Intelligent system to control electric power distribution networks

The use of high voltage power lines transport involves some risks that may be avoided with periodic reviews as imposed by law in most countries. The objective of this work is to reduce the number of these periodic reviews so that the maintenance cost of power lines is also reduced. To reduce the number of transmission towers (TT) to be reviewed, a virtual organization (VO) based system of agents is proposed in conjunction with different artificial intelligence methods and algorithms. This system is able to propose a sample of TT from a selected set to be reviewed and to ensure that the whole set will have similar values without needing to review all the TT. As a result, the system provides a software solution to manage all the review processes and all the TT of Spain, allowing the review companies to use the application either when they initiate a new review process for a whole line or area of TT, or when they want to place an entirely new set of TT, in which case the system would recommend the best place and the best type of structure to use

A Multidimensional Analysis of an Anomalous, High-Impact, Early-Season Ice Storm in Oklahoma

Between 26 to 28 October 2020, an anomalous, high-impact, and early-season ice storm affected much of Oklahoma. This was the first time the National Weather Service (NWS) Forecast Offices in both Norman and Tulsa had issued an ice storm warning during the month of October and the event is the earliest ice storm in the last 40 years. Overall accumulations were consistent with past ice storm events, but the early season nature of this event led to higher impacts due to increased surface area for accumulations on trees that retained leaves from the growing season. This resulted in branch failures, many downed powerlines, and widespread power outages. Due to the early-timing and severe impacts the goal of this study is to investigate the evolution of the event and identify critical physical processes. First, the synoptic-scale and mesoscale features associated with the event were examined. At the synoptic-scale, a 500 hPa Alaskan ridge, ample 700 hPa moisture transport from the eastern Pacific Ocean region, and the progression of cold 850 hPa temperature anomalies from Canada were all noted 14 days before ice storm onset. At the mesoscale, deep-tropospheric ascent contributed to the significant, localized heavy precipitation across central Oklahoma that was collocated with an anomalous, shallow cold air mass. Second, the October 2020 ice storm was compared to 12 past ice storms in Oklahoma that occurred between 1996 and 2017 to investigate whether the synoptic-scale patterns of an early-season storm differ from normal winter season events. The results yielded similar overall patterns, however, the magnitude of anomalies was generally greater for October 2020 than other past cases. Finally, a potential predictability signal was identified where the geopotential height pattern of past ice storms exists simultaneously with cold 2-meter temperature anomalies over much of the CONUS spanning up to two weeks before event onset

Drop impact on dry and liquid infused substrates with micro-wells.

Drop impact on different types of surfaces are important physical concepts that are routinely found in day-to-day life and such studies have immense application for various types of industries. One such important application of drop dynamics is in the field of aviation science which is concerned of very large freezing drizzle drops impacting on airplane wings. Such drops are known as supercooled large droplets (SLD), and they pose a great risk and have been long known to have caused notable accidents in the past. SLDs are liquid drops that can remain in the state of liquid phase and grow into ice after an impact onto a solid body. Sometimes such freezing drizzle can splash and break into multiple daughter or satellite drops, and sometimes they can bounce off the substrate on which the impingement occurs. Due to the importance in aviation safety, researchers over the past decade has studied SLDs, but most of the studies are experimental studies which produced empirical relationship and little numerical simulation that can effectively vary and optimize drop impingement parameters. In this study, numerical simulation is used to study the dynamics of water drops impacting on various types of substrates. The numerical simulation uses a very sharp interface reconstruction method known as moment-of-fluid method. At the interface between the solid-liquid and liquid-gas, lubricant-gas and lubricant-liquid, adaptive mesh refinement is used to correctly capture the moving interface curvatures and directions. To understand the importance of the underneath substrate surface, drop coalescence study has been done to show that merging drops can benefit from surface energy reduction to propel drops with higher kinetic energy, and the degree of curvature greatly affects the propelling behavior. For dry surface comparison, a drop impacting on a large micro-well cavity is studied and compared to a flat substrate. At different contact angles, and impact velocities, it has been shown that for certain range of speeds and wettability, the drops can only rebound from the micro-well cavity but not from the flat substrate. There has been found a notable difference in kinetic energy, spreading area, and wetting area ratios between the two cases. For the third study, a micro-well substrate is filled with lubricant, and drop impact cases at different velocities is studied. In this study we found that cloaking occurs when both lubricant and water interfacial tensions and impact speeds are low. Furthermore, we have observed that the thickness of the encapsulating lubricant layer changes over time. At moderate impact speeds, the lubricant layer is displaced, generating a lubricant-water jet, as we have demonstrated. However, at high impact speeds, a secondary impingement occurs, displacing a significant amount of lubricant and exposing the underlying substrate, which was not visible at lower impact speeds. Additionally, we conducted simulation on microwells infused with lubricant and observed that small spacing between the micro-well walls can limit lubricant drainage and displacement. The use of micro-wells also resulted in less splashing compared to substrates without micro-wells. Finally, we confirmed that microwells are more effective at preserving lubricant than substrates without micro-wells

Extratropical Cyclones and Associated Climate Impacts in the Northeastern United States

