Abstract Accurate and real-time coal-gangue identification is quintessential for advancing the automation of top-coal caving. To tackle the persistent challenges of intense underground background noise and the non-stationary nature of impact signals, this paper proposes a Cross-Modal Spatiotemporal Gated Fusion (CMSGF) method. Specifically, a MoE-based Spectrum-Aware Dynamic Vibration Encoder is developed to accommodate the transient impact characteristics of vibration signals, achieving sample-level adaptive kernel aggregation via VMD-decomposed multi-channel inputs. Concurrently, to resolve the physical property coupling in acoustic features, a Time-Frequency Decoupled Three-Stream Audio Pyramid Network is designed to reconstruct coal-gangue physical attributes across three orthogonal dimensions, significantly enhancing fine-grained discrimination. To bridge the semantic gap between heterogeneous modalities, we further introduce a Bidirectional Symmetric Multi-head Cross-modal Attention (BSCCA) module and a dual-dimensional spatiotemporal gating mechanism, which adaptively filter high-SNR features through a dynamic competition strategy. Experimental results on a simulated top-coal caving platform demonstrate that the proposed method achieves a recognition accuracy of 96.94%. This research provides a low-cost, high-reliability for online coal-gangue monitoring.

Mechanical fault diagnosis under industrial noise interference poses significant challenges, including low feature fusion efficiency and inadequate dynamic noise adaptation. To address these issues, this paper proposes a noiserobust fault diagnosis method that leverages multi-scale feature enhancement and dynamic cross-modal interaction, thereby enhancing noise robustness through hierarchical feature enhancement and an adaptive interaction mechanism. First, using the time and frequency domain features of the original time series data after noise reduction, a Multi-scale time-frequency noise separation and feature enhancement methods (MSTF) framework is constructed to achieve the synergistic optimization of noise suppression and fault feature enhancement. Secondly, a dual-branch architecture is designed: the one-dimensional vibration signal branch uses a multi-scale deep residual network (MS-ResNet) to extract time-domain multi-granularity features, and the time-frequency image branch parses the time-frequency features generated by MSTF through a multi-scale attention interleaving network (MAIN) to realize multi-scale feature extraction. The dynamic cross-modal interactive fusion (DCIF) module is proposed to dynamically adjust the modal weights through a noise-aware gated attention mechanism, addressing the adaptive defects of traditional fixed fusion in response to changes in noise intensity. The multimodal joint optimization strategy is further proposed to construct a multilevel constraint mechanism. Experimental results on the CWRU dataset (SNR = -6 dB) show that the Accuracy of DMRF-Net reaches 97.51 %, which is significantly better than that of the traditional method. The ablation experiments further demonstrate the superiority of the proposed framework. The proposed framework in this study will provide an effective solution for fault diagnosis under complex operating conditions.

Acoustic-vibration coal-gangue recognition is required for intelligent top-coal caving, but non-stationary underground equipment noise can induce distribution shifts between calibration and operating conditions. This paper formulates the task as a noise-scenario generalization problem and proposes RAAF-BiPAC-Mamba, a roughness-guided bidirectional Mamba framework for noise-robust recognition. The framework uses an efficient bidirectional selective state-space backbone to encode 2048-point acoustic-vibration impact responses and introduces two robustness mechanisms. Roughness-aware adaptive fusion (RAAF) uses latent vibration roughness as a modality-reliability cue, whereas the physics-aware constraint with gradient reversal (PAC-GRL) combines gradient-reversal alignment with a label-conditioned, one-sided roughness regularizer. The method is evaluated on a controlled-platform dataset under source-event-disjoint validation, leave-one-noise-scenario-out (LONSO) evaluation, and synthetic held-out equipment-noise overlay testing. Under the primary source-event-disjoint protocol, RAAF-BiPAC-Mamba achieves 93.74% average accuracy across seven noise scenarios and 91.18% under severe mixed noise (S6), improving over Bi-Mamba by +3.25 and +3.75 percentage points and over the adapted HMBCNN-style implementation by +4.00 and +4.20 percentage points, respectively. It also obtains 89.54% Avg-LONSO accuracy and 86.00% accuracy in the held-out mixed-equipment overlay at −6 dB, while using 0.234 M parameters and 0.063 G FLOPs. These results support noise-shift robustness under the evaluated controlled-platform and held-out noise-overlay settings; they do not establish transfer across mine sites, sensor hardware or calibration, geological conditions, or installation configurations.

