• Journal of information and communication convergence engineering
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• 제7권2호
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• pp.135-140
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• 2009

The wide deployment of wireless access technologies and the integration of various access network interfaces into a single terminal, allows mobile end-users to be always connected to the IP network, and to use those interface simultaneously. In this paper the CTE provides various access network interfaces capabilities, allowing reception of data over multiple service providers with different characteristics. Considerations for multiple network and service provider environments are regarded as essential for the successful deployment of convergence services in Next Generation Network (NGN). Event Driven Service (EDS) is regarded as a typical convergence service converging different functions of multiple service providers. This paper first describes the deployment model of NGN convergence services for multiple service provider environments. It also covers the service scenario of EDS, a convergence service for multiple service provider environments in NGN. Multiple provider environments stimulates the unified identifier management, namely ubiquitous identification (U-ID), to enable users to be provided convergence services without awareness of multiple provides. Then the designed structure and procedures of U-ID based EDS are given. Finally, the implementation results tested on Korea Advanced Research Network (KOREN) are described.

• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.49-55
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• 2018

In this paper, we propose schemes of blockchain utilization in tactical communication environment. The military tactical communication environment has similar characteristics with blockchain network such as distributed architecture, decentralization, and the need for data integrity. A communication node constituting a tactical communication network is constituted by a system capable of configuring and connecting a network for each node. When a communication node, having such capabilities, is configured as a node of blockchain network, various functions could be performed. In this paper, we propose utilization schemes of authentication, integrity, record management, and privilege control based blockchain technology. Functions for authentication, integrity verification, and record management need to ensure the stored data and could track history. The requirement of function's characteristics are matched to blockchain which is storing data sequentially and difficult to hack data, so that it could perform functionally and sufficiently well. Functions for authority control should be able to assign different privileges according to the state of the requestor. Smart contract will function when certain conditions are satisfied and it will be able to perform its functions by using it. In this paper, we will look over functions and utilization schemes of blockchain technology which could reliably share and synchronize data in a tactical communication environment composed of distributed network environment.

• International Journal of Contents
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• 제12권1호
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• pp.60-64
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• 2016

As new applications of cloud data management system (CDMS) such as online games, cooperation edit, social network, and so on, are increasing, transaction processing capabilities for CDMS are required. Several transaction processing methods for cloud data management system (CDMS) have been proposed. However, existing transaction processing methods have some problems. Some of them provide limited transaction processing capabilities. Some of them are hard to be integrated with existing CDMSs. In this paper, we proposed a new concurrency control method to support transaction processing capability for CDMS to solve these problems. The proposed method was designed and implemented based on Spark, an in-memory distributed processing framework. It uses RDD (Resilient Distributed Dataset) model to provide fault tolerant to data in the main memory. In our proposed method, database stored in CDMS is loaded to main memory managed by Spark. The loaded data set is then transformed to RDD. In addition, we proposed a multi-version concurrency control method through immutable characteristics of RDD. Finally, we performed experiments to show the feasibility of the proposed method.

• 상하수도학회지
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• 제26권4호
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• pp.547-554
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• 2012

As a national river remediation project has been completed, this study has a special interest on the capabilities to predict water levels at various points of the Geum River. To be endowed with intelligent forecasting capabilities, the author formulate the neuro-genetic algorithm associated with the short-term water level prediction model. The results show that neuro-genetic algorithm has considerable potentials to be practically used for water level forecasting, revealing that (1) model optimization can be obtained easily and systematically, and (2) validity in predicting one- or two-day ahead water levels can be fully proved at various points.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 추계학술대회
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• pp.617-618
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• 2015

본 논문은 농업의 계획, 생산, 유통 및 마케팅 분야에 이르기까지 체계적인 생산 및 활용을 위한 환경조성에 관한 스마트기본구조로 계획단계(pre-production stage), 생산단계(Production stage), 유통 및 마케팅단계(post-production stage)로 구분하여 서술하였다. ICT기반의 스마트 농업의 모형을 기반으로 하여 농산물에 IT 기술을 적용하여 생산성 향상을 표준화를 목표로 구조 및 절차를 기술한다.

• Journal of Multimedia Information System
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• 제6권2호
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• pp.87-90
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• 2019

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

• Journal of information and communication convergence engineering
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• 제22권3호
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• pp.181-188
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• 2024

Herein, an innovative transmission technique that utilizes the satellite aerial terrestrial integrated network (SATIN) architecture in combination with non-orthogonal multiple access (NOMA) communications is proposed. This approach is designed to significantly enhance communication rates, which is critical for modern and future combat capabilities. The effectiveness of the proposed transmission system is validated by conducting a comparative analysis of the sum-throughput results, considering various numbers of transmission nodes within the SATIN structure. The results and analyses reveal that the proposed method outperforms traditional methods such as spatial division multiple access (SDMA) and time division multiple access (TDMA), especially in terms of reducing data loss. This superior performance is primarily due to the advanced capability of NOMA in minimizing interference between signals, resulting in improved sum-throughput outcomes. The implementation of this method is expected to significantly enhance command communications in manned-unmanned combat systems, thereby bolstering overall combat effectiveness through improved transmission rates.

• 터널과지하공간
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• 제3권1호
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• pp.63-79
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• 1993

The design of tunnels in rock masses often demands more informations on geologic features and rock mass properties than acquired by usual field survey and laboratory testings. In practice, the situation that a perfect set of geological and mechanical input data is given to geomechanics design engineer is rare, while the engineers are asked to achieve a high level of reliability in their design products. This study presents an artificial neural network which is developed to resolve the difficulties encountered in conventional design techniques, particulary the problem of deteriorating the confidence of existing numerical techniques such as the finite element, boundary element and distinct element methods due to the incomplete adn vague input data. The neural network has inferring capabilities to identify the possible failure modes, support requirements and its timing for underground openings, from previous case histories. Use of the neural network has resulted in a better estimate of the correlation between systems of rock mass classifications such as the RMR and Q systems. A back propagation learning algorithm together with a multi-layer network structure is adopted to enhance the inferential accuracy and efficiency of the neural network. A series of experiments comparing the results of the neural network with the actual field observations are performed to demonstrate the abilities of the artificial neural network as a new tunnel design assistance system.

• 디지털콘텐츠학회 논문지
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• 제8권3호
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• pp.371-376
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• 2007

차량 내부에서는 컨트롤러, 센서, 텔레매틱스 단말기, 내비게이션, 오디오 및 비디오 등 다양한 모듈들이 CAN이나 MOST와 같은 차량 네트워크를 통해 연결되어 있다. 게다가, 사용자는 이동 중에 무선 모바일 네트워크를 이용하여 ITS나 인터넷에 접속할 수도 있다. 이러한 네트워크의 다양한 활용은 데이터 해킹, 프라이버시 침해, 위치 추적 등과 같은 많은 보안 문제를 야기하게 된다. 또한, 차량 운영 데이터(센서, 제어 데이터)를 해킹함으로써 차량을 고장 내거나 사고를 유발할 수 있는 가능성도 점차 커지고 있다. 본 논문에서는 CAN이나 MOST와 같은 차량 네트워크에 적용할 수 있는 암호화 기능을 가지는 보안 모듈을 제안한다. 이 보안 모듈은 DES, 3-DES, SEED, ECC 및 RSA와 같은 일반적인 암호화 알고리듬과 전자서명 기능을 제공하게 된다.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.77-87
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• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.