Strictly speaking, China's international tourism industry is the outcome of the implementation of economic reform and openness to the outside world. Along with the changes in Chinese political and economic systems, it has grown out of nothing, and experienced progress from small to large, from rapid growth to steady development, heading toward its maturity. This industry is playing an increasingly significant role in the country's national economy.
A novel cationic-COF coated double-shell magnetic sorbent, possessing excellent dispersive capability, high stability, and desirable absorption affinity, was prepared.
In this paper, a novel approach based on deep belief networks (DBN) for electrocardiograph (ECG) arrhythmias classification is proposed. The construction process of ECG classification model consists of two steps: features learning for ECG signals and supervised fine-tuning. In order to deeply extract features from continuous ECG signals, two types of restricted Boltzmann machine (RBM) including Gaussian–Bernoulli and Bernoulli–Bernoulli are stacked to form DBN. The parameters of RBM can be learned by two training algorithms such as contrastive divergence and persistent contrastive divergence. A suitable feature representation from the raw ECG data can therefore be extracted in an unsupervised way. In order to enhance the performance of DBN, a fine-tuning process is carried out, which uses backpropagation by adding a softmax regression layer on the top of the resulting hidden representation layer to perform multiclass classification. The method is then validated by experiments on the well-known MIT-BIH arrhythmia database. Considering the real clinical application, the inter-patient heartbeat dataset is divided into two sets and grouped into four classes (N, S, V, F) following the recommendations of AAMI. The experiment results show our approach achieves better performance with less feature learning time than traditional hand-designed methods on the classification of ECG arrhythmias.
We present the effects of terbium additives upon the microstructure and magnetic properties of Fe83Ga17Tbx alloys (x = 0, 0.2, 0.4, 0.6, and 0.8), prepared by vacuum electric arc-melting and directional solidification techniques. Experiments indicate that small amounts of terbium more than double the saturation magnetostriction of a [110] textured Fe83Ga17 alloy with λ = 72 × 10−6 and lower the magnetostriction saturation field. The pronounced increase in magnetostriction stems from the appearance of [100] texture in polycrystalline alloys. It is verified that [110] and [100] textures are enhanced by the introduction of terbium atoms preferentially residing in a Tb-rich intergranular phase.
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