• 한국정밀공학회지
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• 제11권4호
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• pp.126-137
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• 1994

Performance management of computer networks is intended to improve a given network performance in order for more efficient information exchange between subsystems of an integrated large-scale system. Importance of perfomance management is growing as many functions of the large- scale system depend on the quality of communication services provided by the network. The role of performance management is to manipulate the adjustable protocol parameters on line so that the network can adapt itself to a dynamic environment. This can be divided into two subtasks : performance evaluation to find how changes in protocol parameters affect the network performance and decision making to determine the magnitude and direction of parameter adjustment. This paper is the first part of the two papers focusing on conceptual design, development, and evaluation of performance management for token bus networks. This paper specifically deals with the task of performance evaluation which utilizes the principle of perturbation analysis of discrete event dynamic systems. The developed algorithm can estimate the network performance under a perturbed protocol parameter setting from observations of the network operations under a nominal parameter setting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.3942-3961
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• 2019

Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs. However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration. In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors. We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process. In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN. In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN. In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details. Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements. For scale factor ${\times}8$, our method performs favorably against other methods in terms of gradients similarity.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권5호
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• pp.322-327
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• 2017

문서 분류 문제는 오랜 기간 동안 자연어 처리 분야에서 연구되어 왔다. 우리는 기존 컨볼루션 신경망을 이용했던 연구에서 나아가, 순환 신경망에 기반을 둔 문서 분류를 수행하였고 그 결과를 종합하여 제시하려 한다. 컨볼루션 신경망은 단층 컨볼루션 신경망을 사용했으며, 순환 신경망은 가장 성능이 좋다고 알려져 있는 장기-단기 기억 신경망과 회로형 순환 유닛을 활용하였다. 실험 결과, 분류 정확도는 Multinomial Naïve Bayesian Classifier < SVM < LSTM < CNN < GRU의 순서로 나타났다. 따라서 텍스트 문서 분류 문제는 시퀀스를 고려하는 것 보다는 문서의 feature를 추출하여 분류하는 문제에 가깝다는 것을 확인할 수 있었다. 그리고 GRU가 LSTM보다 문서의 feature 추출에 더 적합하다는 것을 알 수 있었으며 적절한 feature와 시퀀스 정보를 함께 활용할 때 가장 성능이 잘 나온다는 것을 확인할 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제13권6호
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• pp.137-145
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• 2008

대형 무선 센서 네트워크(WSNs)는 일반적으로 수백에서 수천 개의 센서 노드들로 구성되어 있다. 이러한 대형 WSNs는 비용 및 에너지를 고려한 에너지 효율성뿐만 아니라 네트워크의 유지 및 관리가 요구된다. 사용자는 효율적인 시스템을 통해서 사용자 수준의 센싱 서비스 품질을 제공받을 수 있어야 한다. 이 네트워크에서 사용자에게 제공되는 결과 데이터의 품질은 이벤트 검출에 관련된 센서들의 개수가 결정적인 역할을 한다. 그러므로 사용자 요구 품질에 적합한 QoS를 제공할 수 있는 네트워크 프로토콜은 일부 센서 노드들에서 에러가 발생하더라고 전체 시스템 성능에 영향을 주지 않으면서, 동시에 에너지 소비가 최소화되도록 설계되어야 한다. 본 논문에서 제안된 프로토콜은 LEACH(Low Energy Adaptive Clustering Hierarchy) 프로토콜을 기반으로 하며, 지속적인 감시가 요구되는 대형 네트워크에 적합한 역할 기반의 자가 구성 프로토콜을 제안하였다.

• 제어로봇시스템학회논문지
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• 제15권4호
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• pp.413-419
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• 2009

A decentralized adaptive control method is proposed for large-scale systems with unknown time-delayed nonlinear interconnections unmatched in control inputs. It is assumed that the time-delayed interaction terms are bounded by unknown nonlinear bounding functions. The nonlinear bounding functions and uncertain nonlinear functions of large-scale systems are compensated by the function approximation technique using neural networks. The dynamic surface control method is extended to design the proposed memoryless local controller for each subsystem of uncertain nonlinear large-scale time delay systems. Therefore, although the interconnected systems consist of a large number of subsystems, the proposed controller can be designed simply. We prove that all the signals in the total closed-loop system are semiglobally uniformly bounded and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is given to demonstrate the effectiveness and applicability of the proposed scheme.

