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The complex environmental influences often complicate the detection of longitudinal tears in conveyor belts, resulting in insufficient detection accuracy, overlooked detection, and elevated false detection rates. In this study, we propose a new depth learning method specifically designed for detecting longitudinal tears in conveyor belts. This method employs a linear Charge-Coupled Device (CCD) camera to capture images of the conveyor belt. These images are subsequently processed with a modified version of You Only Look Once (YOLO)v7 model to identify instances of longitudinal tearing. The modified YOLOv7 model features Efficient Intersection over Union (EIoU) loss function as a substitute for the original loss function. Furthermore, a Simple Parameter-Free Attention Module (SimAM) is introduced in the detection head to improve detection accuracy. In this method, we introduced the SimSPPFCSPC module as a new spatial pyramid pooling model. This module enhances detection speed while maintaining detection accuracy. Experiment results demonstrate the effectiveness of the proposed method, achieving an impressive precision of 94.6% and a detection speed of approximately 110 Frames Per Second (FPS). Such accuracy and speed meet the requirements for online detection of longitudinal tearing in belt conveyors.

Stability control of the tank gun has emerged as a pivotal issue for moving tank gun control systems (TGCS). As a complex electromechanical integrated system, TGCS of moving tank inevitably possesses significant parametric uncertainties and uncertain nonlinearities. To effectively enhance the stabilization control performance of TGCS, in this study, we introduce an adaptive robust control (ARC) strategy based on radial basis function neural network (RBFNN) compensation. The adaptive technique is employed to address the parametric uncertainties, while the RBFNN is constructed to approximate the uncertain nonlinearities and realize feedforward compensation. Subsequently, to suppress the residual uncertainties, a nonlinear robust feedback control rate is devised to strengthen the robustness of the developed controller. Lyapunov analysis shows that the proposed controller achieves uniform ultimate bounded stability. Extensive simulation and electromechanical experimental results confirm the effectiveness of the proposed controller, which shows outstanding performance in handling strong parametric uncertainties and uncertain nonlinearities.

A method for detecting coal flow on a belt conveyor based on the two-dimensional laser is proposed in this paper to tackle the complications of poor accuracy and real-time detection of coal flow on a belt conveyor. This method collects the point cloud data of the conveyor belt surface and the lateral profile of the coal flow using a two-position laser probe and transmits the data with the industrial Internet to assure the real-time and reliability of the data transmission. The upper computer software of the detection system is designed using LabVIEW software. The improved wavelet threshold algorithm designed by MATLAB is employed for data preprocessing to enhance measurement accuracy. The results demonstrate that the method can accurately detect the coal flow of the belt conveyor, and the detection accuracy reaches more than 95%, presenting the advantages of real-time performance and high accuracy. It has broad application prospects in coal, mines, ports, electric power, and other fields.

The foreign object doped in the conveying belt is the most important factor to cause the tearing of the conveying belt. In order to solve the problem of low accuracy and poor real-time performance of foreign object detection, a new method based on improved Nanodet is proposed in this paper. The hardware of conveyor belt foreign object detection system is designed with ARM processor, and the system software is designed based on Android. It uses a conveyor belt foreign object detection system to detect foreign object. In order to detect foreign object images, a better Nanodet model is suggested. In order to increase detection accuracy while preserving processing speed, the model uses SIoU in place of the original position loss function. When the enhanced Nanodet model is applied to the conveyor belt foreign object detection system, the image of the foreign object appearing on the conveyor belt can be identified. The experimental findings indicate that a conveyor belt foreign object detection system based on an ARM processor and an Android operating system is capable of detecting foreign objects on conveyor belts with an average detection accuracy of 94.3%, a detection speed of 30 frames per second. The application of this method in the detection of foreign objects in conveyor belt can solve the shortcomings of existing methods. At the same time meet the requirements of the conveyor belt foreign object detection site environment.

