scholarly journals Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network

2020 ◽  
Vol 12 (2) ◽  
pp. 253 ◽  
Author(s):  
Sung-Hwan Park ◽  
Hyung-Sup Jung ◽  
Moung-Jin Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Resolution ◽  
Oil Spill ◽  
Ocean Waves ◽  
Probability Of Detection ◽  
The Sun ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Artificial Neural

Oil spill accidents in marine environments have a massive impact on ecosystems. Various methods have been developed to detect oil spills using high-resolution optical imagery. However, ocean waves caused by heavy winds occurring in the accident area cause sun glint in the image, and this severely impedes the ability to detect the oil spill area. The objective of this study was to detect oil spill areas from high-resolution optic images using the artificial neural network (ANN) through effective suppression of severe sun glint effects. To enable this, a directional median filter (DMF) was adapted, and its use was compared with that of a traditional low-pass filter. A performance test was conducted using a KOMPSAT-2 image acquired during oil spill accidents that occurred in the Gulf of Mexico in 2010. The proposed method involved two main steps: (i) The sun glint effects caused by the ocean waves were corrected using the DMF; and (ii) the ANN approach was used to detect the oil spill area. The results show the following: (i) The designed DMF, which considers the size and angle of ocean waves, was proficient in correcting the sun glint effect in a high-resolution optical image; and (ii) oil spill areas were efficiently detected using the ANN approach with the proposed filtering method. The oil spill area was classified with accuracies of approximately 98.12% and 89.56% using the receiver operating characteristic (ROC) curve and probability of detection (POD) measurements, respectively. These results show that the accuracy of the proposed method is improved by about 9% compared to the traditional detecting algorithm.

scholarly journals Spatio-temporal prediction of suspended sediment concentration in the coastal zone using an artificial neural network and a numerical model

2012 ◽  
Vol 14 (3) ◽  
pp. 574-584 ◽  
Author(s):  
B. Bhattacharya ◽  
T. van Kessel ◽  
D. P. Solomatine
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Numerical Model ◽  
Series Data ◽  
Ann Model ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Temporal Prediction ◽  
Artificial Neural

A problem of predicting suspended particulate matter (SPM) concentration on the basis of wind and wave measurements and estimates of bed shear stress done by a numerical model is considered. Data at a location at 10 km offshore from Noordwijk in the Dutch coastal area is used. The time series data have been filtered with a low pass filter to remove short-term fluctuations due to noise and tides and the resulting time series have been used to build an artificial neural network (ANN) model. The accuracy of the ANN model during both storm and calm periods was found to be high. The possibilities to apply the trained ANN model at other locations, where the model is assisted by the correctors based on the ratio of long-term average SPM values for the considered location to that for Noordwijk (for which the model was trained), have been investigated. These experiments demonstrated that the ANN model's accuracy at the other locations was acceptable, which shows the potential of the considered approach.

scholarly journals Comparison and Performance Evaluation of Low Pass Filter Design for various samples using Artificial Neural Network

2016 ◽  
Vol 4 (3) ◽  
pp. 74-79
Author(s):  
Suchi Sharma ◽  
Anjana Goen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Filter Design ◽  
Back Propagation Algorithm ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Feed Forward Back Propagation ◽  
Window Method ◽  
Low Pass ◽  
Artificial Neural

To design any type of filters complex calculation is needed. But with the help of window method, it become simple. In this paper a Low pass filter is designed by window method with the help of Artificial Neural Network. Here, Hann window and Blackman window is used to design filter and Feed Forward Back Propagation algorithm has been taken for neural network. In this paper different data sets for cut off frequency are consider for training and testing purpose to get best results.

scholarly journals A Proposal for Sleep Scoring Analysis Designed by Computer Assisted using Physiological Signals

2021 ◽  
Vol 10 (5) ◽  
pp. 230-235
Author(s):  
Hemu Farooq ◽  
Anuj Jain ◽  
V.K. Sharma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Scoring System ◽  
The Body ◽  
Computer Assisted ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Electrical Signals ◽  
Sleep Scoring ◽  
Artificial Neural

