• Journal of Multimedia Information System
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• v.5 no.2
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• pp.99-104
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.3
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• pp.107-116
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• 2021

We introduce optimization algorithms using Bregman Divergence for solving non-negative matrix factorization (NMF) problems. Bregman divergence is known a generalization of some divergences such as Frobenius norm and KL divergence and etc. Some algorithms can be applicable to not only NMF with Frobenius norm but also NMF with more general Bregman divergence. Matrix Factorization is a popular non-convex optimization problem, for which alternating minimization schemes are mostly used. We develop the Bregman proximal gradient method applicable for all NMF formulated in any Bregman divergences. In the derivation of NMF algorithm for Bregman divergence, we need to use majorization/minimization(MM) for a proper auxiliary function. We present algorithmic aspects of NMF for Bregman divergence by using MM of auxiliary function.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.380-381
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• 2019

This paper presents a grid voltage-sensorless current control design for an LCL-filtered grid-connected inverter with the purpose of enhancing the reliability and reducing the total cost of system. A disturbance observer based on the gradient steepest descent method is adopted to estimate the grid voltages with high accuracy and light computational burden even under distorted grid conditions. The grid fundamental components are effectively extracted from the estimated gird voltages by means of a least-squares algorithm to facilitate the synchronization process without using the conventional phase-locked loop. Finally, the estimated states of inverter system obtained by a discrete current-type full state observer are utilized in the state feedback current controller to realize a stable voltage-sensorless current control scheme. The effectiveness of the proposed scheme is validated through the simulation results.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.90-99
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• 2021

Recently, studies on wearable devices in ubiquitous computing environments have increased and the technology collecting user's activities to provide services has received great attention. We have compared the step counting methods according to sensor molding methods in case of counting steps by using the piezoelectric sensor. We have classified the cases which could result from the course of molding the internal module of a smart shoe as follows: (i) the module is unmolded, (ii) molded but only to the extent that a sensor is fixed or (iii) molded to the extent that a sensor is not moved. Moreover, we have made comparison to verify which algorithm should be used to increase the accuracy of counting steps by the respective cases. Based on the comparison result, we have confirmed that the accuracy of counting steps is higher when using gradient value rather than when using threshold value. In the case of no molding and small molding under the condition of using gradient value, it was turned out to be 100% accuracy for step counting.

• Annual Conference of KIPS
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• 2011.11a
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• pp.298-301
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• 2011

Edge detection is an base research task in the field of image processing. Edge detection can be regarded as a technique for locating pixels of abrupt gray-level change. So with Gradient method, it can be computed easily. But it can't satisfy human naked eye. so in this paper, new algorithm based on the NLM(Non Local Means) is proposed for good performance for human naked eye.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.203-212
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• 2024

The area under the ROC curve (AUC) is one of the most common criteria used to measure the overall performance of binary classifiers for a wide range of machine learning problems. In this article, we propose a L₁-penalized AUC-optimization classifier that directly maximizes the AUC for high-dimensional data. Toward this, we employ the AUC-consistent surrogate loss function and combine the L₁-norm penalty which enables us to estimate coefficients and select informative variables simultaneously. In addition, we develop an efficient optimization algorithm by adopting k-means clustering and proximal gradient descent which enjoys computational advantages to obtain solutions for the proposed method. Numerical simulation studies demonstrate that the proposed method shows promising performance in terms of prediction accuracy, variable selectivity, and computational costs.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.321-324
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• 1997

In this paper, Active Noise Control(ANC) algorithm is proposed based on the estimated frequency estimator of the reference signal. The conventional feedforward ANC algorithms should measure the reference and use it to calculate the gradient of the squared error and filter coefficients. For ANC systems applied to aircrafts and passenger ships, engines from which reference signal is usually measured is so far from seats where main part of controller is placed that the scheme might be difficult to implement or very costly. Feedback ANC algorithm which doesn't need to measure the reference uses the error signal to update the filter and is sensitive to unexpected transient noise like a sneeze, clapping of hands and so on The proposed algorithm estimates frequencies of the desired signal in real time using adaptive notch filter. New frequency estimation algorithm is proposed with the improved convergence rate, threshold SNR and computational simplicity. Reference is not measured but created with the estimated frequencies. It has strong similarity to the conventional feedback control because reference is made from error signal. Enhanced error signal is used to update the controller for better performance under the measurement noise and impact noise. The proposed ANC algorithm is compared with the conventional feedback control.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.807-812
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• 2007

Recently, Extreme learning machine(ELM), a novel learning algorithm which is much faster than conventional gradient-based learning algorithm, was proposed for single-hidden-layer feedforward neural networks. The initial input weights and hidden biases of ELM are usually randomly chosen, and the output weights are analytically determined by using Moore-Penrose(MP) generalized inverse. But it has the difficulties to choose initial input weights and hidden biases. In this paper, an advanced method using the bacterial foraging algorithm to adjust the input weights and hidden biases is proposed. Experiment at results show that this method can achieve better performance for problems having higher dimension than others.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.11
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• pp.1714-1725
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• 1993

This paper analyzes the properties of such algorithm that corresponds to the nonlinear adaptive algorithm with additional update terms, parameterized by the scalar factors a1, a2, a3 and Presents its structure. The analysis of convergence leads to eigenvalues of the transition matrix for the mean weight vector. Regions in which the algorithm becomes stable are demonstrated. The time constant is derived and the computational complexities of MLMS algorithms are compared with those of the conventional LMS, sign, LFG, and QFG algorithms. The properties of convergence in the mean square are analyzed and the expressions of the mean square recursion and the excess mean square error are derived. The necessary condition for the CFG algorithm to be stable is attained. In the computer simulation applied to the system identification the CFG algorithm has the more computation complexities but the faster convergence speed than LMS, LFG and QFG algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.10
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• pp.2310-2316
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• 1997

This paper presents a comparative performance analysis of the stochastic gradient adaptive algorithm based on the least mean absolute third (LMAT) error criterion with other widely-used competing adaptive algorithms. Under the assumption that the signals involved are zero-mean, wide-sense stationary and Gaussian, approximate expressions that characterize the steady-state mean-squared estimation error of the algorithm is dervied. The validity of our derivation is then confirement by computer simulations. The convergence speed is compared under the condition that the LMAT and other competing algorithms converge to the same value for the mean-squared estimation error in the stead-state, and superior convergence property of the LMAT algorithm is observed. In particular, it is shown that the LMAT algorithm converges faster than other algorithms even through the eignevalue spread ratio of the input signal and measurement noise power change.