• KIISE Transactions on Computing Practices
• /
• v.21 no.8
• /
• pp.567-571
• /
• 2015

Transition-based dependency parsing requires much time and efforts to design and select features from a very large number of possible combinations. Recent studies have successfully applied Multi-Layer Perceptrons (MLP) to find solutions to this problem and to reduce the data sparseness. However, most of these methods have adopted greedy search and can only consider a limited amount of information from the context window. In this study, we use a Recurrent Neural Network to handle long dependencies between sub dependency trees of current state and current transition action. The results indicate that our method provided a higher accuracy (UAS) than an MLP based model.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.186-186
• /
• 2022

강수 및 침투 등으로 발생하는 지하수위의 변동을 예측하는 것은 지하수 자원의 활용 및 관리에 필수적이다. 지하수위의 변동은 지하수 자원의 활용 및 관리뿐만이 아닌 홍수 발생과 지반의 응력상태 등에 직접적인 영향을 미치기 때문에 정확한 예측이 필요하다. 본 연구는 인공신경망 중 다층퍼셉트론(Multi Layer Perceptron, MLP)을 이용한 지하수위 예측성능 향상을 위해 MLP의 구조 중 Optimizer를 개량하였다. MLP는 입력자료와 출력자료간 최적의 상관관계(가중치 및 편향)를 찾는 Optimizer와 출력되는 값을 결정하는 활성화 함수의 연산을 반복하여 학습한다. 특히 Optimizer는 신경망의 출력값과 관측값의 오차가 최소가 되는 상관관계를 찾는 연산자로써 MLP의 학습 및 예측성능에 직접적인 영향을 미친다. 기존의 Optimizer는 경사하강법(Gradient Descent, GD)을 기반으로 하는 Optimizer를 사용했다. 하지만 기존의 Optimizer는 미분을 이용하여 상관관계를 찾기 때문에 지역탐색 위주로 진행되며 기존에 생성된 상관관계를 저장하는 구조가 없어 지역 최적해로 수렴할 가능성이 있다는 단점이 있다. 본 연구에서는 기존 Optimizer의 단점을 개선하기 위해 지역탐색과 전역탐색을 동시에 고려할 수 있으며 기존의 해를 저장하는 구조가 있는 메타휴리스틱 최적화 알고리즘을 이용하였다. 메타휴리스틱 최적화 알고리즘 중 구조가 간단한 화음탐색법(Harmony Search, HS)과 GD의 결합모형(HS-GD)을 MLP의 Optimizer로 사용하여 기존 Optimizer의 단점을 개선하였다. HS-GD를 이용한 MLP의 성능검토를 위해 이천시 지하수위 예측을 실시하였으며 예측 결과를 기존의 Optimizer를 이용한 MLP 및 HS를 이용한 MLP의 예측결과와 비교하였다.

• Journal of Convergence for Information Technology
• /
• v.11 no.7
• /
• pp.1-6
• /
• 2021

The purpose of this study is to improve the performance of the artificial neural network system for facial image analysis through the image landmark selection technique. For landmark selection, a CNN-based multi-layer ResNet model for classification of facial image age is required. From the configured ResNet model, a heat map that detects the change of the output node according to the change of the input node is extracted. By combining a plurality of extracted heat maps, facial landmarks related to age classification prediction are created. The importance of each pixel location can be analyzed through facial landmarks. In addition, by removing the pixels with low weights, a significant amount of input data can be reduced.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.10 no.5
• /
• pp.179-186
• /
• 2006

In this study, Multi-Layer Perceptron(MLP) among models of Artificial Neural Network(ANN) is used for the development of a model that evaluates the bending capacities of reinforced concrete beams strengthened by FRP Rebar. And the data of the existing researches are used for materials of ANN model. As the independent variables of input layer, main components of bending capacities, width, effective depth, compressive strength, reinforcing ratio of FRP, balanced steel ratio of FRP are used. And the moment performance measured in the experiment is used as the dependent variable of output layer. The developed model of ANN could be applied by GFRP, CFRP and AFRP Rebar and the model is verified by using the documents of other previous researchers. As the result of the ANN model presumption, comparatively precise presumption values are achieved to presume its bending capacities at the model of ANN(0.05), while observing remarkable errors in the model of ANN(0.1). From the verification of the ANN model, it is identified that the presumption values comparatively correspond to the given data ones of the experiment. In addition, from the Sensitivity Analysis of evaluation variables of bending performance, effective depth has the highest influence, followed by steel ratio of FRP, balanced steel ratio, compressive strength and width in order.

