• 제목/요약/키워드: application layer

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Step-Up 공법에 의한 단층래티스돔의 시공시 안정성 연구 (A Study on the Stability of the Single-Layer Latticed Dome during Erection Using the Step-Up Method)

  • 구충모;정환목;김철환
    • 한국공간구조학회논문집
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    • 제12권4호
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    • pp.109-118
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    • 2012
  • The large-space single-layer lattice dome is relatively simpler in terms of the arrangement of the various framework members and of the design of the junction than the multi-layered lattice dome, can reduce the numbers and quantity of the framework members, and has the merit of exposing the beauty of the framework as it stands. The single-layer lattice dome, however, requires a stability investigation of the whole structure itself, along with an analysis of the stress of the framework members, because an unstable phenomenon called "buckling" occurs when its weight reaches critical levels. Many researchers have systematically conducted researches on the stability evaluation of the single-layer lattice dome. No construction case of a single-layer lattice dome with a 300-m-long span, however, has yet been reported anywhere in the world. The large-space dome structure is difficult to erect due to the gigantic span and higher ceiling compared with other common buildings, and its construction cost is generally huge. The method of erecting a structure causes major differences in the construction cost and period. Therefore, many researchers have been conducting various researches on the method of erecting such structure. The step-up method developed by these authors can reduce the construction cost and period to a great extent compared with the other general methods, but the application of this method inevitably requires the development of system supports in the center section as well as pre-existing supports in the boundary sections. In this research, the safety during the construction of a single-layer lattice dome with 300-m-long span using pre-existing materials was examined in the aspect of structural strength, and the basic data required for manufacturing the supports in the application of the step-up method developed by these authors during the erection of the roof structure were obtained.

Cross-Layer Architecture for QoS Provisioning in Wireless Multimedia Sensor Networks

  • Farooq, Muhammad Omer;St-Hilaire, Marc;Kunz, Thomas
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제6권1호
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    • pp.178-202
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    • 2012
  • In this paper, we first survey cross-layer architectures for Wireless Sensor Networks (WSNs) and Wireless Multimedia Sensor Networks (WMSNs). Afterwards, we propose a novel cross-layer architecture for QoS provisioning in clustered and multi-hop based WMSNs. The proposed architecture provides support for multiple network-based applications on a single sensor node. For supporting multiple applications on a single node, an area in memory is reserved where each application can store its network protocols settings. Furthermore, the proposed cross-layer architecture supports heterogeneous flows by classifying WMSN traffic into six traffic classes. The architecture incorporates a service differentiation module for QoS provisioning in WMSNs. The service differentiation module defines the forwarding behavior corresponding to each traffic class. The forwarding behavior is primarily determined by the priority of the traffic class, moreover the service differentiation module allocates bandwidth to each traffic class with goals to maximize network utilization and avoid starvation of low priority flows. The proposal incorporates the congestion detection and control algorithm. Upon detection of congestion, the congested node makes an estimate of the data rate that should be used by the node itself and its one-hop away upstream nodes. While estimating the data rate, the congested node considers the characteristics of different traffic classes along with their total bandwidth usage. The architecture uses a shared database to enable cross-layer interactions. Application's network protocol settings and the interaction with the shared database is done through a cross-layer optimization middleware.

Hydraulics of a two-layer rotating flow; Application to the Korea Strait

  • Cho, Yang-Ki;Kim, Kuh
    • 한국해안해양공학회:학술대회논문집
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    • 한국해안해양공학회 1995년도 정기학술강연회 발표논문 초록집
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    • pp.9-12
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    • 1995
  • The Korea Strait becomes deeper than 200 m from south to north in general except coastal area, whereas its southern part is shallower than 125 m except for a deep trough (Fig.1). The flow in the Korea Strait could be simplified as two layers (Isobe, 1995); the Tsushima Warm Water in the upper layer and the Korea Strait Bottom Cold Water (KSBCW) in the lower layer (Fig.2). (omitted)

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계층간 최적화를 위해 비디오 트레이스에 기반한 무선 MAC 스케줄러 (A Wireless MAC Scheduler Based on Video Traces for Cross-Layer Optimization)

  • 조성익;편기현
    • 대한전기학회논문지:시스템및제어부문D
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    • 제55권5호
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    • pp.236-239
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    • 2006
  • A wireless MAC scheduler that provides a high level of quality-of-service (QoS) for video-on-demand (VOD) applications while achieving a reasonable level of system throughput is proposed. The proposed scheduler considers both channel qualities of mobiles and the urgency of real-time packets coming from VOD applications in a cross-layer approach between application and MAC layers.

부분 학습구조의 신경회로와 로보트 역 기구학 해의 응용 (A neural network with local weight learning and its application to inverse kinematic robot solution)

  • 이인숙;오세영
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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    • pp.36-40
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    • 1990
  • Conventional back propagation learning is generally characterized by slow and rather inaccurate learning which makes it difficult to use in control applications. A new multilayer perception architecture and its learning algorithm is proposed that consists of a Kohonen front layer followed by a back propagation network. The Kohonen layer selects a subset of the hidden layer neurons for local tuning. This architecture has been tested on the inverse kinematic solution of robot manipulator while demonstrating its fast and accurate learning capabilities.

