• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.262-262
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• 2010

반도체 제조공정 중 식각공정(Etching)은 웨이퍼표면으로부터 화학적, 물리적으로 불필요한 물질들을 선택적으로 제거하는 방법이다. 식각공정 중 하나인 플라즈마 식각(Plasma etching) 공정에서 오버식각(over-etching) 과언더식각(under-etching) 되는것을피하기위해서통계적인방법을기준으로식각종료점(endpoint)를 결정한다. 본 논문의 목표는 통계적인 분석방법을 이용하지 않고 실시간 식각 데이터(realtime etching data)를 사용해서 식각 종료점을 검출하는 것이다. 식각 데이터는 시계열 데이터(time-series data)이기 때문에 간단한 구조와 적은 계산량으로 빠른 수렴속도와 좋은 안정도를 가진 Radial Basis Function Neural Network's (RBF-NN) 를 이용하여 시계열 모델(time-series model)을 구현 하였다. 광학방사분광기(Optical Emission Spectroscopy: OES)로부터 나온 6개의 데이터 세트중에서 4개의 데이터 세트는 RBF-NN을 학습하는데 사용되고 2개의 데이터 세트는 모델의 성과를 시험해 보기 위하여 사용하였다. 학습을 위한 데이터들은 Matrix화 시켜서 목표값을 설정하여 학습시킨다. 실험한 결과 학습한 RBF-NN 모형이 식각 종료점(endpoint)를 정확하게 검출된다는 것을 보여준다.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.286-293
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• 2021

Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.497-503
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• 2005

본 논문에서는 퍼지 RBF 네트워크를 이용한 운송 컨테이너 식별자 인식 시스템을 제안한다. 일반적으로 운송 컨테이너의 식별자들은 크기나 위치가 정형화되어 있지 않고 외부 잡음으로 인하여 식별자의 형태가 변형될 수 있기 때문에 일정한 규칙으로 찾기는 힘들다. 본 논문에서는 이러한 특성을 고려하여 컨테이너 영상에 대해 Canny 마스크를 이용하여 에지를 검출하고, 검출된 에지 정보에서 영상획득 시 외부 광원에 의해 수직으로 길게 발생하는 잡음들을 퍼지 추론 방법을 적용하여 제거한 후에 수직 블록과 수평 블록을 검출하여 컨테이너의 식별자 영역을 추출하고 이진화한다. 이진화된 식별자 영역에 대해 검정색의 빈도수를 이용하여 흰바탕과 민바탕을 구분하고 4방향 윤광선 추적 알고리즘을 적용하여 개별 식별자를 추출한다. 개별 식별자 인식을 위해 퍼지 C-Means 알고리즘을 이용한 퍼지 RBF 네트워크를 제안하여 개별 식별자에 적용한다. 제안된 퍼지 RBF 네트워크는 퍼지 C-Means 알고리즘을 중간층으로 적용하고 중간층과 출력층 간의 학습에는 일반화된 델타 학습 방법과Delta-bar-Delta 알고리즘을 적용하여 학습 성능을 개선한다. 실제 컨테이너 영상을 대상으로 실험한 결과, 기존의 식별자 추출 방법보다 제안된 식별자 추출방법이 개선되었다. 그리고 기존의 ART2 기반 RBF 네트워크보다 제안된 퍼지 RBF 네트워크가 컨테이너 식별자의 학습 및 인식에 있어서 우수함을 확인하였다.

• Environmental Engineering Research
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• v.21 no.4
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• pp.333-340
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• 2016

An Artificial Neural Network including a Radial Basis Function (RBF) and a Time Delay Neural Network (TDNN) was used to predict total dissolved solid (TDS) in the river Zayanderud. Water quality parameters in the river for ten years, 2001-2010, were prepared from data monitored by the Isfahan Regional Water Authority. A factor analysis was applied to select the inputs of water quality parameters, which obtained total hardness, bicarbonate, chloride and calcium. Input data to the neural networks were pH, $Na^+$, $Mg^{2+}$, Carbonate ($CO{_3}^{-2}$), $HCO{_3}^{-1}$, $Cl^-$, $Ca^{2+}$ and Total hardness. For learning process 5-fold cross validation were applied. In the best situation, the TDNN contained 2 hidden layers of 15 neurons in each of the layers and the RBF had one hidden layer with 100 neurons. The Mean Squared Error and the Mean Bias Error for the TDNN during the training process were 0.0006 and 0.0603 and for the RBF neural network the mentioned errors were 0.0001 and 0.0006, respectively. In the RBF, the coefficient of determination ($R^2$) and the index of agreement (IA) between the observed data and predicted data were 0.997 and 0.999, respectively. In the TDNN, the $R^2$ and the IA between the actual and predicted data were 0.957 and 0.985, respectively. The results of sensitivity illustrated that $Ca^{2+}$ and $SO{_4}^{2-}$ parameters had the highest effect on the TDS prediction.

