• Advances in aircraft and spacecraft science
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• v.5 no.3
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• pp.401-410
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• 2018

The aim of this work is to optimize the geometrical parameters as the adhesive thickness and the beveled angle to reduce the edge effect of the scarf and V bounded joint. A finite element analysis is done to define the generated stresses in the bounded joint. The geometrical optimum is obtained using the Experimental Design Method. Results show that the double scarf (V) joint is better than the simple scarf bounded joint.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.129-134
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• 2024

Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

• Korean Journal of Veterinary Research
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• v.47 no.4
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• pp.461-467
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• 2007

A 6-year-old female Maltese dog (body weight 2.0 kg) was referred to the Veterinary Teaching Hospital, Kangwon National University with primary complaints including exercise intolerance and heart murmur. Based on clinical and diagnostic findings including grade V/VI left basal continuous murmur, bounding femoral pulsation, left ventricular enlargement pattern in electrocardiogram, cardiomegaly with aortic bulging on the thoracic radiography, and shunt flow between aorta and pulmonary artery on the echocardiography, the dog was diagnosed as the left-to-right patent ductus arteriosus. The patent ductus arteriosus was successfully treated by lodging a single embolization coil with transjugular approach.

• Journal of the Semiconductor & Display Technology
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• v.19 no.1
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• pp.85-92
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• 2020

Object detection plays a critical role in the field of computer vision, and various researches have rapidly increased along with applying convolutional neural network and its modified structures since 2012. There are representative object detection algorithms, which are convolutional neural networks and YOLO. This paper presents two representative algorithm series, based on CNN and YOLO which solves the problem of CNN bounding box. We compare the performance of algorithm series in terms of accuracy, speed and cost. Compared with the latest advanced solution, YOLO v3 achieves a good trade-off between speed and accuracy.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.177-183
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• 2022

To measure the size and weight of the fish, we developed an automatic fish size measurement system using a deep neural network, where the YOLO (You Only Look Once)v3 model was used. To detect fish, an IP camera with infrared function was installed over the fish pool to acquire image data and used as input data for the deep neural network. Using the bounding box information generated as a result of detecting the fish and the structure for which the actual length is known, the size of the fish can be obtained. A GUI (Graphical User Interface) program was implemented using LabVIEW and RTSP (Real-Time Streaming protocol). The automatic fish size measurement system shows the results and stores them in a database for future work.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.18 no.spc
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• pp.37-50
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• 2020

In this paper, an overview of the scoping calculation results is provided with respect to criticality and radiation shielding of two KBS-3V type PWR SNF disposal systems and one NWMO-type CANDU SNF disposal system of the improved KAERI reference disposal system for SNFs (KRS⁺). The results confirmed that the calculated effective multiplication factors (k_eff) of each disposal system comply with the design criteria (< 0.95). Based on a sensitivity study, the bounding conditions for criticality assumed a flooded container, actinide-only fuel composition, and a decay time of tens of thousands of years. The necessity of mixed loading for some PWR SNFs with high enrichment and low discharge burnup was identified from the evaluated preliminary possible loading area. Furthermore, the absorbed dose rate in the bentonite region was confirmed to be considerably lower than the design criterion (< 1 Gy·hr^-1). Entire PWR SNFs with various enrichment and discharge burnup can be deposited in the KRS⁺ system without any shielding issues. The container thickness applied to the current KRS⁺ design was clarified as sufficient considering the minimum thickness of the container to satisfy the shielding criterion. In conclusion, the current KRS⁺ design is suitable in terms of nuclear criticality and radiation shielding.

