• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.657-659
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• 1998

In order to optimize fuzzy modeling of nonlinear system, we proposed a optimal fuzzy model according to the characteristic of I/O relationship, HCM method, the genetic algorithm, and the objective function with weighting factor. A conventional fuzzy model has difficulty in definition of membership function. In order to solve its problem, the premise structure of the proposed fuzzy model is selected by both the partition of input space and the analysis of input-output relationship using the clustering algorithm. The premise parameters of the fuzzy model are optimized respectively by the genetic algorithm and the consequence parameters of the fuzzy model are identified by the standard least square method. Also, the objective function with weighting factor is proposed to achieve a balance between the performance results for the training and testing data.

• 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.

• ETRI Journal
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• v.37 no.5
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• pp.972-978
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• 2015

This paper proposes an accurate tunable-gain 1/x circuit. The output voltage of the 1/x circuit is generated by using a capacitor charging time that is inversely proportional to the input voltage. The output voltage is independent of the process parameters, because the output voltage depends on the ratios of the capacitors, resistors, and current mirrors. The voltage gain of the 1/x circuit is tuned by a 10-bit digital code. The 1/x circuit was fabricated using a $0.18{\mu}m$ CMOS process. Its core area is $0.011mm^2$ ($144{\mu}m{\times}78{\mu}m$), and it consumes $278{\mu}W$ at $V_{DD}=1.8V$ and $f_{CLK}=1MHz$. Its error is within 1.7% at $V_{IN}=0.05V$ to 1 V.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.830-835
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• 1994

In this paper, a new method of statistically evaluating an output response probability distribution of a memory type non-linear system is practically derived based on a zero-memory type non-linear equivalent system. That is, first, the objective system is approximately and functionally separated into two functional parts, i.e., a zero-memory type non-linear part and a memory type linear part according to the well-known Wiener's idea. A whole mathematical frame of the output probability distribution is evaluated in an approximate but generalized form, based on the equivalent zero-memory type non-linear part. The memory effects between the input and the output of the system are reflected in the statistical parameters and the expansion coefficients.

• Journal of Applied Reliability
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• v.15 no.2
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• pp.131-138
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• 2015

In modern warfare, the number of unmanned systems grow faster than any other weapon systems. Therefore, it is very important to predict and measure the combat effectiveness (CE) of unmanned weapon systems in battlefield for deciding defense budget to acquire those systems. In general, quantitative calculation of weapon effectiveness under complicated battlefield is difficult based on the future network centric warfare. Hence, many papers studied how to measure the combat effectiveness and tried to study a lot of related issues about it. However, there are few papers dealing with the relationship between the UGV (Unmanned Ground Vehicle)'s performance and CE in a ground battlefield. In this paper, we do the sensitivity analysis based on a given scenario in a small unit battle. In order to do that, we developed simulation model using AnyLogic and changed the input parameters such as detection and hitting probabilities. We also assess the simulation outputs according to the variation of input parameters. The MOE used in this simulation model output is survival ratio for Blue force. We hope that this paper will be useful to find which input variable is more effective to increase combat effectiveness in a small unit ground battlefield.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.700-702
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• 2000

The new design methodology of a hybrid fuzzy controller by means of the genetic algorithms is presented. In fuzzy controller which has been widely applied and used. in order to construct the best fuzzy rules that include adjustment of fuzzy sets, a highly skilled techniques using trial and error are required. To deal with such a problem, first, a hybrid fuzzy controller(HFC) related to the optimal estimation of control parameters is proposed. The HFC combined a PID controller with a fuzzy controller concurrently produces the better output performance than any other controller from each control output in steady state and transient state. Second, a auto-tuning algorithms is presented to automatically improve the performance of hybrid fuzzy controller, utilizing the simplified reasoning method and genetic algorithms. In addition, to obtain scaling factors and PID Parameters of HFC using GA, three kinds of estimation modes such as basic, contraction, and expansion mode are effectively utilized. The HFCs are applied to the first-order second-order process with time-delay and DC motor Computer simulations are conducted at step input and the performances of systems are evaluated and also discussed from performance indices.

• Design & Manufacturing
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• v.17 no.4
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• pp.72-78
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• 2023

Injection molding is a process widely used in various industries because of its high production speed and ease of mass production during the plastic manufacturing process, and the product is molded by injecting molten plastic into the mold at high speed and pressure. Since process conditions such as resin and mold temperature mutually affect the process and the quality of the molded product, it is difficult to accurately predict quality through mathematical or statistical methods. Recently, studies to predict the quality of injection molded products by applying artificial neural networks, which are known to be very useful for analyzing nonlinear types of problems, are actively underway. In this study, structural optimization of neural networks was conducted by applying multi-task learning techniques according to the characteristics of the input and output parameters of the artificial neural network. A structure reflecting the characteristics of each process step was applied to the input parameters, and a structure reflecting the quality characteristics of the injection molded part was applied to the output parameters using multi-tasking learning. Building an artificial neural network to predict the three qualities (mass, diameter, height) of injection-molded product under six process conditions (melt temperature, mold temperature, injection speed, packing pressure, pacing time, cooling time) and comparing its performance with the existing neural network, we observed enhancements in prediction accuracy for mass, diameter, and height by approximately 69.38%, 24.87%, and 39.87%, respectively.

• Proceedings of the KIEE Conference
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• 2005.10a
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• pp.155-157
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• 2005

This paper shows how to design a MPPT control of PV system using neural network PI self tuning. The conventional self-tuning methods have the voltage control problem of nonlinear PV system which can't adapt against any kinds of noise or operation circumstances. In this paper, supposed to solve these problem to PI parameters controller algorithm using ANN. In the proposed algorithm, the parameters of the controller were adjusted to reduce by on-line system the error of the output voltage of DC-DC chopper. In this process, EBPA NN was constituted to an output error value of a DC-DC chopper and conspired an input and output. The performance of the self-tuning controller is compared with that of the PI controller tuned by conventional method. The effectiveness of the proposed control method is verified thought the Matlab Simulink.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.3
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• pp.319-332
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• 2014

Insertion loss is used as the character to express the efficiency of EMI filter. In this paper, we studied the better method that can measure the insertion loss of EMI filter exactly than the original method. For the achievement of this, the method measuring both common mode(CM) and differential mode(DM) insertion loss with arbitrary input/output impedance is accomplished using a 4-ports S-parameters system for consideration of unbalanced factor. Using this method, when input/output used in specific system is known, CM/DM insertion loss of EMI filter inserted in the system can be calculated. Finally, we applied 4-ports modeling method to 'X/Y capacitor part' and suggested the algorithm for selecting suitable the value of Y-capacitor using mixed mode S-parameters and mixed mode chain S-parameters.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.144-147
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• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.