scholarly journals Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm

2021 ◽  
Vol 13 (21) ◽  
pp. 4226
Author(s):  
Ning Zhang ◽  
Lin Sun ◽  
Zhendong Sun ◽  
Yu Qu

The Visible Infrared Imaging Radiometer Suite (VIIRS) fire detection algorithm mostly relies on thermal infrared channels that possess fixed or context-sensitive thresholds. The main channel used for fire identification is the mid-infrared channel, which has relatively low temperature saturation. Therefore, when the high temperature of a fire in this channel is used for initial screening, the threshold is relatively high. Although screening results are tested at different levels, few small fires will be lost under these strict test conditions. However, crop burning fires often occur in East Asia at a small scale and relatively low temperature, such that their radiative characteristics cannot meet the global threshold. Here, we propose a new weighted fire test algorithm to accurately detect small-scale fires based on differences in the sensitivity of test conditions to fire. This method reduces the problem of small fires being ignored because they do not meet some test conditions. Moreover, the adaptive threshold suitable for small fires is selected by bubble sorting according to the radiation characteristics of small fires. Our results indicate that the improved algorithm is more sensitive to small fires, with accuracies of 53.85% in summer and 73.53% in winter, representing an 18.69% increase in accuracy and a 28.91% decline in error rate.

2010 ◽  
Vol 30 (4) ◽  
pp. 1129-1131
Author(s):  
Na-juan YANG ◽  
Hui-qin WANG ◽  
Zong-fang MA

Author(s):  
Cheng Chen

The studies of post-communist Russia and China have traditionally been dominated by single-case studies and within-region comparisons. This chapter explores why the CAS of post-communist Russia and China is difficult, why it is rare, and how it could yield significant and unique intellectual payoffs. The cross-regional comparative study of anti-corruption campaigns in contemporary Russia and China is used as an example in this chapter to argue that a well-matched and context-sensitive comparison could reveal significant divergence in the elite politics and institutional capacities of these regimes that would otherwise likely be obscured by single-case studies or studies restricted to one single geographical area such as “Eastern Europe” or “East Asia.” By breaking Russia and China out of their respective “regions,” the CAS perspective thus enables us to better capture the full range of existing diversity of post-communist authoritarianism.


2021 ◽  
Vol 11 (6) ◽  
pp. 2608
Author(s):  
Chien-Hsun Liu ◽  
Willybrordus H. P. Muda ◽  
Cheng-Chien Kuo

A power transformer (PT) in power generation or transmission is critical to maintaining electrical continuity. Fault detection on a PT is needed, especially of incipient faults, which are often caused by a turn-to-turn fault (TTF) before it develops into a more severe fault. We use a hybrid algorithm between conventional and modern techniques to detect a developing fault in a PT. The current response signals from a negative sequence current directional algorithm, extended park vector algorithm (EPVA), differential negative sequence current, and EPVA-fuzzy system are combined to distinguish the possibility of a TTF. The subalgorithms are combined using a hybrid detection algorithm to distinguish the faults. The model is a 10 MVA, three-phase PT with Δ-Y configuration 150/300 kV, simulated using MATLAB Simulink software. The results show that by combining the subalgorithms, several limitations are distinguished within the TTF with a slight increase in accuracy.


Author(s):  
Lin Guo ◽  
Jianjiang Lu ◽  
Yonggang Zhao ◽  
Chengzhi Wang ◽  
Cheng Zhang ◽  
...  

Efficient, environment-friendly, and energy-saving low-temperature denitration (DeNOx) catalysts, applicable in practical flue gas, has a widespread market for use in small-scale boilers. A novel Ce-based low-temperature honeycomb catalyst was tested...


