• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.6
• /
• pp.651-656
• /
• 2014

Corrosion has a significant influence upon the reliability assessment and the maintenance planning of gas pipeline. Corrosion defects occurred on the underground pipeline can be obtained by conducting periodic in-line inspection (ILI). However, little study has been done for practical use of ILI data. This paper deals with remaining lifetime prediction of the gas pipeline in the presence of corrosion defects. Because a pipeline parameter includes uncertainty in its operation, a probabilistic approach is adopted in this paper. A pipeline fails when its operating pressure is larger than the pipe failure pressure. In order to estimate the failure probability, this paper uses First Order Reliability Method (FORM) which is popular in the field of structural engineering. A well-known Battelle code is chosen as the computational model for the pipe failure pressure. This paper develops a Matlab GUI for illustrating failure probability predictions Our result indicates that clustering of corrosion defects is helpful for improving a prediction accuracy and preventing an unnecessary maintenance.

• Korean Journal of Remote Sensing
• /
• v.28 no.2
• /
• pp.191-202
• /
• 2012

A high resolution satellite imagery such as KOMPSAT-2 includes a material containing rational polynomial coefficient (RPC) for three-dimensional geopositioning. However, image geometries which are calculated from the RPC must have inevitable systematic errors. Thus, it is necessary to correct systematic errors of the RPC using several ground control points (GCPs). In this paper, we propose an efficient method for automatic correction of image geometries using tie points of a stereo pair and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) without GCPs. This method includes four steps: 1) tie points extraction, 2) determination of the ground coordinates of the tie points, 3) refinement of the ground coordinates using SRTM DEM, and 4) RPC adjustment model parameter estimation. We validates the performance of the proposed method using KOMPSAT-2 stereo pair. The root mean square errors (RMSE) achieved from check points (CPs) were about 3.55 m, 9.70 m and 3.58 m in X, Y;and Z directions. This means that we can automatically correct the systematic error of RPC using SRTM DEM.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.3
• /
• pp.997-1005
• /
• 2010

Recently, there has been a growing interest in the use of WLAN technology due to its easy deployment, flexibility and so on. Examples of WLAN applications range from standard internet services such as Web access to real-time services with strict latency/throughput requirements such as multimedia video and voice over IP on wireless network environments. Fair and efficient distribution of the traffic loads among APs(Access Points) has become an important issue for improved utilization of WLAN. This paper focuses on an AP selection scheme for achieving better load balance, and hence increasing network resource utilization for each user on wireless network environments. This scheme makes use of active scan patterns and the network delay as main parameters of load measurement and AP selection. This scheme attempts to estimate the AP traffic loads by observing the up/down delay and utilize the results to maximize the link resource efficiency through load balancing. We compared the proposed scheme with the original SNR(Signal to Noise Ratio)-based scheme using the NS-2(Network Simulation.2). We found that the proposed scheme improves the throughput by 12.5% and lower the network up/down link delay by 36.84% and 60.42%, respectively. All in all, the new scheme can significantly increase overall network throughput and reduce up/down delay while providing excellent quality for voice and video services.

• Korean Chemical Engineering Research
• /
• v.57 no.2
• /
• pp.274-282
• /
• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• Korean Journal of Clinical Laboratory Science
• /
• v.48 no.4
• /
• pp.394-400
• /
• 2016

