• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.5
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• pp.441-450
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• 2016

The International Telecommunications Union(: ITU) Radio Regulations(: RR) and in which is defined as the frequency of electromagnetic waves below 3000GHz spread in space without artificial guidance, our country also follows the international definition. As radio waves are electromagnetic waves spreading in space without artificial induction means having a frequency within the range set by the ITU. Frequency distribution for dual-work is to inde 300GHz, among the divided frequency is our daily or less than 90% of the frequency band is in contact saenghwalyong 3GHz. Propagation, but can occur indefinitely without depleting that anyone can create only gatchumyeon transmission equipment, if the radio frequency to use at the same time and space, the soul is the interference occurs is not available radio resources. Due to the physical finiteness used in our country for the first time on such a propagation laws enacted in 1961 and to the state radio resource management, and rules to be used for propagation only if granted the rights.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.2
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• pp.61-71
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• 1993

The stress waves generated by the mechanical energies by impact or the chemical energies by the explosions are transmitted through medium. The wave propagation process through medium is a very complicated procedure due to the reflections and refractions of the waves at the free surfaces and interfaces. In this study the pressure independent Von-Mises model is employed for the wave propagation analysis in the layered systems. Governing equations of this study are conservation equations of momentum and mass in Lagrangian coordinate system which is fixed to the material. Due to the shock-front which violates the continuity assumptions inherent in the differential equations numerical artificial viscosity is used to spread the shock front over several computational zones. These equations are solved by Finite Difference Method with discretized time and space coordinates. The associate normality flow rule as a plastic theory is implemented to find the plastic strains.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.7
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• pp.63-70
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• 2006

This paper describes research of using area of artificial mark on vision based head tracker system. A head tracker system consists of the translational and rotational motions which are detected by web camera. Results of the motion are taken from image processing and neural network. Because of the characteristics of cockpit, the specific color on the helmet is tracked for translational motion. And rotational motion is tracked via neural network. Ratio of two different colored area on the helmet is used as input of network. Neural network algorithms used, such as back-propagation and RBFN (Radial Basis Function Network). Both back-propagation using a characteristic of feedback and RBFN using a characteristic of statistics have a good performances for the tracking of nonlinear system such as a head motion. Finally, this paper analyzes and compares with tracking performance.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.1
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• pp.29-37
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• 2006

Precise control of interior permanent magnet synchronous motor(IPMSM) over wide speed range is an engineering challenge. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using learning mechanism-fuzzy neural network(LM-FNN) and ANN(artificial neural network) control. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility md numerical processing capability. Also, this paper proposes speed control of IPMSM using LM-FNN and estimation of speed using artificial neural network controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. 'The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. Analysis results to verify the effectiveness of the new hybrid intelligent control proposed in this paper.

• Journal of Korean Society of Steel Construction
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• v.16 no.4 s.71
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• pp.443-452
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• 2004

Recently, the Artificial Neural Network(ANN) which can organize complex non-linear problems by effectively applying the parallel computational model that is similar to the human brain, was adopted in the wide department of technology and resulted in many successful applications. In this study, a more appropriate formal method is suggested for the preliminary structural design stage controlled merely by the designer's experience and intuition. To do so, this study proposes a multi-level ANN according to the general progressive structural design procedure, using Back-Propagation Algorithm (BP) and Genetic Algorithm (GA) for the ANN learning. The preliminary structural design of cable-stayed bridges was applied to illustrate the applicability of the study formulated as stated above, and the results of two different learning methods were compared.

• Tunnel and Underground Space
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• v.13 no.6
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• pp.434-443
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• 2003

In this study, it was purposed to develop the new method for the prediction of pollutant concentration in road tunnels. The new method was the use of artificial neural network with the back-propagation algorithm which can model the non-linear system of tunnel environment. This network system was separated into two parts as the visibility and the CO concentration. For this study, data was collected from two highway road tunnels on Yeongdong Expressway. The tunnels have two lanes with one-way direction and adopt the longitudinal ventilation system. The actually measured data from the tunnels was used to develop the neural network system for the prediction of pollutant concentration. The output results from the newly developed neural network system were analysed and compared with the calculated values by PIARC method. Results showed that the prediction accuracy by the neural network system was approximately five times better than the one by PIARC method. In addition, the system predicted much more accurately at the situation where the drivers have to be stayed for a while in tunnels caused by the low velocity of vehicles.

