• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.607-610
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• 2007

In this paper, proposes the monitoring system it will be able to trace the executed of each threads in OpenMP based a parallel program. The monitoring system of existing in uses each threads label information and the analysis technique which uses the access-history was most. This has the problem which raises the time and space complexity which is caused by with massive information creation. In this paper, only the thread which includes interest information it creates tracking information with the target. And it provides information which is intuitive to the user it provides the visualization system for to a same time. The visualization model is composed the images-information of a base. This does to be it will be able to understandable a program execute situation using an image processing technique. Therefore, this paper provides the parallel program an effective debugging environment.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.1
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• pp.111-118
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• 2010

The purposes of this study are to raise the awareness of urban water not being efficiently managed and to propose a method for resolving this issue. To serve these purposes, a methodology was proposed to obtain sensing data in a real-time monitoring method and to build them into a GIS. Some sample data among sensing data was used to perform a series of trend analyses using several polynomial models. As a result of the aforementioned research, the proposed monitoring technique is expected to offer some important information in order to improve the reliability of urban water.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.1
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• pp.27-37
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• 1993

In unmanned machining, One of the most essential issue is the tool management system which includes controlling. identification, presetting and monitoring of cutting tools. Especially the monitoring of tool wear and fracture may be the heart of the system. In this study a computer vision based tool monitoring system is developed. Also an algorithm which can determine the tool condition using this system is presented. In order to enhance practical adaptability the vision system through which two modes of images are taken is located over the rake face of a tool insert. And they are analysed quantitatively and qualitatively with image processing technique. In fact the morphologies of tool fracture or wear are occurred so variously that it is difficult to predict them. For the purpose of this problem the pattern recognition is introduced to classify the modes of the tool such as fracture, crater, chipping and flank wear. The experimental results performed in the CNC turning machine have proved the effectiveness of the proposed system.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.12a
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• pp.31-42
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• 2007

Dc resistivity monitoring has been increasingly used in order to understand the changes of subsurface conditions in terms of conductivity. The commonly adopted interpretation approach which separately inverts time-lapse data may generate inversion artifacts due to measurement error. Eventually the contaminated error amplifies the artifacts when reconstructing the difference images to quantitatively estimate the change of ground condition. In order to alleviate the problems, we defined the subsurface structure as four dimensional (4-D) space-time model and developed 4-D inversion algorithm which can calculate the reasonable subsurface structure continuously changing in time even when the material properties change during data measurements. In this paper, we discussed two case histories of resistivity monitoring to study the ground condition change when the properties of the subsurface material were artificially altered by injecting conductive materials into the ground: (1) dye tracer experiment to study the applicability of electrical resistivity tomography to monitoring of water movement in soil profile and (2) the evaluation of cement grouting performed to reinforce the ground. Through these two case histories, we demonstrated that the 4-D resistivity imaging technique is very powerful to precisely delineate the change of ground condition. Particularly owing to the 4-D inversion algorithm, we were able to reconstruct the history of the change of subsurface material property.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.71-77
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• 2015

This paper suggested dynamic application framework based data collecting and monitoring technique using wireless sensor network. The development of application for wireless measurement node firmware program integrates with various sensors and performs control. Collecting data of the user application is downloaded from the node onboard process wirelessly. In addition, the user application can change the temperature initial value of the nodes, which enables dynamic sampling of the measurement nodes. Therefore, dynamic sampling control of the nodes can reduce the power consumptions of sensors compared to existing wired data monitoring.

• IE interfaces
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• v.24 no.1
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• pp.8-14
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• 2011

Principal Component Analysis (PCA) reduces the dimensionality of the process by creating a new set of variables, Principal components (PCs), which attempt to reflect the true underlying process dimension. However, for highly nonlinear processes, this form of monitoring may not be efficient since the process dimensionality can't be represented by a small number of PCs. Examples include the process of semiconductors, pharmaceuticals and chemicals. Nonlinear correlated process variables can be reduced to a set of nonlinear principal components, through the application of Kernel Principal Component Analysis (KPCA). Support Vector Data Description (SVDD) which has roots in a supervised learning theory is a training algorithm based on structural risk minimization. Its control limit does not depend on the distribution, but adapts to the real data. So, in this paper proposes a non-linear process monitoring technique based on supervised learning methods and KPCA. Through simulated examples, it has been shown that the proposed monitoring chart is more effective than $T^2$ chart for nonlinear processes.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.11
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• pp.1143-1149
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• 2008

