• Journal of KIISE:Information Networking
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• v.29 no.6
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• pp.613-624
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• 2002

Within the past decade, TCP/IP network environment has been explosively widespread all over the world. As the internet and the WWW expand their boundaries, the network traffic caused by data transfers over the internet has also increased. In this paper, we present the design and implementation of a WebTraMAS (Web-based Traffic Monitoring and Analysis System) which can resolve the shortcomings of current management approaches, particularly on the network traffic monitoring and analysis. The WebTraMAS presented in this paper performs the network management activities based on the parameters related to the MIB-II of SNMP and the parameters related to the QoS such as network performance and fault. The proposed WebTraMAS, implemented using the WWW technology, is able for the network manager to manage the network easily and platform independently with the performance analysis of internet traffic.

• Journal of the Korean Applied Science and Technology
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• v.26 no.2
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• pp.151-160
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• 2009

At this study, we are developing a possible control system through remote monitoring for fire protection in various chemical factory facilities. It's possible to do real time confirmation of a normal operation presence of the various equipment installed in a chemical factory through the internet network at a fire fighting head office, an area fire department and a chemical factory situation room using this remote monitoring control system. When occurring, abnormal operation is the remote monitoring control system, which can check this immediately and notify the situation room administrator. After it was tested using developed remote monitoring control system, the remote monitoring for which the internet network was used confirmed possible.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.535-538
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• 2007

System manager needs monitoring tool(S/W) to manage cluster system. But, it is difficult to decide suitable monitoring tool for small SMP cluster system. This document described design of monitoring tool(mon) and development. Mon is monitoring tool for small SMP cluster system using InfiniBand network switch. Function of this tool is monitoring such as computing node(7 node), Infiniband network switch and monitoring of PBS job.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.4
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• pp.174-179
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• 2012

The social and economic level of damages might be highly increased in the case of wide-area black-outages, because of heavy dependence of electricity. Therefore, the development of a wide-area protection relay intelligence techniques is required to prevent massive power outages and minimize the impact strength at failure. The frequency monitoring and prediction for wide-area protection relaying intelligence has been considered as an important technology. In this paper, a network-based frequency monitoring system developed for wide-area protection relay intelligence is presented. In addition, conventional techniques for frequency estimation are compared, and a method for advanced frequency estimation and measurement to improve the precision is proposed. Finally, an integrated monitoring system called K-FNET(Korea-Frequency Monitoring Network) is implemented based on the GPS and various energy monitoring cases are studied.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.43-52
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• 2024

Health Monitoring System is a sophisticating technology and another way to the normal/regular management of the health of the patient. This Health Monitoring Mobile Application is a contribution from our side to the public and to the overall health industry in Pakistan. With the help of Health mobile application, the users will be able to store their medical records, prescriptions and retrieve them later. The users can store and keep track of their vital readings (heart rate, blood pressure, fasting glucose, random glucose). The mobile application also shows hospitals that are nearby in case the user wants to avail of any medical help. An important feature of the application is the symptoms-based disease prediction, the user selects the symptoms which he has and then the application will name certain diseases that match those symptoms based on relevant algorithms. The major advances and issues have been discussed, and as well as potential tasks to health monitoring will be identified and evaluated.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.1-8
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• 2004

In this paper, we propose neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, ART2 neural network is used for fault isolation. The performance and effectiveness of the proposed ART2 neural network based fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4751-4758
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• 2022

Two-phase flow may almost exist in every branch of the energy industry. For the corresponding engineering design, it is very essential and crucial to monitor flow patterns and their transitions accurately. With the high-speed development and success of deep learning based on convolutional neural network (CNN), the study of flow pattern identification recently almost focused on this methodology. Additionally, the photographing technique has attractive implementation features as well, since it is normally considerably less expensive than other techniques. The development of such a two-phase flow pattern online monitoring system is the objective of this work, which seldom studied before. The ongoing preliminary engineering design (including hardware and software) of the system are introduced. The flow pattern identification method based on CNNs and transfer learning was discussed in detail. Several potential CNN candidates such as ALexNet, VggNet16 and ResNets were introduced and compared with each other based on a flow pattern dataset. According to the results, ResNet50 is the most promising CNN network for the system owing to its high precision, fast classification and strong robustness. This work can be a reference for the online monitoring system design in the energy system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1613-1618
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• 2012

For network is growing at a rapid rate, network environment is more complex. The technology of using network traffic to monitor our network in real-time is developed. Cacti is a representative monitoring tool which based on RRDTool(Round Robin Database tool), SNMP(Simple Network Management Protocol). In this paper, it show you how to develop a system which based on Cacti and Libpcap to monitor our monitored objects. At this system, using Libpcap to capture network traffic packets, analyze these packets and then turn out in Cacti in graphical form. So as to achieve monitoring system. This system's execution is efficient and the management is easy and the results are accurate, so it can be widely utilized in the future.

• Convergence Security Journal
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• v.8 no.2
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• pp.41-50
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• 2008

Recently amount of traffic into the network rapidly increase since multimedia streaming services is generally adopted for application. In addition, various network management systems have been suggested for providing a stable service and QoS guarantee. It is necessary for such systems to have QoS monitoring module in order to evaluate acceptance or violation of QoS requirements by analogizing a state information of each node within network. In this paper, we suggest a network management system to evaluate QoS level between end-to-end agents and analysis traffics transmitted between them. The proposed system is implemented for the purpose of collecting network traffic information and monitoring of the view. The proposed system makes user easily understand information of QoS parameters such as throughput, delay and jitter by adopting a method of visual and numerical representation. To achieve this, we purportedly generate test packet into network for confirming acceptance or violation of QoS requirements from point of view of multimedia application service.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.