• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.542-546
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• 2009

In this Paper, a back propagation neural network learning algorithm based on the complex multilayer perceptron is represented for controling and detecting interference of the received signals in cellular mobile communication system. We proposed neural network adaptive correlator which has fast convergence rate and good performance with combining back propagation neural network and the receiver of cellular. We analyzed and proved that NNAC has lower bit error probability than that of traditional RAKE receiver through results of computer simulation in the presence of the tone and narrow - band interference and the co-channel interference.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2762-2764
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• 2001

In this study, we introduce and investigate a class of dynamic perceptron architectures, discuss a comprehensive design methodology and carry out a series of numeric experiments. The proposed dynamic perceptron architectures are called as Polynomial Neural Networks(PNN). PNN is a flexible neural architecture whose topology is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated on the fly. In this sense, PNN is a self-organizing network. PNN has two kinds of networks, Polynomial Neuron(FPN)-based and Fuzzy Polynomial Neuron(FPN)-based networks, according to a polynomial structure. The essence of the design procedure of PN-based Self-organizing Polynomial Neural Networks(SOPNN) dwells on the Group Method of Data Handling (GMDH) [1]. Each node of the SOPNN exhibits a high level of flexibility and realizes a polynomial type of mapping (linear, quadratic, and cubic) between input and output variables. FPN-based SOPNN dwells on the ideas of fuzzy rule-based computing and neural networks. Simulations involve a series of synthetic as well as experimental data used across various neurofuzzy systems. A detailed comparative analysis is included as well.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.85-96
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• 2020

It is important to extract salient object image and to solve the invariance problem for image recognition. In this paper we propose a new hybrid algorithm for invariance and improved classification performance in image recognition, whose algorithm is combined by FT(Frequency-tuned Salient Region Detection) algorithm, Guided filter, Zernike moments, and a simple artificial neural network (Multi-layer Perceptron). The conventional FT algorithm is used to extract initial salient object image, the guided filtering to preserve edge details, Zernike moments to solve invariance problem, and a classification to recognize the extracted image. For guided filtering, guided filter is used, and Multi-layer Perceptron which is a simple artificial neural networks is introduced for classification. Experimental results show that this algorithm can achieve a superior performance in the process of extracting salient object image and invariant moment feature. And the results show that the algorithm can also classifies the extracted object image with improved recognition rate.

• Journal of the Korean Ceramic Society
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• v.41 no.5
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• pp.353-358
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• 2004

Spray drying is a unique drying process for powder production. Spray dried product must be free-flowing in order to fill the pressing dies rapidly, especially in the ceramic production. The important powder characteristics are; the particle size distribu-tion and moisture content of the finished product that can be estimated and adjusted by the spray dryer operation, within limits, through regulation of atomizer and drying conditions. In order to estimate the moisture content of the resultant dried product, we modeled the control system of the drying process using two different Artificial Neural Network (ANN) approaches, namely the Back-Propagation Multiplayer Perceptron (BPMLP) algorithm and the Radial Basis Function (RBF) network. It was found out that the performance of both of the artificial neural network models were quite significant and the total testing error for the 100 data was 0.8 and 0.7 for the BPMLP algorithm and the RBF network respectively.

• IE interfaces
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• v.24 no.3
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• pp.267-273
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• 2011

In the shipbuilding industry, since production processes are so complicated that the data collection for decision making cannot be fully automated, most of production planning and controls are based on the information provided only by field workers. Therefore, without sufficient information it is very difficult to manage the whole production process efficiently. Job status is one of the most important information used for evaluating the remaining processing time in production control, specifically, in block assembly shop. Currently, it is checked by a production manager manually and production planning is modified based on that information, which might cause a delay in production control, resulting in performance degradation. Motivated by these remarks, in this paper we propose an efficient algorithm for identifying job status in block assembly shop for shipbuilding. The algorithm is based on the multi-layer perceptron neural network model using two key factors for input parameters. We showed the superiority of the algorithm by using a numerical experiment, based on real data collected from block assembly shop.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.34-37
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• 2005

An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection. Unlike many existing cloud detection schemes which use thresholding and statistical methods, we used the artificial neural network methods, the multi-layer perceptrons (MLP) with back-propagation algorithm and radial basis function (RBF) networks for cloud detection from Geostationary satellite images. We have used a simple scene (a mixed scene containing only cloud and clear sky). The main results show that the neural networks are able to handle complex atmospheric and meteorological phenomena. The experimental results show that two methods performed well, obtaining a classification accuracy reaching over 90 percent. Moreover, the RBF model is the most effective method for the cloud classification.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1386-1388
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• 1996

In this paper, a recurrent neural network using polynomial is proposed for electric load forecasting. Since the proposed algorithm is based on the bilinear polynomial, it can model nonlinear systems with much more parsimony than the higher order neural networks based on the Volterra series. The proposed Bilinear Recurrent Neural Network(BLRNN) is compared with Multilayer Perceptron Type Neural Network(MLPNN) for electric load forecasting problems. The results show that the BLRNN is robust and outperforms the MLPNN in terms of forecasting accuracy.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1422-1423
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• 2007

This study deals with various neural network algorithms for the on-site partial discharge pattern recognition. For the purpose, the pattern recognition has been carried out on partial discharge data for the typical artificial defect using 9 different neural network models. In order to enhance on-site applicability, artificial defects were installed in the insulation joint box of extra-high voltage xLPE cables and partial discharges were measured by use of the metal foil sensor and a HFCT as a sensor. As the result, it is found out that the accuracy of pattern recognition could be enhanced through the application of the Sigmoid function, the Momentum algorithm and the Genetic algorism on the artificial neural networks. Although Multilayer Perceptron (MLP) algorism showed the best result among 9 neural network algorisms, it is thought that more researches on others would be needed in consideration of on-site application.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1614-1615
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• 2011

It is difficult to differentially diagnose between lung cancer and benign inflammatory lung lesion due to high false positive rate on F-18 FDG-PET. We investigated whether application of artificial neural network to this diagnosis may be helpful. We reviewed the medical records and F-18 FDG PET images of 12 patients, selecting clinical and PET variables such as SUV. For selected variables and confirm, multilayer neural perceptron was applied in crossvalidation method and compared to visual interpretation. Neural network correctly classified the lung lesions in 83%, and reduced greately the false positive rate. However, false negative rate was not influenced. Application of neural network to the differential diagnosis between lung cancer and benigh inflammatory lesion may be helpful. Further studies with more patients are warranted.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1136-1142
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• 2010

In this study, an efficient removal algorithm for specular reflections in a tooth color image is proposed to minimize the artefact interrupting color image segmentation. The pixel values of RGB color channels are initially reversed to emphasize the features in reflective regions, and then those regions are automatically detected by utilizing perceptron artificial neural network model and those prominent intensities are corrected by applying a smoothing spatial filter. After correcting specular reflection regions, multiple seeds in the tooth candidates are selected to find the regional minima and MCWA(Marker-Controlled Watershed Algorithm) is applied to delineate the individual tooth region in a CCD tooth color image. Therefore, the accuracy in segmentation for separating tooth regions can be drastically improved with removing specular reflections due to the illumination effect.