• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.127-140
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• 2021

Recently, sequence data containing time information, such as sensor measurement data and purchase history, has been generated in various applications. So far, many methods for finding sequences that are significantly different from other sequences among given sequences have been proposed. However, most of them have a limitation that they consider only the order of elements in the sequences. Therefore, in this paper, we propose a new anomalous sequence detection method that considers both the order of elements and the time interval between elements. The proposed method uses an extended LSTM autoencoder model, which has an additional layer that converts a sequence into a form that can help effectively learn both the order of elements and the time interval between elements. The proposed method learns the features of the given sequences with the extended LSTM autoencoder model, and then detects sequences that the model does not reconstruct well as anomalous sequences. Using experiments on synthetic data that contains both normal and anomalous sequences, we show that the proposed method achieves an accuracy close to 100% compared to the method that uses only the traditional LSTM autoencoder.

• Journal of Aerospace System Engineering
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• v.18 no.4
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• pp.43-52
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• 2024

This paper presents an anomaly detection system that uses an LSTM-Autoencoder model to identify early-stage anomalies in the blade pitch system of floating wind turbines. The sensor data used in power plant monitoring systems is primarily composed of multivariate time-series data for each component. Comprising two unidirectional LSTM networks, the system skillfully uncovers long-term dependencies hidden within sequential time-series data. The autoencoder mechanism, learning solely from normal state data, effectively classifies abnormal states. Thus, by integrating these two networks, the system can proficiently detect anomalies. To confirm the effectiveness of the proposed framework, a real multivariate time-series dataset collected from a wind turbine model was employed. The LSTM-autoencoder model showed robust performance, achieving high classification accuracy.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.133-144
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• 2020

The number of elderly people with dementia is increasing as fast as the proportion of older people due to aging, which creates a social and economic burden. In particular, dementia care costs, including indirect costs such as increased care costs due to lost caregiver hours and caregivers, have grown exponentially over the years. In order to reduce these costs, it is urgent to introduce a management system to care for dementia patients. Therefore, this study proposes a sensor-based abnormal behavior detection system to manage dementia patients who live alone or in an environment where they cannot always take care of dementia patients. Existing studies were merely evaluating behavior or evaluating normal behavior, and there were studies that perceived behavior by processing images, not data from sensors. In this study, we recognized the limitation of real data collection and used both the auto-encoder, the unsupervised learning model, and the LSTM, the supervised learning model. Autoencoder, an unsupervised learning model, trained normal behavioral data to learn patterns for normal behavior, and LSTM further refined classification by learning behaviors that could be perceived by sensors. The test results show that each model has about 96% and 98% accuracy and is designed to pass the LSTM model when the autoencoder outlier has more than 3%. The system is expected to effectively manage the elderly and dementia patients who live alone and reduce the cost of caring.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.1-13
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• 2022

With the development of network technologies, the security to protect organizational resources from internal and external intrusions and threats becomes more important. Therefore in recent years, the anomaly detection algorithm that detects and prevents security threats with respect to various security log events has been actively studied. Security anomaly detection algorithms that have been developed based on rule-based or statistical learning in the past are gradually evolving into modeling based on machine learning and deep learning. In this study, we propose a deep-autoencoder model that transforms LSTM-autoencoder as an optimal algorithm to detect insider threats in advance using various machine learning analysis methodologies. This study has academic significance in that it improved the possibility of adaptive security through the development of an anomaly detection algorithm based on unsupervised learning, and reduced the false positive rate compared to the existing algorithm through supervised true positive labeling.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.587-590
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• 2020

스마트폰, GPS 장비, 위치 기반 소셜네트워크의 발달로 방대한 이동 경로 데이터 수집이 가능하게 됐다. 이를 통해 다양한 분야에서 GPS 데이터를 가지고 사람의 이동성을 분석하고 POI를 예측하는 기회가 많아졌다. 실생활에서 사람의 이동성은 다양한 상황에 영향을 받지만, 실제 GPS 데이터는 위치, 시간 정보의 수준이다. 따라서 다양한 상황을 내재하는 정보가 사람의 이동성 분석과 POI 예측에 필요하다. 본 논문에서는 POI의 순위, 사용자의 POI 활동, 카테고리 선호도 같은 맥락적 특징을 이용하여 이에 관련된 상황에 맞는 POI 시퀀스를 예측하는 Contextual LSTM 기반 딥러닝 기법을 제안한다. Contextual LSTM은 사람의 이동성에 영향을 주는 시퀀스의 맥락적 특징을 모델에 통합하기 위해 LSTM을 확장한다. 제안된 기법은 HITS 알고리즘과 여러 제약조건 기반으로 추출한 맥락적 특징별로 딥 러닝 모델에 통합하여 각각 POI 시퀀스를 검출했으며, 다양한 맥락적 특징에 대해서 공공 데이터와 수집한 데이터로 평가하였다.

