The non-contact current measurement method with magnetic sensors has become a subject of research. Unfortunately, magnetic sensors fail to distinguish the interested magnetic field from nearby interference and suffer from gauss white noise due to the intrinsic noise of the sensor and external disturbance. In this paper, a novel adaptive filtering-based current reconstruction method with a magnetic sensor array is proposed. Interference-rejection methods based on two classic algorithms, the least-mean-square (LMS) and recursive-least-square (RLS) algorithms, are compared when used in the parallel structure and regular triangle structure of three-phase system. Consequently, the measurement range of RLS-based algorithm is wider than that of LMS-based algorithm. The results of carried out simulations and experiments show that RLS-based algorithms can measure currents with an error of around 1%. Additionally, the RLS-based algorithm can filter the gauss white noise whose magnitude is within 10% of the linear magnetic field range of the sensor.

The compressive strength and water absorption of cement mortars with different water-binder ratio (0.35, 0.45 and 0.55) and fly ash content (0, 10%, 20% and 30%) under water immersion were investigated, and the correlation between them was further analyzed. The internal microstructure and phase composition of mortar was studied by scanning electron microscopy (SEM) and X-ray diffraction (XRD), respectively. The results show that the inside of mortar mixed with fly ash displayed the loose and porous microstructure. Therefore, the incorporation of fly ash reduced the compressive strength of mortar, especially the early strength, and the strength decreased with the increase of fly ash content, and the water absorption of mortar also increased. There was a linear correlation between the compressive strength and water absorption of mortar with the equation: f_c = -3.838β + 62.332, where f_c and β represented the compressive strength and water absorption, respectively. Therefore, when the water absorption of mortar immersed in water was measured, its corresponding compressive strength could be preliminarily inferred through this equation, which was of great significance for detecting and identifying the stability and safety of hydraulic structures.

Cu-2wt%Ag alloy with diameter of 10 mm was fabricated by induction heating directional solidification (IHDS). The effect of different mold temperatures on microstructure of IHDS Cu-2wt%Ag alloy was investigated. The results show that IHDS ­Cu-2wt%Ag alloy is mainly composed of coarse columnar grains at mold temperature of 1075°C. While the mold temperature is at 1100°C, 1150°C and 1200°C, respectively, the IHDS Cu-2wt%Ag alloy is composed of columnar grains and equiaxed grains and the number of grains increases. Meanwhile, the growth direction of columnar grains in the edge of alloys deviates from the direction of continuous casting to form “V” shape. While the mold temperature is controlled at high temperature, the induced current increases, which leads to the enhancement of eddy current in the mold. Therefore, the dendrites fall off to form new grains under the effect of eddy stirring, resulting in an increasing in the number of grains.

In this paper, a rotor current fault monitoring method is proposed based on a sliding mode observer. Firstly, the state-space model of the Double-Fed Induction Generator (DFIG) is constructed by vector transformation. Meanwhile, the stator voltage orientation vector control method is applied to decouple a stator and rotor currents, so as to obtain the correlation between the stator and rotor current. Furthermore, the mathematical model of stator voltage orientation is obtained. Then a state sliding mode observer (SMO) is established for the output current of the rotor of the DFIG. The stability and reachability of the system in a limited time is proved. Finally, the system state is determined by the residuals of the measured and estimated rotor currents. The simulation results show that the method proposed in this paper can effectively monitor the status: a normal state, voltage drop faults, short-circuit faults between windings, and rotor current sensor faults which have the advantages of fast response, high stability.

Super-resolution image reconstruction utilizes two algorithms, where one is for single-frame image reconstruction, and the other is for multi-frame image reconstruction. Singleframe image reconstruction generally takes the first degradation and is followed by reconstruction, which essentially creates a problem of insufficient characterization. Multi-frame images provide additional information for image reconstruction relative to single frame images due to the slight differences between sequential frames. However, the existing super-resolution algorithm for multi-frame images do not take advantage of this key factor, either because of loose structure and complexity, or because the individual frames are restored poorly. This paper proposes a new SR reconstruction algorithm for images using Multi-grained Cascade Forest. Multi-frame image reconstruction is processed sequentially. Firstly, the image registration algorithm uses a convolutional neural network to register low-resolution image sequences, and then the images are reconstructed after registration by the Multi-grained Cascade Forest reconstruction algorithm. Finally, the reconstructed images are fused. The optimal algorithm is selected for each step to get the most out of the details and tightly connect the internal logic of each sequential step. This novel approach proposed in this paper, in which the depth of the cascade forest is procedurally generated for recovered images, rather than being a constant. After training each layer, the recovered image is automatically evaluated, and new layers are constructed for training until an optimal restored image is obtained. Experiments show that this method improves the quality of image reconstruction while preserving the details of the image.

