• Journal of the Korean Institute of Telematics and Electronics
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• v.9 no.5
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• pp.6-11
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• 1972

A general active RC network synthesis procedure which realizes any stable transfer function is described. The network elements are only R's, C's and OA's, and the network configuration are well suited for construction using thin-film RC networks and integrated cil'suit operational amplifiers. Poles and transmission zeros can be adjusted independently to each other and are qu;te insensitive to element variations.

• Computers and Concrete
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• v.3 no.6
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• pp.439-454
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• 2006

This paper develops an artificial neural network (ANN) model for uniformly loaded restrained reinforced concrete (RC) slabs incorporating membrane action. The development of membrane action in RC slabs restrained against lateral displacements at the edges in buildings and bridge structures significantly increases their load carrying capacity. The benefits of compressive membrane action are usually not taken into account in currently available design methods based on yield-line theory. By extending the existing knowledge of compressive membrane action, it is possible to design slabs in building and bridge decks economically with less than normal reinforcement. The processes involved in the development of ANN model such as the creation of a database of test results from previous research studies, the selection of architecture of the network from extensive trial and error procedure, and the training and performance validation of the model are presented. The ANN model was found to predict accurately the ultimate strength of fully restrained RC slabs. The model also was able to incorporate strength enhancement of RC slabs due to membrane action as confirmed from a comparative study of experimental and yield line-based predictions. Practical applications of the developed ANN model in the design process of RC slabs are also highlighted.

• Computers and Concrete
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• v.30 no.3
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• pp.209-224
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• 2022

In this paper, the artificial neural network (ANN) is employed to predict the flexural behavior of reinforced concrete (RC) beams retrofitted with carbon fiber/epoxy composites modified by carbon nanotubes (CNTs). Multiple techniques are used to improve the accuracy of the ANN prediction, as the data represents a multivalued function. These techniques include static ANN modeling, ANN modeling with load history, and ANN modeling with double load history. The developed ANN models are used to predict the load-displacement profiles of beams retrofitted with either CFRP or CNTs modified CFRP, flexural capacity, and maximum displacement of the beams. The results demonstrate that the ANN is able to predict the flexural behavior of the retrofitted RC beams as well as the effect of each parameter including the type of the used epoxy and the presence of the CNTs.

• Structural Engineering and Mechanics
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• v.12 no.5
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• pp.527-540
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• 2001

A new sort of learning algorithm named whole learning algorithm is proposed to simulate the nonlinear and dynamic behavior of RC members for the estimation of structural integrity. A mathematical technique to solve the multi-objective optimization problem is applied for the learning of the feedforward neural network, which is formulated so as to minimize the Euclidean norm of the error vector defined as the difference between the outputs and the target values for all the learning data sets. The change of the outputs is approximated in the first-order with respect to the amount of weight modification of the network. The governing equation for weight modification to make the error vector null is constituted with the consideration of the approximated outputs for all the learning data sets. The solution is neatly determined by means of the Moore-Penrose generalized inverse after summarization of the governing equation into the linear simultaneous equations with a rectangular matrix of coefficients. The learning efficiency of the proposed algorithm from the viewpoint of computational cost is verified in three types of problems to learn the truth table for exclusive or, the stress-strain relationship described by the Ramberg-Osgood model and the nonlinear and dynamic behavior of RC members observed under an earthquake.

• 전기의세계
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• v.24 no.4
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• pp.80-82
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• 1975

This paper presents a synthesis procedure of multi-channel active filters, which realizes an arbitrary N*N matrix of real rational functions in the complex variable s as a voltage transfer matrix. The resultant network reveals a transformerless grounded active RC(2N+1)-terminal network. The active network is consisted of six 2N-port RC networks with 2N single-ended operational amplifiers.

• Journal of the Korean Institute of Telematics and Electronics
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• v.7 no.1
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• pp.9-17
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• 1970

The paper presents pole optimization in RC network of active RC filter using current inversion negative impedance converter. And also empais is placed on improving the stability of the active RC filter. Experimental results obtained with active RC low pass filter, having Chebyshev 2nd order response and modular angle 55$^{\circ}$, cutoff frequency 3.4KC, are shown and compared with theoretical curves.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.164-168
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• 2023

As our community has become increasingly dependent on technology, security has become a bigger concern, which makes it more important and challenging than ever. security can be enhanced with encryption as described in this paper by combining RC6 symmetric cryptographic algorithms with RSA asymmetric algorithms, as well as the Vigenère cipher, to help manage weaknesses of RC6 algorithms by utilizing the speed, security, and effectiveness of asymmetric algorithms with the effectiveness of symmetric algorithm items as well as introducing classical algorithms, which add additional confusion to the decryption process. An analysis of the proposed encryption speed and throughput has been conducted in comparison to a variety of well-known algorithms to demonstrate the effectiveness of each algorithm.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.168-174
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• 2021

The Cognitive radio (CR) is evolving technology for managing the spectrum bandwidth in wireless network. The security plays a vital role in wireless network where the secondary users are trying to access the primary user's bandwidth. During the allocation the any malicious user either he pretends to be primary user or secondary user to access the vital information's such as credentials, hacking the key, network jam, user overlapping etc. This research paper discusses on various types of attack and to prevent the attack in cognitive radio network. In this research, secondary users are identified by the primary user to access the primary network by the secondary users. The secondary users are given authorization to access the primary network. If any secondary user fails to provide the authorization, then that user will be treated as the malicious user. In this paper two approaches are suggested one by applying elliptic curve cryptography and the other method by using priority-based service access.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.12
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• pp.1460-1466
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• 2010

In this paper, we analyzed the performance of radar target classification using the monostatic and bistatic radar cross section(RCS) for four different wire targets. Short time Fourier transform(STFT) and continuous wavelet transform (CWT) were used for feature extraction from the monostatic RCS and the bistatic RCS of each target, and a multi-layered perceptron(MLP) neural network was used as a classifier. Results show that CWT yields better performance than STFT for both the monostatic RCS and the bistatic RCS. And, when STFT was used, the performance of the bistatic RCS was slightly better than that of the monostatic RCS. However, when CWT was used, the performance of the monostatic RCS was slightly better than that of the bistatic RCS. Resultingly, it is proven that bistatic RCS is a good cadndidate for application to radar target classification in combination with a monostatic RCS.

• Structural Engineering and Mechanics
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• v.87 no.6
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• pp.517-527
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• 2023

The severe deterioration of structures has led to extensive research on the development of structural repair techniques using composite materials. Consequently, previous researchers have devised various analytical methods to predict the interface performance of bonded repairs. However, these analytical solutions are highly complex mathematically and necessitate numerous calculations with a large number of iterations to obtain the output parameters. In this paper, an artificial neural network prediction models is used to calculate the interfacial stress distribution in RC beams strengthened with FRP sheet. The R2value for the training data is evaluated as 0.99, and for the testing data, it is 0.92. Closed-form solutions are derived for RC beams strengthened with composite sheets simply supported at both ends and verified through direct comparisons with existing results. A comparative study of peak interfacial shear and normal stresses with the literature gives the usefulness and effectiveness of ANN proposed. A parametrical study is carried out to show the effects of some design variables, e.g., thickness of adhesive layer and FRP sheet.