Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on Airport Runway.

Aircraft failures can result in the leakage of fuel, hydraulic oil, or other lubricants onto the runway during landing or taxiing. Damage to fuel tanks or oil lines during hard landings or accidents can also contribute to these spills. Further, improper maintenance or operational errors may leave oil traces on the runway before take-off or after landing. Identifying oil spills in airport runway videos is crucial to flight safety and accident investigation. Advanced image processing techniques can overcome the limitations of conventional RGB-based detection, which struggles to differentiate between oil spills and sewage due to similar coloration; given that oil and sewage have distinct spectral absorption patterns, precise detection can be performed based on multispectral images. In this study, we developed a method for spectrally enhancing RGB images of oil spills on airport runways to generate HSI images, facilitating oil spill detection in conventional RGB imagery. To this end, we employed the MST++ spectral reconstruction network model to effectively reconstruct RGB images into multispectral images, yielding improved accuracy in oil detection compared with other models. Additionally, we utilized the Fast R-CNN oil spill detection model, resulting in a 5% increase in Intersection over Union (IOU) for HSI images. Moreover, compared with RGB images, this approach significantly enhanced detection accuracy and completeness by 25.3% and 26.5%, respectively. These findings clearly demonstrate the superior precision and accuracy of HSI images based on spectral reconstruction in oil spill detection compared with traditional RGB images. With the spectral reconstruction technique, we can effectively make use of the spectral information inherent in oil spills, thereby enhancing detection accuracy. Future research could delve deeper into optimization techniques and conduct extensive validation in real airport environments. In conclusion, this spectral reconstruction-based technique for detecting oil spills on airport runways offers a novel and efficient approach that upholds both efficacy and accuracy. Its wide-scale implementation in airport operations holds great potential for improving aviation safety and environmental protection.

Three-Dimensional ERT Advanced Detection Method with Source-Position Electrode Excitation for Tunnel-Boring Machines.

Tunnel-boring machines (TBMs) are widely used in urban underground tunnel construction due to their fast and efficient features. However, shield-tunnel construction faces increasingly complex geological environments and may encounter geological hazards such as faults, fracture zones, water surges, and collapses, which can cause significant property damage and casualties. Existing geophysical methods are subject to many limitations in the shield-tunnel environment, where the detection space is extremely small, and a variety of advanced detection methods are unable to meet the required detection requirements. Therefore, it is crucial to accurately detect the geological conditions in front of the tunnel face in real time during the tunnel boring process of TBM tunnels. In this paper, a 3D-ERT advanced detection method using source-position electrode excitation is proposed. First, a source-position electrode array integrated into the TBM cutterhead is designed for the shield-tunnel construction environment, which provides data security for the inverse imaging of the anomalous bodies. Secondly, a 3D finite element tunnel model containing high- and low-resistance anomalous bodies is established, and the GREIT reconstruction algorithm is utilized to reconstruct 3D images of the anomalous body in front of the tunnel face. Finally, a physical simulation experiment platform is built, and the effectiveness of the method is verified by laboratory physical modeling experiments with two different anomalous bodies. The results show that the position and shape of the anomalous body in front of the tunnel face can be well reconstructed, and the method provides a new idea for the continuous detection of shield construction tunnels with boring.

Intelligent Fault Diagnosis Method Based on VMD-Hilbert Spectrum and ShuffleNet-V2: Application to the Gears in a Mine Scraper Conveyor Gearbox.

This paper introduces a fault diagnosis method for mine scraper conveyor gearbox gears using motor current signature analysis (MCSA). This approach solves problems related to gear fault characteristics that are affected by coal flow load and power frequency, which are difficult to extract efficiently. A fault diagnosis method is proposed based on variational mode decomposition (VMD)-Hilbert spectrum and ShuffleNet-V2. Firstly, the gear current signal is decomposed into a series of intrinsic mode functions (IMF) by using VMD, and the sensitive parameters of VMD are optimized by using a genetic algorithm (GA). The Sensitive IMF algorithm judges the modal function sensitive to fault information after VMD processing. By analyzing the local Hilbert instantaneous energy spectrum for fault-sensitive IMF, an accurate expression of signal energy changing with time is obtained to generate the local Hilbert immediate energy spectrum dataset of different fault gears. Finally, ShuffleNet-V2 is used to identify the gear fault state. The experimental results show that the accuracy of the ShuffleNet-V2 neural network is 91.66% after 778 s.

Analyzing point cloud of coal mining process in much dust environment based on dynamic graph convolution neural network.

