• 한국분무공학회지
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• 제28권3호
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• pp.119-125
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• 2023

In this research, a network model was developed to predict combustion instability in an annular gas turbine combustor (GT24) for power generation. The model consisted of various acoustic elements such as several ducts and area changes which could represent a real combustor with a complex geometry, applied mass, momentum, and energy equations to each element. In addition, a one-dimensional network model through a cylindrical coordinate system has been proposed to predict various acoustic modes. As a result of the analysis, the key resonant frequencies such as longitudinal, circumferential, and complex modes were derived from the EV combustor of GT24, and the reliability of the current model was verified through comparison with the 3D Helmholtz solver.

• 한국지능시스템학회논문지
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• 제11권8호
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• pp.701-708
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• 2001

최근 컴퓨터의 지능에 대한 연구가 활발히 진행되고 있으며, 불확실하고 복잡한 동적 환경에서도 적응할 수 있도록 그 영역을 확장해 가고 있다. 본 논문에서는 다치논리를 기반으로 한 다치오토마타 모델을 신경망으로 구현한 다치-신경망 시스템을 제안한다. 또한, 다치오토마타는 신경망으로 구현될 수 있고, 다치-신경망 모델은 다치오토마타로 시뮬레이션될 수 있음을 입증하였다. 그 결과, 다치-신경망 모델은 지능시스템, 뇌의 모델링과 같은 여러 응용 분야에 널리 사용될 수 있을 것으로 기대된다.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1530-1537
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• 2016

Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

• 대한전자공학회논문지TC
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• 제40권12호
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• pp.9-17
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• 2003

본고는 차세대 IP망(NGN)에서의 대역 설계 방법에 관한 것이다. 특히, VoIP 뿐만 아니라 데이터 서비스를 수용하는 에지 라우터에서의 호레벨 및 패킷 레벨 음성 트래픽 대역 설계 방법을 제시하였다. 호레벨 모델은 실제 연결되는 호의 수를 통계적으로 계산하기 위해 평균, 분산과 같은 통계적 기법을 사용하고, 패킷레벨 모델은 M/G/1 큐잉 모델을 활용하여 음성 및 데이터 트래픽의 부하를 나타내었다. 제시된 트래픽 모델을 통해서 계산된, 음성과 데이터 연결을 위한 대역폭을 기반으로 최대 트래픽 부하를 예측할 수 있고, 또한 수치 시험을 통한 이의 결과를 제시하였다.

• 디지털융복합연구
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• 제13권12호
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• pp.13-21
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• 2015

무선 브로드밴드 기술의 발전은 산업계 전반에 다양한 변화를 안겨주고 있다. 새로운 무선 브로드밴드 기술의 등장으로 인해 기존 산업의 경쟁력은 강화되었고 새로운 융합형 서비스와 신생산업들이 등장하게 되었다. 본 연구에서는 차세대 무선 브로드밴드 산업의 동향을 분석하고 이를 활성화시킬 수 있는 방안을 제시하는 것을 목적으로 한다. 이를 위해, 먼저 최근 무선 브로드밴드 산업의 동향을 분석하였고, 무선 브로드밴드 산업 성장에 영향을 주는 요소로 무선 네트워크 기술, 콘텐츠, 서비스를 파악하였다. 파악된 요소별로 차세대 무선 브로드밴드 산업의 동향을 조사하였고, 네트워크 기술 발전을 성장 동력으로 하는 무선 브로드밴드 산업의 선순환 성장 모형을 제시하였다. 끝으로 무선 네트워크 기술 개발 관점에서 무선 브로드밴드 산업을 활성화시킬 수 있는 방안들을 기술하였다. 본 연구의 결과는 차세대 무선 브로드밴드 산업에 대한 통찰력 있는 시각을 제공하며 향후 무선 브로드밴드 산업을 발전시키는 제도방안 수립에 도움을 줄 것으로 기대된다.

• 한국전자통신학회논문지
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• 제17권5호
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• pp.825-832
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• 2022

본 논문은 영농형 태양광 발전 시스템의 전력 생산량을 수집·저장하여 지능적인 예측 모델을 구현하기 위한 예측 및 진단 모델의 설계와 구현에 대해 논한다. 제안된 모델은 시계열 데이터에 특화된 순환신경망 기법인 RNN, LSTM, GRU 모델을 이용하여 태양광 발전량을 예측하고 각 모델의 하이퍼 파라미터를 다르게 주어 비교 분석하고, 성능을 평가했다. 그 결과 세 모델 모두 MSE, RMSE 지표는 0에 매우 가까우며, R2 지표는 1에 가까운 성능을 보였다. 이를 통해 제안하는 예측 모델은 태양광 발전량을 예측하기에 적합한 모델임을 알 수 있고, 이러한 예측을 이용하여 영농형 태양광 시스템에서 지능적인 운영관리 기능에 적용될 수 있음을 보였다.

