• 전력전자학회논문지
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• 제19권3호
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• pp.290-296
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• 2014

This paper presents a design strategy for the control of the Z-source inverter (ZSI). For the Z-network capacitor voltage control, the average current model is derived to describe the dynamics of the voltage control and the controller outputs the average current command for the capacitor. Z-network inductor current reference is derived from the average current model of the Z-network capacitor. The inner current control loop outputs the average voltage command for the Z-network inductor and the shoot-through duty ratio of the ZSI is calculated from the output using the average voltage model of the Z-network inductor. The gain values of the current and voltage controllers are directly obtained by the Z-network parameters and desired bandwidth of each controller without a gain tuning process.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2018년도 하계학술대회
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• pp.63-66
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• 2018

Recently, a convolutional neural network (CNN) models at single image super-resolution have been very successful. Residual learning improves training stability and network performance in CNN. In this paper, we compare four convolutional neural network models for super-resolution (SR) to learn nonlinear mapping from low-resolution (LR) input image to high-resolution (HR) target image. Four models include general CNN model, global residual learning CNN model, local residual learning CNN model, and the CNN model with global and local residual learning. Experiment results show that the results are greatly affected by how skip connections are connected at the basic CNN network, and network trained with only global residual learning generates highest performance among four models at objective and subjective evaluations.

• 로봇학회논문지
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• 제17권2호
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• pp.133-141
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• 2022

This paper introduces model compression algorithms which make a deep neural network smaller and faster for embedded systems. The model compression algorithms can be largely categorized into pruning, quantization and knowledge distillation. In this study, gradual pruning, quantization aware training, and knowledge distillation which learns the activation boundary in the hidden layer of the teacher neural network are integrated. As a large deep neural network is compressed and accelerated by these algorithms, embedded computing boards can run the deep neural network much faster with less memory usage while preserving the reasonable accuracy. To evaluate the performance of the compressed neural networks, we evaluate the size, latency and accuracy of the deep neural network, DenseNet201, for image classification with CIFAR-10 dataset on the NVIDIA Jetson Xavier.

• 정보처리학회논문지C
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• 제8C권1호
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• pp.68-74
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• 2001

본 논문에서는 네트워크 관리 시스템에서 SNMP(Simple Network Management Protocol) 프로토콜을 이용한 중앙 집중형 방식과 이동 에이젠트(mobile agent)를 이용한 분산방식 각각의 성능을 평가하기 위한 성능평가 모델을 제시한다. 먼저 균등 네트워크 상에서 적용 가능한 모델을 제시하고 실제 실험을 통하여 각 파라미터 값의 변화에 따른 네트워크 수행 시간을 비교한다. 다음으로 비균등 네트워크에 적용 가능하도록 모델을 확장하고, 제시한 모델을 응용하여 실제 가상의 시나리오 하에서 네트워크 소요시간을 줄일 수 있는 방안과 그에 대한 실험 결과를 제시한다. 본 성능평가 모델은 차후 네트워크 관리시스템을 개발할 때에, 효율적인 패러타임(paradigm)과 수행 구조를 선택하는데 도움이 될 수 있을 것이다.

• 디지털산업정보학회논문지
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• 제11권4호
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• pp.33-45
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• 2015

Currently, Internet is used an essential tool in the business area. Despite this importance, there is a risk of network attacks attempting collection of fraudulence, private information, and cyber terrorism. Firewalls and IDS(Intrusion Detection System) are tools against those attacks. IDS is used to determine whether a network data is a network attack. IDS analyzes the network data using various techniques including expert system, data mining, and state transition analysis. This paper tries to compare the performance of two data mining models in detecting network attacks. They are decision tree (C4.5), and neural network (FANN model). I trained and tested these models with data and measured the effectiveness in terms of detection accuracy, detection rate, and false alarm rate. This paper tries to find out which model is effective in intrusion detection. In the analysis, I used KDD Cup 99 data which is a benchmark data in intrusion detection research. I used an open source Weka software for C4.5 model, and C++ code available for FANN model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권2호
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• pp.680-699
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• 2015