Environmental perception is an important research direction of coal mine sustainable development. There is much dust in the underground working environment of coal mine. This study is to identify the marker (ball) in the coal mine, which provides a basic to convert the coordinate of large-scale fully mechanized mining face point cloud to the geodetic coordinate. Firstly, in the face of the phenomenon that the uneven distribution of underground point cloud is more serious, this study further has studied on the basis of complete and incomplete geometry point cloud and generated multi-density geometry point cloud for the first time. Secondly, aiming at the problem that the geometric features of underground point cloud are not obvious enough, this study has increased the weight of point cloud normal vector in the training process of network model, so that the network model is more sensitive to different geometric features. Finally, this study has used a variety of advanced deep neural networks to directly analyze point clouds to verify the proposed method. The results show that the method proposed in this study has been combined with the dynamic graph convolution neural network (DGCNN) established earlier, which can more accurately identify the ball in tens of millions of the point clouds of coal mining process. Most importantly, this work is not only of great significance to improve the production efficiency and safety in fully mechanized mining face but also lays a foundation for realizing intelligence in the mining field and avoiding the harm of dust explosion and other accidents to workers.

A Big Coal Block Alarm Detection Method for Scraper Conveyor Based on YOLO-BS.

With the aim of solving the problem of coal congestion caused by big coal blocks in underground mine scraper conveyors, in this paper we proposed the use of a YOLO-BS (YOLO-Big Size) algorithm to detect the abnormal phenomenon of coal blocks on scraper conveyors. Given the scale of the big coal block targets, the YOLO-BS algorithm replaces the last layer of the YOLOv4 algorithm feature extraction backbone network with the transform module. The YOLO-BS algorithm also deletes the redundant branch which detects small targets in the PAnet module, which reduces the overall number of parameters in the YOLO-BS algorithm. As the up-sampling and down-sampling operations in the PAnet module of the YOLO algorithm can easily cause feature loss, YOLO-BS improves the problem of feature loss and enhances the convergence performance of the model by adding the SimAM space and channel attention mechanism. In addition, to solve the problem of sample imbalance in big coal block data, in this paper, it was shown that the YOLO-BS algorithm selects focal loss as the loss function. In view of the problem that the same lump coal in different locations on the scraper conveyor led to different congestion rates, we conducted research and proposed a formula to calculate the congestion rate. Finally, we collected 12,000 image datasets of coal blocks on the underground scraper conveyor in Daliuta Coal Mine, China, and verified the performance of the method proposed in this paper. The results show that the processing speed of the proposed method can reach 80 fps, and the correct alarm rate can reach 93%. This method meets the real-time and accuracy requirements for the detection of abnormal phenomena in scraper conveyors.

Optimization design of two-stage amplification micro-drive system without additional motion based on particle swarm optimization algorithm.

With the increasing requirements of precision mechanical systems in electronic packaging, ultra-precision machining, biomedicine and other high-tech fields, it is necessary to study a precision two-stage amplification micro-drive system that can safely provide high precision and a large amplification ratio. In view of the disadvantages of the current two-stage amplification and micro-drive system, such as poor security, low motion accuracy and limited amplification ratio, an optimization design of a precise symmetrical two-stage amplification micro-drive system was completed in this study, and its related performance was studied. Based on the guiding principle of the flexure hinge, a two-stage amplification micro-drive mechanism with no parasitic motion or non-motion direction force was designed. In addition, the structure optimization design of the mechanism was completed using the particle swarm optimization algorithm, which increased the amplification ratio of the mechanism from 5 to 18 times. A precise symmetrical two-stage amplification system was designed using a piezoelectric ceramic actuator and two-stage amplification micro-drive mechanism as the micro-driver and actuator, respectively. The driving, strength, and motion performances of the system were subsequently studied. The results showed that the driving linearity of the system was high, the strength satisfied the design requirements, the motion amplification ratio was high and the motion accuracy was high (relative error was 5.31%). The research in this study can promote the optimization of micro-drive systems.

Research on imaging method of driver's attention area based on deep neural network.

In the driving process, the driver's visual attention area is of great significance to the research of intelligent driving decision-making behavior and the dynamic research of driving behavior. Traditional driver intention recognition has problems such as large contact interference with wearing equipment, the high false detection rate for drivers wearing glasses and strong light, and unclear extraction of the field of view. We use the driver's field of vision image taken by the dash cam and the corresponding vehicle driving state data (steering wheel angle and vehicle speed). Combined with the interpretability method of the deep neural network, a method of imaging the driver's attention area is proposed. The basic idea of this method is to perform attention imaging analysis on the neural network virtual driver based on the vehicle driving state data, and then infer the visual attention area of the human driver. The results show that this method can realize the reverse reasoning of the driver's intention behavior during driving, image the driver's visual attention area, and provide a theoretical basis for the dynamic analysis of the driver's driving behavior and the further development of traffic safety analysis.

