• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.716-718
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• 2004

The false alarm data in intrusion detection systems are divided into false positive and false negative. The false positive makes bad effects on the performance of intrusion detection system. And the false negative makes bad effects on the efficiency of intrusion detection system. Recently, the most of works have been studied the data mining technique for analysis of alert data. However, the false alarm data not only increase data volume but also change patterns of alert data along the time line. Therefore, we need a tool that can analyze patterns that change characteristics when we look for new patterns. In this paper, we focus on the false positives and present a framework for analysis of false alarm pattern from the alert data. In this work, we also apply incremental data mining techniques to analyze patterns of false alarms among alert data that are incremental over the time. Finally, we achieved flexibility by using dynamic support threshold, because the volume of alert data as well as included false alarms increases irregular.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2129-2147
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• 2019

This paper introduces an adaptive face recognition method based on a Novel Incremental Kernel Nonparametric Discriminant Analysis (IKNDA) that is able to learn through time. More precisely, the IKNDA has the advantage of incrementally reducing data dimension, in a discriminative manner, as new samples are added asynchronously. Thus, it handles dynamic and large data in a better way. In order to perform face recognition effectively, we combine the Gabor features and the ordinal measures to extract the facial features that are coded across local parts, as visual primitives. The variegated ordinal measures are extraught from Gabor filtering responses. Then, the histogram of these primitives, across a variety of facial zones, is intermingled to procure a feature vector. This latter's dimension is slimmed down using PCA. Finally, the latter is treated as a facial vector input for the advanced IKNDA. A comparative evaluation of the IKNDA is performed for face recognition, besides, for other classification endeavors, in a decontextualized evaluation schemes. In such a scheme, we compare the IKNDA model to some relevant state-of-the-art incremental and batch discriminant models. Experimental results show that the IKNDA outperforms these discriminant models and is better tool to improve face recognition performance.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.9
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• pp.1073-1080
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• 2012

Incremental forming is a cold working process in which a small part of the material is being deformed and the area of local deformation is moving over the entire material. In this paper, we study description schemes to perform finite element analysis for the incremental forming. The selected description schemes to examine are the Lagrangian description and the arbitrary Lagrangian-Eulerian (ALE) description. The sliding boundary scheme coupled with ALE is also examined to overcome the distortion problems of elements on the contact surface. Results show that the ALE description with the sliding boundary scheme is most favorable in overcoming the distortion of elements. This description leads to make the simulation continued to the final stage of the incremental forming. On the other hand, the Lagrangian description as well as the original ALE description makes the elements much distorted and the analysis is stopped long before arriving at the final shape of deformation.

• Journal of Power Electronics
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• v.12 no.1
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• pp.164-171
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• 2012

Variable step size maximum power point trackers (MPPTs) are widely used in photovoltaic (PV) systems to extract the peak array power which depends on solar irradiation and array temperature. One essential factor which judges system dynamics and steady state performances is the scaling factor (N), which is used to update the controlling equation in the tracking algorithm to determine a new duty cycle. This paper proposes a novel stability study of variable step size incremental resistance maximum power point tracking (INR MPPT). The main contribution of this analysis appears when developing the overall small signal model of the PV system. Therefore, by using linear control theory, the boundary value of the scaling factor can be determined. The theoretical analysis and the design principle of the proposed stability analysis have been validated using MATLAB simulations, and experimentally using a fixed point digital signal processor (TMS320F2808).

• Transactions of the Korean Society of Automotive Engineers
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• v.3 no.1
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• pp.97-107
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• 1995

Global postbuckling analysis is accomplished for one-dimensional and two-dimensional delaminations. A new finite element model, which can be used to model the global postbuckling analysis of one-dimensional and two-dimensional delaminations, is presented. In order to calculate the strain energy release rate, geometrically nonlinear analysis is accomplished, and the incremental crack closure technique is introduced. To check the effectiveness of the finite element models and the incremental crack closure technique, the simplified closed-form sloution for a through-the-width delamination with plane strain condition is derived and compared with the finite element result. The finite element results show good agreement with the closed-foul1 solutions. The present method was extended to calculate the strain energy release rate for two-dimensional delamination. For a symmetric circular delamination, the strain energy release rate shows great variation along the delamination front. and the delamination growth appears to occur perpendicular to the loading direction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2496-2511
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• 2014

