An ontology-aided, natural language-based approach for multi-constraint BIM model querying

Being able to efficiently retrieve the required building information is critical for construction project stakeholders to carry out their engineering and management activities. Natural language interface (NLI) systems are emerging as a time and cost-effective way to query Building Information Models (BIMs). However, the existing methods cannot logically combine different constraints to perform fine-grained queries, dampening the usability of natural language (NL)-based BIM queries. This paper presents a novel ontology-aided semantic parser to automatically map natural language queries (NLQs) that contain different attribute and relational constraints into computer-readable codes for querying complex BIM models. First, a modular ontology was developed to represent NL expressions of Industry Foundation Classes (IFC) concepts and relationships, and was then populated with entities from target BIM models to assimilate project-specific information. Hereafter, the ontology-aided semantic parser progressively extracts concepts, relationships, and value restrictions from NLQs to fully identify constraint conditions, resulting in standard SPARQL queries with reasoning rules to successfully retrieve IFC-based BIM models. The approach was evaluated based on 225 NLQs collected from BIM users, with a 91% accuracy rate. Finally, a case study about the design-checking of a real-world residential building demonstrates the practical value of the proposed approach in the construction industry

Multi-photon entanglement and interferometry

Multi-photon interference reveals strictly non-classical phenomena. Its applications range from fundamental tests of quantum mechanics to photonic quantum information processing, where a significant fraction of key experiments achieved so far comes from multi-photon state manipulation. We review the progress, both theoretical and experimental, of this rapidly advancing research. The emphasis is given to the creation of photonic entanglement of various forms, tests of the completeness of quantum mechanics (in particular, violations of local realism), quantum information protocols for quantum communication (e.g., quantum teleportation, entanglement purification and quantum repeater), and quantum computation with linear optics. We shall limit the scope of our review to "few photon" phenomena involving measurements of discrete observables.Comment: 71 pages, 38 figures; updated version accepted by Rev. Mod. Phy

Key aspects for effective mathematical modelling of fractional-diffusion in cardiac electrophysiology: A quantitative study

Microscopic structural features of cardiac tissue play a fundamental role in determining complex spatio-temporal excitation dynamics at the macroscopic level. Recent efforts have been devoted to the development of mathematical models accounting for non-local spatio-temporal coupling able to capture these complex dynamics without the need of resolving tissue heterogeneities down to the micro-scale. In this work, we analyse in detail several important aspects affecting the overall predictive power of these modelling tools and provide some guidelines for an effective use of space-fractional models of cardiac electrophysiology in practical applications. Through an extensive computational study in simplified computational domains, we highlight the robustness of models belonging to different categories, i.e., physiological and phenomenological descriptions, against the introduction of non-locality, and lay down the foundations for future research and model validation against experimental data. A modern genetic algorithm framework is used to investigate proper parameterisations of the considered models, and the crucial role played by the boundary assumptions in the considered settings is discussed. Several numerical results are provided to support our claims.Italian National Group of Mathematical Physics (GNFM-INdAM); NSF grant No. 1762553; NIH grant No. 1R01HL143450-0

Automated Mapping of Adaptive App GUIs from Phones to TVs

With the increasing interconnection of smart devices, users often desire to adopt the same app on quite different devices for identical tasks, such as watching the same movies on both their smartphones and TV. However, the significant differences in screen size, aspect ratio, and interaction styles make it challenging to adapt Graphical User Interfaces (GUIs) across these devices. Although there are millions of apps available on Google Play, only a few thousand are designed to support smart TV displays. Existing techniques to map a mobile app GUI to a TV either adopt a responsive design, which struggles to bridge the substantial gap between phone and TV or use mirror apps for improved video display, which requires hardware support and extra engineering efforts. Instead of developing another app for supporting TVs, we propose a semi-automated approach to generate corresponding adaptive TV GUIs, given the phone GUIs as the input. Based on our empirical study of GUI pairs for TV and phone in existing apps, we synthesize a list of rules for grouping and classifying phone GUIs, converting them to TV GUIs, and generating dynamic TV layouts and source code for the TV display. Our tool is not only beneficial to developers but also to GUI designers, who can further customize the generated GUIs for their TV app development. An evaluation and user study demonstrate the accuracy of our generated GUIs and the usefulness of our tool.Comment: 30 pages, 15 figure

