• Title/Summary/Keyword: Input and Output Parameters

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Electrical Characteristics of CMOS Circuit Due to Channel Region Parameters in LDMOSFET

  • Kim, Nam-Soo;Cui, Zhi-Yuan;Lee, Hyung-Gyoo;Kim, Kyoung-Won
    • Transactions on Electrical and Electronic Materials
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    • v.7 no.3
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    • pp.99-102
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    • 2006
  • The electrical characteristics of CMOS inverter with LDMOSFET are studied for high power and digital circuit application by using two dimensional MEDICI simulator. The simulation is done in terms of voltage transfer characteristic and on-off switching properties of CMOS inverter with variation of channel length and channel doping levels. The channel which surrounds a junction-type source in LDMOSFET is considered to be an important parameter to decide a circuit operation of CMOS inverter. The digital logic levels of input voltage show to increase with increase of n-channel length and doping levels while the logic output levels show to the almost constant.

An Artificial Pancreas Using the Pole Assignment Self-Tuning Algorithm (PASTR을 이용한 인공췌장의 연구)

  • 김영철;우응제;박광석;민병구;양흥석
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.34 no.7
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    • pp.257-266
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    • 1985
  • A new method for the artificial beta cell which can be used to control the hyperglycemia in diabetic patients was represented. The relationship between the insulin infusion rate and the blood glucose concentration was described by the second order ARMA model, and the time varying parameters were identified by exponentially weighted least squares estimator. The design of controller was based on the pole assignment self tuning altorithm with discrete blood sampling and the constraints of input and output responsse rate were considered. The results of animal experiments show that this method may be a fruitful approach for regulating the blood glucose level. We expect that this device can be used as both therapeutic and research tools providing that its stability and reliability are improved a little more.

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Application of artificial neural networks (ANNs) and linear regressions (LR) to predict the deflection of concrete deep beams

  • Mohammadhassani, Mohammad;Nezamabadi-pour, Hossein;Jumaat, Mohd Zamin;Jameel, Mohammed;Arumugam, Arul M.S.
    • Computers and Concrete
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    • v.11 no.3
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    • pp.237-252
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    • 2013
  • This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN's MSE values are 40 times smaller than the LR's. The test data R value from ANN is 0.9931; thus indicating a high confidence level.

A Robust Fault Detection method for Uncertain Systems with Modelling Errors (모델링 오차를 갖는 불확정 시스템에서의 견실한 이상 검출기)

  • 권오주;이명의
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.39 no.7
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    • pp.729-739
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    • 1990
  • This paper deals with the fault detection problem in uncertain linear/non-linear systems having both undermodelling and noise. A robust fault detection method is presented which accounts for the effects of noise, model mismatch and nonlinearities. The basic idea is to embed the unmodelled dynamics in a stochastic process and to use the nominal model with a predetermined fixed denominator. This allows the input /output relationship to be represented as a linear function of the system parameters and also facilitate the quatification of the effect of noise, model mismatch and linearization errors on parameter estimation by the Bayesian method. Comparisons are made via simulations with traditional fault detection methods which do not account for model mismatch or linearization errors. The new method suggested in this paper is shown to have a marked improvement over traditional methods on a number of simulations, which is a consequence of the fact that the new method explicitly for the effects of undermodelling and linearization errors.

A Practical Stability Control Strategy for DC/DC Converters

  • Jiang, Lin;Li, Po
    • Journal of Electrical Engineering and Technology
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    • v.13 no.3
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    • pp.1232-1240
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    • 2018
  • This paper aims at designing an intelligent controller, based on control Lyapunov Function strategy integrated with fabricating discrete model of Buck and Boost converters and analyzing energy changes during the DC/DC progress to realize tracing reference current on Buck and Boost converters. In addition, practical stability phenomenon research and transient performance analysis has been proposed to give an insight to the influence of controller parameters in achieving an enhanced output performance and how the time of sample period affect the error of practical stability will be illustrated. The novelty of this controller in comparison to other schemes lies in the improved performance of practical stability.

Nano-medicine effectiveness in pediatric patients: An artificial intelligence investigation

  • Shaona Wang;Fan Yang
    • Advances in nano research
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    • v.15 no.2
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    • pp.129-139
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    • 2023
  • Emerge of nanotechnology has affected many aspects of our life and also triggers research studies in many fields. Nano-medicine are proven to be effective in encountering diseases. In the present study, aspects of the applications and effectiveness of nano-medicine in pediatrics patients are studied. In this regard, using experimental data of previous published researches, combination and dose of nano-medicines are optimized using response surface method and neural-fuzzy inference network. The input parameters of the selected multiple nano-medicines are dose and type and the output is the effectiveness of the combinations using IC50 parameter. A detailed parameter study is presented to observe effects of each inputs on the IC50. The results indicate that personalized scaling of nano-medicine is required in therapy of pediatric diseases such as cancers.

