• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.861-865
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• 1994

By using Neural Networks, a 8$\times$8 Banyan network controller is designed and implemented. In order to solve internal blocking and output blocking, Winner-Take-All method is used. The longer queue takes higher priority. First-in-first-out method is used among the non-blocking cells in the queue selected.The required time to select a cell is 2.7 $\mu$sec for 155Mbps. The implemented controller using Xilinx FPGA chip selects cells within 2.5$\mu$sec.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.506-507
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• 2007

Lateral Information Propagation Neural Networks (LIPN) is proposed for on-line interpolation. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1872-1879
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• 2016

The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectroscopy to extract a clear spectrum of plastic material. In addition, to improve the classification ability of fuzzy Radial Basis Function Neural Networks, we apply supervised learning based clustering method instead of unsupervised clustering method. The conditional fuzzy C-Means clustering method, which is a kind of supervised learning based clustering algorithms, is used to determine the location of radial basis functions. The conditional fuzzy C-Means clustering analyzes the data distribution over input space under the supervision of auxiliary information. The auxiliary information is defined by using k Nearest Neighbor approach.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1869_1870
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• 2009

본 논문에서는 다항식 기반 Type-2 Fuzzy Neural Networks(T2FNN)를 설계하고 이를 패턴분류 문제에 적용하여 그 성능을 분석한다. T2FNN은 Fuzzy C-Means(FCM)을 Type-2 Fuzzy C-Means로 확장시킨 것이라 할 수 있으며, Input layer, Fuzzyification layer, Inference layer, Deffuzification layer의 4층 네트워크로 구성된다. interval Type-1 퍼지 집합인 후반부의 연결가중치는 Gradient Descent Method를 이용하여 학습한다. 제안된 RBF 신경회로망은 모의데이터와 패턴인식 성능 평가에 많이 사용되는 machine learning 데이터에 적용하여 패턴 분류기로서의 성능을 평가받는다.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.108-114
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• 2008

Polynomial networks have been known to have excellent properties as classifiers and universal approximators to the optimal Bayes classifier. In this paper, the use of polynomial neural networks is proposed for efficient implementation of the polynomial-based classifiers. The polynomial neural network is a trainable device consisting of some rules and three processes. The three processes are assumption, effect, and fuzzy inference. The assumption process is driven by fuzzy c-means and the effect processes deals with a polynomial function. A learning algorithm for the polynomial neural network is developed and its performance is compared with that of previous studies.

• Journal of the Korean Mathematical Society
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• v.47 no.6
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• pp.1223-1238
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• 2010

The purpose of this work is to identify the internal structure of the electrical networks with data obtained from only a part of network or the boundary of network. To be precise, it is discussed whether we can identify resistors or electrical conductivities of each link inside networks by the measurement of voltage on the boundary which is induced by a prescribed current on the boundary. As a result, it is shown that the structure of the resistor network can be determined uniquely by only one pair of the data (current, voltage) on the boundary, if the resistors satisfy an appropriate condition. Besides, several useful results about the energy functionals, which means the electrical power, are included.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.800-802
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• 2000

In this paper, we propose the Self-Organizing Networks(SON) based on competitive Fuzzy Polynomial Neuron(FPN) for the optimal design of nonlinear process system. The SON architectures consist of layers with activation nodes based on fuzzy inference rules. Here each activation node is presented as FPN which includes either the simplified or regression Polynomial fuzzy inference rules. The proposed SON is a network resulting from the fusion of the Polynomial Neural Networks(PNN) and a fuzzy inference system. The conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as liner, quadratic and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are studied. Chaotic time series data used to evaluate the performance of our proposed model.

• Journal of the Korean Ceramic Society
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• v.54 no.1
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• pp.66-69
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• 2017

In this study, the fabrication and electrical properties of aligned single-walled carbon nanotube (SWCNT) networks using a template-based fluidic assembly process are presented. This complementary metal-oxide-semiconductor (CMOS)-friendly process allows the formation of highly aligned lateral nanotube networks on $SiO_2/Si$ substrates, which can be easily integrated onto existing Si-based structures. To measure outstanding electrical properties of organized SWCNT devices, interfacial contact resistance between organized SWCNT devices and Ti/Au electrodes needs to be improved since conventional lithographic cleaning procedures are insufficient for the complete removal of lithographic residues in SWCNT network devices. Using optimized purification steps and controlled developing time, the interfacial contact resistance between SWCNTs and contact electrodes of Ti/Au is reached below 2% of the overall resistance in two-probe SWCNT platform. This structure can withstand current densities ${\sim}10^7A{\cdot}cm^{-2}$, equivalent to copper at similar dimensions. Also failure current density improves with decreasing network width.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.114-116
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• 1993

A novel Neural networks controller for Buck type DC-DC converter is presented and compared with the operation of sliding node coupled several control strategies for the converter. The connection weights of neural networks are trained by error back propagation algorithm. The behavior of the control system that arises fred the use of those methods is analyzed from the viewpoint of dynamic and steady state errors and simulation results are presented.

• Transactions on Electrical and Electronic Materials
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• v.18 no.6
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• pp.345-349
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• 2017

Reconfiguration is an important method to minimize power loss and load interruption by creating an optimal configuration of a system. Furthermore, by increasing demand and value of consumption, construction of new power plants can be postponed in networks by reconfiguration and proper arrangement of linkage switches. This method is feasible for radial networks, which create meshes of linkage switches. One convenient way to achieve a system with minimal power loss and interruption is to utilize capacitors. Optimal placement and sizing of capacitors in such applications is an important issue in the literature. In this paper, cat swarm optimization is introduced as a new metaheuristic algorithm to achieve this purpose. Simulation has been carried out in two feasible networks, 69-bus and 33-bus systems.