• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.71-80
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• 2005

More than 100 bridges have been annually collapsed or seriously damaged by scouring in Korea. It is extremely hard to understand the complicated scour mechanism and estimate the scour depth with accuracy in fields, however since scouring is a very complex manifestation of sediment transport unable to describe with a simple mathematical tool. In order to obtain the reasonable solution to bridge scouring, therefore, the interdisciplinary co-operation is strongly recommended. In this study the special task force team for the scour problems around Incheon 2nd bridge piers is made, in which all kinds of scour oriented researches such as oceangraphic survey, hydraulic model test, numerical simulation, scour rate test, real-time scour monitoring, etc will be carried out. This paper provides this interdisciplinary procedure in details.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.2
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• pp.393-406
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• 1993

In resistance spot welding process, size of molten nuggest have been utilized to assess the integrity of the weld quality. However real-time monitoring of the nugget size is an extremely difficult problem. This paper describes the design of an artificial neural networks(ANN) estimator to predict the nugget size for on-line use of weld quality monitoring. The main task of the ANN estimator is to realize the mapping characteristics from the sampled dynamic resistance signal to the actual negget size through training. The structure of the ANN estimator including the number of hidden layers and nodes in a layer is determined by an estimation error analysis. A series of welding experiments are performed to assess the performance of the ANN estimator. The results are quite promissing in that real-time estimation of the invisible nugget size can be achieved by analyzing the dynamic resistance signal without any conventional destructive testing of welds.

• Smart Structures and Systems
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• v.17 no.4
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• pp.631-646
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• 2016

Although the deployment of wireless sensors for structural sensing and monitoring is becoming popular, supplying power to these sensors remains as a daunting task. To address this issue, there have been large volume of ongoing energy harvesting studies that aimed to find a way to scavenge energy from surrounding ambient energy sources such as vibration, light and heat. In this study, a magnetic resonance based wireless power transfer (MR-WPT) system is proposed so that sensors inside a concrete structure can be wirelessly powered by an external power source. MR-WPT system offers need-based active power transfer using an external power source, and allows wireless power transfer through 300-mm thick reinforced concrete with 21.34% and 17.29% transfer efficiency at distances of 450 mm and 500 mm, respectively. Because enough power to operate a typical wireless sensor can be instantaneously transferred using the proposed MR-WPT system, no additional energy storage devices such as rechargeable batteries or supercapacitors are required inside the wireless sensor, extending the expected life-span of the sensor.

• Smart Structures and Systems
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• v.5 no.4
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• pp.483-494
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• 2009

Damage detection has been proven to be a challenging task in structural health monitoring (SHM) due to the fact that damage cannot be measured. The difficulty associated with damage detection is related to electing a feature that is sensitive to damage occurrence and evolution. This difficulty increases as the damage size decreases limiting the ability to detect damage occurrence at the micron and submicron length scale. Damage detection at this length scale is of interest for sensitive structures such as aircrafts and nuclear facilities. In this paper a new photonic sensor based on photonic crystal (PhC) technology that can be synthesized at the nanoscale is introduced. PhCs are synthetic materials that are capable of controlling light propagation by creating a photonic bandgap where light is forbidden to propagate. The interesting feature of PhC is that its photonic signature is strongly tied to its microstructure periodicity. This study demonstrates that when a PhC sensor adhered to polymer substrate experiences micron or submicron damage, it will experience changes in its microstructural periodicity thereby creating a photonic signature that can be related to damage severity. This concept is validated here using a three-dimensional integrated numerical simulation.

• IE interfaces
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• v.22 no.2
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• pp.126-134
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• 2009

As product quality and yield are essential factors in semiconductor manufacturing, monitoring the main manufacturing steps is a critical task. For the purpose, FDC(Fault detection and classification) is used for diagnosing fault states in the processes by monitoring data stream collected by equipment sensors. This paper proposes an FDC model based on decision tree which provides if-then classification rules for causal analysis of the processing results. Unlike previous decision tree approaches, we reflect the structural aspect of the data stream to FDC. For this, we segment the data stream into multiple subregions, define structural features for each subregion, and select the features which have high relevance to results of the process and low redundancy to other features. As the result, we can construct simple, but highly accurate FDC model. Experiments using the data stream collected from etching process show that the proposed method is able to classify normal/abnormal states with high accuracy.

