ABSTRACT
Three-dimensional (3D) printer-based self-healing capsules, embedded in cement composites, were proposed to heal cracks, as they allow for various structural designs of capsules, repeatable fabrication, and strength analysis. Out of many 3D printing methods, such as fusion deposition modeling (FDM), powder layer fusion, and PolyJet printing, FDM was used to design, analyze, and produce new self-healing capsules, which are widely used due to their high-speed, low-cost, and precise manufacturing. However, the PLA extruded in the FDM had low adhesion energy between stacked layers, which caused a degradation of the performance of the self-healing capsule, because it had different strengths depending on the angle between the stacked layers and the applied load within the concrete structure. Therefore, in this paper, specimens were produced, in accordance with ASTM specifications, using the FDM PLA method, and mechanical properties were obtained through tensile, shear, and compression tests. Additionally, the isotropic fracture characteristics of the four types of capsules were analyzed through finite element method analysis. Subsequently, the 3D-printed capsules were produced, and the fracture strength was analyzed in the x, y and z directions of the applied load through a compression test. As a result, the newly proposed capsule design was verified to have an isotropic fracture strength value of 1400% in all directions compared to conventional spherical thin film capsules.
ABSTRACT
This paper proposes a novel approach to high-dynamic-range (HDR) imaging of dynamic scenes to eliminate ghosting artifacts in HDR images when in the presence of severe misalignment (large object or camera motion) in input low-dynamic-range (LDR) images. Recent non-flow-based methods suffer from ghosting artifacts in the presence of large object motion. Flow-based methods face the same issue since their optical flow algorithms yield huge alignment errors. To eliminate ghosting artifacts, we propose a simple yet effective alignment network for solving the misalignment. The proposed pyramid inter-attention module (PIAM) performs alignment of LDR features by leveraging inter-attention maps. Additionally, to boost the representation of aligned features in the merging process, we propose a dual excitation block (DEB) that recalibrates each feature both spatially and channel-wise. Exhaustive experimental results demonstrate the effectiveness of the proposed PIAM and DEB, achieving state-of-the-art performance in terms of producing ghost-free HDR images.
ABSTRACT
At present, RFID is installed on mobile devices such as mobile phones or PDAs and provides a means to obtain information about objects equipped with an RFID tag over a multi-channeled telecommunication networks. To use mobile RFIDs, reader collision problems should be addressed given that readers are continuously moving. Moreover, in a multichannel environment for mobile RFIDs, interference between adjacent channels should be considered. This work first defines a new concept of a reader collision problem between adjacent channels and then suggests a novel reader anti-collision algorithm for RFID readers that use multiple channels. To avoid interference with adjacent channels, the suggested algorithm separates data channels into odd and even numbered channels and allocates odd-numbered channels first to readers. It also sets an unused channel between the control channel and data channels to ensure that control messages and the signal of the adjacent channel experience no interference. Experimental results show that suggested algorithm shows throughput improvements ranging from 29% to 46% for tag identifications compared to the GENTLE reader anti-collision algorithm for multichannel RFID networks.