Quantum metrology with nonclassical states of atomic ensembles

Quantum technologies exploit entanglement to revolutionize computing, measurements, and communications. This has stimulated the research in different areas of physics to engineer and manipulate fragile many-particle entangled states. Progress has been particularly rapid for atoms. Thanks to the large and tunable nonlinearities and the well developed techniques for trapping, controlling and counting, many groundbreaking experiments have demonstrated the generation of entangled states of trapped ions, cold and ultracold gases of neutral atoms. Moreover, atoms can couple strongly to external forces and light fields, which makes them ideal for ultra-precise sensing and time keeping. All these factors call for generating non-classical atomic states designed for phase estimation in atomic clocks and atom interferometers, exploiting many-body entanglement to increase the sensitivity of precision measurements. The goal of this article is to review and illustrate the theory and the experiments with atomic ensembles that have demonstrated many-particle entanglement and quantum-enhanced metrology.Comment: 76 pages, 40 figures, 1 table, 603 references. Some figures bitmapped at 300 dpi to reduce file siz

Wind power plants hybridised with solar power: A generation forecast perspective

ABSTRACT: aggregation for the operation of power systems is an area of recent research. Accurate forecasts are crucial for extracting those benefits and promote an optimal integration of such plants into power systems and electricity markets. This study focuses on the hybridisation of existing wind power plants with different shares of solar photovoltaic capacity and investigates how these power plants can reduce their combined forecast errors and thus, increasing profitability in electricity markets. The work uses a forecast methodology based on a sequential forward feature selection algorithm which employs two different objective functions and an artificial neural network approach previously presented but, in this case, it is applied to the specific case of hybrid power plants. The methodology uses as input data from a numerical weather prediction model and iteratively selects meteorological features to achieve the different objective functions implemented, namely i) minimisation of the root mean square error; or ii) maximisation of the market remuneration. The methodology developed was applied to three case studies in Portugal with different levels of wind and solar generation complementarity. The results show that the hybrid power plants can increase market value by up to 5% and total remuneration can increase by up to 30% when compared with the existing wind power plant, while it is possible to reduce the forecast errors by nearly 4%. The obtained results highlight the need to select the most relevant meteorological features to maximise the accuracy of the power forecast and the renewable power producers revenues in a market environment.info:eu-repo/semantics/publishedVersio

Quantifying the spatio-temporal temperature dynamics of Greater London using thermal Earth observation

PhD ThesisUrban areas are highly sensitive to extreme events such as heatwaves. In order to understand how cities will respond to thermal stress it is critical to quantify not only their temporal temperature dynamics but also their spatial temperature variability. However, many cities lack weather station networks with a sufficient spatial distribution to characterise spatio-temporal intraurban temperature dynamics. One means by which spatially complete measurements of urban temperature may be derived is to employ satellite thermal Earth observed data. While some success has been achieved in understanding the temperature characteristics of cities using such data, relatively little work has been undertaken on establishing the use of long time-series Earth observed data as a supplement or alternative to screen-level air temperatures frequently utilised in urban climatological studies. In this thesis a software framework, centred around the use of a spatial database, is developed which can be used to gain an improved understanding of how satellite thermal Earth observed data can be used in the long timeseries analysis of urban temperature dynamics. The utility of the system is demonstrated by processing a 23 year time series (1985-2008) of 1,141 Advanced Very High Resolution Radiometer (AVHRR) images and hourly United Kingdom (UK) Met Office weather station measurements for the Greater London area. London was selected as the region of interest as it is the UK’s only megacity, and has been shown to exhibit both a significant urban heat island and a severe increase in population mortality during previous heatwave events. The software framework was employed to conduct two inter-related sets of analysis. First, the relationship over time between AVHRR estimated surface temperature (EST) and screen-level air temperature records is investigated and quantified. The resulting relationships are then used to produce an empirical model that can predict spatially complete summer-season air temperi atures for London. Cross-validation testing of the model at selected London weather stations showed model root mean square error (RMSE) ranging from 2.70 to 2.94°C and absolute errors in air temperature estimation of 0.45 to 1.67°C. A key finding of the thesis is that the minimal variation in prediction error between the different stations indicate a level of spatial robustness in the model across the urban surface, that is within the limits of the AVHRR EST precision. In addition, the model was used to estimate spatially averaged air temperatures over the Greater London area for selected summers, and showed a maximum error in air temperature prediction of 1.44°C. Furthermore, the prediction error for the heatwave summer of 2003 was 0.51°C, suggesting that such a model can successfully be used to estimate air temperatures for extreme heatwave summers. Such predictions are directly relevant to future assessments of urban population exposure to heatwaves, and it is envisaged that they could be used in conjunction with a population vulnerability index to create a spatially complete heatwave risk map for London. This work is then extended to investigate the utility of satellite estimated surface temperature measurements to characterise temporally and spatially intra-urban heatwave dynamics using the commonly employed urban heat island intensity metric (UHII). Analysis of the AVHRR EST found that the data are highly sensitive to local meteorological conditions, and that temporal aggregation at the monthly scale is required to provide robust data-sets for inter-year analysis of summer temperatures and generation of the UHII metric. Statistical testing of EST and air-temperature derived UHII for the heatwave summer of 2003 against other non-heatwave summers showed no significant increase in intensity at the 95% confidence level. This raises questions as to the applicability of the UHII metric to capture increases in urban temperatures during a heatwave event.Engineering and Physical Sciences Research Council and the School of Civil Engineering and Geoscience

