Shufa Sun

Fire spread prediction is a crucial technology for fighting forest fires. Most existing fire spread models focus on making predictions after a specific time, and their predicted performance decreases rapidly in continuous prediction due to error accumulation when using the recursive method. Given that fire spread is a dynamic spatiotemporal process, this study proposes an expanded neural network of long short-term memory based on self-attention (SA-EX-LSTM) to address this issue. The proposed model predicted the combustion image sequence based on wind characteristics. It had two detailed feature transfer paths, temporal memory flow and spatiotemporal memory flow, which assisted the model in learning complete historical fire features as well as possible. Furthermore, self-attention mechanisms were integrated into the model’s forgetting gates, enabling the model to select the important features associated with the increase in fire spread from massive historical fire features. Datasets for model training and testing were derived from nine experimental ground fires. Compared with the state-of-the-art spatiotemporal prediction models, SA-EX-LSTM consistently exhibited the highest predicted performance and stability throughout the continuous prediction process. The experimental results in this paper have the potential to positively impact the application of spatiotemporal prediction models and UAV-based methods in the field of fire spread prediction.

Forest vehicle operation causes different degrees of compaction damage to the soil, which is related to the pressure-bearing characteristics of the soil. However, scholars have not profoundly investigated the pressure-bearing factors of forest soil. In this paper, disturbed brown coniferous forest soil was collected layer by layer, dried, screened, and tested with indoor pressing-plate tests with different pressing-plate shapes and diameters (side lengths). A kind of pressure–subsidence ( P–Z) curve of hard soil, which is different from those of farmland soil and homogeneous remolded soil, was obtained and drawn as the P–Z curve. The results show that in the process of pressure subsidence, the forest soil gradually changed from loose to compact. Furthermore, the change of pressure-bearing subsidence of layered soil from this forest region was characterized first by the rapid increase of soil subsidence with the increase of pressure. Then, the subsidence speed became slower with the increase of pressure; finally, subsidence speed was much less affected by the increase in pressure. According to the pressure-bearing subsidence curve of forest soil, a new subsidence model is put forward in this paper. The new model has a good prediction effect on the subsidence curve of forest soil. This paper aims to provide a theoretical basis for studying soil pressure-bearing characteristics and the development of vehicles in high-passing forest areas.

At present, most studies are focused on converting the vehicle kinetic energy into electrochemical energy for battery storage. During each deceleration period, the kinetic energy is first converted into electromagnetic energy and then stored in the chemical form before being released as the kinetic energy in next acceleration period, which leads to a low transmission efficiency. Secondly, the efficiency of the kinetic energy recovery is limited by the rated capacity of electric drive devices. Thirdly, a single-axis front-drive electric powertrain can only recover the kinetic energy of front wheels. The system proposed in this paper, which included a flywheel, an electromagnetic coupler, and two gear pairs, was arranged in the rear axis. This new configuration could recycle the kinetic energy of the rear wheels for front-driving vehicles. Most of the energy between the wheels and the flywheel was transmitted in the form of mechanical energy, and the power transmitted by the mechanical port of the electromagnetic coupler was not limited by its rated power. Moreover, the battery only needs to recover the slip power of the coupler. Finally, a test bench based on the proposed system was designed and built under deceleration and cruising conditions. The experimental results also proved the functionality of the proposed system.

The simulation of forest fire spread is a key problem for the management of fire, and Cellular Automata (CA) has been used to simulate the complex mechanism of the fire spread for a long time. The simulation of CA is driven by the rate of fire spread (ROS), which is hard to estimate, because some input parameters of the current ROS model cannot be provided with a high precision, so the CA approach has not been well applied yet in the forest fire management system to date. The forest fire spread simulation model LSTM-CA using CA with LSTM is proposed in this paper. Based on the interaction between wind and fire, S-LSTM is proposed, which takes full advantage of the time dependency of the ROS. The ROS estimated by the S-LSTM is satisfactory, even though the input parameters are not perfect. Fifteen kinds of ROS models with the same structure are trained for different cases of slope direction and wind direction, and the model with the closest case is selected to drive the transmission between the adjacent cells. In order to simulate the actual spread of forest fire, the LSTM-based models are trained based on the data captured, and three correction rules are added to the CA model. Finally, the prediction accuracy of forest fire spread is verified though the KAPPA coefficient, Hausdorff distance, and horizontal comparison experiments based on remote sensing images of wildfires. The LSTM-CA model has good practicality in simulating the spread of forest fires.

Modeling forest fire spread is a very complex problem, and the existing models usually need some input parameters which are hard to get. How to predict the time series of forest fire spread rate based on passed series may be a key problem to break through the current technical bottleneck. In the process of forest fire spreading, spread rate and wind speed would affect each other. In this paper, three kinds of network models based on Long Short-Term Memory (LSTM) are designed to predict fire spread rate, exploring the interaction between fire and wind. In order to train these LSTM-based models and validate their effectiveness of prediction, several outdoor combustion experiments are designed and carried out. Process data sets of forest fire spreading are collected with an infrared camera mounted on a UAV, and wind data sets are recorded using a anemometer simultaneously. According to the close relationship between wind and fire, three progressive LSTM based models are constructed, which are called CSG-LSTM, MDG-LSTM and FNU-LSTM, respectively. A Cross-Entropy Loss equation is employed to measure the model training quality, and then prediction accuracy is computed and analyzed by comparing with the true fire spread rate and wind speed. According to the performance of training and prediction stage, FNU-LSTM is determined as the best model for the general case. The advantage of FNU-LSTM is further demonstrated by doing comparison experiments with the normal LSTM and other LSTM based models which predict both fire spread rate and wind speed separately. The experiment has also demonstrated the ability of the model to the real fire prediction on the basis of two historical wildland fires.

A single VLP-16 LiDAR estimation method based on a single-frame 3D laser point cloud is proposed to address the problem of estimating negative obstacles’ geometrical features in structured environments. Firstly, a distance measurement method is developed to determine the estimation range of the negative obstacle, which can be used to verify the accuracy of distance estimation. Secondly, the 3D point cloud of a negative obstacle is transformed into a 2D elevation raster image, making the detection and estimation of negative obstacles more intuitive and accurate. Thirdly, we compare the effects of a StatisticalOutlierRemoval filter, RadiusOutlier removal, and Conditional removal on 3D point clouds, and the effects of a Gauss filter, Median filter, and Aver filter on 2D image denoising, and design a flowchart for point cloud and image noise reduction and denoising. Finally, a geometrical feature estimation method is proposed based on the elevation raster image. The negative obstacle image in the raster is used as an auxiliary line, and the number of pixels is derived from the OpenCV-based Progressive Probabilistic Hough Transform to estimate the geometrical features of the negative obstacle based on the raster size. The experimental results show that the algorithm has high accuracy in estimating the geometric characteristics of negative obstacles on structured roads and has a practical application value for LiDAR environment perception research.