intensity estimation
Recently Published Documents


TOTAL DOCUMENTS

332
(FIVE YEARS 122)

H-INDEX

24
(FIVE YEARS 5)

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 551
Author(s):  
Chih-Wei Lin ◽  
Xiuping Huang ◽  
Mengxiang Lin ◽  
Sidi Hong

Precipitation intensity estimation is a critical issue in the analysis of weather conditions. Most existing approaches focus on building complex models to extract rain streaks. However, an efficient approach to estimate the precipitation intensity from surveillance cameras is still challenging. This study proposes a convolutional neural network known as the signal filtering convolutional neural network (SF-CNN) to handle precipitation intensity using surveillance-based images. The SF-CNN has two main blocks, the signal filtering block (SF block) and the gradually decreasing dimension block (GDD block), to extract features for the precipitation intensity estimation. The SF block with the filtering operation is constructed in different parts of the SF-CNN to remove the noise from the features containing rain streak information. The GDD block continuously takes the pair of the convolutional operation with the activation function to reduce the dimension of features. Our main contributions are (1) an SF block considering the signal filtering process and effectively removing the useless signals and (2) a procedure of gradually decreasing the dimension of the feature able to learn and reserve the information of features. Experiments on the self-collected dataset, consisting of 9394 raining images with six precipitation intensity levels, demonstrate the proposed approach’s effectiveness against the popular convolutional neural networks. To the best of our knowledge, the self-collected dataset is the largest dataset for monitoring infrared images of precipitation intensity.


MAUSAM ◽  
2021 ◽  
Vol 48 (2) ◽  
pp. 157-168
Author(s):  
R. R. KELKAR

    ABSTRACT. Capabilities of meteorological satellites have gone a long way in meeting requirements of synoptic analysis and forecasting of tropical cyclones. This paper shows the impact made by the satellite data in the intensity estimation and track prediction of tropical cyclones in the Indian Seas and also reviews the universally applied Dvorak algorithm for performing tropical cyclone intensity analysis. Extensive use of Dvorak's intensity estimation scheme has revealed many of its limitations and elements of subjectivity in the analysis of tropical cyclones over the Arabian Sea and the Bay of Bengal, which, like cyclones in other ocean basins, also exhibit wide structural variability as seen in the satellite imagery. Satellite-based cyclone tracking techniques include: (i) use of satellite-derived mean wind flow,             (ii) animation of sequence of satellite images and extrapolation of the apparent motion of the cloud system and (iii) monitoring changes in the upper level moisture patterns in the water vapour absorption channel imagery. Satellite-based techniques on tropical cyclone intensity estimation and track prediction have led to very significant improvement in disaster warning and consequent saving of life and property.    


2021 ◽  
Vol 7 ◽  
pp. e736
Author(s):  
Olufisayo Ekundayo ◽  
Serestina Viriri

Facial Expression Recognition (FER) has gained considerable attention in affective computing due to its vast area of applications. Diverse approaches and methods have been considered for a robust FER in the field, but only a few works considered the intensity of emotion embedded in the expression. Even the available studies on expression intensity estimation successfully assigned a nominal/regression value or classified emotion in a range of intervals. Most of the available works on facial expression intensity estimation successfully present only the emotion intensity estimation. At the same time, others proposed methods that predict emotion and its intensity in different channels. These multiclass approaches and extensions do not conform to man heuristic manner of recognising emotion and its intensity estimation. This work presents a Multilabel Convolution Neural Network (ML-CNN)-based model, which could simultaneously recognise emotion and provide ordinal metrics as the intensity estimation of the emotion. The proposed ML-CNN is enhanced with the aggregation of Binary Cross-Entropy (BCE) loss and Island Loss (IL) functions to minimise intraclass and interclass variations. Also, ML-CNN model is pre-trained with Visual Geometric Group (VGG-16) to control overfitting. In the experiments conducted on Binghampton University 3D Facial Expression (BU-3DFE) and Cohn Kanade extension (CK+) datasets, we evaluate ML-CNN’s performance based on accuracy and loss. We also carried out a comparative study of our model with some popularly used multilabel algorithms using standard multilabel metrics. ML-CNN model simultaneously predicts emotion and intensity estimation using ordinal metrics. The model also shows appreciable and superior performance over four standard multilabel algorithms: Chain Classifier (CC), distinct Random K label set (RAKEL), Multilabel K Nearest Neighbour (MLKNN) and Multilabel ARAM (MLARAM).


MAUSAM ◽  
2021 ◽  
Vol 57 (1) ◽  
pp. 159-164
Author(s):  
B. R. LOE ◽  
R. K. GIRI ◽  
B. L. VERMA ◽  
S. BALI ◽  
SOMA SEN ROY

lkj & m".kdfVca/kh; pØokr dh rhozrk dk vkdyu djus ds fy, lewps fo’o esa O;kogkfjd :i ls mi;ksx dh tkus okyh M~oksjd rduhd esa mixzg ls izkIr fp=ksa dk mi;ksx fd;k tkrk gSA blesa O;ofLFkr laogu ds laca/k esa fo’ys"kd }kjk fd, x, foospu lfgr dqN izk;ksfxd ekunaMksa ds vk/kkj ij mixzg ls izkIr fp= ds iSVuZ dh igpku dh tkrh gSA fofHkUu fo’ys"k.k dsUnzksa }kjk fdlh ,d pØokr dk vkdyu djus esa gksus okyh fo"k;ijd foospu laca/kh folaxfr;k¡ daI;wVj ij vk/kkfjr ,yxksfjFe ds ek/;e ls de gqbZA bl la’kksf/kr rduhd dks fodflr fo"k;ijd M~oksjd rduhd ¼,- vks- Mh- Vh-½ dgk x;k vkSj ;g iw.kZ fodflr m".k dfVca/kh; pØokrksa ds fy, mi;qDr gSA bl 'kks/k&Ik= esa o"kZ 2004 esa vk, rhu m".kdfVca/kh; pØokrksa ds laca/k esa ,- vks- Mh- Vh- ds dk;Z & fu"iknu dk ewY;kdau fd;k x;k gSA buds rqYukRed fo’ys"k.k ls ;g irk pyk fd ,- vks- Mh- Vh- rduhd M~oksjd rduhd ds vk/kkj ij fd, x, pØokr dh rhozrk ds vkdyuksa] tks m".kdfVca/kh; fo’ys"k.k dsUnzksa ds mixzg ls izkIr fp=ksa ds fo’ys"kdksa }kjk O;kogkfjd :Ik ls rS;kj fd, x,] ds  eqdkcys dh jghA  Dvorak technique operationally used all over the world for estimating the tropical cyclone intensity is based on satellite observations. It involves image pattern recognition based on certain empirical rules along with the analyst interpretation of organized convection.  The computer-based algorithm can minimize these subjective judgement discrepancies between different analysis centers estimating the same storm.  This modified version is called Advanced Objective Dvorak Technique (AODT) and which is applicable for well-developed tropical cyclones. In this paper the performance of the AODT is evaluated on three cases of the year 2004 tropical cyclones. Comparative analysis indicates the technique to be competitive with, the Dvorak-based intensity estimates produced operationally by satellite analysts from tropical analysis centers.


Sign in / Sign up

Export Citation Format

Share Document