• Journal of Information Processing Systems
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• v.18 no.1
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• pp.115-129
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• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.1
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• pp.15-23
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• 2020

In this paper, we propose a new method to detect traffic accidents in video from vehicle-mounted cameras (vehicle black box). We use the distance between vehicles to determine whether an accident has occurred. To calculate the position of each vehicle, we use object detection and tracking method. By the way, in a crowded road environment, it is so difficult to decide an accident has occurred because of parked vehicles at the edge of the road. It is not easy to discriminate against accidents from non-accidents because a moving vehicle and a stopped vehicle are mixed on a regular downtown road. In this paper, we try to increase the accuracy of the vehicle accident detection by using not only the motion of the surrounding vehicle but also ego-motion as the input of the Recurrent Neural Network (RNN). We improved the accuracy of accident detection compared to the previous method.

• Smart Structures and Systems
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• v.29 no.1
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• International Journal of Contents
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• v.18 no.2
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• pp.68-80
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• 2022

The health of the human heart is commonly measured using ECG (Electrocardiography) signals. To identify any anomaly in the human heart, the time-sequence of ECG signals is examined manually by a cardiologist or cardiac electrophysiologist. Lightweight anomaly detection on ECG signals in an embedded system is expected to be popular in the near future, because of the increasing number of heart disease symptoms. Some previous research uses deep learning networks such as LSTM and BiLSTM to detect anomaly signals without any handcrafted feature. Unfortunately, lightweight LSTMs show low precision and heavy LSTMs require heavy computing powers and volumes of labeled dataset for symptom classification. This paper proposes an ECG anomaly detection system based on two level BiLSTM for acceptable precision with lightweight networks, which is lightweight and usable at home. Also, this paper presents a new threshold technique which considers statistics of the current ECG pattern. This paper's proposed model with BiLSTM detects ECG signal anomaly in 0.467 ~ 1.0 F₁ score, compared to 0.426 ~ 0.978 F₁ score of the similar model with LSTM except one highly noisy dataset.

• ETRI Journal
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• v.44 no.2
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• pp.300-312
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• 2022

Recently, public safety services have attracted significant attention for their ability to protect people from crimes. Rapid detection of dangerous situations (that is, abnormal situations where someone may be harmed or killed) is required in public safety services to reduce the time required to respond to such situations. This study proposes a novel danger detection technology based on multimodal data, which includes data from multiple sensors (for example, accelerometer, gyroscope, heart rate, air pressure, and global positioning system sensors), and multilog data, which includes contextual logs of humans and places (for example, contextual logs of human activities and crime-ridden districts) over time. To recognize human activity (for example, walk, sit, and punch), the proposed technology uses multimodal data analysis with an attitude heading reference system and long short-term memory. The proposed technology also includes multilog data analysis for detecting whether recognized activities of humans are dangerous. The proposed danger detection technology will benefit public safety services by improving danger detection capabilities.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.286-286
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• 2023

물의 전체 순환 구조에서 토양수분이 차지하는 정량적 비중은 상대적으로 작지만, 강우-유출 과정의 비선형에 영향을 미치는 지배적 요인 중 하나이고, 토양 침식과 산사태, 농업생산량, 기후 변화 대응 등 광범위한 주제와 연관되어 있어, 토양수분의 물리과정에 대한 이해 증진과 예측 기술의 지속적인 개선이 필요하다. 본 연구에서는 금오공과대학교 유역 내에서 토양수분과 기상 요소를 실시간 관측하고, 기계학습 기법을 이용하여 토양수분을 단기 예측하는 기술을 개발하고 평가한다. 구체적으로는, 토양 관측 장비인 TEROS를 사용하여 표층 지점의 10cm, 심층 지점의 40cm에서의 토양수분, 토양장력과 토양온도를, 기상 관측 장비인 ATMOS를 사용하여 태양복사, 강수량, 기온, 풍속, 대기압 등 다양한 기상 요소를, 실시간 클라우드 방식으로 1여 년간 수집한 데이터를 활용한다. 또한, 과거 및 실시간 데이터를 기반으로 LSTM(Long-Short Term Memory) 기법을 사용하여 토양수분 예측 모형을 구축하고, 선행 예측 시간에 따른 모의 정확도를 평가한다. 기상 요소의 누적 등 자료 분석 방법이 표층 및 심층 토양수분 예측에 미치는 영향, 그리고 예측 모형 개선 방향에 대해 토의한다. 실시간 현장 관측 자료 및 인공지능 기반 단기 토양수분 예측 모의 기술은 소규모 유역의 수문순환 분석 및 물리기반 모형의 개선 등 다양한 분야에서 활용할 수 있을 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.92-92
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• 2023

