An Efficient Spatial-Temporal Convolution Recurrent Neural Network Surrogate Model for History Matching

Summary Surrogate modeling has shown to be effective in improving the solving efficiency for history matching in the development of oil and gas, but the traditional surrogate models are difficult to directly deal with the high-dimensional spatial uncertain parameters, such as the permeability field. In this paper, we introduce a new deep-learning-based surrogate modeling framework, image-to-sequence regression, which can directly predict the production data from the high-dimensional spatial parameters. Specifically, a spatial-temporal convolution recurrent neural network surrogate model is proposed based on a densely connected convolutional neural network (CNN) model and a stacked multilayer long short-term memory (LSTM) model. And a surrogate-based history-matching workflow is then developed by combining the proposed surrogate model with an improved ensemble smoother data assimilation algorithm. Two case studies on a 2D and a 3D reservoir model demonstrate that the proposed surrogate model can effectively predict production data and improve the efficiency of history matching.

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Parameter Tuning onto Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) Network for Feature Selection in Classification of High-Dimensional Bioinformatics Datasets

Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing - Springer Tracts in Nature-Inspired Computing ◽

10.1007/978-981-15-6695-0_2 ◽

2020 ◽

pp. 21-42

Author(s):

Richard Millham ◽

Israel Edem Agbehadji ◽

Hongji Yang

Keyword(s):

Neural Network ◽

Feature Selection ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Parameter Tuning ◽

High Dimensional ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

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Recurrent neural network-based acute concussion classifier using raw resting state EEG data

Scientific Reports ◽

10.1038/s41598-021-91614-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Karun Thanjavur ◽

Arif Babul ◽

Brandon Foran ◽

Maya Bielecki ◽

Adam Gilchrist ◽

...

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Resting State ◽

Short Term Memory ◽

Characteristic Curve ◽

Health Concern ◽

Adolescent Athletes ◽

Individual Level ◽

Diffuse Brain Injury ◽

Eeg Data

AbstractConcussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, the most significant challenge in concussion management is the lack of objective, clinically- accepted, brain-based approaches for determining whether an athlete has suffered a concussion. Here, we report on our efforts to address this challenge. Specifically, we introduce a deep learning long short-term memory (LSTM)-based recurrent neural network that is able to distinguish between non-concussed and acute post-concussed adolescent athletes using only short (i.e. 90 s long) samples of resting state EEG data as input. The athletes were neither required to perform a specific task nor expected to respond to a stimulus during data collection. The acquired EEG data were neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and validated using data from 27 male, adolescent athletes with sports related concussion, benchmarked against 35 non-concussed adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of > 90% and achieved an ensemble median Area Under the Receiver Operating Characteristic Curve (ROC/AUC) equal to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state, raw EEG data. Our concussion classifier represents a promising first step towards the development of an easy-to-use, objective, brain-based, automatic classification of concussion at an individual level.

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Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189622 ◽

2021 ◽

pp. 1-11

Author(s):

Sang-Ki Jeong ◽

Dea-Hyeong Ji ◽

Ji-Youn Oh ◽

Jung-Min Seo ◽

Hyeung-Sik Choi

Keyword(s):

Neural Network ◽

Control System ◽

Flow Velocity ◽

Recurrent Neural Network ◽

Pid Controller ◽

Short Term Memory ◽

Ocean Currents ◽

Ocean Current ◽

Movement Speed ◽

Marine Research

In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9].

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