Identifying Balls Feature in a Large-Scale Laser Point Cloud of a Coal Mining Environment by a Multiscale Dynamic Graph Convolution Neural Network.

In the process of coal mining, a certain amount of gas will be produced. Environmental perception is very important to realize intelligent and unmanned coal mine production and operation and to reduce the accident rate of gas explosion and other disasters. The identification of geometric features of the coal mine working face is the main part of the environmental perception of the working face. In this study, we identify geometric features in a large-scale coal mine working face point cloud (we take the ball as an example) so as to provide a method for the environmental perception of the coal mine working face. On the basis of previous research, we upgrade the dynamic graph convolution neural network (DGCNN) for directly processing point clouds from two aspects: extracting local features and global features of point clouds. First, a multiscale dynamic graph convolution neural network (MS-DGCNN) is proposed, and the combination of max-pooling and average-pooling is used as the symmetry function. Second, we use MS-DGCNN to learn the features of a variety of geometric point clouds in the point cloud data set we make and then look for the ball in the large-scale point cloud of the coal mining working face. Finally, we compare the performance of MS-DGCNN with that of other deep neural networks directly processing point clouds. This study enables MS-DGCNN to obtain more powerful feature expression ability and enhance the generalization of the model. In addition, this study provides a solid foundation for the geometric feature identification of MS-DGCNN in the environmental perception of the coal mine working face and creates a precedent for the application of MS-DGCNN in the field of energy. At the same time, this study makes a beneficial exploration for the development of a transparent coal mine working face.

Coal Wall and Roof Segmentation in the Coal Mine Working Face Based on Dynamic Graph Convolution Neural Networks.

The intersection line information of the point cloud between the coal wall and the roof can not only accurately reflect the direction information of the scraper conveyor but also provide a preliminary basis for realizing the intelligent coal mine. However, the indirect method of using deep learning to segment the point cloud of coal mine working face cannot make full use of the rich information provided by the point cloud data. The direct method of using deep learning to segment the point cloud ignores the local feature relationship between points. Therefore, we propose to use dynamic graph convolution neural networks (DGCNNs) to segment the point cloud of the coal wall and roof so as to obtain the intersection line between them. First, in view of the characteristics of heavy dust and strong electromagnetic interference in the environment of the coal mine working face, we have installed an underground inspection robot so that we use light detection and ranging to obtain the point cloud of the coal mine working face. At the same time, we put forward a fast labeling method of the point cloud of the coal mine working face and an efficient training method of the depth neural network. Second, on the basis of edge convolution, being the greatest innovation of DGCNNs, we analyze the influence of the number of layers, K value, and output feature dimension of edge convolution on the effect of DGCNNs segmenting the point cloud of the coal mine working face and obtaining the intersection line of the coal wall and roof. Finally, we compare DGCNNs with PointNet and PointNet++. The results show that the DGCNN exhibits the best performance. What is more, the results provide a research foundation for the application of DGCNNs in the field of energy. Last but not least, the research results provide a direct and key basis for the adjustment of the scraper conveyor, which is of great significance for an intelligent coal mine working face and accurate construction of a geological information model.

Geometric Feature Extraction of Point Cloud of Chemical Reactor Based on Dynamic Graph Convolution Neural Network.

Geometric features are an important factor for the classification of drugs and other transport objects in chemical reactors. The moving speed of drugs and other transport objects in chemical reactors is fast, and it is difficult to obtain their features by imaging and other methods. In order to avoid the mistaken and missed distribution of drugs and other objects, a method of extracting geometric features of the drug's point cloud in a chemical reactor based on a dynamic graph convolution neural network (DGCNN) is proposed. In this study, we first use MATLAB R2019a to add a random number of noise points in each point cloud file and label the point cloud. Second, k-nearest neighbor (KNN) is used to construct the adjacency relationship of all nodes, and the effect of DGCNN under different k values and the confusion matrix under the optimal k value are analyzed. Finally, we compare the effect of DGCNN with PointNet and PointNet++. The experimental results show that when k is 20, the accuracy, precision, recall, and F1 score of DGCNN are higher than those of other k values, while the training time is much shorter than that of k = 25, 30, and 35; in addition, the effect of DGCNN in extracting geometric features of the point cloud is better than that of PointNet and PointNet++. The results show that it is feasible to use DGCNN to analyze the geometric characteristics of drug point clouds in a chemical reactor. This study fills the gap of the end-to-end extraction method for a point cloud's corresponding geometric features without a data set. In addition, this study promotes the institutionalization, standardization, and intelligent design of safe production and management of drugs and other objects in the chemical reactor, and it has positive significance for the production cost and resource utilization of the whole pharmaceutical process. At the same time, it provides a new method for the intelligent processing of point cloud data.

