• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.911-917
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• 2017

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

• 정보처리학회논문지A
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• 제17A권4호
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• pp.181-188
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• 2010

본 연구에서는 데이터마이닝 기법을 이용하여 네트워크 사용자의 행동 패턴을 분석하는 시스템을 제안하고자 한다. 시스템 구현을 위해 네트워크 탭 장비를 사내 네트워크에 설치하여 패킷을 복제한다. 복제된 패킷은 고속의 메인 메모리DB를 통하여 데이터베이스에 저장된다. 저장 된 데이터는 데이터 마이닝 기법을 이용하여 행동패턴을 분석하고 네트워크 관리자에게 실시간 보고한다. 또한 이기종간의 데이터를 공유하기 위해 표준의 XML 문서 교환 방식을 적용한다. 행동패턴 분석 시스템은 추후 저가 구현 및 쉬운 설치를 고려하여 임베디드형 셋톱박스 기반을 제안한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2580-2582
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• 2004

In this paper, a Neural Network(NN) based approach for classification of two dimensions images. The proposed algorithm is able to apply in the actual industry. The described diagnostic algorithm is presented to defect surface failures on tiles. A way to get data for a digital image process is several kinds of it. The tiles are scanned and the digital images are preprocessed and classified using neural networks. It is important to reduce the amount of input data with problem specific preprocessing. The auto-associative neural network is used for feature generation and selection while the probabilistic neural network is used for classification. The proposed algorithm is evaluated experimentally using one hundred of the real tile images. Sample image data to preprocess have histogram. The histogram is used as input value of probabilistic neural network. Auto-associative neural network compress input data and compressed data is classified using probabilistic neural network. Classified sample images are determined by human state. So it is intervened human subjectivity. But digital image processing and neural network are better than human classification ability. Therefore it is very useful of quality control improvement.

• 한국국방경영분석학회지
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• 제32권1호
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• pp.133-158
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• 2006

정보통신 기술의 급속한 발달은 군의 전장 환경에도 커다란 변화를 가져오고 있다. 특히, 멀티미디어 정보를 이용하여 다양한 대용량의 정보유통 서비스 수요가 급증할 것으로 예상된다. 이와 같은 사용자의 요구사항을 충족하기 위해서는 국방정보통신망의 업그레이드는 불가피하다. 네트워크의 용량을 증가시키기 위해서는 예산 투자가 당연하다. 하지만 데이터 트래픽의 양을 과학적인 방법으로 측정하는 기법이 없는 것이 현실이다. 본 논문은 미래 군 전술종합정보통신 체계에서 요구되는 정보유통소요량에 대하여 네트워크 시뮬레이션 기법을 적용한 툴(Tool) 기반의 과학적이고 신뢰성 있는 정보유통소요량을 분석 및 예측 할 수 있는 기법을 제시하고자 한다.

• 한국통신학회논문지
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• 제40권3호
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• pp.602-612
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• 2015

현재 표준 차량 통신 프로토콜인 IEEE 802.11p는 차량 간 한 홉 전송을 수행하기 때문에 차량 환경에서 효율적인 정보 전달을 수행하는데 한계가 있다. 본 논문은 차량 환경에서 효율적인 정보 전달을 위해 무선 근거리 통신 중 하나인 LTE-D2D 기술을 사용한 차량 네트워크를 제안한다. 이때 전송 메시지 형태는 IP 패킷 옵션을 지닌 이름 기반 정보 메시지를 사용하고 일반 차량 노드는 요청하는 메시지를 중간 매개 노드인 대형 차량 노드로 전송하여 정보를 전송 받는다. 성능 분석을 통해 셀룰러 네트워크와 제안된 LTE-D2D 차량 네트워크에서의 패킷전달 시간에 따른 데이터 처리율을 비교하였다.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2008년도 정보통신설비 학술대회
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• pp.45-50
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• 2008

Many applications of sensor network require connection to the Internet. The transmission protocol of traditional sensor network was designed within the sensor network itself. However, based on 6LoWPAN which can be accessed using IPv6, direct connection is possible between the sensor network and the TCP/IP network outside. Transmission of data in applications of sensor network falls into two main categories. One is a small packet that is periodically produced such as packet related to temperature and humidity. The other is a relatively large packet that brings about network overheads such as images. We investigated the conformance test and pros and cons of application data over the transmission protocol of Zigbee and 6LoWPAN. As a result, both Zigbee and 6LoWPAN have shown low rate of loss for periodic data and have in creased reliability of data transfer. When transmitting streaming image data, both ACK, non ACK mode of Zigbee and UDP of 6LoWPAN minimized transmission time but suffered the consequences of high packet loss. Even though TCP of 6LoWPAN required a long transmission time, we were able to confirm that no loss has occurred.

