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As computer and multimedia technology advances and as the variety of knowledge display forms expands, there are more ways for people to obtain knowledge and information. The best course in musicology is voice performance. A crucial issue is how to select and categorize valuable curriculum materials of varying quality levels. This paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning technology and tests it on the corresponding dataset, beginning with multidimensional association rule mining technology and the premise that multimedia data contain numerous useful characteristics. Experiments demonstrate the classification precision and data integration capacity of the proposed algorithm. Vocal performance is the most engaging course in musicology. To cultivate corresponding talents, we can rely on college and university offline courses and the rapidly developing multimedia technology to train talents online. An important topic is how to efficiently select and classify curriculum materials of varying quality in order to provide them to students with different learning needs. We can achieve the automatic classification of the course content by leveraging the superior learning capabilities of artificial intelligence and machine learning technology. Consequently, this paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning-related technology and conducts an experimental test on the corresponding dataset, beginning with multidimensional association rule mining technology and the perspective that multimedia information itself contains a large number of useful characteristics. Experiments demonstrate that the proposed algorithm has a high level of classification accuracy and data integration capability. [ABSTRACT FROM AUTHOR]

In order to achieve a quantitative analysis of the training effects of athletes, this paper combines machine learning algorithms to evaluate and analyze athletes' training effects and analyzes the evaluation algorithms based on machine learning interpretable models. Moreover, after analyzing a variety of algorithms, this paper selects an intelligent evaluation method suitable for this model and builds an intelligent evaluation system based on the current athletes' training needs. In addition, this paper verifies the effect of the system with the support of intelligent algorithms and experiments. The experimental research results show that the athlete training effect evaluation system based on the machine learning algorithm proposed in this paper has good results, and it can be applied to subsequent athletes' sports training evaluation. [ABSTRACT FROM AUTHOR]

Irony recognition is an important research direction in text sentiment analysis, which contributes to discover ironic tone to judge text emotion correctly. The object of this paper is the application of deep learning to irony recognition, and the research aims to solve the problem that existing machine learning algorithms have difficulty in discriminating ironic tones. Aiming at the irony recognition of Chinese social comments, DC-BiGRU-CNN is proposed from the perspective of structural optimization rather than a text vectorization mechanism point of view, which is a dual-channel CNN combined with BiGRU, and incorporates attention mechanism and multigranularity convolutional neural network as the main framework. This paper first briefly discusses the difficulties of irony recognition and provides an overview of existing text sentiment analysis algorithms. This is followed by a detailed discussion of DC-BiGRU-CNN. Furtherly, it is compared with the main irony recognition methods on a social comment dataset containing Chinese ironic comments, and the experimental results show that DC-BiGRU-CNN can improve the accuracy of irony recognition. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Image recognition tasks involve an increasingly high amount of symmetric positive definite (SPD) matrices data. SPD manifolds exhibit nonlinear geometry, and Euclidean machine learning methods cannot be directly applied to SPD manifolds. The kernel trick of SPD manifolds is based on the concept of projecting data onto a reproducing kernel Hilbert space. Unfortunately, existing kernel methods do not consider the connection of SPD matrices and linear projections. Thus, a framework that uses the correlation between SPD matrices and projections to model the kernel map is proposed herein. To realize this, this paper formulates a Hilbert–Schmidt independence criterion (HSIC) regularization framework based on the kernel trick, where HSIC is usually used to express the interconnectedness of two datasets. The proposed framework allows us to extend the existing kernel methods to new HSIC regularization kernel methods. Additionally, this paper proposes an algorithm called HSIC regularized graph discriminant analysis (HRGDA) for SPD manifolds based on the HSIC regularization framework. The proposed HSIC regularization framework and HRGDA are highly accurate and valid based on experimental results on several classification tasks. [ABSTRACT FROM AUTHOR]

In this paper, a fault-tolerant control system based on back-stepping integral sliding mode controller (BISMC) is designed and analyzed for both nonlinear translational and rotational subsystems of the quadrotor unmanned aerial vehicles (UAVs). The novelty of this paper is about combination of a classic controller with a repetitive algorithm to reduce the response time to actuator faults and have better tracking performance. The actuator fault is defined based on the loss of effectiveness and bias fault. Next, the iterative learning control algorithm (ILCA) is used to compensate for the unknown fault input according to previous recorded experiences. In the normal condition (without actuators fault), BISMC can force the actual trajectories toward the desired commands and reduce chattering about control signals, and in the presence of the actuators fault or external disturbances, the mentioned learning algorithm can incline the accuracy of the tracking performance and compensate for the occurred error. The Lyapunov theory illustrates that the proposed control strategy can stabilize the system despite the actuators' fault and external disturbances. The simulation results show the effectiveness of the proposed scheme in comparison with another method. [ABSTRACT FROM AUTHOR]