• Journal of the Korean Geotechnical Society
• /
• v.20 no.8
• /
• pp.77-87
• /
• 2004

In this paper a back-propagation neural network model is developed to estimate the preconsolidation pressure of Korean soft soils based on 176 oedometer tests and 63 piezocone test results, which were compiled from 11 sites - western and southern parts of Korea. Only 147 data were used for the training of the neural network and 29 data, which were not used during the training phase, were used for the verification of trained network. Empirical and theoretical models were compared with the developed neural network model. A simple 4-4-9-1 multi-layered neural network has been developed. The cone tip resistance $q_T$ penetration pore pressure $u_2$, total overburden pressure $\sigma_{vo}$ and effective overburden pressure $\sigma'_{vo}$ were selected as input variables. The developed neural network model was validated by comparing the prediction results of the proposed neural network model for the new data which were not used for the training of the model with the measured preconsolidation pressures. It can also predict more precise and reliable preconsolidation pressures than the analytical and empirical model. Furthermore, it can be carefully concluded that neural network model can be used as a generalized model for prediction of preconsolidation pressure throughout Korea since developed model shows good performance for the new data which were not used in both training and testing data.

• Journal of Korea Water Resources Association
• /
• v.44 no.2
• /
• pp.97-107
• /
• 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 Journal of the Acoustical Society of Korea
• /
• v.39 no.5
• /
• pp.454-460
• /
• 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.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.35-35
• /
• 2019

본 연구에서는 분포형 수문 모형 Drying Stream Assessment Tool and Water Flow Tracking (DrySAT-WTF)을 활용해 우리나라의 1976년부터 2015년까지의 유출량을 산정하고, 이를 다층퍼셉트론(Multi Layer Perceptron) 인경신경망 모형(Artificial Neural Network Model)에 적용해 미래 유출을 예측하였다. DrySAT-WFT은 전국 표준 유역을 대상으로 하천 건천화 원인 추적 및 평가를 위해 개발된 모형으로 유출모의를 위한 기상자료 외에 건천화 영향 요소를 고려하기 위한 산림 높이, 도로망, 지하수 이용량, 토지이용, 토심 변화에 대한 DB를 적용 가능한 것이 특징이다. DrySAT-WFT를 위한 기상자료로 모의 기간에 대한 일별 강우량, 상대습도, 평균풍속, 평균 및 최고, 최저 기온, 일조시간을 구축하였으며, 연대별 건천화 영향 요소 DB를 구축하여 적용하였다. 전국 다목적 댐 보 12지점의 유량을 활용해 모형의 보정(2005-2010) 및 검증(2011-2015)을 실시한 결과, 평균 결정계수(Coefficient of determination, $R^2$)는 0.76, 모형효율성계수(Nash-Sutcliffe efficiency, NSE)는 0.62, 평균제곱근오차(average root mean square error, RMSE)는 3.09로 신뢰성 있는 유출 모의 결과를 나타내었다. 미래 유출량 예측을 위한 MLP-ANN은 1976년부터 2015년까지의 유출 모의 결과를 Training Set으로 훈련하여 $R^2$가 0.5 이상이 되어 신뢰성을 확보하였고, 2016년부터 2018년까지의 기간을 1개월 단위로 실제 유출량과 예측 유출량을 비교하며 적용성을 검증 및 향상시켰다.

• Journal of the Korean Geotechnical Society
• /
• v.16 no.1
• /
• pp.83-97
• /
• 2000

In the present study, artificial neural network based on the multi-layer perceptron is used and an optimum model is chosen through the process of efficiency evaluation in order to develop a system predicting maximum displacements of the earth retaining walls at various excavation stages. By analyzing the measured field data collected at various urban excavation sites in Korea, factors influencing on the behaviors of the excavation wall are examined. Among the measured data collected, reliable data are further selected on the basis of the performance ratio and are used as a data base. Data-based measurements are also utilized for both teaming and verifying the artificial neural network model. The learning is carried out by using the back-propagation algorithm based on the steepest descent method. Finally, to verify a validity of the formulated artificial neural network system, both the magnitude and the occurring position of the maximum horizontal displacement are predicted and compared with measured data at real excavation sites not included in the teaming process.

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