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With the expansion of grid-connected photovoltaic power generation, the problem of small-disturbance stability caused by the dynamic characteristics of its converter control system becomes increasingly prominent. Due to the small capacity of a single photovoltaic power generation unit, the grid-connected photovoltaic power generation system often contains a large number of photovoltaic power generation units. As a result, the model of the system is high-order. And the large numbel of photovolatic power generation units greatly increases the complexity of stability analysis. Therefore, in order to propose a relatively simple and effective stability discrimination method, a reduced-order model of small-disturbance stability analysis of grid-connected photovoltaic power generation system under DC voltage control timescale through the timescale decomposition method is established in this paper. Then the small-disturbance stability criterion of the system is deduced by applying the Routh-Hurwitz criterion on the basis of this model. From the criterion, the key factors affecting the small-disturbance stability of the system under DC voltage control timescale are analyzed. The main unfavorable affecting factors are heavy loading condition, condition of weak grid connection, improper setting of parameters of converter control systems. The correctness and effectiveness of the proposed stability criterion are verified by simulation examples. The results show that the stability criterion can effectively evaluate the small-disturbance stability of the grid-connected photovoltaic power generation system under DC voltage timescale. The evaluation method does not need to establish a complex high-order model of the system, which greatly simplifies the calculation and analysis process.

Gesture plays an important role in human–machine interaction. However, the insufficient accuracy and high complexity of gesture recognition have blocked its widespread application. A gesture recognition method that combines state machine and bidirectional long short-term memory (Bi-LSTM) fusion neural network is proposed to improve the accuracy and efficiency. Firstly, gestures with large movements are categorized into simple trajectory gestures and complex trajectory gestures in advance. Afterwards, different recognition methods are applied for the two categories of gestures, and the final result of gesture recognition is obtained by combining the outputs of the two methods. The specific method used is a state machine that recognizes six simple trajectory gestures and a bidirectional LSTM fusion neural network that recognizes four complex trajectory gestures. Finally, the experimental results show that the proposed simple trajectory gesture recognition method has an average accuracy of 99.58%, and the bidirectional LSTM fusion neural network has an average accuracy of 99.47%, which can efficiently and accurately recognize 10 gestures with large movements. In addition, by collecting more gesture data from untrained participants, it was verified that the proposed neural network has good generalization performance and can adapt to the various operating habits of different users. [ABSTRACT FROM AUTHOR]

Spinocerebellar ataxias, also called autosomal dominant cerebellar ataxias, are a group of neurological genetic diseases characterised by chronic, progressive cerebellar ataxia. The clinical hallmark of spinocerebellar ataxia is the loss of balance and coordination, accompanied by slurred speech. Spinocerebellar ataxia type 11 is a rare subtype of spinocerebellar ataxia caused by mutations in the tau tubulin kinase 2 gene. Patients with spinocerebellar ataxia are clinically characterised by slowly progressive cerebellar ataxia, trunk and limb ataxia, and eye movement abnormalities with occasional pyramidal features. Peripheral neuropathy and dystonia are rare. According to the literature, only nine families affected with spinocerebellar ataxia have been reported worldwide. Herein, a series of spinocerebellar ataxia cases are discussed in detail to determine the potential research direction of this dysfunction, including its epidemiology, clinical features, genetic characteristics, diagnosis and differential diagnosis, pathogenic mechanisms, treatment, prognosis, follow‐up, genetic counselling and future perspectives, and to improve the overall understanding of spinocerebellar ataxia among clinicians, researchers and patients. [ABSTRACT FROM AUTHOR]

The resonance conditions of two-way collinear mixing are derived when a longitudinal wave and a transverse wave are propagating in the opposite directions based on cubic nonlinearity. The analytical solutions for mixing waves are obtained. An interesting result is that the resonant longitudinal wave and the resonant transverse wave can be generated, respectively, when considering different resonance conditions. This is different from the case with quadratic nonlinearity that only resonant transverse wave can be generated. For the convenience of applications, the wave mixing of a time-harmonic longitudinal pulse and a time-harmonic transverse pulse is also investigated. A clear mixing process is presented by discussing the parameters introduced in the derivation. Numerical calculation is conducted on material Al, the hexagonal waveforms of the resonant transverse waves are depicted when considering different cases. The results could provide new opportunities for mixing wave applications in nondestructive evaluation. [ABSTRACT FROM AUTHOR]