• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2007년도 학술대회
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• pp.327-330
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• 2007

This article discusses a capacity planning method in QoS-guaranteed IP networks such as BcN (Broadband convergence Network). Since IP based networks have been developed to transport best-effort data traffic, the introduction of multi-service component in BcN requires fundamental modifications in capacity planning and network dimensioning. In this article, we present the key issues of the capacity planning in multi-service IP networks. To provide a foundation for network dimensioning procedure, we describe a systematic approach for classification and modeling of BcN traffic based on the QoS requirements of BcN services. We propose a capacity planning framework considering data traffic and real-time streaming traffic separately. The multi-service Erlang model, an extension of the conventional Erlang B loss model, is introduced to determine required link capacity for the call based real-time streaming traffic. The application of multi-service Erlang model can provide significant improvement in network planning due to sharing of network bandwidth among the different services.

• 인터넷정보학회논문지
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• 제23권5호
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• pp.17-23
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• 2022

Network slicing is a promising paradigm and significant evolution for adjusting the heterogeneous services based on different requirements by placing dynamic virtual network functions (VNF) forwarding graph (VNFFG) and orchestrating service function chaining (SFC) based on criticalities of Quality of Service (QoS) classes. In system architecture, software-defined networks (SDN), network functions virtualization (NFV), and edge computing are used to provide resourceful data view, configurable virtual resources, and control interfaces for developing the modified deep reinforcement learning agent (MDRL-A). In this paper, task requests, tolerable delays, and required resources are differentiated for input state observations to identify the non-critical/critical classes, since each user equipment can execute different QoS application services. We design intelligent slicing for handing the cross-domain resource with MDRL-A in solving network problems and eliminating resource usage. The agent interacts with controllers and orchestrators to manage the flow rule installation and physical resource allocation in NFV infrastructure (NFVI) with the proposed formulation of completion time and criticality criteria. Simulation is conducted in SDN/NFV environment and capturing the QoS performances between conventional and MDRL-A approaches.

• 기술혁신연구
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• 제25권3호
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• pp.1-24
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• 2017

최근 기술융합의 핵심현상으로 사물인터넷이 대두되면서 사물인터넷의 기술트렌드 및 기술융합에 관해 많은 연구들이 진행되고 있다. 그러나 기존 연구들의 대부분이 사물인터넷 기술 동향에 대한 정성적 연구에 그치고 있어 기술융합의 구체적 양상을 파악하기 어려운 실정이다. 따라서 본 연구에서는 특허 데이터를 기술의 대용데이터로 간주하고, 동시 분류분석과 텍스트마이닝을 바탕으로 사물인터넷 융합 네트워크를 구축하고 융합의 특성을 분석하였다. 본 연구에서는 먼저 문헌연구를 통해 사물인터넷의 융합을 일으키는 주요 기술군을 디바이스, 네트워크, 플랫폼, 서비스 네 가지로 정의한 후, "Internet of Things" 키워드를 중심으로 미국 특허청에서 수집된 923개 특허의 클래스를 네 가지 기술군에 할당하여 이들 간 관계를 파악하였다. 대부분의 클래스 및 키워드가 디바이스에 관련되어 있으므로, 본 연구에서는 융합 현상을 디바이스 융합과 전체 융합으로 나누어 기술융합 양상을 파악하였다. 디바이스 중심의 사물인터넷 기술을 분석한 결과 센서 디바이스를 비롯한 헬스케어 디바이스, 냉장 및 냉동 장치, 에너지관리 디바이스, 로봇, 임베디드 등이 주요 융합 그룹으로 도출되었다. 전체 기술을 대상으로 분석한 결과 사물인터넷 요소기술을 중심으로 스마트 헬스케어, 스마트 홈, 무인자동차 등 사물인터넷의 다양한 응용영역들이 기술융합을 이루고 있는 것으로 파악되었다. 본 연구 결과는 사물인터넷 기술융합 활성화를 위한 정책 및 전략 수립에 효과적으로 활용될 수 있을 것으로 기대된다.

• 대한전기학회논문지
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• 제39권8호
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• pp.856-867
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• 1990

In this paper, the convergence and the learning accuracy of the back-propagation (BP) method in neural network are investigated by 1) analyzing the reason for decelerating the convergence of BP method and examining the rapid deceleration of the convergence when the learning is executed on the part of sigmoid activation function with the very small first derivative and 2) proposing the modified logistic activation function by defining, the convergence factor based on the analysis. Learning on the output patterns of binary as well as analog forms are tested by the proposed method. In binary output patter, the test results show that the convergence is accelerated and the learning accuracy is improved, and the weights and thresholds are converged so that the stability of neural network can be enhanced. In analog output patter, the results show that with extensive initial transient phenomena the learning error is decreased according to the convergence factor, subsequently the learning accuracy is enhanced.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.160-162
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• 2006

In this paper, a heterogeneous home network is designed and implemented based on the PLC (power line communications) and the IEEE 802.15.4. This paper presents the need of the heterogeneous home network and the convergence sub-layer. The convergence sub-layer is designed and implemented on the Xelline power line communication modem with IEEE 802.15.4 communication module.

• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.216-224
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• 2017

Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. Recently, some advances have been made in pig monitoring; however, detecting each pig is still challenging problem. In this paper, we propose a new color image-based monitoring system for the detection of the individual pig using a fast region-based convolution neural network with consideration of detecting touching pigs in a crowed pigsty. The experimental results with the color images obtained from a pig farm located in Sejong city illustrate the efficiency of the proposed method.

• ETRI Journal
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• 제30권2호
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• pp.216-226
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• 2008

This paper proposes a novel transport network architecture for the next generation network (NGN) based on the optical burst switching technology. The proposed architecture aims to provide efficient delivery of various types of network traffic by satisfying their quality-of-service constraints. To this end, we have developed a soft-state bandwidth reservation mechanism, which enables NGN transport nodes to dynamically reserve bandwidth needed for active data burst flows. The performance of the proposed mechanism is evaluated by means of numerical analysis and NS2 simulation. Our results show that the packet delay is kept within the constraint for each traffic flow and the burst loss rate is remarkably improved.

• Journal of Communications and Networks
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• 제8권4호
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• pp.410-421
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• 2006

In this work, we propose an analytic framework for dimensioning the link capacity of broadband access networks which provide universal broadband access services to a diverse kind of customers such as patient and impatient customers. The proposed framework takes into account the flow-level quality of service (QoS) of a connection as well as the packet-level QoS, via which a simple and systematic provisioning and operation of the network are provided. To that purpose, we first discuss the necessity of flow-aware network dimensioning by reviewing the networking technologies of the current and future access network. Next, we propose an analytic model for dimensioning the link capacity for an access node of broadband convergence networks which takes into account both the flow and packet level QoS requirements. By carrying out extensive numerical experiment for the proposed model assuming typical parameters that represent real network environment, the validity of the proposed method is assessed.

• ETRI Journal
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• 제31권2호
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• pp.231-233
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• 2009

We propose a nonreciprocal filter based on coarse wavelength division multiplexing (CWDM) that reduces the upstream channel insertion loss in a passive optical network (PON). We also propose a method to increase the number of channels/optical network units (ONUs) in PON systems using the proposed filter to reduce the service cost per subscriber. Experimental results show that the PON system with the proposed 4-channel filter can reduce the power budget of the upstream and increase the number of ONUs by 3 to 4 times that of a conventional time-division multiplexing PON.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.816-839
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• 2023

In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons. In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO). Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO. Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation. Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images. The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.