• Journal of The Korean Society of Agricultural Engineers
• /
• v.63 no.4
• /
• pp.23-32
• /
• 2021

For agricultural water management, it is essential to establish the digital infrastructure data such as agricultural watershed, irrigated area and canal network in rural areas. Approximately 70,000 irrigation facilities in agricultural watershed, including reservoirs, pumping and draining stations, weirs, and tube wells have been installed in South Korea to enable the efficient management of agricultural water. The total length of irrigation and drainage canal network, important components of agricultural water supply, is 184,000 km. Major problem faced by irrigation facilities management is that these facilities are spread over an irrigated area at a low density and are difficult to access. In addition, the management of irrigation facilities suffers from missing or errors of spatial information and acquisition of limited range of data through direct survey. Therefore, it is necessary to establish and redefine accurate identification of irrigated areas and canal network using up-to-date high resolution images. In this study, previous existing data such as RIMS (Rural Infrastructure Management System), smart farm map, and land cover map were used to redefine irrigated area and canal network based on appropriate image data using satellite imagery, aerial imagery, and drone imagery. The results of the building the digital infrastructure in rural areas are expected to be utilized for efficient water allocation and planning, such as identifying areas of water shortage and monitoring spatiotemporal distribution of water supply by irrigated areas and irrigation canal network.

• Journal of Korea Multimedia Society
• /
• v.24 no.5
• /
• pp.684-694
• /
• 2021

Resting-state Functional Magnetic Resonance Imaging(fMRI) data detects the temporal correlations in Blood Oxygen Level Dependent(BOLD) signal and these temporal correlations are regarded to reflect intrinsic cortical connectivity, which is deactivated during attention demanding, non-self referential tasks, called Default Mode Network(DMN). The relationship between fMRI and anatomical connectivity has not been studied in detail, however, the preceded studies have tried to clarify this relationship using Diffusion Tensor Imaging(DTI) and fMRI. These studies use method that fMRI data assists DTI data or vice versa and it is used as guider to perform DTI tractography on the brain image. In this study, we hypothesized that functional connectivity in resting state would reflect anatomical connectivity of DMN and the combined images include information of fMRI and DTI showed visible connection between brain regions related in DMN. In the previous study, functional connectivity was determined by subjective region of interest method. However, in this study, functional connectivity was determined by objective and advanced method through Independent Component Analysis. There was a stronger connection between Posterior Congulate Cortex(PCC) and PHG(Parahippocampa Gyrus) than Anterior Cingulate Cortex(ACC) and PCC. This technique might be used in several clinical field and will be the basis for future studies related to aging and the brain diseases, which are needed to be translated not only functional connectivity, but structural connectivity.

• Applied Chemistry for Engineering
• /
• v.22 no.5
• /
• pp.522-525
• /
• 2011

In this study, an established enzyme-linked immunosorbent assay and immuno-chromatography technique are combined to fabricate an immuno-strip sensor for the detection of S. aureus. The immuno-strip is manufactured by using four different functional membranes. The capture antibody is immobilized on the nitrocellulose membrane due to the high affinity and the capillary action through porous membranes induces a flow of sample. A colorimetric signal is appeared according to the enzyme reaction and is analyzed by the digital camera (qualitative analysis) and home-made image analysis software (quantitative analysis). Under the optimal conditions, samples with S. aureus in the range of $2.7{\times}10^4{\sim}2.7{\times}10^7CFU/mL$ can be detected by the colorimetric method within 30 min.

• Korean Journal of Metals and Materials
• /
• v.48 no.3
• /
• pp.203-209
• /
• 2010

An application of the instrumented indentation technique has been expanded from the measurements of hardness and elastic modulus to the analysis of residual stress. A slope of the indentation loading curve increases (or decreases) according to compressive (or tensile) residual stress. A theoretical equation has been established for quantifying residual stress from the slope change. However, a precise observation of the remnant indents is indispensible because the theoretical approach needs actual contact information. In addition, the conventional hardness test is still used for predicting the residual stress distribution of welded joints. Thus, we observed the three-dimensional morphologies of the remnant indents formed on artificial stress states and analyzed stress effects on morphological recovery of the indents. First, a depth recovery ratio, which has been regarded as a sensitive stress indicator, did not show a clear dependency with the residual stress. Thus an analysis on volumetric recovery was tried in this study and yielded a inverse proportional behavior with the residual stress. In addition, an elastic to plastic volume recovery ratio showed more significant correlation with the residual stress.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.1
• /
• pp.85-91
• /
• 2011

The technique, which is able to detect and recognize characters in videos such as a movie or TV drama, can be used for applications which are database management of a general user's facial images for the suppliers of PVR(personal video recorder), mobile phones, and multimedia, etc. In this paper, we propose a face detection algorithm. It searches the character through cast indexing when the scene is changed in video. It is consisted of three stages. The first step is the detection-step of the scene change after producing a paused image. The second step is the face detection-step using color information. The final step is the detection-step which detects its features by the facial boundary. According to the experimental result, it has detected faces in different conditions successfully and more advanced than the existing other one that are using only color information.