Characterization of multiphase flows integrating X-ray imaging and virtual reality

Multiphase flows are used in a wide variety of industries, from energy production to pharmaceutical manufacturing. However, because of the complexity of the flows and difficulty measuring them, it is challenging to characterize the phenomena inside a multiphase flow. To help overcome this challenge, researchers have used numerous types of noninvasive measurement techniques to record the phenomena that occur inside the flow. One technique that has shown much success is X-ray imaging. While capable of high spatial resolutions, X-ray imaging generally has poor temporal resolution. This research improves the characterization of multiphase flows in three ways. First, an X-ray image intensifier is modified to use a high-speed camera to push the temporal limits of what is possible with current tube source X-ray imaging technology. Using this system, sample flows were imaged at 1000 frames per second without a reduction in spatial resolution. Next, the sensitivity of X-ray computed tomography (CT) measurements to changes in acquisition parameters is analyzed. While in theory CT measurements should be stable over a range of acquisition parameters, previous research has indicated otherwise. The analysis of this sensitivity shows that, while raw CT values are strongly affected by changes to acquisition parameters, if proper calibration techniques are used, acquisition parameters do not significantly influence the results for multiphase flow imaging. Finally, two algorithms are analyzed for their suitability to reconstruct an approximate tomographic slice from only two X-ray projections. These algorithms increase the spatial error in the measurement, as compared to traditional CT; however, they allow for very high temporal resolutions for 3D imaging. The only limit on the speed of this measurement technique is the image intensifier-camera setup, which was shown to be capable of imaging at a rate of at least 1000 FPS. While advances in measurement techniques for multiphase flows are one part of improving multiphase flow characterization, the challenge extends beyond measurement techniques. For improved measurement techniques to be useful, the data must be accessible to scientists in a way that maximizes the comprehension of the phenomena. To this end, this work also presents a system for using the Microsoft Kinect sensor to provide natural, non-contact interaction with multiphase flow data. Furthermore, this system is constructed so that it is trivial to add natural, non-contact interaction to immersive visualization applications. Therefore, multiple visualization applications can be built that are optimized to specific types of data, but all leverage the same natural interaction. Finally, the research is concluded by proposing a system that integrates the improved X-ray measurements, with the Kinect interaction system, and a CAVE automatic virtual environment (CAVE) to present scientists with the multiphase flow measurements in an intuitive and inherently three-dimensional manner

Influence of spatial cues on the identification and the localization of objects in the auditory foreground

Thesis (Sc. D.)--Harvard-MIT Division of Health Sciences and Technology, 2007.Includes bibliographical references (p. 169-182).The ability to form auditory objects is important in the natural environment where sounds arriving at our ears are a resultant of all spectro-temporal components that may have arisen from different auditory events. It has been shown that auditory spatial cues are effective for grouping acoustical energy across time or across frequency. However, little is known about the effect of spatial cues on scene analysis when more than one auditory object is being presented. In this thesis dissertation, a novel two-object paradigm was used to investigate how spatial cues influence the identification and the localization of object in the auditory foreground. Specifically, the effect of both spatial and non-spatial cues on auditory grouping and object identification was ascertained. Using an acoustic pointer and the same stimuli for the object identification task, the apparent spatial location of these objects was measured to test the hypothesis that only the spatial attributes of the components grouped to form an object influences the localization of the same object. A conceptual model was generated to highlight the role of spatial cues in object formation, and the dissociation between the auditory computation of "what" and "where" was further investigated. In current technology, object segregation presents a fundamental challenge for the hearing impaired, hearing aid design and speech recognition algorithms. It is hopeful that the findings in this dissertation will inspire new biologically-based algorithms for auditory scene analysis and in turn, influence designs in assistive hearing devices and other technological development that is dependent on multi-source segregation.by Adrian Kuo Ching Lee.Sc.D