Sleep is utterly regarded as compulsory component for a person’s prosperity and is an exceedingly important element for wellbeing of a healthy person. It is a condition in which an individual is physically and mentally at rest. The conception of sleep is considered extremely peculiar and is a topic of discussion and researchers all over the world has been attracted by this concept. Sleep analysis and its stages is analyzed to be useful in sleep research and sleep medicine area. By properly analyzing the sleep scoring system and its different stages has proven helpful for diagnosing sleep disorders. As it’s seen, sleep stage classification by manual process is a hectic procedure as it takes sufficient time for sleep experts to perform data analysis. Besides, mistakes and irregularities in between classification of same data can be recurrent. Therefore, the use of automatic scoring system in order to support reliable classification is highly in greater use. The scheduled work provides an insight to use the automatic scheme which is based on real time EMG signals and Artificial neural network. EMG is an electro neurological diagnostic tool which evaluates and records the electrical activity generated by muscle cells. The sleep scoring analysis can be applied by recording Electroencephalogram (EEG), Electromyogram (EMG), and Electrooculogram (EOG) based on epoch and this method is termed as PSG test or polysomnography test. The epoch measured has length segments for a period of 30 seconds. The standard database of EMG records was gathered from various hospitals in sleep laboratory which gives the different stages of sleep. These are Waking, Non-REM1 (stage-1), NonREM2 (stage-2), Non-REM3 (stage-3), REM. The collection of data was done for the period of 30 second known as epoch, for seven hours. The dataset obtained from the biological signal was managed so that necessary data is to be extracted from degenerated signal utilized for the purpose of study. As a matter of fact, it is known electrical signals are distributed throughout the body and is needed to be removed. These unwanted signals are termed as artifacts and they are removed with the help of filters. In this proposed work, the signal is filtered by making use of low-pass filter called Butterworth. The withdrawn characteristics were instructed and categorized by utilizing Artificial Neural Network (ANN). ANN, on the other hand is highly complicated network and utilizing same in the field of biomedical when contracted with electrical signals, acquired from human body is itself a novel. The precision obtained by the help of the procedure was discovered to be satisfactory and hence the process is very useful in clinics of sleep, especially helpful for neuro-scientists for discovering the disturbance in sleep.

scholarly journals Artificial neural network model for precipitation forecast over Western Himalaya using satellite images

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 83-90
Author(s):  
PIYUSH JOSHI ◽  
M.S. SHEKHAR ◽  
ASHAVANI KUMAR ◽  
J.K. QUAMARA
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Images ◽  
India Meteorological Department ◽  
Western Himalaya ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Ann Model ◽  
Skill Scores ◽  
Artificial Neural

Kalpana satellite images in real time available by India meteorological department (IMD), contain relevant inputs about the cloud in infra-red (IR), water vapor (WV), and visible (VIS) bands. In the present study an attempt has been made to forecast precipitation at six stations in western Himalaya by using extracted grey scale values of IR and WV images. The extracted pixel values at a location are trained for the corresponding precipitation at that location. The precipitation state at 0300 UTC is considered to train the model for precipitation forecast with 24 hour lead time. The satellite images acquired in IR (10.5 - 12.5 µm) and WV (5.7 - 7.1 µm) bands have been used for developing Artificial Neural Network (ANN) model for qualitative as well as quantitative precipitation forecast. The model results are validated with ground observations and skill scores are computed to check the potential of the model for operational purpose. The probability of detection at the six stations varies from 0.78 for Gulmarg in Pir-Panjal range to 0.95 for Dras in Greater Himalayan range. Overall performance for qualitative forecast is in the range from 61% to 84%. Root mean square error for different locations under study is in the range 5.81 to 8.7.

A Temperature-Compensated High-Resolution Microwave Sensor Using Artificial Neural Network

2020 ◽  
Vol 30 (9) ◽  
pp. 919-922 ◽  
Author(s):  
Nazli Kazemi ◽  
Mohammad Abdolrazzaghi ◽  
Petr Musilek ◽  
Mojgan Daneshmand
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Resolution ◽  
Microwave Sensor ◽  
Artificial Neural
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