The use of web service has been increased rapidly, with an increase in the number of available services, finding the exact service is the challenging task. Service discovery is the most significant job to complete the service discoverers needs. In order to achieve the efficient service discovery, we focus on designing a cluster based service discovery model for service registering and service provisioning among all mobile nodes in a mobile ad hoc network (MANETs). A dynamic backbone of nodes (i.e. cluster heads) that forms a service repository to which MANET nodes can publish their services and/or send their service queries. The designed model is based on storing services with their service description on cluster head nodes that are found in accordance with the proposed cluster head election model. In addition to identifying and analyzing the system parameters for finding the effectiveness of our model, this paper studies the stability analysis of the network, overhead of the cluster, and bandwidth utilization and network traffic is evaluated using analytic derivations and experimental evaluation has been done.

• 한국자동차공학회논문집
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• 제12권4호
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• pp.110-118
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• 2004

In this paper, a blackbox approach is carried out to model the nonlinear dynamic bushing model. One-axis durability test is performed to describe the mechanical behavior of typical vehicle elastomeric components. The results of the tests are used to develop an empirical bushing model with an artificial neural network. The back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra's algorithm of ‘NARMAX’ form is employed in the neural network bushing module. A numerical example is carried out to verify the developed bushing model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권9호
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• pp.3110-3125
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• 2014

Network coding improves TCP's performance in lossy wireless networks. However, the complex congestion window evolution of network coded TCP (TCP-NC) makes the analysis of end-to-end throughput challenging. This paper analyzes the evolutionary process of TCP-NC against lossy links. An analytic model is established by applying a two-dimensional Markov chain. With maximum window size, end-to-end erasure rate and redundancy parameter as input parameters, the analytic model can reflect window evolution and calculate end-to-end throughput of TCP-NC precisely. The key point of our model is that by the novel definition of the states of Markov chain, both the number of related states and the computation complexity are substantially reduced. Our work helps to understand the factors that affect TCP-NC's performance and lay the foundation of its optimization. Extensive simulations on NS2 show that the analytic model features fairly high accuracy.

• 한국지능시스템학회논문지
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• 제16권6호
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• pp.785-790
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• 2006

RFID를 이용한 공급망 관리에서 제품의 이동 경로에 대한 추적과 상품에 대한 정보 수집은 중요한 이슈사항이다. 본 논문에서는 효과적인 제품 추적 모델과 기존에 운영 중인 상품정보 동기화 네트워크와 RFID 네트워크의 통합 연계 방안에 대한 연구를 진행하였다. 정보는 RFID 네트워크에서 발생하는 동적 자료와 상품정보 동기화 네트워크에서 발생하는 정적 정보로 구성되고, 통합 모델은 장기적으로 2개의 네트워크 사이에 상호운용성을 제공할 것이다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제6권4호
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• pp.203-210
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• 2017

본 논문에서는 이미지 캡션 생성과 모델 전이에 효과적인 심층 신경망 모델을 제시한다. 본 모델은 멀티 모달 순환 신경망 모델의 하나로서, 이미지로부터 시각 정보를 추출하는 컨볼루션 신경망 층, 각 단어를 저차원의 특징으로 변환하는 임베딩 층, 캡션 문장 구조를 학습하는 순환 신경망 층, 시각 정보와 언어 정보를 결합하는 멀티 모달 층 등 총 5 개의 계층들로 구성된다. 특히 본 모델에서는 시퀀스 패턴 학습과 모델 전이에 우수한 LSTM 유닛을 이용하여 순환 신경망 층을 구성하며, 캡션 문장 생성을 위한 매 순환 단계마다 이미지의 시각 정보를 이용할 수 있도록 컨볼루션 신경망 층의 출력을 순환 신경망 층의 초기 상태뿐만 아니라 멀티 모달 층의 입력에도 연결하는 구조를 가진다. Flickr8k, Flickr30k, MSCOCO 등의 공개 데이터 집합들을 이용한 다양한 비교 실험들을 통해, 캡션의 정확도와 모델 전이의 효과 면에서 본 논문에서 제시한 멀티 모달 순환 신경망 모델의 높은 성능을 확인할 수 있었다.