Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%

Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%

Factors influencing abandoned farmland in hilly and mountainous areas, and the governance paths: A case study of Xingning City

The current global pandemic has laid bare the importance of national food security to human survival. Many cultivated lands in the hilly, mountainous, and other marginalized areas have been abandoned on a large scale, resulting in a tremendous waste of agricultural resources, thereby threatening national food security. Here, we studied abandoned farmland in Xingning City, a mountainous area in northern Guangdong province. According to the "seeding—growing—harvesting" life cycle of cultivated plots, spatial superposition method and remote sensing change detection method were applied to identify abandoned arable land. Logistic regression model was used to reveal the influencing factors and occurrence mechanism of abandoned cropland at plot scale, and cluster analysis was used to discuss the classification and management strategies. Result showed that 16.83% of the cultivated land in the study area was severely abandoned, attributed to poor location, poor basic conditions, and fragmentation of the land. Further, the abandoned farmland was divided into output-driving type, cultivation condition-driving type, and plot-condition driving type. Based on these types, we proposed some countermeasures, such as adjusting agricultural structures, tamping agricultural infrastructures, strengthening land circulation, popularizing appropriate scale operations. These measures provide a reference to effectively curb abandoned farmland and improving the utilization efficiency of cultivated land, especially in recent years