95,927 research outputs found
UrbanDenoiser - Denoise for urban seismological noise
No full textWe develop a deep-learning-based denoising algorithm, UrbanDenoiser, to filter out urban seismological noise.
The trained deep-learning model of UrbanDenoiser is in 210906-023102.zip, which is a transfer learning from DeepDenoiser (https://github.com/wayneweiqiang/DeepDenoiser), using waveform datasets containing rich noise sources from the urban Long Beach dense array and high signal-to-noise ratio (SNR) earthquake signals from the rural San Jacinto dense array.
With the denoised Long Beach dense array data by UrbanDenoiser, we relocate the seismisity beneath Long Beach, and store the earthquake catalog in LB Catalog_2012_061-067.txt.
The first column is the date. The second column is the number of the three-second window during which the earthquake happened referenced to the starting time of each day (UTC). The third to fifth columns represent the earthquake location in our 3D imaging volume, with the grid spacing of 200 m in each direction (Depth, E-W, N-S). California State Plane Coordinate System Zone 7 is applied here. The origin of the 3D imaging volume represents (1293400 m, 1226000 m, 0 m) in Zone 7. The last column represents the number of median absolute deviation (MAD) the back-projected amplitudes exceeding the detection threshold.
This work is under the Creative Commons (CC) license 3.0: Attribution-NonCommercial-NoDerivs 3.0 https://creativecommons.org/licenses/by-nc-nd/3.0/
If you use any part of this program in your research, please cite:
Lei Yang, Xin Liu, Weiqiang Zhu, Liang Zhao, Gregory C. Beroza. 2022. Toward Improved Urban Earthquake Monitoring through Deep-Learning-Based Noise Suppression. Science Advances, DOI: 10.1126/sciadv.abl3564
Large-scale Forest Stand Height mapping for the northeast of U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI
No full text<p><span>The forest height mosaic for the northeastern parts of China and U.S are generated based on a global-to-local inversion approach proposed in </span><span><span>(Yu et al., 2023)</span></span><span> by making use of Spaceborne repeat-pass InSAR and spaceborne GEDI data. The sparsely but extensively distributed LiDAR samples provided by NASA’s GEDI mission are used to parametrize the semi-empirical repeat-pass InSAR scattering model(Lei et al., 2017) and to obtain forest height estimates. Compared to our previous efforts </span><span><span>(Lei et al., 2018, Lei and Siqueira, 2022)</span></span><span>, this paper further removes the assumptions that were made given the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage inversion scheme. This approach allows a better use of local GEDI samples to achieve finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion.<span> </span>This approach is further fully automated to enable a large-scale forest mapping capability. Two forest height mosaic maps were generated for the entire northeastern regions of U.S. and China with total area of 18 million hectares and 112 million hectares, respectively. The validation of the forest height estimates demonstrates much improved accuracy achieved by the proposed approach compared to the previous efforts i.e., reducing from RMSE of 3-4 m on the order of 3-6-hectare aggregated pixel size to RMSE 3-4 m on the order of 0.81-hectare pixel size. The proposed fusion approach not only addresses the sparse spatial sampling problem inherent to the GEDI mission, but also improve the accuracy of forest height estimates compared to the GEDI-interpolated maps by a factor of 20% at 30-m resolution. The extensive evaluation of forest height inversion against LVIS LiDAR data indicates an accuracy 3-4 m on the order of 0.81 hectare over smooth areas and 4-5 m over hilly areas in U.S., whereas the forest height estimates over northeastern China are best compared with small footprint LiDAR validation data even at an accuracy of even below 3.5 m with R2 mostly above 0.6. Such a forest height inversion accuracy at sub-hectare pixel size provides promising values towards the existing and future spaceborne LiDAR (</span><span>JAXA’s MOLI, NASA’s GEDI, China’s TECIS</span><span>) and InSAR missions (</span><span>NASA-ISRO’s NISAR, JAXA’s ALOS-4 and China’s LuTan-1</span><span>). This fusion prototype can work as a cost-effective solution for public users to obtain a wall-to-wall forest height mapping at large scale when only spaceborne repeat-pass InSAR data are available and freely accessible.</span></p>
Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models
No full textExperimental and numerical investigations are presented for a theory-guided machine learning (ML) model that combines the Hashin failure theory (HFT) and the classical lamination theory (CLT) to optimize and accelerate the design of composite laminates. A finite element simulation with the incorporation of the HFT and CLT were used to generate the training dataset. Instead of directly mapping the relationship between the ply angles of the laminate and its strength and stiffness, a multi-layer interconnected neural network (NN) system was built following the logical sequence of composite theories. With the forward prediction by the NN system and the inverse optimization by genetic algorithm (GA), a benchmark case of designing a composite tube subjected to the combined loads of bending and torsion was studied. The ML models successfully provided the optimal layup sequences and the required fiber modulus according to the preset design targets. Additionally, it shows that the machine learning models, with the guidance of composite theories, realize a faster optimization process and requires less training data than models with direct simple NNs. Such results imply the importance of domain knowledge in helping improve the ML applications in engineering problems