An Improved Method for Spot Position Detection of a Laser Tracking and Positioning System Based on a Four-Quadrant Detector.

For the laser tracking and positioning system of a moving target using a four-quadrant detector, the accuracy of laser spot position detection has a serious impact on the tracking performance of the system. For moving target tracking, the traditional spot position detection method of a four-quadrant detector cannot give better consideration to both detection accuracy and operation speed. In view of this, an improved method based on piecewise low-order polynomial least squares fitting and a Kalman filter is proposed. Firstly, the tracking and positioning mathematical model of the system is created, and the experimental device is established. Then, the shortcomings of traditional methods are analyzed, and the improved method and the real-time tracking and positioning algorithm of the system are studied. Finally, through the experiment, the system operation effects are compared and analyzed before and after the improvement. The experimental results of system dynamic tracking show that, the least squares fitting of the experimental data using a 5-segment and quadratic polynomial can achieve better results. By using the improved method, the maximum tracking distance of a moving object is increased from 12 m to more than 30 m. At a distance of 7.5 m, the maximum tracking speed can reach 2.11 m/s, and the root mean square error (RMSE) of the position is less than 4.59 mm. At 15.5 m, the maximum tracking speed is 2.04 m/s and the RMSE is less than 5.42 mm. Additionally, at 23.5 m, it is 1.13 m/s and 5.71 mm.

An Online Calibration Method for a Galvanometric System Based on Wavelet Kernel ELM.

The online calibration method of a two-dimensional (2D) galvanometer requires both high precision and better real-time performance to meet the needs of moving target position measurement, which presents some challenges for traditional calibration methods. In this paper, a new online calibration method is proposed using the wavelet kernel extreme learning machine (KELM). Firstly, a system structure is created and its experiment setup is established. The online calibration method is then analyzed based on a wavelet KELM algorithm. Finally, the acquisition methods of the training data are set, two groups of testing data sets are presented, and the verification method is described. The calibration effects of the existing methods and wavelet KELM methods are compared in terms of both accuracy and speed. The results show that, for the two testing data sets, the root mean square errors (RMSE) of the Mexican Hat wavelet KELM are reduced by 16.4% and 38.6%, respectively, which are smaller than that of the original ELM, and the standard deviations (Sd) are reduced by 19.2% and 36.6%, respectively, indicating the proposed method has better generalization and noise suppression performance for the nonlinear samples of the 2D galvanometer. Although the online operation time of KELM is longer than ELM, due to the complexity of the wavelet kernel, it still has better real-time performance.

Three Dimensional Distribution of Sensitive Field and Stress Field Inversion of Force Sensitive Materials under Constant Current Excitation.

Force sensitive conductive composite materials are functional materials which can be used as the sensitive material of force sensors. However, the existing sensors only use one-dimensional electrical properties of force sensitive conductive materials. Even in tactile sensors, the measurement of contact pressure is achieved by large-scale arrays and the units of a large-scale array are also based on the one-dimensional electrical properties of force sensitive materials. The main contribution of this work is to study the three-dimensional electrical properties and the inversion method of three-dimensional stress field of a force sensitive material (conductive rubber), which pushes the application of force sensitive material from one dimensional to three-dimensional. First, the mathematical model of the conductive rubber current field distribution under a constant force is established by the effective medium theory, and the current field distribution model of conductive rubber with different geometry, conductive rubber content and conductive rubber relaxation parameters is deduced. Secondly, the inversion method of the three-dimensional stress field of conductive rubber is established, which provides a theoretical basis for the design of a new tactile sensor, three-dimensional stress field and space force based on force sensitive materials.

A Human Body Pressure Distribution Imaging System Based on Wavelet Analysis and Resistance Tomography.

In this paper, a pressure distribution sensing system based on wavelet analysis and resistance tomography is proposed to overcome the shortcomings of a traditional electrode type pressure distribution sensor, which needs to be arranged with many electrodes and has a high production cost. The system uses ADS1256, a constant current source module, a serial communication module, a Raspberry host, a touch screen, and other components. The wavelet transform is used to preprocess the collected signal to improve the anti-jamming performance of the system. The method of resistance tomography is used to realize the real-time imaging of pressure distribution. Finally, the reliability of the system is verified using conductive silica gel as a sensitive material. The experimental results show that wavelet analysis preprocessing can significantly improve the quality of pressure distribution imaging.