• The Journal of the Acoustical Society of Korea
• /
• 제22권1E호
• /
• pp.3-10
• /
• 2003

This paper describes two kinds of neural networks for stereoscopic vision, which have been applied to an identification of human speech. In speech recognition based on the stereoscopic vision neural networks (SVNN), the similarities are first obtained by comparing input vocal signals with standard models. They are then given to a dynamic process in which both competitive and cooperative processes are conducted among neighboring similarities. Through the dynamic processes, only one winner neuron is finally detected. In a comparative study, with, the average phoneme recognition accuracy on the two-layered SVNN was 7.7% higher than the Hidden Markov Model (HMM) recognizer with the structure of a single mixture and three states, and the three-layered was 6.6% higher. Therefore, it was noticed that SVNN outperformed the existing HMM recognizer in phoneme recognition.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
• /
• pp.122-127
• /
• 2001

This study investigates the effectiveness of time delay neural networks(TDNN) for the time dependent prediction domain. Although it is well-known fact that the back-propagation neural network(BPN) performs well in pattern recognition tasks, the method has some limitations in that it can only learn an input mapping of static (or spatial) patterns that are independent of time of sequences. The preliminary results show that the accuracy of TDNN is higher than the standard BPN with time lag. Our proposed approaches are demonstrated by the stork market prediction domain.

• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2005년도 춘계 학술기술논문발표대회 논문집
• /
• pp.119-124
• /
• 2005

It is necessary for prediction of recycled aggregate concrete(RAC) strength at the early stage that facilitate concrete form removal and scheduling for construction. However, to predict RAC strength is difficult because of being influenced by complicated many factors. Therefore, this research suggest optimized estimation method that can reflect many factors. One way is Case-Based Reasoning(CBR) that solved new problems by adapting solutions to similar problems solved in the past, which are solved in the case library. Other way is Artificial Neural Networks(ANN) that solved new problems by training using a set of data, which is representative of problem domain. This study is to propose comparing accuracy of the estimating the compressive strength of recycled aggregate concrete using Case-Based Reasoning(CBR) and Artificial Neural Networks(ANN).

• Journal of Electrical Engineering and Technology
• /
• 제3권1호
• /
• pp.108-114
• /
• 2008

Polynomial networks have been known to have excellent properties as classifiers and universal approximators to the optimal Bayes classifier. In this paper, the use of polynomial neural networks is proposed for efficient implementation of the polynomial-based classifiers. The polynomial neural network is a trainable device consisting of some rules and three processes. The three processes are assumption, effect, and fuzzy inference. The assumption process is driven by fuzzy c-means and the effect processes deals with a polynomial function. A learning algorithm for the polynomial neural network is developed and its performance is compared with that of previous studies.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2007년도 학술대회 논문집 전문대학교육위원
• /
• pp.83-87
• /
• 2007

Correlation Propagation Neural Networks is proposed for On-line interpolation. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D CPNN hardware has been implemented with general purpose analog ICs to test the interpolation capability of the proposed neural networks. Experiments with static and dynamic signals have been done upon the CPNN hardware.

• Smart Structures and Systems
• /
• 제6권2호
• /
• pp.147-165
• /
• 2010

Impact damage detection in composite structures has gained a considerable interest in many engineering areas. The capability to detect damage at the early stages reduces any risk of catastrophic failure. This paper compares two advanced signal processing methods for impact location in composite aircraft structures. The first method is based on a modified triangulation procedure and Genetic Algorithms whereas the second technique applies Artificial Neural Networks. A series of impacts is performed experimentally on a composite aircraft wing-box structure instrumented with low-profile, bonded piezoceramic sensors. The strain data are used for learning in the Neural Network approach. The triangulation procedure utilises the same data to establish impact velocities for various angles of strain wave propagation. The study demonstrates that both approaches are capable of good impact location estimates in this complex structure.

• Smart Structures and Systems
• /
• 제25권4호
• /
• pp.459-469
• /
• 2020

The presence of cracks in mechanical components is a very important problem that, if it is not detected on time, can lead to high economic costs and serious personal injuries. This work presents a methodology focused on identifying cracks in unbalanced rotors, which are some of the most frequent mechanical elements in industry. The proposed method is based on Artificial Neural Networks that give a solution to the presented inverse problem. They allow to estimate unknown crack parameters, specifically, the crack depth and the eccentricity angle, depending on the dynamic behavior of the rotor. The necessary data to train the developed Artificial Neural Network have been obtained from the frequency spectrum of the displacements of the well- known cracked Jeffcott rotor model, which takes into account the crack breathing mechanism during a shaft rotation. The proposed method is applicable to any rotating machine and it could contribute to establish adequate maintenance plans.

• 대한전기학회논문지
• /
• 제40권8호
• /
• pp.816-824
• /
• 1991

It is well known that dynamic control is needed for fast and accurate control. Neural networks are ideal for representing the strongly nonlinear relationship in the dynamic equations including complex unmodeled effects. It thus creates many advantages over conventional methods such as simple, fast and accurate control through neural network's inherent learning and massive parallelism. In this paper, dynamic control of the full six degrees of freedom of an industrial robot arm will be presented using neural networks. Moreover, through application to a real robot the usefulness of neurocontrol is demonstrated. The back propagation and feedback-error learning is used to train the neurocontroller. Simulated control of a PUMA 560 arm demonstrates that it moves at high speed with good accuracy and generalizes over untrained trajectories as well as adapt to unforseen load changes and sensor noise.

• 제어로봇시스템학회논문지
• /
• 제13권5호
• /
• pp.422-428
• /
• 2007

This paper presents a neural network based adaptive control approach to a reconfigurable flight control law that keeps handling qualities in the presence of faults or failures to the control surfaces of an aircraft. This approach removes the need for system identification for control reallocation after a failure and the need for an accurate aerodynamic database for flight control design, thereby reducing the cost and time required to develope a reconfigurable flight controller. Neural networks address the problem caused by uncertainties in modeling an aircraft and pseudo control hedging deals with the nonlinearity in actuators and the reconfiguration of a flight controller. The effect of the reconfigurable flight control law is illustrated in results of a nonlinear simulation of an unmanned aerial vehicle Durumi-II.

• Journal of the Korean Data and Information Science Society
• /
• 제14권3호
• /
• pp.641-651
• /
• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the prediction ability of hybrid models to reflect the structural change.