The pathogenesis of porcine contagious pleuropneumonia is poorly understood. In the present study, a mouse model of intranasal infection by Actinobacillus pleuropneumoniae (App) was used to examine lung inflammation. The pathogical results of lung tissues showed that App-infected mice showed dyspnea and anorexia, with severe damage by acute hemorrhage, and infiltration of eosinophils and lymphocytes, as well as increased expression of caspase-1 p20, interleukin (IL)-1β, IL-6, IL-8, IL-18 and tumor necrosis factor (TNF)-α. Caspase-1 inhibitors reduced both lung tissue damage and the expression of caspase-1 p20, IL-1β, IL-6, IL-8, TNF-α and IL-18 in infected mice. These findings suggest that the caspase-1 dependent pyroptosis involved in the pathogenesis of the mouse pleuropneumonia caused by App and the inhibition of caspase-1 reduced the lung injury of this pleuropneumonia

Micro-defects detection in solidified castings of aluminum alloy has always been a hot topic, and the method employed is mainly depends upon the size and shape of the specimens. In present paper, the amount and distribution characters of micro-defects in a series of 2219 aluminum alloy ingot, with diameters of φ1380 mm, φ1250 mm, φ1000 mm, φ850 mm and φ630 mm, prepared by direct chill casting were investigated by means of metallographic, respectively. Samples were cut along the radius direction from slices in the steady casting stage. The result reveals that typical micro-defects are consist of inclusions, porosity and shrinkage under optical microscope, and the total amount of micro-defect per unit area in an ingot slightly decreased with the increase of its diameter. Meanwhile, defects were classified into 2 types according to its size, the results suggesting that defects greater than 40 μm account for the largest proportion among the counted two kinds of defects. Moreover, the distribution of defects greater than 40 μm along the radial direction was detected, its amount increases as its distance from the side, indicating that the micro-defects greater than 40 μm distributed the most in the center zone of ingots and the larger the ingot diameter, the more obvious the tendency was.

The underground complicated testing environment and the fan operation instability cause large random errors and outliers of the wind speed signals. The outliers and large random errors result in distortion of mine wind speed monitoring, which possesses safety hazards in mine ventilation system. Application of Kalman filter in velocity monitoring can improve the accuracy of velocity measurement and eliminate the outliers. Adaptive Kalman Filter was built by automatically adjusting process noise covariance and measurement noise covariance depending on the differences between measured and expected speed signals. We analyzed the fluctuation of airflow flow using data of wind speed flow and distribution characteristics of the tunnel obtained by the Laser Doppler Velocimetry system (LDV) studies. A state-space model was built based on the tunnel airflow fluctuations and wind speed signal distribution. The adaptive Kalman Filter was calculated according to the actual measurement data and the Expectation Maximization (EM) algorithm. The adaptive Kalman filter was used to shield fluid pulsation while preserving system-induced fluctuations. Using the Kalman filter to treat offline wind speed signal acquired by LDV, the reliability of Kalman filter wind speed state model and the characteristics of adaptive Kalman Filter were investigated. Results showed that the adaptive Kalman filter effectively eliminated the outliers and reduced the root-mean-squares error (RMSE), and the adaptive Kalman filter had better performance than the traditional Kalman filter in eliminating outliers and reducing RMSE. Field experiments in online wind speed monitoring were conducted using the optimized adaptive Kalman Filter. Results showed that adaptive Kalman filter treatment could monitor the wind speed with smaller RMSE compared with LVD monitor. The study data demonstrated that the adaptive Kalman filter is reliable and suitable for online signal processing of mine wind speed monitor.

In this study, the effect of the emergence angle of a source array on acoustic transmission in a typical shallow sea is simulated and analyzed. The formula we derived for the received signal based on the Normal Mode indicates that the signal is determined by the beamform on the modes of all sources and the samplings of all modes at the receiving depth. Two characteristics of the optimal emergence angle (OEA) are obtained and explained utilizing the aforementioned derived formula. The observed distributions of transmission loss (TL) for different sources and receivers are consistent with the obtained characteristics. The results of this study are valuable for the development and design of active sonar detection.