Coal-gangue recognition technology plays an important role in the intelligent realization of integrated working faces and coal quality improvement. However, the existing methods are easily affected by high dust, noise, and other disturbances, resulting in unstable recognition results that make it difficult to meet the needs of industrial applications. To realize accurate recognition of coal-gangue in noisy environments, this paper proposes an end-to-end multi-scale feature fusion convolutional neural network (MCNN-BILSTM) based gangue recognition method, which can automatically learn and fuse complementary information from multiple signal components of vibration signals. It combines traditional filtering methods and the idea of multi-scale learning, which can expand the breadth and depth of the feature learning process. the breadth and depth of the feature learning process. Moreover, to strengthen the expression of key features, a feature weighting method based on the attention mechanism is combined to give adaptive weights to different features. Finally, the experimental platform of a tail beam of coal-gangue impact hydraulic support is built, and several comparative experiments are carried out. The comprehensive comparison experiments show that the method shows strong adaptability, robustness, and noise resistance under various complex noise environments, and is suitable for complex practical industrial sites.

To address the issues of severe interference of equipment operating noise and information loss caused by single extraction methods during coal gangue audio feature extraction, a coal gangue audio classification method based on improved EfficientNet is proposed. The method adopted a feature extraction approach combining Mel spectrogram and Gammatone frequency cepstral coefficients to effectively capture low-frequency information and detailed features in gangue audio. EfficientNet-B0 was selected as the backbone network, and the following improvements were made: the original multi-scale channel attention module was replaced with a convolutional block attention module, resulting in the Convolutional Attention Feature Fusion (CAFF) module. This module allowed the network to autonomously assign different weight information to features in different spatial positions, generating new effective features. Additionally, a Frequency-domain Channel Attention (FCA) module was embedded in parallel within the original MBConv module, strengthening the representation ability of feature maps and thereby improving overall network performance. The experimental results demonstrated that after introducing the CAFF module, the model's accuracy improved by 0.61%, the F1 score increased by 0.52%, and convergence was faster, indicating that the CAFF module effectively enhanced the model's ability to capture spectral features. After integrating the FCA module, accuracy improved by 0.45%, and the F1 score increased by 0.62%, showing that combining these modules further enhanced the model's generalization ability and its ability to process complex features. The improved EfficientNet model achieved an accuracy of 91.90%, with a standard deviation of 0.108, significantly outperforming other comparable audio classification models.

Fast and accurate coal-gangue identification techniques are essential for intelligent integrated mining and improved coal quality. However, existing methods are susceptible to high dust, noise, and other disturbances, resulting in unstable recognition results that cannot meet the demands of industrial applications. To address these challenges, this paper proposes a coal-gangue recognition method based on the fusion of a multi-scale parallel MCNN-BITCN network with an attention mechanism and the Improved Sparrow Search Algorithm (ISSA). The method combines a bidirectional spatio-temporal convolutional network (BITCN) with a multi-branch convolutional neural network (MCNN) to deeply mine and expand the extracted features. The time-frequency features are then fused through a cross-attention mechanism and fed into a fully connected layer. An improved sparrow search algorithm (ISSA) is used to generate the input weights and biases of the hidden layer nodes. This paper constructs an experimental platform to simulate the impact of coal-gangue on the tail beam of hydraulic support and conducts several comparative experiments. Results show that the MCNN-BITCN model maintains 83.76

Infrared and visible image fusion seeks to integrate complementary information from both modalities, generating a single, comprehensive image for subsequent visual tasks. However, existing fusion methods often inadequately capture local details and long-range contextual information, resulting in information loss and edge blurring. In this paper, we propose a Dual-branch Multi-cascade Hierarchical Network (DMHNet) for infrared and visible image fusion. DMHNet utilizes a dual-branch architecture to extract both local and global features. Furthermore, we introduce a feature fusion module that employs feature embedding and a bi-directional calibrated attention mechanism to facilitate global information exchange and achieve efficient fusion of complementary features. Additionally, DMHNet employs progressive feature linking for cross-stage feature fusion. This strategy preserves low-level details and enhances high-level semantic information, while simultaneously minimizing gradient loss from the source images. Extensive experiments on benchmark datasets demonstrate that DMHNet surpasses stateof-the-art methods across both qualitative and quantitative metrics, confirming its efficacy for multimodal image fusion.

