Damage to School Buildings During the 2015 Nepal Earthquake and Reconstruction Strategy

The 2015 Nepal Earthquake (Mw7.8) affected more than 9,000 schools in the country. Damage distribution in the 14 most-affected administrative districts shows that the construction practices were an important determent for the level of damage extended. Use of improper construction materials, lack of construction supervision, and non-compliance with the existing building codes during design and construction probably contributed to the severe damage of most of the school buildings. Preliminary damage assessment results show that in the most-affected districts, about 86% schools were affected by the earthquake and about one million students were out of their schools for a long time. The damage survey data indicate that about 30% classrooms collapsed, about 13% classrooms sustained major damage, and about 17% classrooms sustained minor damage within the 14 districts. Such evidence of loss and damage in the earthquake disasters provides an opportunity to learn lessons for the future preparedness and to encounter the disaster challenges. Based on the damage analysis data and experience of reconstruction process after the 2015 Nepal Earthquake, this paper highlights the steps to be considered during reconstruction strategy planning for school buildings after an earthquake disaster.

LETR: An End-to-End Detector of Reconstruction Area in Blade’s Adaptive Machining with Transformer

In the leading/trailing edge’s adaptive machining of the near-net-shaped blade, a small portion of the theoretical part is retained for securing aerodynamic performance by manual work. However, this procedure is time-consuming and depends on the human experience. In this paper, we defined retained theoretical leading/trailing edge as the reconstruction area. To accelerate the reconstruction process, an anchor-free neural network model based on Transformer was proposed, named LETR (Leading/trailing Edge Transformer). LETR extracts image features from an aspect of mixed frequency and channel domain. We also integrated LETR with the newest meta-Acon activation function. We tested our model on the self-made dataset LDEG2021 on a single GPU and got an mAP of 91.9\%, which surpassed our baseline model, Deformable DETR by 1.1\%. Furthermore, we modified LETR’s convolution layer and named the new model after GLETR (Ghost Leading/trailing Edge Transformer) as a lightweight model for real-time detection. It is proved that GLETR has fewer weight parameters and converges faster than LETR with an acceptable decrease in mAP (0.1\%) by test results.

Tianwen-1 Mars entry vehicle trajectory and atmosphere reconstruction preliminary analysis

The Tianwen-1 Mars entry vehicle successfully landed on the surface of Mars in southern Utopia Planitia on May 15, 2021, at 7:18 (UTC+8). To acquire valuable Martian flight data, a scientific instrumentation package consisting of a flush air data system and a multilayer temperature-sensing system was installed aboard the entry vehicle. A combined approach was applied in the entry, descent, and landing trajectory reconstruction using all available data obtained by the inertial measurement unit and the flush air data system. An aerodynamic database covering the entire flight regime was generated using computational fluid dynamics methods to assist in the reconstruction process. A preliminary analysis of the trajectory reconstruction result, along with the atmosphere reconstruction and aerodynamic performance, was conducted. The results show that the trajectory agrees closely with the nominal trajectory and the wind-relative attitude. Suspected wind occurred at the end of the trajectory.

Algorithm of 3D Virtual Reconstruction of Ancient Buildings in Qing Dynasty Based on Image Sequence

Aiming at the problem of low accuracy and poor integrity of traditional Qing Dynasty ancient architecture 3D virtual reconstruction algorithm, a 3D virtual reconstruction algorithm of Qing Dynasty ancient architecture based on image sequence is proposed. Acquire the sequence images of ancient buildings in the Qing Dynasty through the pinhole camera model, analyze the projective space and reconstruction space of the sequence images, redefine the similarity measurement coefficient according to the improved 2DPCA-SIFT feature matching algorithm, match the feature points of the ancient architecture images in the Qing Dynasty, and use random sampling to be consistent. The algorithm solves the basic matrix, removes the interference error in the image reconstruction process, and realizes the design of the three-dimensional reconstruction algorithm through image sequence fusion. The experimental results show that, compared with the existing methods, the completeness of the three-dimensional virtual reconstruction 3D model of ancient Qing Dynasty buildings constructed by the designed algorithm is 87.26% on average, and the completeness and accuracy of the 3D model construction of the subparts of the ancient Qing Dynasty buildings of this method are better. The height of the building fully shows that the designed building has good performance in the construction of the three-dimensional model of ancient buildings in the Qing Dynasty.

