• Smart Structures and Systems
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• 제30권6호
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• pp.583-599
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• 2022

In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.411-419
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• 2017

The Wireless Mesh Network (WMN) is a multi-hop wireless network consisting of mesh routers and clients, where the mesh routers have minimal mobility and form the backbone. The WMN is primarily designed to access outer network to mesh clients through backhaul gateways. As traffic converges on the gateways, traffic hotspots are likely to form in the neighborhood of the gateways. In this paper, we propose Congestion Aware Multi-path Routing (CAMR) protocol to tackle this problem. Upon congestion, CAMR divides the clients under a mesh STA into two groups and returns a different path for each group. The CAMR protocol triggers multi-path routing in such a manner that the packet reordering problem is avoided. Through simulations, we show that CAMR improves the performance of the WMN in terms of throughput, delay and packet drop ratio.

• Journal of Advanced Marine Engineering and Technology
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• 제33권1호
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• pp.120-130
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• 2009

In recent years, many mobile wireless communication devices and applications have been deployed on the planet. The mobile multi-hop wireless network models appeared to provide means to access to networks where few infrastructure exists. However, the mobile multi-hop wireless networks have weaker points in attacks and intrusions than the wired and one-hop wireless networks. In this paper, the secure routing issues in most mobile multi-hop wireless network models are surveyed in depth. The state-of-the-art technologies and research activities are explained. Finally, the issues and technologies for the secure routing specific to a maritime network model are sufficiently discussed as conclusions.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2000년도 전력전자학술대회 논문집
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• pp.464-467
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• 2000

In this paper we introduce the network based multi-motors control system using CAN(Controller Area Network) The traditional multi-motors control system has many problems in the view of reliability and economy because of the amount and complexity of wiring noise and maintenance problems etc, These problems are serious especially when the motor controllers are separated widely CAN is generally applied in car networking in order to reduce the complexity of the related wiring harnesses. These traditional CAN application techniques are modified to achieve the real time communication for the multi-motor control system. And also the position synchronization technique is developed and the proposed methods are verified experimentally.

• ETRI Journal
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• 제36권6호
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• pp.960-967
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• 2014

In this paper, an analytical framework is proposed for the optimization of network performance through joint congestion control, channel allocation, rate allocation, power control, scheduling, and routing with the consideration of fairness in multi-channel wireless multihop networks. More specifically, the framework models the network by a generalized network utility maximization (NUM) problem under an elastic link data rate and power constraints. Using the dual decomposition technique, the NUM problem is decomposed into four subproblems - flow control; next-hop routing; rate allocation and scheduling; power control; and channel allocation - and finally solved by a low-complexity distributed method. Simulation results show that the proposed distributed algorithm significantly improves the network throughput and energy efficiency compared with previous algorithms.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2023년도 제67차 동계학술대회논문집 31권1호
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• pp.467-468
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• 2023

현재 게임 시장에서 VR 게임이 가지는 영향력은 점차 증가하는 추세이다. 기존의 VR게임들은 대부분 Multi-Play를 지원하지 않는다. 이에 따라 본 논문에서는 Photon Network와 XR Plugin을 사용하여 2명의 플레이어가 함께 즐길 수 있는 Arcade 장르의 VR 경쟁 Multi-Game을 구현하였다. 이에 필요한 서버는 리슨 서버 방식으로 Master Client가 게임을 시작하면, Game에 참가한 다른 Client Player는 Photon Network의 RPC 기능을 사용하고 Player의 동작, Game 진행 상황 등을 실시간으로 Server에 동기화하여 Multi-Play게임을 할 수 있다.

• 방송공학회논문지
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• 제25권5호
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• pp.742-749
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• 2020

본 논문에서는 하나의 공용 신경망을 사용하여 음소와 감정을 모두 인식하는 방법과 공용 신경망 학습을 위한 다중 학습 방법을 제안한다. 공용 신경망은 동일한 동작을 수행하여 두 정보를 모두 인식하며, 이는 인간이 하나의 청각기관으로 여러 정보를 동시에 인식하는 구조에 해당한다. 다중 학습은 여러 정보를 위한 공통 모델링을 진행하므로 여러 정보에 대한 일반화된 학습을 진행시켜 기존의 정보별 개별 학습에서 나타나는 과적합을 감소시키고 인식 성능을 향상시킨다. 또한, 다중 학습에서 음소 인식에 가중치를 부여하여 음소 인식 성능을 추가 향상시키는 방법을 제안한다. 동일한 특성벡터와 신경망을 사용할 때, 제안한 다중 학습이 적용된 공용 신경망의 성능이 각 정보별로 학습시킨 개별 신경망에 비하여 우수한 것을 확인하였다.

• Current Optics and Photonics
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• 제8권3호
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• pp.215-224
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• 2024

In medium- and long-range optical imaging systems, atmospheric turbulence causes blurring and distortion of images, resulting in loss of image information. An image-restoration method based on an adaptive feature-fusion multi-input-multi-output (MIMO) dense U-shaped network (Unet) is proposed, to restore a single image degraded by atmospheric turbulence. The network's model is based on the MIMO-Unet framework and incorporates patch-embedding shallow-convolution modules. These modules help in extracting shallow features of images and facilitate the processing of the multi-input dense encoding modules that follow. The combination of these modules improves the model's ability to analyze and extract features effectively. An asymmetric feature-fusion module is utilized to combine encoded features at varying scales, facilitating the feature reconstruction of the subsequent multi-output decoding modules for restoration of turbulence-degraded images. Based on experimental results, the adaptive feature-fusion MIMO dense U-shaped network outperforms traditional restoration methods, CMFNet network models, and standard MIMO-Unet network models, in terms of image-quality restoration. It effectively minimizes geometric deformation and blurring of images.

• ETRI Journal
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• 제37권2호
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• pp.369-379
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• 2015

Fault management of virtualized network environments using user-driven network provisioning systems (NPSs) is crucial for guaranteeing seamless virtual network services irrespective of physical infrastructure impairment. The network service interface (NSI) of the Open Grid Forum reflects the need for a common standard management API for the reservation and provisioning of user-driven virtual circuits (VCs) across global networks. NSI-based NPSs (that is, network service agents) can be used to compose user-driven VCs for mission-critical applications in a dynamic multi-domain. In this article, we first attempt to outline the design issues and challenges faced when attempting to provide mission-critical applications using dynamic VCs with a protection that is both user-driven and trustworthy in a dynamic multi-domain environment, to motivate work in this area of research. We also survey representative works that address inter-domain VC protection and qualitatively evaluate them and current NSI against the issues and challenges.

• 한국경영과학회지
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• 제38권1호
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• pp.139-152
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• 2013

The purpose of this study is to claim the validity of tuning the architecture of neural network models for multi-class classification. A neural network model for multi-class classification is basically constructed by building a series of neural network models for binary classification. Building a neural network model, we are required to set the values of parameters such as number of hidden nodes and weight decay parameter in advance, which draws special attention as the performance of the model can be quite different by the values of the parameters. For better performance of the model, it is absolutely necessary to have a prior process of tuning the parameters every time the neural network model is built. Nonetheless, previous studies have not mentioned the necessity of the tuning process or proved its validity. In this study, we claim that we should tune the parameters every time we build the neural network model for multi-class classification. Through empirical analysis using wine data, we show that the performance of the model with the tuned parameters is superior to those of untuned models.