Face recognition is considered a complex biometrics in the field of image processing mainly due to the constraints imposed by variation in the appearance of facial images. These variations in appearance are affected by differences in expressions and/or occlusions (sunglasses, scarf etc.). This paper discusses incremental Kernel Fisher Discriminate Analysis on sub-classes for dealing with partial occlusions and variant expressions. This framework focuses on the division of classes into fixed size sub-classes for effective feature extraction. For this purpose, it modifies the traditional Linear Discriminant Analysis into incremental approach in the kernel space. Experiments are performed on AR, ORL, Yale B and MIT-CBCL face databases. The results show a significant improvement in face recognition.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.177-183
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• 2015

Incremental sheet metal forming is a manufacturing process to produce thin parts using sheet metals by a series of small incremental deformation. The process rarely needs dedicated dies and molds, thus, preparation time for the process is relatively short as to be compared to conventional metal forming. Spring back in sheet metal working is very common, which causes critical errors in dimensions. Incremental sheet metal forming is not fully investigated yet. Hence, incremental sheet metal forming frequently produces inaccurate parts. This paper proposes a method to minimize dimensional errors to improve shape accuracy of products manufactured by incremental forming. This study conducts experiments using an exclusive incremental forming machine and the material for these experiments are sheets of aluminum AL1015. This research defines a process parameter and selects a few factors for the experiments. The parameters employed in this paper are tool feed rate, tool diameter, step depth, material thickness, forming method, dies applied, and tool path method. In addition, their levels for each factor are determined. The plan of the experiments is designed using orthogonal array $L_8$ ($2^7$) which requires minimum number of experiments. Based on the measurements, dimensional errors are collected both on the tool contacted surfaces and on the non-contacted surfaces. The distances between the formed surfaces and the CAD models are scanned and recorded using a commercial software product. These collected data are statistically analyzed and ANOVAs (analysis of variances) are drawn up. From the ANOVAs, this paper concludes that the process parameters of tool diameter, forming depth, and forming method are the significant factors to reduce the errors on the tool contacted surface. On the other hand, the experimental factors of forming method and dies applied are the significant factors on the non-contacted surface. However, the negative forming method always produces better accuracy than the positive forming method.

• Journal of KIISE
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• v.43 no.12
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• pp.1396-1403
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• 2016

In streaming data analysis where underlying data distribution may be changed or the concept of interest can drift with the progress of time, the ability to adapt to concept drift can be very powerful especially in the process of incremental learning. In this paper, we develop a general framework for an adaptive incremental classifier on data stream with concept drift. A distribution, representing the performance pattern of a classifier, is constructed by utilizing the distance between the confidence score of a classifier and a class indicator vector. A hypothesis test is then performed for concept drift detection. Based on the estimated p-value, the weight of outdated data is set automatically in updating the classifier. We apply our proposed method for two types of linear discriminant classifiers. The experimental results on streaming data with concept drift demonstrate that the proposed adaptive incremental learning method improves the prediction accuracy of an incremental classifier highly.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.10
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• pp.941-950
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• 2011

In this paper, we analysis the end-to-end performance of the incremental cooperative communication with relay selection. In the conventional cooperative scheme, the source(S) broadcasts the signal to the relay(R) and the destination(D) at 1st phase, and the R forwards the signal to the D at 2nd phase. Although this scheme can improve performance and provide diversity gain, it suffers from decreasing spectrum efficiency. In order to overcome this problem, the incremental cooperative model can be used. In this paper, we study two incremental cooperative method : the first uses ARQ with threshold SNR and the second uses HARQ with channel coding. we also evaluated performance of the incremental cooperative communication based on the R arrangement by using both methods.

• ETRI Journal
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• v.40 no.3
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• pp.291-302
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• 2018

The deployment of an incremental hybrid decode-amplify and forward relaying scheme is a promising and superior solution for cellular networks to meet ever-growing network traffic demands. However, the selection of a suitable relaying protocol based on the signal-to-noise ratio threshold is important in realizing an improved quality of service. In this paper, an incremental hybrid relaying protocol is proposed using polar codes. The proposed protocol achieves a better performance than existing turbo codes in terms of capacity. Simulation results show that the polar codes through an incremental hybrid decode-amplify-and-forward relay can provide a 38% gain when ${\gamma}_{th(1)}$ and ${\gamma}_{th(2)}$ are optimal. Further, the channel capacity is improved to 17.5 b/s/Hz and 23 b/s/Hz for $2{\times}2$ MIMO and $4{\times}4$ MIMO systems, respectively. Monte Carlo simulations are carried out to achieve the optimal solution.