• Journal of KIISE:Information Networking
• /
• v.36 no.3
• /
• pp.204-211
• /
• 2009

Proxy Mobile IPv6(PMIPv6) is that network-based mobility management protocol that network supports mobile node's mobility on behalf of the Mobile Node(MN). In PMIPv6 network, data packets from a Correspondent Node(CN) to a MN will always traverse the MN's Local Mobility Anchor(LMA). Even though, CN and MN might be located close to each other or within the same PMIPv6 domain. To solve this problem, several PMIPv6 Route Optimization(RO) schemes have been proposed. However, these RO schemes may result in a high signaling cost when MN moves frequently between MAGs. For this reason, we propose an adaptive route optimization(ARO) scheme. We analyze the performance of the ARO. Analytical results indicate that the ARO outperforms previous schemes in terms of signaling overhead.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2022.06a
• /
• pp.1315-1317
• /
• 2022

최근, 심층신경망 기반 오디오 부호화기가 활발히 연구되고 있다. 심층신경망 기반 오디오 부호화기는 기존의 전통적인 오디오 부호화기보다 구조적으로 간단하지만, 네트워크의 복잡도를 증가시키지 않고 인지적 성능향상을 기대하는 것은 어렵다. 이 문제를 해결하기 위하여 인간의 청각적 특성을 활용한 심리음향모델 기반 손실함수를 사용한 기법들이 소개되었다. 심리음향 모델 기반 손실함수를 사용한 오디오 부호화기는 양자화 잡음을 잘 제어하였지만, 여전히 지각적인 향상이 필요하다. 본 논문에서는 심층신경망 기반 오디오 부호화기를 위한 Multi-time Scale 손실함수의 지역 손실함수 윈도우 크기의 최적화 제안한다. Multi-time Scale 손실함수의 지역 손실함수 계산을 위한 윈도우 크기를 조절하며, 이를 통하여 오디오 부호화에 적합한 윈도우 사이즈를 결정한다. 실험을 통해 얻은 최적의 Multi-time Scale 손실함수를 사용하여 네트워크를 훈련하였고, 주관적 평가를 통해 기존의 심리음향모델 기반 손실함수보다 좋은 음성 품질을 보여주는 것을 확인하였다.

• Journal of KIISE
• /
• v.44 no.7
• /
• pp.726-732
• /
• 2017

In practical distributed sensing and prediction applications over wireless sensor networks (WSN), environmental sensing activities are highly dynamic because of noisy sensory information from moving source signals. The recent distributed online convex optimization frameworks have been developed as promising approaches for solving approximately stochastic learning problems over network of sensors in a distributed manner. Negligence of mobility consequence in the original distributed saddle point algorithm (DSPA) could strongly affect the convergence rate and stability of learning results. In this paper, we propose an integrated sliding windows mechanism in order to stabilize predictions and achieve better convergence rates in cooperative detection of a moving source signal scenario.

• Journal of the Institute of Convergence Signal Processing
• /
• v.14 no.1
• /
• pp.57-61
• /
• 2013

A hybrid genetic algorithm based learning method for the morphological neural networks (MNN) is proposed. The morphological neural networks are based on max-plus algebra, therefore, it is difficult to optimize the coefficients of MNN by the learning method with derivative operations. In order to solve the difficulty, a hybrid genetic algorithm based learning method to optimize the coefficients of MNN is used. Through the image compression/reconstruction experiment using test images extracted from standard image database(SIDBA), it is confirmed that the quality of the reconstructed images obtained by the proposed method is better than that obtained by the conventional neural networks.

• The Journal of the Korea Contents Association
• /
• v.11 no.12
• /
• pp.578-585
• /
• 2011

Nowadays, wireless local area networks are widely deployed so that stations can potentially associate with an access point. The AP selection strategy is one of the significant research areas for wireless local area networks. The selection strategy solves the relevant problem is which AP can be selected and associated with a station so that the efficient resource utilization can be obtained. Rather than merely choosing the AP with the strongest received signal strength, however, we calculate effective throughput for each neighboring AP and use it as a basis for AP selection. Referencing the throughput is better than referencing the signal strength only because the network may contain a severe load imbalance. We have performed computer simulations using OPNET modeler in order to verify the performance of the proposed scheme. The results show us that the proposed selection method outperforms that of the conventional one in terms of throughput and delay.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2015.11a
• /
• pp.13-15
• /
• 2015

