• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1231-1242
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• 2019

This paper presents a two-dimensional attention-based long short-memory (2D-ALSTM) model for stock index prediction, incorporating input attention and temporal attention mechanisms for weighting of important stocks and important time steps, respectively. The proposed model is designed to overcome the long-term dependency, stock selection, and stock volatility delay problems that negatively affect existing models. The 2D-ALSTM model is validated in a comparative experiment involving the two attention-based models multi-input LSTM (MI-LSTM) and dual-stage attention-based recurrent neural network (DARNN), with real stock data being used for training and evaluation. The model achieves superior performance compared to MI-LSTM and DARNN for stock index prediction on a KOSPI100 dataset.

• Smart Media Journal
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• v.9 no.4
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• pp.26-35
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• 2020

Attempts to predict future stock prices have been studied steadily since the past. However, unlike general time-series data, financial time-series data has various obstacles to making predictions such as non-stationarity, long-term dependence, and non-linearity. In addition, variables of a wide range of data have limitations in the selection by humans, and the model should be able to automatically extract variables well. In this paper, we propose a 'sliding time step normalization' method that can normalize non-stationary data and LSTM autoencoder to compress variables from all variables. and 'moving transfer learning', which divides periods and performs transfer learning. In addition, the experiment shows that the performance is superior when using as many variables as possible through the neural network rather than using only 100 major financial variables and by using 'sliding time step normalization' to normalize the non-stationarity of data in all sections, it is shown to be effective in improving performance. 'moving transfer learning' shows that it is effective in improving the performance in long test intervals by evaluating the performance of the model and performing transfer learning in the test interval for each step.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.194-197
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• 2015

Long Short-term Memory Network(LSTM) 기반 Recurrent Neural Network(RNN)는 순차 데이터를 모델링 할 수 있는 딥 러닝 모델이다. 기존 RNN의 그래디언트 소멸 문제(vanishing gradient problem)를 해결한 LSTM RNN은 멀리 떨어져 있는 이전의 입력 정보를 볼 수 있다는 장점이 있어 음성 인식 및 필기체 인식 등의 분야에서 좋은 성능을 보이고 있다. 또한 LSTM RNN 모델에 의존성(전이 확률)을 추가한 LSTM CRF모델이 자연어처리의 한 분야인 개체명 인식에서 우수한 성능을 보이고 있다. 본 논문에서는 한국어 문장의 지배소가 문장 후위에 나타나는 점에 착안하여 Backward 방식의 LSTM CRF 모델을 제안하고 이를 한국어 의미역 결정에 적용하여 기존 연구보다 더 높은 성능을 얻을 수 있음을 보인다.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.242-252
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• 2021

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.201-203
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• 2022

Over the past five years, the number of patients treated for diabetes has increased by 27.7% to 3.22 million, and since blood sugar is still checked through finger blood collection, continuous blood glucose measurement and blood sugar peak confirmation are difficult and painful. To solve this problem, based on blood sugar data measured for 14 days, three months of blood sugar prediction data are provided to diabetics using artificial intelligence technology.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.28-31
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• 2021

전세계적으로 우울증은 정신 건강 질환으로써 문제가 되고 있으며, 이를 해결하기 위해 일상생활에서의 우울증 탐지에 대한 연구가 진행되고 있다. 따라서 본 논문에서는 일상생활에 밀접하게 연관되어 있는 AI 스피커를 사용한 어텐션 메커니즘(Attention Mechanism) 기반 멀티모달 우울증 감지 시스템을 제안한다. 제안된 방법은 AI 스피커로부터 수집할 수 있는 음성 및 텍스트 데이터를 수집하고 CNN(Convolutional Neural Network)과 BiLSTM(Bidirectional Long Short-Term Memory Network)를 통해 각 데이터에서의 학습을 진행한다. 학습과정에서 Self-Attention 을 적용하여 특징 벡터에 추가적인 가중치를 부여하는 어텐션 메커니즘을 사용한다. 최종적으로 음성 및 텍스트 데이터에서 어텐션 가중치가 추가된 특징들을 합하여 SoftMax 를 통해 우울증 점수를 예측한다.

