Ensemble Fog Prediction

2017 ◽  
pp. 477-509 ◽  
Author(s):  
Jun Du ◽  
Binbin Zhou
Keyword(s):  
Fog Prediction

scholarly journals FogNet: A multiscale 3D CNN with double-branch dense block and attention mechanism for fog prediction

2021 ◽  
pp. 100038
Author(s):  
Hamid Kamangir ◽  
Waylon Collins ◽  
Philippe Tissot ◽  
Scott A. King ◽  
Hue Thi Hong Dinh ◽  
...  
Keyword(s):  
Attention Mechanism ◽  
Fog Prediction ◽  
3D Cnn

Efficient fog prediction with multi-objective evolutionary neural networks

2018 ◽  
Vol 70 ◽  
pp. 347-358 ◽  
Author(s):  
A.M. Durán-Rosal ◽  
J.C. Fernández ◽  
C. Casanova-Mateo ◽  
J. Sanz-Justo ◽  
S. Salcedo-Sanz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Multi Objective ◽  
Evolutionary Neural Networks ◽  
Fog Prediction

scholarly journals Kin-FOG: Automatic Simulated Freezing of Gait (FOG) Assessment System for Parkinson’s Disease

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2416 ◽  
Author(s):  
Sara Soltaninejad ◽  
Irene Cheng ◽  
Anup Basu
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Freezing Of Gait ◽  
The Elderly ◽  
Assessment System ◽  
Microsoft Kinect ◽  
Data Sets ◽  
Fog Prediction ◽  
Foot Joint ◽  
Rehabilitation Clinic

Parkinson’s disease (PD) is one of the leading neurological disorders in the world with an increasing incidence rate for the elderly. Freezing of Gait (FOG) is one of the most incapacitating symptoms for PD especially in the later stages of the disease. FOG is a short absence or reduction of ability to walk for PD patients which can cause fall, reduction in patients’ quality of life, and even death. Existing FOG assessments by doctors are based on a patient’s diaries and experts’ manual video analysis which give subjective, inaccurate, and unreliable results. In the present research, an automatic FOG assessment system is designed for PD patients to provide objective information to neurologists about the FOG condition and the symptom’s characteristics. The proposed FOG assessment system uses an RGB-D sensor based on Microsoft Kinect V2 for capturing data for 5 healthy subjects who are trained to imitate the FOG phenomenon. The proposed FOG assessment system is called “Kin-FOG”. The analysis of foot joint trajectory of the motion captured by Kinect is used to find the FOG episodes. The evaluation of Kin-FOG is performed by two types of experiments, including: (1) simple walking (SW); and (2) walking with turning (WWT). Since the standing mode has features similar to a FOG episode, our Kin-FOG system proposes a method to distinguish between the FOG and standing episodes. Therefore, two general groups of experiments are conducted with standing state (WST) and without standing state (WOST). The gradient displacement of the angle between the foot and the ground is used as the feature for discriminating between FOG and standing modes. These experiments are conducted with different numbers of FOGs for getting reliable and general results. The Kin-FOG system reports the number of FOGs, their lengths, and the time slots when they occur. Experimental results demonstrate Kin-FOG has around 90% accuracy rate for FOG prediction in both experiments for different tasks (SW, WWT). The proposed Kin-FOG system can be used as a remote application at a patient’s home or a rehabilitation clinic for sending a neurologist the required FOG information. The reliability and generality of the proposed system will be evaluated for bigger data sets of actual PD subjects.

Comparison of Several Numerical Methods for Fog Prediction

2019 ◽  
Vol 32 (2) ◽  
pp. 193-201
Author(s):  
G. A. Zarochentsev ◽  
K. G. Rubinstein ◽  
V. I. Bychkova ◽  
R. Yu. Ignatov ◽  
Yu. I. Yusupov
Keyword(s):  
Numerical Methods ◽  
Fog Prediction

scholarly journals Toward Improving Coastal-Fog Prediction (C-FOG)

2021 ◽  
Author(s):  
Clive E. Dorman ◽  
Andrey A. Grachev ◽  
Ismail Gultepe ◽  
Harindra J. S. Fernando
Keyword(s):  
Fog Prediction ◽  
Coastal Fog

scholarly journals Prediction of Freezing of Gait in Parkinson’s Disease Using a Random Forest Model Based on an Orthogonal Experimental Design: A Pilot Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Zhonelue Chen ◽  
Gen Li ◽  
Chao Gao ◽  
Yuyan Tan ◽  
Jun Liu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Experimental Design ◽  
False Positive Rate ◽  
Freezing Of Gait ◽  
Orthogonal Experimental Design ◽  
Fog Prediction ◽  
Positive Rate ◽  
Extraction Parameters ◽  
Main Effects

PurposeThe purpose of this study was to introduce an orthogonal experimental design (OED) to improve the efficiency of building and optimizing models for freezing of gait (FOG) prediction.MethodsA random forest (RF) model was developed to predict FOG by using acceleration signals and angular velocity signals to recognize possible precursor signs of FOG (preFOG). An OED was introduced to optimize the feature extraction parameters.ResultsThe main effects and interaction among the feature extraction hyperparameters were analyzed. The false-positive rate, hit rate, and mean prediction time (MPT) were 27%, 68%, and 2.99 s, respectively.ConclusionThe OED was an effective method for analyzing the main effects and interactions among the feature extraction parameters. It was also beneficial for optimizing the feature extraction parameters of the FOG prediction model.

Radiation Fog Prediction Using a Simple Numerical Model

1999 ◽  
Vol 155 (1) ◽  
pp. 57-80 ◽  
Author(s):  
W. D. Meyer ◽  
G. V. Rao
Keyword(s):  
Numerical Model ◽  
Radiation Fog ◽  
Fog Prediction ◽  
Simple Numerical Model
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