SUMMARYThis paper describes the development of a novel compact magneto-rheological (MR) fluid brake with high transmitted torque and a simple structure. The MR fluid brake has two shearing disks with an electromagnetic coil located between them. Such a structure enables the brake to have a small radial dimension and a large torque transmission capacity. In the design process, a Bingham viscoplastic model is used to predict the transmitted torque. Electromagnetic finite element analysis (FEA) is performed to assist the magnetic circuit design and structural parameters' optimization. The novel brake design is prototyped and studied. Experimental results show that a compact MR fluid brake with high transmitted torque is successfully achieved.
This work is an extension of our previous study on the development of a linear variable differential sensor (LVDS)-based magnetorheological (MR) damper with self-sensing capability, where a new MR damper integrated with LVDS technology was developed and prototyped, then its self-induced performance under static and dynamic working conditions was experimentally evaluated. The results of the static and dynamic experiments indicated that the self-induced voltage was proportional to the displacement of the damper. Moreover, the damping performance of this new MR damper was also evaluated through an experimental study. Compared with our previous study, the new MR damper performed better in terms of its self-induced sensing ability and damping capacity.
Context. Arch filament systems (AFSs) are usually considered as the chromospheric manifestations of the emerging flux regions (EFRs) seen in Hα observations. Moving magnetic features (MMFs) look similar to EFRs in magnetograms, but often appear in the decaying phase of an active region (AR) and behave differently from EFRs. A possible relation between AFS and MMF would be important for revealing a common mechanism for building up basic structures on the Sun. Aims. Based on Hα and magnetic field observations with high spatial resolution, we study the evolution of MMFs around a sunspot, as well as their related AFSs from birth to death. Methods. The multiwavelength observations from the New Vacuum Solar Telescope (NVST) and the Solar Dynamic Observatories (SDO) are co-aligned in the spatial and the temporal sense. MMFs appeared near the northern end of a light bridge (LB). Their related AFSs were carefully identified and traced from their appearance to disappearance based on Hα, EUV data, and magnetograms. Results. In the main sunspot of AR NOAA 11711 during April 1−4, 2013, many slow-speed MMFs with a polarity opposite to that of the sunspot appeared from the close vicinity of the northern end of a LB. Different from other smaller MMFs around the sunspot, these MMFs were always related to arch filaments and eventually formed AFSs with three twisting branches. The total flux involved in the AFSs was estimated to be about 2.7 × 10 21 Mx. The largest MMF "M1" evolved into a small pore that led to an intensity reduction in the continuum intensity images. The appearance and evolution of the AFSs near the sunspot seems to be controlled by MMFs emanating from the penumbra. Owing to continual magnetic cancellation between the MMFs and their surrounding opposite flux, the AFSs gradually disintegrated and finally disappeared. Conclusions. The appearance and evolution of the AFSs near the sunspot seem to be controlled by these MMFs emanating from the penumbra.
Extracting buildings and roads from remote sensing images is very important in the area of land cover monitoring, which is of great help to urban planning. Currently, a deep learning method is used by the majority of building and road extraction algorithms. However, for existing semantic segmentation, it has a limitation on the receptive field of high-resolution remote sensing images, which means that it can not show the long-distance scene well during pixel classification, and the image features is compressed during down-sampling, meaning that the detailed information is lost. In order to address these issues, Hybrid Multi-resolution and Transformer semantic extraction Network (HMRT) is proposed in this paper, by which a global receptive field for each pixel can be provided, a small receptive field of convolutional neural networks (CNN) can be overcome, and the ability of scene understanding can be enhanced well. Firstly, we blend the features by branches of different resolutions to keep the high-resolution and multi-resolution during down-sampling and fully retain feature information. Secondly, we introduce the Transformer sequence feature extraction network and use encoding and decoding to realize that each pixel has the global receptive field. The recall, F1, OA and MIoU of HMPR obtain 85.32%, 84.88%, 85.99% and 74.19%, respectively, in the main experiment and reach 91.29%, 90.41%, 91.32% and 84.00%, respectively, in the generalization experiment, which prove that the method proposed is better than existing methods.
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