• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 3부
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• pp.12-18
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• 2008

본 논문은 촉감기반 네트워크 시스템을 위한 적용형 전송기법들에 대한 기존의 연구들을 소개한다. 우선 촉감기반 네트워크 시스템의 구조와 데이터 유행에 따라서 촉감기반 네트워크 시스템을 분류하고 촉감기반 시스템을 위한 네트워크 QoS 요구 조건에 관한 기존의 연구들을 정리한다. 촉감기반 시스템을 위한 기존의 네트워크 적응형 전송기법에 대해서 네트워크 지연 및 지터 보상 기법, 손실 제어, 그리고 전송률 제어 기법들로 나누어 소개한다. 촉감기반 네트워크 시스템을 위한 기존의 연구들을 정리하므로써 더 효율적인 네트워크 적응형 전송 기법들의 개발을 위한 기반을 마련하고자 한다.

• 정보통신설비학회논문지
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• 제3권2호
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• pp.81-89
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• 2004

In this paper, we describe ubiquitous environments and the trend of convergence that is an evolution path of. current telecommunication, and show the concept of broadband convergence network, service feature and evolution path. In order to converge wire and wireless communication, telecommunication and broadcasting, voice and data efficiently, broadband convergence network divides a network into service layer, control layer, transport layer, ubiquitous access and terminal layer. Broadband convergence network will be a network that can provide and control broadband multimedia services with QoS and security of different and customized level. Then we depict characteristics and types of broadband multimedia service, and describe the characteristic of broadband convergence network. Finally, we show ubiquitous network based on the broadband convert- gence network to provide ubiquitous service which is a future telecommunication service. We also describe requirements of ubiquitous network such as an intelligent and context based platform, convergence terminals, ubi- quitous computing devices, etc.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.411-425
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• 2021

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

• Journal of Information Processing Systems
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• 제9권2호
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• pp.301-314
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• 2013

In a parallel processing system, Multi-stage Interconnection Networks (MINs) play a vital role in making the network reliable and cost effective. The MIN is an important piece of architecture for a multiprocessor system, and it has a good impact in the field of communication. Optical Multi-stage Interconnection Networks (OMINs) are the advanced version of MINs. The main problem with OMINs is crosstalk. This paper, presents the (1) Destination Based Modified Omega Network (DBMON) and the (2) Destination Based Scheduling Algorithm (DBSA). DBSA does the scheduling for a source and their corresponding destination address for messages transmission and these scheduled addresses are passed through DBMON. Furthermore, the performance of DBMON is compared with the Crosstalk-Free Modified Omega Network (CFMON). CFMON also minimizes the crosstalk in a minimum number of passes. Results show that DBMON is better than CFMON in terms of the average number of passes and execution time. DBSA can transmit all the messages in only two passes from any source to any destination, through DBMON and without crosstalk. This network is the modified form of the original omega network. Crosstalk minimization is the main objective of the proposed algorithm and proposed network.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권1호
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• pp.111-118
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

• Genomics & Informatics
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• 제13권3호
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• pp.76-80
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• 2015

Type 2 diabetes mellitus is a complex metabolic disorder associated with multiple genetic, developmental and environmental factors. The recent advances in gene expression microarray technologies as well as network-based analysis methodologies provide groundbreaking opportunities to study type 2 diabetes mellitus. In the present study, we used previously published gene expression microarray datasets of human skeletal muscle samples collected from 20 insulin sensitive individuals before and after insulin treatment in order to construct insulin-mediated regulatory network. Based on a motif discovery method implemented by iRegulon, a Cytoscape app, we identified 25 candidate regulons, motifs of which were enriched among the promoters of 478 up-regulated genes and 82 down-regulated genes. We then looked for a hierarchical network of the candidate regulators, in such a way that the conditional combination of their expression changes may explain those of their target genes. Using Genomica, a software tool for regulatory network construction, we obtained a hierarchical network of eight regulons that were used to map insulin downstream signaling network. Taken together, the results illustrate the benefits of combining completely different methods such as motif-based regulatory factor discovery and expression level-based construction of regulatory network of their target genes in understanding insulin induced biological processes and signaling pathways.

• Journal of information and communication convergence engineering
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• 제9권6호
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• pp.625-628
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• 2011

Dynamic sensor network(DSN) technology which is based on swarming flight type air node offers analyzed and acquired information on target data gathered by air nodes in rotation flight or 3 dimension array flight. Efficient operation of dynamic sensor network based on air node is possible when problems of processing time, data transmission reliability, power consumption and intermittent connectivity are solved. Delay tolerant network (DTN) can be a desirable alternative to solve those problems. DTN using store-and-forward message switching technology is a solution to intermittent network connectivity, long and variable delay time, asymmetric data rates, and high error rates. However, all processes are performed at the bundle layer, so high power consumption, long processing time, and repeated reliability technique occur. DSN based on swarming flight type air node need to adopt store-and-forward message switching technique of DTN, the cancelation scheme of repeated reliability technique, fast processing time with simplified layer composition.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2003년도 추계학술대회
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• Journal of Electrical Engineering and Technology
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• 제8권6호
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• pp.1626-1636
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• 2013

Nowadays, a rapid development in wind power technologies is occurring compared with other renewable energies. This advance in technology has facilitated a new generation of wind turbines with larger capacity and higher efficiency. As the height of the turbines and the distance between turbines increases, the monitoring and control of this new generation wind turbines presents new challenges. This paper presents the architectural design, simulation, and evaluation of hybrid communication networks for a large-scale wind turbine (WT). The communication network of WT is designed based on logical node (LN) concepts of the IEC 61400-25 standard. The proposed hybrid network architectures are modeled and evaluated by OPNET. We also investigate network performance using three different technologies: Ethernet-based, WiFi-based, and ZigBee-based. Our network model is validated by analyzing the simulation results. This work contributes to the design of a reliable communication network for monitoring and controlling a wind power farms (WPF).

• 경영과학
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• 제32권4호
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• pp.81-96
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• 2015

This paper models knowledge diffusion on an inter-organizational network. Based on literatures related to knowledge diffusion, the model considers critical factors that affect diffusion behavior including nodal property, relational property, and environmental property. We examine the relationships among network structure, knowledge search, and diffusion performance. Through a massive simulation runs based on the agent-based model, we find that the average path length of a network decreases a firm's cumulative knowledge stock, whereas the clustering coefficient of a firm has no significant relationship with the firm's knowledge. We also find that there is an interaction effect of network structure and the range of knowledge search on knowledge diffusion. Specifically, in a network of a larger average path length (APL) the marginal effect of search conduct is significantly greater than in that of a smaller APL.