• 한국경영과학회지
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• 제36권4호
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• pp.1-16
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• 2011

It is important not only to introduce the C4I(Command and Control, Communication, Computer, Intelligence) system for realizing the NCW(Network Centric Warfare) but also to evaluate the synergistic effect by the C4I system. However, the study effort for evaluating the system's synergistic effect is insufficient compared with introducing the system. Therefore, in this paper, we proposed a model that measures the synergistic effect of combat power by the warfare information system. To measure the synergistic effect of warfare information system, the network power must be considered, so we also proposed a new methodology for measurement of network power based on SNA(Social Network Analysis), not Metcalfe's law. A model we proposed is a model that measures the raised combat power by the network effectiveness. The methodology and model we proposed in this paper will be used usefully to analyze the practical effect of constructing future warfare information system.

• 한국컴퓨터정보학회논문지
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• 제22권6호
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• pp.25-32
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• 2017

We are living in networks which are regarded as a society. However, it is difficult to designate a specific position or the impact over sociological relationships and virtual links. In this paper, we conceptualize two themes of the network as Physical Network and Virtual Network, and observe their cross-network effects. New concept called Networked Community (NC) is then introduced to walk through both PN and VN by using the element of connections say connectivity feature. Through modeling NC by the theme of network transposition and egocentric network, we try to comprehend all possible networks for detecting the problems and solutions by using both sides' idea. Experimental results show that we can model real-world problems and then analyze them through NC by measurable and structural manner.

• 한국기계기술학회지
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• 제20권6호
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• pp.776-782
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• 2018

After CNN basic structure was introduced by LeCun in 1989, there has not been a major structure change except for more deep network until recently. The deep network enhances the expression power due to improve the abstraction ability of the network, and can learn complex problems by increasing non linearity. However, the learning of a deep network means that it has vanishing gradient or longer learning time. In this study, we proposes a CNN structure with MLP layer. The proposed CNNs are superior to the general CNN in their classification performance. It is confirmed that classification accuracy is high due to include MLP layer which improves non linearity by experiment. In order to increase the performance without making a deep network, it is confirmed that the performance is improved by increasing the non linearity of the network.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.389-395
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• 2022

A Mobile Sensor Network is widely used in real time applications. A critical need in Mobile Sensor Network is to achieve energy efficiency during routing as the sensor nodes have scarce energy resource. The nodes' mobility in MWSN poses a challenge to design an energy efficient routing protocol. Clustering helps to achieve energy efficiency by reducing the organization complexity overhead of the network which is proportional to the number of nodes in the network. This paper proposes"EERA: Energy Efficient Routing Algorithm for Mobile Sensor Network" is divided into five phases. 1, Cluster Formation 2.Cluster head and Transmission head selection 3.Path Establishment / Route discovery and 4,Data Transmission. Experimental Analysis has been done and is found that the proposed method performs better than the existing method with respect to four parameters.

• 한국통신학회:학술대회논문집
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• 한국통신학회 1986년도 춘계학술발표회 논문집
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• pp.119-124
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• 1986

This paper describes the development of the KORNET that may be regarded as the latest public packet-swiched computer communication network. The KORNET project included the development of the network management center (NMC), a network concentrator. For the KORNET we use the virtual circuit(VC) method, a distributed adaptive routing algorithm, and a dynamic buffer management algorithm. The NMC acts as a nerve center of the network, performing such function as network monitoring, subscriber and network management and routing management, etc. As for the NNP and NC hardware, we have implemented them with the 16-bit multitask/multiprocessor technology using MC68000 microprocessors. Softwares have been developed using C language is required for real time processing. All the network protocols we have developed comply completely with the latest CCITT recommendations including X.25, X,3 , X.28 and X.29.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.111-118
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• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권9호
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• pp.2017-2036
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• 2012

Based on the current solutions to the problem of energy hole, this paper proposed a nonuniform node distribution clustering algorithm, NNDC. Firstly, we divide the network into rings, and then have an analysis and calculation on nodes' energy consumption in each ring of the network when clustering algorithm is applied to collect data. We also put forward a scheme of nonuniform node distribution on the basis of the proportion of nodes' energy consumption in each ring, and change nodes' active/hibernating states under density control mechanism when network coverage is guaranteed. Simulation shows NNDC algorithm can satisfyingly balance nodes' energy consumption and effectively avoid the problem of energy hole.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1441-1461
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• 2017

Nowadays, Dynamic Adaptive Streaming over HTTP (DASH) has become very popular in streaming multimedia contents. In DASH, a client estimates current network bandwidth and then determines an appropriate video quality with bitrate matching the estimated bandwidth. Thus, estimating accurately the available bandwidth is a significant premise in the quality of video streaming, especially when network traffic fluctuates substantially. To cope with this challenge, researchers have presented various filters to estimate network bandwidth adaptively. However, experiment results show that current schemes either adapt slowly to network changes or adapt fast but are very sensitive to delay jitter and produce sharply changed estimation. This paper presents a novel bandwidth estimation scheme based on Moving Average Convergence Divergence (MACD). We applied an MACD indicator and its two thresholds to classifying network states into stable state and agile state, based on the network state different filters are applied to estimate network bandwidth. In the paper, we studied the performance of various MACD indicators and the threshold values on bandwidth estimation. Then we used a DASH proxy-based environment to compare the performance of the presented scheme with current well-known schemes. The simulation results illustrate that the MACD-based bandwidth estimation scheme performs superior to existing schemes both in the speed of adaptively to network changes and in stability in bandwidth estimation.

• 한국자동차공학회논문집
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• 제19권4호
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• pp.54-63
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• 2011

An educational network system based on CAN protocol internal to a passenger ground vehicle has been developed. The developed network system has been applied to a commercial plug-in electrical vehicle and verified the educational applicability. To apply this in-vehicle network technology based on CAN, a suitable electric vehicle has been chosen and a CAN network structure has been designed, developed and manufactured. Since the commercial electric vehicle chosen as a test bed has its own proprietary electric network, we explain how the original electric network has been utilized and how the new network system has been designed. The developed network system on a real vehicle has been tested to show the applicability and the performance. Finally, the system has been applied at few classrooms to demonstrate how the in-vehicle network system works and to teach how to analyse the CAN signals. The developed system proven to be effective for educational purpose.

• 한국산림과학회지
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• 제84권1호
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• pp.63-70
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• 1995

이 논문에서는 neural network기법에 의해 소규모 임분의 시업계획을 분석하는 방법과 적용성을 평가하였다. 이를 위해서 적정한 임분시업체계를 계산하기 위한 neural network 모델을 개발하고, neural network의 구조체계와 network을 교육시키기 위해 요구되는 자료량의 측면에서 적용성을 검토하였다. 연구목적상 모델의 교육 및 비교분석에 요구되는 적정 시업체계에 대한 자료는 기존의 비선형 시업체계분석모델을 이용하였다. 이 시업체계 분석모델은 동령급 구조의 긴잎 소나무(Pinus palustris) 단순림의 적정시업체계를 분석하는 모델로서 전림수확생장함수에 의해 임분의 생장이 예측되는 모델이다. neural network 모델의 적용성 검토에 요구되는 분석자료들은 이 비선형 시업체계분석모델에 의해 제시된 긴잎소나무 임분의 적정 시업체제분석 결과들을 이용하였다.