• Smart Structures and Systems
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• v.33 no.2
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• pp.93-103
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• 2024

Despite the rapid development of sensors, structural health monitoring (SHM) still faces challenges in monitoring due to the degradation of devices and harsh environmental loads. These challenges can lead to measurement errors, missing data, or outliers, which can affect the accuracy and reliability of SHM systems. To address this problem, this study proposes a classification method that detects anomaly patterns in sensor data. The proposed classification method involves several steps. First, data scaling is conducted to adjust the scale of the raw data, which may have different magnitudes and ranges. This step ensures that the data is on the same scale, facilitating the comparison of data across different sensors. Next, informative features in the time and frequency domains are extracted and used as input for a deep neural network model. The model can effectively detect the most probable anomaly pattern, allowing for the timely identification of potential issues. To demonstrate the effectiveness of the proposed method, it was applied to actual data obtained from a long-span cable-stayed bridge in China. The results of the study have successfully verified the proposed method's applicability to practical SHM systems for civil infrastructures. The method has the potential to significantly enhance the safety and reliability of civil infrastructures by detecting potential issues and anomalies at an early stage.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.6
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• pp.49-59
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• 2008

SIFT(Scale Invariant Feature Transform) is an algorithm to extract vectors at pixels around keypoints, in which the pixel colors are very different from neighbors, such as vortices and edges of an object. The SIFT algorithm is being actively researched for various image processing applications including 3-D image constructions, and its most computation-intensive stage is a keypoint localization. In this paper, we develope a fixed-point model of the keypoint localization and propose its efficient hardware architecture for embedded applications. The bit-length of key variables are determined based on two performance measures: localization accuracy and error rate. Comparing with the original algorithm (implemented in Matlab), the accuracy and error rate of the proposed fixed point model are 93.57% and 2.72% respectively. In addition, we found that most of missing keypoints appeared at the edges of an object which are not very important in the case of keypoints matching. We estimate that the hardware implementation will give processing speed of $10{\sim}15\;frame/sec$, while its fixed point implementation on Pentium Core2Duo (2.13 GHz) and ARM9 (400 MHz) takes 10 seconds and one hour each to process a frame.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.245-253
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• 2011

Due to the imprudent spending of the fossil fuels, the environment was contaminated seriously and the exhaustion problems of the fossil fuels loomed large. Therefore people become taking a great interest in alternative energy resources which can solve problems of fossil fuels. The wind power energy is one of the most interested energy in the new and renewable energy. However, the plants of wind power energy and the traditional power plants should be balanced between the power generation and the power consumption. Therefore, we need analysis and prediction to generate power efficiently using wind energy. In this paper, we have performed a research to predict power generation patterns using the wind power data. Prediction approaches of datamining area can be used for building a prediction model. The research steps are as follows: 1) we performed preprocessing to handle the missing values and anomalous data. And we extracted the characteristic vector data. 2) The representative patterns were found by the MIA(Mean Index Adequacy) measure and the SOM(Self-Organizing Feature Map) clustering approach using the normalized dataset. We assigned the class labels to each data. 3) We built a new predicting model about the wind power generation with classification approach. In this experiment, we built a forecasting model to predict wind power generation patterns using the decision tree.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.195-202
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• 2022

Underground Geospatial Information Map Management System(UGIMMS) integrates various underground facilities in the underground space into 3D mesh data, and supports to check the 3D image and location of the underground facilities in the mobile app. However, there is a problem that it takes a long time to run in the app because various underground facilities can exist in some areas executed by the app and can be seen layer by layer. In this paper, we propose a deep learning-based K-means vertex clustering algorithm as a method to reduce the execution time in the app by reducing the size of the data by reducing the number of vertices in the 3D mesh data within the range that does not cause a problem in visibility. First, our proposed method obtains refined vertex feature information through a deep learning encoder-decoder based model. And second, the method was simplified by grouping similar vertices through K-means vertex clustering using feature information. As a result of the experiment, when the vertices of various underground facilities were reduced by 30% with the proposed method, the 3D image model was slightly deformed, but there was no missing part, so there was no problem in checking it in the app.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.171-187
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• 2023

When a person's face is recognized through a recording device such as a low-pixel surveillance camera, it is difficult to capture the face due to low image quality. In situations where it is difficult to recognize a person's face, problems such as not being able to identify a criminal suspect or a missing person may occur. Existing studies on face recognition used refined datasets, so the performance could not be measured in various environments. Therefore, to solve the problem of poor face recognition performance in low-quality images, this paper proposes a method to generate high-quality images by performing image quality improvement on low-quality facial images considering various environments, and then improve the performance of facial feature point detection. To confirm the practical applicability of the proposed architecture, an experiment was conducted by selecting a data set in which people appear relatively small in the entire image. In addition, by choosing a facial image dataset considering the mask-wearing situation, the possibility of expanding to real problems was explored. As a result of measuring the performance of the feature point detection model by improving the image quality of the face image, it was confirmed that the face detection after improvement was enhanced by an average of 3.47 times in the case of images without a mask and 9.92 times in the case of wearing a mask. It was confirmed that the RMSE for facial feature points decreased by an average of 8.49 times when wearing a mask and by an average of 2.02 times when not wearing a mask. Therefore, it was possible to verify the applicability of the proposed method by increasing the recognition rate for facial images captured in low quality through image quality improvement.

