• Journal of Biomedical Engineering Research
• /
• v.12 no.3
• /
• pp.191-202
• /
• 1991

This paper describes ECG data compression algorithm using neural network. As a learning method, we use back error propagation algorithm. ECG data compression is performed using learning ability of neural network. CSE database, which is sampled 12bit digitized at 500samp1e/sec, is selected as a input signal. In order to reduce unit number of input layer, we modify sampling ratio 250samples/sec in QRS complex, 125samples/sec in P & T wave respectively. hs a input pattern of neural network, from 35 points backward to 45 points forward sample Points of R peak are used.

• The Journal of the Acoustical Society of Korea
• /
• v.13 no.6
• /
• pp.5-14
• /
• 1994

This paper is about an experiment of speaker-independent automatic Korean spoken words recognition using Multi-Layered Perceptron and Error Back-propagation algorithm. The object words are 50 citynames of D.D.D local numbers. 43 of those are 2 syllables and the rest 7 are 3 syllables. The words were not segmented into syllables or phonemes, and some feature components extracted from the words in equal gap were applied to the neural network. That led independent result on the speech duration, and the PARCOR coefficients calculated from the frames using linear predictive analysis were employed as feature components. This paper tried to find out the optimum conditions through 4 differerent experiments which are comparison between total and pre-classified training, dependency of recognition rate on the number of frames and PAROCR order, recognition change due to the number of neurons in the hidden layer, and the comparison of the output pattern composition method of output neurons. As a result, the recognition rate of $89.6\%$ is obtaimed through the research.

• Journal of Navigation and Port Research
• /
• v.32 no.8
• /
• pp.589-596
• /
• 2008

In this paper, Artificial Neural Network(ANN) is applied to automatic berthing control for a ship. ANN is suitable for a maneuvering such as ship's berthing, because it can describe non-linearity of the system. Multi-layer perceptron which has more than one hidden layer between input layer and output layer is applied to ANN. Using a back-propagation algorithm with teaching data, we trained ANN to get a minimal error between output value and desired one. For the automatic berthing control of a containership, we introduced low speed maneuvering mathematical models. The berthing control with the structure of 8 input layer units in ANN is compared to 6 input layer units. From the simulation results, the berthing conditions are satisfied, even though the berthing paths are different.

• Journal of the Korean Institute of Telematics and Electronics T
• /
• v.35T no.3
• /
• pp.46-52
• /
• 1998

In this paper, after inputting the computer with the attached number plate on the vehicle, using it, the color decomposition method and back propagation neural network proposed the extractable method of the vehicle number plate at high speed. This method separated R, G, B signal form input moving vehicle image to computer through video camera, then after transform this R, G, B signal into input image data of the computer by using color depth of vehicle number plate and store up binary value in the memory frame buffer. After adapting character's recognition algorithm, also improving this, by adapting back propagation neural network makes the vehicle number plate recognition system. Also minimalizing the similar color's confusion, adapting horizontal and vertical extracting algorithm by using the vehicle's rectangular architecture shows the extract and character's recognition of the vehicle number plate at high speed.

• The Transactions of the Korea Information Processing Society
• /
• v.3 no.2
• /
• pp.381-389
• /
• 1996

A simple modification of the standard back-propagation algorithm to eliminate redundant connections(weights and biases) is described. It was motivated by speculations from the distribution of the magnitudes of the weights and the biases, analysis of the classification boundary, and the nonlinearity of the sigmoid function. After initial training, this algorithm eliminates all connections of which magnitude is below a threshold by setting them to zero. The algorithm then conducts retraining in which all weights and biases are adjusted to allow important ones to recover. In studies with Boolean functions, the algorithm reconstructed the theoretical minimum architecture and eliminated the connections which are not necessary to solve the functions. For simulated random signal classification problems, the algorithm produced the result which is consistent with the idea that easier problems require simpler networks and yield lower misclassification rates. Furthermore, in comparison, our algorithm produced better generalization than the standard algorithm by reducing over fitting and pattern memorization problems.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.28 no.5
• /
• pp.320-325
• /
• 2015

This paper dealt with a defect identification algorithm which is based on single partial discharge (PD) pulse analysis in gas insulated structure. Four types of electrode systems such as a needle-plane, a plane-needle, a free particle and a crack inside spacer were fabricated to simulate defects in gas insulated switchgear (GIS). We measured single PD pulse by an oscilloscope with a sampling rate of 5 GS/s and a frequency bandwidth of 1 GHz. Data aquisition and signal processing were controlled by a LabVIEW program. Physical shapes of PD pulses were compared with kurtosis, skewness and time-based parameters as rising time, falling time and pulse-width. These parameters were analysed by an algorithm with a back propagation algorithm (BPA). By applying the algorithm, the identification rate was 97% for the needle-plane electrode, 96% for the plane-needle electrode, 91% for the free particle and 93% for the crack inside spacer. The results verified that the algorithm could identify the type of defects in GIS.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1991.06a
• /
• pp.73-77
• /
• 1991

As the basic analysis to solve the stop consonants in phoneme based speech recognition using Back Propagation learning algorithm, changes in hidden units, training set and iteration. Also we propose an efficient processing method of separation between consonants and vowels.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.05a
• /
• pp.27-30
• /
• 2000

본 논문에서는 선형 이진 신경회로망(Linear Binary Neural Network)을 이용하여 이진 영상으로부터 골격(skeleton)을 추출하는 병렬 구조를 제안하였다. 기존의 골격 추출 알고리즘으로부터 이진함수를 추출하고 이를 MSP Term Grouping Algorithm을 이용하여 학습시켰다. 결과에서는 기존의 역전파(Back-propagation) 학습알고리즘을 사용한 신경회로망보다 더 쉽게 하드웨어로 구현할 수 있음을 보여준다.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.131-134
• /
• 1997

This paper presents a new approach to the design of intelligent control system for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.3
• /
• pp.115-122
• /
• 1994

The problems in back-propagation(BP) generally are learning speed and misclassification due to lacal minimum. In this paper, to solve these problems, the classical modified methods of BP are reviewed and an extension of the BP to compensate the sigmoide-prime function around the extremity where the actual output of a unit is close to zero or one is proposed. The proposed method is not onlu faster than the conventional methods in learning speed but has an advantage of setting variables easily because it shows good classification results over the vast and uncharted space about the variations of learning rate, etc.. And it is simple for hardware implementation.