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A New Prestress Loss Calculation Model of Anchor Cable in Pile–Anchor Structure
Mathematics, 2022 Pile–anchor structures are widely used in foundation excavation and slope reinforcement due to their safety and reliability. However, the pile–anchor structures have the common problem of the prestress loss of anchor cables, which may reduce the ...
Xing Gao +5 more
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Analysis of Re-Tensioning Time of Anchor Cable Based on New Prestress Loss Model
Mathematics, 2021 Considering the interaction among anchor cable, frame beam and rock mass, a new model of prestress loss of anchor cable was established. The accuracy and applicability of the new model were verified by comparing the field monitoring data and the ...
Keyou Shi +3 more
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Anchor-Free Smoke and Flame Recognition Algorithm with Multi-Loss
Fire, 2023 Fire perception based on machine vision is essential for improving social safety. Object recognition based on deep learning has become the mainstream smoke and flame recognition method.
Gang Li +4 more
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Anchor Loss Reduction of Lamb Wave Resonator by Pillar-Based Phononic Crystal [PDF]
Micromachines, 2021 Energy leakage via anchors in substrate plates impairs the quality factor (Q) in microelectromechanical system (MEMS) resonators. Most phononic crystals (PnCs) require complicated fabrication conditions and have difficulty generating a narrow bandgap at ...
Yinjie Tong, Tao Han
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Torque-Dependent Anchor Loss and Fourth-Harmonic Damping Anisotropy in Coriolis Vibratory Gyroscopes [PDF]
SensorsThe quality factor (Q) and its circumferential non-uniformity are essential for the resolution and long-term stability of Coriolis vibratory gyroscopes (CVGs).
Ning Wang +4 more
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Micromachines, 2023 In this paper, a multi-material radial phononic crystal (M-RPC) structure is proposed to reduce the anchor-point loss of piezoelectric micro-electro-mechanical system (MEMS) resonators and improve their quality factor.
Qian Yang +3 more
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Proxy Anchor Loss for Deep Metric Learning [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Existing metric learning losses can be categorized into two classes: pair-based and proxy-based losses. The former class can leverage fine-grained semantic relations between data points, but slows convergence in general due to its high training complexity. In contrast, the latter class enables fast and reliable convergence, but cannot consider the rich
Sungyeon Kim +3 more
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Micromachines, 2022 This paper presents a type of single-phase double “I” hole phononic crystal (DIH-PnC) structure, which is formed by vertically intersecting double “I” holes.
Lixia Li +4 more
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A Noble, Easy and Conceptual Radiographic Analysis to Assess the Type of Tooth Movement (Molar Distalization) [PDF]
Journal of Clinical and Diagnostic Research, 2015 Context and Aim: Bodily tooth movement is desirable in the field of Orthodontics and so is with molar distalization. Till date there is no such analysis available which could gauge and quantify the molar distalization and the type of tooth movement ...
Abhishek Bansal +3 more
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Micromachines, 2023 This article presents a new design of supporting tethers through the concept of force distribution. The transmitted force applied on tethers will be distributed on the new tether design area, resulting in low acoustic energy transferred to anchor ...
Mohammed Awad +3 more
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