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

Mesoscale Model Simulation of Coastal Fog

1997 ◽  
Author(s):  
Christopher A. Davis
Keyword(s):  
Mesoscale Model ◽  
Model Simulation ◽  
Coastal Fog

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

Numerical study of a coastal fog event over Casablanca, Morocco

2014 ◽  
Vol 141 (690) ◽  
pp. 1894-1905 ◽  
Author(s):  
Driss Bari ◽  
Thierry Bergot ◽  
Mohamed El Khlifi
Keyword(s):  
Numerical Study ◽  
Coastal Fog

Simulations of Coastal Fog in the Canadian Atlantic with the Weather Research and Forecasting Model

2021 ◽  
Author(s):  
Reneta Dimitrova ◽  
Ashish Sharma ◽  
Harindra J. S. Fernando ◽  
Ismail Gultepe ◽  
Ventsislav Danchovski ◽  
...  
Keyword(s):  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Coastal Fog ◽  
Weather Research

scholarly journals ENSO Influence on Coastal Fog-Water Yield in the Atacama Desert, Chile

2018 ◽  
Vol 18 (1) ◽  
pp. 127-144 ◽  
Author(s):  
Camilo del Río ◽  
Daniela Rivera ◽  
Alexander Siegmund ◽  
Nils Wolf ◽  
Pilar Cereceda ◽  
...  
Keyword(s):  
Atacama Desert ◽  
Water Yield ◽  
Fog Water ◽  
Coastal Fog

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.

Anomalous Conditions in the Slope Water Area in 1959

1964 ◽  
Vol 21 (2) ◽  
pp. 327-333 ◽  
Author(s):  
L. V. Worthington
Keyword(s):  
Coastal Water ◽  
North Atlantic ◽  
Cold Water ◽  
Large Data ◽  
Water Area ◽  
Research Vessel ◽  
Weather Pattern ◽  
High Incidence ◽  
Coastal Fog ◽  
Deep Current

An oceanographic section made with the research vessel Crawford in June 1959 showed that the proportion of Labrador-Coastal Water to Slope Water at the meridian 57°30′W was unusually large. Data from this section combined with bathythermograms taken elsewhere in the Slope Water area and direct deep current measurements suggest that there was an abnormal influx of cold water from the Labrador Basin in 1959. This suggestion is strengthened by the high incidence of coastal fog in the summer of 1959. It is suggested that the cause of this influx was an abnormal North Atlantic weather pattern in January 1959.

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
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