• Journal of the Korea Society of Computer and Information
• /
• v.10 no.5 s.37
• /
• pp.11-16
• /
• 2005

Recently, there are a lot of endeavor to implement a fuzzy theory to artificial neural network. Goh proposed the fuzzy single layer perceptron algorithm and advanced fuzzy perceptron based on the generalized delta rule to solve the XOR Problem and the classical Problem. However, it causes an increased amount of computation and some difficulties in application of the complicated image recognition. In this paper, we propose an enhanced fuzzy single layer Perceptron using the dynamic adjustment of threshold. This method is applied to the XOR problem, which used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for image application. In a result of experiment, it does not always guarantee the convergence. However, the network show improved the learning time and has the high convergence rate.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.28B no.5
• /
• pp.394-403
• /
• 1991

This paper described the development of input driven multilayer perceptron(IDMLP) neural network and it's application to the Korean spoken digit recognition. The IDMPLP neural network used here and the learning algorithm for this network was proposed newly. In this model, weight value is integer and transfer function in the neuron is hard limit function. According to the result of the network learning for the some kinds of input data, the number of network layers is one or more by the difficulties of classifying the inputs. We tested the recognition of binaried data for the spoken digit 0 to 9 by means of the proposed network. The experimental results are 100% and 96% for the learning data and test data, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.7
• /
• pp.1243-1248
• /
• 2008

As development in technology of bioinformatics recently mates it possible to operate micro-level experiments, we can observe the expression pattern of total genome through on chip and analyze the interactions of thousands of genes at the same time. In this thesis, we used CDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer. It analyzed and compared performance of each of the experiment result using existing DT, NB, SVM and multi-perceptron neural network classifier combined the similar scale combination method after constructing class classification model by extracting significant gene list with a similar scale combination method proposed in this paper through normalization. Result classifying in Multi-Perceptron neural network classifier for selected 200 genes using combination of PC(Pearson correlation coefficient) and ED(Euclidean distance coefficient) represented the accuracy of 98.84%, which show that it improve classification performance than case to experiment using other classifier.

• Steel and Composite Structures
• /
• v.44 no.2
• /
• pp.241-254
• /
• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Journal of Electrical Engineering and Technology
• /
• v.6 no.6
• /
• pp.769-775
• /
• 2011

Custom power devices such as dynamic voltage restorer (DVR) and DSTATCOM are used to improve the power quality in distribution systems. These devices require real power to compensate the deep voltage sag during sufficient time. An interline DVR (IDVR) consists of several DVRs in different feeders. In this paper, a neural network is proposed to control the IDVR performance to achieve optimal mitigation of voltage sags, swell, and unbalance, as well as improvement of dynamic performance. Three multilayer perceptron neural networks are used to identify and regulate the dynamics of the voltage on sensitive load. A backpropagation algorithm trains this type of network. The proposed controller provides optimal mitigation of voltage dynamic. Simulation is carried out by MATLAB/Simulink, demonstrating that the proposed controller has fast response with lower total harmonic distortion.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.4 no.2
• /
• pp.189-201
• /
• 2001

For a design of multi-layer armor, the extensive full scale or sub-scale penetration test data are required. In generally, the collection of penetration data is in need of time-consuming and expensive processes. However, the application of numerical or analytical method is very limited due to poor understanding about penetration mechanics. In this paper, we have developed a neural network analyzer which can be used as a design tool for a new armor. Calculation results show that the developed neural network analyzer can predict relatively exact penetration depth of a new armor through the effective analysis of the pre-existing penetration database.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1624-1627
• /
• 2005

We proposed a design technique for auto-landing guidance and control system. This technique utilizes linear controller and neural network. Main features of this technique is to use conventional linear controller and compensate for the error coming from the model uncertainties and/or reference model mismatch. In this study, the multi-perceptron neural network with single hidden layer is adopted to compensate for the errors. Glide-slope capture logic for auto-landing guidance and control system is designed in this technique. From the simulation results, it is observed that the responses of velocity and pitch angle to commands are fairly good, which are directly related to control inputs of throttle and elevator, respectively.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.169.2-169
• /
• 2001

In this paper, we propose the face recognition system using the neural network. A difficult procedure in constructing the entire recognition systems is the feature extraction from the face imga. And a key poing is the design of the matching function that relates the set of feature values to the appropriate face candidates. We use the length and angle values as feature values that are extracted from the face image normalized to the range of [0,1]. These features values are applied to the input layer of the neural network. Then, these multi-layered perceptron learns or gives otput result. By using the neural network we need not to design the matching function. This function may have nonlinear attributes considerably and would be ...

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.7
• /
• pp.29-38
• /
• 1997

A closed-loop system modeling of an active/semiactive suspension system has been accomplished through an artificial neural network. A 7DOF full model as a system's equation of motion has been derived and an output feedback linear quadratic regulator has been designed for control purpose. A training set of a sample data has been obtained through a computer simulation. A 7DOF full model with LQR controller simulated under several road conditions such as sinusoidal bumps and rectangular bumps. A general multilayer perceptron neural network is used for dynamic modeling and target outputs are fedback to the a layer. A backpropagation method is used as a training algorithm. Model validation of new dataset have been shown through computer simulations.

• Laser Solutions
• /
• v.2 no.3
• /
• pp.19-31
• /
• 1999

This paper introduces the monitoring scheme of laser welding quality using neural network. The developed monitoring scheme detects light signal emitting from plasma formed above the weld pool with optic sensor and DSP-based signal processor, and analyzes to give a guidance about the weld quality. It can automatically detect defects of laser weld and further give an information about what kind of defects it is, specially partial penetration and porosity among the interior defects. Those could be detected only by naked eyes or X-ray after welding, which needs more processes and costs in mass production. The monitoring scheme extracts four feature vectors from signal processing results of optical measuring data. In order to classify pattern for extracted feature vectors and to decide defects, it uses single-layer neural network with perceptron learning. The monitoring result using only the first feature vector shows confidence rate in recognition of 90%($\pm$5) and decides whether normal status or defects status in real time.