Thermodynamic Analysis of Wind Energy Systems

This chapter studies the efficiency performance of wind energy systems evaluated by energy and exergy analyses. The theories of energy and exergy analyses along with efficiency calculation for horizontal-axis wind turbines (WTs) are provided by a lucid explanation. A 1.5 MW WT is selected for the thermodynamic analysis using reanalyzed meteorological data retrieved from the National Aeronautics and Space Administration’s (NASA) Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), data set. Matlab scripts are developed to calculate the energy and exergy efficiencies using the MERRA-2 data set. The energy efficiency presents higher magnitude than the exergy efficiency based on the theoretical derivation and the calculated time series of efficiencies. Comparison of impacts of four meteorological variables (wind speed, pressure, temperature, and humidity ratio) on WT efficiencies shows that although wind speed dominates the turbine’s efficiency performance, other meteorological variables also play important roles. In addition, uncertainties of the meteorological variables are represented by the best-fit distributions, which are critically important for evaluating the reliability of wind power performance considering realistic meteorological uncertainty

Orientation-Aware 3D SLAM in Alternating Magnetic Field from Powerlines

Identifying new sensing modalities for indoor localization is an interest of research. This paper studies powerline-induced alternating magnetic field (AMF) that fills the indoor space for the orientation-aware three-dimensional (3D) simultaneous localization and mapping (SLAM). While an existing study has adopted a uniaxial AMF sensor for SLAM in a plane surface, the design falls short of addressing the vector field nature of AMF and is therefore susceptible to sensor orientation variations. Moreover, although the higher spatial variability of AMF in comparison with indoor geomagnetism promotes location sensing resolution, extra SLAM algorithm designs are needed to achieve robustness to trajectory deviations from the constructed map. To address the above issues, we design a new triaxial AMF sensor and a new SLAM algorithm that constructs a 3D AMF intensity map regularized and augmented by a Gaussian process. The triaxial sensor’s orientation estimation is free of the error accumulation problem faced by inertial sensing. From extensive evaluation in eight indoor environments, our AMF-based 3D SLAM achieves sub-1m to 3m median localization errors in spaces of up to 500 m2 , sub-2° mean error in orientation sensing, and outperforms the SLAM systems based on Wi-Fi, geomagnetism, and uniaxial AMF by more than 30%

KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding

With the advent of pre-trained language models (LMs), increasing research efforts have been focusing on infusing commonsense and domain-specific knowledge to prepare LMs for downstream tasks. These works attempt to leverage knowledge graphs, the de facto standard of symbolic knowledge representation, along with pre-trained LMs. While existing approaches leverage external knowledge, it remains an open question how to jointly incorporate knowledge graphs representing varying contexts, from local (e.g., sentence), to document-level, to global knowledge, to enable knowledge-rich and interpretable exchange across these contexts. Such rich contextualization can be especially beneficial for long document understanding tasks since standard pre-trained LMs are typically bounded by the input sequence length. In light of these challenges, we propose KALM, a Knowledge-Aware Language Model that jointly leverages knowledge in local, document-level, and global contexts for long document understanding. KALM first encodes long documents and knowledge graphs into the three knowledge-aware context representations. It then processes each context with context-specific layers, followed by a context fusion layer that facilitates interpretable knowledge exchange to derive an overarching document representation. Extensive experiments demonstrate that KALM achieves state-of-the-art performance on three long document understanding tasks across 6 datasets/settings. Further analyses reveal that the three knowledge-aware contexts are complementary and they all contribute to model performance, while the importance and information exchange patterns of different contexts vary with respect to different tasks and datasets