• Geomechanics and Engineering
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• v.17 no.4
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• pp.375-383
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• 2019

In underground retreating longwall coal mining, hard roof collapse is one of the most challenging safety problems for mined-out areas. Identifying precursors for hard roof collapse is of great importance for the development of warning systems related to collapse geohazards and ground control. In this case study, the Xinhe mine was chosen because it is a standard mine and the minable coal seam usually lies beneath hard strata. Real-time monitoring of hard roof collapse was performed in longwall face 5301 of the Xinhe mine using support resistance and microseismic (MS) monitoring; five hard roof collapse cases were identified. To reveal the characteristics of MS activity during hard roof collapse development and to identify its precursors, the change in MS parameters, such as MS event rate, energy release, bursting strain energy, b value and the relationships with hard roof collapse, were studied. This research indicates that some MS parameters showed irregularity before hard roof collapse. For the Xinhe coalmine, a substantial decrease in b value and a rapid increase in MS event rate were reliable hard roof collapse precursors. It is suggested that the b value has the highest predictive sensitivity, and the MS event rate has the second highest.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.6
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• pp.133-140
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• 1999

In this study, a monitoring method to detect chip disposal state in drilling system based on neural network was proposed and its performance was evaluated. If chip flow is bad during drilling, not only the static component but also the fluctuation of dynamic component of drilling. Drilling torque is indirectly measured by sensing spindle motor power through a AC spindle motor drive system. Spindle motor power being measured drilling, four quantities such as variance/mean, mean absolute deviation, gradient, event count were calculated as feature vectors and then presented to the neural network to make a decision on chip disposal state. The selected features are sensitive to the change of chip disposal state but comparatively insensitive to the change of drilling condition. The 3 layerd neural network with error back propagation algorithm has been used. Experimental results show that the proposed monitoring system can successfully recognize the chip disposal state over a wide range of drilling condition even though it is trained under a certain drilling condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2804-2823
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• 2013

Wireless sensor network (WSN) is a promising technology for monitoring physical phenomena at fine-grained spatial and temporal resolution. However, the typical approach of sending each sensed measurement out of the network for detailed spatial analysis of transient physical phenomena may not be an efficient or scalable solution. This paper focuses on in-network physical phenomena detection schemes, particularly the distributed computation of the boundary of physical phenomena (i.e. event), to support energy efficient spatial analysis in wireless sensor networks. In-network processing approach reduces the amount of network traffic and thus achieves network scalability and lifetime longevity. This study investigates the recent advances in distributed event detection based on in-network processing and includes a concise comparison of various existing schemes. These boundary detection schemes identify not only those sensor nodes that lie on the boundary of the physical phenomena but also the interior nodes. This constitutes an event geometry which is a basic building block of many spatial queries. In this paper, we introduce the challenges and opportunities for research in the field of in-network distributed event geometry boundary detection as well as illustrate the current status of research in this field. We also present new areas where the event geometry boundary detection can be of significant importance.

• Journal of Satellite, Information and Communications
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• v.11 no.3
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• pp.51-57
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• 2016

Many kinds of telemetry should be monitored to check the state of spacecraft and it leads the time consumption. However, it is very important to define the status of satellite in short time because the contact number and time of low earth orbit satellite is limited. Also, on-board fault management should be prepared for non-contact operation because of the sever space environment. In this paper, on-board and ground autonomous operation method for the safety enhancement is described. Immediate fault detection and response is possible in ground by explicit anomaly detection through satellite event and error information. Also, satellite operation assistant system is proposed for ground autonomy that collect event sequence in accordance with related telemetry and recommend or execute an appropriate action for abnormal state. Critical parameter monitoring method with checking rate, mode and threshold is developed for on-board autonomous fault management. If the value exceeds the limit, pre-defined command sequence is executed.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1263-1271
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• 1999

This paper designs the performance analysis layer for the various performance analysis of parallel programs using event expressions are similar to the normal program language to analyze the events which display a dynamic state exchange of a program. The event expressions suggest operations for overloading and functions which are needed in performance analysis, such as a filtering operation, data format translation functions, performance analysis, static functions, and etc. By using the event expressions, the programmer can modify the event trace data to analyze the performance and analyze more easily and variously than the pre-developed tools.

• Journal of Biomedical Engineering Research
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• v.29 no.4
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• pp.295-301
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• 2008

A wireless 3 channel ECG monitoring system was developed so that it could monitor the health and movement state during subject's daily life. The developed system consists of a wireless biomedical signal acquisition device, a personal healthcare server, and a remote medical server. Three experiments were performed to evaluate the accuracy, reliability and operability, applicability during daily life of the developed device. First, ECG signals were measured using the developed device and commercial reference device during sitting and marking time and compared to verify the accuracy of R-R intervals. Second, the reliable data transmission to remote server was verified on two types of simulated emergency event using patient simulator. Third, during five types of motion in daily life, the accuracy of data transmission to remote server using CDMA network was verified on two types of event occurring. By acquiring and comparing subject's biomedical signal and motion signal, the accuracy, reliability and operability, applicability during daily life of the developed device were verified. In addition, PDA-phone based wireless system enabled subject to be monitored without any constraints. Therefore, the developed system is expected to be applicable for monitoring the aged and chronic diseased people and giving first-aid in emergency.

• Journal of the Korea Society for Simulation
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• v.7 no.2
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• pp.137-152
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• 1998

One of the components that constitute the simulation models is the state variables whose values are determined by the time related simulation process. Embedding rule-based expert systems into the simulation models should provide a systematic way of handling these time-dependent variables without distracting the essential problem solving capabilities of the expert systems which are well suited for expressing the decision making function of complex cases. The expert system, however, is inefficient in dealing with the time elapsing characteristics of target system compare to the simulation models. To solve the problem, this paper provides an interruptible inference engine whose inferencing process can be interrupted when the variables' value, which are used as the parameters of the rules, are not yet determined due to the time dependent nature of the state variables. The process is resumed when the variables are ready. The elapse of time is calculated by time-advance function of the simulation model to which the expert system has been embedded. The example modeling shown exploits the embedded interruptible inferencing capability for the controlling and monitoring of metal grating process.

• KIEE International Transactions on Power Engineering
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• v.4A no.4
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• pp.214-220
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• 2004

Power quality monitoring is the cornerstone for power quality analysis, diagnosis and improvement. The measurement of power quality (PQ) evolves from instantaneous metering to continuous monitoring. Furthermore, recent technologies enable us to construct more flexible, reliable, rapid and economical power quality monitoring system (PQMS). Therefore, this paper presents an improved PQMS with a new distributed monitoring structure. The proposed PQMS consists of a PQ meter, PQ analyzer and GUI. The PQ meter only collects raw data and the PQ analyzer performs power quality analysis. It has several advantages compared to conventional structures in economic efficiency, modularity, speed, etc. PQ monitoring algorithms to catch steady-state trends and to detect PQ events are also adapted to the proposed structure. Using the proposed structure and monitoring algorithm, a prototype PQMS is constructed and real-time testing is performed.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.2
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• pp.247-252
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• 2006

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn until signal is growing to abnormal state that the signal is over or under the set point. therefore cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without any additional sensors. By analyzing the data with high correlation coefficient(CC), correlation level of interactive data can be defined. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC. FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.281-286
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• 2005

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. By analyzing this data having high correlation coefficient(CC), correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.