• International Journal of Precision Engineering and Manufacturing
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• v.9 no.1
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• pp.12-18
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• 2008

This paper describes two run-to-run controllers, a nonlinear multiple exponential-weight moving-average (NMEWMA) controller and a dynamic model-tuning minimum-variance (DMTMV) controller, for photolithography processes. The relationships between the input recipes (exposure dose and focus) and output variables (critical dimensions) were formed using an experimental design method, and the photolithography process model was built using a multiple regression analysis. Both the NMEWMA and DMTMV controllers could update the process model and obtain the optimal recipes for the next run. Quantified improvements were obtained from simulations and real photolithography processes.

• Bulletin of the Korean Mathematical Society
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• v.49 no.1
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• pp.135-143
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• 2012

We present two kinds of construction methods for self-dual codes over $\mathbb{F}_2+u\mathbb{F}_2$. Specially, the second construction (respectively, the first one) preserves the types of codes, that is, the constructed codes from Type II (respectively, Type IV) is also Type II (respectively, Type IV). Every Type II (respectively, Type IV) code over $\mathbb{F}_2+u\mathbb{F}_2$ of free rank larger than three (respectively, one) can be obtained via the second construction (respectively, the first one). Using these constructions, we update the information on self-dual codes over $\mathbb{F}_2+u\mathbb{F}_2$ of length 9 and 10, in terms of the highest minimum (Hamming, Lee, or Euclidean) weight and the number of inequivalent codes with the highest minimum weight.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.73-84
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• 2020

As the Internet began to become popular, there have been hacking and attacks on networks including systems, and as the techniques evolved day by day, it put risks and burdens on companies and society. In order to alleviate that risk and burden, it is necessary to detect hacking and attacks early and respond appropriately. Prior to that, it is necessary to increase the reliability in detecting network intrusion. This study was conducted on applying weight initialization and weight optimization to the KDD'99 dataset to improve the accuracy of detecting network intrusion. As for the weight initialization, it was found through experiments that the initialization method related to the weight learning structure, like Xavier and He method, affects the accuracy. In addition, the weight optimization was confirmed through the experiment of the network intrusion detection dataset that the Adam algorithm, which combines the advantages of the Momentum reflecting the previous change and RMSProp, which allows the current weight to be reflected in the learning rate, stands out in terms of accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.895-904
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• 2007

This paper proposes a robust sub-optimal smart antenna in Code Division Multiple Access (CDMA) basestation. It makes use of the property of the Least Mean Square (LMS) algorithm and the Conjugate Gradient Method (CGM) algorithm for beamforming processes. The weight update takes place at symbol level which follows the PN correlators of receiver module under the assumption that the post correlation desired signal power is far larger than the power of each of the interfering signals. The proposed algorithm is simple and has as low computational load as five times of the number of antenna elements(O(5N)) as a whole per each snapshot. The output Signal to Interference plus Noise Ratio (SINR) of the proposed smart antenna system when the weight vector reaches the steady state has been examined. It has been observed in computer simulations that proposed beamforming algorithm improves the SINR significantly compared to the single antenna case. The convergence property of the weight vector has also been investigated to show that the proposed hybrid algorithm performs better than CGM and LMS during the initial stage of the weight update iteration. The Bit Error Rate (BER) characteristics of the proposed array has also been shown as the processor input Signal to Noise Ratio (SNR) varies.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.691-696
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• 2018

We propose an algorithm to update weight to use the mean square error method and mutual coupling matrix in a coherent channel. The algorithm proposed in this paper estimates the desired signal by using the updated weight. The updated weight is obtained by covariance matrix using mean square error and mutual coupling matrix. The MUSIC algorithm, which is direction of arrival estimation method, is mostly used in the desired signal estimation. The MUSIC algorithm has a good resolution because it uses subspace techniques. The proposed method estimates the desired signal by updating the weights using the mutual coupling matrix and mean square error method. Through simulation, we analyze the performance by comparing the classical MUSIC and the proposed algorithm in a coherent channel. In this case of the coherent channel for estimating at the three targets (-10o, 0o, 10o), the proposed algorithm estimates all the three targets (-10o, 0o, 10o). But the classical MUSIC algorithm estimates only one target (x, x, 10o). The simulation results indicate that the proposed method is superior to the classical MUSIC algorithm for desired signal estimation.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.9
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• pp.45-53
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• 2015