In a nuclear power plant, loose part monitoring and its diagnostic technique is one of the major issues for ensuring the structural integrity of the reactor system. Typically, accelerometers are mounted on the surface of a reactor vessel to localize impact location cavsed by the impact of metallic substances on the reactor system. However, in some cases, the number of the accelerometers is not enough to estimate the impact location precisely. In such a case, one of alternative plan is to utilize another type sensors that can measure the vibration of the reactor structure even though the measuring frequency ranges are different from each others. The AE sensors installed on the reactor structure can be utilized as additional sensors for loose part monitoring. In this paper, we proposed a new method to estimate impact location by using both accelerometer signal and AE signal, simultaneously. The feasibility of the proposed method is verified by an experiment. The experimental results demonstrate that we can enhance the reliability and precision of the loose part monitoring.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.285-301
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• 2021

Recently, with the diversification of media services and the development of smart devices, users have more opportunities to use digital content, such as movies, dramas, and music; consequently, the size of the copyright market expands simultaneously. However, there are piracy sites that generate revenue by illegal use of copyrighted works. This has led to losses for copyright holders, and the scale of copyrighted works infringed due to the ever-increasing number of piracy sites has increased. To prevent this, government agencies respond to copyright infringement by monitoring piracy sites using online monitoring and countermeasure strategies for infringement. However, the detection and blocking process consumes a significant amount of time when compared to the rate of generating new piracy sites. Hence, online monitoring is less effective. Additionally, given that piracy sites are sophisticated and refined in the same way as legitimate sites, it is necessary to accurately distinguish and block a site that is involved in copyright infringement. Therefore, in this study, we analyze features of piracy sites and based on this analysis, we propose an intelligent detection technique for piracy sites that automatically classifies and detects whether a site is involved in infringement.

• Structural Engineering and Mechanics
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• v.87 no.5
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• pp.451-460
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• 2023

Recently, the use of smartphones for structural health monitoring in civil engineering has drawn increasing attention due to their rapid development and popularization. In this study, the structural responses and dynamic characteristics of a 421-m-tall skyscraper during the landfall of Typhoon Muifa are monitored using an iPhone 13. The measured building acceleration responses are first corrected by the resampling technique since the sampling rate of smartphone-based measurement is unstable. Then, based on the corrected building acceleration, the wind-induced responses (i.e., along-wind and across-wind responses) are investigated and the serviceability performance of the skyscraper is assessed. Next, the amplitude-dependency and time-varying structural dynamic characteristics of the monitored supertall building during Typhoon Muifa are investigated by employing the random decrement technique and Bayesian spectral density approach. Moreover, the estimated results during Muifa are further compared with those of previous studies on the monitored building to discuss its long-term time-varying structural dynamic characteristics. The paper aims to demonstrate the applicability and effectiveness of smartphones for structural health monitoring of high-rise buildings.

• Structural Monitoring and Maintenance
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• v.10 no.1
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• pp.43-62
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• 2023

Damage monitoring is a prerequisite step to ensure the safety and performance of concrete structures. Smart aggregate (SA) technique has been proven for its advantage to detect early-stage internal cracks in concrete. In this study, a 1-D CNN-based method is developed for autonomously classifying the damage feature in a concrete anchorage zone using the raw impedance signatures of the embedded SA sensor. Firstly, an overview of the developed method is presented. The fundamental theory of the SA technique is outlined. Also, a 1-D CNN classification model using the impedance signals is constructed. Secondly, the experiment on the SA-embedded concrete anchorage zone is carried out, and the impedance signals of the SA sensor are recorded under different applied force levels. Finally, the feasibility of the developed 1-D CNN model is examined to classify concrete damage features via noise-contaminated signals. The results show that the developed method can accurately classify the damaged features in the concrete anchorage zone.