• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.53.6-53
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• 2001

In this paper, we are concerned with a tracking filter algorithm which can track a maneuvering target. Among the novel tracking filter algorithms, the input estimation (IE) filter can be summarized as estimating the unknown maneuver input and compensating the state according to the estimated input, and the variable dimension filter (VDF) can be summarized as detecting the maneuver of target and changing the dimension of the target dynamics to accomodate the maneuver of target They have some goods and bads with respect to each other. The variable dimension filter with input estimation (VDIEF) is constructed by combining the two filtering algorithms. However, it requires too much computational burden while it has good performance. We propose another variable dimension with input estimation ...

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2876-2878
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• 2005

In this paper, we propose Genetic Algorithms(GAs)-based Optimal Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and modified quadratic, and is connected as various kinds of multi-variable inputs. The conventional PNN depends on experience of a designer that select No. of input variable, input variable and polynomial type. Therefore it is very difficult a organizing of optimized network. The proposed algorithm identified and selected No. of input variable, input variable and polynomial type by using Genetic Algorithms(GAs). In the sequel the proposed model shows not only superior results to the existing models, but also pliability in organizing of optimal network. The study is illustrated with the ACI Distance Relay Data for application to power systems.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.149-151
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• 2005

In this paper, we propose the Optimal Polynomial Neural Networks(PNN) for nonlinear process. The PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and modified quadratic, and is connected as various kinds of multi-variable inputs. The conventional PNN depends on experience of a designer that select No. of input variable, input variable and polynomial type. Therefore it is very difficult a organizing of optimized network. The proposed algorithm identified and selected No. of input variable, input variable and polynomial type by using Genetic Algorithms(GAs). In the sequel the proposed model shows not only superior results to the existing models, but also pliability in organizing of optimal network. Medical Imaging System(MIS) data is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.8
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• pp.34-40
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• 1992

This paper shows a new relational formula of multiplexing gain versus variable input bit rates useful for designing Nx1 ATM(Asynchronous Transfer Mode) multiplexer which mixes several asynchronous bit streams with different transmission rates. The relation between multiplexing gain and input bit stream speeds is derived from the occupied mean lenght(the width per unit time) of cells and the occupation probability of the number of cells at an arbitrary instant when the rates of the periodic cell strams change randomly. And the relation between multiplexing gain and variable bit rates from different number of input bit streams is analyzed accordingly. Under the condition of unlimited multiplexing speed, the more number of input bit streams increases, the bigger the multiplexing gain becomes. While for the case which restricts the multiplexing speed to a limited value, the multiplexing gain becomes smaller contrarily as the number of input bit streams continues too invrease beyond a boundary value. It is shown that for designing an ATM multiplexer according to the latter case, the combination of input bit streams should be determined such as its total bit rate is lower thean, but most apprpaximate to, the multiplexed output speed. Also the general formula evaluating the most significant parameters which should be needed to design the multiplexer is derived.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.163-167
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• 2000

This paper presents the digital control of single-phase power factor correction(PFC) converter which has the variable gain according to the condition of inner control loop error. Generally the gain of inner current control loop in single-stage PFC converter has a constant magnitude. This has a bad influence on the power factor because current loop doesn't operate smoothly in the condition that input voltage is low In particular a digital controller has more time delay than an analog controller and degrades This drops the phase margin of the total digital PFC system,. It causes the problem that the gain of current control loop isn't increased enough. In addition the oscillation happens in the peak value of the input voltage open loop PFC system gain changes according to ac input voltage. These aspects make the design of the digital PFC controller difficult The digital PFC controller presented in this paper has a variable gain of current control loop according to input voltage. The 1kW converter was used to verify the efficiency of the digital PFC controller.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.157-157
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• 2000

In the fuzzy control for the multi-variable system, it is difficult to obtain the fuzzy rule. Therefore, the parallel structure of the independent single input-single output fuzzy controller using a pairing between the input and output variable is applied to the multi-variable system. The concept of relative gain matrix is used to obtain the input-output pairs. However, among the input/output variables which are not paired the interactive effects should be taken into account. these mutual coupling of variables affect the control performance. Therefore, for the control system with a strong coupling property, the control performance is sometimes lowered. In this paper, the effect of mutual coupling of variables is considered by tile introduction of a simple compensator. This compensator adjusts the degree of coupling between variables using a neural network. In this proposed neuro-fuzzy controller, the Neural network which is realized by back-propagation algorithm, adjusts the mutual coupling weight between variables.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.11
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• pp.141-150
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• 2000

Continuously variable transmission(CVT) combined differential gear unit has many advantages, which are the decrease of CVT size, the increase of overall efficiency, the extension of speed ratio range, and the generation of geared neutral. It is known that such CVT can be classified into the input coupled type and the output coupled type according to the coupling location of continuously variable unit(CVU). In this paper, six different configurations of input coupled type CVT combined V-belt CVU and 2K-H I type differential gear unit are proposed. Some useful theoretical formula related to speed ratio, power flow and efficiency are derived and analyzed. The propriety of derived formula and theoretical analysis are proven by various experiments.

• Korean Journal of Construction Engineering and Management
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• v.13 no.5
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• pp.94-102
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• 2012

Recently, the global financial crisis and the increasing number of unsold houses in Korea are construction companies to assess their efficiency. The most important factor in analyzing the efficiency of a company is the input-output variable. However, systematic stud the core input-output variables, which have a great influence on the efficiency analysis. Thus, to the core input-output variables for efficiency analysis of construction companies, this study propose a model that includes all combinations of input-output variables and to find the core input-output variables using the Data Envelopment Analysis(DEA) model and Principal Component Analysis(PCA). Existing research and theories were studied variables and 21 models were established to measure efficiency. were obtained that the core input and output variable in 2006 the number of employees and sales. For 2008, the core input variable was capital stock and the core output variable was quarterly net profit. For 2010, the core input variable was fixed asset and the core output variable was sales. Through obtaining the variables that greatly affect the efficiency of construction companies, it is considered that individual construction companies will be able to prepare a priority strategy to enhance efficiency.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.21 no.4
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• pp.321-328
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• 2008

In this paper, variable gain amplifier(VGA) with a gain slope controller has been proposed and verified by circuit simulations and measurements. The proposed VGA has a gain control, gain slope switch and variable gain range. The input source coupled pair with diode connected load is used for VGA gain stage. The gain slope controller with switch can control VGA gain slope. The proposed VGA is fabricated in $0.18{\mu}m$ CMOS process for multi -standard wireless receiver. The proposed two stage VGA consumes min. 2.0 mW to max. 2.6 mW in gain control range and gives input IP3 of -3.77 dBm and NF of 28.7 dB at 1.8 V power supply under -25 dBm, 1 MHz input. The proposed VGA has 37 dB(-16 dB $\sim$ 21 dB) variable gain range, and 8 dB gain range control per 0.3 V control voltage, and can provide variable gain, positive and negative gain slope control, and gain range control. This VGA characteristics provide design flexibility in multi-standard wireless receiver.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.601-604
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• 2000

In this paper, we compare the classification performance of both data fusion and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. Since the relationship between input & output is not typically known, we use Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: Clustering based logistic regression turns out to provide the highest classification accuracy when input variables are weakly correlated and the variance of data is high. When there is high correlation among input variables, variable bagging performs better than logistic regression. When there is strong correlation among input variables and high variance between observations, bagging appears to be marginally better than logistic regression but was not significant.