• ETRI Journal
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• 제44권2호
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• pp.208-219
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• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.271-280
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• 2018

Recently, big data has been growing rapidly due to the development of IT technology. Especially in the medical field, big data is utilized to provide services such as patient-customized medical care, disease management and disease prediction. In Korea, 'National Health Alarm Service' is provided by National Health Insurance Corporation. However, the prediction model has a problem of short-term prediction within 3 days and unreliability of social data used in prediction model. In order to solve these problems, this paper proposes a disease prediction model using medicine prescription data generated from actual patients. This model predicts the total number of patients and the risk of disease in each region and uses the ARIMA model for long-term predictions.

• 농업과학연구
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• 제47권4호
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2009년도 춘계 학술대회 제21권1호
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• pp.199-200
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• 2009

본 연구는 균열이 발생한 철근콘크리트 구조 부재에서 공용중 철근 부식과 콘크리트의 건조수축 및 크리프의 영향을 고려한 장기 균열폭을 예측할 수 있도록 부착특성에 기반한 모델을 개발한 것이다.

• 한국지반공학회지:지반
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• 제13권2호
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• pp.65-76
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• 1997

난지도 쓰레기 매립지 뿐만 아니라 대도시 주변 매립지의 사후활용 방안에 대한 관심이 점차로 고조되고 있다. 본 논문에서는 난지도 쓰레기 매립지에서 2년여간 계측된 침하자료를 분석하여 침하양상을 규명하고자 하였다. 침하분석결과 난지도 쓰레기 매립지의 침하양상은 미국내 24개 매립지에 대해 분석된 침하경향과 유사함을 알 수 있었다. 계측된 침하자료에 대한 해석을 근거로 할 경우, Bjarngard와 Edgers의 침하모델이 난지도 매립지의 장기침하량 예측에 적합한 것으로 사료된다. 10년 후 장기침하량을 예측할 때 Bjarngard와 Edgers의 침하모델은 Power Creep Model과 상당한 예측의 차이를 보였다. 난지도 쓰레기 매립지 침하양상에 대한 분석이 이루어지지 않은 상황에서 단지 기존의 침하모델만을 사용하는 것은 장기침하량 예측시 상당한 오류를 범할 수 있음을 알 수 있었다.

• 한국전자통신학회논문지
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• 제16권2호
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• pp.329-338
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• 2021

본 연구는 풍력발전의 합리적인 운영 계획과 에너지 저장창치의 용량산정을 위한 풍력 발전량을 예측한다. 예측을 위해 물리적 접근법과 통계적 접근법을 결합하여 풍력 발전량의 예측 방법을 제시하고 풍력 발전의 요인을 분석하여 변수를 선정한다. 선정된 변수들의 과거 데이터를 수집하여 딥러닝을 이용해 풍력 발전량을 예측한다. 사용된 모델은 Bidirectional LSTM(:Long short term memory)과 CNN(:Convolution neural network) 알고리즘을 결합한 하이브리드 모델을 구성하였으며, 예측 성능 비교를 위해 MLP 알고리즘으로 이루어진 모델과 오차를 비교하여, 예측 성능을 평가하고 그 결과를 제시한다.

• 한국해안·해양공학회논문집
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• 제33권6호
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• pp.238-245
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• 2021

본 연구에서는 LSTM 모델을 활용하여 낙동강 하구역의 DO 농도 예측을 위한 최적 모델 조건과 적합한 예측변수를 찾기 위한 Case study를 수행하였다. 모델 매개변수 case study 결과, Epoch = 300과 Sequence length = 1에서 상대적으로 높은 정확도를 보였다. 예측변수 case study 결과, DO와 수온을 예측변수로 했을 때 가장 높은 정확도를 보였으며, 이는 DO 농도와 수온의 높은 상관성에 기인한 것으로 판단된다. 상기 결과로부터 낙동강 하구역의 DO 농도 예측에 적합한 LSTM 모델 조건과 예측변수를 찾을 수 있었다.

• Wind and Structures
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• 제38권2호
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• pp.147-160
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• 2024

The aerodynamic force is a significant component that influences the stability and safety of structures. It has unstable properties and depends on computer precision, making its long-term prediction challenging. Accurately estimating the aerodynamic traits of structures is critical for structural design and vibration control. This paper establishes an unsteady aerodynamic time series prediction model using Long Short-Term Memory (LSTM) network. The unsteady aerodynamic force under varied Reynolds number and angles of attack is predicted by the LSTM model. The input of the model is the aerodynamic coefficients of the 1 to n sample points and output is the aerodynamic coefficients of the n+1 sample point. The model is predicted by interpolation and extrapolation utilizing Unsteady Reynolds-average Navier-Stokes (URANS) simulation data of flow around a circular cylinder, square cylinder and airfoil. The results illustrate that the trajectories of the LSTM prediction results and URANS outcomes are largely consistent with time. The mean relative error between the forecast results and the original results is less than 6%. Therefore, our technique has a prospective application in unsteady aerodynamic force prediction of structures and can give technical assistance for engineering applications.

• 스마트미디어저널
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• 제12권11호
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• pp.134-144
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• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.