• 한국컴퓨터정보학회논문지
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• 제23권9호
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• pp.35-42
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• 2018

In this paper, we propose an optimal streaming service method based on Hybrid CDN/P2P architecture. Recently, video streaming utilizes a CDN (Content Delivery Network) operation technique based on a Proxy Server, which is an end node located close to a user. However, since CDN has a fixed network traffic bandwidth and data information exchange among CDNs in the network is not smooth, it is difficult to guarantee traffic congestion and quality of image service. In the hybrid CDN/P2P network, a data selection technique is used to select only the data that is expected to be continuously requested among all the data in order to guarantee the QoS of the user who utilizes the limited bandwidth efficiently. In order to search user requested data, this technique effectively retrieves the storage information of the constituent nodes of CDN and P2P, and stores the new image information and calculates the deletion priority based on the request possibility as needed. Therefore, the streaming service scheme proposed in this paper can effectively improve the quality of the video streaming service on the network.

• Journal of Communications and Networks
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• 제18권3호
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• pp.493-501
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• 2016

In-network storage is an effective technique for avoiding network congestion and reducing power consumption in continuous data collection in wireless sensor networks. In recent years, network coding based storage design has been proposed as a means to achieving ubiquitous access that permits any query to be satisfied by a few random (nearby) storage nodes. To maintain data consistency in continuous data collection applications, the readings of a sensor over time must be sent to the same set of storage nodes. In this paper, we present an efficient approach to updating data at storage nodes to maintain data consistency at the storage nodes without decoding out the old data and re-encoding with new data. We studied a transmission strategy that identifies a set of storage nodes for each source sensor that minimizes the transmission cost and achieves ubiquitous access by transmitting sparsely using the sparse matrix theory. We demonstrate that the problem of minimizing the cost of transmission with coding is NP-hard. We present an approximation algorithm based on regarding every storage node with memory size B as B tiny nodes that can store only one packet. We analyzed the approximation ratio of the proposed approximation solution, and compared the performance of the proposed coding approach with other coding schemes presented in the literature. The simulation results confirm that significant performance improvement can be achieved with the proposed transmission strategy.

• BMB Reports
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• 제41권3호
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• pp.184-193
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• 2008

Living organisms are comprised of various systems at different levels, i.e., organs, tissues, and cells. Each system carries out its diverse functions in response to environmental and genetic perturbations, by utilizing biological networks, in which nodal components, such as, DNA, mRNAs, proteins, and metabolites, closely interact with each other. Systems biology investigates such systems by producing comprehensive global data that represent different levels of biological information, i.e., at the DNA, mRNA, protein, or metabolite levels, and by integrating this data into network models that generate coherent hypotheses for given biological situations. This review presents a systems biology framework, called the 'Integrative Proteomics Data Analysis Pipeline' (IPDAP), which generates mechanistic hypotheses from network models reconstructed by integrating diverse types of proteomic data generated by mass spectrometry-based proteomic analyses. The devised framework includes a serial set of computational and network analysis tools. Here, we demonstrate its functionalities by applying these tools to several conceptual examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권11호
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• pp.5243-5263
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• 2017

Data center networks provide critical bandwidth for the continuous growth of cloud computing, multimedia storage, data analysis and other businesses. The problem of low link bandwidth utilization in data center network is gradually addressed in more hot fields. However, the current scheduling strategies applied in data center network do not adapt to the real-time dynamic change of the traffic in the network. Thus, they fail to distribute resources due to the lack of intelligent management. In this paper, we present an improved adaptive traffic scheduling strategy utilizing the simulated annealing genetic algorithm (SAGA). Inspired by the idea of software defined network, when a flow arrives, our strategy changes the bandwidth demand dynamically to filter out the flow. Then, SAGA distributes the path for the flow by considering the scheduling of the different pods as well as the same pod. It is implemented through software defined network technology. Simulation results show that the bisection bandwidth of our strategy is higher than state-of-the-art mechanisms.