• Transactions of the Korean Society of Automotive Engineers
• /
• v.4 no.4
• /
• pp.18-26
• /
• 1996

Based on experimental analysis, the characteristics of pulsating pressure wave propagation is clarified by testing of 4-stroke gasoline engine. The pulsating pressure wave in exhaust system is generated by pulsating gas flow due to working of exhaust valve. The pulsating pressure wave is closely concerned to the loss of engine power according to back pressure and exhaust noise. It is difficult to exactly calculate pulsating pressure wave propagation in exhaust system because of nonlinear effect. Therefore, in the first step for solving these problems, this paper contains experimental model and analysis method which are applied two-port network analysis. Also, it shows coherence function, frequency response function, back pressure, and gradient of temperature in exhaust system.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.18 no.4
• /
• pp.430-435
• /
• 2009

The cooling stage greatly affects the product quality in the injection molding process. The cooling system that minimizes temperature variance in the product surface will improve the quality and the productivity of products. The cooling circuit optimization problem that was once solved by a response surface method with 4 design variables. It took too much time for the optimization as an industrial design tool. It is desirable to reduce the optimization time. Therefore, we tried the back-propagation algorithm of artificial neural network(BPN) to find an optimum solution in the cooling circuit design in this research. We tried various ways to select training points for the BPN. The same optimum solution was obtained by applying the BPN with reduced number of training points by the fractional factorial design.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.4 no.4
• /
• pp.374-385
• /
• 2012

The indoor noise of a ship is usually determined using the A-weighted sound pressure level. However, in order to better understand this phenomenon, evaluation parameters that more accurately reflect the human sense of hearing are required. To find the level of the satisfaction index of the noise inside a naval vessel such as "Loudness" and "Annoyance", psycho-acoustic evaluation of various sound recordings from the naval vessel was performed in a laboratory. The objective of this paper is to develop a single index of "Loudness" and "Annoyance" for noise inside a naval vessel according to a psycho-acoustic evaluation by using psychological responses such as Noise Rating (NR), Noise Criterion (NC), Room Criterion (RC), Preferred Speech Interference Level (PSIL) and loudness level. Additionally, in order to determine a single index of satisfaction for noise such as "Loudness" and "Annoyance", with respect to a human's sense of hearing, a back-propagation neural network is applied.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.6
• /
• pp.67-75
• /
• 1993

ECG pattern was classified using a back-propagation neural network. An improved feature extractor of ECG is proposed for better classification capability. It is consisted of preprocessing ECG signal by an FIR filter faster than conventional one by a factor of 5. QRS complex recognition by moving-window integration, and peak extraction by quadratic approximation. Since the FIR filter had a periodic frequency spectrum, only one-fifth of usual processing time was required. Also, segmentation of ECG signal followed by quadratic approximation of each segment enabled accurate detection of both P and T waves. When improtant features were extracted and fed into back-propagation neural network for pattern classification, the required number of nodes in hidden and input layers was reduced compared to using raw data as an input, also reducing the necessary time for study. Accurate pattern classification was possible by an appropriate feature selection.

• ETRI Journal
• /
• v.30 no.4
• /
• pp.546-554
• /
• 2008

Phase-locked loops (PLLs) are among the most important mixed-signal building blocks of modern communication and control circuits, where they are used for frequency and phase synchronization, modulation, and demodulation as well as frequency synthesis. The growing popularity of PLLs has increased the need to test these devices during prototyping and production. The problem of distinguishing and classifying the responses of analog integrated circuits containing catastrophic faults has aroused recent interest. This is because most analog and mixed signal circuits are tested by their functionality, which is both time consuming and expensive. The problem is made more difficult when parametric variations are taken into account. Hence, statistical methods and techniques can be employed to automate fault classification. As a possible solution, we use the back propagation neural network (BPNN) to classify the faults in the designed charge-pump PLL. In order to classify the faults, the BPNN was trained with various training algorithms and their performance for the test structure was analyzed. The proposed method of fault classification gave fault coverage of 99.58%.

