• Journal of Communications and Networks
• /
• v.14 no.3
• /
• pp.310-318
• /
• 2012

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.

• Nuclear Engineering and Technology
• /
• v.34 no.4
• /
• pp.406-414
• /
• 2002

In TSPA of long-term post closure radiological safety on permanent disposal of HLW in Korea, appropriate management of input and output data through QA is necessary. The robust QA system is developed using the T2R3 principles applicable for five major steps in R&D＇s. The proposed system is implemented in the web-based system so that all participants in TSPA are able to access the system. In addition, the internet based input database for TSPA is developed. Currently data from literature surveys, domestic laboratory and field experiments as well as expert elicitation are applied for TSPA.

• International journal of advanced smart convergence
• /
• v.9 no.4
• /
• pp.156-166
• /
• 2020

In this paper, to improve spectral efficiency and mitigate interference in coordinated millimeter-wave systems, we proposes an optimal user group and beam selection scheme. The proposed scheme improves spectral efficiency by mitigating intra- and inter-cell interferences (ICI). By examining the effective channel capacity for all possible user combinations, user combinations and beams with minimized ICI can be selected. However, implementing this in a dense environment of cells and users requires highly complex computational abilities, which we have investigated applying multiclass classifiers based on machine learning. Compared with the conventional scheme, the numerical results show that our proposed scheme can achieve near-optimal performance, making it an attractive option for these systems.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.10
• /
• pp.2601-2619
• /
• 2012

A differencing multiuser detection (MUD) method is proposed for multiple-input multiple-output (MIMO) direct sequence (DS) ultra-wideband (UWB) system to cope with the multiple access interference (MAI) and the computational efficiency in Nakagami fading channel. The method, which combines a multiuser-interference-cancellation-based decision feedback equalizer using error feedback filter (MIC DFE-EFF), a coefficient optimization algorithm (COA) and a differencing algorithm (DA), is termed as MIC DFE-EFF (COA) with DA for short. In the paper, the proposed MUD method is illuminated from the rudimental MIC DFE-EFF to the advanced MIC DFE-EFF (COA) with DA step by step. Firstly, the MIC DFE-EFF system performance is analyzed by minimum mean square error criterion. Secondly, the COA is investigated for optimization of each filter coefficient. Finally, the DA is introduced to reduce the computational complexity while sacrificing little performance. Simulations show a significant performance gain can be achieved by using the MIC DFE-EFF (COA) with DA detector. The proposed MIC DFE-EFF (COA) with DA improves both bit error rate performance and computational efficiency relative to DFE, DFE-EFF, parallel interference cancellation (PIC), MIC DFE-EFF and MIC DFE-EFF with DA, though it sacrifices little system performance, compared with MIC DFE-EFF (COA) without DA.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.8
• /
• pp.3-10
• /
• 2014

Millimeter wave (mmWave) has attracted great interest recently and the necessity of Millimeter Mobile Broadband (MMB) system has appeared based on the 4 Generation Long Term Evolution-Advanced (LTE-A) Specification. Currently, there are many studies about the mmWave communication channel. And it is subject of interest to analyze the performance in MMB channel environments. In this paper, we design the MMB system for 5th Generation mobile communication and propose channel models through the analysis of the mmWave propagation characteristics. Also, we have analyzed the performance of the MMB system of 28 GHz band in MMB channel environments.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39B no.7
• /
• pp.440-448
• /
• 2014

A $4{\times}10$ Gb/s Transimpedance Amplifier (TIA) array is implemented in $0.13{\mu}m$ CMOS process technology, which will be used in the receiver of TWDM-PON system. A technology for bandwidth enhancement of a given $4{\times}10$ Gb/s TIA presented under inductor peaking technology and a single 1.2V power supply based low voltage design technology. It achieves 3 dB bandwidth of 7 GHz in the presence of a 0.5 pF photodiode capacitance. The trans-resistance gain is $50dB{\Omega}$, while 48 mW/ 1channel from a 1.2 V supply. The input sensitivity of the TIA is -27 dBm. The chip size is $1.9mm{\times}2.2mm$.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.4
• /
• pp.1307-1323
• /
• 2014

With the exhaustion of global IPv4 addresses, IPv6 technologies have attracted increasing attentions, and have been deployed widely. Meanwhile, new applications running over IPv6 networks will change the traditional traffic characteristics obtained from IPv4 networks. Traditional models obtained from IPv4 cannot be used for IPv6 network monitoring directly and there is a need to investigate those changes. In this paper, we explore the flow features of IPv6 traffic and compare its difference with that of IPv4 traffic from flow level. Firstly, we analyze the differences of the general flow statistical characteristics and users' behavior between IPv4 and IPv6 networks. We find that there are more elephant flows in IPv6, which is critical for traffic engineering. Secondly, we find that there exist many one-way flows both in the IPv4 and IPv6 traffic, which are important information sources for abnormal behavior detection. Finally, in light of the challenges of analyzing massive data of large-scale network monitoring, we propose a group flow model which can greatly reduce the number of flows while capturing the primary traffic features, and perform a comparative measurement analysis of group users' behavior dynamic characteristics. We find there are less sharp changes caused by abnormity compared with IPv4, which shows there are less large-scale malicious activities in IPv6 currently. All the evaluation experiments are carried out based on the traffic traces collected from the Northwest Regional Center of CERNET (China Education and Research Network), and the results reveal the detailed flow characteristics of IPv6, which are useful for traffic management and anomaly detection in IPv6.

• Journal of Internet Computing and Services
• /
• v.21 no.5
• /
• pp.119-127
• /
• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.12
• /
• pp.3330-3344
• /
• 2023

This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.12
• /
• pp.4823-4827
• /
• 2014

Background: Since inconsistent results have been reported regarding the relation between the matrix metalloproteinase-2 (MMP-2) -1306C>T polymorphism and susceptibility for breast cancer, we performed a meta-analysis to investigate the issue. Materials and Methods: An internet search of PubMed and EMBASE was performed to identify eligible studies. Pooled odds ratios (ORs) with their corresponding confidence intervals (CIs) were calculated to evaluate any association between MMP-2 -1306C>T polymorphism and breast cancer susceptibility. Results: Nine case-control studies were included in the meta-analysis, involving 9,858 cases and 10,871 controls. Overall, there was no evidence of any association between the MMP-2 -1306C>T polymorphism and breast cancer susceptibility in different genetic models (T-allele vs C-allele: OR=0.95, 95%CI, 0.82-1.10, p=0.49; TT vs CC: OR=1.03, 95%CI, 0.90-1.19, p=0.66; TT+TC vs CC: OR=0.93, 95%CI, 0.78-1.10, p=0.38; TT vs TC+CC: OR=1.02, 95%CI, 0.89-1.17, p=0.77). In the subgroup analysis by ethnicity, CC was associated with a significant increase in breast susceptibility among Latin-Americans in the dominant model (OR=0.61, 95%CI, 0.40-0.93, p=0.02), but the association disappeared in other models. No significant association was observed among Europeans, East Asians and others in different genetic models. In the subgroup analysis by their source of controls, no significant association between MMP-2 -1306C>T polymorphism and breast cancer susceptibility was noted among population-based studies and hospital-based studies in different genetic models. Conclusions: The results of this meta-analysis suggest that MMP-2 -1306C>T polymorphism is not associated with breast cancer susceptibility, although the association among Latin-Americans in the dominant model was significant.