• Smart Media Journal
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• v.13 no.4
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• pp.48-56
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• 2024

In the livestock industry, detecting environmental outliers and predicting data are crucial tasks. Outliers in livestock environment data, typically gathered through time-series methods, can signal rapid changes in the environment and potential unexpected epidemics. Prompt detection and response to these outliers are essential to minimize stress in livestock and reduce economic losses for farmers by early detection of epidemic conditions. This study employs two methods to experiment and compare performances in setting thresholds that define outliers in livestock environment data outlier detection. The first method is an outlier detection using Mean Squared Error (MSE), and the second is an outlier detection using a Dynamic Threshold, which analyzes variability against the average value of previous data to identify outliers. The MSE-based method demonstrated a 94.98% accuracy rate, while the Dynamic Threshold method, which uses standard deviation, showed superior performance with 99.66% accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.171-174
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• 2024

IoT 가 전 산업에 널리 활용되면서 생성되는 데이터 양이 급증하고 있다. 그러나 경량, 저가, 저전력 IoT 는 대용량 데이터를 처리, 저장, 전송하기 어렵다. 그러나 이러한 문제를 해결하기 위한 종래의 방법들은 복잡도와 성능의 트레이드오프 문제가 있다. 본 논문은 IoT 기기의 효율적 리소스 사용을 위한 오토 인코더 데이터 압축 기법을 제안한다. 실험 결과에 따르면 제안한 기법은 종래 기술에 비해 평균 60.61% 축소된 데이터 크기를 보였다. 또한, 제안된 기법으로 압축된 데이터를 사용하여 모델 학습을 진행한 결과에 따르면 RNN 과 LSTM 모델에 제안한 방법을 적용했을 때 모두 97% 이상의 정확도를 보였다.

• Information Systems Review
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• v.23 no.1
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• pp.23-43
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• 2021

Recently, many studies have been conducted to increase the accuracy of stock price prediction by analyzing candlestick charts using artificial intelligence techniques. However, these studies failed to consider the time-series characteristics of candlestick charts and to take into account the emotional state of market participants in data learning for stock price prediction. In order to overcome these limitations, this study produced input data by combining volatility index and candlestick charts to consider the emotional state of market participants, and used the data as input for a new method proposed on the basis of combining variantion autoencoder (VAE) and attention mechanisms for considering the time-series characteristics of candlestick chart. Fifty firms were randomly selected from the S&P 500 index and their stock prices were predicted to evaluate the performance of the method compared with existing ones such as convolutional neural network (CNN) or long-short term memory (LSTM). The results indicated the method proposed in this study showed superior performance compared to the existing ones. This study implied that the accuracy of stock price prediction could be improved by considering the emotional state of market participants and the time-series characteristics of the candlestick chart.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.717-724
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• 2020

Due to the important role played by emotion in human interaction, affective computing is dedicated in trying to understand and regulate emotion through human-aware artificial intelligence. By understanding, emotion mental diseases such as depression, autism, attention deficit hyperactivity disorder, and game addiction will be better managed as they are all associated with emotion. Various studies for emotion recognition have been conducted to solve these problems. In applying machine learning for the emotion recognition, the efforts to reduce the complexity of the algorithm and improve the accuracy are required. In this paper, we investigate emotion Electroencephalogram (EEG) feature reduction and classification using Stack AutoEncoder (SAE) and Long-Short-Term-Memory/Recurrent Neural Networks (LSTM/RNN) classification respectively. The proposed method reduced the complexity of the model and significantly enhance the performance of the classifiers.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.435-438
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• 2020

시장예측 문제를 해결하기 위하여 과거부터 꾸준한 연구가 진행되어왔다. 하지만 금융 시계열 데이터에는 분산이 일정하지 않으며 Non-stationarity 등 예측을 하는 것에 있어서 여러 가지 방해 요인이 존재한다. 또한 광범위한 데이터 변수는 기존에 사람이 직접 경험적으로 선택하는 것에 한계가 있기 때문에, 모델이 변수를 자동으로 추출할 수 있어야 한다. 본 논문에서는 여러 가지 금융 시계열 데이터의 문제를 고려하여 타임 스텝 정규화를 제안하며 자동 변수 추출을 위해 LSTM 형태의 오토 인코더 모델을 학습하였으며 LSTM 네트워크를 이용하여 시장 예측하는 모델을 제안한다. 해당 시스템은 실제 주식 거래나 시장 거래를 위하여 온라인 학습이 가능하며 긴 기간을 테스트 구간으로 실험한 결과 미래의 수익률을 예측하는 것에 있어서 우수한 성능을 보였다.