The b-type natriuretic peptide (BNP) values and increase on functional disorder in the ventricle, and are used as an index to diagnose heart failure and predict the prognosis. BNP values is known to be relevant to dyssystole in congestive heart failure. This study aimed to identify correlation between the BNP values and the items that indicate the diastolic function in echocardiography. The research divided 188 patients who went through the BNP test and echocardiography in the hospital into the groups with the BNP values; <100, 100-300, 301-600, 601-900, and >901 pg/mL. As the BNP values increase, there was relevance with the echocardiography items of ejection fraction, size of left atrium, E velocity, A velocity, Deceleration time, E/A ratio, E', A', S' and E/E'. In comparison on the groups divided based on the BNP values, E/E' had the highest relevance. The research also categorized 67 patients who diagnosed with heart failure. In comparison on the groups of the heart failure patients, the BNP values of the three groups of Grade I: $623.0{\pm}459.7pg/mL$, Grade II: $1013.2{\pm}1155.1pg/mL$ and Grade III: $1693.4{\pm}1544.0pg/mL$, respectively (p<0.01). As the grade was higher, there was a higher relevance with the echocardiography items of ejection fraction, size of left atrium, E velocity, A velocity, Deceleration time, E/A ratio, E', A', S' and E/E' (p<0.001). Higher BNP values had a higher relevance with the items that indicate the diastolic function in echocardiography and the BNP values of the Restrictive physiology group were the highest in echocardiography. So the BNP values was thought to be valuable to predict diastolic function of heart.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.12
• /
• pp.157-167
• /
• 2009

Recently, the leakage of confidential information and personal information is taking place on the Internet more frequently than ever before. Most of such online security incidents are caused by attacks on vulnerabilities in web applications developed carelessly. It is impossible to detect an attack on a web application with existing firewalls and intrusion detection systems. Besides, the signature-based detection has a limited capability in detecting new threats. Therefore, many researches concerning the method to detect attacks on web applications are employing anomaly-based detection methods that use the web traffic analysis. Much research about anomaly-based detection through the normal web traffic analysis focus on three problems - the method to accurately analyze given web traffic, system performance needed for inspecting application payload of the packet required to detect attack on application layer and the maintenance and costs of lots of network security devices newly installed. The UTM(Unified Threat Management) system, a suggested solution for the problem, had a goal of resolving all of security problems at a time, but is not being widely used due to its low efficiency and high costs. Besides, the web filter that performs one of the functions of the UTM system, can not adequately detect a variety of recent sophisticated attacks on web applications. In order to resolve such problems, studies are being carried out on the web application firewall to introduce a new network security system. As such studies focus on speeding up packet processing by depending on high-priced hardware, the costs to deploy a web application firewall are rising. In addition, the current anomaly-based detection technologies that do not take into account the characteristics of the web application is causing lots of false positives and false negatives. In order to reduce false positives and false negatives, this study suggested a realtime anomaly detection method based on the analysis of the length of parameter value contained in the web client's request. In addition, it designed and suggested a WAF(Web Application Firewall) that can be applied to a low-priced system or legacy system to process application data without the help of an exclusive hardware. Furthermore, it suggested a method to resolve sluggish performance attributed to copying packets into application area for application data processing, Consequently, this study provide to deploy an effective web application firewall at a low cost at the moment when the deployment of an additional security system was considered burdened due to lots of network security systems currently used.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.42 no.3 s.303
• /
• pp.47-52
• /
• 2005

Obstructive Sleep Apnea (OSA) is a representative symptom of sleep disorder caused by the obstruction of upper airway. Because OSA causes not only excessive daytime sleepiness and fatigue, hypertension and arrhythmia but also cardiac arrest and sudden death during sleep in the severe case, it is very important to detect the occurrence and the frequency of OSA. OSA is usually diagnosed through the laboratory-based Polysomnography (PSG) which is uncomfortable and expensive. Therefore researches to improve the disadvantages of PSG are needed and studies for the detection of OSA using only one or two parameters are being made as alternatives to PSG. In this paper, we developed an algorithm for the detection of OSA based on Heart Rate Variability (HRV). The proposed method is applied to the ECG data sets provided from PhysioNet which consist of learning set and training set. We extracted features for the detection of OSA such as average and standard deviation of 1 minute R-R interval, power spectrum of R-R interval and S-peak amplitude from data sets. These features are applied to the input of neural network. As a result, we obtained sensitivity of $89.66\%$ and specificity of $95.25\%$. It shows that the features suggested in this study are useful to detect OSA.