본 논문에서는 하나의 중계기가 하나의 송신기로부터 신호를 받아 증폭한 후에 수신기로 재전송하는 방식으로 송신기와 수신기 사이의 통신을 돕는 네트워크를 고려하였다. 중계기가 독자적인 에너지원이 없는 경우 일정한 양을 에너지를 확보하여 중계에 사용하기 위해서 송신기로부터의 신호를 에너지로 하베스팅하는 모델을 생각하였다. 또한, 나아가 현재의 다양한 무선통신 네트워크에서 사용중인 적응변조를 적용하여 항상 일정이상의 비트오율을 만족할 수 있는 더욱 현실적인 모델이 되도록 하였다. 이러한 모델에서 정해진 만큼의 시간을 하베스팅에 사용했을 경우 처리율을 구하였으며, 나아가 그 시간을 최적화하여 유도한 처리율을 최대화하는 문제를 만들었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.05a
• /
• pp.201-204
• /
• 2011

최근 차량 내부의 전장장치 사이에 운전자의 편의성의 요구 수준이 높아지고 있으며 이에 대응해 상호통신을 위한 차량용 네트워크 기술이 빠르게 발전하고 있다. 이에 따라 이들 인포테인먼트 시스템 중 최적화된 내부 통신기술이 필요하게 되었다. 차량용 네트워크인 MOST(Media Oriented Systems Transport)는 오디오, 비디오 신호를 동기적으로 전송할 수 있는 자동차용 멀티미디어 시스템에 가장 광범위 하게 사용되고 있는 네트워크이며 넓은 대역폭과 오디오, 비디오 데이터의 실시간 전송 및 코딩을 지원한다. Windows CE는 다양한 라이브러리를 지원함으로써 다양한 GUI 및 네트워크 프로젝터 개발이 가능하다. 이러한 기술적 변화에 맞춰서 본 논문에서는 Windows CE 6.0 기반의 보드와 차량용 인포테이먼트 네트워크인 MOST 네트워크를 이용하여 MOST 네트워크 상태를 관리할 수 있고 MOST 네트워크 동작 및 정보, 상태 등 다양한 정보를 한눈에 확인할 수 있는 시스템을 구성하였다.

• Journal of Korean Society of Transportation
• /
• v.17 no.2
• /
• pp.127-135
• /
• 1999

Most existing progression bandwidth models maximize the single or multi weighted sum of bandwidths in the both directions to improve traffic mobility on an arterial, but they cannot be applied to general networks. Even though a few models formulating a looped network problem cannot be applied to networks have not loops. Also they have some defects in optimizing phase sequences. Therefore, the objective of this study is to develope a mathematical formulation of the synchronization problem for a general traffic network. The goal is achieved successfully by introducing the signal phasing for each movement and expanding the mixed integer linear programming of MAXBAND. The experiments indicate that the proposed model can formulate the general traffic network problem mere efficiently than any other model. In conclusion, this model may optimize signal time to smooth progression in the general networks.

• Journal of Korean Society of Transportation
• /
• v.27 no.1
• /
• pp.27-34
• /
• 2009

The operational effect of traffic signal control improvement was evaluated using the Two-Fluid Model. The parameters engaged in the Two-Fluid Model becomes food indicators to measure the quality of traffic flow due to the improvement of traffic signal operation. A series of experiment were conduced for the 31 signalized intersections in Uijeongbu City. To estimate the parameters in the Two-Fluid Model the trajectory informations of individual vehicles were collected using the CORSIM and Run Time Extension. The test results showed 35 percent decrease of average minimum trip time per unit distance. One of the parameters in the Two-Fluid Model is a measure of the resistance of the network to the degraded operation with the increased demand. The test result showed 28 percent decrease of this parameter. In spite of the simulation results of the arterial flow, it was concluded that the Two-Fluid Model is useful tool to evaluate the improvement of the traffic signal control system from the macroscopic aspect.

• Journal of IKEEE
• /
• v.23 no.2
• /
• pp.529-533
• /
• 2019

Combined cycle power plants are often used to produce power. These days prediction of power plant output based on operating parameters is a major concern. This paper presents an approach to using computational intelligence technique to predict the output power of combined cycle power plant. Computational intelligence techniques have been developed and applied to many real world problems. In this paper, tree architectures of fuzzy neural networks are considered to predict the output power. Tree architectures of fuzzy neural networks have an advantage of reducing the number of rules by selecting fuzzy neurons as nodes and relevant inputs as leaves optimally. For the optimization of the networks, two-step optimization method is used. Genetic algorithms optimize the binary structure of the networks by selecting the nodes and leaves as binary, and followed by random signal-based learning further refines the optimized binary connections in the unit interval. To verify the effectiveness of the proposed method, combined cycle power plant dataset obtained from the UCI Machine Learning Repository Database is considered.