• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.454-460
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• 2020

Artificial intelligence assistants that provide speech recognition operate through cloud-based voice recognition with high accuracy. In cloud-based speech recognition, Wake-Up-Word (WUW) detection plays an important role in activating devices on standby. In this paper, we compare the performance of Convolutional Neural Network (CNN)-based WUW detection models for mobile devices by using Google's speech commands dataset, using the spectrogram and mel-frequency cepstral coefficient features as inputs. The CNN models used in this paper are multi-layer perceptron, general convolutional neural network, VGG16, VGG19, ResNet50, ResNet101, ResNet152, MobileNet. We also propose network that reduces the model size to 1/25 while maintaining the performance of MobileNet is also proposed.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.111-128
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• 2017

Existing studies for recommender have focused on recommending an appropriate item based on the customer preference. However, it has not yet been studied actively to recommend purchase timing for the repurchase product despite of its importance. This study aims to propose MLP and RNN models based on the only simple purchase history data to predict the timing of customer repurchase and compare performances in the perspective of prediction accuracy and quality. As an experiment result, RNN model showed outstanding performance compared to MLP model. The proposed model can be used to develop CRM system which can offer SMS or app based promotion to the customer at the right time. This model also can be used to increase sales for repurchase product business by balancing the level of order as well as inducing repurchase of customer.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.47-55
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• 2010

In this paper, we proposed a recognition system of smile facial expression for smile treatment training. The proposed system detects face candidate regions by using Haar-like features from camera images. After that, it verifies if the detected face candidate region is a face or non-face by using SVM(Support Vector Machine) classification. For the detected face image, it applies illumination normalization based on histogram matching in order to minimize the effect of illumination change. In the facial expression recognition step, it computes facial feature vector by using PCA(Principal Component Analysis) and recognizes smile expression by using a multilayer perceptron artificial network. The proposed system let the user train smile expression by recognizing the user's smile expression in real-time and displaying the amount of smile expression. Experimental result show that the proposed system improve the correct recognition rate by using face region verification based on SVM and using illumination normalization based on histogram matching.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.399-412
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• 2010

The goal of this research is to apply the neural networks models for the temporal disaggregation of the yearly pan evaporation (PE) data, Republic of Korea. The neural networks models consist of multilayer perceptron neural networks model (MLP-NNM) and generalized regression neural networks model (GRNNM), respectively. And, for the performances evaluation of the neural networks models, they are composed of training and test performances, respectively. The three types of data such as the historic, the generated, and the mixed data are used for the training performance. The only historic data, however, is used for the testing performance. From this research, we evaluate the application of MLP-NNM and GRNNM for the temporal disaggregation of nonlinear time series data. We should, furthermore, construct the credible monthly PE data from the temporal disaggregation of the yearly PE data, and can suggest the available data for the evaluation of irrigation and drainage networks system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.6
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• pp.56-68
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• 2003

Difficult problems In implementing digital neural network hardware are the extension of synapses and the programmability for relocating neurons. In this paper, the structure of a new hardware is proposed for solving these problems. Our structure based on traditional SIMD can be dynamically and easily reconfigured connections of network without synthesizing and mapping original design for each use. Using additional modular processing unit the numbers of neurons find synapses increase. To show the extensibility of our structure, various models of neural networks : multi-layer perceptrons and Kohonen network are formed and tested. The performance comparison with software simulation shows its superiority in the aspects of performance and flexibility.

• Journal of KIISE:Information Networking
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• v.27 no.3
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• pp.323-330
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• 2000