At present, conveyor belt tearing is the most serious fault of belt conveyor, causing the greatest loss. This paper aims to overcome the issues of poor accuracy, low precision, and poor real-time performance in the longitudinal tear detection of the conveyor belt of the belt conveyor. Specifically, this paper presents a method of longitudinal tear detection of conveyor belt based on the sound signal. According to the recognition of the tearing sound signal, the longitudinal tearing of conveyor belt can be detected. A dynamic MFCC feature extraction method is proposed to extract the sound signal feature. An improved DenseNet neural network model is designed, which is used to classify the longitudinal sound of the belt conveyor to realize the longitudinal tearing detection of the conveyor belt.The experimental results demonstrate that the method in this paper achieves the sound detection of the longitudinal tear of the conveyor belt, and the average accuracy of the longitudinal tear detection of the conveyor belt of the belt conveyor reaches 95.42%, which satisfies the requirements of the longitudinal tear detection of the conveyor belt of the belt conveyor.Applying this method to the longitudinal tearing detection of conveyor belt can solve the shortcomings of existing methods and realize the detection of the longitudinal tearing fault of conveyor belt.

Automatic speaker verification (ASV) is an emerging biometric verification technique with more and more applications. However, both verification accuracy and anti-spoofing should be considered carefully before putting ASV into practice, where anti-spoofing is also called replay detection in which voice is recorded, stored and replayed to deceive ASV systems. Cascaded decision of anti-spoofing and ASV is a straightforward solution to tackle the two issues. In this paper, joint decision of anti-spoofing and ASV was investigated in a multi-task learning framework with contrastive loss in order to improve the cascaded decision approach. A modified triplet loss was firstly constructed to supervise deep neural networks to extract embedding vectors containing information of both speaker identity and spoofing. The embedding vectors were subsequently taken as input features by back-end classifiers towards speaker and spoofing classification. The experimental results on both ASVspoof 2017 and ASVspoof 2019 showed that the proposed joint decision approach with triplet loss outperformed the corresponding baselines, a recent work on joint decision with Gaussian back-end fusion and our previous joint decision approach with cross-entropy loss.

Even though speaker recognition has gained significant progress in recent years, its performance is known to be deteriorated severely with the existence of strong background noises. Inspired by a recently proposed clean-frame selection approach, this work investigates a relatively elegant weighting method when computing the Baum-Welch statistics of Gaussian mixture models (GMMs) in i-vector extraction. By introducing weighting parameters to the frames of enrollment/testing utterances, the optimization problem is redefined and solved. New updating rules are derived by incorporating weights to the computation of posterior probabilities, mean vectors, and covariance matrices of the GMM. The experiments conducted on the Speakers in the Wild (SITW) database show that the proposed algorithm has significantly improved the performance of i-vector-based speaker recognition systems in noisy environments. Compared with the GMM i-vector baseline, the equal error rate is reduced from 5.75 to 4.72 and the minimum value of cost function (Cdetmin) is reduced from 0.4825 to 0.4505. Slight but significant superiority is also observed over the method with an additional feature enhancement frontend by using deep neural networks.

Permutation-based steganography in polygonal meshes can provide considerably large embedding capacities for hiding secret messages. However, corresponding steganalysis techniques against such methods have never been studied. This paper identifies the essential differences between naturally generated meshes and meshes produced by permutation-based steganography methods. It is found that the two types of meshes differ significantly in the distribution of topological distances between consecutive mesh elements. Therefore, by measuring the orderliness of the vertex list and the face list of meshes, we develop solutions for several mesh steganalysis problems. These solutions are effective, leading to high detection accuracy; and they are also universal, requiring no knowledge such as which steganography method is used and what data embedding rate is adopted for the detection mechanism to work. Moreover, this paper also presents a security-improved permutation-based mesh steganography approach, by taking advantage of the connectivity information of polygonal meshes and establishing a good trade-off between embedding capacity and undetectability. Without bringing global changes, our approach embeds secret messages into local neighborhoods on meshes. As a result, meshes generated by the proposed steganography approach tend to have natural structures that are unlikely to draw suspicions to steganalyzers.