This paper describes the Howard University system for the language identification shared task of the Second Workshop on Computational Approaches to Code Switching.Our system is based on prior work on Swahili-English token-level language identification.Our system primarily uses character n-gram, prefix and suffix features, letter case and special character features along with previously existing tools.These are then combined with generated label probabilities of the immediate context of the token for the final system.
Codeswitching is a very common behavior among Swahili speakers, but of the little computational work done on Swahili, none has focused on codeswitching.This paper addresses two tasks relating to Swahili-English codeswitching: word-level language identification and prediction of codeswitch points.Our two-step model achieves high accuracy at labeling the language of words using a simple feature set combined with label probabilities on the adjacent words.This system is used to label a large Swahili-English internet corpus, which is in turn used to train a model for predicting codeswitch points.
This research investigates the effect of geological discontinuities on the electric breakdown distributions near vertical and horizontal lightning conductors (VLC and HLC) in various ground conditions. To investigate this effect, numerical simulations were carried out using COMSOL Multiphysics, a state-of-the-art finite element analysis software. The simulations took into consideration detailed models of the conductor geometries, ground structures, and parameters of the lightning discharge for a variation of distance ratios (D/hc) between the conductor and earth discontinuity. Results show that for D/hc = 1.5, the electric field strength near the conductor and discontinuity interface is 20% lower than that obtained from the homogeneous model without a conductor. On the other hand, for D/hc ≥ 3.5, the electric breakdown strength has agreed well with the conductor less model, indicating negligible impact on discharge attraction. The equipotential lines and field intensities around the interface have shown non-uniform distributions, with maximum field strengths directly at the interface surface. Results have brought forth important practical implications in developing an efficient lightning protection system over different earth natures and emphasized the role of accurate numerical modeling approaches with consideration to boundary conditions.
The paper introduces a comprehensive framework designed to determine the optimal siting, sizing, and scheduling for distributed energy resources within the context of a microgrid, particularly in response to transmission network disruptions caused by the influence of severe weather events. The proposed methodology uses Voronoi polygons to approximate the primary substations' service areas, and a vulnerability assessment followed by a risk assessment is performed to identify the areas with a high risk of an outage. The climate ensemble models are used to generate climate scenarios for the identified areas to simulate the possible future conditions that the system might encounter. The generated scenarios are simulated and utilized in a two-stage hybrid stochastic optimization model to find the optimal design of the microgrid. The proposed hybrid algorithm consists of Particle Swarm Optimization and Mixed-Integer Linear Programming, and it is used to find the distributed energy resources optimal size and scheduling for different generation portfolios. The framework is validated using a wildfire case study for the transmission grid of California, USA.The results show Voronoi polygons' reasonable accuracy in approximating each substation's service area and the framework's robustness in finding the optimum microgrid design while ensuring the system's resilience across all considered scenarios.
Modern electrical power networks make extensive use of high voltage direct current transmission systems based on voltage source converters due to their advantages in terms of both cost and flexibility. Moreover, incorporating a direct current link adds more complexity to the optimal power flow computation. This paper presents a new meta-heuristic technique, named self-adaptive bonobo optimizer, which is an improved version of bonobo optimizer. It aims to solve the optimal power flow for alternating current power systems and hybrid systems AC/DC, to find the optimal location of the high voltage direct current line in the network, with a view to minimize the total generation costs and the total active power transmission losses. The self-adaptive bonobo optimizer was tested on the IEEE 30-bus system, and the large-scale Algerian 114-bus electric network. The obtained results were assessed and contrasted with those previously published in the literature in order to demonstrate the effectiveness and potential of the suggested strategy.
Extreme weather events stemming from climate change can cause significant damage and disruption to power systems. Failure to mitigate and adapt to climate change and its cascading effects can lead to short and long term issues. The profound costs of outage in power systems, integrated with the impacts on individual safety and security from loss of critical services, necessitate an urgent need to guarantee resilience in electric power systems. This article proposes a framework to optimize the electricity sector design to be more resilient to climate change and extreme weather events by using distributed generators. The proposed framework considers components dynamic behavior and interdependencies under an uncertain environment. The climate data and socio economic factors are used to generate demand and supply pattern scenarios. The generated scenarios are simulated and utilized in a stochastic optimization model to find the optimal resilience based system design.
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