• Journal of Information Processing Systems
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• 제5권1호
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• pp.33-40
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• 2009

Ever since the network-based malicious code commonly known as a 'worm' surfaced in the early part of the 1980's, its prevalence has grown more and more. The RCS (Random Constant Spreading) worm has become a dominant, malicious virus in recent computer networking circles. The worm retards the availability of an overall network by exhausting resources such as CPU capacity, network peripherals and transfer bandwidth, causing damage to an uninfected system as well as an infected system. The generation and spreading cycle of these worms progress rapidly. The existing studies to counter malicious code have studied the Microscopic Model for detecting worm generation based on some specific pattern or sign of attack, thus preventing its spread by countering the worm directly on detection. However, due to zero-day threat actualization, rapid spreading of the RCS worm and reduction of survival time, securing a security model to ensure the survivability of the network became an urgent problem that the existing solution-oriented security measures did not address. This paper analyzes the recently studied efficient dynamic network. Essentially, this paper suggests a model that dynamically controls the RCS worm using the characteristics of Power-Law and depth distribution of the delivery node, which is commonly seen in preferential growth networks. Moreover, we suggest a model that dynamically controls the spread of the worm using information about the depth distribution of delivery. We also verified via simulation that the load for each node was minimized at an optimal depth to effectively restrain the spread of the worm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2157-2177
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• 2024

Demand response (DR) refers to the customers' active reaction with respect to the changes of market pricing or incentive policies. DR plays an important role in improving network reliability, minimizing operational cost and increasing end users' benefits. Hence, the integration of DR in the microgrid (MG) management is gaining increasing popularity nowadays. This paper proposes a day-ahead MG scheduling framework in conjunction with DR and investigates the impact of DR in optimizing load profile and reducing overall power generation costs. A linear responsive model considering time of use (TOU) price and incentive is developed to model the active reaction of customers' consumption behaviors. Thereafter, a novel multi-swarm sine cosine algorithm (MSCA) is proposed to optimize the total power generation costs in the framework. In the proposed MSCA, several sub-swarms search for better solutions simultaneously which is beneficial for improving the population diversity. A cooperative learning scheme is developed to realize knowledge dissemination in the population and a competitive substitution strategy is proposed to prevent local optima stagnation. The simulation results obtained by the proposed MSCA are compared with other meta-heuristic algorithms to show its effectiveness in reducing overall generation costs. The outcomes with and without DR suggest that the DR program can effectively reduce the total generation costs and improve the stability of the MG network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4364-4384
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• 2021

The dynamic network state and the mobility of the terminals make the service function chain (SFC) orchestration mechanisms based on static and deterministic assumptions hard to be applied in SDN/NFV mobile edge computing networks. Designing dynamic and online SFC orchestration mechanism can greatly improve the execution efficiency of compute-intensive and resource-hungry applications in mobile edge computing networks. In order to increase the overall profit of service provider and reduce the resource cost, the system running time is divided into a sequence of time slots and a dynamic orchestration scheme based on an improved column generation algorithm is proposed in each slot. Firstly, the SFC dynamic orchestration problem is formulated as an integer linear programming (ILP) model based on layered graph. Then, in order to reduce the computation costs, a column generation model is used to simplify the ILP model. Finally, a two-stage heuristic algorithm based on greedy strategy is proposed. Four metrics are defined and the performance of the proposed algorithm is evaluated based on simulation. The results show that our proposal significantly provides more than 30% reduction of run time and about 12% improvement in service deployment success ratio compared to the Viterbi algorithm based mechanism.

• Nuclear Engineering and Technology
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• 제54권12호
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• pp.4776-4797
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• 2022

In the one-dimensional thermal-hydraulic (TH) codes, a subcooled boiling model to predict the void fraction profiles in a vertical channel consists of wall heat flux partitioning, the vapor condensation rate, the bubbly-to-slug flow transition criterion, and drift-flux models. Model performance has been investigated in detail, and necessary refinements have been incorporated into the Safety and Performance Analysis Code (SPACE) developed by the Korean nuclear industry for the safety analysis of pressurized water reactors (PWRs). The necessary refinements to models related to pumping factor, net vapor generation (NVG), vapor condensation, and drift-flux velocity were investigated in this study. In particular, a new NVG empirical correlation was also developed using artificial neural network (ANN) techniques. Simulations of a series of subcooled flow boiling experiments at pressures ranging from 1 to 149.9 bar were performed with the refined SPACE code, and reasonable agreement with the experimental data for the void fraction in the vertical channel was obtained. From the root-mean-square (RMS) error analysis for the predicted void fraction in the subcooled boiling region, the results with the refined SPACE code produce the best predictions for the entire pressure range compared to those using the original SPACE and RELAP5 codes.