• Smart Structures and Systems
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• v.24 no.5
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• pp.669-681
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• 2019

Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV's flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amount of image data obtained from unmanned inspection equipment consisting of the UAV and imaging devices (vision and IR cameras).

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.71-77
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• 2011

In this paper, we propose a learning based super resolution algorithm using local block for image enhancement of vehicle license plate. Local block is defined as the minimum measure of block size containing the associative information in the image. Proposed method essentially generates appropriate local block sets suitable for various imaging conditions. In particular, local block training set is first constructed as ordered pair between high resolution local block and low resolution local block. We then generate low resolution local block training set of various size and blur conditions for matching to all possible blur condition of vehicle license plates. Finally, we perform association and merging of information to reconstruct into enhanced form of image from training local block sets. Representative experiments demonstrate the effectiveness of the proposed algorithm.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.1 no.1
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• pp.31-40
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• 2020

The rapid development of technologies in unmanned aerial vehicles (UAVs) has led to their use in various areas. UAVs are mainly used for commercial purposes, but their utilization is increasingly important in other areas because their operation cost is less than satellites and aerial imaging. The utilization of UAVs in the environment/ecology area is relatively new. Therefore, identifying the trends of UAV-related spatial information is significant in basic research for UAV utilization. This study quantitatively identified domestic and international research trends related to UAV utilization and analyzed research areas. An attempt was also made to identify upcoming UAV-related topics in the environment/ecology research field using text mining to analyze the bibliographic information of global research literature. Domestic UAV-related studies were classified into seven clusters where basic research on "UAV technology/industry trends" was abundant, and studies on data collection and analysis through UAV remote sensing technology have increased since 2015. Eight clusters were identified for international studies where the most active research area international was "remote sensing technology/data analysis". In addition, Canopy, Classification, Forest, Leaf Area Index, Normalized Difference Vegetation Index, Temperature, Tree, and Atmosphere appeared as the main keywords related to environment and ecology. The appearance frequencies and association strengths were high because the advancement in UAV optical sensor technology and the rapid development of image processing technology enabled the acquisition of data that could not be obtained from existing spatial information. They are recognized as future research topics as related domestic studies have begun corresponding to international research.

• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1179-1187
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• 2023

Recent advancements in modern transportation have led to the active development of various biomedical signal and medical imaging technologies. Particularly, in the field of cognitive/neuroscience, the importance of electroencephalography (EEG) measurement and the development of accurate EEG measurement technology in moving vehicles represent a challenging area. This study aims to extensively investigate and analyze the trends in technology research utilizing EEG during driving. For this purpose, the Scopus database was used to explore EEG-related research conducted since the year 2000, resulting in the selection of about 40 papers. This paper sheds light on the current trends and future directions in signal processing technology, EEG measurement device development, and in-vehicle driver state monitoring technology. Additionally, a ultra compact 32-channel EEG measurement module was designed. By implementing it simply and measuring and analyzing EEG signals, in-vehicle EEG module's functionality was checked. This research anticipates that the technology for measuring and analyzing biometric signals during driving will contribute to driver care and health monitoring in the era of autonomous vehicles.

• Architectural research
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• v.18 no.1
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• pp.13-20
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• 2016

Smart Home has achieved remarkable developments over the past few decades. In the ICT(Information and Communications Technology) field, 'app ecosystem'-a collection of multiple devices such as mobile phones and tablets, software (operating system and development tools), companies (manufacturers, carriers, app-stores, etc.) and the process through which data is transferred/shared by a user from one device to another device or by the device itself-has come into wide use since the advent of the smart phone. Due to the synergy effect of the 'app ecosystem', it has been applied to various fields such as televisions and automobile industries. As a result, both the Smart TV and connected vehicle have developed their own ecosystem. Although much research has been conducted on these two ecosystems, there is a lack of research regarding 'App Ecosystem based Smart Home' (AESH). This research focuses on the building scenarios based on 'Tracking, Analyzing, Imaging, Deciding, and Acting (T.A.I.D.A), a future prediction method process. Rather than taking an approach from the perspective of providing and applying advanced technology for research on building future scenarios, this paper focuses on research from the perspective of architectural planning. As a result, two future scenarios of AESH are suggested.

