• 한국정보전자통신기술학회논문지
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• 제11권2호
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• pp.204-208
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• 2018

본 논문에서는 시계열 데이터인 주가의 변동 패턴을 학습하고, 주가 가격을 예측하기 적합한 주가 예측 딥러닝 모델을 제시하고 평가하였다. 일반신경망에 시계열 개념이 추가되어 은닉계층에 이전 정보를 기억시킬 수 있는 순환신경망이 시계열 데이터인 주가 예측 모델로 적합하다. 순환신경망에서 나타나는 기울기 소멸문제를 해결하며, 장기의존성을 유지하기 위하여, 순환신경망의 내부에 작은 메모리를 가진 LSTM을 사용한다. 또한, 순환신경망의 시계열 데이터의 직전 패턴 기반으로만 학습하는 경향을 보이는 한계를 해결하기 위하여, 데이터의 흐름의 역방향에 은닉계층이 추가되는 양방향 LSTM 순환신경망을 이용하여 주가예측 모델을 구현하였다. 실험에서는 제시된 주가 예측 모델에 텐서플로우를 이용하여 주가와 거래량을 입력 값으로 학습을 하였다. 주가예측의 성능을 평가하기 위해서, 실제 주가와 예측된 주가 간의 평균 제곱근 오차를 구하였다. 실험결과로는 단방향 LSTM 순환신경망보다, 양방향 LSTM 순환신경망을 이용한 주가예측 모델이 더 작은 오차가 발생하여 주가 예측 정확성이 향상되었다.

• 한국해양공학회지
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• 제35권4호
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• pp.287-295
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• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제7권12호
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• pp.461-468
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• 2018

개발자들은 소프트웨어 개발과 유지보수 작업 중 하나의 코드를 수정하는데 들이는 시간보다 이를 위해 코드를 탐색하고 이해하는데 더 많은 시간을 소모한다. 코드를 탐색하는 시간을 줄이기 위하여 기존 연구들은 데이터 마이닝과 통계적 언어모델 기법을 이용하여 수정할 코드를 추천하여 왔다. 그러나 이 경우 모델의 학습 데이터와 입력되는 데이터가 정확하게 일치하지 않으면 추천이 발생하지 않는다. 이 논문에서 우리는 딥러닝의 기법 중 하나인 Recurrent Neural Networks에 동작기록을 학습시켜 기존 연구의 상기 문제점 없이 수정할 코드의 위치를 추천하는 방법을 제안한다. 제안 방법은 RNN과 동작기록을 활용한 추천 기법으로 평균 약 91%의 정확도와 71%의 재현율을 달성함으로써 기존의 추천방법보다 코드 탐색 시간을 더욱 줄일 수 있게 해 준다.

• ETRI Journal
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• 제40권2호
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• pp.257-265
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• 2018

Pronouns are frequently dropped in Korean sentences, especially in text messages in the mobile phone environment. Restoring dropped pronouns can be a beneficial preprocessing task for machine translation, information extraction, spoken dialog systems, and many other applications. In this work, we address the problem of dropped pronoun recovery by resolving two simultaneous subtasks: detecting zero-pronoun sentences and determining the type of dropped pronouns. The problems are statistically modeled by encoding the sentence and classifying types of dropped pronouns using a recurrent neural network (RNN) architecture. Various RNN-based encoding architectures were investigated, and the stacked RNN was shown to be the best model for Korean zero-pronoun recovery. The proposed method does not require any manual features to be implemented; nevertheless, it shows good performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.81.2-81
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• 2002

$\textbullet$ Introduction- Concept and meaning of the emotional Recognition $\textbullet$ The feature of 4-emotions $\textbullet$ Pitch(approach) $\textbullet$ Simulator-structure, RNN(learning algorithm), evaluation function, solution search method $\textbullet$ Result

• 한국진공학회:학술대회논문집
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• 한국진공학회 2012년도 제42회 동계 정기 학술대회 초록집
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• 한국항행학회논문지
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• 제25권5호
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• pp.409-414
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• 2021

미세먼지에 대한 심각성이 사회적으로 대두됨에 따라 대중들은 미세먼지 예보에 대한 정보의 높은 신뢰성을 요구하고 있다. 이에 따라 다양한 신경망 알고리즘을 이용하여 미세먼지 예측을 위한 연구가 활발히 진행되고 있다. 본 논문에서는 미세먼지 예측을 위해 다양한 알고리즘으로 연구되고 있는 신경망 알고리즘들 중 대표적인 알고리즘들의 예측 성능 비교를 진행하였다. 신경망 알고리즘 중 DNN(deep neural network), RNN(recurrent neural network), LSTM(long short-term memory)을 이용하였으며, 하이퍼 파라미터 탐색을 이용하여 최적의 예측 모델을 설계하였다. 각 모델의 예측 성능 비교 분석 결과, 실제 값과 예측 값의 변화 추이는 전반적으로 좋은 성능을 보였다. RMSE와 정확도를 기준으로 한 분석에서는 DNN 예측 모델이 다른 예측 모델에 비해 예측 오차에 대한 안정성을 갖는 것을 확인하였다.

• 품질경영학회지
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• 제47권3호
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.

• Communications for Statistical Applications and Methods
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• 제28권1호
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• pp.39-57
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• 2021

Time series prediction is an area of great interest to many people. Algorithms for time series prediction are widely used in many fields such as stock price, temperature, energy and weather forecast; in addtion, classical models as well as recurrent neural networks (RNNs) have been actively developed. After introducing the attention mechanism to neural network models, many new models with improved performance have been developed; in addition, models using attention twice have also recently been proposed, resulting in further performance improvements. In this paper, we consider time series prediction by introducing attention twice to an RNN model. The proposed model is a method that introduces H-attention and T-attention for output value and time step information to select useful information. We conduct experiments on stock price, temperature and energy data and confirm that the proposed model outperforms existing models.

• 한국지반공학회논문집
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• 제36권3호
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• pp.5-14
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• 2020

콘크리트궤도는 고속, 고밀도의 운행선로에서도 차량의 주행안정성이 높고, 이용승객에게 좋은 승차감을 제공할 수 있으며, 궤도 보수 비용을 대폭 절감시킬 수 있는 장점을 가지고 있어 고속선을 중심으로 점차 적용 구간이 늘어나고 있다. 하지만, ◯◯고속철도 토공 구간의 노반 침하로 기인된 콘크리트궤도의 2차적 침하가 발생하는 사례가 빈번히 발생하고 있어, 이로 인한 철도 운행의 안정성 문제가 대두되고 있다. 허용 잔류침하량을 초과하는 콘크리트궤도에 대한 효율적인 유지보수 방안 수립을 위해서는 공용 기간 중 발생하는 콘크리트궤도의 장기 침하 거동을 합리적으로 예측하는 것이 필수적이다. 따라서, 본 연구에서는 다양한 인공지능 기술 중 순환신경망을 활용하여 콘크리트궤도의 장기 침하 거동을 예측하는 모델을 개발하였다. 또한 기존 모델의 침하 예측 결과와 비교를 통해 개발된 모델의 적용 가능성을 모색하였다.