Adaptive support-weight based algorithm can produce better disparity map compared to generic area-based algorithms and also can be implemented as a realtime system. In this paper, we propose a realtime system based on geodesic support-weight which performs better segmentation of objects in the window. The data scheduling is analyzed for efficient hardware design and better performance and the parallel architecture for weight update which takes the longest delay is proposed. The exponential function is efficiently designed using a simple step function by careful error analysis. The proposed architecture is designed with verilogHDL and synthesized using Donbu Hitek 0.18um standard cell library. The proposed system shows 2.22% of error rate and can run up to 260Mhz (25fps) operation frequency with 182K gates.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.112-124
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• 2004

A neural network based adaptive reconfigurable flight controller is presented for a class of discrete-time nonlinear flight systems in the presence of variations of aerodynamic coefficients and control effectiveness decrease caused by control surface damage. The proposed adaptive nonlinear controller is developed making use of the backstepping technique for the angle of attack, sideslip angle, and bank angle command following without two time separation assumption. Feedforward multilayer neural networks are implemented to guarantee reconfigurability for control surface damage as well as robustness to the aerodynamic uncertainties. The main feature of the proposed controller is that the adaptive controller is developed under the assumption that all of the nonlinear functions of the discrete-time flight system are not known accurately, whereas most previous works on flight system applications even in continuous time assume that only the nonlinear functions of fast dynamics are unknown. Neural networks learn through the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. The boundness of the error states and neural networks weight estimation errors is also investigated by the discrete-time Lyapunov derivatives analysis. To show the effectiveness of the proposed control law, the approach is i]lustrated by applying to the nonlinear dynamic model of the high performance aircraft.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.2
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• pp.158-165
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• 2003

Image similarity is often decided by computing the distance between two feature vectors. Unfortunately, the feature vector cannot always reflect the notion of similarity in human perception. Therefore, most current image retrieval systems use weights measuring the importance of each feature. In this paper new initial weight selection and update rules are proposed for image retrieval purpose. In order to obtain the purpose, database images are first divided into groups based on human perception and, inner and outer query are performed, and, then, optimal feature weights for each database images are computed through searching the group where the result images among retrieved images are belong. Experimental results on 2000 images show the performance of proposed algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.119-126
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• 2004

Location management is very important issue in wireless communication system to trace mobile users' exact location. In this study, we propose a dynamic location area management scheme which determines the size of dynamic location area considering each user's characteristic. In determining the optimal location area size, we consider the measurement data as well as the historical data, which contains call arrival rate and average speed of each mobile user. In this mixture of data, the weight of historical data is derived by linear searching method which guarantees the minimal cost of location management. We also introduce the regularity index which can be calculated by using the autocorrelation of historical data itself. Statistical validation shows that the regularity index is the same as the weight of measurement data. As a result, the regularity index is utilized to incorporate the historical data into the measurement data. By applying the proposed scheme, the location management cost is shown to decrease. Numerical examples illustrate such an aspect of the proposed scheme.

• The Journal of Information Technology
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• v.4 no.1
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• pp.71-79
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• 2001

This paper presents an adaptive back propagation algorithm that update the learning parameter by the generated error, adaptively and configuration of the range for the initial connecting weight according to the different maximum target value from minimum target value. This algorithm is expected to escaping from the local minimum and make the best environment for the convergence. On the simulation tested this algorithm on three learning pattern. The first was 3-parity problem learning, the second was $7{\times}5$ dot alphabetic font learning and the third was handwritten primitive strokes learning. In three examples, the probability of becoming trapped in local minimum was reduce. Furthermore, in the alphabetic font and handwritten primitive strokes learning, the neural network enhanced to loaming efficient about 27%~57.2% for the standard back propagation(SBP).