• Tunnel and Underground Space
• /
• v.26 no.1
• /
• pp.1-11
• /
• 2016

This study deals with the case that was the field application of the microseismic monitoring techniques for the stability monitoring in a domestic mine. The usefulness and limitations of the microseismic techniques were examined through analyzing the microseismic monitored data. The target limestone mine adopted a hybrid room-and-pillar mining method to improve the extraction ratio. The accelerometers were installed in each vertical pillar within the test bed which has the horizontal cross-section $50m{\times}50m$. The measured signals were divided into 4 types; blasting induced signal, drilling induced signal, damage induced signal, and electric noise. The stability analysis was performed based on the measured damage induced signals. After the blasting in the mining section close to the test bed, the damage of the pillar was increased and rockfall near the test bed could be estimated from monitored microseismic data. It was possible to assess the pillar stability from the changes of daily monitored data and the proposed safety criteria from the accumulated monitored data. However, there was a difficulty to determine the 3D microseismic source positions due to the 2D local sensor arrays. Also, it was needed to use real-time monitoring methods in domestic mines. By complementing the problems encountered in the mine application and comparing microseismic monitored data with mining operations, the microseismic monitoring technique can be used as a better safety method.

• Journal of the Korean Geotechnical Society
• /
• v.23 no.4
• /
• pp.15-24
• /
• 2007

Two kinds of temperature monitoring technology have been introduced in this study, which can measure coincidently temperatures at many points along a single length of cable. One is to use a thermal sensor cable comprizing of addressable thermal sensors. The other is to use an optic fiber sensor with Distributed Temperature Sensing (DTS) system. The differences between two technologies can be summarized as follows: A thermal sensor cable has a concept of "point sensing" that can measure temperature only at a predefined position. The accuracy and resolution of temperature measurement are up to the capability of the individual thermal sensor. On the other hand, an optic fiber sensor has a concept of "distributed sensing" because temperature is measured practically at all points along the fiber optic cable by analysing the intensity of Raman back-scattering when a laser pulse travels along the fiber. Thus, the temperature resolution depends on the measuring distance, measuring time and spatial resolution. The purpose of this study is to investigate the applicability of two different temperature monitoring techniques in technical and economical sense. To this end, diverse experiments with two techniques were performed and two techniques are applied under the same condition. Considering the results, the thermal sensor cable will be well applicable to the assessment of groundwater flow, geothermal distribution and grouting efficiency within about loom distance, and the optic fiber sensor will be suitable for long distance such as pipe line inspection, tunnel fire detection and power line monitoring etc.

• Economic and Environmental Geology
• /
• v.50 no.5
• /
• pp.401-414
• /
• 2017

To prevent the damages from earthquakes, various researches have been conducted around the world focusing on earthquake prediction and forecasting for several decades. Among various precursory phenomena, changes in groundwater level and quality are considered to be reliable for estimating the time of earthquake occurrence and its magnitude. In effects, some countries impacted by frequent earthquakes have established and operated the groundwater monitoring network for earthquake surveillance and prediction. In Korea, recently researches have begun for using groundwater monitoring techniques for earthquake prediction. In this paper, the groundwater monitoring networks of China, Japan, and the United States were reviewed focusing on the facilities and results of researches to deduce the tasks for earthquake prediction researches using groundwater monitoring techniques in Korea. In results, research needs are suggested in the implementation of groundwater monitoring networks for specifically earthquake surveillance with the real-time monitoring and the measures to quantify the degrees of abnormal changes in the relationship of distance from the earthquake epicenter.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.23 no.5
• /
• pp.445-454
• /
• 2003

Health monitoring is a major concern not only in the design and manufacturing but also in service stages for composite laminated structures. Excessive loads or low velocity impact can cause matrix cracks and delaminations that may severely degrade the load carrying capability of the composite laminated structures. To develop the health monitoring techniques providing on-line diagnostics of smart composite structures can be helpful in keeping the composite structures sound during their service. In this study, we discuss the signal processing techniques and some applications for health monitoring of composite structures using piezoceramic sensors and fiber optic sensors.

• Journal of Environmental Health Sciences
• /
• v.41 no.2
• /
• pp.90-101
• /
• 2015

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results ($r^2=92.64$,$Q{^2}_{LOO}=91.45$,$Q{^2}_{Ext}=88.17$). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.

• Journal of the Korea Computer Industry Society
• /
• v.2 no.8
• /
• pp.1109-1116
• /
• 2001

This paper describes a mechanism and its aspects for monitoring user actions and behaviors on the web operated under the layered architecture that utilizes intelligent agents, and by which personalized information services, for instance one-to-one marketing, is easily facilitated and developed. Intelligent agent is one of techniques which enables to automate the whole process for providing personalized services that includes monitoring, logging a user actions and analyzing behaviors based on his or her profiles, and then selecting, organizing customizable contents which are at last delivered to the users browser or mail box also by intelligent agents. Monitoring technologies can utilize to help better construct the process in providing personalized services in that those services require intelligence in operating. As a result, implementing effective personalized services, however, depends on how well to define various users interests and needs, and how correctly to detect and log the actions when hey are done by users.