In deep-sea mining operations, lifting pipes experience significant large deformations and nonlinear vibrations, which affect both the structural design and operational safety of the lifting system. This study considers the lateral bending and longitudinal deformation of the pipe and proposes a geometric nonlinear dynamic modeling method suitable for deep-sea pipelines with large displacements, based on the modified multi-rigid-body discrete element method. The model and program are validated by comparing the numerical results with experimental measurements and Abaqus simulation results. The model is applied to simulate the actual lifting pipe system, exploring its nonlinear longitudinal vibration characteristics and sensitivity to key control parameters, thereby identifying critical influencing factors. Results indicate that the effective tension along the arc length of the lifting pipe gradually decreases and experiences a necking change at the step positions, while longitudinal vibrations exhibit an amplitude amplification effect, increasing the instability of the pipe. The dynamic response of the lifting pipe is highly sensitive to changes in the excitation frequency and amplitude of external disturbances at the top, with nonlinear characteristics. It is insensitive to changes in internal medium flow velocity and density but significantly sensitive to changes in buffer station mass. Therefore, optimizing the mass range of the buffer station and the excitation parameters of top disturbances is crucial for ensuring the reliability and structural safety of the lifting pipe.

Accurate identification of coal and gangue is a crucial guarantee for efficient and safe mining of top coal caving face. This article proposes a coal-gangue recognition method based on an improved beluga whale optimization algorithm (IBWO), convolutional neural network, and long short-term memory network (CNN-LSTM) multi-modal fusion model. First, the mutation and memory library mechanisms are introduced into the beluga whale optimization to explore the solution space fully, prevent falling into local optimum, and accelerate the convergence process. Subsequently, the image mapping of the audio signal and vibration signal is performed to extract Mel-Frequency Cepstral Coefficients (MFCC) features, generating rich sample data for CNN-LSTM. Then the multi-head attention mechanism is introduced into CNN-LSTM to speed up the training speed and improve the classification accuracy. Finally, the IBWO-CNN-LSTM coal-gangue recognition model is constructed by the optimal hyperparameter combination obtained by IBWO to realize the automatic recognition of coal-gangue. The benchmark function proves that IBWO is superior to other optimization algorithms. By building an experimental platform for the impact of coal and gangue falling on the tail beam of hydraulic support, multiple experimental data collection is carried out. The experimental results show that the proposed coal-gangue recognition model has better performance than other recognition models, and the accuracy rate reaches 95.238%. The multi-modal fusion strategy helps to improve the accuracy and robustness of coal-gangue recognition.

The coal-gangue recognition technology plays an important role in the intelligent realization of fully mechanized caving face and the improvement of coal quality. Although great progress has been made for the coal-gangue recognition in recent years, most of them have not taken into account the impact of the complex environment of top coal caving on recognition performance. Herein, a hybrid multi-branch convolutional neural network (HMBCNN) is proposed for coal-gangue recognition, which based on improved Mel Frequency Cepstral Coefficient (MFCC) as well as Mel spectrogram, and attention mechanism. Firstly, the MFCC and its smooth feature matrix are input into each branch of one-dimensional multi-branch convolutional neural network, and the spliced features are extracted adaptively through multi-head attention mechanism. Secondly, the Mel spectrogram and its first-order derivative are input into each branch of the two-dimensional multi-branch convolutional neural network respectively, and the effective time-frequency information is paid attention to through the soft attention mechanism. Finally, at the decision-making level, the two networks are fused to establish a model for feature fusion and classification, obtaining optimal fusion strategies for different features and networks. A database of sound pressure signals under different signal-to-noise ratios and equipment operations is constructed based on a large amount of data collected in the laboratory and on-site. Comparative experiments and discussions are conducted on this database with advanced algorithms and different neural network structures. The results show that the proposed method achieves higher recognition accuracy and better robustness in noisy environments.

Stability of obstacle–crossing and structural optimization are important issues in the research of tracked mobile robots. In this paper, in order to fully understand the obstacle–surmounting ability of the robot, the relationship between the position of the center of gravity and the posture of the front and rear swing arms is analyzed. Based on the motion mechanism of the robot crossing obstacles, the geometric model and the dynamic model are established for the key states in the obstacle crossing process. Based on these models, a multi-objective optimization problem for the maximum obstacle–crossing height and minimum driving torque is established during the obstacle crossing process of the robot, which must meet geometric, slip, and stability constraints. To effectively handle the optimization problem of tracked mobile robots, an improved non–dominated sorting genetic algorithm with elite strategy version II based on adaptive genetic strategy (NSGA-II-AGS) is proposed in this paper. Some meaningful relationships between the objective function and the design variables are obtained through sensitivity analysis. Finally, the robot's obstacle-crossing ability was verified through virtual simulation and prototype experiments. These excellent performances enable the proposed NSGA-II-AGS to be qualified for dealing with the multi-objective optimization problem.

