Patrol unmanned aerial vehicles (UAVs) in coal mines have high requirements for environmental perception. Because there are no GPS signals in a mine, it is necessary to use simultaneous localization and mapping (SLAM) to realize environmental perception for UAVs. Combined with complex coal mine environments, an integrated navigation algorithm for unmanned helicopter inertial measurement units (IMUs), light detection and ranging (LiDAR) systems, and depth cameras based on probabilistic membrane computing‐based SLAM (PMC‐SLAM) is proposed. First, based on an analysis of the working principle of each sensor, the mathematical models for the corresponding sensors are given. Second, an algorithm is designed for the membrane, and a probabilistic membrane system is constructed. The probabilistic SLAM map is constructed by sparse filtering. The experimental results show that PMC can further improve the accuracy of map construction. While adapting to the trend of intelligent precision mining in coal mines, this approach provides theoretical support and application practice for coal mine disaster prevention and control.

Unmanned helicopter for mission inspection has good application value in intelligent coal mining, and attitude control is important. In this paper, membrane computing is introduced to realize attitude optimization control of an unmanned helicopter. First, we give the application scenarios of unmanned helicopters in coal mines. Secondly, we establish a dynamic model of an unmanned helicopter with environmental participation, and the attitude model of the helicopter is deduced based on this model. Further, the cellular membrane system suitable for the attitude model of an unmanned helicopter under the control parameters of environment mapping is constructed, and the cellular membrane controller based on the characteristics and operation rules of the membrane system is designed. The robust performance of the controller is proved theoretically, and by the semiphysical experiments, the performance of trajectory tracking is almost consistent and attitude angle control is less than±1°, in the range of±2°by wind disturbance. Compared with the linear feedback controller in the same experimental environment, the performance of the membrane controller is improved by nearly 0.4026 on average. It shows that the cellular membrane controller constructed has good effectiveness and robustness. This will provide a good application value for membrane computing in the field of accurate coal mining.

Infrared and visible image fusion is the process of integrating information from multiple sensor images to obtain a comprehensive understanding. However, existing fusion algorithms face challenges in achieving high-quality fused images. In this article, a novel fusion framework based on saliency detection dynamic threshold neural P (SD-DTNP) systems is proposed. The non-subsampled shearlet transform (NSST) is first introduced to obtain key features at different scales and orientations. To overcome the disadvantage of visual saliency map (VSM)-based algorithms resulting in unnatural fused images, a novel fusion rule of the contrast saliency DTNP (CS-DTNP) system is designed to fuse the low-frequency sub-band. The high-frequency sub-bands are fused by combining the multiscale morphological gradient (MSMG) with the DTNP system, which can avoid the influence of noise and integrate more structural information. Experiments demonstrate that, compared with seven advanced fusion algorithms, the proposed algorithm not only improves the spatial consistency of the final image but also preserves the salient and detailed information.

Fake sites are serious threats for both consumers and merchants. They mimic authentic sites and attempt to obtain money by fraud, steal personal information such as credit card numbers from consumers, ruin merchants' reputation and so on. Usually, it is difficult for consumers to distinguish between authentic sites and fake sites. In this paper, we propose a method to detect fake sites with high accuracy based on analyzing structures of HTML source codes of websites. For the analysis, we leverage a combination of a tree kernel and the support vector machine (SVM). We first test 32 different types of tree kernels with small datasets, and then, select a few kernels that have exhibited the best accuracy scores. Further, we test them with larger datasets, and finally, focus on a single kernel that has shown the best accuracy and speed. The AUC, F-measure, Accuracy scores of the kernel exceed 0.97 with a large dataset with 3001 instances, and we conclude that the method can perform at the practical level.

Aiming to investigate the disadvantage of the optimization algorithm of membrane computing (a P system) in which it is difficult to take advantage of parallelism in MATLAB, leading to a slow optimization speed, a digital-specific hardware solution (field-programmable gate array, FPGA) is proposed to design and implement the single-cell-membrane algorithm (SCA). Because the SCA achieves extensive global searches by the symmetric processing of the solution set, with independent and symmetrically distributed submembrane structures, the FPGA-hardware-based design of the SCA system includes a control module, an HSP module, an initial value module, a fitness module, a random number module, and multiple submembrane modules with symmetrical structures. This research utilizes the inherent parallel characteristics of the FPGA to achieve parallel computations of multiple submembrane modules with a symmetric structure inside the SCA, and it achieves a high degree of parallelism of rules inside the modules by using a non-blocking allocation. This study uses the benchmark Sphere function to verify the performance of the FPGA-designed SCA system. The experimental results show that, when the FPGA platform and the MATLAB platform obtain a similar calculation accuracy, the average time-consuming of the FPGA is 0.00041 s, and the average time-consuming of MATLAB is 0.0122 s, and the calculation speed is improved by nearly 40 times. This study uses the FPGA design to implement the SCA, and it verifies the advantages of the membrane-computing maximum-parallelism theory and distributed structures in computing speed. The realization platform of membrane computing is expanded, which provides a theoretical basis for further development of the distributed computing model of population cells.

For the trajectory planning problem under the nonlinear and strongly coupled characteristics of unmanned helicopters, membrane computing with distributed parallel processing capability is introduced for unmanned helicopter trajectory planning. The global and local spatial information is temporally characterized; the temporal characterization algorithm under mapping information is designed; the hierarchical discriminant regression algorithm is designed based on incremental principal component analysis to realize the process of building and identifying trees in trajectory planning; and the pulsed neural membrane system (PNMS) with spatio-temporal coding function under membrane computing is constructed. Compared with the RRT algorithm in two experimental environments, the original path length, the trimmed path length, the time used to plan the trajectory, and the number of search nodes have different levels of improvement; the feasibility and effectiveness of the PNMS in unmanned helicopter trajectory planning are verified. It expands the theoretical research of membrane computing in the field of optimal control and provides theoretical support for the subsequent application practice.