• Composites Research
• /
• v.14 no.6
• /
• pp.47-58
• /
• 2001

This paper describes applications of cure monitoring techniques by using embedded fiber optic strain sensors, which are extrinsic Fabry-Perot interoferometric (EFPI) and/or fiber Bra99 grating (FBG) sensors, to three kinds of molding methods of autoclave, FW and RTM molding methods. In these applications, internal strain of high-temperature curing resin was monitored by EFPI sensors. From theme experimental results, it was shown that strain caused by thermal shrink at cooling stage could be measured well. In addition, several specific matters to these molding methods were considered. As thor an autoclave molding of unidirectional FRP laminates, it was confirmed that off-axis strain of unidirectional FRP could be monitored by EFPI sensors. As for FW molding using room-temperature (RT) cured resin, it was found that the strain outputs from EFPI sensors represented curing shrinkage as well as thermal strain and the convergence meant finish of cure reaction. It was also shown that this curing shrinkage should be evaluated with consideration on logarithmic change in stiffness of matrix resin. As for a RTM melding, both EFPI and FBC sensors were employed to measure strain. The results showed that FBG sensors hale also good potential for strain monitoring at cooling stage, while the non-uniform thermal residual strain of textile affected the FBG spectrum after molding. This study has proven that embedded fiber optic strain sensors hale practical ability of cure monitoring of FRP. However, development of automatic installation methods of sensors remains as a problem to be solved for applications to practical products.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.22 no.12
• /
• pp.1227-1236
• /
• 2012

The low speed machinery faults are usually caused by the bearing failure of the rolling elements. As the life time of the bearing is limited, the condition monitoring of bearing is very important to maintain the continuous operation without failures. A few monitoring techniques using time domain, frequency domain and fuzzy neural network vibration analysis are introduced to detect and diagnose the faults of the low speed machinery. This paper presents a method of fault detection for the rolling element bearing in the low speed machinery using the Wiener filtering and shock pulse counting techniques. Wiener filter is used for noise cancellation and it clearly makes the shock pulse emerge from the time signal with the high level of noise. The shock pulse counting is used to determine the various faults obviously from the shock signal with transient pulses not related with the bearing fault. Machine fault simulator is used for the experimental measurement in order to verify this technique is the powerful tool for the low speed machine compared with the frequency analysis. The test results show that the method proposed is very effective parameter even for the signal with high contaminated noise, speed variation and very low energy. The presented method shows the optimal tool for the condition monitoring purpose to detect the various bearing fault with high accuracy.

• Journal of Korean Society for Atmospheric Environment
• /
• v.17 no.5
• /
• pp.375-384
• /
• 2001

Optical remote sensing techniques are particularly advantageous over the conventional fixed point methods because with these methods large-area monitoring can be possible and sample preparation difficulties are avoidable. Instruments based on the differential optical absorption spectroscopy (DOAS) technique are widely used for monitoring air pollutants in urban areas in recent years. In this study, $O_3$, SO$_2$, NO$_2$, and VOCs (benzene, toluene, xylene, and styrene) are measured continuously at Sihwa industrial area using a DOAS from February to November. 1999. Intercomparison between the DOAS method and the conventional methods (filed point samplers for $O_3$, NO$_2$, and SO$_2$, and adsorbent sampling methods and gas chromatography for VOCs) are performed simultaneously at the same site. The time series of the DOAS data and that of fixed point method show good match at the view point of the tendency, but the absolute concentration values of these two methods differ quite a lot from each other; correlation coefficients shows 0.78 for $O_3$and 0.97 for SO$_2$. However, the results of VOCs measurements are not quite satisfactory ; the spectral interference with $O_2$and $O_3$appears to be the major cause of the errors for VOCs .

• Computational Structural Engineering
• /
• v.10 no.4
• /
• pp.303-313
• /
• 1997

Though many techniques for the damage assessment of structures have been studied recently, most of them can be only applied to simple structures. Therefore, practical damage assessment techniques that evaluate the damage location and the damage state for large structures need to be developed. In this study, a damage assessment technique using a neural network is developed, in which the bilevel damage assessment procedure is proposed to evaluate the damage of a large structure from the limited monitoring data. The procedure is as follows ; first, for the rational selection of damage critical members, the members that affect the probability of failure or unusual structural behavior are selected by sensitivity analysis. Secondly, the monitoring points and the number of sensors that are sensitive to the damage severity of the selected members are also selected through the sensitivity analysis with a proposed sensitivity measurement format. The validity and applicability of the developed technique are demonstrated by various examples, and it has been shown that the practical information on the damage state of the selected critical members can be assessed even though the limited monitoring data have been used.