• Bulletin of the Korean Mathematical Society
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• v.47 no.1
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• pp.89-102
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• 2010

This paper concerns the problem of global robust stability of a time-delay discontinuous system with a positive-defined connection matrix under polytopic-type uncertainty. In order to give the stability condition, we firstly address the existence of solution and equilibrium point based on the properties of M-matrix, Lyapunov-like approach and the theories of differential equations with discontinuous right-hand side as introduced by Filippov. Second, we give the delay-independent and delay-dependent stability condition in terms of linear matrix inequalities (LMIs), and based on Lyapunov function and the properties of the convex sets. One numerical example demonstrate the validity of the proposed criteria.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1072-1077
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• 2004

This paper provides a new approach to analyze the stability of a general multi-link TCP Vegas, which is a kind of feedback-based congestion algorithm. Whereas the conventional approaches use the approximately linearized model of the TCP Vegas along equilibrium pints, this approach models a multi-link TCP Vegas network in the form of a piecewise linear multiple time-delay system. And then, based on the exactly characterized dynamic model, this paper presents a new stability criterion via a piecewise and multiple delay-dependent Lyapunov-Krasovskii function. Especially, the resulting stability criterion is formulated in terms of linear matrix inequalities (LMIs).

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2430-2434
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• 2003

This paper provides a new approach to analyze the stability of TCP Vegas, which is a kind of feedback-based congestion control algorithm. Whereas the conventional approaches use the approximately linearized model of the TCP Vegas along equilibrium points, this approach uses the exactly characterized dynamic model to get a new stability criterion via a piecewise and delay-dependent Lyapunov-Krasovskii function. Especially, the resulting stability criterion is formulated in terms of linear matrix inequalities (LMIs). Using the new criterion, this paper shows that the current TCP Vegas algorithm is stable in the sufficiently wide region of network delay and link capacity.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.703-715
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• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.6
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• pp.821-827
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• 1995

This paper presents a process control language for constructing multiloop control system. which include advanced control algorithms. In order to make controller, this language uses function blocks that do specific operations. Then, the total control algorithm is a set of function blocks, of which each block is represented as a function code. The function code is a line of simple ASCII codes denoting function, input, output, parameters. It is possible to use variables as input/output port of any block. Compared with other language using function block concept, the proposed one enables to use advanced control algorithms undefinitely, such as fuzzy, neural network, predictive controller, etc., because vector and matrix variables as input/output can be used freely in this language. To raise flexibility, we put an intermediate level, which is C-language code, between function code and target-dependent operation code.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.275-280
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• 2005

The principal context of this research is the approach to an artificial neural network algorithm which solves multivariable nonlinear equation systems by estimating the state of line power flow. First a dynamical neural network with feedback is used to find the minimum value of the objective function at each iteration of the state estimator algorithm. In second step a two-layer neural network structures is derived to implement all of the different matrix-vector products that arise in neural network state estimator analysis. For hardware requirements, as they relate to the total number of internal connections, the architecture developed here preserves in its structure the pronounced sparsity of power networks for which state the estimator analysis is to be carried out. A principal feature of the architecture is that the computing time overheads in solution are independent of the dimensions or structure of the equation system. It is here where the ultrahigh-speed of massively parallel computing in neural networks can offer major practical benefit.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.2
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• pp.224-233
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• 1991

The multilayer expanson of single layer NN (Neural Network) was needed to solve the linear seperability problem as shown by the classic example using the XOR function. The EBP (Error Back Propagation ) learning rule is often used in multilayer Neural Networks, but it is not without its faults: 1)D.Rimmelhart expanded the Delta Rule but there is a problem in obtaining Ca from the linear combination of the Weight matrix N between the hidden layer and the output layer and H, wich is the result of another linear combination between the input pattern and the Weight matrix M between the input layer and the hidden layer. 2) Even if using the difference between Ca and Da to adjust the values of the Weight matrix N between the hidden layer and the output layer may be valid is correct, but using the same value to adjust the Weight matrixd M between the input layer and the hidden layer is wrong. Recognitron III was proposed to solve these faults. According to simulation results, since Recognitron III does not learn the three layer NN itself, but divides it into several single layer NNs and learns these with learning patterns, the learning time is 32.5 to 72.2 time faster than EBP NN one. The number of patterns learned in a EBP NN with n input and output cells and n+1 hidden cells are 2**n, but n in Recognitron III of the same size. [5] In the case of pattern generalization, however, EBP NN is less than Recognitron III.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.615-625
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• 2020

The document-term frequency matrix is a term extracted from documents in which the group information exists in text mining. In this study, we generated the document-term frequency matrix for document classification according to research field. We applied the traditional term weighting function term frequency-inverse document frequency (TF-IDF) to the generated document-term frequency matrix. In addition, we applied term frequency-inverse gravity moment (TF-IGM). We also generated a document-keyword weighted matrix by extracting keywords to improve the document classification accuracy. Based on the keywords matrix extracted, we classify documents using a deep neural network. In order to find the optimal model in the deep neural network, the accuracy of document classification was verified by changing the number of hidden layers and hidden nodes. Consequently, the model with eight hidden layers showed the highest accuracy and all TF-IGM document classification accuracy (according to parameter changes) were higher than TF-IDF. In addition, the deep neural network was confirmed to have better accuracy than the support vector machine. Therefore, we propose a method to apply TF-IGM and a deep neural network in the document classification.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.3
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• pp.84-90
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• 1985

A general procedure for finding the immittances of a general, uniformly coupled, n-line structure in an inhomogeneous medium is Presented. The expressions derived in terms of the normal modes of the system are in a convenient matrix form and can be used to compute or to derive the explicit expressions for the elements of the In-port immittance matrix. As an example, the closed form expressions for the elements of the admittance matrix of a sym-metrical four-line eight-port structure are given.

• 한국생물공학회:학술대회논문집
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• 2002.04a
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• pp.30-33
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• 2002

We have shown that it is possible to form a fibrilar network of fibronectin on a polyelectrolyte polymer film whose dimensions are similar to those reported on the extra cellular matrix. The fibronectin network was observed to form only when the charge density of the polymer was in excess of the natural charge density of the cell wall. Furthermore, the self-organized fibronectin layer was much thicker than the polymer film, indicating that long ranged interaction may playa key role in the assembly process. It is therefore important to understand the structure of the polymer layer/protein interface. Here we report on a neutron reflectivity study where we explore the structure of the polyelectrolyte layer, in this case sulfonated polystyrene (PSSx,), with varying degree of sulfonation (x<30%), as a function of sulfur content and counter ion concentration. These results are then correlated with systemic study of the adsorption and the multilayer formation of fibronectin as a function of incubation time for various sulfonation levels of $PSSx.^1$