• Journal of Ocean Engineering and Technology
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• v.18 no.1
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• pp.10-15
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• 2004

For effective conservation of the oceans and harbors, long-term and systematic development of the ocean and harbor monitoring system is essential. A monitoring system capable of real-time and accurate data acquisition is necessary for dealing with the level of contamination by situations, such as red tide and foods. This paper introduces an effective and economical real-time harbor environmental monitoring system that utilizes PCS wireless data communication technology. The monitoring system has various functions, such as multiple communication, TCP/IP protocol for wireless internet access, system time synchronization, and bi-directional communication between the measuring device and the server. The system has been implemented at Shinseondae harbor pier in Busan to validate the system's stability and effectiveness in data acquisition. The acquired real-time ocean and harbor environmental data is expected to have a large effect, when shared with the public through the Internet.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.63-68
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• 2003

For effective conservation of ocean and harbor, long-term and systematic development of the ocean and harbor monitoring system is essential. The monitoring system capable of real-time and accurate data acquisition is necessary for dealing with contamination such as red tide and the flood. This paper introduces the effective and economical real-time harbor environmental monitoring system that utilizes PCS wireless data communication technology. The monitoring system has various functions such as multiple communication, TCP/IP protocol for wireless internet access, system time synchronization, bi-directional communication between the measuring device and the server. The system has been implemented at Shinseondae harbor pier in Busan to validate the systems stability and effectiveness in data acquisition. The acquired real-time ocean and harbor environmental data is expected to have a large effect, when shared by public through internet.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1074-1081
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• 2011

As robotics systems are becoming more complex there is the need to promote component based robot development, where systems can be constructed as the composition and integration of reusable building block. One of the most important challenges facing component based robot development is safeguarding against software component failures and malfunctions. The health monitoring of the robot software is most fundamental factors not only to manage system at runtime but also to analysis information of software component in design phase of the robot application. And also as a lot of monitoring events are occurred during the execution of the robot software components, a simple data treatment and efficient memory management method is required. In this paper, we propose an efficient events monitoring and data management method by modeling robot software component and monitoring factors based on robot software framework. The monitoring factors, such as component execution runtime exception, Input/Output data, execution time, checkpoint-rollback are deduced and the detail monitoring events are defined. Furthermore, we define event record and monitor record pool suitable for robot software components and propose a efficient data management method. To verify the effectiveness and usefulness of the proposed approach, a monitoring module and user interface has been implemented using OPRoS robot software framework. The proposed monitoring module can be used as monitoring tool to analysis the software components in robot design phase and plugged into self-healing system to monitor the system health status at runtime in robot systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.11-17
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• 2008

Typical wireless sensor networks used in structural monitoring are continuous types wherein data transmission is progressive at all time that may include irrelevant and insignificant data and information. Continuous types of wireless monitoring systems often pose problems of handling large-sized data that may deteriorate the performance of the system. The proposed method is to suggest an event-triggered monitoring system that captures and transmits relevant data only. An error signal generated by the Principal Components Analysis (PCA) is utilized as an index for event detection and selective data transmission. With this new monitoring scheme, the remote server is relieved of unwanted data by receiving only relevant information from the wireless sensor networks. The performance of the proposed scheme was verified with simulation studies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.5
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• pp.1082-1086
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• 2007

This research has a purpose of constructing the data monitoring system that makes two-tier work state in the brick production factory to unification by reusing cinder. Monitoring system automatically manages data by using data managing processes such as a state managing process, a location managing process, a badness managing process, a circumstances managing process. In this research, the data management monitoring system manufactures state information of each processes received from RFID and transmits them to data monitoring system. Analyzed data through this system reuses the cinder, so it can effectively manage the production process of the factory which produces bricks through processing automation, faulty-ratio minimization, real-time monitoring and loading managing.

• Journal of the Korean GEO-environmental Society
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• v.16 no.5
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• pp.43-53
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• 2015

In this study, detection methods of outlier in various monitoring data that fit into big data category were developed and outlier detections were conducted for both artificial data and real field monitoring data. Rule-based methods applied rate of change and probability of error for monitoring data are effective to detect a large-scale short faults and constant faults having no change within a certain period. There are however, problems with misjudgement that consider the normal data with a large scale variation as outlier caused by using independent single dataset. Rule-based methods for noise faults detection have a limit to application of real monitoring data due to the problem with a choice of proper window size of data and finding of threshold for outlier judgment. A correlation analysis among different two datasets were very effective to detect localized outlier and abnormal variation for short and long-term monitoring dataset if reasonable range of training data could be selected.

• Proceedings of the KSR Conference
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• 2002.10a
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• pp.497-502
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• 2002

TGIS(Train General Information System) is monitoring Train status and analyzing fault happened. TGIS informed a lot of Passenger data, Driving data, Status data and Fault data to 'Depot'. For dealing them more properly, TGIS need specified data and specialized data handling method. This paper suggest Optimize data of Train Monitoring System and Apply that to Main Line Car.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.99-106
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• 2010

In this paper, we propose an industrial fluid monitoring system which performs the flow control function and monitors fluid pressure transmitted from MFC(Mass Flow Controller) unit. This system consists of MFC unit, channel device, and monitoring management software. MFC unit transmits the measured data of the fluid pressure to the channel device which would provide the input/output interface between management software, monitoring and MFC unit. The monitoring and management software control and analyze by monitoring real time measurements of fluid pressure from each channel of MFC unit. This software can process 20 channels and 0.1 monitoring cycle which gives 200 data measurement per second (i.e., 720,000 data/hour). At this time, the storage space increases in proportion to the rise of input data. This growth of data and storage space makes loss of data access efficiency. Therefore, it demands the implementation by sensing scheme of change scope and data, which can effectively manage the data.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.2
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• pp.77-85
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• 2017

Due to the expansion of accommodation space caused by increase of population along with lifestyle changes, most of people spend their time indoor except for the travel time. Because of this, environmental change of indoor is very important, and it affects people's health and economy in resources. But, most of people don't acknowledge the importance of indoor environment. Thus, monitoring system for sustaining and managing indoor environment systematically is needed, and big data clusters should be used in order to save and manage numerous sensor data collected from many spaces. In this paper, we design a big data cluster for the indoor environment monitoring in order to store the sensor data and monitor unit of the huge building Implementation design big data cluster-based system for the analysis, and a distributed file system and building a Hadoop, HBase for big data processing. Also, various sensor data is saved for collection, and effective indoor environment management and health enhancement through monitoring is expected.

• Smart Structures and Systems
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• v.17 no.4
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• pp.669-690
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• 2016

Advances in sensor technologies have led to the instrumentation of sensor networks for bridge monitoring and management. For a dense sensor network, enormous amount of sensor data are collected. The data need to be managed, processed, and interpreted. Data management issues are of prime importance for a bridge management system. This paper describes a data management infrastructure for bridge monitoring applications. Specifically, NoSQL database systems such as MongoDB and Apache Cassandra are employed to handle time-series data as well the unstructured bridge information model data. Standard XML-based modeling languages such as OpenBrIM and SensorML are adopted to manage semantically meaningful data and to support interoperability. Data interoperability and integration among different components of a bridge monitoring system that includes on-site computers, a central server, local computing platforms, and mobile devices are illustrated. The data management framework is demonstrated using the data collected from the wireless sensor network installed on the Telegraph Road Bridge, Monroe, MI.