• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.86-86
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• 2016

본 연구는 가뭄 예측에 대한 오차를 알고리즘과 결합하여 다층 퍼셉트론 (Multi-layer Perceptron, MLP) 네트워크 구조를 인공신경망 모형에 적용하고, 표준강수지수(Standard Precipitation Index, SPI)를 입 력 및 출력 변수로 구성하여 가뭄예측을 시도하였다. 예측모델을 평가하기 위해 기상청 산하의 59개 관측소에 대한 1980년부터 2015년까지의 기상자료를 적용하였으며, 수립된 자료를 활용하여 한반도 전역의 가뭄에 대한 시공간적인 분석을 수행하였다. 단기가뭄 예측성능을 평가하기 위해 2000년에서 2015년까지 16년간의 모의결과를 ROC 분석을 통하여 시공간적 단기가뭄 예측성능을 평가하고 혼동행렬(Conversion Matrix) 구성에 대한 조건적 확률의 다각적 검토를 통해 모델 예측에 대한 정확성(Accuracy), 신뢰성(Precision) 등 다양한 예측성능에 대한 평가를 수행하고 2016년 가뭄전망을 제시하고자 한다.

• Journal of Navigation and Port Research
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• v.35 no.1
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• pp.83-91
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• 2011

The accuracy of forecasting is remarkably important to reduce total cost or to increase customer services, so it has been studied by many researchers. In this paper, the artificial neural network (ANN), one of the most popular nonlinear forecasting methods, is compared with autoregressive integrated moving average(ARIMA) model through performing a prediction of container traffic. It uses a hybrid methodology that combines both the linear ARIAM and the nonlinear ANN model to improve forecasting performance. Also, it compares the methodology with other models in performance for prediction. In designing network structure, this work specially applies the genetic algorithm which is known as the effectively optimal algorithm in the huge and complex sample space. It includes the time delayed neural network (TDNN) as well as multi-layer perceptron (MLP) which is the most popular neural network model. Experimental results indicate that both ANN and Hybrid models outperform ARIMA model.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.550-559
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• 2018

The effectiveness of deep neural networks (DNNs) for detection and classification of micro-Doppler signals generated by human walking and background noise sources is investigated. Previous research included a complex process for extracting meaningful features that directly affect classifier performance, and this feature extraction is based on experiences and statistical analysis. However, because a DNN gradually reconstructs and generates features through a process of passing layers in a network, the preprocess for feature extraction is not required. Therefore, binary classifiers and multiclass classifiers were designed and analyzed in which multilayer perceptrons (MLPs) and DNNs were applied, and the effectiveness of DNNs for recognizing micro-Doppler signals was demonstrated. Experimental results showed that, in the case of MLPs, the classification accuracies of the binary classifier and the multiclass classifier were 90.3% and 86.1%, respectively, for the test dataset. In the case of DNNs, the classification accuracies of the binary classifier and the multiclass classifier were 97.3% and 96.1%, respectively, for the test dataset.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.773-774
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• 2016

배경잡음은 음성신호의 특징을 왜곡하기 때문에 음성인식 시스템의 인식율 향상의 방해요소가 된다. 따라서 본 논문에서는 배경잡음이 존재하는 환경에서의 음성인식을 실시하기 위해서, 신경회로망과 Mel 주파수 켑스트럼 계수를 사용하여 연속음성 식별 알고리즘을 제안한다. 본 논문의 실험에서는 본 알고리즘을 사용하여 배경잡음이 섞인 음성신호에 대하여 음성인식의 식별율 개선을 실현할 수 있도록 연구를 진행하며, 본 알고리즘이 유효하다는 것을 실험을 통하여 명백히 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2521-2526
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• 2011

