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Posted 20 hours ago

Spot's Fire Engine

£4.105£8.21Clearance
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LFB said the electrified vehicle, developed by manufacturer Emergency One, has “minimal differences” to its 143 current fire engines. It has a range of more than 200 miles and can pump water continuously for four hours.

fire engines on the run for Northamptonshire Fire Four new fire engines on the run for Northamptonshire Fire

Active fire detection methods can be divided into two types: those that are based on a manual design algorithm, primarily the threshold method, and the alternative approach, based on deep learning, including shallow neural networks and image-level deep networks. Spotfire as a platform provides all the solutions that can cater the data analytics and reporting requirements. Spotfire can connect to literally any data source available out there and pull data in seconds." Zhonghua Hong 1 Zhizhou Tang 1 Haiyan Pan 1* Yuewei Zhang 2* Zhongsheng Zheng 1 Ruyan Zhou 1 Zhenling Ma 1 Yun Zhang 1 Yanling Han 1 Jing Wang 1 Shuhu Yang 1 Therefore, the objective of the study is to propose an active fire detection system using a novel convolutional neural network (FireCNN) based on Himawari-8 satellite imageries, to fill the research gap of this area. The presented FireCNN uses multi-scale convolution and residual acceptance design, which can effectively extract the accurate characteristics of fire spots, and to improve the fire detection accuracy. The main contributions of our study are as follows. 1) We developed a novel active fire detection convolutional neural network (FireCNN) based on Himawari-8 satellite images. The new method utilizes multi-scale convolution to comprehensively assess the characteristics of fire spots and uses residual structures to retain the original characteristics, which makes it able to extract the key features of the fire spots. 2) A new Himawari-8 active fire detection dataset was created, which includes a training set and a test set. The training set includes 654 fire spots and 1,308 non-fire spots, and the test set includes 1,169 fire spots and 2,338 non-fire spots. The remainder of the article is organised as follows. In the Data section, we explain the source and composition of the data and pre-processing steps and provide basic information regarding the study area as well as a detailed description of the database established in this study. In the Methodology section, the proposed algorithm is described in detail, and both the traditional threshold method and deep learning method used in the experiment are introduced. In the Experiment section, the relevant settings of the experiment, the parameters used for evaluation, and the analysis of the results are described. Finally, the key findings of the study are summarized, and possible future research is briefly discussed. Data Data and Pre-Processing

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where Z ( λ ) = ( λ ) − m e a n ( λ ) s t d ( λ ), m e a n ( λ ) and s t d ( λ ) represents the mean and standard deviation of the band in the study area.

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Spotfire is an excellent tool for yield analysis, engineering verification, and design of experiments." Spotfire is great at visualizing very large data sets. With hundreds or thousands of process inputs and outputs you can easily see correlation / causation when one part of the manufacturing process changes and the effect it has on others."

The feature extraction component includes three convolution modules of different scales and residual edges. The convolution modules are Conv-2, Conv-3, and Conv-4; that is, the size of the convolution kernel is 2, 3, and 4. Each convolution module includes two convolutional layers and a maximum pooling layer, and each convolutional layer is followed by a rectified linear unit (ReLU) activation function. In this study, convolutional neural networks were used in the convolution module to select features. Through convolutional layers of different scales, feature selection and extraction can be performed in different ranges, which is not only beneficial to reduce the weight of the features with poor correlation with wildfire in the original feature, but also a more comprehensive analysis of the relationship between different quantitative features and extract the key features. In the pooling layer, we chose to use the maximum pooling to retain the key features to the greatest extent, while reducing the dimension of the features to facilitate subsequent calculations. The residual edge in the convolution module prevents the loss of original features and effectively solves the problem of neural network degradation. The feature extraction component fuses the features extracted by the three convolution modules of different scales with the original features as the output. Fully Connected Layer Classifier

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