• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2003.10a
• /
• pp.125-130
• /
• 2003

This Paper describes a Nonlinear adoptive noise canceller using Neural Network for Machine Tools Controller System. Back-Propagation Learning Algorithm based MLP (Multi Layer Perceptron)is used an adaptive filters. In this Paper. it assume that the noise of primary input in the adaptive noise canceller is not the same characteristic as that of the reference input. Experimental results show that the neural network base noise canceller outperforms the linear noise canceller. Especially to make noise cancel close to realtime, Primary Input is divided by Unit and each divided pan is processed for very short time than all the processed data are unified to whole data.

• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10b
• /
• pp.1377-1382
• /
• 1990

In Multi-Layer Perceptron (MLP) which realizes continuous mappings, the output errors is directly affected by the weight errors which may be caused by the limited precision of digital or analog hardware in implementations. So, it is important to study the sensitivity due to the perturbation of connection weights between neurons. In this paper, we derive a sensitivity function to the statistical weight perturbations in MLP with differentiable activation functions. This sensitivity function can be regarded as an ensemble average of deterministic sensitivity measures due to the perturbations of weights. Hence, this sensitivity function can be used as the criteria for selecting weights with the minimum sensitivity among possible sets of connection weights in MLP. For the verification of the validity of the proposed sensitivity function, computer simulations have been performed and through the simulations we find good agreement between the theoretical and simulation results.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.32B no.8
• /
• pp.1110-1118
• /
• 1995

Over the past few years, a wide variety of techniques have been developed which make a reliable recognition of speech signal. Multi-layer perceptron(MLP) which has excellent pattern recognition properties is one of the most versatile networks in the area of speech recognition. This paper describes an automatic speech recognition system which use both MLP and lexicon. In this system., the recognition is performed by a network search algorithm which matches words in lexicon to MLP output scores. We also suggest a recognition algorithm which incorperat durational information of each phone, whose performance is comparable to that of conventional continuous HMM(CHMM). Performance of the system is evaluated on the database of 26 vocabulary size from 9 speakers. The experimental results show that the proposed algorithm achieves error rate of 7.3% which is 5.3% lower rate than 12.6% of CHMM.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2017.01a
• /
• pp.19-20
• /
• 2017

인공지능이 사회적으로 대두되면서 많은 양의 관련 연구가 시작되고 있다. 본 논문에서는 다층 퍼셉트론 신경망에서 역전파 학습의 진행 과정을 시각화 하는 것을 목표로 하고 있다. 다층 퍼셉트론 신경망은 학습의 진행 과정과 그 방식은 잘 알려져 있으나 각 신경의 값이 어떻게 변화되어 가는 지는 눈에 보이지 않는다. 이러한 과정에 대해 시각화를 통해 값이 변하는 과정을 눈으로 쉽게 관찰할 수 있도록 하는 것이 이 논문의 목표이다. 본 연구결과는 향후 다층 퍼셉트론 신경망을 기반으로 하는 다른 모델의 시각화에 대한 기초자료로 활용될 수 있을 것이다.

• MALSORI
• /
• no.45
• /
• pp.35-45
• /
• 2003

Laryngeal diseases cause significant changes in the quality of speech production. Automatic detection of laryngeal diseases by voice is attractive because of its nonintrusive nature. In this paper, we apply speech recognition techniques to detection of laryngeal cancer, and investigate which feature parameters and classification methods are appropriate for this purpose. Linear Predictive Cepstral Coefficients (LPCC) and Mel-Frequency Cepstral Coefficients (MFCC) are examined as feature parameters, and parameters reflecting the periodicity of speech and its perturbation are also considered. As for classifier, multilayer perceptron neural networks and Gaussian Mixture Models (GMM) are employed. According to our experiments, higher order LPCC with the periodic information parameters yields the best performance.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.6
• /
• pp.795-801
• /
• 1998

In this paper, we propose a machine vision algorithm for inspecting inserts which are used for milling and turning operations. Major defects of the inserts are breakage and crack on insert surfaces. Among the defects, breakages on the face of the inserts can be detected through three stages of the algorithm developed in this paper. In the first stage, a multi-layer perceptron is used to recognize the inserts being inspected. Edge detection of the insert image is performed in the second stage. Finally, in the third stage breakages on the insert face are identified using Hough transform. The overall algorithm is tested on real specimens and the results show that the algorithm works fairly well.

• Proceedings of the IEEK Conference
• /
• 2002.06c
• /
• pp.9-12
• /
• 2002

This paper proposes a dynamic moment algorithm to control oscillaion before the convergence of the KR(Kernel Relaxation). The proposed dynamic moment algorithm can be controlled to convergence speed and performance according to the change of the dynamic moment by teaming training. we used SONAR data that is a neural network classifier standard evaluation data in order to do impartial performance evaluation. The proposed algorithm has been applied to the KP (kernel perceptron), KPM(kernel perceptron with margin) and KLMS(kernel lms) as the kernel method presented recently. The simulation results of proposed algorithm have better the convergence performance than those using none and static moment.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.17 no.7 s.94
• /
• pp.1783-1793
• /
• 1993

Pattern recognition technique using fuzzy c-means algorithm and multilayer perceptron was applied to classify tool wear states in turning. The tool wear states were categorized into the three regions 'Initial', 'Normal', 'Severe' wear. The root mean square(RMS) value of acoustic emission(AE) and current signal was used for the classification of tool wear states. The simulation results showed that a fuzzy c-means algorithm was better than the conventional pattern recognition techniques for classifying ambiguous informations. And normalized RMS signal can provide good results for classifying tool wear. In addition, a fuzzy c-means algorithm(success rate for tool wear classification : 87%) is more efficient than the multilayer perceptron(success rate for tool wear classification : 70%).

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2004.10a
• /
• pp.345-351
• /
• 2004

This Paper describes a nonlinear adaptive noise filter using neural network for digital controller system. Back-Propagation Learning Algorithm based MLP (Multi Layer Perceptron)is used an adaptive filters. In this paper. it assume that the noise of primary input in the adaptive noise canceller is not the same characteristic as that of the reference input. Experimental reaults show that the neural network base noise canceller outperforms the linear noise canceller. Especially to make noise cancel close to realtime, Primary input is divided by unit and each divided part is processed for very short time than all the processed data are unified to whole data.

• Journal of Korea Multimedia Society
• /
• v.21 no.3
• /
• pp.391-399
• /
• 2018

In this paper, we construct and compare various guitar chord classification systems using perceptron neural network and convolutional neural network without pre-processing other than Fourier transform to identify the optimal chord classification system. Conventional guitar chord classification schemes use, for better feature extraction, computationally demanding pre-processing techniques such as stochastic analysis employing a hidden markov model or an acoustic data filtering and hence are burdensome for real-time chord classifications. For this reason, we construct various perceptron neural networks and convolutional neural networks that use only Fourier tranform for data pre-processing and compare them with dataset obtained by playing an electric guitar. According to our comparison, convolutional neural networks provide optimal performance considering both chord classification acurracy and fast processing time. In particular, convolutional neural networks exhibit robust performance even when only small fraction of low frequency components of the data are used.