• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.90-95
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• 2012

Remotely sensed data provide valuable information on land monitoring due to multi-temporal observation over large areas. Especially, high resolution imagery with 0.6~1.0 m spatial resolutions contain a wealth of information and therefore are very useful for thematic mapping and monitoring change in urban areas. Recently, remote sensing technology has been successfully utilized for natural disaster monitoring such as forest fire, earthquake, and floods. In this paper, an efficient change detection method based on texture differences observed from high resolution multi-temporal data sets is proposed for mapping disaster damage and extracting damage information. It is composed of two parts: feature extraction and detection process. Timely and accurate information on disaster damage can provide an effective decision making and response related to damage.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.236.2-236.2
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• 2012

We present the interim results of simultaneous time monitoring observations of SiO v=1, 2, J =1-0, $^{29}SiO$ v=0, J =1-0, and $H_2O$ $6_{16}-5_{23}$ maser lines toward about 60 relatively strong SiO and/or H2O maser sources using the single dishes of the Korean VLBI Network from 2009 September to 2012 June. These monitoring sources are composed of representative semiregular variables, Miras, water fountain sources, preplantary nebulae and SiO maser sources of star forming regions etc. The variations of intensity ratios between SiO and $H_2O$ masers and velocity structures are investigated according to stellar optical phases and observational epochs. Several individual sources which show an interesting feature will be presented here.

• Structural Monitoring and Maintenance
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• v.8 no.4
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.98-107
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• 1995

The in-process detection of drill wear and breakage is one of the most importnat technical problems in unmaned machining system. In this paper, the monitoring system is developed to monitor abnormal drilling states such as drill breakage, drill wear and unstable cutting using motor current. Drill breakage is detected by level monitoring. Tool wear is classified by fuzzy pattern recognition. The key feature for classification of tool wear is the estimated flank wear which is calculated by the proposed flank wear model. The characteristic of the model is not sensitive to the variation of cutting conditions but is sensitive to drill wear state. Unstable cutting states due to the unsmooth chip disposal and the overload are monitored by the variance/mean ratio of spindle motor current. Variance/mean ratio also includes the information about the prediction of drill wear and drill breakage. The evaluation experiments have shown that the developed system works very well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1597-1610
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• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.83-92
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• 2020

Recently, many companies and organizations are interested in predictive process monitoring for the efficient operation of business process models. Traditional process monitoring focused on the elapsed execution state of a particular process instance. On the other hand, predictive process monitoring focuses on predicting the future execution status of a particular process instance. In this paper, we implement the function of the business process remaining time prediction, which is one of the predictive process monitoring functions. In order to effectively model the remaining time, normalization by activity is proposed and applied to the predictive model by taking into account the difference in the distribution of time feature values according to the properties of each activity. In order to demonstrate the superiority of the predictive performance of the proposed model in this paper, it is compared with previous studies through event log data of actual companies provided by 4TU.Centre for Research Data.

• International Journal of Aeronautical and Space Sciences
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• v.1 no.1
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• pp.48-61
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• 2000

Korea Aerospace Research Institute (KARI) have gained experience with Helicopter Vibration Health Monitoring (VHM) System technology with the help of UK GKN-WHL. GKN-WHL have had many years of experience with the research and development of vibration analysis techniques to improve the health monitoring of helicopter transmissions. This activity was targeted at transmission rig testing at first, but the techniques have been progressively developed where they are now used as a part of integrated Health and Usage Monitoring (HUM) systems on many types of in-service and new helicopters. The technique development process has been considerably aided by an ever expanding database of transmission monitoring experience from both the rig testing and aircraft operations. This experience covers a wide range of failure types from naturally occurring faults to crack propagation studies and covering a wide range of transmission configurations. Primarily based on accelerometer signals GKN-WHL's vibration analysis methods have also been applied to a variety of other sensor types. The transition from an experimental environment to operational VHM systems has been a lengthy process, there being a need to demonstrate technique reliability as well as effectiveness to both regulatory (Airworthiness Authority) and commercial organizations. Another important feature of this process has been the development of close relationships with a number of VHM system hardware and software suppliers. Such an experienced GKN-WHL provides various raw vibration data which was acquired from transmission ground test rig and allow KARI to develop it's own analysis program. KARI made a program and then analyzed the data to coma pre with the results of GKN-WHL. The KARI's results both time domain signals and statistical values show comparable to GKN's.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.217-225
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• 2016

In this study, a dementia patient care monitoring system is suggested that uses a wearable type bluetooth beacon to prevent them from going missing. This system shows whether the patients stay within the manageable area and sends a warning message to their monitoring managers' or guardians' smart devices when they leave it. The feature of the system is that it provides the service based on indoor location that makes the beacon worn by dementia patients continuously transmit their location information to the managing server through the smart terminal installed indoors or in hospitals and that enables the monitoring managers or the guardians to receive messages sent from the server that tell the patients' whereabouts through their smart devices. The system suggested in this paper is believed to be a system that effectively contributes to the prevention of the dementia patients' going astray from the hospitals and facilities where they are taking care.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.1-8
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• 2016

Recently, buildings tend to be large size, complex shape and functional. As the size of buildings is becoming massive, the need for structural health monitoring(SHM) technique is ever-increasing. Various SHM techniques have been studied for buildings which have different dynamic characteristics and are influenced by various external loads. Generally, the visual inspection and non-destructive test for an accessible point of structures are performed by experts. But nowadays, the system is required which is online measurement and detect risk elements automatically without blind spots on structures. In this study, in order to consider the response of non-linear structures, proposed a signal feature extraction and the adaptive threshold setting algorithm utilized to determine the abnormal behavior by using statistical methods such as control chart, root mean square deviation, generalized extremely distribution. And the performance of that was validated by using the acceleration response of structures during earthquakes measuring system of forced vibration tests and actual operation.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.45-52
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• 2021

In this paper, we propose a method of generating theme, background amd signal music usage monitoring information based on a blockchain, in which the music usage informations are recorded by the monitoring tool using feature-based filtering of monitoring organizations. Theme, background and signal music are music inserted into the broadcasting contents of broadcaster. Since they are recognized as created contents just like normal music, there are lyricists and composers who have the right for those music and all copyright holders of them have to receive the corresponding copyright fees, once the music was used in the broadcast. However, there are problems with inaccurate monitoring results for music usage, due to the omission of usage details and non-transparent settlement method. In order to solve these problems, If the information generation method proposed in this paper, accurate music usage history can be created, the details are stored in the blockchain without changes or omissions, and transparent settlement and distribution are possible by smart contract, avoiding the current non-transparent settlement method.