• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2002.11a
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• pp.524-527
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• 2002

본 연구에서는 Support Vector Machine(SVM)을 이용한 호스트 기반 침임 탐지 방법을 제안한다. 침입 탐지는 침입과 정상을 판단하는 이진분류 문제이므로 이진분류에 뛰어난 성능을 발휘하는 SVM을 이용하여 침입 탐지 시스템을 구현하였다. 먼저 감사자료를 system call level에서 분석한 후, sliding window기법에 의해 패턴 feature를 추출하고 training set을 구성하였다. 여기에 SVM을 적용하여 decision model을 생성하였고, 이에 대한 판정 테스트 결과 90% 이상의 높은 침입탐지 적중률을 보였다.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.234-236
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• 2004

Transmission control protocol(TCP) is protocol used in internet. TCP is seldom transmission error and is protocol based on wire environment. TCP uses 3 way handshake ways, data transmission control through windows size, data transmission control through reception confirmation, sliding window for packet delivery. In this study, designed TCP packet ion module for analyze the TCP segments & correct information about TCP. TCP capture in internet using designed TCP module and analysed TCP segments composition. Through this, could analyze the correct information of protocol in network.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.167.5-167
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• 2001

Single point attitude determination method provides an optimal attitude minimizing the Wahba loss function. However, for the insufficient number of measurement vectors, the conventional single point methods has no unique solution. Thus, we introduce the sequential method to and an optimal attitude minimizing the windowed loss function. In this paper, this function is de ned as the sum of square errors for all measurement vectors within the axed sliding window. For simple implementation, the proposed algorithm is rewritten as a recursive form. Moreover, the covariance matrix is derived and expressed as a recursive form. Finally, we apply this algorithm to the attitude determination system with three LOS measurement sensors.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.129-135
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• 2016

In this paper, we propose a novel tracking algorithm called slide window tracker (SWT) suitable for maneuvering target. To efficiently estimate trajectory of moving target, we adopt a sliding piecewise linear window which includes past trace information. By adjusting the window parameters, the proposed algorithm is to reduce measurement noise and to track fast maneuvering target with little computational increment as compared to ${\alpha}-{\beta}$ tracker. Throughout the computer simulations, we verify outstanding tracking performance of the SWT algorithm in noisy linear and nonlinear trajectories. Also, we show that the SWT algorithm is not sensitive to initial model parameter selection, which gives large degree of freedom in applying the SWT algorithm to unknown time-varying measurement environments.

• Science of Emotion and Sensibility
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• v.21 no.3
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• pp.83-102
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• 2018

The process model of emotion regulation suggests that cognitive reappraisal and expressive suppression engage at different time points in the regulation process. Although multiple brain regions and networks have been identified for each strategy, no articles have explored changes in network characteristics or network connectivity over time. The present study examined (a) the whole-brain network and six other resting-state networks, (b) their modularity and global efficiency, which is an index of the efficiency of information exchange across the network, (c) the degree and betweenness centrality for 160 brain regions to identify the hub nodes with the most control over the entire network, and (d) the intra-network and inter-network functional connectivity (FC). Such investigations were performed using a traditional large-scale FC analysis and a relatively recent sliding window correlation analysis. The results showed that the right inferior orbitofrontal cortex was the hub region of the whole-brain network for both strategies. The present findings of temporally altering functional activity of the networks revealed that the default mode network (DMN) activated at the early stage of reappraisal, followed by the task-positive networks (cingulo-opercular network and fronto-parietal network), emotion-processing networks (the cerebellar network and DMN), and sensorimotor network (SMN) that activated at the early stage of suppression, followed by the greater recruitment of task-positive networks and their functional connection with the emotional response-related networks (SMN and occipital network). This is the first study that provides neuroimaging evidence supporting the process model of emotion regulation by revealing the temporally varying network efficiency and intra- and inter-network functional connections of reappraisal and suppression.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.175-182
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• 2017

