• Computational Structural Engineering
• /
• v.9 no.3
• /
• pp.145-155
• /
• 1996

This paper suggests a new algorithm of automatic mesh generation over planar domains with arbitrarily shaped boundaries and control curves. The algorithm is based on the method of recursively subdividing the domain by the path connecting, with minimum penalty value, two points on the super-loop, which consists of the boundaries and the control curves, The algorithm is not subject to any limitation on the shape of the domain, and its process can be fully automated. Therefore, this algorithm can be implemented into computer programs which require minimal user intervention while generating finite element meshes over complicated domains. This algorithm can also be easily extended for application to the generation of meshes over curved surfaces, or to the adaptive mesh generation.

• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.2
• /
• pp.50-58
• /
• 2024

Body temperature plays an important role in medicine, some diseases are characterized by changes in human body temperature. Monitoring body temperature also allows doctors to monitor the effectiveness of medical treatments. Accurate body temperature measurement is key to detecting fevers, especially fevers related to infection with the SARS-CoV-2 virus that caused the recent Covid-19 pandemic in the world. The solution of measuring body temperature using a thermal camera is fast but has a high cost and is not suitable for some organizations with difficult economic conditions today. Use a medical thermometer to measure body temperature directly for a slow rate, making it easier to spread disease from person to person. In this paper, we propose a completely automatic body temperature measurement system that can adjust the height according to the person taking the measurement, has a measurement logging system and is monitored via the internet. Experimental results show that the proposed method has successfully created a fully automatic human body measurement system. Furthermore, this research also helps the school's scientists and students gain more knowledge and experience to apply Internet of Things technology in real life.

• Journal of Biomedical Engineering Research
• /
• v.10 no.2
• /
• pp.114-116
• /
• 1989

A method has been proposed for the fully automatic detection of left ventricular endocardial boundary in 2D short axis echocardiogram using geometric model. The procedure has the following three distinct stages. First, the initial center is estimated by the initial center estimation algorithm which is applied to decimated image. Second, the center estimation algorithm is applied to original image and then best-fit elliptic model estimation is processed. Third, best-fit boundary is detected by the cost function which is based on the best-fit elliptic model. The proposed method shows effective result without manual intervention by a human operator.

• Proceedings of the KOSOMBE Conference
• /
• v.1996 no.11
• /
• pp.155-156
• /
• 1996

The intercommissural(AC-PC) line is automatically detected for HR and PET images. With the detected AC-PC lines from MR and PET images, fully non-iterative automatic co- registration is accomplished. It provides a new automated method for image co-registration.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.2
• /
• pp.542-551
• /
• 2000

In this paper, a novel technique is presented for automatic brain region segmentation in single channel MR image data sets for 3D visualization and analysis. The method detects brain contours in 2D and 3D processing of four steps. The first and the second make a head mask and an initial brain mask by automatic thresholding using a curve fitting technique. The stage 3 reconstructs 3D volume of the initial brain mask by cubic interpolation and generates an intermediate brain mask using morphological operation and labeling of connected components. In the final step, the brain mask is refined by automatic thresholding using curve fitting. This algorithm is useful for fully automatic brain region segmentation of T1-weighted, T2-weighted, PD-weighted, SPGR MRI data sets without considering slice direction and covering a whole volume of a brain. In the experiments, the algorithm was applied to 20 sets of MR images and showed over 0.97 in comparison with manual drawing in similarity index.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.44 no.1
• /
• pp.79-84
• /
• 2007

In this paper, a fully-integrated low phase noise multi-band CMOS VCO using automatic level controller (ALC) and switched LC tank has been presented. The proposed VCO has been fabricated in a 0.13-um CMOS process. The switched LC tank has been designed with a pair of capacitors and two pairs of inductors switched using MOS switch. By using this structure, four band (2.986 ${\sim}$ 3.161, 3.488 ${\sim}$ 3.763, 4.736 ${\sim}$ 5.093, and 5.35 ${\sim}$ 5.887 GHz) operation is achieved in a single VCO. The VCO with 1.2 V power supply has phase noise of -118.105 dBc/Hz @ 1 MHz at 2.986 GHz and -113.777 dBc/Hz @ 1 MHz at 5.887 GHz, respectively. The reduced phase noise has been approximately -1 ${\sim}$ -3 dBc/Hz @ 1 MHz in the broadest tuning range, 2.986 ${\sim}$ 5.887 GHz. The VCO has consumed 4.2 ${\sim}$ 5.4 mW in the entire frequency band.