On a Modified Structure of Planar Multiport Power Divider/Combiner at 2 GHz (평면 다수 입출력 전력 분배/결합회로의 2 GHz에서의 구조 수정 연구)

  • Han, Yong-In;Jo, Chi-Sung;Kim, Ihn-Seok
    • Journal of Advanced Navigation Technology
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    • v.6 no.4
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    • pp.279-290
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    • 2002
  • In this paper, tapered shape of multiport power divider/combiner modified for 2 GHz range from the model published by [10] is proposed. Parameters determining electrical property of the circuit structure have been analyzed by HFSS simulation. For input matching, balance of output signals and phase linearity at each output port, one circular hole has been etched out on the circuit surface. 1:2 and 1:3 power dividers/combiners designed by this study have been compared with the same circuits designed by the method of [10] in terms of S-parameters. As a result, it has been found that the modified structure of power divider/combiner have improved return loss more than 20 dB and another 18 dB, respectively, at 2 GHz.

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A Novel Epsilon Near Zero Tunneling Circuit Using Double-Ridge Rectangular Waveguide

  • Kim, Byung-Mun;Son, Hyeok-Woo;Hong, Jae-Pyo;Cho, Young-Ki
    • Journal of electromagnetic engineering and science
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    • v.14 no.1
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    • pp.36-42
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    • 2014
  • In this paper, an epsilon near zero (ENZ) tunneling circuit using a double-ridge rectangular waveguide (RWG) is proposed for the miniaturization of a waveguide component. The proposed ENZ channel and is located in the middle of the input-output RWG (IORWG). The ratio of the height to the width of the channel waveguide is very small compared to the IORWG. By properly adjusting the ridge dimensions, the tunneling frequency of the proposed ENZ channel can be lowered to near the cut-off frequency of the IORWG. For the proposed ENZ tunneling circuit, the approach adopted for extracting the effective permittivity, effective permeability;normalized effective wave impedance, and propagation constant from the simulated scattering parameters was explained. The extracted parameters verified that the proposed channel is an ENZ channel and electromagnetic energy is tunneling through the channel. Simulation and measurement results of the fabricated ENZ channel structure agreed.

Self-Organizing Fuzzy Modeling Using Creation of Clusters (클러스터 생성을 이용한 자기구성 퍼지 모델링)

  • Koh, Taek-Beom
    • Journal of the Korean Institute of Intelligent Systems
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    • v.12 no.4
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    • pp.334-340
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    • 2002
  • This paper proposes a self-organizing fuzzy modeling which can create a new hyperplane-shaped cluster by applying multiple regression to input/output data with relatively large fuzzy entropy, add the new cluster to fuzzy rule base and adjust parameters of the fuzzy model in repetition. Tn the coarse tuning, weighted recursive least squared algorithm and fuzzy C-regression model clustering are used and in the fine tuning, gradient descent algorithm is used to adjust parameters of the fuzzy model precisely And learning rates are optimized by utilizing meiosis-genetic algorithm. To check the effectiveness and feasibility of the suggested algorithm, four representative examples for system identification are examined and the performance of the identified fuzzy model is demonstrated in comparison with that of the conventional fuzzy models.

Iterative Channel Estimation for MIMO-OFDM System in Fast Time-Varying Channels

  • Yang, Lihua;Yang, Longxiang;Liang, Yan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.9
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    • pp.4240-4258
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    • 2016
  • A practical iterative channel estimation technique is proposed for the multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system in the high-speed mobile environment, such as high speed railway scenario. In the iterative algorithm, the Kalman filter and data detection are jointed to estimate the time-varying channel, where the detection error is considered as part of the noise in the Kalman recursion in each iteration to reduce the effect of the detection error propagation. Moreover, the employed Kalman filter is from the canonical state space model, which does not include the parameters of the autoregressive (AR) model, so the proposed method does not need to estimate the parameters of AR model, whose accuracy affects the convergence speed. Simulation results show that the proposed method is robust to the fast time-varying channel, and it can obtain more gains compared with the available methods.