• Proceedings of the KSME Conference
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• 2003.04a
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• pp.31-36
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• 2003

In this study a relationship between SP curves and tensile properties was investigated by FE analysis on SP test with various assumed tensile properties. For the accuracy of FE analysis, SP test and tensile test were performed and those results were compared with FE analysis results. The yield load(Py) defined from the intersection point of two lines tangent to the elastic bending region and plastic bending region. And it was related specifically with yield stress(${\sigma}_0$) in FE analysis result curves. The slopes of FE analysis result curves normalized by yield stress(${\sigma}_0$) reflected the change of tensile properties regardless of yield stress(${\sigma}_0$) variation. Empirical relations were derived from these results. Tensile properties from these relations showed good agreement in FE analysis curve and tested curve.

• Journal of Korea Multimedia Society
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• v.5 no.6
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• pp.616-624
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• 2002

An extraction method of central objects in the color images is proposed, in this paper. A central object is defined as a comparatively consist of the central object in the image. First of all. an input image and its decreased resolution images are segmented. Segmented regions are classified as the outer or the inner region. The outer region is adjacent regions are included by a same region in the decreased resolution image. Then core object regions and core background regions are selected from the inner region and the outer region respectively. Core object regions are the representative regions for the object and are selected by using the information about the information about the region size and location. Each inner regions is classified into foreground or background regions by comparing values of a color histogram intersection of the inner region against the core object region and the core background regions. The core object region and foreground regions consist of the central object in the image.

• Korean Journal of Computational Design and Engineering
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• v.10 no.2
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• pp.114-120
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• 2005

This paper presents a novel method for radius measurement of fillet regions of polygonal models by using optimum onhogonal planes. The objective function for finding an optimum onhogonal plane is designed based on the orthogonality between the normal vectors of the faces in a filet region and the plane that is to be found. Direct search methods are employed to solve the defined optimization problem since no explicit derivatives of the object function can be calculated. Once an optimum orthogonal plane is obtained, the intersection between the onhogonal plane and the faces of interest is calculated, and necessary point data in the fillet region for measuring radii are extracted by some manipulation. Then, the radius of the fillet region in question is measured by least squares fitting of a circle to the extracted point data. The proposed radius measuring method could eliminate the burden of defining a plane for radius measurement, and automatically find a necessary optimum orthogonal plane. It has an advantage in that it can measure fillet radii without prior complicated segmentation of fillet regions and explicit information of neighboring surfaces. The proposed method is demonstrated trough some mea-surement examples.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.5
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• pp.554-562
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• 2004

An empirical relationship between parameters from SP curves and tensile properties has been systematically investigated by experimental tests and FEM simulations. A series of SP and tensile tests were performed. SP tests were also simulated by FE analysis with various tensile properties. It was found that the yield loads(Py) and the maximum loads( $P_{MAX}$) in SP curves were linearly related with the yield strength($\sigma$$_{o}$) and the tensile strength($\sigma$$_{UTS}$), respectively. The yield loads defined from the intersection point of two lines tangent to the elastic bending region and plastic bending region showed better relation to the yield strength than those from offset line. The maximum loads in SP curves showing plastic instability region was linearly related with the tensile strengths. The slope of SP curves in simulation results had a close correlation with the hardening coefficient and hardening strength as well.l.l.l.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.784-791
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• 2022

Tack coat is a thin layer of asphalt between the existing pavement and asphalt overlay. During construction, insufficient tack coat layering can later cause surface defects such as slippage, shoving, and rutting. This paper proposed a method for tack coat inspection improvement using an unmanned aerial vehicle (UAV) and deep learning neural network for automatic non-uniform assessment of the applied tack coat area. In this method, the drone-captured images are exploited for assessment using a combination of Mask R-CNN and Grey Level Co-occurrence Matrix (GLCM). Mask R-CNN is utilized to detect the tack coat region and segment the region of interest from the surroundings. GLCM is used to analyze the texture of the segmented region and measure the uniformity and non-uniformity of the tack coat on the existing pavements. The results of the field experiment showed both the intersection over union of Mask R-CNN and the non-uniformity measured by GLCM were promising with respect to their accuracy. The proposed method is automatic and cost-efficient, which would be of value to state Departments of Transportation for better management of their work in pavement construction and rehabilitation.

