Automatic Fog Detection with Alcohol Detector

Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson’s disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging wearable devices and machine-learning methods to develop and evaluate automated detection of FOG and quantification of its severity. Seventy-one subjects with FOG completed a FOG-provoking test while wearing three wearable sensors (lower back and each ankle). Subjects were videotaped before (OFF state) and after (ON state) they took their antiparkinsonian medications. Annotations of the videos provided the “ground-truth” for FOG detection. A leave-one-patient-out validation process with a training set of 57 subjects resulted in 84.1% sensitivity, 83.4% specificity, and 85.0% accuracy for FOG detection. Similar results were seen in an independent test set (data from 14 other subjects). Two derived outcomes, percent time frozen and number of FOG episodes, were associated with self-report of FOG. Bother derived-metrics were higher in the OFF state than in the ON state and in the most challenging level of the FOG-provoking test, compared to the least challenging level. These results suggest that this automated machine-learning approach can objectively assess FOG and that its outcomes are responsive to therapeutic interventions.

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Arctic Fog Detection Using Infrared Spectral Measurements

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-18-0100.1 ◽

2019 ◽

Vol 36 (8) ◽

pp. 1643-1656

Author(s):

Li Yi ◽

King-Fai Li ◽

Xianyao Chen ◽

Ka-Kit Tung

Keyword(s):

Sea Ice ◽

Chukchi Sea ◽

Open Water ◽

Detection Algorithm ◽

Probability Of Detection ◽

Satellite Observations ◽

The Arctic ◽

Water Surface Area ◽

Infrared Spectral ◽

Fog Detection

AbstractThe rapid increase in open-water surface area in the Arctic, resulting from sea ice melting during the summer likely as a result of global warming, may lead to an increase in fog [defined as a cloud with a base height below 1000 ft (~304 m)], which may imperil ships and small aircraft transportation in the region. There is a need for monitoring fog formation over the Arctic. Given that ground-based observations of fog over Arctic open water are very sparse, satellite observations may become the most effective way for Arctic fog monitoring. We developed a fog detection algorithm using the temperature difference between the cloud top and the surface, called ∂T in this work. A fog event is said to be detected if ∂T is greater than a threshold, which is typically between −6 and −12 K, depending on the time of the day (day or night) and the surface types (open water or sea ice). We applied this method to the coastal regions of Chukchi Sea and Beaufort Sea near Barrow, Alaska (now known as Utqiaġvik), during the months of March–October. Training with satellite observations between 2007 and 2014 over this region, the ∂T method can detect Arctic fog with an optimal probability of detection (POD) between 74% and 90% and false alarm rate (FAR) between 5% and 17%. These statistics are validated with data between 2015 and 2016 and are shown to be robust from one subperiod to another.

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Optical Thickness and Effective Radius Retrievals of Low Stratus and Fog from MTSAT Daytime Data as a Prerequisite for Yellow Sea Fog Detection

Remote Sensing ◽

10.3390/rs8010008 ◽

2015 ◽

Vol 8 (1) ◽

pp. 8 ◽

Cited By ~ 6

Author(s):

Li Yi ◽

Boris Thies ◽

Suping Zhang ◽

Xiaomeng Shi ◽

Jörg Bendix

Keyword(s):

Optical Thickness ◽

Yellow Sea ◽

Effective Radius ◽

Sea Fog ◽

Fog Detection

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Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data

Remote Sensing ◽

10.3390/rs12193181 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3181

Author(s):

Ji-Hye Han ◽

Myoung-Seok Suh ◽

Ha-Yeong Yu ◽

Na-Young Roh

Keyword(s):

South Korea ◽

Geographic Location ◽

Surface Characteristics ◽

Detection Algorithm ◽

Time Of Day ◽

Probability Of Detection ◽

Bias Ratio ◽

Quantitative Validation ◽

Fog Detection ◽

Total Average

Fog affects transportation due to low visibility and also aggravates air pollutants. Thus, accurate detection and forecasting of fog are important for the safety of transportation. In this study, we developed a decision tree type fog detection algorithm (hereinafter GK2A_FDA) using the GK2A/AMI and auxiliary data. Because of the responses of the various channels depending on the time of day and the underlying surface characteristics, several versions of the algorithm were created to account for these differences according to the solar zenith angle (day/dawn/night) and location (land/sea/coast). Numerical model data were used to distinguish the fog from low clouds. To test the detection skill of GK2A_FDA, we selected 23 fog cases that occurred in South Korea and used them to determine the threshold values (12 cases) and validate GK2A_FDA (11 cases). Fog detection results were validated using the visibility data from 280 stations in South Korea. For quantitative validation, statistical indices, such as the probability of detection (POD), false alarm ratio (FAR), bias ratio (Bias), and equitable threat score (ETS), were used. The total average POD, FAR, Bias, and ETS for training cases (validation cases) were 0.80 (0.82), 0.37 (0.29), 1.28 (1.16), and 0.52 (0.59), respectively. In general, validation results showed that GK2A_FDA effectively detected the fog irrespective of time and geographic location, in terms of accuracy and stability. However, its detection skill and stability were slightly dependent on geographic location and time. In general, the detection skill and stability of GK2A_FDA were found to be better on land than on coast at all times, and at night than day at any location.

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Fog detection and warning, a novel approach to sensor location

1999 IEEE Africon. 5th Africon Conference in Africa (Cat. No.99CH36342) ◽

10.1109/afrcon.1999.820751 ◽

1999 ◽

Cited By ~ 4

Author(s):

K.R. MacHutchon ◽

A. Ryan

Keyword(s):

Sensor Location ◽

Novel Approach ◽

Fog Detection

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Evaluation of Sea fog Detection Accuracy Based on Geostationary Satellite Image Using Machine Learning

10.5194/egusphere-egu2020-22319 ◽

2020 ◽

Author(s):

NaKyeong Kim ◽

Suho Bak ◽

Minji Jeong ◽

Hongjoo Yoon

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Satellite Remote Sensing ◽

Geostationary Satellite ◽

Machine Learning Algorithms ◽

Detection Accuracy ◽

Sea Fog ◽

Short Period ◽

Wide Range ◽

Fog Detection

<p><span>A sea fog is a fog caused by the cooling of the air near the ocean-atmosphere boundary layer when the warm sea surface air moves to a cold sea level. Sea fog affects a variety of aspects, including maritime and coastal transportation, military activities and fishing activities. In particular, it is important to detect sea fog as they can lead to ship accidents due to reduced visibility. Due to the wide range of sea fog events and the lack of constant occurrence, it is generally detected through satellite remote sensing. Because sea fog travels in a short period of time, it uses geostationary satellites with higher time resolution than polar satellites to detect fog. A method for detecting fog by using the difference between 11 &#956;m channel and 3.7 &#956;m channel was widely used when detecting fog by satellite remote sensing, but this is difficult to distinguish between lower clouds and fog. Traditional algorithms are difficult to find accurate thresholds for fog and cloud. However, machine learning algorithms can be used as a useful tool to determine this. In this study, based on geostationary satellite imaging data, a comparative analysis of sea fog detection accuracy was conducted through various methods of machine learning, such as Random Forest, Multi-Layer Perceptron, and Convolutional Neural Networks.</span></p>

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Towards night fog detection through use of in-vehicle multipurpose cameras

2011 IEEE Intelligent Vehicles Symposium (IV) ◽

10.1109/ivs.2011.5940486 ◽

2011 ◽

Cited By ~ 22

Author(s):

Romain Gallen ◽

Aurelien Cord ◽

Nicolas Hautiere ◽

Didier Aubert

Keyword(s):

Fog Detection

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