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Hopfield 신경 회로망의 개선과 Layer Assignment 문제에의 응용 (A Modified Hopfield Network and It's application to the Layer Assignment)

  • 김규현;황희영;이종호
    • 대한전기학회논문지
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    • 제40권2호
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    • pp.234-237
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    • 1991
  • A new neural network model, based on the Hopfield crossbar associative network, is presented and shown to be an effective tool for the NP-Complete problems. This model is applied to a class of layer assignment problems for VLSI routing. The results indicate that this modified Hopfield model, improves stability and accuracy.

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접근풍속과 건물 변동풍압력에 대한 연속파동변화법의 적용 (A Continuous Wavelet Study on Approach Wind and Building Pressure)

  • 함희정
    • 산업기술연구
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    • 제25권B호
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    • pp.89-97
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    • 2005
  • Application of proper orthogonal decomposition (POD) and continuous wavelet transform (CWT) is introduced to study wind speed and building roof pressures of flow separation region. In this study, a detailed analysis of the approach wind flow, wind-induced building pressure and the relation between the two fields was carried out using the POD technique and CWT analysis. The results show potential of the application of POD and CWT in characterization of spatio-temporal and spectral properties of the approach wind and its induced dynamic pressure events. Some of findings resulting from the application of this analysis can be summarized as follows: (1) The POD first principal coordinate of the roof pressure in the separated shear layer is closely correlated with the longitudinal component of oncoming flow. (2) The CWT analysis suggests that the extreme peak pressure in the separated shear layer is due to condensed large-scale eddy motions.

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전자제품 생산의 조정공정을 위한 신경회로망 응용 (An Application of Neural Ntwork For the Adjustment Process during Electronics Production)

  • 장석호;정영기;감도영;우광방
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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    • pp.310-313
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    • 1996
  • In this paper, a neural control algorithm is proposed on the automation of adjustment process. The adjustment processes in camcoder production line are modelled, and the processes are adjusted automatically by means of off-line supervisory trained multi-layer neural network. We have made many experiments on the several adjustment processes by using the control algorithm. There are many unexpected troubles to achieve the desirable adjust time in the practical application. To overcome those, some auxiliary algorithms are demanded. As a result, our proposed algorithm has some advantages - simple architecture, easy extraction of the training data without expertises, adaptability to the varying systems, and wide application for the other resemble processes.

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cGAN을 이용한 OCT 이미지의 층 분할 (Segmenting Layers of Retinal OCT Images using cGAN)

  • 권오흠;권기룡;송하주
    • 한국멀티미디어학회논문지
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    • 제23권12호
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    • pp.1476-1485
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    • 2020
  • Segmenting OCT retinal images into layers is important to diagnose and understand the progression of retinal diseases or identify potential symptoms. The task of manually identifying these layers is a difficult task that requires a lot of time and effort even for medical professionals, and therefore, various studies are being conducted to automate this using deep learning technologies. In this paper, we use cGAN-based neural network to automatically segmenting OCT retinal images into seven terrain-type regions defined by six layer boundaries. The network is composed of a Segnet-based generator model and a discriminator model. We also proposed a dynamic programming algorithm for refining the outputs of the network. We performed experiments using public OCT image data set and compared its performance with the Segnet-only version of the network. The experimental results show that the cGAN-based network outperforms Segnet-only version.

Real-time Classification of Internet Application Traffic using a Hierarchical Multi-class SVM

  • Yu, Jae-Hak;Lee, Han-Sung;Im, Young-Hee;Kim, Myung-Sup;Park, Dai-Hee
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제4권5호
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    • pp.859-876
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    • 2010
  • In this paper, we propose a hierarchical application traffic classification system as an alternative means to overcome the limitations of the port number and payload based methodologies, which are traditionally considered traffic classification methods. The proposed system is a new classification model that hierarchically combines a binary classifier SVM and Support Vector Data Descriptions (SVDDs). The proposed system selects an optimal attribute subset from the bi-directional traffic flows generated by our traffic analysis system (KU-MON) that enables real-time collection and analysis of campus traffic. The system is composed of three layers: The first layer is a binary classifier SVM that performs rapid classification between P2P and non-P2P traffic. The second layer classifies P2P traffic into file-sharing, messenger and TV, based on three SVDDs. The third layer performs specialized classification of all individual application traffic types. Since the proposed system enables both coarse- and fine-grained classification, it can guarantee efficient resource management, such as a stable network environment, seamless bandwidth guarantee and appropriate QoS. Moreover, even when a new application emerges, it can be easily adapted for incremental updating and scaling. Only additional training for the new part of the application traffic is needed instead of retraining the entire system. The performance of the proposed system is validated via experiments which confirm that its recall and precision measures are satisfactory.