An event camera is a neuromimetic sensor inspired by the human retinal imaging principle, which has the advantages of high dynamic range, high temporal resolution, and low power consumption. Due to the interference of hardware and software and other factors, the event stream output from the event camera usually contains a large amount of noise, and traditional denoising algorithms cannot be applied to the event stream. To better deal with different kinds of noise and enhance the robustness of the denoising algorithm, based on the spatio-temporal distribution characteristics of effective events and noise, an event stream noise reduction and visualization algorithm is proposed. The event stream enters fine filtering after filtering the BA noise based on spatio-temporal density. The fine filtering performs time sequence analysis on the event pixels and the neighboring pixels to filter out hot noise. The proposed visualization algorithm adaptively overlaps the events of the previous frame according to the event density difference to obtain clear and coherent event frames. We conducted denoising and visualization experiments on real scenes and public datasets, respectively, and the experiments show that our algorithm is effective in filtering noise and obtaining clear and coherent event frames under different event stream densities and noise backgrounds.

The mechanical fault diagnosis of HVCB is important to ensure the stability of electric power systems. Aiming at the problem of poor diagnostic performance of deep learning methods under limited samples, this paper proposes an HVCB operating mechanism fault diagnosis model (multi-channel CNN-SABO-SVM, MCCSS) based on multimodal data fusion features and Subtraction-Average-Based Optimizer (SABO). This model extracts and fuses features from the input two-dimensional data using a multi-channel CNN network and then uses the multimodal data fusion features to diagnose HVCB faults. Additionally, the SVM is used instead of the Softmax classifier to classify the fused features of vibration and sound, compensating for the poor diagnostic performance and generalization ability of the CNN network in small sample data scenarios. To further enhance the fault diagnosis performance of the SVM, the SABO is introduced for hyperparameter optimization of the SVM classifier. An HVCB fault test platform was established to train and test the model with limited data. The experimental results show that, compared with the multi-channel CNN-SVM and the CNN model based on unimodal signals, the proposed multi-channel CNN-SABO-SVM model improves the accuracy by 2.66% and 10.66%, respectively, and effectively addresses the challenge of circuit breaker fault diagnosis with limited samples.

为了研究复杂阶梯状扬矿管在采矿船升沉运动和海流作用下的纵向振动特性,利用连续弹性杆振动理论,对5000?m长扬矿管纵向振动性能进行分析.?首先,根据达朗贝尔原理建立扬矿管纵向振动数学模型,采用分离变量法推导管道固有频率方程;然后,进行振型的质量归一化处理;最后,利用ABAQUS软件建立扬矿管有限元模型,对管道的纵向动态响应进行研究.?研究结果表明:扬矿管的一阶纵向共振频率处于矿区海浪能量集中的频带内,随着中间矿仓质量的增加扬矿管固有频率减小,中间矿仓质量对高阶固有频率的影响更加明显;随着海浪频率的增加,纵向振幅、轴向力和轴向应力先增大后减小,并在一阶固有频率时达到峰值,其峰值分别发生在扬矿管5000、0、1000?m处;随着采矿船升沉幅值的增加,扬矿管的动态响应逐渐增大,当升沉幅值大于1.5?m时,扬矿管动态响应的增长速度变缓;扬矿管发生一阶纵向共振时,振动位移和轴向力先增大后作等幅稳态振荡;随着海水深度的增加,沿管长方向的振动幅值逐渐增大,振动平衡位置发生下移,振动响应时间发生延迟,同时轴向力和轴向应力逐渐减小,且轴向应力在每两级阶梯管间急剧变大.

Abstract. A reliable optimization of dynamic vibration absorber (DVA) parameters is extremely important to analyze its dynamic damping characteristics and improve its vibration suppression performance. In this paper, we will discuss a parameter optimization method of the Voigt and three-element DVA models according to the H∞ optimization criterion. The particle swarm optimization method is an effective heuristic optimization algorithm; however, it is easy to lose diversity and fall into local extremum. To solve this problem, the adaptive multiswarm particle swarm optimization (AM-PSO) is used to search the solution of the DVA models. Particles in AM-PSO are adaptively divided into multiple swarms, and the variable substitution learning strategy is utilized to reduce their computational complexity and improve the algorithm's global search capability. In addition, the AM-PSO method is employed to optimize the parameters of DVA models and compared with the genetic algorithm and PSO. The simulation results show that the AM-PSO algorithm has superior performance. Also, the adaptive multiswarm numerical design method discussed herein will push the field towards practical applications, including traditional DVA and related complex three-element DVA.