• Journal of the korean academy of Pediatric Dentistry
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• v.43 no.3
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• pp.313-319
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• 2016

Wolf-Hirschhorn syndrome (WHS), associated with the deletion of the short arm of chromosome 4, causes multiple congenital malformations. Patients suffer from various deformities, including mental and growth disorders, epilepsy, hypotonia, congenital heart defects, and atypical craniofacial features. The "Greek warrior helmet appearance" is the most characteristic feature, with a prominent glabella, high arched eyebrow, broad nasal bridge, and hypertelorism. Cleft lip with or without cleft palate is observed in 30% of patients. Dental structure anomalies also exist including multiple tooth agenesis and over-retained primary molars caused by MSX1 gene impairment, and cone-shaped and taurodontic teeth. This case, a 9-year-old girl with WHS, showed intellectual disability, delayed growth development, previous occurrence of seizures, otitis media, and the typical facial features of WHS. Dental findings included multiple congenital missing teeth, over-retained primary teeth, and severe caries on the primary molars. Dental treatments were performed under general anesthesia. This report documents the characteristics of WHS, including general and oral features, and discusses the importance of oral hygiene and preventive dental management.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6888-6894
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• 2015

Bluetooth 4.0 is the most appropriate technology for Internet of Things, which can be used to enhance and expand the existing areas and areas with a variety of applications. In this paper, an example of the services of the Internet of Things, we developed a tour guide system using the Bluetooth 4.0 and WiFi sensor technology. iBeacon-based push service have been limited to iOS smart phone series, non-iOS family and lower version of iOS 6 smart phones can not receive push-based services. This paper proposed in iBeacon and WiFi sensor is more than tourists, while maintaining the existing system (non-iOS smartphone users) can provide the service to you. Service tourist information as well as that can provide the advertising associated with the service. With 10 kinds of smart phones used in Korea was the experiment on the MAC information collected. This experiment is to track the behaviour of tourists in history can provide customized services can be based. Tourist destinations as well as amusement parks, resorts, etc. If you want to apply this system in a crowded place for user behavior information-gathering feature views over the a prevention of a missing child features such as notifications, risk, prevention is possible for a variety of applications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.546-554
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• 2019

Typically, closed-circuit television (CCTV) monitoring is mainly used for post-processes (i.e. to provide evidence after an incident has occurred), but by using a streaming video feed, machine-based learning, and advanced image recognition techniques, current technology can be extended to respond to crimes or reports of missing persons in real time. The multi-CCTV cooperation technique developed in this study is a program model that delivers similarity information about a suspect (or moving object) extracted via CCTV at one location and sent to a monitoring agent to track the selected suspect or object when he, she, or it moves out of range to another CCTV camera. To improve the operating efficiency of local government CCTV control centers, we describe here the partial automation of a CCTV control system that currently relies upon monitoring by human agents. We envisage an integrated crime prevention service, which incorporates the cooperative CCTV network suggested in this study and that can easily be experienced by citizens in ways such as determining a precise individual location in real time and providing a crime prevention service linked to smartphones and/or crime prevention/safety information.

• The Journal of the Petrological Society of Korea
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• v.25 no.2
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• pp.121-142
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• 2016

The Satkatbong pluton was studied with other plutons together, but some fundamental petrological characteristics were missing. This study mainly reports the petrography and geochemistry of the Satkatbong pluton comparing with the Daebo and the Bulguksa granitoids in south Korea. The Satkatbong pluton, which is host rock including a number of Mafic Magmatic Enclaves (MME), is north-south shaped dioritic pluton, located along the east coast of south Korea. The Satkatbong pluton seems to be unconformable with Cretaceous sedimentary rocks from fieldwork result. In geochemistry, the Satkatbong pluton, which is roughly similar with the Daebo granitoids, is classified into calc-alkali series rock and volcanic arc granitoid Tectonically. The fact that AlT value in marginal parts of amphiboles in the Satkatbong pluton is lower than other granitoids implies emplacement depth of the Satkatbong pluton was relatively shallow. The Satkatbong pluton shows different geochemical feature compared to the adjacent adakitic Yeongdeok granite. This seems to be caused by mafic mantle material expected from the occurrence of MMEs.