• Journal of Korean Society of societal Security
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• v.1 no.3
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• pp.59-67
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• 2008

The most influential facility causing traffic disaster on the urban road is intersection. Accordingly, this study elected a region for case study from seabang three-way junction, partial section of Dongmoon Ro in Kwang-Ju city, to the intersection of Mudeung Library Entrance. It is believed that the signal progression is very effective on the basis of short interval of intersection and massive traffic volume. The signal progression was simulated by using TRANSYT-7F model. The following is summary of the simulation: According to the change of cycle length, P.I. delay and fuel consumption showed the tendency of being increased in case that cycle length becomes long or short, centering around the best cycle length. In the event of progressing the cycle length, the average speed per vehicle is increased by 11.39Km per hour and P.I value is improved by 40.65% so that it resulted in 42.86% improvement in the total travel time. Moreover, the fuel consumption in line with the progression practice produced fuel saving of 31.04%.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.25-34
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• 2022

Brain computed tomography (CT) is useful for brain lesion diagnosis, such as brain hemorrhage, due to non-invasive methodology, 3-dimensional image provision, low radiation dose. However, there has been numerous misdiagnosis owing to a lack of radiologist and heavy workload. Recently, object detection technologies based on artificial intelligence have been developed in order to overcome the limitations of traditional diagnosis. In this study, the applicability of a deep learning-based YOLOv5s model was evaluated for brain hemorrhage detection using brain CT images. Also, the effect of hyperparameters in the trained YOLOv5s model was analyzed. The YOLOv5s model consisted of backbone, neck and output modules. The trained model was able to detect a region of brain hemorrhage and provide the information of the region. The YOLOv5s model was trained with various activation functions, optimizer functions, loss functions and epochs, and the performance of the trained model was evaluated in terms of brain hemorrhage detection accuracy and training time. The results showed that the trained YOLOv5s model is able to provide a bounding box for a region of brain hemorrhage and the accuracy of the corresponding box. The performance of the YOLOv5s model was improved by using the mish activation function, the stochastic gradient descent (SGD) optimizer function and the completed intersection over union (CIoU) loss function. Also, the accuracy and training time of the YOLOv5s model increased with the number of epochs. Therefore, the YOLOv5s model is suitable for brain hemorrhage detection using brain CT images, and the performance of the model can be maximized by using appropriate hyperparameters.

• The Journal of Korea Robotics Society
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• v.11 no.1
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• pp.1-8
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• 2016

In this paper, we propose a new algorithm of the guidance line extraction for autonomous agricultural robot based on vision camera in paddy field. It is the important process for guidance line extraction which finds the central point or area of rice row. We are trying to use the central region data of crop that the direction of rice leaves have convergence to central area of rice row in order to improve accuracy of the guidance line. The guidance line is extracted from the intersection points of extended virtual lines using the modified robust regression. The extended virtual lines are represented as the extended line from each segmented straight line created on the edges of the rice plants in the image using the Hough transform. We also have verified an accuracy of the proposed algorithm by experiments in the real wet paddy.

• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2183-2195
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• 2020

A mathematical model for the flowrate and rotation speed of RCP during idling was established. The numerical calculation method and dimensionless method were used to analyze the flow, head, torque and pressure and speed changes under idle conditions. Regularity, using the Q criterion vortex identification judgment method combined with surface flow spectrum morphology analysis to diagnose the vortex dynamic characteristics on RCP blade. On impeller blade, there is two oscillations in the pressure ratio on pressure surface in blade outlet region. The velocity on the suction surface is two times more oscillating than the inlet of blade, and there is an intersection with the velocity ratio curve on pressure surface. On blade of guide vane, the pressure ratio increases along the inlet to outlet direction, and the speed ratio decreases with the increase of idle time. There is a vortex that rotates counterclockwise on the suction surface, and the streamline on the suction surface of blade is subjected to the entrainment and blocking action of the vortex creates a large reverse flow in the main flow region. There are two vortices at the outlet of guide vane suction side and the vortices are in opposite directions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2458-2482
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• 2023

Medical image segmentation techniques based on convolution neural networks indulge in feature extraction triggering redundancy of parameters and unsatisfactory target localization, which outcomes in less accurate segmentation results to assist doctors in diagnosis. In this paper, we propose a multi-level semantic-rich encoding-decoding network, which consists of a Pooling-Conv-Former (PCFormer) module and a Cbam-Dilated-Transformer (CDT) module. In the PCFormer module, it is used to tackle the issue of parameter explosion in the conservative transformer and to compensate for the feature loss in the down-sampling process. In the CDT module, the Cbam attention module is adopted to highlight the feature regions by blending the intersection of attention mechanisms implicitly, and the Dilated convolution-Concat (DCC) module is designed as a parallel concatenation of multiple atrous convolution blocks to display the expanded perceptual field explicitly. In addition, MultiHead Attention-DwConv-Transformer (MDTransformer) module is utilized to evidently distinguish the target region from the background region. Extensive experiments on medical image segmentation from Glas, SIIM-ACR, ISIC and LGG demonstrated that our proposed network outperforms existing advanced methods in terms of both objective evaluation and subjective visual performance.