• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.4
• /
• pp.410-418
• /
• 1999

This paper describes the use of residual and parameter identification methods for fault detection in an air handling unit. Faults can be detected by comparing expected condition with the measured faulty data using residuals. Faults can also be detected by examining unmeasurable parameter changes in a model of a controlled system using a system identification technique. In this study, AutoRegressive Moving Average with seXtrnal input(ARMAX) and AutoRegressive with eXternal input(ARX) models with both single-input/single-input and multi-input/single-input structures are examined. Model parameters are determined using the Kalman filter recursive identification method. Regression equations are calculated from normal experimental data and are used to compute expected operating variables. These approaches are tested using experimental data from a laboratory's variable-air-volume air-handling-unit.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.2
• /
• pp.366-373
• /
• 2017

In this paper, we propose a variable weighted average (VWA) learning method in order to improve the performance of the fuzzy ART neural network that has been developed by Grossberg. In a conventional method, the Fast Commit Slow Recode (FCSR), when an input pattern falls in a category, the representative pattern of the category is updated at a fixed learning rate regardless of the degree of similarity of the input pattern. To resolve this issue, a variable learning method proposes reflecting the distance between the input pattern and the representative pattern to reduce the FCSR's category proliferation issue and improve the pattern recognition rate. However, these methods still suffer from the category proliferation issue and limited pattern recognition rate due to inevitable excessive learning created by use of fuzzy AND. The proposed method applies a weighted average learning scheme that reflects the distance between the input pattern and the representative pattern when updating the representative pattern of a category suppressing excessive learning for a representative pattern. Our simulation results show that the newly proposed variable weighted average learning method (VWA) mitigates the category proliferation problem of a fuzzy ART neural network by suppressing excessive learning of a representative pattern in a noisy environment and significantly improves the pattern recognition rates.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.42 no.8 s.338
• /
• pp.35-42
• /
• 2005

This paper describes novel flip-flops with improved robustness and reduced power consumption. Variable sampling window flip-flop (VSWFF) adjusts the width of the sampling window according to input data, providing robust data latching as well as shorter hold time. The flip-flop also reduces power consumption for higher input switching activities as compared to the conventional low-power flip-flop. Clock swing-reduced variable sampling window flip-flop (CSR-VSWFF) reduces clock power consumption by allowing the use of a small swing clock. Unlike conventional reduced clock swing flip-flops, it requires no additional voltage higher than the supply voltage, eliminating design overhead related to the generation and distribution of this voltage. Simulation results indicate that the proposed flip-flops provide uniform latency for narrower sampling window and improved power-delay product as compared to conventional flip-flops. To evaluate the performance of the proposed flip-flops, test structures were designed and implemented in a $0.3\mu m$ CMOS process technology. Experimental result indicates that VSWFF yields power reduction for the maximum input switching activity, and a synchronous counter designed with CSR-VSWFF improves performance in terms of power consumption with no use of extra voltage higher than the supply voltage.

• Communications for Statistical Applications and Methods
• /
• v.29 no.2
• /
• pp.251-261
• /
• 2022

As AI has a wide range of influence on human social life, issues of transparency and ethics of AI are emerging. In particular, it is widely known that due to the existence of historical bias in data against ethics or regulatory frameworks for fairness, trained AI models based on such biased data could also impose bias or unfairness against a certain sensitive group (e.g., non-white, women). Demographic disparities due to AI, which refer to socially unacceptable bias that an AI model favors certain groups (e.g., white, men) over other groups (e.g., black, women), have been observed frequently in many applications of AI and many studies have been done recently to develop AI algorithms which remove or alleviate such demographic disparities in trained AI models. In this paper, we consider a problem of using the information in the sensitive variable for fair prediction when using the sensitive variable as a part of input variables is prohibitive by laws or regulations to avoid unfairness. As a way of reflecting the information in the sensitive variable to prediction, we consider a two-stage procedure. First, the sensitive variable is fully included in the learning phase to have a prediction model depending on the sensitive variable, and then an imputed sensitive variable is used in the prediction phase. The aim of this paper is to evaluate this procedure by analyzing several benchmark datasets. We illustrate that using an imputed sensitive variable is helpful to improve prediction accuracies without hampering the degree of fairness much.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.29 no.7
• /
• pp.38-46
• /
• 2015