본 연구에서는 대한민국 도시 유역에 대하여 딥러닝 네트워크 기반의 분산형 수문 모형을 개발하였다. 개발된 모형은 완전연결계층(Fully Connected Layer)으로 연결된 여러 개의 장단기 메모리(LSTM-Long Short-Term Memory) 은닉 유닛(Hidden Unit)으로 구성되었다. 개발된 모형을 사용하여 연구 지역인 중랑천 유역을 분석하기 위해 1km² 해상도의 239개 모델 격자 셀에서 10분 단위 레이더-지상 합성 강수량과 10분 단위 기온의 시계열을 입력으로 사용하여 10분 단위 하도 유량을 모의하였다. 모형은 보정과(2013~2016년)과 검증 기간(2017~2019년)에 대한 NSE 계수는각각 0.99와 0.67로 높은 정확도를 보였다. 본 연구는 모형을 추가적으로 심층 분석하여 다음과 같은 결론을 도출하였다: (1) 모형을 기반으로 생성된 유출-강수 비율 지도는 토지 피복 데이터에서 얻은 연구 지역의 불투수율 지도와 유사하며, 이는 모형이 수문학에 대한 선험적 정보에 의존하지 않고 입력 및 출력 데이터만으로 강우-유출 분할과정을 성공적으로 학습하였음을 의미한다. (2) 모형은 연속 수문 모형의 필수 전제 조건인 토양 수분 의존 유출 프로세스를 성공적으로 재현하였다; (3) 각 LSTM 은닉 유닛은 강수 자극에 대한 시간적 민감도가 다르며, 응답이 빠른 LSTM 은닉 유닛은 유역 출구 근처에서 더 큰 출력 가중치 계수를 가졌는데, 이는 모형이 강수 입력에 대한 직접 유출과 지하수가 주도하는 기저 흐름과 같이 응답 시간의 차이가 뚜렷한 수문순환의 구성 요소를 별도로 고려하는 메커니즘을 가지고 있음을 의미한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.336-336
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• 2023

가뭄은 가장 심각한 기상 재해 중 하나로 농업 생산, 사회경제 등 다양한 분야에 영향을 미친다. 국내의 경우 광주·전남지역이 1990년대 이후 30년 만에 제한 급수 위기에 처하는 역대 최악의 가뭄으로 지역민들은 심각한 피해가 발생하였다. 유럽의 경우 2022년 당시 500년 만에 찾아온 가뭄으로 인해 3분의 2에 해당하는 지역이 피해를 입었으며, 미국 서부 지역은 2000년부터 2021년까지 1200년 만에 가장 극심한 대가뭄을 겪은 것으로 나타났다. 지구온난화에 따른 기후변화로 인해 가뭄의 빈도와 강도가 증가함에 따라 피해도 커질 것으로 예상된다. 가뭄의 부정적인 영향으로 인해 정확하고 신뢰할 수 있는 가뭄 예측 기술이 필요하다. 본 연구에서는 가뭄예측을 위한 입력변수로서 GPM IMERG (The Integrated Multi-satellitE Retrievals for GPM) 강수량 자료와 NOAA에서 제공하는 8가지 북반구 대기패턴 자료 간의 상관성을 분석하였다. 입력변수 간의 상관성과 중장기 가뭄 예측을 위하여 딥러닝 모델 중 시계열 데이터에서 높은 예측 성능을 보이는 LSTM(Long Short Term-Memory)을 적용하여 가뭄을 예측하고자 한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.37-37
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• 2023

There is currently a high-accuracy modern forecasting method that uses machine learning algorithms or artificial neural network models to forecast river water levels or flowrate. As a result, this study aims to develop a mathematical model based on artificial neural networks to effectively forecast river water levels upstream of Tranh Culvert in North Vietnam's Bac Hung Hai irrigation system. The mathematical model was thoroughly studied and evaluated by using hydrological data from six gauge stations over a period of twenty-two years between 2000 and 2022. Furthermore, the results of the developed model were also compared to those of the long-short-term memory neural networks model. This study performs four predictions, with a forecast time ranging from 6 to 24 hours and a time step of 6 hours. To validate and test the model's performance, the Nash-Sutcliffe efficiency coefficient (NSE), mean absolute error, and root mean squared error were calculated. During the testing phase, the NSE of the model varies from 0.981 to 0.879, corresponding to forecast cases from one to four time steps ahead. The forecast results from the model are very reasonable, indicating that the model performed excellently. Therefore, the proposed model can be used to forecast water levels in North Vietnam's irrigation system or rivers impacted by tides.