• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.10A
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• pp.1003-1013
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• 2006

There is a serious problem in an 802.11g VoWLAN (Voice over Wireless LAN) terminal that talk time is less than 30% compared with an 802.11b terminal. It is almost impossible to achieve talk time level of the 802.11b MAC transmission method because IEEE 802.11g uses OFDM modulation, which is a kind of multi-carrier method and OFDM transmission speed is 54 Mbps faster than normal modulation. In this paper, a new concept of a Holdover time as a power saving method during a call with 802.11g terminal is suggested for the first time. Increase in the number of engaged terminals as a result of holdover time causes to QoS problem because of the increase in the number of back-off and then contention window. In this paper, to solve the QoS problem, a new approach is suggested such that when in down lint the sequence number of 802.11 G.711 is analyzed in the MAC of the terminal and then the Hold over time depending on loss rate is changed. Also, consumption of an electric current of 802.11b/g and MAC parameter's performance due to busy traffic caused by increase in the number of terminal are analyzed and then real data using VQT and Airopeek are analyzed.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.39-47
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• 2009

Recently, the threat of DDoS attacks is increasing and many companies are planned to deploy the DDoS defense solutions in their networks. The DDoS attack usually transmits heavy traffic data to networks or servers and they cannot handle the normal service requests because of running out of resources. Since it is very hard to prevent the DDoS attack beforehand, the strategic plan is very important. In this work, we have conducted modeling and simulation of the DDoS attack by changing the number of servers and estimated the duration that services are available. In this work, the modeling and simulation is conducted using OPNET Modeler. The simulation result can be used as a parameter of trade-off analysis of DDoS defense cost and the service's value. In addition, we have presented a way of estimating the cost effectiveness in deployment of the DDoS defense system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.33-40
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• 2009

The critical lag of crosswalk pedestrians is an important parameter in analyzing traffic operation at unsignalized crosswalks, however there is few research in this field in Korea. The purpose of this study is to develop a model to estimate the critical lag. Among the elements which influence the critical lag, the age of pedestrians and the length of crosswalks, which have fuzzy characteristics, and the each lag which is rejected or accepted are collected on crosswalks of which lengths range from 3.5 m to 10.5 m. The values of the critical lag range from 2.56 sec. to 5.56 sec. The age and the length are divided to the 3 fuzzy variables each, and the critical lag of each case is estimated according to Raff's technique, so a total of 9 fuzzy rules are established. Based on the rules, an ANFIS (Adaptive Neuro-Fuzzy Inference System) model to estimate the critical lag is built. The predictability of the model is evaluated comparing the observed with the estimated critical lags by the model. Statistics of $R^2$, MAE, MSE are 0.96, 0.097, 0.015 respectively. Therefore, the model is evaluated to explain the result well. During this study, it is found that the critical lag increases rapidly over the pedestrian's age of 40 years.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1A
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• pp.11-19
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• 2006

In this study ship collision risk analysis is performed to determine the design vessel for collision impact analysis of suspension bridge. Method II in AASHTO LRFD bridge design specifications which is a more complicated probability based analysis procedure is used to select the design vessel for collision impact. From the assessment of ship collision risk for each bridge pier exposed to ship collision, the design impact lateral strength of bridge pier is determined. The analysis procedure is an iterative process in which a trial impact resistance is selected for a bridge component and a computed annual frequency of collapse(AF) is compared to the acceptance criterion, and revisions to the analysis variables are made as necessary to achieve compliance. The acceptance criterion is allocated to each pier using allocation weights based on the previous predictions. This AF allocation method is compared to the pylon concentration allocation method to obtain safety and economy in results. This method seems to be more reasonable than the pylon concentration allocation method because AF allocation by weights takes the design parameter characteristics quantitatively into consideration although the pylon concentration allocation method brings more economical results when the overestimated design collision strength of piers compared to the strength of pylon is moderately modified. The design vessel for each pier corresponding with the design impact lateral strength obtained from the ship collision risk assessment is then selected. The design impact lateral strength can vary greatly among the components of the same bridge, depending upon the waterway geometry, available water depth, bridge geometry, and vessel traffic characteristics. Therefore more researches on the allocation model of AF and the selection of design vessel are required.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.155-159
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• 2004

Recently, there are many trials about Artificial neural networks : ANNs structure and studying method of researches for forecasting traffic volume. ANNs have a powerful capabilities of recognizing pattern with a flexible non-linear model. However, ANNs have some overfitting problems in dealing with a lot of parameters because of its non-linear problems. This research deals with the application of a variety of model selection criterion for cancellation of the overfitting problems. Especially, this aims at analyzing which the selecting model cancels the overfitting problems and guarantees the transferability from time measure. Results in this study are as follow. First, the model which is selecting in sample does not guarantees the best capabilities of out-of-sample. So to speak, the best model in sample is no relationship with the capabilities of out-of-sample like many existing researches. Second, in stability of model selecting criterion, AIC3, AICC, BIC are available but AIC4 has a large variation comparing with the best model. In time-series analysis and forecasting, we need more quantitable data analysis and another time-series analysis because uncertainty of a model can have an effect on correlation between in-sample and out-of-sample.