• Journal of Ocean Engineering and Technology
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• v.18 no.4 s.59
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• pp.40-45
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• 2004

An efficient numerical model of the modified mild slope equation, based on the robust iterative method is presented. The model developed is verified against other numerical experimental results, related to wave reflection from an arc-shaped bar and wave transformation over a circular shoal. The results show that the modified mild slope equation model is capable of producing accurate results for wave propagation in a region where water depth varies substantially, while the conventional mild slope equation model yeilds large errors, as the mild slope assumption is violated.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.163-171
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• 1997

Function approximation from a set of input-output pairs has numerous applications in scientific and engineering areas. Multilayer feedforward neural networks have been proposed as a good approximator of nonlinear function. The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data we employed. When errorneous traning data are employed, the learned mapping can oscillate badly between data points. In this paper we propose a robust BP learning algorithm that is resistant to the errorneous data and is capable of rejecting gross errors during the approximation process, that is stable under small noise perturbation and robust against gross errors.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.12
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• pp.59-68
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• 1999

The timing characteristics of an ASIC are analyzed based on the propagation delays of each gate and interconnect wire. The gate delay can be modeled using the two-dimensional delay table whose index variables are the input transition time and the output load capacitance. The AWE technique can be adopted as an algorithm to compute the interconnect delay. Since these delays are affected by the interaction to the two-dimensional delay table and the AWE technique. A method to model this effect has been proposed through the effective capacitance and the gate driver model under the assumption of single driving gate. This paper presents a new technique to handle the multiple CMOS gates driving interconnect wire by extending previous approach. This technique has been implemented in C language and applied to several interconnect circuits driven by multiple CMOS gates. In most cases, we found a few tens of speed-up and only a few percents of errors in computing both of gate and interconnect delays, compared to SPICE.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• The KIPS Transactions:PartC
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• v.9C no.4
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• pp.563-572
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• 2002

Wireless mobile networks tend to drop a large portion of packets due to propagation errors rather than congestion. To Improve reliability over noisy wireless channels, wireless networks can employ forward error correction (FEC) techniques. Static FEC algorithms, however, can degrade the performance by poorly matching their overhead to the degree of the underlying channel error, especially when the channel path loss rate fluctuates widely. This paper investigates the benefits of an adaptable FEC mechanism for wireless networks with severe packet loss by analytical analysis or measurements over a real wireless network called sensor network. We show that our adaptive FEC named FECA (FEC-level Adaptation) technique improves the performance by dynamically tuning FEC strength to the current amount of wireless channel loss. We quantify these benefits through a hybrid simulation integrating packet-level simulation with bit-level details and validate that FECA keeps selecting the appropriate FEC-level for a constantly changing wireless channel.

• Journal of KIISE:Information Networking
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• v.30 no.6
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• pp.755-763
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• 2003

To improve performance over noisy wireless channels, mobile wireless networks employ forward error correction(FEC) techniques. The performance of static FEC algorithms, however, degrades by poorly matching the overhead of their correction code to the degree of the fluctuating underlying channel error. This paper proposes an adaptive FEC technique called FECA(FEC-level Adaptation), which dynamically tunes FEC strength to the currently estimated channel error rate at the data link layer. FECA is suitable for wireless networks whose error rate is high and slowly changing compared to the round-trip time between two communicating nodes. One such example network would be a sensor network in which the average bit error rate is higher than $10^{-6}$ and the detected error rate at one time lasts a few hundred milliseconds on average. Our experiments show that FECA performs 15% in simulations with theoretically modeled wireless channels and in trace-driven simulations based on the data collected from real sensor networks better than any other static FEC algorithms.

• The Journal of the Acoustical Society of Korea
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• v.27 no.1
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• pp.1-11
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• 2008

To overcome the mid frequency limitation of geometrical acoustic techniques, the phased geometrical method was suggested by introducing the phase information into the sound propagation from the source. By virtue of phase information, the phased tracing method has a definite benefit in taking the interference phenomenon at mid frequencies into account. Still, this analysis technique has suffered from difficulties in dealing with low frequency phenomena, so called, wave nature of sound. At low frequencies, diffraction at corners, edges, and obstacles can cause errors in simulating the transfer function and the impulse response. Due to the use of real valued absorption coefficient, simulated results have shown a discrepancy with measured data. Thus, incorrect phase of the reflection characteristic of a wall should be corrected. In this work, the uniform theory of diffraction was integrated into the phased beam tracing method (PBTM) and the result was compared to the ordinary PBTM. By changing the phase of the reflection coefficient, effects of phase information were investigated. Incorporating such error compensation methods, the acoustic prediction by PBTM can be further extended to low frequency range with improved accuracy in the room acoustic field.

• Journal of the Korean Ceramic Society
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• v.42 no.7 s.278
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• pp.449-454
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• 2005

A waveguide method is developed to study the materials with relatively large dielectric constants at microwave range. Basically, the method is similar to the previous waveguide methods represented by short-circuit line and transmission/reflection measurement methods. However, the complex permittivity is not determined by the shift in resonance frequencies, but by numerical analysis of measured scattering parameters. In order to enhance microwave penetration into the specimen with relatively large permittivity, a dielectric plate with lower permittivity is employed for impedance matching. The influences of air gap between the specimen and waveguide wall are evaluated, and the corresponding errors are estimated. The propagation of higher order modes is also considered. Experimental results for several reference ceramics are presented.

• Environmental Engineering Research
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• v.22 no.3
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• pp.302-311
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• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.

• 전기의세계
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• v.30 no.11
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• pp.734-746
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• 1981

This paper suggests on-load torque measurement for 3 phase induction motors by input -voltage and current utilizing symmetric coordinate analysis technique on the basis of the induction motor equivalent circuit. In this paper, two cases are treated with, i.e, one is the case where the motors' exciting current and primary leakage impedance voltage drop are compensated automatically, adopting the ideal wattmeter whose current coil impedance and voltage coil impedance are 0 and .inf. respectively, and the other is the case where non-ideal wattmeter is adopted and the compensation above is made by computation. As a result of the case study, following conclusions are obtained. 1) By proper combination of the error propagation law and the limit of power consumption, the desirable overall measurement error of the apparatus can be obtained on the basis of the inherent errors of CT and PT. 2) The measurement error is larger in current simulation circuit than in voltage simulation circuit. 3) Between the two cases, the latter is more advantageous than the former from the viewpoint of feasibility and the measurement error. 4) As the attachment of Ammeter in the current simulation circuit influences the measurement error considerably, its internal impedance should be large considerably. 5) The larger the consumption power of the apparatus is, the easier the feasibility is.