• Proceedings of the IEEK Conference
• /
• 2000.07b
• /
• pp.839-842
• /
• 2000

A new and modified neural network model Is proposed for CT image reconstruction from four projection directions only. The model uses the Resilient Back-Propagation (Rprop) algorithm, which is derived from the original Back-Propagation, for adaptation of its weights. In addition to the error in projection directions of the image being reconstructed, the proposed network makes use of errors in pixels between an image which passed the median filter and the reconstructed one. Improved reconstruction was obtained, and the proposed method was found to be very effective in CT image reconstruction when the given number of projection directions is very limited.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.29B no.1
• /
• pp.94-101
• /
• 1992

The multilayer perceptron, trained by the error back-propagation learning rule, has been known as a mapping network which can represent arbitrary functions. However depending on the complexity of a function and the initial weights of the multilayer perceptron, the error back-propagation learning may fall into a local minimum or a flat area which may require a long learning time or lead to unsuccessful learning. To solve such difficulties in training the multilayer perceptron by standard error back-propagation learning rule, the paper proposes a learning method which progressively enlarges the learning domain from a small area to the entire region. The proposed method is devised from the investigation on the roles of hidden nodes and connection weights in the multilayer perceptron which approximates a function of one variable. The validity of the proposed method was illustrated through simulations for a function of one variable and a function of two variable with many extremal points.

• Journal of information and communication convergence engineering
• /
• v.2 no.3
• /
• pp.177-181
• /
• 2004

In this paper, we suggest an Iris recognition system with an excellent recognition rate and confidence as an alternative biometric recognition technique that solves the limit in an existing individual discrimination. For its implementation, we extracted coefficients feature values with the wavelet transformation mainly used in the signal processing, and we used neural network to see a recognition rate. However, Scale Conjugate Gradient of nonlinear optimum method mainly used in neural network is not suitable to solve the optimum problem for its slow velocity of convergence. So we intended to enhance the recognition rate by using Levenberg-Marquardt Back-propagation which supplements existing Scale Conjugate Gradient for an implementation of the iris recognition system. We improved convergence velocity, efficiency, and stability by changing properly the size according to both convergence rate of solution and variation rate of variable vector with the implementation of an applied algorithm.

• Journal of information and communication convergence engineering
• /
• v.21 no.3
• /
• pp.233-238
• /
• 2023

This study presents a method for identifying partial discharge defects in an eco-friendly gas insulated system using a backpropagation algorithm. Four partial discharge (PD) electrode systems, namely, a free-moving particle, protrusion on the conductor, protrusion on the enclosure, and voids, were designed to simulate PD defects that can occur during the operation of eco-friendly gas-insulated switchgear. The PD signals were measured using an ultrahigh-frequency sensor as a nonconventional method based on IEC 62478. To identify the types of PD defects, the PD parameters of single PD pulses in the time and frequency domains and the phase-resolved partial discharge patterns were extracted, and a back-propagation algorithm in the artificial neural network was designed using a virtual instrument based on LabVIEW. The backpropagation algorithm proposed in this paper has an accuracy rate of over 90% for identifying the types of PD defects, and the result is expected to be used as a reference database for asset management and maintenance work for eco-friendly gas-insulated power equipment.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.5 no.3
• /
• pp.83-91
• /
• 1995

A neural network with one or more layers of hidden units can be trained using the well-known error back propagation algorithm. According to this algorithm, the synaptic weights of the network are updated during the training by propagating back the error between the expected output and the output provided by the network. However, the error back propagation algorithm is characterized by slow convergence and the time required for training and, in some situation, can be trapped in local minima. A theoretical formulation of a new fast learning method based on tabu search method is presented in this paper. In contrast to the conventional back propagation algorithm which is based solely on the modification of connecting weights of the network by trial and error, the present method involves the calculation of the optimum weights of neural network. The effectiveness and versatility of the present method are verified by the XOR problem. The present method excels in accuracy compared to that of the conventional method of fixed values.