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Accurate diagnosis of Lithium-ion batteries (Li-ion batteries) degradation plays a critical role in improving the maintenance of energy storage technology. This paper presents a method based on a novel deep network model combined with a data transformation technique to diagnose Li-ion battery degradation modes. Different from conventional studies based on specific experimental and numerical methods to estimate and predict the degradation, the proposed method is based on data-driven approach, by leveraging datasets consisting of voltage/capacity curves, these were converted into incremental capacity (IC) curves and then transformed into images using the gramian angular summation field (GASF) technique. The study adopted two models: Inception-v3 and the proposed model, both underwent fine-tuning and a subsequent transfer learning process. Degradation modes, namely loss of lithium inventory (LLI) and the loss of active materials in both the positive (LAMPE) and negative electrodes (LAMNE), were diagnosed in relation to IC curves. Finally, the model was tested using two different datasets, and the results showed that the proposed method achieved high performance, especially across three Li-ion batteries, three degradation modes, three cells, and various cycles (totaling 378 cases) the proposed method outperformed in 233 cases, thereby outperforming other methods in comparison. Our method provides a flexible data-driven approach that accurately predicts various degradation modes across different cell chemistries throughout their lifespan. [ABSTRACT FROM AUTHOR]

The rapid growth of online music education has led to increased security risks from cyber intrusions. This paper proposes public cloud networks oriented deep neural networks for effective intrusion detection in online music education environments. Specifically, a novel intrusion detection framework is developed, comprising fuzzy logic based feature selection, chronological salp swarm algorithm optimized deep belief networks, and gated recurrent unit integrated convolutional neural networks. Detailed methodologies are presented for the fuzzy logic system, chronological salp optimization, deep belief network architecture, and convolutional neural networks. Comprehensive experiments are conducted on the NSL-KDD and CICIDS2017 datasets. Various deep neural networks are evaluated and compared, including multi-layer perceptrons, convolutional neural networks, deep belief networks, recurrent neural networks, and the proposed models. Experimental results demonstrate that the proposed fuzzy feature selection and chronological salp swarm algorithm optimized deep belief network achieves a test accuracy of 97.33 %, outperforming other peer models. The gated recurrent unit integrated convolutional neural network obtains a test accuracy of 98.46 %, superior to state-of-the-art methods. While the experiments on the newly created dataset for intrusion detection in cloud-based online music education demonstrate that the proposed models outperform the benchmarks. The experiments verify the effectiveness of the proposed deep learning frameworks for intrusion detection in online music education cloud networks. [ABSTRACT FROM AUTHOR]

• One dimensional emotion recognition method is proposed to realize the dimensional emotion recognition by combing the attention mechanism and global second-order deep representations. • The residual attention network is used to extract emotionally relevant features to solve the negative influence of emotionally irrelevant features on the recognition performance. • The global second-order pooling network is integrated to learn the interdependence relationship between the long-distance features, increasing the nonlinear emotion representation learning ability of the trained model. • The experiments on the Affectnet dataset demonstrate the validity of the proposed model which is superior to some existing methods. For the emotion recognition tasks, the emotion-related features and the emotionally irrelevant ones are fully extracted by the deep neural network is particularly desirable. In this paper, a dimensional emotion recognition algorithm is proposed by combing the attention mechanism and global second-order feature representations. More specially, the residual attention network (RAN) is utilized to select the features related to the task, and then the global second-order pooling network is employed to model the correlation between these features to complete the modeling of the feature interdependence relationship, improving the deficiency of the RAN in this point so as to enhance the capacity of extracting the emotion-related features and the emotionally irrelevant ones. The experiments on the AffectNet dataset demonstrate the validity of the proposed model, which is comparable to and even superior to those from the support vector regression method, the convolutional neural network (CNN)-based method, and the recently-developed CNN-based variants. [Display omitted] [ABSTRACT FROM AUTHOR]