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 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 Data-driven prognosis method using hybrid deep recurrent neural network

2020 ◽  
Vol 93 ◽  
pp. 106351 ◽  
Author(s):  
Min Xia ◽  
Xi Zheng ◽  
Muhammad Imran ◽  
Muhammad Shoaib
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Data Driven ◽  
Deep Recurrent Neural Network

scholarly journals A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5133
Author(s):  
Yongfeng Suo ◽  
Wenke Chen ◽  
Christophe Claramunt ◽  
Shenhua Yang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Computational Time ◽  
Identification System ◽  
Trajectory Prediction ◽  
Ground Truth Data ◽  
Redundant Data ◽  
Computation Efficiency ◽  
Path Distance

Ship trajectory prediction is a key requisite for maritime navigation early warning and safety, but accuracy and computation efficiency are major issues still to be resolved. The research presented in this paper introduces a deep learning framework and a Gate Recurrent Unit (GRU) model to predict vessel trajectories. First, series of trajectories are extracted from Automatic Identification System (AIS) ship data (i.e., longitude, latitude, speed, and course). Secondly, main trajectories are derived by applying the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Next, a trajectory information correction algorithm is applied based on a symmetric segmented-path distance to eliminate the influence of a large number of redundant data and to optimize incoming trajectories. A recurrent neural network is applied to predict real-time ship trajectories and is successively trained. Ground truth data from AIS raw data in the port of Zhangzhou, China were used to train and verify the validity of the proposed model. Further comparison was made with the Long Short-Term Memory (LSTM) network. The experiments showed that the ship’s trajectory prediction method can improve computational time efficiency even though the prediction accuracy is similar to that of LSTM.

Bayesian recurrent neural network as a tool for reconstructing dynamical systems from multidimensional data

2020 ◽  
Author(s):  
Alexander Feigin ◽  
Aleksei Seleznev ◽  
Dmitry Mukhin ◽  
Andrey Gavrilov ◽  
Evgeny Loskutov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Cost Function ◽  
Recurrent Neural Network ◽  
Bayesian Optimization ◽  
Data Driven ◽  
High Dimensional ◽  
Model Learning ◽  
Low Dimensional ◽  
The Cost

<p>We suggest a new method for construction of data-driven dynamical models from observed multidimensional time series. The method is based on a recurrent neural network (RNN) with specific structure, which allows for the joint reconstruction of both a low-dimensional embedding for dynamical components in the data and an operator describing the low-dimensional evolution of the system. The key link of the method is a Bayesian optimization of both model structure and the hypothesis about the data generating law, which is needed for constructing the cost function for model learning.  The form of the model we propose allows us to construct a stochastic dynamical system of moderate dimension that copies dynamical properties of the original high-dimensional system. An advantage of the proposed method is the data-adaptive properties of the RNN model: it is based on the adjustable nonlinear elements and has easily scalable structure. The combination of the RNN with the Bayesian optimization procedure efficiently provides the model with statistically significant nonlinearity and dimension.<br>The method developed for the model optimization aims to detect the long-term connections between system’s states – the memory of the system: the cost-function used for model learning is constructed taking into account this factor. In particular, in the case of absence of interaction between the dynamical component and noise, the method provides unbiased reconstruction of the hidden deterministic system. In the opposite case when the noise has strong impact on the dynamics, the method yield a model in the form of a nonlinear stochastic map determining the Markovian process with memory. Bayesian approach used for selecting both the optimal model’s structure and the appropriate cost function allows to obtain the statistically significant inferences about the dynamical signal in data as well as its interaction with the noise components.<br>Data driven model derived from the relatively short time series of the QG3 model – the high dimensional nonlinear system producing chaotic behavior – is shown be able to serve as a good simulator for the QG3 LFV components. The statistically significant recurrent states of the QG3 model, i.e. the well-known teleconnections in NH, are all reproduced by the model obtained. Moreover, statistics of the residence times of the model near these states is very close to the corresponding statistics of the original QG3 model. These results demonstrate that the method can be useful in modeling the variability of the real atmosphere.</p><p>The work was supported by the Russian Science Foundation (Grant No. 19-42-04121).</p>

scholarly journals A 4D Trajectory Prediction Model Based on the BP Neural Network

2019 ◽  
Vol 29 (1) ◽  
pp. 1545-1557 ◽  
Author(s):  
Zhi-Jun Wu ◽  
Shan Tian ◽  
Lan Ma
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Clustering Algorithm ◽  
Spline Interpolation ◽  
Absolute Error ◽  
Maximum Absolute Error ◽  
Trajectory Prediction ◽  
Model Based ◽  
Crossing Point

Abstract To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation (BP) neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based on the BP neural network. It was trained by Automatic Dependent Surveillance – Broadcast trajectory from Qingdao to Beijing and used to predict the flight trajectory at future moments. In this paper, the model is evaluated by the common measurement index such as maximum absolute error, mean absolute error and root mean square error. It also gives an analysis and comparison of the predicted over-point time, the predicted over-point altitude, the actual over-point time and the actual over-point altitude. The results indicate that the predicted 4D trajectory is close to the real flight data, and the time error at the crossing point is no more than 1 min and the altitude error at the crossing point is no more than 50 m, which is of high accuracy.

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