• Transactions of the Korean Society of Pressure Vessels and Piping
• /
• v.16 no.2
• /
• pp.1-9
• /
• 2020

Large-scale industrial facility structures continue to deteriorate due to the effects of operating and environmental conditions. The problems of these industrial facilities are potentially causing economic losses, environmental pollution, casualties, and national losses. Accordingly, in order to prevent disaster accidents of large structures in advance, the necessity of diagnosing structures using non-destructive inspection techniques is being highlighted. The defect occurrence, location and defect type of the structure are important parameters for predicting the remaining life of the structure, so continuous defect observation is very important. Recently, many researchers have been actively researching real-time monitoring technology to solve these problems. Structure Health Monitoring Inspection is a technology that can identify and respond to the occurrence of defects in real time, but there is a limit to check the degree of defects and the direction of growth of defects. In order to compensate for the shortcomings of these technologies, the importance of defect imaging techniques is emerging, and in order to find defects in large structures, a method of inspecting a wide range using guided ultrasonic is effective. The work presented here introduces a calculation for the shape factor for evaluation of the damaged area, as well as a variable β parameter technique to correct a damaged shape. Also, we perform research in modeling simulation and an experiment for comparison with a suggested inspection method and verify its validity. The curved structure image obtained by the advanced RAPID algorithm showed a good match between the defect area and the shape.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.6
• /
• pp.1209-1216
• /
• 2020

In this study, we propose a method for detecting coastal surface wastes using an UAV(Unmanned Aerial Vehicle) remote sensing method and an object detection algorithm based on deep learning. An object detection algorithm based on deep neural networks was proposed to detect coastal debris in aerial images. A deep neural network model was trained with image datasets of three classes: PET, Styrofoam, and plastics. And the detection accuracy of each class was compared with Darknet-53. Through this, it was possible to monitor the wastes landing on the shore by type through unmanned aerial vehicles. In the future, if the method proposed in this study is applied, a complete enumeration of the whole beach will be possible. It is believed that it can contribute to increase the efficiency of the marine environment monitoring field.

• Journal of the Korean Society of Radiology
• /
• v.15 no.3
• /
• pp.281-291
• /
• 2021

This study aimed to measure the quantitative changes in images according to the use of compressed sensing in expressing the slow flow rate in TOF MRA test using magnetic resonance imaging. This study set different blood flow rate sections by using auto-injector and flow phantom and compared changes in the SNR, CNR, SSIM, and RMSE measurements by different CS factors between TOF with CS and TOF without CS. One-way ANOVA was performed to test the effect on the image induced by the increase of the CS factor. The results revealed that TOF MRA with CS significantly decreased scan time without significantly affecting SNR and CNR compared to TOF MRA with CS. On the other hand, the differences in SSIM and RMSE between TOF with CS and TOF without CS increased as the CS factor increased. Therefore, it is necessary to efficiently reduce scan time by adapting the CS technique while considering the appropriate range of the CS factor. Additionally, more studies are needed to evaluate CS factors and the similarity precision of images further.

• Journal of Biomedical Engineering Research
• /
• v.42 no.3
• /
• pp.100-106
• /
• 2021

Measuring Intima-media thickness (IMT) with ultrasound images can help early detection of coronary artery disease. As a result, numerous machine learning studies have been conducted to measure IMT. However, most of these studies require several steps of pre-treatment to extract the boundary, and some require manual intervention, so they are not suitable for on-site treatment in urgent situations. in this paper, we propose to use deep learning networks U-Net, Attention U-Net, and Pretrained U-Net to automatically segment the intima-media complex. This study also applied the HE, HS, and CLAHE preprocessing technique to wireless portable ultrasound diagnostic device images. As a result, The average dice coefficient of HE applied Models is 71% and CLAHE applied Models is 70%, while the HS applied Models have improved as 72% dice coefficient. Among them, Pretrained U-Net showed the highest performance with an average of 74%. When comparing this with the mean value of IMT measured by Conventional wired ultrasound equipment, the highest correlation coefficient value was shown in the HS applied pretrained U-Net.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.11
• /
• pp.1437-1444
• /
• 2020

With the development of computer vision systems, many advances have been made in the fields of surveillance, biometrics, medical imaging, and autonomous driving. In the field of autonomous driving, in particular, the object detection technique using deep learning are widely used, and the paved road detection is a particularly crucial problem. Unlike the ROI detection algorithm used in general object detection, the structure of paved road in the image is heterogeneous, so the ROI-based object recognition architecture is not available. In this paper, we propose a deep neural network architecture for atypical paved road detection using Semantic segmentation network. In addition, we introduce the multi-scale semantic segmentation network, which is a network architecture specialized to the paved road detection. We demonstrate that the performance is significantly improved by the proposed method.