• IEIE Transactions on Smart Processing and Computing
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• v.2 no.3
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• pp.148-159
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• 2013

An analysis of the throughput and stability region of random access systems is currently of interest in research and industry. This study evaluated the performance of a multiuser random access channel with a retransmission gain. The channel was composed of a media access control (MAC) determined by the transmission probabilities and a multiuser communication channel characterized by the packet reception probabilities as functions of the number of packet transmissions and the collision status. The analysis began with an illustrative two-user channel, and was extended to a general multiuser channel. For the two-user channel, a sufficient condition was derived, under which the maximum throughput was achieved with a control-free MAC. For the channel with retransmission gain, the maximum steady throughput was obtained in a closed form. The condition under which the random access channel can acquire retransmission gain was also obtained. The stability region of the general random access channel was derived. These results include those of the well-known orthogonal channel, collision channel and slotted Aloha channel with packet reception as a special instance. The analytical and numerical results showed that exploiting the retransmission gain can increase the throughput significantly and expand the stability region of the random access channel. The analytical results predicted the performance in the simulations quite well.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.22 no.7
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• pp.1030-1040
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• 1998

One of the main unresolved issues in large-eddy simulation(LES) of wall-bounded turbulent flows is the requirement of high spatial resolution in the near-wall region, especially in the spanwise direction. Such high resolution required in the near-wall region is generally used throughout the computational domain, making simulations of high Reynolds number, complex-geometry flows prohibitive. A grid-embedding strategy using a nonconforming spectral domain-decomposition method is proposed to address this limitation. This method provides an efficient way of clustering grid points in the near-wall region with spectral accuracy. LES of transitional and turbulent channel flow has been performed to evaluate the proposed grid-embedding technique. The computational domain is divided into three subdomains to resolve the near-wall regions in the spanwise direction. Spectral patching collocation methods are used for the grid-embedding and appropriate conditions are suggested for the interface matching. Results of LES using the grid-embedding strategy are promising compared to LES of global spectral method and direct numerical simulation. Overall, the results show that the spectral domain-decomposition grid-embedding technique provides an efficient method for resolving the near-wall region in LES of complex flows of engineering interest, allowing significant savings in the computational CPU and memory.

• BMB Reports
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• v.28 no.4
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• pp.353-358
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• 1995

Epitope tagging is the process of fusing a set of amino acid residues that are recognized as an antigenic determinant to a protein of interest. Tagging a protein with an epitope facilitates various immunochemical analyses of the tagged protein with a specific monoclonal antibody. The monoclonal antibody H8 has subtype specificity for an epitope derived from the preS2 region of hepatitis B virus surface antigen. Previous studies on serial deletions of the preS2 region indicated that the preS2 epitope was located in amino acid residues 130~142. To test whether the amino acid sequence in this interval is sufficient to confer on proteins the antigenicity recognizable by the antibody H8, the set of amino acid residues in the interval was tagged to the amino terminal of ${\beta}$-galactosidase and to the carboxyl terminal of the truncated $p56^{lck}$ fragment. The tagged ${\beta}$-galactosidase, expressed in Escherichia coli, maintained the enzymatic activity and was immunoprecipitated efficiently with H8. The tagged $p56^{lck}$ fragment, synthesized in an in vitro translation system, was also immunoprecipitated specifically with H8. These results demonstrate that the amino acid sequence of the preS2 region can be used efficiently for the epitope tagging approach.

• Journal of radiological science and technology
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• v.27 no.3
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• pp.19-24
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• 2004

MRI imaging is one of the imaging techniques showing anatomical structures of human body for medical diagnosis, and has been researched in order to provide better quality of anatomical information. In this study, we propose a very useful method to extract an interest areas and how to diagnose necrolysis of femoral neck disease automatically. Regions of femoral neck is set using anatomical features and Hough transform and advantages of both region extension and histogram-based region segmentation method are combined for better region segmentation. As a result of the proposed method, good imaging quality was obtained for femoral neck with both normal and severe necrosis as well as for femoral neck in early stage of necrolysis.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.4
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• pp.233-238
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• 2009

In this paper, we propose the methods to select the stable region for the detect region which is required in the system used the face to the ROI in the auto-focus digital camera. this method regards the face region as the ROI in the progressive input frame and focusing the region in the mobile camera embeded ISP module automatically. The learning algorithm to detect the face is the Adaboost algorithm. we proposed the method to detect the slanted face not participate in the train process and postprocessing method for the results of detection, and then we proposed the stabilization method to sustain the region not shake for the region. we estimated the capability for the stabilization algorithm using the RMS between the trajectory and regression curve.

• Journal of the Korean association of regional geographers
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• v.14 no.4
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• pp.382-393
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• 2008

Tourism Industry can be treated as means for activating the economy of regions and must be considered as new alternative. As this trend, recently. the interest of tourism area connected with health industry is increasing gradually. And so, this study investigated the domestic abroad cases and established the promotion model of health tourism through experts' opinion to push ahead health tourism as strategy industry of Jeju region. Also this study proposed the planning to improve the competition ability of Jeju region by executing the fundamental research through the survey focused on tourists.

• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.5
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• pp.731-739
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• 1987

This study has been performed on multiple steady-state natural convection in the upflow region induced by an inclined isothermal plate immersed in pure cold water. The newly found additional steady-state solutions are of considerable practical interest because the heat-transfer rates for a pair of solutions with determining physical parameters and boundary conditions otherwise identical are sometimes vastly different. The results are as follows: First, in the largely upflow region, two solutions exist for 0.15157

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.687-688
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• 2023

Bone age assessment is a crucial task in pediatric radiology for assessing growth and development in children. In this paper, we explore the potential of Vision Transformer, a state-of-the-art deep learning model, for bone age assessment using X-ray images. We generate heatmap outputs using a pre-trained Vision Transformer model on a publicly available dataset of hand X-ray images and show that the model tends to focus on the overall hand and only the bone part of the image, indicating its potential for accurately identifying the regions of interest for bone age assessment without the need for pre-processing to remove background noise. We also suggest two methods for extracting the region of interest from the heatmap output. Our study suggests that Vision Transformer holds great potential for bone age assessment using X-ray images, as it can provide accurate and interpretable output that may assist radiologists in identifying potential abnormalities or areas of interest in the X-ray image.