This paper propose a variable parameter estimations for variable over current load of five-phase squirrel-cage induction motor(IM) to servo control system. In order to high performance control of AC motor using a field oriented control(FOC) and direct torque control(DTC) algorithm, there are required precise motor parameters for slip calculation, flux observer, controller gain, torque command of current components, rotor position, speed estimation, and so on. We are suggest a analyzed estimation results of the motor parameters that developing five-phase squirrel-cage IM have a stator of concentrated winding for experimental within variable over current load at rated input frequency. There are results of stator winding measurement, no-load test, locked-rotor test, variable over current load test, and estimated parameters of equivalent circuits using manufactured experimental apparatus by IEEE Standard Test Procedure for Polyphase Induction Motors and Generators 112-2004.

• Journal of the Korean Data and Information Science Society
• /
• v.16 no.2
• /
• pp.195-205
• /
• 2005

In decision tree analysis, C4.5 and CART algorithm have some problems of computational complexity and bias on variable selection. But QUEST algorithm solves these problems by dividing the step of variable selection and split point selection. When input variables are continuous, QUEST algorithm uses ANOVA F-test under the assumption of normality and homogeneity of variances. In this paper, we investigate the influence of violation of normality assumption and effect of the transformation of variables in the QUEST algorithm. In the simulation study, we obtained the empirical powers of variable selection and the empirical bias of variable selection after transformation of variables having various type of underlying distributions.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.4
• /
• pp.1059-1066
• /
• 2016

In this paper we propose a robust version of varying coefficient models, which is based on the regularized regression with L1 regularization. We use the iteratively reweighted least squares procedure to solve L1 regularized objective function of varying coefficient model in locally weighted regression form. It provides the efficient computation of coefficient function estimates and the variable selection for given value of smoothing variable. We present the generalized cross validation function and Akaike information type criterion for the model selection. Applications of the proposed model are illustrated through the artificial examples and the real example of predicting the effect of the input variables and the smoothing variable on the output.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.4
• /
• pp.402-409
• /
• 1999

Variable structure control law is well known to be a robust control algorithm and evolution strategy is used as an effective search algorithm in optimization problems. In this paper, we propose a variable structure controller with time-varying switching surface. We calculate the maximum value of seitching surface gradient that is of the 3rd order polynomial form. Evolution strategy is used to optimize the parameters of the switching surface gradient. Finally, the proposed method is applied to position tracking control for BLDC motor. Experimental results show that the proposed method is more useful than the conventional variable structure controller.

• Journal of Welding and Joining
• /
• v.11 no.3
• /
• pp.34-47
• /
• 1993

Finite element models were developed for thermal and residual stress analysis for the specific welding problems. They were used to evaluate the effectiveness of the various welding heat input models, such as ramp heat input function and lumped pass models. Through the parametric studies, thermal-mechanical modeling sensitivity to the ramp function and lumping techniques was determined by comparing the predicted results with experimental data. The kinetics for residual stress formation during welding can be developed by iteration of various proposed mechanisms in the parametric study. A ramp heat input function was developed to gradually apply the heat flux with variable amplitude to the model. This model was used to avoid numerical convergence problems due to an instantaneous increase in temperature near the fusion zone. Additionally, it enables the model to include the effect of a moving arc in a two-dimensional plane. The ramp function takes into account the variation in the out of plane energy flow in a 2-D model as the arc approaches, travels across, and departs from each plane under investigation. A lumped pass model was developed to reduce the computation cost in the analysis of multipass welds. Several weld passes were assumed as one lumped pass in this model. Recommendations were provided about ramp lumping techniques and the optimum number of weld passes that can be combined into a single thermal input.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.528-531
• /
• 1997

This paper deals with the kinematic and dynamic modeling of a 3 degree-of-freedom redundantly actuated mobile robot for the purpose of analysis and control. Each wheel is driven by two motors for steering and driving. Therefore, the system becomes force-redundant since the number of input variable is greater than the number of output variable. The kinematic and dynamic models in terms of three independent joint variables are derived. Also, a load distribution method to determine the input loads is introduced. Finally we demonstrate the feasibility of the proposed algorithms through simulation.