• KIPS Transactions on Computer and Communication Systems
• /
• v.9 no.2
• /
• pp.37-44
• /
• 2020

In this paper, we proposed that a fully automatic multi-class whole heart segmentation algorithm using deep learning. The proposed method is based on U-Net architecture which consist of recurrent convolutional block, residual multi-dilated convolutional block. The evaluation was accomplished by comparing automated analysis results of the test dataset to the manual assessment. We obtained the average DSC of 96.88%, precision of 95.60%, and recall of 97.00% with CT images. We were able to observe and analyze after visualizing segmented images using three-dimensional volume rendering method. Our experiment results show that proposed method effectively performed to segment in various heart structures. We expected that our method can help doctors and radiologist to make image reading and clinical decision.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.39 no.3
• /
• pp.399-407
• /
• 2019

In Korea, the construction industry has been known to have the highest risk of safety accidents compared to other industries. Therefore, in order to improve safety in the construction industry, several researches have been carried out from the past. This study aims at improving safety of labors in construction site by constructing an effective automatic safety helmet detection system using object detection algorithm based on image data of construction field. Deep learning was conducted using Region-based Fully Convolutional Network (R-FCN) which is one of the object detection algorithms based on Convolutional Neural Network (CNN) with Transfer Learning technique. Learning was conducted with 1089 images including human and safety helmet collected from ImageNet and the mean Average Precision (mAP) of the human and the safety helmet was measured as 0.86 and 0.83, respectively.

• Journal of Drive and Control
• /
• v.17 no.1
• /
• pp.37-43
• /
• 2020

An intelligent excavator can be divided into Machine Guidance (MG), semi-automatic, and unmanned by technology. The MG technology excavator is equipped with a tilt sensor on each link of the excavator and a GPS is installed on the excavator body to inform the user of the position of the excavator bucket end. Machine control (MC) technology that assists the user's work can be divided into semi-automatic and fully automatic technology. The semi-automatic MC equipment has already been commercialized by Komatsu and Caterpillar. The MC excavator is equipped with an electro-hydraulic system, sensors and controllers to control the excavator bucket end according to the user's needs. In this study, the semi-automated excavator modified based on manual excavator, is equipped with an electro-hydraulic system, a controller system, multi-sensors and a control algorithm is developed to assist in excavation work such as leveling and grading. By applying the developed technology, it was possible to confirm productivity improvement compared to manual digging and leveling work. In the future, further research to improve the accuracy of the hydraulic precision control and collaborative work with heterogeneous construction equipment such as dump truck and automated collaboration tasks technology could be developed.

• Journal of Advanced Navigation Technology
• /
• v.17 no.1
• /
• pp.115-122
• /
• 2013

Disease diagnostics based on medical imaging is getting popularity day by day. Presence of the atherosclerosis is one of the causes of narrowing of carotid arteries which may block partially or fully blood flow into the brain. Serious brain strokes may occur due to such types of blockages in blood flow. Early detection of the plaque and taking precautionary steps in this regard may prevent from such type of serious strokes. In this paper, we present an automatic image segmentation technique for carotid artery ultrasound images based on active contour approach. In our experimental study, we assume that ultrasound images are properly aligned before applying automatic image segmentation. We have successfully applied the automatic segmentation of carotid artery ultrasound images using snake based model. Qualitative comparison of the proposed approach has been made with the manual initialization of snakes for carotid artery image segmentation. Our proposed approach successfully segments the carotid artery images in an automated way to help radiologists to detect plaque easily. Obtained results show the effectiveness of the proposed approach.