Detection of Partially Structural Collapse Using Long‐Term Small Displacement Data from Satellite Images

The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long‐term structural health monitoring (SHM). However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR‐based SHM. Conversely, the major challenge of the long‐term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis‐squared distance. The first method presented in this work develops an artificial neural network‐based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher– student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long‐term displacement samples extracted from a few SAR images of TerraSAR‐X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR‐based SHM applications

Online hybrid learning methods for real-time structural health monitoring using remote sensing and small displacement data

Structural health monitoring (SHM) by using remote sensing and synthetic aperture radar (SAR) images is a promising approach to assessing the safety and the integrity of civil structures. Apart from this issue, artificial intelligence and machine learning have brought great opportunities to SHM by learning an automated computational model for damage detection. Accordingly, this article proposes online hybrid learning methods to firstly deal with some major challenges in data-driven SHM and secondly detect damage via small displacement data from SAR images in a real-time manner. The proposed methods contain three main parts: (i) data augmentation by Hamiltonian Monte Carlo and slice sampling for addressing the problem of small displacement data, (ii) data normalization by an online deep transfer learning algorithm for removing the effects of environmental and/or operational variability from augmented data, and (iii) feature classification via a scalar novelty score. The major contributions of this research include proposing two online hybrid unsupervised learning methods and providing effective frameworks for online damage detection. A small set of displacement samples extracted from SAR images of TerraSar-X regarding a long-term monitoring scheme of the Tadcaster Bridge in United Kingdom is applied to validate the proposed methods

A prenatally detected adrenal cyst treated by adrenal-sparing surgery: case report and review of the literature

A neonatal case of left adrenal cyst detected in utero and successfully treated by adrenal-sparing surgery is presented and discussed with review of the literature. Incidentally discovered prenatal adrenal masses present a diagnostic dilemma. Benign and malignant conditions can present as a fetal suprarenal mass. There is a wide spectrum of management modalities ranging from followup by serial sonographic scanning during pregnancy to early primary excision of the mass. We report a neonate with prenatal diagnosis of a cystic mass arising from the left adrenal gland. Postnatal excision of the mass without adrenalectomy was carried out. Frozen sections of the mass and a biopsy of the left adrenal gland confirmed the benign nature of the cyst and normal adrenal tissue. The uniloculated cyst was reported as a pseudocyst. After surgery, the recovery was uneventful, and the patient was discharged 4 days postoperatively in good condition. On the basis of this case and review of the literature, we may conclude that early primary surgical excision is recommended for either diagnosis or treatment if the results of prenatal or postnatal imaging studies are unreliable for the precise diagnosis of suprarenal mass. Adrenal-sparing surgery is recommended if pathological evaluation of frozen sections has confirmed the benign nature of the mass.Keywords: adrenal-sparing surgery, neonates, prenatal diagnosis, suprarenal cys

Development Of Al-B-C Master Alloy Under External Fields

This study investigates the application of external fields in the development of an Al-B-C alloy, with the aim of synthesizing in situ Al3BC particles. A combination of ultrasonic cavitation and distributive mixing was applied for uniform dispersion of insoluble graphite particles in the Al melt, improving their wettability and its subsequent incorporation into the Al matrix. Lower operating temperatures facilitated the reduction in the amount of large clusters of reaction phases, with Al3BC being identified as the main phase in XRD analysis. The distribution of Al3BC particles was quantitatively evaluated. Grain refinement experiments reveal that Al-B-C alloy can act as a master alloy for Al-4Cu and AZ91D alloys, with average grain size reduction around 50% each at 1wt%Al-1.5B-2C additions

SiC substrate effects on electron transport in the epitaxial graphene layer

Cataloged from PDF version of article.Hall effect measurements on epitaxial graphene (EG) on SiC substrate have been carried out as a function of temperature. The mobility and concentration of electrons within the two-dimensional electron gas (2DEG) at the EG layers and within the underlying SiC substrate are readily separated and characterized by the simple parallel conduction extraction method (SPCEM). Two electron carriers are identified in the EG/SiC sample: one high-mobility carrier (3493 cm(2)/Vs at 300 K) and one low-mobility carrier (1115 cm(2)/Vs at 300 K). The high mobility carrier can be assigned to the graphene layers. The second carrier has been assigned to the SiC substrate

LIGHTNESS: a function-virtualizable software defined data center network with all-optical circuit/packet switching

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Modern high-performance data centers are responsible for delivering a huge variety of cloud applications to the end-users, which are increasingly pushing the limits of the currently deployed computing and network infrastructure. All-optical dynamic data center network (DCN) architectures are strong candidates to overcome those adversities, especially when they are combined with an intelligent software defined control plane. In this paper, we report the first harmonious integration of an optical flexible hardware framework operated by an agile software and virtualization platform. The LIGHTNESS deeply programmable all-optical circuit and packet switched data plane is able to perform unicast/multicast switch-over on-demand, while the powerful software defined networking (SDN) control plane enables the virtualization of computing and network resources creating a virtual data center and virtual network functions (VNF) on top of the data plane. We experimentally demonstrate realistic intra DCN with deterministic latencies for both unicast and multicast, showcasing monitoring, and database migration scenarios each of which is enabled by an associated network function virtualization element. Results demonstrate a fully functional complete unification of an advanced optical data plane with an SDN control plane, promising more efficient management of the next-generation data center compute and network resources.Peer ReviewedPostprint (author's final draft