• Journal of the Korea Society of Computer and Information
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• v.1 no.1
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• pp.83-94
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• 1996

The test statistic in ANOVA tests has a single or doubly noncentral F distribution and the noncentral F distribution is applied to the calculation of the power functions of tests of general linear hypotheses. In this paper. the evaluation of the cumulative function of the single noncentral F distribution is applied to the neural network theory. The neural network consists of the multi-layer perceptron structure and learning process has the algorithm of the backpropagation. Numerical comparisons are made between the results obtained by neural network theory and the Patnaik's values.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.73-90
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• 2018

Despite of increasing studies for product recommendation, the recommendation of product repurchase timing has not yet been studied actively. This study aims to propose deep neural network models usingsimple purchase history data to predict the repurchase timing of each customer and compare performances of the models from the perspective of prediction quality, including expected ROI of promotion, variability of precision and recall, and diversity of target selection for promotion. As an experiment result, a recurrent neural network (RNN) model showed higher promotion ROI and the smaller variability compared to MLP and other models. The proposed model can be used to develop a CRM system that can offer SMS or app-based promotionsto the customer at the right time. This model can also be used to increase sales for product repurchase businesses by balancing the level of ordersas well as inducing repurchases by customers.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.2
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• pp.87-93
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• 2019

This research deals with posture optimization for humanoid robot against external forces using genetic algorithm and neural network. When the robot takes a motion to push an object, the torque of each joint is generated by reaction force at the palm. This study aims to optimize the posture of the humanoid robot that will change this torque. This study finds an optimized posture using a genetic algorithm such that torques are evenly distributed over the all joints. Then, a number of different optimized postures are generated from various the reaction forces at the palm. The data is to be used as training data of MLP(Multi-Layer Perceptron) neural network with BP(Back Propagation) learning algorithm. Humanoid robot can find the optimal posture at different reaction forces in real time using the trained neural network include non-training data.

• Journal of The Korean Association of Information Education
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• v.12 no.3
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• pp.267-274
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• 2008

A neural network is a modeling technique useful for finding out hidden patterns from data through repetitive learning process and for predicting target values for new data. In this study, we built multilayer perceptron neural networks for prediction of the students' computer literacy based on their personal characteristics, home and social environment, and academic record of other subjects. Prediction performance of the network was compared with that of a widely used prediction method, the regression model. From our experiments, it was found that personal characteristic features best explained computer proficiency level of a student, whereas the features of home and social environment resulted in the worse prediction accuracy among all. Moreover, the developed neural network model produced far more accurate prediction than the regression model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.327-328
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• 2006

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.364-367
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• 1995

In this paper, a design method of learning flight control system via input matching is proposed. The proposed learning control system is a simple structure which has an artificial neural network and feedback mechanism, and it is a useful method to control nonlinear systems.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.280-285
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• 2004

In this study, we made a comparative study of backpropagation neural network and probabilistic neural network and bayesian classifier and perceptron as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to four algorithms. Here, feature variable is composed of time domain signal itself and frequency domain signal itself. Through this process, we comfirmed advantages/disadvantages of four algorithms and identified application methods of four algorithms.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.12
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• pp.59-67
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• 1991

In this paper, we aimed to develop associative pattern recognizer based on neural network for aircraft identification. For obtaining invariant feature space description of an object regardless of its scale change and rotation, Log-polar sampling technique recently developed partly due to its similarity to the human visual system was introduced with Fourier transform post-processing. In addition to the recognition results, image recall was associatively performed and also used for the visualization of the recognition reliability. The multilayer perceptron model was learned by backpropagation algorithm.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1353-1355
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• 1996

This paper presents classification of high impedance fault pattern using linear prediction coefficients. A feature of neutral phase current is extracted by the linear predictive coding. This feature is classified into faults by a multilayer perceptron neural network. Neural network successfully classifies test data into three faults and one normal state.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.149-152
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• 2009

본 논문에서는 오차역전파 알고리즘을 사용한 3층 구조의 퍼셉트론형 신경회로망으로 네트워크의 학습을 실시하여, N비트의 패리티판별에 필요한 최소의 중간유닛수의 해석에 관한 연구이다. 따라서 본 논문은 제안한 퍼셉트론형 신경회로망의 중간 유닛의 수를 변화시켜 N 비트의 패리티 판별 실험을 실시하였다. 본 시스템은 패리티 판별의 실험을 통하여 N 비트 패리티 판별이 가능하다는 것을 실험으로 확인한다.