Noise suppression is one of the key issues for the partial discharge (PD) ultra-high frequency (UHF) method to detect and diagnose the insulation defect of high voltage electrical equipment. However, most existing denoising algorithms are unable to reduce various noises simultaneously. Meanwhile, these methods pay little attention to the feature preservation. To solve this problem, a new denoising method for UHF PD signals is proposed. Firstly, an automatic selection method of mode number for the variational mode decomposition (VMD) is designed to decompose the original signal into a series of band limited intrinsic mode functions (BLIMFs). Then, a kurtosis-based judgement rule is employed to select the effective BLIMFs (eBLIMFs). Next, a singular spectrum analysis (SSA)-based thresholding technique is presented to suppress the residual white noise in each eBLIMF, and the final denoised signal is synthesized by these denoised eBLIMFs. To verify the performance of our method, UHF PD data are collected from the computer simulation, laboratory experiment and a field test, respectively. Particularly, two new evaluation indices are designed for the laboratorial and field data, which consider both the noise suppression and feature preservation. The effectiveness of the proposed approach and its superiority over some traditional methods is demonstrated through these case studies.
3D printing of carbon fiber reinforced plastics can produce lightweight components with higher efficiency and more complex structure. For the short carbon fiber reinforced plastics, the composites are firstly made by compounding, then they are processed to filaments, powders or other needed forms, finally the components are printed by Fused Deposition Modeling (FDM), Selected Laser Sintering (SLS) or other methods. The tensile strength of the nylon-based component is more than 70 MPa. Companies such as EOS, Stratasys and Farsoon can provide the materials and equipments. For the continuous carbon fiber reinforced plastics, the divided carbon fibers and plastic filaments or impregnated carbon fiber filaments are firstly prepared, then the components are printed by FDM or other methods. The average tensile strength of the nylon-based component is more than 200 MPa. Companies such as Markforged and Arevo Labs have commercialized the 3D printing equipment/platform for the continuous fiber reinforced plastics.
Sorona fiber was produced from renewable resources with excellent performance of ordinary synthetic. In this paper, throughout the production process and internal molecular structure of Sorona fiber, the author introduced its environmental protection, tensile elasticity, easy dyeing and other properties and the current application.
Human activity recognition (HAR) is widely used in healthcare, personal fitness, physical training and military, etc. How to distinguish various human activities accurately (such as running, walking, walking upstairs and downstairs, jumping and standing) has become an important problem in human-computer interaction. The computer vision method requires a large amount of computing resources, and it is not highly accuracy and can be easily disturbed by other objects in the background. The sensor-based method can achieve high accuracy, and it requires few computing resources, and is not disturbed by the background. This paper proposes a method based on the one-dimensional convolutional neural network (1D-CNN) to classify the sensor signals of some different activities. For comparison, this paper applies some widely used methods to accomplish the recognition task with the same dataset. Then, it tests the proposed 1D-CNN model with different datasets, for the purpose of testing its generality across users. The experimental results show that the proposed model achieves an accuracy of 95.12% with the said datasets, which is higher than those of the other methods by about 8% on average. This indicates that the proposed method has good performance in terms of generality across users, and at the same time provides a higher accuracy. The obtained results can improve the accuracy of current technologies.
We have developed a SmartHomeCage system that allow for insertion of normal rodent cages to monitor animal's behaviors in its homecage with bedding, food and water for days. Each apparatus is comprised of multiple sensors and up to 16 apparatuses can be simultaneously operated by one PC. Parameters measured include sleep/wake states, locomotion (traveling distance and speed), rearing, movement patterns and rotations. For validating the system, we showed that cocaine (30 mg/kg, ip) produced an increase in activity including distance traveled and rearing. The nociceptin receptor agonist SR 14150 (10 mg/kg, ip) that was previously shown to induce hypolocomotion produced a decrease in activity. Using a dark box inserted in the normal cage, we were able to assess the anxiolytic effect of diazepam (3mg/kg, i.p.), as indicated by an increased latency of first entry to the dark box and increased the transition number between light and dark compartments. In pilot studies through collaboration with universities, we showed this system is sensitive enough to quantify mouse behavioral changes following experimental spinal injury or middle cerebral artery occlusion. Sleep/wake state automated classification using the SmartHomeCage will be further validated by comparing with simultaneous EEG recordings. This higher throughput behavioral monitoring system for behavioral phenotype of transgenic and gene knockout mice are currently underway.
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