• Journal of information and communication convergence engineering
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• v.2 no.1
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• pp.36-39
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• 2004

In this paper, a method of improving the learning speed and convergence rate is proposed to exploit the advantages of artificial neural networks and neuro-fuzzy systems. This method is applied to the XOR problem, n bit parity problem, which is used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for practical image application. As a result of experiment, it does not always guarantee convergence. However, the network showed considerable improvement in learning time and has a high convergence rate. The proposed network can be extended to any number of layers. When we consider only the case of the single layer, the networks had the capability of high speed during the learning process and rapid processing on huge images.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.15-22
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• 2016

In satellite communication, transmission performance is degraded due to long propagation delay and relatively high data loss compared to terrestrial network. In this paper, We propose Hybrid TCP PEP scheme with XOR coding and Hybla TCP, which reduces the transmission performance degradation due to the transmission delay time. Experimental results show that the proposed method improves the file transfer rate by more than 10% in the environment with high channel error rate. Therefore, Hybrid TCP, which is a mixture of XOR coding method and TCP Hybla, is considered to contribute to the improvement of transmission speed in satellite communication when applied to connection split PEP.

• Journal of Korea Multimedia Society
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• v.2 no.4
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• pp.490-495
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• 1999

In this paper, a method of improving the learning time and convergence rate is proposed to exploit the advantages of artificial neural networks and fuzzy theory to neuron structure. This method is applied to the XOR Problem, n bit parity problem which is used as the benchmark in neural network structure, and recognition of digit image in the vehicle plate image for practical image application. As a result of the experiments, it does not always guarantee the convergence. However, the network showed improved the teaming time and has the high convergence rate. The proposed network can be extended to an arbitrary layer Though a single layer structure Is considered, the proposed method has a capability of high speed 3earning even on large images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.7
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• pp.747-749
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• 2016

We consider a time-division multiple-access relay channel (MARC), in which two source nodes (SNs) transmit data with different data rate to a destination node (DN) with the help of a relay node (RN) using network coding (NC). However, due to its asymmetric data rate, the RN cannot combine the received bits by XOR NC. In this paper, we compare with the problem of asymmetric data rates by using zero padding and hierarchical 16QAM.

• Tunnel and Underground Space
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• v.6 no.4
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• pp.316-325
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• 1996

A new accelerated neural network adopting modified sigmoid function was developed and applied to estimate engineering properties of rock from insufficient geological data. Developed network was tested on the well-known XOR and character recognition problems to verify the validity of the algorithms. Both learning speed and recognition rate were improved. Test learn on the Lee and Sterling's problems showed that learning time was reduced from tens of hours to a few minutes, while the output pattern was almost the same as other studies. Application to the various case studies showed exact coincidence with original data or measured results.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.842-846
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• 1993

Because of complexity, neural network is difficult to learn. So if internal representation[1] can be performed successfully, it is possible to use perceptron learning rule. As a result, learning is easier. Therefore the method of internal representations applied to the "XOR" problem, and the "spirals" problem. And then using the above results, the structure of neural network for computing is embodied.mputing is embodied.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.277-282
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• 2003

In this paper we propose Using Higher Order Neuron on the Supervised Learning Machine of the Kohonen Feature Map. The architecture of proposed model adopts the higher order neuron in the input layer of Kohonen Feature Map as a Supervised Learning Machine. It is able to estimate boundary on input pattern space because or the higher order neuron. However, it suffers from a problem that the number of neuron weight increases because of the higher order neuron in the input layer. In this time, we solved this problem by placing the second order neuron among the higher order neuron. The feature of the higher order neuron can be mapped similar inputs on the Kohonen Feature Map. It also is the network with topological mapping. We have simulated the proposed model in respect of the recognition rate by XOR problem, discrimination of 20 alphabet patterns, Mirror Symmetry problem, and numerical letters Pattern Problem.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1652-1662
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• 1995

In this paper, a hybrid neural network is proposed to improve the learning ability of a neural network. The union of the characteristics of a Self-Organizing Neural Network model and of multi-layer perceptron model using the backpropagation learning method gives us the advantage of reduction of the learning error and the learning time. In learning process, the proposed hybrid neural network reduces the number of nodes in hidden layers to reduce the calculation time. And this proposed neural network uses the fuzzy feedback values, when it updates the responding region of each node in the hidden layer. To show the effectiveness of this proposed hybrid neural network, the boolean function(XOR, 3Bit Parity) and the solution of inverse kinematics are used. Finally, this proposed hybrid neural network is applied to the visual tracking control of a PUMA560 robot, and the result data is presented.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.407-420
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• 2021

Deep neural networks are widely used to solve various problems. However, the deep neural network with a deep hidden layer frequently has a vanishing gradient or exploding gradient problem, which is a major obstacle to learning the deep neural network. In this paper, we propose a parametric activation function to alleviate the vanishing gradient problem that can be caused by nonlinear activation function. The proposed parametric activation function can be obtained by applying a parameter that can convert the scale and location of the activation function according to the characteristics of the input data, and the loss function can be minimized without limiting the derivative of the activation function through the backpropagation process. Through the XOR problem with 10 hidden layers and the MNIST classification problem with 8 hidden layers, the performance of the original nonlinear and parametric activation functions was compared, and it was confirmed that the proposed parametric activation function has superior performance in alleviating the vanishing gradient.

• ETRI Journal
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• v.24 no.5
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• pp.373-380
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• 2002

This paper presents a new method for a visual cryptography scheme that uses phase masks and an interferometer. To encrypt a binary image, we divided it into an arbitrary number of slides and encrypted them using an XOR process with a random key or keys. The phase mask for each encrypted image was fabricated nuder the proposed phase-assignment rule. For decryption, phase masks were placed on any path of the Mach-Zehnder interferometer. Through optical experiments, we confirmed that a secret binary image that was sliced could be recovered by the proposed method.