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.

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.

The central pattern generator (CPG) is widely applied in biped gait generation, and the particle swarm optimization (PSO) algorithm is commonly used to solve optimization problems for CPG network controllers. However, the canonical PSO algorithms fail to balance exploration and exploitation, resulting in reduced optimization accuracy and stability, decreasing the control effectiveness of CPG controllers. In order to address this issue, a balanced PSO (BPSO) algorithm is proposed, which achieves better performance by balancing the algorithm's exploration and exploitation capabilities. The BPSO algorithm's solving process consists of two phases: the free exploration phase (FEP), which emphasizes exploration, and the attention exploration phase (AEP), which emphasizes exploitation. The proportion of each phase during optimization is controlled by an adjustable parameter. The BPSO algorithm is subjected to qualitative, numerical, convergence, and statistical analyses based on 13 benchmark functions. The experimental results from the benchmark functions demonstrate that the BPSO algorithm outperforms other comparison algorithms. Finally, a linear walking optimization method for humanoid robots based on the BPSO algorithm is established and tested in the Webots simulator. Comparative results with two other optimization methods show that the BPSO-based optimization method enables the robot to achieve greater walking distance and smaller lateral deviation within a fixed number of iterations. Compared to the other two methods, walking distance increases by at least 60.98% and lateral deviation decreases by at least 1.96%. This research contributes to enhancing the locomotion capabilities of CPG-controlled humanoid robots, enriching biped gait optimization theory and promoting the application of CPG gait control methods in humanoid robots.

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.

BACKGROUND:Diabetes, as a significant disease affecting public health, requires early detection for effective management and intervention. However, imbalanced datasets pose a challenge to accurate diabetes prediction. This imbalance often results in models performing poorly in predicting minority classes, affecting overall diagnostic performance. OBJECTIVES:To address this issue, this study employs a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Random Under-Sampling (RUS) for data balancing and uses Optuna for hyperparameter optimization of machine learning models. This approach aims to fill the gap in current research concerning data balancing and model optimization, thereby improving prediction accuracy and computational efficiency. METHODS:First, the study uses SMOTE and RUS methods to process the imbalanced diabetes dataset, balancing the data distribution. Then, Optuna is utilized to optimize the hyperparameters of the LightGBM model to enhance its performance. During the experiment, the effectiveness of the proposed methods is evaluated by comparing the training results of the dataset before and after balancing. RESULTS:The experimental results show that the enhanced LightGBM-Optuna model improves the accuracy from 97.07% to 97.11%, and the precision from 97.17% to 98.99%. The time required for a single search is only 2.5 seconds. These results demonstrate the superiority of the proposed method in handling imbalanced datasets and optimizing model performance. CONCLUSIONS:The study indicates that combining SMOTE and RUS data balancing algorithms with Optuna for hyperparameter optimization can effectively enhance machine learning models, especially in dealing with imbalanced datasets for diabetes prediction.

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.

Traditional coal-gangue recognition methods usually do not consider the impact of equipment noise, which severely limits its adaptability and recognition accuracy. This paper mainly studies the more accurate recognition of coal-gangue in the noise site environment with the operation of shearer, conveyor, transfer machine and other device in the process of top coal caving. Mel Frequency Cepstrum Coefficients (MFCC) smoothing method was introduced to express the intrinsic feature of sound pressure more clearly in the coal-gangue recognition site. Then, a multi-branch convolution neural network (MBCNN) model with three branches was developed, and the smoothed MFCC feature was incorporated into this model to realize the recognition of falling coal and gangue in noisy environment. The sound pressure signal datasets under the operation of different device were constructed through a great deal of laboratory and site data acquisition. Comparative experiments were carried out on noiseless dataset, single noise dataset and simulated site dataset, and the results show that our method can provide higher correct recognition accuracy and better robustness. The proposed coal-gangue recognition approach based on MBCNN and MFCC smoothing can not only recognize the state of falling coal or gangue, but also recognize the operational state of site device.

The policy of balance between exploration capability and exploitation capability directly affects the solution performance of the meta-heuristic algorithm in a limited time. In order to better balance the exploration and exploitation capabilities of the algorithm and meet the solution requirements of complex real-world problems, the adaptive balance optimization algorithm (ABOA) is proposed in this paper. The algorithm consists of a global search phase (GSP) and a local search phase (LSP) and is controlled by a fixed parameter. ABOA not only considers the balance of exploration and exploitation capabilities of the algorithm throughout the whole iterative process but also focuses on the balance of exploration and exploitation in both GSP and LSP. The search in both phases is focused around the respective search centers from outside to inside. ABOA balances the exploration and exploitation capabilities of the algorithm throughout the search process by two adaptive policies: changing the search area and changing the search center. Fifty-two unconstrained benchmark test functions were employed to evaluate the performance of ABOA. The results of ABOA were compared with nine excellent optimization algorithms available in the literature. The statistical results and Friedman test showed that ABOA was significantly competitive. Finally, the results of the examined engineering design problems showed that ABOA can solve the constrained optimization problem better compared to other methods.

To solve the problem of inaccurate object segmentation caused by unbalanced samples for in-vehicle point cloud, an improved semantic segmentation network RangeNet++ based on asymmetric loss function (AsL-RangeNet++) is proposed, which uses asymmetric loss (AsL) function and Adam optimizer to calculate and adjust object weights, achieve optimal point cloud segmentation. AsL-RangeNet++ can solve the problem of unbalance between positive and negative samples and label error in multi-label classification by calculating the weights of positive and negative samples respectively and more accurately segments the point cloud of small targets. A large number of experiments on the widely used SemanticKITTI dataset show that the proposed method has higher segmentation accuracy and better adaptability than the current mainstream methods.

The K-Nearest Neighbor (KNN) algorithm is a classical machine learning algorithm. Most KNN algorithms are based on a single metric and do not further distinguish between repeated values in the range of K values, which can lead to a reduced classification effect and thus affect the accuracy of fault diagnosis. In this paper, a hybrid metric-based KNN algorithm is proposed to calculate a composite metric containing distance and direction information between test samples, which improves the discriminability of the samples. In the experiments, the hybrid metric KNN (HM-KNN) algorithm proposed in this paper is compared and validated with a variety of KNN algorithms based on a single distance metric on six data sets, and an HM-KNN application method is given for the forward gait stability control of a bipedal robot, where the abnormal motion is considered as a fault, and the distribution of zero moment points when the abnormal motion is generated is compared. The experimental results show that the algorithm has good data differentiation and generalization ability for different data sets, and it is feasible to apply it to the walking stability control of bipedal robots based on deep neural network control.

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.

激励轨迹的选取和优化是机器人动力学参数辨识的重要基础.为了提高机器人动力学参数的辨识精度,以SCARA机器人为研究对象,设计了基于双层自适应遗传算法的机器人激励轨迹优化方案.运用Newton-Euler法建立了机器人的动力学模型,并对机器人的动力学模型进行线性分离,得到了机器人的最小惯性参数集和对应的观测矩阵.分析机器人的参数辨识方程,确定了观测矩阵条件数最小的优化目标.针对传统遗传算法进行改进,提出了双层自适应机制,提升了算法的全局搜索能力和搜索效率.最后利用MATLAB和ADAMS进行联合仿真实验,使用递推最小二乘法计算机器人的最小惯性参数集.实验结果表明,使用改进的双层自适应遗传算法得到的激励轨迹可以保证机器人动力学参数的辨识精度.