Noises are usually generated by various external causes and low quality devices in image data acquisition and recording as well as by channel interference in image transmission. Since these noise signals result in the loss of information, subsequent image processing is subject to the corruption of the original image. In general, image processing is performed in the mixed noise environment where common types of noise, known to be Gaussian and impulse, are present. This study proposes an iterative weighted mean filter for reducing mixed type of noise. Impulse noise pixels are first turned off in the input image, then $3{\times}3$ sliding window regions are processed by replacing center pixel with the result of weighted mean mask operation. This filtering processes are iterated until all the impulse noise pixels are replaced. Applied to images corrupted by Gaussian noise with ${\sigma}=10$ and different levels of impulse noise, the proposed filtering method improved the PSNR by up to 12.98 dB, 1.97 dB, 1.97 dB respectively, compared to SAWF, AWMF, MMF when impulse noise desities are less than 60%.

• Journal of IKEEE
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• v.10 no.1 s.18
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• pp.30-39
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• 2006

The TCP protocol can provide some reliability using sliding window mechanism for data transmission, flow control, and congestion control. However, TCP has some limitations in that it has basically been designed solely for wired communication environments. If traditional TCP protocol is used also in wireless networks, the end-to-end data transmission performance degrades dramatically due to frequent packet losses caused by transmission errors and hand-offs. While there have been some research efforts on TCP enhancements considering the mobility of wireless communication devices, in this paper we propose a new method to improve the TCP performance by combining the Snoop and the Freeze-TCP methods. In the proposed scheme, the TCP end-to-end semantics is maintained and no changes of existing protocols in sending systems or in routers are required. It has the advantage of simple implementation because TCP code changes are limited to mobile devices for applying the Freeze-TCP and it requires only to add Snoop modules in base stations. Accordingly, the proposed scheme can operate well in the existing networks. Finally, in this study, we compared the performance of the proposed scheme with traditional TCP, other approaches through simulations using ns-2.

• Journal of Internet Computing and Services
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• v.8 no.4
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• pp.41-49
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• 2007

Ethernet passive optical network(EPON) is the next-generation technology for supporting services of high-quality at low-cost. In the EPON, all optical network units(ONUs) have to share a limited uplink channel for upstream data. In order to satisfy bandwidth demands of users on high-capacity local access networks(LANs), the optical line terminal(OLT) efficiently divides and allocates time slots of uplink channel to all ONUs. We discuss previous schemes for dynamic bandwidth allocation(DBA), such as interleaved polling with adaptive cycle time(IPACT) and sliding cycle time(SLICT). In this paper, dynamic right sizing of maximum-windows(DRSM), as a novel bandwidth allocation service, is proposed for more effective and efficient time slot allocation of the uplink channel. DRSM which is based on past information of bandwidth allocated by OLT calculates maximum available bandwidth and dynamically alters the maximum window size for the next ONU. This scheme does not only exert every effort to meet bandwidth demands of ONUs with the possible scope, it also seeks fairness of bandwidth allocation among ONUs.

• KIEAE Journal
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• v.14 no.3
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• pp.87-95
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• 2014

The purpose of this study is to evaluate the correlation coefficients between the airtightness and sound insulation performance of Han-style windows in New Han-ok. To achieve these goals, field measurements were accomplished in 18 bedrooms of 16 Han-oks in which actual residents were living, and then lab measurements were proceeded in the reverberation lab for evaluating the sound insulation performance. Followings are results. The results of the correlation analysis between the airtightness(Air change per hour at 50 Pa, ACH50) and sound insulation performance(Sound reduction index, Rw) in bedrooms of actual Han-oks, it was found that there were no significant correlation between two evaluating values. On the other hand, it was analyzed that the correlation coefficients of total 24 structures(double casement windows, single casement window, casement and sliding windows, single sliding window, 6 types per each structure) were located on 0.6757 exponentially and 0.4154 lineary in the lab evaluating conditions. But, The results of evaluating 4 structure classificatorily, it was found that there were high correlation coefficients(0.8665~0.9273 at ACH50, 0.8414~0.9346 at Rw). These results were signified that the correlation coefficients were changed according to the each structure and case by case analysis were necessary at the same time.

• The Journal of Information Systems
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• v.29 no.1
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• pp.241-265
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• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.