• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.167-174
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• 2021

The purpose of this study is to classify TIFF images, PNG images, and JPEG images using deep learning, and to compare the accuracy by verifying the classification performance. The TIFF, PNG, and JPEG images converted from chest X-ray DICOM images were applied to five deep neural network models performed in image recognition and classification to compare classification performance. The data consisted of a total of 4,000 X-ray images, which were converted from DICOM images into 16-bit TIFF images and 8-bit PNG and JPEG images. The learning models are CNN models - VGG16, ResNet50, InceptionV3, DenseNet121, and EfficientNetB0. The accuracy of the five convolutional neural network models of TIFF images is 99.86%, 99.86%, 99.99%, 100%, and 99.89%. The accuracy of PNG images is 99.88%, 100%, 99.97%, 99.87%, and 100%. The accuracy of JPEG images is 100%, 100%, 99.96%, 99.89%, and 100%. Validation of classification performance using test data showed 100% in accuracy, precision, recall and F1 score. Our classification results show that when DICOM images are converted to TIFF, PNG, and JPEG images and learned through preprocessing, the learning works well in all formats. In medical imaging research using deep learning, the classification performance is not affected by converting DICOM images into any format.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.920-925
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• 2010

A home network system is made up of home devices and wire and wireless network can not only be the subject of cyber attack from a variety factors of threatening, but also have security weakness in cases of hacking, vicious code, worm virus, DoS attack, tapping of communication network, and more. As a result, a variety of problems such as abuse of private life, and exposure and stealing of personal information arose. Therefore, the necessity for a security protocol to protect user asset and personal information within a home network is gradually increasing. Thus, this dissertation designs and suggests a home network security protocol using user authentication and approach-control technology to prevent the threat by unauthorized users towards personal information and user asset in advance by providing the gradual authority to corresponding devices based on authorized information, after authorizing the users with a Public Key Certificate.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.877-884
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• 1994

In the power system state estimation, the J(x)-index test and normalized residuals ${\gamma}$S1NT have been the presence of bad measurements and identify their location. But, these methods require the complete re-estimation of system states whenever bad data is identified. This paper presents back-propagation neural network medel using autoregressive filter for identification of bad measurements. The performances of neural network method are compared with those of conventional mehtods and simulation results show the geed performance in the bad data identification based on the neural network under sample power system.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.1-12
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• 2020

In the medical field, numerical imbalance of data due to differences in disease prevalence is a common problem. It reduces the performance of a artificial intelligence network, leading to difficulties in learning a network with good performance. Recently, generative adversarial network (GAN) technology has been introduced as a way to address this problem, and its ability has been demonstrated by successful applications in various fields. However, it is still difficult to achieve good results in solving problems with performance degraded by numerical imbalances because the image resolution of the previous studies is not yet good enough and the structure in the image is modeled locally. In this paper, we propose a multi-scale conditional generative adversarial network based on attention mechanism, which can produce high resolution images to solve the numerical imbalance problem of chest X-ray image data. The network was able to produce images for various diseases by controlling condition variables with only one network. It's efficient and effective in that the network don't need to be learned independently for all disease classes and solves the problem of long distance dependency in image generation with self-attention mechanism.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1936-1945
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• 2008

In Korea, TCN has applied to the Korean High-speed Train (HSR350X) through G7 High-speed Train development project. TCN is the most suitable international standard communication network for distributed control systems that is adopted for high-speed of vehicle, safety and flexibility. TCN is the network exclusively for the high-speed train and electrical trains. This TCN satisfies the network standards. The network standards are real time communication, fault tolerance design, integrated data system, resistance of environment, automated recognition for modification of vehicle formation and maintenance. The purpose of this research is applying the development of WTB analyzer which is part of communication network system TCN, to check the communication of high-speed trains and electrical trains.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.321-324
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• 2018

Recently, the evolution of V2X (Vehicle to Everything) technology is accelerating. In particular, it can be seen that C-V2X (Cellular V2X) technology and services combined with mobile telecommunication network are developing rapidly. However, in Korea, e-Call and emergency communication services are inferior to the developed communication technologies and the proportion of vehicles arriving at Golden Hour is considerably low. Therefore, this paper designed the communication architecture with C-V2X and Android operating systems, and presented ways to improve existing e-Call services using V2N (Vehicle to Nomadic Device) communication based on it.

• Journal of Biomedical Engineering Research
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• v.19 no.1
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• pp.1-8
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• 1998

Clustered microcalcifications on X-ray mammograms are an important sign for early detection of breast cancer. This paper proposes a computer-aided diagnosis method for the detection of clustered microcalcifications and marking their locations on digitized mammograms. The proposed detection method consists of the region of interest (ROI) selection, the film-artifact removal, the surrounding texture analysis method for the detection of clustered microcalcifications, which is based on the second-order histogram in two nested surrounding regions on the current pixel. This paper also describes the effectiveness of the proposed film-artifact removal filter in terms of the classification performance with the receiver operating-characteristics(ROC) analysis. A three-layer backpropagation neural network is employed as a classifier. The appropriate marking for the locations of clustered microcalcifications can be used to alert radiologists to locations of suspicious lesions.

• The Transactions of the Korea Information Processing Society
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• v.5 no.1
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• pp.40-48
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• 1998

Linear size expanders have been studied in many fields for the practical use, which make it possible to connect large numbers of device chips in both parallel communication systems and parallel computers. One major limitation on the efficiency of parallel computer designs has been the highly cost of parallel communication between processors and memories. Linear order concentrators can be used to construct theoretically optimal interconnection network schemes. Existing explicitly defined constructions are based on expanders, which have large constant factors, thereby rendering them impractical for reasonable sized networks. For these objectives, we use the more detailed matching points in permutation functions, to find out the bigger expansion constant from an equation, $\mid\Gamma_x\mid\geq[1+d(1-\midX\mid/n)]\midX\mid$. This paper presents an improvement of expansion constant on constructing concentrators using expanders, which realizes the reduction of the size in a superconcentrator by a constant factor. As a result, this paper shows an explicit construction of (n, 5, $1-\sqrt{3/2}$) expander. Thus, superconcentrators with 209n edges can be obtained by applying to the expanders of Gabber and Galil's construction.

• Journal of IKEEE
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• v.16 no.2
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• pp.86-94
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• 2012

Synchronization MAC Protocol such as S-MAC and T-MAC utilize duty cycling technique which peroidically operate wake-up and sleep state for reducing energy consumption. But synchronization MAC showed low energy efficiency because of additional control packets. For better energy consumption, Asychronization MAC protocols are suggested. For example, B-MAC, and X-MAC protocol adopt Low Power Listening (LPL) technique with CSMA algorithm. All nodes in these protocols joining a network with independent duty cycle schedules without additional synchronization control packets. For this reason, asynchronous MAC protocol improve energy efficiency. In this study, a low-power MAC protocol which is based on X-MAC protocol for wireless sensor network is proposed for better energy efficiency. For this protocol, we suggest preamble numbering, and virtual-synchronization technique between sender and receive node. Using TelosB mote for evaluate energy efficiency.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.125-134
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• 2024

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.