이동통신망의 발전으로 인해 한정적인 주파수 자원을 효율적으로 사용하여 폭증하는 가입자를 수용하기 위해 셀의 반경은 점점 작아지고 있다. 가입자에게 신규호의 실패보다 더 민감한 핸드오프호가 자주 발생함에 따라 핸드오프호 처리의 중요성이 증대되었다. 따라서, 셀마다 핸드오프호를 위한 예약 채널을 두어 어느 정도 신규호의 블록킹율의 증가를 감수하더라도, 핸드오프호의 강제종료율을 낮추는 방법이 제안되었다. 이러한 예약 채널 할당 기법에서는 예약 채널을 몇 개로 할 것인가가 중요한 문제가 된다. 왜냐하면 예약 채널 수를 과다하게 설정하면 핸드오프가 빈번하지 않은 셀에서는 채널의 낭비를 초래하고, 적게 설정하면 핸드오프가 빈번한 셀은 핸드오프 강제종료율이 높아지게 되기 때문이다. 이러한 문제를 해결하기 위해 본 논문은 신경망 모델 중 다층 퍼셉트론을 이용하여 셀에서 요구되는 최적의 예약율을 구하여 셀의 환경이 변할 때마다 적용할 수 있는 방법을 제안한다. 본 논문에서는 모의 실험을 통해 이동통신 시스템에서의 핸드오프 예약율을 주기적으로 최적화 시킴으로써 핸드오프가 자주 발생하는 셀에서는 핸드오프 강제종료율을 낮추고, 핸드오프가 빈번하지 않은 셀은 채널의 손실을 막아 시스템의 전체적인 효율이 향상됨을 보인다.

• Journal of Korea Water Resources Association
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• v.44 no.2
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• pp.97-107
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• 2011

For the purpose of enhancing usability of NWP (Numerical Weather Prediction), the quantitative precipitation prediction scheme was suggested. In this research, precipitation by leading time was predicted using 3-hour rainfall accumulation by meso-scale numerical weather model and AWS (Automatic Weather Station), precipitation water and relative humidity observed by atmospheric sounding station, probability of rainfall occurrence by leading time in June and July, 2001 and August, 2002. Considering the nonlinear process of ranfall producing mechanism, the ANN (Artificial Neural Network) that is useful in nonlinear fitting between rainfall and the other atmospheric variables. The feedforward multi-layer perceptron was used for neural network structure, and the nonlinear bipolaractivation function was used for neural network training for converting negative rainfall into no rain value. The ANN simulated rainfall was validated by leading time using Nash-Sutcliffe Coefficient of Efficiency (COE) and Coefficient of Correlation (CORR). As a result, the 3 hour rainfall accumulation basis shows that the COE of the areal mean of the Korean peninsula was improved from -0.04 to 0.31 for the 12 hr leading time, -0.04 to 0.38 for the 24 hr leading time, -0.03 to 0.33 for the 36 hr leading time, and -0.05 to 0.27 for the 48 hr leading time.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.3
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• pp.274-280
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• 1992

In this paper, an Improved Error Back Propagation Algorithm is introduced, which avoids Network Paralysis, one of the problems of the Error Backpropagation learning rule. For this purpose, we analyzed the reason for Network Paralysis and modified the Activation Function Derivative of the standard Error Backpropagation Algorithm which is regarded as the cause of the phenomenon. The characteristics of the modified Activation Function Derivative is analyzed. The performance of the modified Error Backpropagation Algorithm is shown to be better than that of the standard Error Back Propagation algorithm by various experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.542-546
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• 2009

In this Paper, a back propagation neural network learning algorithm based on the complex multilayer perceptron is represented for controling and detecting interference of the received signals in cellular mobile communication system. We proposed neural network adaptive correlator which has fast convergence rate and good performance with combining back propagation neural network and the receiver of cellular. We analyzed and proved that NNAC has lower bit error probability than that of traditional RAKE receiver through results of computer simulation in the presence of the tone and narrow - band interference and the co-channel interference.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.1062-1070
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• 2003

In this paper, we observe one of the aquatic insect, fish(Medaka)'s behavior which reacts to giving toxic chemicals until lethal conditions using automatic tracking sl$.$stem. For the result, we define the Pattern A is a normal movement of fish and Pattern B is after giving the chemicals. In order to detect the movement of fish automatically, these patterns are selected for the training data of the artificial neural networks. The average recognition rates of the pattern B are remarkably increased after inputs of toxic chemical(diazinon) while the Pattern A is decreased distinctively. This study demonstrates that artificial neural networks are useful method for detecting presence of toxicoid in environment as for an alternative of in-situ behavioral monitoring tool.