• Journal of Biosystems Engineering
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• v.33 no.5
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• pp.340-347
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• 2008

Dipstick urinalysis is used as a routine test for a screening test of UTI (urinary tract infection) in primary practice because urine dipstick test is simple. The result of dipstick urinalysis brings medical professionals to make a microscopic examination and urine culture for exact UTI diagnosis, therefore it is emphasized on a role of screening test. The objective of this study was to the classification between UTI patients and normal subjects using hybrid neural network classifier with enhanced clustering performance in urine dipstick screening test. In order to propose a classifier, we made a hybrid neural network which combines with RBF layer, summation & normalization layer and L VQ artificial neural network layer. For the demonstration of proposed hybrid neural network, we compared proposed classifier with various artificial neural networks such as back-propagation, RBFNN and PNN method. As a result, classification performance of proposed classifier was able to classify 95.81% of the normal subjects and 83.87% of the UTI patients, total average 90.72% according to validation dataset. The proposed classifier confirms better performance than other classifiers. Therefore the application of such a proposed classifier expect to utilize telemedicine to classify between UTI patients and normal subjects in the future.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.505-515
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• 2000

THe purpose of this study is to forecast of runoff hydrographs according to rainfall event in a stream. The neural network theory as a hydrologic blackbox model is used to solve hydrological problems. The Back-Propagation(BP) algorithm by the Levenberg-Marquardt(LM) techniques and Radial Basis Function(RBF) network in Neural Network(NN) models are used. Runoff hydrograph is forecasted in Bocheongstream basin which is a IHP the representative basin. The possibility of a simulation for runoff hydrographs about unlearned stations is considered. The results show that NN models are performed to effective learning for rainfall-runoff process of hydrologic system which involves a complexity and nonliner relationships. The RBF networks consist of 2 learning steps. The first step is an unsupervised learning in hidden layer and the next step is a supervised learning in output layer. Therefore, the RBF networks could provide rather time saved in the learning step than the BP algorithm. The peak discharge both BP algorithm and RBF network model in the estimation of an unlearned are a is trended to observed values.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6484-6487
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• 2013

This paper reports the direct controller for nonlinear plants using a neural network. The controller was composed of an approximate controller and a neural network auxiliary controller. The approximate controller provides rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not place too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network was trained and the system showed stable performance for the inputs it has been trained for. The simulation results showed that it was quite effective and could realize satisfactory control of the nonlinear system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.91-94
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• 2003

본 논문에서는 RBF 네트워크의 중간층과 출력층 사이의 연결강도를 효율적으로 조정하기 위해 퍼지 논리 시스템을 이용하여 학습률과 모멘텀을 동적으로 조정하는 개선된 RBF 네트워크를 제안한다. 입력층과 중간층 사이의 학습 구조로 ART2를 적용하고 중간층과 출력층 사이의 연결 강도 조정 방법으로는 제안된 학습률 자동 조정 방식을 적용한다. 제안된 방법의 학습 성능을 평가하기 위해 기존의 delta-bar-delta 알고리즘, 기존의 ART2 기반의 RBF 네트워크와 비교 분석한 결과, 제안된 방법이 학습 속도와 수렴성에서 개선된 것을 확인하였다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.202-206
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• 2005

우리나라 주민등록증은 주소지, 주민등록 번호, 지문 등 개개인의 방대한 정보를 가진다. 그런데 현재의 플라스틱 주민등록증은 위?변조가 쉬워 사회적으로 많은 문제를 일으키고 있다. 이러한 문제점을 해결하기 위하여 주민등록증을 전산화 하여 주민등록증 위조여부를 판단하고 있다. 본 논문에서는 주민등록증 영상을 자동 인식할 수 있는 개선된 ART2기반 RBF 네트워크를 이용한 주민등록증 자동 인식 방법을 제안한다. 제안된 방법은 주민등록증 영상에서 위치 정보와 수직 및 수평 히스토그램 방법을 이용하여 주민등록번호와 발행일 영역을 추출한다. 그리고 추출된 주민등록번호와 발행일 영역에서 4 방향 윤곽선 추적 알고리즘으로 개별 문자를 추출한다. 추출된 개별 코드는 개선된 ART2 기반 RBF 네트워크를 제안하여 인식에 적용한다. 제안된 ART2 기반 RBF 네트워크는 ART2알고리즘을 중간층으로 적용하고 중간층과 출력층 간의 학습은 일반화된 델타 학습에 모멘텀을 적용하여 학습 성능을 개선한다. 실제 주민등록증 영상을 이용하여 실험한 결과, 제안된 ART2기반 RBF 네트워크가 주민등록증 인식에 효율적인 것을 확인하였다.