2021 ◽  
Vol 13 (2) ◽  
pp. 196
Author(s):  
Xiaoman Lu ◽  
Xiaoyang Zhang ◽  
Fangjun Li ◽  
Mark A. Cochrane ◽  
Pubu Ciren

Smoke from fires significantly influences climate, weather, and human health. Fire smoke is traditionally detected using an aerosol index calculated from spectral contrast changes. However, such methods usually miss thin smoke plumes. It also remains challenging to accurately separate smoke plumes from dust, clouds, and bright surfaces. To improve smoke plume detections, this paper presents a new scattering-based smoke detection algorithm (SSDA) depending mainly on visible and infrared imaging radiometer suite (VIIRS) blue and green bands. The SSDA is established based on the theory of Mie scattering that occurs when the diameter of an atmospheric particulate is similar to the wavelength of the scattered light. Thus, smoke commonly causes Mie scattering in VIIRS blue and green bands because of the close correspondence between smoke particulate diameters and the blue/green band wavelengths. For developing the SSDA, training samples were selected from global fire-prone regions in North America, South America, Africa, Indonesia, Siberia, and Australia. The SSDA performance was evaluated against the VIIRS aerosol detection product and smoke detections from the ultraviolet aerosol index using manually labeled fire smoke plumes as a benchmark. Results show that the SSDA smoke detections are superior to existing products due chiefly to the improved ability of the algorithm to detect thin smoke and separate fire smoke from other surface types. Moreover, the SSDA smoke distribution pattern exhibits a high spatial correlation with the global fire density map, suggesting that SSDA is capable of detecting smoke plumes of fires in near real-time across the globe.


2017 ◽  
Vol 2 (2) ◽  
pp. 155-167
Author(s):  
Deb Cleland

Charting the course: The world of alternative livelihood research brings a heavy history of paternalistic colonial intervention and moralising. In particular, subsistence fishers in South East Asia are cyclical attractors of project funding to help them exit poverty and not ‘further degrade the marine ecosystem’ (Cinner et al. 2011), through leaving their boats behind and embarking on non-oceanic careers. What happens, then, when we turn an autoethnographic eye on the livelihood of the alternative livelihood researcher? What lexicons of lack and luck may we borrow from the fishers in order to ‘render articulate and more systematic those feelings of dissatisfaction’ (Young 2002) of an academic’s life’s work and our work-life? What might we learn from comparing small-scale fishers to small-scale scholars about how to successfully ‘navigate’ the casualised waters of the modern university? Does this unlikely course bring any ideas of ‘possibilities glimmering’ (Young 2002) for ‘exiting’ poverty in Academia?


Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
BinBin Zhang ◽  
Fumin Zhang ◽  
Xinghua Qu

Purpose Laser-based measurement techniques offer various advantages over conventional measurement techniques, such as no-destructive, no-contact, fast and long measuring distance. In cooperative laser ranging systems, it’s crucial to extract center coordinates of retroreflectors to accomplish automatic measurement. To solve this problem, this paper aims to propose a novel method. Design/methodology/approach We propose a method using Mask RCNN (Region Convolutional Neural Network), with ResNet101 (Residual Network 101) and FPN (Feature Pyramid Network) as the backbone, to localize retroreflectors, realizing automatic recognition in different backgrounds. Compared with two other deep learning algorithms, experiments show that the recognition rate of Mask RCNN is better especially for small-scale targets. Based on this, an ellipse detection algorithm is introduced to obtain the ellipses of retroreflectors from recognized target areas. The center coordinates of retroreflectors in the camera coordinate system are obtained by using a mathematics method. Findings To verify the accuracy of this method, an experiment was carried out: the distance between two retroreflectors with a known distance of 1,000.109 mm was measured, with 2.596 mm root-mean-squar error, meeting the requirements of the coarse location of retroreflectors. Research limitations/implications The research limitations/implications are as follows: (i) As the data set only has 200 pictures, although we have used some data augmentation methods such as rotating, mirroring and cropping, there is still room for improvement in the generalization ability of detection. (ii) The ellipse detection algorithm needs to work in relatively dark conditions, as the retroreflector is made of stainless steel, which easily reflects light. Originality/value The originality/value of the article lies in being able to obtain center coordinates of multiple retroreflectors automatically even in a cluttered background; being able to recognize retroreflectors with different sizes, especially for small targets; meeting the recognition requirement of multiple targets in a large field of view and obtaining 3 D centers of targets by monocular model-based vision.


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