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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Civil Space Technology Initiative: a First Step
This is the first published overview of OAST's focused program, the Civil Space Technology Initiative, (CSTI) which started in FY88. This publication describes the goals, technical approach, current status, and plans for CSTI. Periodic updates are planned
Proposal of a health care network based on big data analytics for PDs
Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians
Novel approach for three-dimensional integral documentation of machine rooms in hospitals using portable mobile mapping system
10 p.In this paper, a novel method for the documentation and evaluation of the machine roomsin hospitals is presented. The approach is based on data acquired with a portable mobile mappingsystem (PMMS), GeoSlam Zeb-Revo, which has proved to be effective for three-dimensional (3D) mappingof indoor environments, as well as for 3D modeling of individual thermal and fluid-mechanics equipment.An automatic data processing workflow based on the extraction of quantitative and qualitative geometricalfeatures from the point clouds provided by the PMMS is developed with the aim of evaluating the stateand adequate distributions of machineries and, in this way, generating a complete three-dimensional map ofthe industrial environment to be used for maintenance, inspection, and inventory tasks in accordance withsafety standards. The extracted parameters are statistically tested to evaluate the adequacy of the proposedmethodology and, in this way, demonstrate its potential for the application in the context of hospital facilitiesS
Laser powder bed additive manufacturing: A review on the four drivers for an online control
Online control of Additive Manufacturing (AM) processes appears to be the next challenge in the transition toward Industry 4.0 (I4.0). Although many efforts have been dedicated by industry and research in the last decades, there remains substantial room for improvement. Additionally, the existing scientific literature lacks a wide-ranging identification and classification of the primary drivers that enable online control of AM processes. This article focuses on online control of one of the most industrially widespread AM processes: metal Laser Powder Bed Fusion (L-PBF), with particular emphasis on two subcategories, namely Selective Laser Sintering (SLS) and Selective Laser Melting (SLM). Through a systematic literature review, this article initially identified over 200 manuscripts. The search was conducted utilizing a defined research query within the Scopus database, double checked on Scholar. The results were refined through multiple phases of inclusion/exclusion criteria, culminating in the selection of 95 pertinent papers. This article aims to provide a systematic and comprehensive review of four identified drivers i) Online controllable input parameters, ii) Online observable output signatures, iii) Online sensing techniques, iv) Online feedback strategies, adopted from the general Deming control loop Plan-Do-Check-Act (PDCA). Ultimately, this article delves into the challenges and prospects inherent in the online control of metal L-PBF
Machine Learning Based Defect Detection in Robotic Wire Arc Additive Manufacturing
In the last ten years, research interests in various aspects of the Wire Arc Additive Manufacturing (WAAM) processes have grown exponentially. More recently, efforts to integrate an automatic quality assurance system for the WAAM process are increasing. No reliable online monitoring system for the WAAM process is a key gap to be filled for the commercial application of the technology, as it will enable the components produced by the process to be qualified for the relevant standards and hence be fit for use in critical applications in the aerospace or naval sectors. However, most of the existing monitoring methods only detect or solve issues from a specific sensor, no monitoring system integrated with different sensors or data sources is developed in WAAM in the last three years. In addition, complex principles and calculations of conventional algorithms make it hard to be applied in the manufacturing of WAAM as the character of a long manufacturing cycle. Intelligent algorithms provide in-built advantages in processing and analysing data, especially for large datasets generated during the long manufacturing cycles. In this research, in order to establish an intelligent WAAM defect detection system, two intelligent WAAM defect detection modules are developed successfully. The first module takes welding arc current / voltage signals during the deposition process as inputs and uses algorithms such as support vector machine (SVM) and incremental SVM to identify disturbances and continuously learn new defects. The incremental learning module achieved more than a 90% f1-score on new defects. The second module takes CCD images as inputs and uses object detection algorithms to predict the unfused defect during the WAAM manufacturing process with above 72% mAP. This research paves the path for developing an intelligent WAAM online monitoring system in the future. Together with process modelling, simulation and feedback control, it reveals the future opportunity for a digital twin system
A Proposal for a Three Detector Short-Baseline Neutrino Oscillation Program in the Fermilab Booster Neutrino Beam
A Short-Baseline Neutrino (SBN) physics program of three LAr-TPC detectors
located along the Booster Neutrino Beam (BNB) at Fermilab is presented. This
new SBN Program will deliver a rich and compelling physics opportunity,
including the ability to resolve a class of experimental anomalies in neutrino
physics and to perform the most sensitive search to date for sterile neutrinos
at the eV mass-scale through both appearance and disappearance oscillation
channels. Using data sets of 6.6e20 protons on target (P.O.T.) in the LAr1-ND
and ICARUS T600 detectors plus 13.2e20 P.O.T. in the MicroBooNE detector, we
estimate that a search for muon neutrino to electron neutrino appearance can be
performed with ~5 sigma sensitivity for the LSND allowed (99% C.L.) parameter
region. In this proposal for the SBN Program, we describe the physics analysis,
the conceptual design of the LAr1-ND detector, the design and refurbishment of
the T600 detector, the necessary infrastructure required to execute the
program, and a possible reconfiguration of the BNB target and horn system to
improve its performance for oscillation searches.Comment: 209 pages, 129 figure
2020 NASA Technology Taxonomy
This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world
Snapshot hyperspectral imaging : near-infrared image replicating imaging spectrometer and achromatisation of Wollaston prisms
Conventional hyperspectral imaging (HSI) techniques are time-sequential and rely on
temporal scanning to capture hyperspectral images. This temporal constraint can limit
the application of HSI to static scenes and platforms, where transient and dynamic
events are not expected during data capture.
The Near-Infrared Image Replicating Imaging Spectrometer (N-IRIS) sensor described
in this thesis enables snapshot HSI in the short-wave infrared (SWIR), without the
requirement for scanning and operates without rejection in polarised light. It operates in
eight wavebands from 1.1μm to 1.7μm with a 2.0° diagonal field-of-view. N-IRIS
produces spectral images directly, without the need for prior topographic or image
reconstruction. Additional benefits include compactness, robustness, static operation,
lower processing overheads, higher signal-to-noise ratio and higher optical throughput
with respect to other HSI snapshot sensors generally.
This thesis covers the IRIS design process from theoretical concepts to quantitative
modelling, culminating in the N-IRIS prototype designed for SWIR imaging. This effort
formed the logical step in advancing from peer efforts, which focussed upon the visible
wavelengths. After acceptance testing to verify optical parameters, empirical laboratory
trials were carried out. This testing focussed on discriminating between common
materials within a controlled environment as proof-of-concept. Significance tests were
used to provide an initial test of N-IRIS capability in distinguishing materials with
respect to using a conventional SWIR broadband sensor.
Motivated by the design and assembly of a cost-effective visible IRIS, an innovative
solution was developed for the problem of chromatic variation in the splitting angle
(CVSA) of Wollaston prisms. CVSA introduces spectral blurring of images. Analytical
theory is presented and is illustrated with an example N-IRIS application where a sixfold
reduction in dispersion is achieved for wavelengths in the region 400nm to 1.7μm,
although the principle is applicable from ultraviolet to thermal-IR wavelengths.
Experimental proof of concept is demonstrated and the spectral smearing of an
achromatised N-IRIS is shown to be reduced by an order of magnitude. These
achromatised prisms can provide benefits to areas beyond hyperspectral imaging, such
as microscopy, laser pulse control and spectrometry
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