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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 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.

Exoskeleton as a real-time interaction with the wearer’s intelligent robot, in recent years, becomes a hot topic mouth class research in the field of robotics. Wearable exoskeleton outside the body, combined with the organic body, plays a role in the protection and support. By wearing an exoskeleton robot, it is possible to expand the wearer’s athletic ability, increase muscle endurance, and enable the wearer to complete tasks that he or she cannot perform under natural conditions. Based on the above advantages, the exoskeleton robot in military medical care and rehabilitation has broad application prospects. This paper describes the multimodal model of machine learning research status and research significance of the text on the exoskeleton robot applications, and on the basis of a detailed study of gait. It mainly involves: analysis and planning and obtaining motion information processing, pattern recognition and analysis of gait and the gait conversion process, and the EEG and joint position, foot pressure, such as different modes of data as input to machine learning models to improve the timeliness, accuracy and safety of gait planning. Since the common movement process involves the transformation process of gait, this paper studies the gait transformation process including squatting, walking on the ground and standing.

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.

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.

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.

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.

Shaoyao Gancao Decoction (SGD) is a classic and representative oral administration of traditional Chinese medicine formula. It is composed of two Chinese herbal medicines, Paeoniae Radix Alba [Paeonia lactiflora Pall] and Glycyrrhizae Radix et Rhizoma. The clinical study found SGD could effectively reduce clinical symptoms and improve the level of inflammation in osteoarthritis (OA) patients. The aim of this study is to identify the efficacy and molecular mechanism of SGD in the treatment of OA, and find the new therapeutic target through RNA sequencing (RNA-Seq) to provide theoretical support for its clinical application. Destabilization of the medial meniscus (DMM) OA rat model was established in vivo. Hematoxylineosin staining, safranin O/fast green staining and immunohistochemistry were used to observe changes of cartilage Histology and extracellular matrix (ECM) of cartilage cells. In vitro, the chondrocyte-like cells were derived from ATDC5 cells and induced by interleukin-1 beta to establish the model. The medial meniscotibial ligament (MTT) test was used to identify the effects of SGD on chondrocyte-like cell proliferation, and immunocytochemistry was used to assess changes in chondrocyte ECM. The differentially expressed genes (DEGs) were obtained by RNA-Seq. Meanwhile, the core targets were found through bioinformatics analysis, and then verified by qRT-PCR and Western Blotting. The inflammatory factors IL-1β, IL-6 and TNF-α were detected by ELISA. SGD could alleviate cartilage degeneration, and reduce ECM degradation in OA by upregulating COL2A1 and downregulating MMP-13. 120 key targets were screened from DEGs by RNA-Seq. Based on further bioinformatics analysis, interleukin 17 receptor B (IL-17RB), interleukin 23 receptor and growth differentiation factor 5 were finally selected as core targets. IL-17RB has rarely been reported in previous studies about OA, and worthy of further study. Subsequently, it was found that the gene and protein expressions of IL-17RB were significantly reversed in model group after SGD treatment. Moreover, SGD could inhibit the release of inflammatory factors by mediating IL-17RB in OA. SGD reduced the release of inflammatory factors IL-1β, IL-6 and TNF-α, upregulated COL2A1 and downregulated MMP-13 to alleviate degradation of ECM, and reduced the cartilage degeneration and progression of OA by reducing IL-17RB in articular cartilage.

TFRD has been widely used in China to treat osteoporosis (OP). However, the specific molecular mechanism of TFRD against OP has not been fully clarified. Our previous studies have also proved that TFRD could attenuate OP and the clinical equivalent dose of 67.5mg/kg/d is the effective dose for TFRD treating OP. Therefore, this study used 67.5mg/kg as the dosage of TFRD in combination with multi omics to investigate the mechanism of action of TFRD in the treatment of OP. The aim of this study was to further elucidate molecular mechanism of TFRD for treating OP based on metagenomic and metabolomic analyses. In this study, hematoxylin-eosin (H&E) staining, micro computed tomography (micro-CT) and bone mineral density (BMD) analysis were used to observe pharmacological effects of TFRD against ovariectomized (OVX)-induced OP. Subsequently, multiomics analysis including metagenomics, untargeted and short chain fatty acids (SCFAs) metabolomics were carried out to identify whether the anti-osteoporosis mechanism of TFRD correlated with gut microbiota and related metabolites. Our results indicate that TFRD could improve the microstructure and density of trabecular bone in OVX rats. 17 differential species, which mainly from Akkermansia , Bacteroides , and Phascolarctobacterium genus, 14 related differential metabolites and acetic acid in SCFAs were significantly altered by OVX and reversed by TFRD. Furthermore, according to results of untargeted metabolomics analysis, it was found that several metabolic pathways such as phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis and so on might play an important role in TFRD against OP. In order to further study the relationship between gut microbiota and related metabolites, spearman correlation analysis was used, and showed that gut microbiota such as Akkermansia muciniphila might be closely related to several metabolites and metabolic pathways. These findings suggest that TFRD treatment could reduce the effects of OVX-induced OP by altering community composition and abundance of gut microbiota, regulating metabolites and SCFAs. It was speculated that the gut microbiota especially Akkermansia muciniphila and related metabolites might play an important role in TFRD against OP, and deserve further study by follow-up experiment. This conclusion provides new theoretical support for mechanism research of TFRD against OP.

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.