End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction

Metal artifacts are common in CT-guided interventions due to the presence of metallic instruments. These artifacts often obscure clinically relevant structures, which can complicate the intervention. In this work, we present a deep learning CT reconstruction called iCTU-Net for the reduction of metal artifacts. The network emulates the filtering and back projection steps of the classical filtered back projection (FBP). A U-Net is used as post-processing to refine the back projected image. The reconstruction is trained end-to-end, i.e., the inputs of the iCTU-Net are sinograms and the outputs are reconstructed images. The network does not require a predefined back projection operator or the exact X-ray beam geometry. Supervised training is performed on simulated interventional data of the abdomen. For projection data exhibiting severe artifacts, the iCTU-Net achieved reconstructions with SSIM = 0.970±0.009 and PSNR = 40.7±1.6. The best reference method, an image based post-processing network, only achieved SSIM = 0.944±0.024 and PSNR = 39.8±1.9. Since the whole reconstruction process is learned, the network was able to fully utilize the raw data, which benefited from the removal of metal artifacts. The proposed method was the only studied method that could eliminate the metal streak artifacts.

Unified Approach For Multiscale Radar Cross-Section Measurement by Synthetic Aperture Imaging

<div><div>*This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</div></div><div><br></div>In this study, we present an improved and unified approach for image-based radar cross-section (RCS) measurement by 2-D synthetic aperture radar (SAR) imaging with an arbitrary curved antenna scanning trajectory. Because RCS is a quantity defined in the far-field distance of an object under test, direct RCS measurement of an electrically large target is often infeasible owing to the spatial limitation of the measurement facility. The method proposed in this study belongs to the class of techniques referred to as the image-based near-field to far-field transformation (NFFFT) to convert the near-field data of scattering experiment into the far-field RCS. In a previous study, we have developed an NFFFT based on 3-D SAR imaging with an arbitrary antenna scanning surface. However, the previous approach is only applicable to the surface scanning which is impossible for a certain case such as measurement using airborne SAR or vehicle-borne SAR. Therefore, one requires an alternative method that can accommodate an arbitrary scanning curve, which is the subject of this study. We derive a generalized correction factor for image-based NFFFT which is designed to ensure the integral transformation in the image reconstruction process be self-consistent for electrically small scatterers. We provide a series of numerical simulations, an indoor experiment, and an airborne SAR experiment to validate that the proposed scheme can be utilized for various situations ranging from near-field to far-field distance.

Unified Approach For Multiscale Radar Cross-Section Measurement by Synthetic Aperture Imaging

<div><div>*This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</div></div><div><br></div>In this study, we present an improved and unified approach for image-based radar cross-section (RCS) measurement by 2-D synthetic aperture radar (SAR) imaging with an arbitrary curved antenna scanning trajectory. Because RCS is a quantity defined in the far-field distance of an object under test, direct RCS measurement of an electrically large target is often infeasible owing to the spatial limitation of the measurement facility. The method proposed in this study belongs to the class of techniques referred to as the image-based near-field to far-field transformation (NFFFT) to convert the near-field data of scattering experiment into the far-field RCS. In a previous study, we have developed an NFFFT based on 3-D SAR imaging with an arbitrary antenna scanning surface. However, the previous approach is only applicable to the surface scanning which is impossible for a certain case such as measurement using airborne SAR or vehicle-borne SAR. Therefore, one requires an alternative method that can accommodate an arbitrary scanning curve, which is the subject of this study. We derive a generalized correction factor for image-based NFFFT which is designed to ensure the integral transformation in the image reconstruction process be self-consistent for electrically small scatterers. We provide a series of numerical simulations, an indoor experiment, and an airborne SAR experiment to validate that the proposed scheme can be utilized for various situations ranging from near-field to far-field distance.