This paper proposes a speaker recognition algorithm using a perceptron neural network and LPC (Linear Predictive Coding) cepstrum coefficients. The proposed algorithm first detects the voiced sections at each frame. Then, the LPC cepstrum coefficients which have speaker characteristics are obtained by the linear predictive analysis for the detected voiced sections. To classify the obtained LPC cepstrum coefficients, a neural network is trained using the LPC cepstrum coefficients. In this experiment, the performance of the proposed algorithm was evaluated using the speech recognition rates based on the LPC cepstrum coefficients and the neural network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.360-364
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• 2009

현대사회는 인구의 고령화에 따른 노인인구의 증가 및 만성질환자의 증가에 따른 의료수요 급증이 예상되고 있다. 하지만 현재의 의료서비스 인프라는 증가하는 의료수요를 충족하기에는 역부족이 따르며, 이러한 문제점을 해결하기위해 정보통신기술과 헬스케어기술이 결합된 유비쿼터스 헬스케어기술이 부각되고 있다. 본 연구에서는 일상생활 중 움직임에 따른 활동 상태를 판별하여 운동량의 모니터링을 통한 건강관리뿐만 아니라 낙상 등과 같은 응급상황의 모니터링이 가능한 시스템을 구현하고자 하였다. 이를 위하여 3축 가속도센서를 이용하여 인체의 움직임에 따른 활동 가속도 신호를 계측할 수 있는 센서 및 시스템을 구현하였다. 또한 계측된 센서신호를 PC또는 휴대용 단말기로 무선전송하기위하여 무선센서네트워크 기술을 적용한 데이터 전송시스템을 구현하였다. 계측된 가속도 신호로부터 활동 상태를 판별하기위해 다층 퍼셉트론 알고리즘을 적용한 분류알고리즘을 제안하였으며, 분류알고리즘의 성능평가를 통해 실제 활동상태 모니터링에 적용 가능함을 확인하였다.

• 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 the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.2843-2852
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• 2013

In the nested mobile network environment, the mobile node selects one of multiple mobile routers. The MR(Mobile Router) by existing top-down or bottom-up methods may not be the optimal MR if the numbers of mobile nodes and routers are substantially increased, and the scale of the network is increased drastically. Since an inappropriate MR decision causes handover or binding renewal to mobile nodes, determining of the optimal MR is important for efficiency. In this paper, we propose an algorithm that decides on the optimal MR using MR QoS(Quality of Service) information, and we describe how to understand the various structured MLP(Multi-Layered Perceptron) based on the algorithm. In conclusion, we prove the ability of the suggested neural network for a nesting mobile network through the performance analysis of each learned MLP.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.35-47
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• 2020

Box-office prediction is important to movie stakeholders. It is necessary to accurately predict box-office and select important variables. In this paper, we propose a multivariate time series classification and important variable selection method to improve accuracy of predicting the box-office. As a research method, we collected daily data from KOBIS and NAVER for South Korean movies, selected important variables using Random Forest and predicted multivariate time series using Deep Learning. Based on the Korean screen quota system, Deep Learning was used to compare the accuracy of box-office predictions on the 73^rd day from movie release with the important variables and entire variables, and the results was tested whether they are statistically significant. As a Deep Learning model, Multi-Layer Perceptron, Fully Convolutional Neural Networks, and Residual Network were used. Among the Deep Learning models, the model using important variables and Residual Network had the highest prediction accuracy at 93%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.305-313
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• 2021

The upcoming warfare will be network-centric warfare with the acquiring and sharing of information on the battlefield through the connection of the entire weapon system. Therefore, the amount of information generated increases, but the technology of evaluating the information is insufficient. Threat assessment is a technology that supports a quick decision, but the information has many uncertainties and is difficult to apply to an advanced battlefield. This paper proposes a threat assessment based on artificial intelligence while removing the target uncertainty. The artificial intelligence system used was a fuzzy inference system and a multi-layer perceptron. The target was classified by inputting the unique characteristics of the target into the fuzzy inference system, and the classified target information was input into the multi-layer perceptron to calculate the appropriate threat value. The validity of the proposed technique was verified with the threat value calculated by inputting the uncertain target to the trained artificial neural network.