• Nuclear Engineering and Technology
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• v.33 no.5
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• pp.468-482
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• 2001

As a process of Direct Containment Heating (DCH) issue resolution for Korean Standard Nuclear Power Plants (KSNPs), a containment load/strength assessment with two different approaches, the probabilistic and the deterministic, was performed with all plant-specific and phenomena-specific data. In case of the probabilistic approach, the framework developed to support the Zion DCH study, Two-Cell Equilibrium (TCE) coupled with Latin Hypercubic Sampling (LHS), provided a very efficient tool to resolve DCH issue. In case of the deterministic approach, the evaluation methodology using the sophisticated mechanistic computer code, CONTAIN 2.0 was developed, based on findings from DCH-related experiments or analyses. For three bounding scenarios designated as Scenarios V, Va, and VI, the calculation results of TCE/LHS and CONTAIN 2.0 with the conservatism or typical estimation for uncertain parameters, showed that the containment failure resulted from DCH loads was not likely to occur. To verify that these two approaches might be conservative , the containment loads resulting from typical high-pressure accident scenarios (SBO and SBLOCA) for KSNPs were also predicted. The CONTAIN 2.0 calculations with boundary and initial conditions from the MAAP4 predictions, including the sensitivity calculations for DCH phenomenological parameters, have confirmed that the predicted containment pressure and temperature were much below those from these two approaches, and, therefore, DCH issue for KSNPS might be not a problem.

• Korean Journal of Veterinary Research
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• v.50 no.1
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• pp.49-53
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• 2010

A 5-year-old female Cocker spaniel dog (body weight 7.0 kg) was presented with primary complaints of exercise intolerance and loud precordial thrill which was noticed since she was a puppy. Physical examination revealed a grade V/VI continuous murmur over the maximal point of the left basal area, bounding femoral pulse, but no differential cyanosis. Tall R waves were detected in electrocardiogram, suggesting left ventricular enlargement. Diagnostic imaging studies showed enlarged left ventricle, bulged descending aorta (dAo), markedly dilated right pulmonary artery, and continuous shunt flow between the dAo and main pulmonary artery. Based on these findings, the dog was diagnosed as left to right shunted patent ductus arteriosus (PDA). The patent ductus arteriosus was treated by lodging a PDA duct occluder via the transvenous approach. Clinical signs were markedly improved after the ductal occlusion, the shunt flow was mildly persistent. The case presented is the first case of PDA occluded by the PDA duct occluder via the transvenous approach in a small breed of dog. Although the residual shunt flow was mildly persisted, the dog was clinically normal without detectable murmurs.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.110-116
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• 2021

This paper proposed a real-time earlobe detection system using deep learning on the web. Existing deep learning-based detection methods often find independent objects such as cars, mugs, cats, and people. We proposed a way to receive an image through the camera of the user device in a web environment and detect the earlobe on the server. First, we took a picture of the user's face with the user's device camera on the web so that the user's ears were visible. After that, we sent the photographed user's face to the server to find the earlobe. Based on the detected results, we printed an earring model on the user's earlobe on the web. We trained an existing YOLO v5 model using a dataset of about 200 that created a bounding box on the earlobe. We estimated the position of the earlobe through a trained deep learning model. Through this process, we proposed a real-time earlobe detection system on the web. The proposed method showed the performance of detecting earlobes in real-time and loading 3D models from the web in real-time.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.4
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• pp.59-71
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• 2001

The Current CORBA shows some limitations for its successful deployment in real time system applications. Recently, OMG adopted Real-Time CORBA specification, which is defined as an extension to CORBA. The goal of the Real-Time CORBA is to provide a standard for CORBA ORB implementations that support 'end to end predictability'. In order to support 'end-to-end predictability', Real Time CORBA specifies many components such as priority model, communication protocol configuration, thread management, and etc. Among them, 'priority model' is the most important mechanism for avoiding or bounding priority inversion in CORBA invocations. In this paper, we present our efforts on a design ,and implementation of the Priority Model in Real-Time CORBA specification. The implementation is done as an extension of omniORB2(v.3.0.0), a popular open source non real time ORB. Experiment results demonstrate that our priority model implementation shows better performance and predictability than the non real-time ORB.