• Journal of the Korean Society for Railway
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• v.13 no.1
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• pp.71-77
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• 2010

It is increasing annually that the cost for bridge Maintenance Repair & Rehabilitation (MR&R) in developed countries. Based on Intelligent Technology, Bridge Management System (BMS) is developed for optimization of Life Cycle Cost (LCC) and reliability to predict long-term bridge deteriorations. However, such data are very limited amongst all the known bridge agencies, making it difficult to reliably predict future structural performances. To alleviate this problem, an Artificial Neural Network (ANN) based Backward Prediction Model (BPM) for generating missing historical condition ratings has been developed. Its reliability has been verified using existing condition ratings from the Maryland Department of Transportation, USA. The function of the BPM is to establish the correlations between the known condition ratings and such non-bridge factors as climate and traffic volumes, which can then be used to obtain the bridge condition ratings of the missing years. Since the non-bridge factors used in the BPM can influence the variation of the bridge condition ratings, well-selected non-bridge factors are critical for the BPM to function effectively based on the minimized discrepancy rate between the BPM prediction result and existing data (deck; 6.68%, superstructure; 6.61%, substructure; 7.52%). This research is on the generation of usable historical data using Artificial Intelligence techniques to reliably predict future bridge deterioration. The outcomes (Long-term Bridge deterioration Prediction) will help bridge authorities to effectively plan maintenance strategies for obtaining the maximum benefit with limited funds.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.1
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• pp.51-56
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• 2012

Crashworthy fuel cells have been widely implemented to rotorcraft and rendered a great contribution for improving the survivability of crews and passengers. Since the embryonic stage of military rotorcraft history began, the US army has developed and practised a detailed military specification documenting the unique crashworthiness requirements for rotorcraft fuel cells to prevent most fatality due to post-crash fire. Foreign manufacturers have followed their long term experience to develop their fuel cells, and have reflected the results of crash impact tests on the trial-and-error based design and manufacturing procedures. Since the crash impact test itself takes a long-term preparation efforts together with costly fuel cell specimens, a series of numerical simulations of the crash impact test with digital mock-ups is necessary even at the early design stage to minimize the possibility of trial-and-error with full-scale fuel cells. In the present study a number of numerical simulations on fuel cell crash impact tests are performed with a crash simulation software, Autodyn. The resulting equivalent stresses are further analysed to evaluate a number of appropriate design parameters and the artificial neural network and simulated annealing method are simultaneously implemented to optimize the crashworthy performance of fuel cells.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.694-706
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• 2019

In this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study showed that the LSTM model outperforms the gradient boosting model, a general machine learning model known to have relatively good prediction performance, for the time series classification of the cryptocurrency price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB model.

• Wind and Structures
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• v.36 no.6
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• pp.379-392
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• 2023

Structural health monitoring is used to ensure the well-being of civil structures by detecting damage and estimating deterioration. Wind flow applies external loads to high-rise buildings, with the horizontal force component of the wind causing structural displacements in high-rise buildings. This study proposes a deep learning-based predictive model for measuring lateral displacement response in high-rise buildings. The proposed long short-term memory model functions as a sequence generator to generate displacements on building floors depending on the displacement statistics collected on the top floor. The model was trained with wind-induced displacement data for the top floor of a high-rise building as input. The outcomes demonstrate that the model can forecast wind-induced displacement on the remaining floors of a building. Further, displacement was predicted for each floor of the high-rise buildings at wind flow angles of 0° and 45°. The proposed model accurately predicted a high-rise building model's story drift and lateral displacement. The outcomes of this proposed work are anticipated to serve as a guide for assessing the overall lateral displacement of high-rise buildings.