• Nuclear Engineering and Technology
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• v.52 no.1
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• pp.78-86
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• 2020

The operation and monitoring (OM) interface is the digital medium between nuclear power system and operators. The cognitive load of OM interface has an important effect on the operation errors made by operator during OM task between operator and computer. The cognitive load model of OM interface is constructed for analysing the composition and influencing factors of OM interface cognitive load. And to study the coping strategies and methods for cognitive load of nuclear power system. An experiment method based on eye movement is proposed to measure the cognitive load of OM interface. Experiment case is carried out with 20 subjects and typical OM interface of a nuclear power system simulator. The OM interface is optimized based on the experiment results. And the results comparison between the original OM interface and the optimized OM interface shows that the cognitive load model and proposed method is valuable contributions in reducing the cognitive load and improving the interaction efficiency of OM tasks.

• Journal of Soil and Groundwater Environment
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• v.28 no.spc
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• pp.18-39
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• 2023

Rapid development of geophysical exploration and hydrogeologic monitoring techniques has yielded remarkable increase of datasets related to groundwater systems. Increased number of datasets contribute to understanding of general aquifer characteristics such as groundwater yield and flow, but understanding of complex heterogenous aquifers system is still a challenging task. Recently, applications of data science technique have become popular in the fields of geophysical explorations and monitoring, and such attempts are also extended in the groundwater field. This work reviewed current status and advancement in utilization of data science in groundwater field. The application of data science techniques facilitates effective and realistic analyses of aquifer system, and allows accurate prediction of aquifer system change in response to extreme climate events. Due to such benefits, data science techniques have become an effective tool to establish more sustainable groundwater management systems. It is expected that the techniques will further strengthen the theoretical framework in groundwater management to cope with upcoming challenges and limitations.

• Computers and Concrete
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• v.32 no.5
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• pp.475-486
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• 2023

Nondestructive evaluation (NDE) is an important task of civil engineering structure monitoring and inspection, but minor damage such as small cracks in local structure is difficult to observe. If cracks continued expansion may cause partial or even overall damage to the structure. Therefore, monitoring and detecting the structure in the early stage of crack propagation is important. The crack detection technology based on machine vision has been widely studied, but there are still some problems such as bad recognition effect for small cracks. In this paper, we proposed a deep learning method based on sweep signals to evaluate concrete surface crack with a width less than 1 mm. Two convolutional neural networks (CNNs) are used to analyze the one-dimensional (1D) frequency sweep signal and the two-dimensional (2D) time-frequency image, respectively, and the probability value of average damage (ADPV) is proposed to evaluate the minor damage of structural. Finally, we use the standard deviation of energy ratio change (ERVSD) and infrared thermography (IRT) to compare with ADPV to verify the effectiveness of the method proposed in this paper. The experiment results show that the method proposed in this paper can effectively predict whether the concrete surface is damaged and the severity of damage.

• Korean Journal of Cognitive Science
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• v.14 no.4
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• pp.1-7
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• 2003

The common features of the behavioral inhibition and the action monitoring that are considered as one of the executive functions were investigated using event-related brain potentials (ERPs) and source localization analysis. The electrophysiological correlates of behavioral inhibition and action monitoring ate analyzed when the subjects performed the Go/NoGo task. Two ERP components of behavioral inhibition termed as N200 and P300 in NoGo condition were differ from those of Go condition, that is the amplitudes of NoGo N200 and P300 are largest on the fronto-central region, which may reflect the inhibitory control of frontal lobe required in NoGo condition. The error-related negativity (ERN) observed on the fronto-central region when the subjects committed error was much larger in amplitude and faster in latency than those of the correct-related negativity (CRN), which may indicate that the signal of action monitoring is much more required for the error response. The correlation analysis for the ERP components of behavioral inhibition and action monitoring revealed the significant negative correlation among the latencies of NoGo N200 and P300 and the amplitude of ERN, which may reflects that the faster subjects inhibit response, the more monitor their own action. The close relationship between behavioral inhibition and action monitoring was also supported by the results of source localization analysis, which showed the common neural sources of NoGo N200 and ERN was anterior cingulate cortex.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.316-325
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• 2018

In this paper, to integrate distributed IT resources and manage human resource efficiently as purpose of cost reduction, infrastructure of wind turbine monitoring system have been designed and developed on the basis of SaaS cloud. This infrastructure hierarchize data according to related task and services. Softwares to monitor conditions via the infrastructure are also developed. Softwares are made up of DB design, field measurement, data transmission and monitoring programs. The infrastructure is able to monitor conditions from SCADA data and additional sensors. Total time delay from field measurement to monitoring is defined by modeling of step-wise time delay in condition monitoring algorithms. Since vibration data are acquired by measurements of high resolution, the delay is unavoidable and it is essential information for application of O&M program. Monitoring target is gearbox in wind turbine of MW-class and it is operating for 10 years, which means that accurate monitoring is essential for its efficient O&M in the future. The infrastructure is in operation to deal with the gearbox conditions with high resolution of 50 TB data capacity, annually.