Graph-Based Classification and Urban Modeling of Laser Scanning and Imagery: Toward 3D Smart Web Services

Recently, remotely sensed data obtained via laser technology has gained great importance due to its wide use in several fields, especially in 3D urban modeling. In fact, 3D city models in urban environments are efficiently employed in many fields, such as military operations, emergency management, building and height mapping, cadastral data upgrading, monitoring of changes as well as virtual reality. These applications are essentially composed of models of structures, urban elements, ground surface and vegetation. This paper presents a workflow for modeling the structure of buildings by using laser-scanned data (LiDAR) and multi-spectral images in order to develop a 3D web service for a smart city concept. Optical vertical photography is generally utilized to extract building class, while LiDAR data is used as a source of information to create the structure of the 3D building. The building reconstruction process presented in this study can be divided into four main stages: building LiDAR points extraction, piecewise horizontal roof clustering, boundaries extraction and 3D geometric modeling. Finally, an architecture for a 3D smart service based on the CityGML interchange format is proposed.

Synthesis of Mesoporous Cu-Ni/Al2O4 Catalyst for Hydrogen Production via Hydrothermal Reconstruction Route

Mesoporous Cu-Ni/Al2O4 catalyst of high surface area (176 m2g−1) is synthesized through a simple hydrothermal reconstruction process by using low-cost activated alumina as the aluminate source without organic templates. The desired mesoporous structure of the catalyst is formed by the addition of Cu2+ and Ni2+ metal ions in the gel solution of the activated alumina followed by hydrothermal treatment at 70 °C and calcination at temperatures in the range of 600 to 800 °C. To consider the environmental concern, we found the concentration of the Cu2+ and Ni2+ ion in the residual filtrate is less than 0.1 ppm which satisfies the effluent standard in Taiwan (<1.0 ppm). The effects of the pH value, hydrothermal treatment time, and calcination temperature on the structure, morphology and surface area of the synthesized Cu-Ni/Al2O4 composites are investigated as well. In addition, the Cu-Ni/Al2O4 catalyst synthesized at pH 9.0 with a hydrothermal treatment time of 24 h and a calcination temperature of 600 °C is used for hydrogen production via the partial oxidation of methanol. The conversion efficiency is found to be >99% at a reaction temperature of around 315 °C, while the H2 yield is 1.99 mol H2/mol MeOH. The catalyst retains its original structure and surface area following the reaction process, and is thus inferred to have a good stability. Overall, the hydrothermal reconstruction route described herein is facile and easily extendable to the preparation of other mesoporous metal-alumina materials for catalyst applications.

Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry

Creation of head 3D models from videos or pictures of the head by using close-range photogrammetry techniques has many applications in clinical, commercial, industrial, artistic, and entertainment areas. This work aims to create a methodology for improving 3D head reconstruction, with a focus on using selfie videos as the data source. Then, using this methodology, we seek to propose changes for the general-purpose 3D reconstruction algorithm to improve the head reconstruction process. We define the improvement of the 3D head reconstruction as an increase of reconstruction quality (which is lowering reconstruction errors of the head and amount of semantic noise) and reduction of computational load. We proposed algorithm improvements that increase reconstruction quality by removing image backgrounds and by selecting diverse and high-quality frames. Algorithm modifications were evaluated on videos of the mannequin head. Evaluation results show that baseline reconstruction is improved 12 times due to the reduction of semantic noise and reconstruction errors of the head. The reduction of computational demand was achieved by reducing the frame number needed to process, reducing the number of image matches required to perform, reducing an average number of feature points in images, and still being able to provide the highest precision of the head reconstruction.