• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.6
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• pp.114-123
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• 1985

This is the second part of the four-part paper describing the development of a packet-switched computer network named the cORNET In this paper, following the first par paper that describes the concepts of the KORNET and the development of the network management center (NMC), wc present the design of the KORNET and the development of the network node processor (NNP) The initial configuration of the KORNET consists of three NNP's and one NMC. We have developed each NNP as a microprocessor-based (Mc68000) multiprocessor system, and implemented the NMC using a super-mini computer (Mv/8000) For the KORNET we use the virtual circuit (VC) method as the packet service strategy and the distributed adaptive routing algorithm to adapt efficiently the variation of node and link status. Also, we use a dynamic buffer management algorithm for efficient storage management. Thc hardware of the NNP system has been designed with emphasis on modularity so that it may be expanded esily . Also, the software of the NNP system has been developed according to the CCITT recommendations X.25, X.3, X.28 and X.29.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.427-432
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• 2023

In this study, MEC (: Multi-access Edge Computing) proposes a cloud service network configuration for various tests of autonomous vehicles to which V2X (: Vehicle to Everything) is applied in Wave, LTE, and 5G networks and MEC App (: Application) applied V2X service function test verification of two domains (operator (KT, SKT, LG U+), network type (Wave, LTE (including 3G), 5G)) in a specific region. In 4G networks of domestic operators (SKT, KT, LG U+ and Wave), MEC summarized the improvement effects through V2X function blocks and traffic offloading for the purpose of bringing independent network functions. And with a high level of QoS value in the V2X VNF of the 5G network, the traffic steering function scenario was demonstrated on the destination-specific traffic path.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.7
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• pp.907-912
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• 1993

In this Paper we have proposed a new method to implement the interpolation of the functions, using a neural network. The architecture of neural network is a three-layer perceptron and the training algorithm is a modified error back propagation algorithm adding neurons to hidden layer. The interpolated functions are sin(7 X), 3rd order polynomial 0.5$\times$3_2$\times$2+X+2.5 and rectangular pulse 0.99 U (X-0.2) -0.99 U(X-0.8) +0.01, where U(X) is the unit step. The root mean squred errors of the interpolated functions are 0.00258, 0.00164 and 0.00116 respectively.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.185-191
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• 2008

In this paper, experimental studies of a neural network (NN) control technique for non-model based position control of the x-y table robot are presented. Decentralized neural networks are used to control each axis of the x-y table robot separately. For an each neural network compensator, an inverse control technique is used. The neural network control technique called the reference compensation technique (RCT) is conceptually different from the existing neural controllers in that the NN controller compensates for uncertainties in the dynamical system by modifying desired trajectories. The back-propagation learning algorithm is developed in a real time DSP board for on-line learning. Practical real time position control experiments are conducted on the x-y table robot. Experimental results of using neural networks show more excellent position tracking than that of when PD controllers are used only.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2131-2153
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• 2022

To achieve accurate detection of tuberculosis (TB) areas in chest radiographs, we design a chest X-ray TB area detection algorithm. The algorithm consists of two stages: the chest X-ray TB classification network (CXTCNet) and the chest X-ray TB area detection network (CXTDNet). CXTCNet is used to judge the presence or absence of TB areas in chest X-ray images, thereby excluding the influence of other lung diseases on the detection of TB areas. It can reduce false positives in the detection network and improve the accuracy of detection results. In CXTCNet, we propose a channel attention mechanism (CAM) module and combine it with DenseNet. This module enables the network to learn more spatial and channel features information about chest X-ray images, thereby improving network performance. CXTDNet is a design based on a sparse object detection algorithm (Sparse R-CNN). A group of fixed learnable proposal boxes and learnable proposal features are using for classification and location. The predictions of the algorithm are output directly without non-maximal suppression post-processing. Furthermore, we use CLAHE to reduce image noise and improve image quality for data preprocessing. Experiments on dataset TBX11K show that the accuracy of the proposed CXTCNet is up to 99.10%, which is better than most current TB classification algorithms. Finally, our proposed chest X-ray TB detection algorithm could achieve AP of 45.35% and AP50 of 74.20%. We also establish a chest X-ray TB dataset with 304 sheets. And experiments on this dataset showed that the accuracy of the diagnosis was comparable to that of radiologists. We hope that our proposed algorithm and established dataset will advance the field of TB detection.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1209-1212
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• 2005

By using x86 processor for office environment, an improved Network Display System is implemented in this paper. The Network Display System is developed based on the x86 processor, and the system contains ethernet controller that can be used internet by stand alone. The Windows CE.NET is adopted as an operating system, and TFT-LCD monitor system is embedded..

• Journal of the military operations research society of Korea
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• v.26 no.1
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• pp.115-124
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• 2000

In this paper, we are introduced a certification system for open network environment. The Kerberos which was developed by MIT uses a secret key cryptosystem for authentication. It is secure and efficient for closed network users to authenticate each others. However, the kerberos has a disadvantage of managing a lot of secret keys for In this paper, we are introduced a certification system for open network environment. users in the open network environment. This paper suggests a method that uses X.509 to provide public keys with certification to Kerberos users for authentication in the X.500 directory standard. And we also suggest the smartcard as data storage device to enhance the security and availability.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.66
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• pp.19-26
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• 2001

I propose the control chart pattern to provide a more comprehensive scheme for detecting process X and R shifts using individual observations in start-up process. It is important to automate the identification of special disturbances to facilitate real-time manufacturing. This papers formulates X-R charts for interpretation by artificial neural networks. In this papers, which uses the backpropagation algorithm, two samples are fed into the trained neural network to provide outputs ranging from 0 to 1. Simulation results sow that the performance of the proposed control chart using the neural network(NNCC) is quite promising. Using these NN charts, guidelines are given for detecting and classifying process X and R shifts.

• The Journal of the Korea institute of electronic communication sciences
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• v.1 no.1
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• pp.70-80
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• 2006

The large portion of communication service areas move from a legacy wire-line voice service to mobile data service. For the purpose of satisfaction on market need, many communication systems should be installed and upgraded based on a mobile wide-band transmission facility. Recently, large part of communication service is based on internet protocol by packet switch techniques and required new technologies such as multimedia processing, QoS achievement, and mobility managememobile communication network such as IS-95A/B and CDMA2000-1X. In this paper, I analyzed the network architecture and service provision methods. in CDMA2000-1X nt. In korea, a CDMA communication technique is standardized for digital mobile communication systems. By using the analysed results, I will extract an efficient method for network evolution and a core technique for next generation mobile communication network.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1315-1318
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• 2003

In this paper, a neural network based approach using a self-organizing feature map is proposed for the segmentation of X ray images. A number of algorithms based on such approaches as histogram analysis, region growing, edge detection and pixel classification have been proposed for segmentation of general images. However, few approaches have been applied to X ray image segmentation because of blur of the X ray image and vagueness of its edge, which are inherent properties of X ray images. To this end, we develop a new model based on the neural network to detect objects in a given X ray image. The new model utilizes Mumford-Shah functional incorporating with a modified adaptive SOFM. Although Mumford-Shah model is an active contour model not based on the gradient of the image for finding edges in image, it has some limitation to accurately represent object images. To avoid this criticism, we utilize an adaptive self organizing feature map developed earlier by the authors.[1] It's learning rule is derived from Mumford-Shah energy function and the boundary of blurred and vague X ray image. The evolution of the neural network is shown to well segment and represent. To demonstrate the performance of the proposed method, segmentation of an industrial part is solved and the experimental results are discussed in detail.