• Computers and Concrete
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• v.21 no.1
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• pp.47-54
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• 2018

Artificial neural network models can be successfully used to simulate the complex behavior of many problems in civil engineering. As compared to conventional computational methods, this popular modeling technique is powerful when the relationship between system parameters is intrinsically nonlinear, or cannot be explicitly identified, as in the case of concrete behavior. In this investigation, an artificial neural network model was developed to assess the residual compressive strength of self-compacted concrete at elevated temperatures ($20-900^{\circ}C$) and various relative humidity conditions (28-99%). A total of 332 experimental datasets, collected from available literature, were used for model calibration and verification. Data used in model development incorporated concrete ingredients, filler and fiber types, and environmental conditions. Based on the feed-forward back propagation algorithm, systematic analyses were performed to improve the accuracy of prediction and determine the most appropriate network topology. Training, testing, and validation results indicated that residual compressive strength of self-compacted concrete, exposed to high temperatures and relative humidity levels, could be estimated precisely with the suggested model. As illustrated by statistical indices, the reliability between experimental and predicted results was excellent. With new ingredients and different environmental conditions, the proposed model is an efficient approach to estimate the residual compressive strength of self-compacted concrete as a substitute for sophisticated laboratory procedures.

• Textile Coloration and Finishing
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• v.12 no.1
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• pp.61-67
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• 2000

This study was carried out to develope antimicrobial artificial suede by coating with water soluble polyurethane resin and the copolymer of N,N'-dialkyl-N,N'-dialkyl ammonium chloride (DADAAC) and acrylamide as a antimicrobial additve. The copolymer of DADAAC and acrylamide was synthesized by free radical initiation and intra-intermolecular propagation, and the prepared copolymers had sufficient compatibility with water soluble polyurethane resin. The MIC values of the prepared copolymers and antimicrobial characteristics of the artificial suede coated by polyurethane were evaluated. With the increase in the proportion of DADAAC, which is antimicrobially active part in the DADAAC/acrylamide copolymers, the MIC value becomes lower. The MIC value of DADAAC-AA (1 : 1) copolymer is below 30 ppm against S. aureus, and below 90 ppm against K pneumoniae. The artificial suede coated by water soluble polyurethane resin with 1.0% owl concentration of DADAAC/acrylamide copolymer has good antimicrobial fastness as to show colony reduction of above 90% and 80% against S. aureus and K. pneumoniae respectively in the shake flask test after 10 times of washing, and above 95% and 85% after 10 times of dry-cleaning. The elastic recovery of coated suede fabric is not affected up to 1.0% owf concentration of DADAAC-AA copolymer in the polyurethane coating.

• Journal of Korean Society on Water Environment
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• v.21 no.1
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• pp.21-28
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• 2005

The training and prediction modeling using an artificial neural network was implemented to predict the turbidity of treated water as well as to estimate the optimized feed concentration of polyaluminium chloride (PACl) in a water treatment plant. The parameters used in the input layers were pH, temperature, turbidity and alkalinity, while those in output layers were PACl and turbidity of treated water. Levenberg-Marquadt method of feedforward back-propagation perceptron in the neural network toolbox of MATLAB program was used in this study. Correlation coefficients of the training data with the measured data were 0.9997 for PACl and 0.6850 for turbidity and those of the testing data with measured data were 0.9140 for PACl and 0.3828 for turbidity, when four parameters at input layer, 12-12 nodes each at both the first and the second hidden layers, and two parameters(PACl and turbidity) at output layer were used. Although the predictability of PACl was improved, compared to that of the previous studies to use the only coagulant dose as output layer, turbidity in treated water could not be predicted well. Acquisition of more data through several years obtained with the advanced on-line measuring system could make the artificial neural network useful and practical in actual water treatment plants.

• Journal of radiological science and technology
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• v.43 no.3
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• pp.195-208
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• 2020

Artificial intelligence(AI) is a field of computer science that is defined as allowing computers to imitate human intellectual behavior, even though AI's performance is to imitate humans. It is grafted across software-based fields with the advantages of high accuracy and speed of processing that surpasses humans. Indeed, the AI based technology has become a key technology in the medical field that will lead the development of medical image analysis. Therefore, this article introduces and discusses the concept of deep learning-based medical imaging analysis using the principle of algorithms for convolutional neural network(CNN) and back propagation. The research cases application of the AI based medical imaging analysis is used to classify the various disease(such as chest disease, coronary artery disease, and cerebrovascular disease), and the performance estimation comparing between AI based medical imaging classifier and human experts.