• 전자공학회논문지B
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• 제33B권12호
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• pp.95-105
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• 1996

This paper presents structure optimization of a feedforward neural netowrk controller using the genetic algorithm. It is important to design the neural network with minimum structure for fast response and learning. To minimize the structure of the feedforward neural network, a genralization of multilayer neural netowrks, the genetic algorithm uses binary coding for the structure and floating-point coding for weights. Local search with an on-line learnign algorithm enhances the search performance and reduce the time for global search of the genetic algorithm. The relative fitness defined as the multiplication of the error and node functions prevents from premature convergence. The feedforward neural controller of smaller size outperformed conventional multilayer perceptron network controller.

• 한국펄프종이공학회:학술대회논문집
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• 한국펄프종이공학회 2007년도 추계학술발표논문집
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• pp.127-134
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• 2007

Paper is composed network of fibers. Since paper is plain, most cases paper is considered two-dimensional. But network of fibers creates a network of pores, and pores between fibers are most important part of the paper structure. So we have to make an approach to the paper by three-dimensionally. Pore structure in the Z-direction of the paper can affect directly not only basic properties od the paper such as density, porosity, opacity and strength but also coverage of the coating colors during coatong and printing properties. We studied effect of raw materials of the papermaking and physical treatment on the pore structure and properties of the paper.

• 대한전기학회논문지:전력기술부문A
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• 제48권4호
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• pp.434-443
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• 1999

In this paper, neural network predictive PID (NNPPID) control system is proposed to reduce the vibration of building structure. NNPPID control system is made up predictor, controller, and self-tuner to yield the parameters of controller. The neural networks predictor forecasts the future output based on present input and output of building structure. The controller is PID type whose parameters are yielded by neural networks self-tuning algorithm. Computer simulations show displacements of single and multi-story structure applied to NNPPID system about disturbance loads-wind forces and earthquakes.

• 풍력에너지저널
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• 제9권4호
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• pp.16-23
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• 2018

A damage detection method for the tripod support structure of offshore wind turbines is presented for structural health monitoring. A finite element model of a prototype tripod support structure is established and the modal properties are calculated. The degree and location of the damage are estimated based on the neural network technique using the changes of natural frequencies and mode shape due to the damage. The stress distribution occurring in the support structure is obtained by a dynamic analysis for the wind turbine system to select the output data of the neural network. The natural frequencies and mode shapes for 36 possible damage scenarios were used for the input data of the learned neural network for damage assessment. The estimated damages agreed reasonably well with the accurate ones. The presented method could be effectively applied for damage detection and structural health monitoring of various types of support structures of offshore wind turbines.

• 산업경영시스템학회지
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• 제12권20호
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• pp.47-53
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• 1989

This paper presents an algorithm CAPFACT to evaluate the reliability of a capacitated two terminal network such as a communication network, a power distribution network, and a pipeline network. The network is good(working) if and only if it is possible to transmit successfully the required system capacity from one specified terminal to the other. This paper defines new Capacitated series-parallel reduction to be applied to a series-parallel structure of the network. New Capacitated factoring method is applied to a non-series-parallel structure. The method is based on the factoring theorem given by Agrawal and Barlow. According to the existing studies on the reliability evaluation of the network that the capacity is not considered, the factoring method using reduction is efficient. The CAPFACT is more efficient than Aggarwal algorithm which enumerated and combined the paths. The efficiency is proved by the result of testing the number of operations and cpu time on FORTRAN compiler of VAX-11/780 at Hanyang University.

• KIEAE Journal
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• 제14권6호
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• pp.23-29
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• 2014

It is essential to investigate the structure and the main characteristics of BSN (Bio-Sensor Network) platform in built smart healthcare environment while designing healthy housing facilities. For this study, WSN (Wireless Sensor Network) data transmission technologies have been employed with medical sensors, and optimal medical devices would provide various Web 2.0 services by connecting to the WiBro network. The BSN platform normally recognizes in surroundings of WBAN (Wireless Body Area Network) or WPAN (Wireless Personal Area Network), and it is possible to manage sensor nodes by utilizing SOAP (Simple Object Access Protocol) and REST (REpresentational State Transfer). In addition, the feature of SNMP (Simple Network Management Protocol) for mobile gateway is also included for being adapted to huge network structure. Finally, BSN platform will play a role as important clues for developing personal WSN service models for smart healthy housing properties.

• 보건교육건강증진학회지
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• 제33권3호
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• pp.1-11
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• 2016

Objectives: The challenging issue of public health program is to strengthen partnership and network between health resources. This study identified the structure and characteristics of school health program network. Methods: In this paper we collected data from schools and organizations in 4 local communities in 2014 that participated to school health program. Using social network analysis techniques we measured the number of component, diameter, density, average degree, node centralization for each network. Results: We determined that networks shared some common organizational structure such as less density, low average degree, and short diameter. Networks were dominated by the health center, and directions of collaborations between nodes were mostly one-way. Conclusions: These findings can help to depict the network of school health program. The further research is necessary to define causal relationship between network effectiveness and public health outcomes.

• Journal of information and communication convergence engineering
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• 제21권1호
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• pp.9-16
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• 2023

The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

• International Journal of Control, Automation, and Systems
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• 제3권4호
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the 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. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• E2M - 전기 전자와 첨단 소재
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• 제9권4호
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• pp.364-371
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• 1996

In this study, in order to investigate the applicability of IPN structure to epoxy resin which has been widely used as electrical and electronic insulating materials, DC dielectric breakdown properties and morphology were compared and analyzed according to variation of network structure, using the single network structure specimen formed of epoxy resin alone, interpenetrating polymer network specimen formed of epoxy resin/methacrylic acid resin, and interpenetrating polymer network specimen formed of epoxy resin/methacrylic acid resin/polyurethane resin. As results of the measunnent of DC dielectric breakdown strength at 50[.deg. C] and 130[>$^{\circ}C$], IPN specimen formed of epoxn, resin 100[phr] and methacrylic acid resin 35[phr] was the most excellent, and which corresponded to the SEM phenomena. The effect of IPN was more remarkable at high temperature region than at low temperature region. It is supposed that the defect of epoxy resin, dielectric breakdown strength is lowered remarkably at high temperature region, be complemented according to introducing IPN method.