• 한국정보통신설비학회:학술대회논문집
• /
• 한국정보통신설비학회 2009년도 정보통신설비 학술대회
• /
• pp.314-319
• /
• 2009

In this paper, the network performance of a turbo coded optical code division multiple access (COMA) system with cross-layer, which is between physical and network layers, concept is analyzed and simulated We consider physical and MAC layers in a cross-layer concept. An intensity-modulated/direct-detection (IM/DD) optical system employing pulse position modulation (PPM) is considered In order to increase the system performance, turbo codes composed of parallel concatenated convolutional codes (PCCCs) is utilized. The network performance is evaluated in terms of bit error probability (BEP). From the simulation results, it is demonstrated that turbo coding offers considerable coding gain with reasonable encoding and decoding complexity. Also, it is confirmed that the performance of such an optical COMA network can be substantially improved by increasing the interleaver length and the number of iterations in the decoding process. The results of this paper can be applied to implement the indoor optical wireless LANs.

• Journal of Electrical Engineering and Technology
• /
• 제5권4호
• /
• pp.552-560
• /
• 2010

This paper presents an application of layered Artificial Neural Network controller to study load frequency control problem in power system. The objective of control scheme guarantees that steady state error of frequencies and inadvertent interchange of tie-lines are maintained in a given tolerance limitation. The proposed controller has been designed for a two-area interconnected power system. Only one artificial neural network controller (ANN), which controls the inputs of each area in the power system together, is considered. In this study, back propagation-through time algorithm is used as neural network learning rule. The performance of the power system is simulated by using conventional integral controller and ANN controller, separately. For the first time comparative study has been carried out between SMES and CES unit, all of the areas are included with SMES and CES unit separately. By comparing the results for both cases, the performance of ANN controller with CES unit is found to be better than conventional controllers with SMES, CES and ANN with SMES.

• 인터넷정보학회논문지
• /
• 제2권3호
• /
• pp.9-17
• /
• 2001

유선망과 기지국을 가지고 있지 않는 이동 노드들로만 구성된 Ad-Hoc 무선망에서는 잦은 호스트의 이동과 토폴로지 변화에 의해 패킷의 손실이 자주 발생한다. 이러한 환경 하에서 요구되는 Ad-Hoc 라우팅 프로토콜에 초점을 둔 많은 연구에서 인터넷 트래픽 성능 분석에 대한 연구가 미진하다. 따라서, 본 논문에서는 AODV 라우팅 프로토콜을 사용하여 Ad-Hoc 무선망의 트래픽을 모의 실험하였으며, 시뮬레이션 평가 요소인 Ad-Hoc 무선망의 크기, 이동하는 노드의 이동 속도를 변화시켜 인터넷 트래픽인 TCP/Reno와 TCP/Sack 성능을 모의 실험을 통해 분석하였다. 모의 실험 결과 TCP/Reno가 TCP/Sack보다 노드의 이동 속도나 노드 수에 다소 민감하다는 것을 알 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권9호
• /
• pp.4197-4219
• /
• 2017

Cloud computing is a new service to provide dynamic, scalable virtual resource services via the Internet. Cloud market is available to multiple cloud computing resource providers and users communicate with each other and participate in market transactions. However, since cloud computing is facing with more and more security issues, how to complete the allocation process effectively and securely become a problem urgently to be solved. In this paper, we firstly analyze the cloud resource allocation problem and propose a mathematic model based on combinatorial double auction. Secondly, we introduce a trust evaluation mechanism into our model and combine genetic algorithm with simulated annealing algorithm to increase the efficiency and security of cloud service. Finally, by doing the overall simulation, we prove that our model is highly effective in the allocation of cloud resources.

• 조명전기설비학회논문지
• /
• 제20권10호
• /
• pp.41-46
• /
• 2006

상업용으로 계통에 연계되어 운전되는 대부분의 풍력발전기는 유도발전기가 주로 사용되고 있으며 동기발전기와 다른 양상을 보이고 있어 계통에서의 사고발생 후 정상상태로 복귀할 수 있는 최대의 시간(임계 제거시간)을 이용하여 과도상태를 해석하고 있다. 본 연구에서는 풍력 발전시스템이 연계된 계통에 대해 임계 제거시간에 미치는 요인을 분석하였으며 계통해석 프로그램인 Digsilent Power Factory를 이용하였다. 임계 제거시간에 미치는 요인으로는 연계되는 계통의 단락용량(단락전류). 풍력발전 용량, 풍력발전기 역률, 풍력 발전시스템과 연계되는 계통사이의 전용선 길이, 부하 용량이나 역률 등이 있으며 이들의 변화에 대한 임계 제거시간의 영향을 분석하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권5호
• /
• pp.2081-2101
• /
• 2016

Successive interference cancellation (SIC) is considered to be a promising technique to mitigate multi-user interference and achieve concurrent uplink transmissions, but the optimal power allocation (PA) issue for SIC users is not well addressed. In this article, we focus on the optimization of the PA ratio of users on an SIC channel and analytically obtain the optimal PA ratio with regard to the signal-to-interference-plus-noise ratio (SINR) threshold for successful demodulation and the sustainable demodulation error rate. Then, we design an efficient resource allocation (RA) scheme using the obtained optimal PA ratio. Finally, we compare the proposal with the near-optimum RA obtained by a simulated annealing search and the RA scheme with random PA. Simulation results show that our proposal achieves a performance close to the near-optimum and much higher performance than the random scheme in terms of total utility and Jain's fairness index. To demonstrate the applicability of our proposal, we also simulate the proposal in various network paradigms, including wireless local area network, body area network, and vehicular ad hoc network.