• 대한임베디드공학회논문지
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• 제18권2호
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• pp.67-73
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• 2023

Tree routing is one of appropriate routing schemes in wireless sensor network because the complexity of this approach is relatively low. But, congestion at a specific node may happen because a parent node toward a sink node is usually selected in one hop way, specially where large number of node are deployed. As feasible solution for this problem, multiple paths and sinks schemes can be applied. However, the performance of these schemes are not proved and analyzed yet. In this paper, we conduct diverse simulaton scenarios performance evaluation for these cases to identify the improvement and analyze the impact of schemes. The performance is measured in the aspects of packet transmission rate, throughput, and end-to-end delay as a function of amount of network traffic.

• 대기
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• 제20권3호
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• pp.343-353
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• 2010

A regional wind network with complex surface conditions must be designed with sufficient space and time resolution to resolve the local circulations. In this study, the spatial variations of the wind field observed in the Seoul and Jeju regional networks were evaluated in terms of annual, seasons, and months to assess the spatial homogeneity of wind fields within the regional networks. The coherency of the wind field as a function of separation distance between stations indicated that significant coherency was sometimes not captured by the network, as inferred by low correlations between adjacent stations. A meso-velocity scale was defined in terms of the spatial variability of the wind within the network. This problem is predictably most significant with weak winds, dull prevailing wind, clear skies and significant topography. The relatively small correlations between stations imply that the wind at a given point cannot be estimated by interpolating winds from the nearest stations. For the Seoul and Jeju regional network, the meso-velocity scale has typically a same order of magnitude as the speed of the network averaged wind, revealing the large spatial variability of the Jeju network station imply topography and weather. Significant scatter in the relationship between spatial variability of the wind field and the wind speed is thought to be related to thermally-generated flows. The magnitude of the mesovelocity scale was significantly different along separation distance between stations, wind speed, intensity of prevailing wind, clear and cloudy conditions, topography. Resultant wind vectors indicate much different flow patterns along condition of contributing factors. As a result, the careful considerations on contributing factors such as prevailing wind in season, weather, and complex surface conditions with topography and land/sea contrast are required to assess the spatial variations of wind field on a regional network. The results in the spatial variation from the mesovelocity scale are useful to represent the characteristics of regional wind speed including lower surface conditions over the grid scale of large scale atmospheric model.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 1999년도 학술발표회 논문집
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• pp.775-780
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• 1999

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 2007년도 정기학술대회
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• pp.321-329
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• 2007

This paper reports on the application of network analysis approaches to investigate the characteristics of graph representations of Japanese word associations. Two semantic networks are constructed from two separate Japanese word association databases. The basic statistical features of the networks indicate that they have scale-free and small-world properties and that they exhibit hierarchical organization. A graph clustering method is also applied to the networks with the objective of generating hierarchical structures within the semantic networks. The method is shown to be an efficient tool for analyzing large-scale structures within corpora. As a utilization of the network clustering results, we briefly introduce two web-based applications: the first is a search system that highlights various possible relations between words according to association type, while the second is to present the hierarchical architecture of a semantic network. The systems realize dynamic representations of network structures based on the relationships between words and concepts.

• International journal of advanced smart convergence
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• 제7권2호
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• pp.95-100
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• 2018

In this paper, we propose a binarized multi-scale module to accelerate the speed of the pose estimating deep neural network. Recently, deep learning is also used for fine-tuned tasks such as pose estimation. One of the best performing pose estimation methods is based on the usage of two neural networks where one computes the heat maps of the body parts and the other computes the part affinity fields between the body parts. However, the convolution filtering with a large kernel filter takes much time in this model. To accelerate the speed in this model, we propose to change the large kernel filters with binarized multi-scale modules. The large receptive field is captured by the multi-scale structure which also prevents the dropdown of the accuracy in the binarized module. The computation cost and number of parameters becomes small which results in increased speed performance.