• Journal of Ecology and Environment
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• v.41 no.3
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• pp.85-92
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• 2017

Unmanned aerial vehicles (UAVs) are a new and yet constantly developing part of forest inventory studies and vegetation-monitoring fields. Covering large areas, their extensive usage has saved time and money for researchers and conservationists to survey vegetation for various data analyses. Post-processing imaging software has improved the effectiveness of UAVs further by providing 3D models for accurate visualization of the data. We focus on determining the coniferous tree coverage to show the current advantages and disadvantages of the orthorectified 2D and 3D models obtained from the image photogrammetry software, Pix4Dmapper Pro-Non-Commercial. We also examine the methodology used for mapping the study site, additionally investigating the spread of coniferous trees. The collected images were transformed into 2D black and white binary pixel images to calculate the coverage area of coniferous trees in the study site using MATLAB. The research was able to conclude that the 3D model was effective in perceiving the tree composition in the designated site, while the orthorectified 2D map is appropriate for the clear differentiation of coniferous and deciduous trees. In its conclusion, the paper will also be able to show how UAVs could be improved for future usability.

• Journal of Korean Neurosurgical Society
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• v.53 no.1
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• pp.49-51
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• 2013

A case of traumatic spondyloptosis of the cervical spine at the C6-C7 level is reported. The patient was treated succesfully with a anterior-posterior combined approach and decompression. The patient had good neurological outcome after surgery. A-51-year-old female patient was transported to our hospital's emergency department after a vehicle accident. The patient was quadriparetic (Asia D, MRC power 4/5) with severe neck pain. Plain radiographs, computerize tomography and spinal magnetic resonance imaging (MRI) showed C6-7 spondyloptosis and C5, C6 posterior element fractures. Gardner-Wells skeleton traction was applied. Spinal alignment was reachived by traction and dislocation was decreased to a grade 1 spondylolisthesis. Then the patient was firstly operated by anterior approach. Anterior stabilization and fusion was firstly achieved. Seven days after first operation the patient was operated by a posterior approach. The posterior stabilization and fusion was achieved. Postoperative lateral X-rays and three-dimensional computed tomography showed the physiological realignment and the correct screw placements. The patient's quadriparesis was improved significantly. Subaxial cervical spondyloptosis is a relatively rare clinical entity. In this report we present a summary of the clinical presentation, the surgical technique and outcome of this rarely seen spinal disorder.

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.112-122
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• 2020

In 3D computer graphics, a depth map is an image that provides information related to the distance from the viewpoint to the subject's surface. Stereo sensors, depth cameras, and imaging systems using an active illumination system and a time-resolved detector can perform accurate depth measurements with their own light sources. The 3D image information obtained through the depth map is useful in 3D modeling, autonomous vehicle navigation, object recognition and remote gesture detection, resolution-enhanced medical images, aviation and defense technology, and robotics. In addition, the depth map information is important data used for extracting and restoring multi-view images, and extracting phase information required for digital hologram synthesis. This study is oriented toward a recent research trend in deep learning-based 3D data analysis methods and depth map information extraction technology using a convolutional neural network. Further, the study focuses on 3D image processing technology related to digital hologram and multi-view image extraction/reconstruction, which are becoming more popular as the computing power of hardware rapidly increases.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.7
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• pp.687-693
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• 2013

The service conditions of railway cars have become more difficult in recent years due to increased speed. Faulty components in the railcars may result in service interruption, or in extreme cases, derailment. Thus, it is important to diagnose and monitor the main components of railcars. Temperature monitoring is one of the basic methods used to diagnose abnormal conditions in the main components of railway cars, such as in bearings, reduction gears, and traction motors. In this study, we developed a monitoring system for the main components, using an infrared thermography technique. This technique has the advantage of infrared thermal camera imaging of temperature contours in the components. Various hardware and software components of the monitoring system are used to acquire the sensor data, to identify potential problems in railcar operation.

• Journal of Trauma and Injury
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• v.30 no.3
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• pp.107-111
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• 2017

The fat embolism syndrome (FES) represents a condition, usually with traumatic etiology, which may pose challenges to diagnosis while its treatment usually requires supportive measures in the intensive care units (ICUs). The clinical criteria, including respiratory and cerebral dysfunction and a petechial rash, along with imaging studies help in diagnosis. Here we present three case reports of young male who developed FES and were admitted to our ICUs after long bones fractures emerging after vehicle crashes and we briefly review FES literature. All patients' treatment was directed towards: 1) the restoration of circulating volume with fresh blood and/or plasma; 2) the correction of acidosis; and 3) immobilization of the affected part. All patients recovered and were released to the orthopedic wards. The incidence of cases of patients with FES admitted in our ICUs records a significant decrease. This may be explained in terms effective infrastructure reforms in Greece which brought about significant improvement in early prevention and management.