Reasoning about complex agent knowledge - Ontologies, Uncertainty, rules and beyond

Ph.DDOCTOR OF PHILOSOPH

Strong optical response and light emission from a monolayer molecular crystal

Excitons in two-dimensional (2D) materials are tightly bound and exhibit rich physics. So far, the optical excitations in 2D semiconductors are dominated by Wannier-Mott excitons, but molecular systems can host Frenkel excitons (FE) with unique properties. Here, we report a strong optical response in a class of monolayer molecular J-aggregates. The exciton exhibits giant oscillator strength and absorption (over 30% for monolayer) at resonance, as well as photoluminescence quantum yield in the range of 60-100%. We observe evidence of superradiance (including increased oscillator strength, bathochromic shift, reduced linewidth and lifetime) at room-temperature and more progressively towards low temperature. These unique properties only exist in monolayer owing to the large unscreened dipole interactions and suppression of charge-transfer processes. Finally, we demonstrate light-emitting devices with the monolayer J-aggregate. The intrinsic device speed could be beyond 30 GHz, which is promising for next-generation ultrafast on-chip optical communications

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe

Uncertainty Quantification of a Coupled Model for Wind Prediction at a Wind Farm in Japan

Reliable and accurate short-term prediction of wind speed at hub height is very important to optimize the integration of wind energy into existing electrical systems. To this end, a coupled model based on the Weather Research Forecasting (WRF) model and Open Source Field Operation and Manipulation (OpenFOAM) Computational Fluid Dynamics (CFD) model is proposed to improve the forecast of the wind fields over complex terrain regions. The proposed model has been validated with the quality-controlled observations of 15 turbine sites in a target wind farm in Japan. The numerical results show that the coupled model provides more precise forecasts compared to the WRF alone forecasts, with the overall improvements of 26%, 22% and 4% in mean error (ME), root mean square error (RMSE) and correlation coefficient (CC), respectively. As the first step to explore further improvement of the coupled system, the polynomial chaos expansion (PCE) approach is adopted to quantitatively evaluate the effects of several parameters in the coupled model. The statistics from the uncertainty quantification results show that the uncertainty in the inflow boundary conditions to the CFD model affects more dominantly the hub-height wind prediction in comparison with other parameters in the turbulence model, which suggests an effective approach to parameterize and assimilate the coupling interface of the model

Multivariable neural network to postprocess short‐term, hub‐height wind forecasts

Abstract This work introduces a novel error correction method for short‐term, hub‐height wind speed forecasting systems aimed at power output prediction. We present a multivariable neural network that is trained to reduce the error in wind speed predictions out of a numerical weather prediction (NWP) model, by exploiting hidden information in additional atmospheric variables, that is, wind direction, temperature, and pressure. The unique layout of the network was influenced by that of denoising autoencoders, and their ability to learn mapping functions. The predicted values from the NWP model, which incorporate errors due to numerical discretization, inaccuracies in initial/boundary conditions and parametrizations, complex terrain features, etc., are mapped to a more accurate prediction in which the errors have been reduced. To show the performance of the proposed model, training and validation are carried out with 4 years of forecasted and observed data for fifteen sites in a wind farm in Awaji Island, Japan, in a challenging zone with complex topography and therefore complicated, highly fluctuating wind patterns. Moreover, a single variable (i.e., wind speed) network is also implemented in order to expose the contribution and usefulness of including additional atmospheric variables. The results show a considerable reduction in the root mean square error as well as an increase in the correlation coefficient. As expected, it is found that multiple meteorological variables as inputs offer a huge advantage when compared with the equivalent single‐variable correction method

Verifying OWL and ORL Ontologies in PVS

The Semantic Web vision is being realized to reach the full potential of the Web. Semantic data modeling is the foundation of the Semantic Web. The Web Ontology Language (OWL) and OWL Rules Language (ORL) provides basic machinery to the semantic mark-up for data. However, there is limited tool support for OWL and no tool support currently for ORL. In this paper, we propose to model OWL and ORL language semantics in PVS specification language so that OWL and ORL ontologies can be transformed and verified in the Prototype Verification System (PVS). PVS user-defined proof strategies are also developed to automate the proof process