There is growing concern that some aspects of severe weather could become more frequent and extreme across the northeastern United States (USNE) as a consequence of climate change. Extratropical cyclones and frontal systems are a common factor in a variety of severe weather hazards in the region. This dissertation examines three types of meteorological events impacting the USNE – ice storms, heavy rainfall, and high-wind events. The first research topic utilizes the Weather Research and Forecasting (WRF) model in a case study of the December 2013 New England ice storm. In this analysis, a series of tests are conducted to examine how choice of planetary boundary layer physics and other factors affect the model skill in comparison to observations. The results show that near-surface variables are highly sensitive to model setup, highlighting the need for careful testing prior to use. The second research topic explores large-scale teleconnections associated with the documented increase in summer precipitation across the USNE over the past two decades. It is shown that the precipitation surplus occurs in likely teleconnection with increased frequency of high pressure blocking over Greenland. As the current generation of climate models do not correctly depict seasonal patterns or trends in precipitation for the USNE, identifying the association between Greenland blocking and recent precipitation changes across the USNE is crucial for understanding the shortcomings for climate projections for the region. The third research topic is an analysis of the frequency and intensity of mid-autumn wind storms in New England. Fall season storms can have dominant cold-season characteristics, while also being fueled by warm-season moisture sources or the result of an extratropical transition. While the results show an increase in storm total precipitation, there are no significant trends in overall wind storm frequency or intensity with respect to central pressure or surface wind speeds. Nevertheless, storm severity is only one factor that contributes to damage from high wind events. As a whole, this dissertation provides insights to how precipitation and storms are changing across the USNE, while highlighting some of the challenges of weather and climate prediction at regional scales

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

ARCHITECTURE, MODELS, AND ALGORITHMS FOR TEXTUAL SIMILARITY

Identifying similar pieces of texts remains one of the fundamental problems in computational linguistics. This dissertation focuses on the textual similarity measurement and identification problem by studying a variety of major tasks that share common properties, and presents our efforts to address 7 closely-related similarity tasks given over 20 public benchmarks, including paraphrase identification, answer selection for question answering, pairwise learning to rank, monolingual/cross-lingual semantic textual similarity measurement, insight extraction on biomedical literature, and high performance cross-lingual pattern matching for machine translation on GPUs. We investigate how to make textual similarity measurement more accurate with deep neural networks. Traditional approaches are either based on feature engineering which leads to disconnected solutions, or the Siamese architecture which treats inputs independently, utilizes single representation view and straightforward similarity comparison. In contrast, we focus on modeling stronger interactions between inputs and develop interaction-based neural modeling that explicitly encodes the alignments of input words or aggregated sentence representations into our models. As a result, our multiple deep neural networks show highly competitive performance on many textual similarity measurement public benchmarks we evaluated. Our multi-perspective convolutional neural networks (MPCNN) uses a multiplicity of perspectives to process input sentences with multiple parallel convolutional neural networks, is able to extract salient sentence-level features automatically at multiple granularities with different types of pooling. Our novel structured similarity layer encourages stronger input interactions by comparing local regions of both sentence representations. This model is the first example of our interaction-based neural modeling. We also provide an attention-based input interaction layer on top of the MPCNN model. The input interaction layer models a closer relationship of input words by converting two separate sentences into an inter-related sentence pair. This layer utilizes the attention mechanism in a straightforward way, and is another example of our interaction-based neural modeling. We then provide our pairwise word interaction model with very deep neural networks (PWI). This model directly encodes input word interactions with novel pairwise word interaction modeling and a novel similarity focus layer. The use of very deep architecture in this model is the first example in NLP domain for better textual similarity modeling. Our PWI model outperforms the Siamese architecture and feature engineering approach on multiple tasks, and is another example of our interaction-based neural modeling. We also focus on the question answering task with a pairwise ranking approach. Unlike traditional pointwise approach of the task, our pairwise ranking approach with the use of negative sampling focuses on modeling interactions between two pairs of question and answer inputs, then learns a relative order of the pairs to predict which answer is more relevant to the question. We demonstrate its high effectiveness against competitive previous pointwise baselines. For the insight extraction on biomedical literature task, we develop neural networks with similarity modeling for better causality/correlation relation extraction, as we convert the extraction task into a similarity measurement task. Our approach innovates in that it explicitly models the interactions among the trio: named entities, entity relations and contexts, and then measures both relational and contextual similarity among them, finally integrate both similarity evaluations into considerations for insight extraction. We also build an end-to-end system to extract insights, with human evaluations we show our system is able to extract insights with high human acceptance accuracy. Lastly, we explore how to exploit massive parallelism offered by modern GPUs for high-efficiency pattern matching. We take advantage of GPU hardware advances and develop a massive parallelism approach. We firstly work on phrase-based SMT, where we enable phrase lookup and extraction on suffix arrays to be massively parallelized and vastly many queries to be carried out in parallel. We then work on computationally expensive hierarchical SMT model, which requires matching grammar patterns that contain ''gaps''. In order to get high efficiency for the similarity identification task on GPUs, we show developing massively parallel algorithms on GPUs is the most important approach to fully utilize GPU's raw processing power, and developing compact data structures on GPUs is helpful to lower GPU's memory latency. Compared to a highly-optimized, state-of-the-art multi-threaded CPU implementation, our techniques achieve orders of magnitude improvement in terms of throughput