithm is functionally composed of three main parts-parameter estimation, fault detection, and isolation, When a change in the system occurs, the errors between the system output and the estimated output cross a predetermined threshold, and once a fault in the system is detected, and in this zone the estimated parameters are transferred to the fault classifier by ART2(adaptive resonance theory 2) neural network for fault isolation. Since ART2 neural network is an unsupervised neural network fault classifier does not require the knowledge of all possible faults to isolate the faults occurred in the system. Simulations are carried out to evaluate the performance of the proposed ...