The role of the turbulent scales in the settling velocity of heavy particles in homogeneous isotropic turbulence
No full textDielectrophoretic force and torque on an ellipsoid in an arbitrary time varying electric field
No full textDielectrophoretic force and torque on a sphere in an arbitrary time varying electric field
No full textExperimental study on the impact resistance and damage tolerance of thermoplastic FMLs
No full textThis study aimed to enhance the impact resistance of fiber metal laminates (FMLs) and achieve lightweight optimization by incorporating a new thermoplastic resin, a titanium alloy and ultra-high-molecular-weight polyethylene (UHMWPE) fiber to produce a novel type of FMLs (PEFMLs). The impact resistance of PEFMLs was analyzed through low-velocity impact tests conducted at different energy levels. Subsequently, the residual compression-after-impact (CAI) strength of the PEFMLs was evaluated through compression tests on the impacted specimens. The experimental findings revealed that PEFMLs exhibited subcritical failure when subjected to impact energies less than 35 J, with a penetration energy threshold of 55 J. Higher impact energies resulted in larger damage areas and increased plate buckling of PEFMLs, consequently leading to reduced CAI strength. The presence of metal, thermoplastic resin and UHMWPE in the PEFMLs effectively dissipated a substantial amount of impact energy while maintaining their structural integrity during both the impact and compression processes
Mogannia hainana Shen, Lei & Yang 2006
No full textMogannia hainana Shen, Lei & Yang, 2006 Figures 14–15 Mogannia hainana Shen, Lei & Yang, 2006: 175. Description. Head (Fig. 14 A–D) ochraceous; postclypeus longer than vertex in dorsal view; apex of vertex with tuft of brown hairs; compound eye black; ocellus ochraceous. Distance between lateral ocellus and corresponding eye slightly longer than distance between lateral ocelli. Lateral part of face brown. Rostrum brown, extending to apex of mid coxae. Pronotum (Fig. 14 C) ochraceous, without distinct marks. Mesonotum (Fig. 14 C) ochraceous, slightly narrower than pronotal collar; with dark longitudinal fasciae laterally in dorsal view; cruciform elevation ochraceous. Ventral surface of thorax dark brown. Legs (Fig. 14 G) mostly dark brown; fore femur with primary spine prostrate, nearly lying flat; secondary and subapical spines erect; secondary spine sharp and short; subapical spine broadened, nearly flat. Fore wing (Fig. 14 A–B) with basal half mostly brown, basal cell light yellow, apical half hyaline; basal membrane of fore wing and base of hing wing red. Abdomen (Fig. 14 A–B) cylindrical and ochraceous in dorsal view. Timbal cover (Fig. 14 F) fuscous, short and more or less triangular, with apex rounded; timbal with eight ribs and seven intercalary ribs, caudal five ribs fused at base. Ventral surface of abdomen ochraceous. Male operculum (Fig. 14 E) blackish brown, short, falcate; tympanum exposed; opercula widely separated from each other. Male genitalia (Fig. 15 A–D). Pygofer barrel-shaped in ventral view; dorsal beak long, protruding upwards in lateral view; basal lobe of pygofer short, with apex rounded; upper lobe of pygofer a little short, digitate and curved upwards in lateral view; distal shoulder remarkably convex near upper lobe, forming somewhat large triangular process in lateral view. Clasper in ventral with median clasper process long, digitate, apical half slightly curved inward; lateral clasper lobe short and rounded. Prominent lobe-like process at both sides of base of aedeagus long, digitate. Aedeagus somewhat truncate subapically in ventral view; eight spine-like processes apically and subapically, which are all curved inwards in lateral view. Posterior margin of sternite VII rounded. Female pygofer with dorsal beak slender; posterior margin of sternite VII with large median incision. Measurements (103, 10Ƥ) (in mm). Body length: 3 11.5–12.5, Ƥ 12.0–14.0; fore wing length: 3 12.0–13.0, Ƥ 12.0–14.0; fore wing width: 3 3.5–4.5, Ƥ 4.0– 5.5; width of head including eyes: 3 2.5 –3.0, Ƥ 2.5 –3.0; pronotum width (including pronotal collar): 3 4.5 –5.0, Ƥ 4.5–5.5; mesonotum width: 3 3.5 –4.0, Ƥ 3.5 –4.0. Material examined. 13, 1Ƥ (IZAS), Hainan Prov., Mt. Wuzhishan, 16 -V- 1960, coll. Li Binfu; 43 (IZAS), Hainan Prov., Mt. Wuzhishan, 24 -VI- 1960, coll. Li Binfu; 13 (IZAS)?Hainan Prov., Mt. Wuzhishan, 4 -VIII- 1960, coll. Lin Binfu; 13 (IZAS), Hainan Prov., Mt. Wuzhishan, 10 -VI- 1960, coll. Zhang Xuezhong; 13 (NWAFU), Hainan Prov., Mt. Jianfengling, 6 -V- 1964, coll. Liu Shengli; 1 Ƥ (SYSU), Hainan Prov., Mt. Jianfengling, 14 -VII- 1983, coll. Liang Shaoying; 923, 8Ƥ (NWAFU), Hainan Prov., Mt. Bawangling, 27 -V- 2011, coll. Yang Mingsheng; 83, 3Ƥ (NWAFU), Hainan Prov., Mt. Qixianling, 15 -V- 2011, coll. Yang Mingsheng; 73, 5Ƥ (NWAFU), Hainan Prov., Mt. Limushan, 22 -V- 2011, coll. Yang Mingsheng. Biology. This species is distributed in Hainan Province. Males often sing on leaves of shrubs, and singing usually occurs in the afternoon. They often perch on the surface of leaves towards sunshine. Variations. Some examined specimens with ground colour of body mostly ochraceous or even a bit reddish, basal half of fore wing with various stripes, and aedeagus of male genitalia with six spine-like processes. Distribution. China (Hainan).Published as part of Chen, Xiao, Yang, Mingsheng & Wei, Cong, 2012, Review of the cicada genus Mogannia Amyot & Serville from China, with descriptions of three new species (Hemiptera: Cicadidae), pp. 1-35 in Zootaxa 3568 on pages 21-23, DOI: 10.5281/zenodo.24624
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