scholarly journals Development of ship behavior prediction model by using Recurrent Neural Network

2020 ◽  
Vol 143 (0) ◽  
pp. 77-82
Author(s):  
Yoshihiro SHIO ◽  
Hiroko ITOH ◽  
Yasumi KAWAMURA ◽  
Sonoko KAWASHIMA
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Recurrent Neural Network ◽  
Behavior Prediction ◽  
Ship Behavior

scholarly journals Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6460
Author(s):  
Dae-Yeon Kim ◽  
Dong-Sik Choi ◽  
Jaeyun Kim ◽  
Sung Wan Chun ◽  
Hyo-Wook Gil ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Glucose Level ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Percentage Error ◽  
Glucose Prediction

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

scholarly journals Rating Player Actions in Soccer

2021 ◽  
Vol 3 ◽  
Author(s):  
Uwe Dick ◽  
Maryam Tavakol ◽  
Ulf Brefeld
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Recurrent Neural Network ◽  
Performance Indicators ◽  
Data Driven ◽  
Tracking Data ◽  
Trajectory Prediction ◽  
Study Approach

We present a data-driven model that rates actions of the player in soccer with respect to their contribution to ball possession phases. This study approach consists of two interconnected parts: (i) a trajectory prediction model that is learned from real tracking data and predicts movements of players and (ii) a prediction model for the outcome of a ball possession phase. Interactions between players and a ball are captured by a graph recurrent neural network (GRNN) and we show empirically that the network reliably predicts both, player trajectories as well as outcomes of ball possession phases. We derive a set of aggregated performance indicators to compare players with respect to. to their contribution to the success of their team.

scholarly journals Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4211 ◽  
Author(s):  
Miao Gao ◽  
Guoyou Shi ◽  
Shuang Li
Keyword(s):  
Neural Network ◽  
Collision Avoidance ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Automatic Identification ◽  
Identification System ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Ship Behavior

The real-time prediction of ship behavior plays an important role in navigation and intelligent collision avoidance systems. This study developed an online real-time ship behavior prediction model by constructing a bidirectional long short-term memory recurrent neural network (BI-LSTM-RNN) that is suitable for automatic identification system (AIS) date and time sequential characteristics, and for online parameter adjustment. The bidirectional structure enhanced the relevance between historical and future data, thus improving the prediction accuracy. Through the “forget gate” of the long short-term memory (LSTM) unit, the common behavioral patterns were remembered and unique behaviors were forgotten, improving the universality of the model. The BI-LSTM-RNN was trained using 2015 AIS data from Tianjin Port waters. The results indicate that the BI-LSTM-RNN effectively predicted the navigational behaviors of ships. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could potentially be applied as the predictive foundation for various intelligent systems, including intelligent collision avoidance, vessel route planning, operational efficiency estimation, and anomaly detection systems.

scholarly journals Bayesian Regularization Algorithm Based Recurrent Neural Network Method and NSGA-II for the Optimal Design of the Reflector

Machines ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 63
Author(s):  
Xinyong Zhang ◽  
Liwei Sun ◽  
Lingtong Qi
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Recurrent Neural Network ◽  
Bayesian Regularization ◽  
Nsga Ii ◽  
Regularization Algorithm ◽  
Modal Frequency ◽  
Dimensional Parameters ◽  
Optimization Search ◽  
Machine Structure

The optical-mechanical system of a space camera is composed of several complex components, and the effects of several factors (weight, gravity, modal frequency, temperature, etc.) on its system performance need to be considered during ground tests, launch, and in-orbit operation. In order to meet the system specifications of the optical camera system, the dimensional parameters of the optical camera structure need to be optimized. There is a highly nonlinear functional relationship between the dimensional parameters of the optical machine structure and the design indexes. The traditional method takes a significant amount of time for finite element calculation and is less efficient. In order to improve the optimization efficiency, a recurrent neural network prediction model based on the Bayesian regularization algorithm is proposed in this paper, and the NSGA-II is used to globally optimize multiple prediction objectives of the prediction model. The reflector of the space camera is used as an example to predict the weight, first-order modal frequency, and gravitational mirror deformation root mean square of the reflector, and to complete the lightweight design. The results show that the prediction model established by BR-RNN-NSGA-II offers high prediction accuracy for the design indexes of the reflector, which all reach over 99.6%, and BR-RNN-NSGA-II can complete the multi-objective optimization search efficiently and accurately. This paper provides a new idea of optimization of optical machine structure, which enriches the theory of complex structure design.

Sign in / Sign up

Export Citation Format

Share Document