Accurate identification of lithology is the basis and key process of fine logging interpretation and evaluation. However, reservoirs formed by different sedimentary environments and tectonic movements generally have the characteristics of complex and diverse lithology and strong heterogeneity, which brings great difficulty to the identification of reservoir lithology. This paper proposes an automatic identification technology for lithology logging based on the GWO-SVM algorithm model. The technology is actually applied, and the results are compared with the results of the support vector machine cross-validation optimization model, PNN (probabilistic neural network) model, and ELM (extreme learning machine) model. The results show that the GWO-SVM lithology logging recognition model can efficiently solve the lithology recognition and classification problems in complex reservoir analysis and has strong adaptability and higher recognition accuracy. [ABSTRACT FROM AUTHOR]

To a large extent, classical boosting denoising algorithms can improve denoising performance. However, these algorithms can only work well when the denoisers are linear. In this paper, we propose a boosting algorithm that can be used for a nonlinear denoiser. We further implement the proposed algorithm into a shrinkage curve learning denoising algorithm, which is a nonlinear denoiser. Concurrently, the convergence of the proposed algorithm is proved. Experimental results indicate that the proposed algorithm is effective and the dependence of the shrinkage curve learning denoising algorithm on training samples has improved. In addition, the proposed algorithm can achieve better performance in terms of visual quality and peak signal-to-noise ratio (PSNR). [ABSTRACT FROM AUTHOR]

In the order picking process of the warehouse center, considering the rapid increase in the volume of orders arriving at the picking center at the time of the promotional festival, a hybrid operation mode with multiple picking tables is used to meet the picking requirements of the huge number of real-time orders. Therefore, in this paper, a hybrid picking mode is proposed, taking into account both the idle degree of picking stations and their order item centers of gravity, and a new reinforcement learning algorithm embedding mechanism (PRL) with placeholder control is designed to solve the problem of a huge number of real-time item orders arriving at the picking center system on promotional holidays and in inconsistent quantities, and numerical simulations are performed for this algorithm. The experimental results show that the PRL algorithm in hybrid picking mode can handle a huge number of orders simultaneously and improve picking efficiency effectively. [ABSTRACT FROM AUTHOR]

Traditional and typical iterative learning control algorithm shows that the convergence rate of error is very low for a class of regular linear systems. A fast iterative learning control algorithm is designed to deal with this problem in this paper. The algorithm is based on the traditional P-type iterative learning control law, which increases the composition of adjacent two overlapping quantities, the tracking error of previous cycle difference signals, and the current error difference. Using convolution to promote Young inequalities proved strictly that, in terms of Lebesgue-p norm, when the number of iterations tends to infinity, the tracking error converges to zero in the system and presents the convergence condition of the algorithm. Compared with the traditional P-type iterative learning control algorithm, the proposed algorithm improves convergence speed and evades the defect using the norm metric's tracking error. Finally, the validation of the effectiveness of the proposed algorithm is further proved by simulation results. [ABSTRACT FROM AUTHOR]

With the continuous development of neural network theory itself and related theories and related technologies, neural network is one of the main branches of intelligent control technology. Artificial neural network is a nonlinear and adaptive information processing composed of a large number of processing units. In this paper, an adaptive fuzzy neural network (FNN) is used to construct an intelligent system architecture for English learning, and activation function is used to apply the knowledge of computer science and linguistics to English learning. The network neural structure diagram is presented. English machine learning model framework is established based on recursive neural network. On this basis, feature vector extraction and normalization algorithm are used to meet the needs of neural network model. After acquiring the feature vectors of users' learning styles, the clustering algorithm is used to effectively form a variety of learning styles. The validity of the English learning model was verified by designing the functional flow based on tests. Accurate mastery can activate the corresponding brain regions not only to improve the efficiency of learning, but also to better facilitate language learning. [ABSTRACT FROM AUTHOR]