• 한국통신학회논문지
• /
• 제21권2호
• /
• pp.534-540
• /
• 1996

This paper presents an Adaptive Time-Delay Recurrent Neural Network (ATRN) for learning and recognition of temporal correlations of temporal patterns. The ATRN employs adaptive time-delays and recurrent connections, which are inspired from neurobiology. In the ATRN, the adaptive time-delays make the ATRN choose the optimal values of time-delays for the temporal location of the important information in the input parrerns, and the recurrent connections enable the network to encode and integrate temporal information of sequences which have arbitrary interval time and arbitrary length of temporal context. The ATRN described in this paper, ATNN proposed by Lin, and TDNN introduced by Waibel were simulated and applied to the chaotic time series preditcion of Mackey-Glass delay-differential equation. The simulation results show that the normalized mean square error (NMSE) of ATRN is 0.0026, while the NMSE values of ATNN and TDNN are 0.014, 0.0117, respectively, and in temporal learning, employing recurrent links in the network is more effective than putting multiple time-delays into the neurons. The best performance is attained bythe ATRN. This ATRN will be sell applicable for temporally continuous domains, such as speech recognition, moving object recognition, motor control, and time-series prediction.

• Smart Structures and Systems
• /
• 제5권4호
• /
• pp.415-426
• /
• 2009

This study focuses on the concept of multi-scale wireless sensor networks for damage detection in civil infrastructure systems by first over viewing the general network philosophy and attributes in the areas of data acquisition, data reduction, assessment and decision making. The data acquisition aspect includes a scalable wireless sensor network acquiring acceleration and strain data, triggered using a Restricted Input Network Activation scheme (RINAS) that extends network lifetime and reduces the size of the requisite undamaged reference pool. Major emphasis is given in this study to data reduction and assessment aspects that enable a decentralized approach operating within the hardware and power constraints of wireless sensor networks to avoid issues associated with packet loss, synchronization and latency. After over viewing various models for data reduction, the concept of a data-driven Bivariate Regressive Adaptive INdex (BRAIN) for damage detection is presented. Subsequent examples using experimental and simulated data verify two major hypotheses related to the BRAIN concept: (i) data-driven damage metrics are more robust and reliable than their counterparts and (ii) the use of heterogeneous sensing enhances overall detection capability of such data-driven damage metrics.

• Nuclear Engineering and Technology
• /
• 제52권11호
• /
• pp.2565-2571
• /
• 2020

Background: Understanding the behaviour of nuclear fuel claddings by conducting burst test on single cladding tube under simulated loss-of-coolant accident conditions and developing theoretical cum empirical predictive computer codes have been the focus of several investigations. The developed burst criterion (a) assumes symmetrical deformation of cladding tube in contrast to experimental observation (b) interpolates the properties of Zircaloy-4 cladding in mixed α+β phase (c) does not account for azimuthal temperature variations. In order to overcome all these drawbacks of burst criterion, it is reasoned that artificial intelligence technique may be a better option to predict the burst parameters. Methods: Artificial neural network models based on feedforward backpropagation algorithm with logsig transfer function are developed. Results: Neural network architecture of 2-4-4-3, that is model with two hidden layers having four nodes in each layer is found to be the most suitable. The mean, maximum, and minimum prediction errors for this optimised model are 0.82%, 19.62%, and 0.004%, respectively. Conclusion: The burst stress, burst temperature, and burst strain obtained from burst criterion have average deviation of 19%, 12%, and 53% respectively whereas the developed neural network model predicted these parameters with average deviation of 6%, 2%, and 8%, respectively.

• 한국해양공학회지
• /
• 제20권4호
• /
• pp.24-30
• /
• 2006

Different methods exist for the estimation of thermaldeformation of plates in the line heating process. These are based on the assumption of residual strains in the heat-affected zone, known as the method of inherent strains, or simulated relations between heating conditions and residual deformations. The purpose of this paper is to develop a simulator of thermal deformation in the line heating, using the artificial neural network. Curvature deformations for the plate-forming are investigated, which can be used as a prime deformation parameter in the process. The curvature of plates are calculated using the approximation of plate surface by NURBS. Line heating experiments for 11 specimens of different thickness and heating conditions were performed. Two neural networks predicting the maximum temperature and curvature deformations at the heating line are studied. It was concluded that the thermal deformations predicted by the neural network can be used in a line heating simulator, which is considered an attractive and practical alternative to the existing methods.