Ping Xie

Research and Design of the Remote Monitoring System for Power Quality in Wind Farms

Rapid development of wind power generation and increasing growth of installed capacity of wind turbines make it necessary to study the power quality in wind farms. To monitoring the power quality in wind farms automatically and in real time, this paper presents a remote monitoring system for power quality based on LabVIEW software and Internet technology. The remote panel technology in LabVIEW and the Browser/Client (B/S) mode are adopted. This system mainly consists of monitoring hardware devices, the server, LabVIEW application software and remote clients. In the system, the remote clients in monitoring center workstations can monitor in real time power quality parameters of each wind turbine in wind farms through the VI control panel in the Internet browser. Thus it enables to improve automation level of wind farms and reduce operation and maintenance cost. The feasibility and coordination of the developed system are validated by experimental simulating results in the laboratory environment.

Effect of pulsed transcranial ultrasound stimulation at different number of tone-burst on cortico-muscular coupling

BackgroundPulsed transcranial ultrasound stimulation (pTUS) can modulate the neuronal activity of motor cortex and elicit muscle contractions. Cortico-muscular coupling (CMC) can serve as a tool to identify interaction between the oscillatory activity of the motor cortex and effector muscle. This research aims to explore the neuromodulatory effect of low-intensity, pTUS with different number of tone burst to neural circuit of motor-control system by analyzing the coupling relationship between motor cortex and tail muscle in mouse. The motor cortex of mice was stimulated by pulsed transcranial ultrasound with different number of tone bursts (NTB = 100 150 200 250 300). The local field potentials (LFPs) in tail motor cortex and electromyography (EMG) in tail muscles were recorded simultaneously during pTUS. The change of integral coupling strength between cortex and muscle was evaluated by mutual information (MI). The directional information interaction between them were analyzed by transfer entropy (TE).ResultsAlmost all of the MI and TE values were significantly increased by pTUS. The results of MI showed that the CMC was significantly enhanced with the increase of NTB. The TE results showed the coupling strength of CMC in descending direction (from LFPs to EMG) was significantly higher than that in ascending direction (from EMG to LFPs) after stimulation. Furthermore, compared to NTB = 100, the CMC in ascending direction were significantly enhanced when NTB = 250, 300, and CMC in descending direction were significantly enhanced when NTB = 200, 250, 300.ConclusionThese results confirm that the CMC between motor cortex and the tail muscles in mouse could be altered by pTUS. And by increasing the NTB (i.e. sonication duration), the coupling strength within the cortico-muscular circuit could be increased, which might further influence the motor function of mice. It demonstrates that, using MI and TE method, the CMC could be used for quantitatively evaluating the effect of pTUS with different NTBs, which might provide a new insight into the effect of pTUS neuromodulation in motor cortex.

Cortical Rhythmic Activity and Neurophysiology Mechanism during Movement Observation and Motor Imagery

Previous studies proved that movement/action observation and motor imagery are benefit for relearning of movement function after stroke and considered as a method for exploring the neurophysiology mechanism. To analyze and compare the features of activated cortical neurons between movement observation and motor imagery, 10 healthy subjects whose EEG signals were recorded during hand grasp movement observation and motor imagery were recruited. Gabor filter was introduced to analyze time-frequency spectrum power for EEG recorded at somatosensory and visual cortex, then time-frequency quantification of ERD/ERS was performed based on Gabor filter. At last, ERD index was built to distinguish between left hand and right hand and compare movement observation with motor imagery quantitatively. The results showed that movement observation also could activate somatosensory cortex, which issimilar to motor imagery,, and contralateral dominant ERD in a and b range was found during motor imagery. In addition, classification accuracy of motor imagery based on ERDI was greater than it of movement observation. Moreover, movement observation was associated with activation of visual cortex where a significant suppression was found. The present study provide neurophysiology basis and realization approach of movement observation and motor imagery for application in clinical rehabilitation and BCI system.

A novel method of non-stationary sEMG signal analysis and decomposition using a latent process model

To solve the problems of conventional signal analysis methods about non-stationary and frequency characteristics of surface electromyogrphy (sEMG) is of great significance to rehabilitation robot control with EMG-based human-computer interfaces (HCI). In this paper, the latent process models of sEMG signals were developed based on the combination of time-varying auto-regression (TVAR) model and dynamic linear model (DLM), which decomposed the signals into several components, and each component represents different time-frequency behavior of sEMG signals. On the basis of the latent process model, time-varying parameters, modulus and wavelength features were extracted. The fusing features of sEMG signals in two elbow movement conditions (elbow flexion and elbow extension) were adopted for clustering analysis and classification of data was visualized by using self-organizing map (SOM). An experiment with 9 healthy participants was carried out to verify the validity of this algorithm. The result implied that latent process model is a meaningful and valuable non-stationary sEMG signal analysis method which may be promising in rehabilitation robot control.

A NewFault Detection and Diagnosis Method Based on Wigner-Ville Spectrum Entropy for the Rolling Bearing

The rolling bearings are one of the most critical components in rotary machinery. To prevent unexpected bearing failure, it is crucial to develop the effective fault detection and diagnosis techniques to realize equipment’s near-zero downtime and maximum productivity. In this paper, a new fault detection and diagnosis method based on Wigner-Ville spectrum entropy (WVSE) is proposed. First, the local mean decomposition (LMD) and the Wigner-Ville distribution (WVD) are combined to develop a new feature extraction approach to extract the fault features in time-frequency domain of the bearing vibration signals. Second, the concept of the Shannon entropy is integrated into the WVD to define the Wigner-Ville spectrum entropy to quantify the energy variation in time-frequency distribution under different work conditions. The research results from the bearing vibration signals demonstrate that the proposed method based on WVSE can identify different fault patterns more accurately and effectively comparing with other methods based on singular spectrum entropy (SSE) or power spectrum entropy (PSE).