Carbon microfiber was prepared through shear pulverization using the self-designed pan-mill type equipment at ambient temperature from short carbon fiber (CF). The effects of shear stress on structure transformations, particles size, microfiber morphology, surface functional groups and crystalline properties during pulverization were studied by laser diffraction particle size analyzer, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), x-ray photoelectron spectroscopy (XPS) and wide-angle x-ray diffraction (WAXD), respectively. SEM analysis indicated that CF was milled into microfiber due to the strong shear and squeezing force. The average particle size of carbon microfiber was reduced to 12.7 lm and specific surface area was increased to 0.6 m 2 /g after 40 milling cycles. FT-IR and XPS analyses showed that the oxygen-containing groups increased after shear pulverization, and WAXD results illustrated that shear stress offered by mill discs had an obvious damage on the crystal structure of CF, leading to a decrease of crystallinity. Thermal analysis showed that carbon microfiber exhibited good thermal stability. The pan-milling shear pulverization technique is an environment-friendly and efficient method for preparing carbon microfiber.
With the rapid development of the Industrial Internet of Things (IIoT) and edge computing techniques, in situ intelligent sensors are continuously generating increasing and vast amounts of time-series data. In many industrial applications, particularly highly distributed systems positioned in remote areas, repeated transmission of large amounts of raw data onto the remote server is not possible. This poses a significant challenge to the timely processing of these data in IIoT. Analyzing and processing all the raw data remotely in the cloud server is impractical and has very low efficiency owing to network latency and the limited cloud computing resources. Failure of detecting abnormal data may result in major production safety problems. Therefore, businesses are moving machine learning capabilities to the edge to enable real-time actions in the field. In this study, we present a machine-learning-based edge-cloud framework to solve this problem. First, robust random cut forest and isolation forest algorithms are employed at the edge gateway to the collected data for the detection of anomalously changing data. Subsequently, these preprocessed time-series data are transmitted to cloud services for data trend prediction and missing data completion using the long short-term memory recurrent neural network method feed in conjunction with the original sequence of historical data combined with the first-order forward difference data. The experimental results show that the machine-learning-based edge-cloud-assisted oil production IIoT system can improve substantially the efficiency and accuracy of time-series data analyses.
The measurement of radiated emission (RE) in an anechoic chamber becomes very challenging at high frequencies, up to 60 GHz, because the scanning plane of the receiver is in measurement standard deviation from the actual wavefront. As a result, the RE intensity of the devices may be underestimated, resulting in electromagnetic interference. The deviation between the electric field at the far-field vertical scanning point and the actual wavefront is researched. Then, in an anechoic chamber, a hybrid deep learning amendment model of convolutional neural network (CNN) and transformer is proposed to correct the RE measurement at a 3 m distance. The results indicate that the correction is reliable, with an average error of 6.35% for a 3 m distance in a semianechoic chamber and less than 4.83% for other test scenarios. The proposed method provides a promising solution for RE measurement at a millimeter wave band in an anechoic chamber.
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