The lifting pipe is a key component of deep sea mining whose dynamic response directly affects the safety of the lifting operation. The objective of this paper was to investigate the effects of heave motion and sailing velocity of mining vessel and the buffer mass on the dynamic response of lifting pipe. First, an equivalent model of the lifting pipe was established, and the natural frequency and dynamic response of the lifting pipe equivalent model were determined with consideration of the wave action by the method of separated variables. Secondly, the reliability of the equivalent model was verified by simulating a 5000 m stepped pipe with OrcaFlex software. Then the dynamic displacement, axial tension, axial stress of the lifting pipe under different sea conditions and sailing velocities were studied, and the main factors affecting the dynamic response of the pipe described. By comparing the simulation results of actual and equivalent models, the equivalent model can be used to analyze the longitudinal vibration characteristics of the lifting pipe. The sailing velocity of the mining vessel has little effect on the dynamic response of the lifting pipe, but the surface wave has a significant effect.

为了研究复杂阶梯状扬矿管在采矿船升沉运动和海流作用下的纵向振动特性，利用连续弹性杆振动理论，对5 000 m长扬矿管纵向振动性能进行分析.首先，根据达朗贝尔原理建立扬矿管纵向振动数学模型，采用分离变量法推导管道固有频率方程；然后，进行振型的质量归一化处理；最后，利用ABAQUS软件建立扬矿管有限元模型，对管道的纵向动态响应进行研究.研究结果表明：扬矿管的一阶纵向共振频率处于矿区海浪能量集中的频带内，随着中间矿仓质量的增加扬矿管固有频率减小，中间矿仓质量对高阶固有频率的影响更加明显；随着海浪频率的增加，纵向振幅、轴向力和轴向应力先增大后减小，并在一阶固有频率时达到峰值，其峰值分别发生在扬矿管5 000、0、1 000 m处；随着采矿船升沉幅值的增加，扬矿管的动态响应逐渐增大，当升沉幅值大于1.5 m时，扬矿管动态响应的增长速度变缓；扬矿管发生一阶纵向共振时，振动位移和轴向力先增大后作等幅稳态振荡；随着海水深度的增加，沿管长方向的振动幅值逐渐增大，振动平衡位置发生下移，振动响应时间发生延迟，同时轴向力和轴向应力逐渐减小，且轴向应力在每两级阶梯管间急剧变大.

In deep-sea mining, the coupling dynamic response between the mining vessel and the lifting pipe is a significant problem, which directly affects the structural design of the lifting system and the safety of field operation. The characteristics of coupled motion model have not been fully considered in the existing research. Therefore, this paper uses time-domain coupled numerical model as the research object, considering ocean current, surface wave, pipe dynamics and vessel-pipe contact mechanics, to study the dynamic behavior of the lifting pipe and mining vessel during the process of deep-sea mining using AQWA and OrcaFlex softwares. The response amplitude operator (RAO) is used to compare the measured and simulations dynamic response of the mining vessel. There is a very good agreement in RAO between the experiments and simulations. The coupling simulation results show that the coupling effect has a significant effect on the time domain dynamic response of the lifting pipe, but has little effect on the average effective tension and longitudinal amplitude along the pipe length. The research results of this paper are of great significance to the safety design of deep-sea mining lifting system and the planning of deep-sea operation activities.

深海采矿船与扬矿管系统的耦合动力分析对采矿系统的研发应用具有重要意义.为了研究波浪引起的扬矿管和采矿船之间的耦合动力响应,采用等效截断设计原则,对扬矿管截断模型的弹性模量、轴向刚度以及单位长度质量等特征参数进行优选,得到了合理的截断模型参数.基于相似理论建立了深海采矿5000m扬矿管耦合动力学试验模型,解决了深海采矿系统在有限水深试验条件下进行模型试验的问题.同时开展了不同波高、不同波向角和不同矿仓质量下采矿系统模型的耦合运动试验,结果表明,波高、波向角和矿仓质量对耦合响应的幅值、耦合作用时间均有较大影响.该试验模型的建立,为后续管道稳定性和升沉补偿特性的研究提供了技术基础.