A novel extract-transform-load methodology applying computer vision to build electrically equivalent models of electricity grids (i.e., digital twins of smart grids) is proposed. Here, we standardise information from electricity grids in various formats into a single visual input for object and text recognition, transformation and loading into a computer simulator. The proposed method is tested using state of the art computer vision algorithms: YOLOv8 for object detection (re-trained using two generic datasets of geometric figures) and PaddleOCR for text recognition. Validation is performed using two real electricity distribution networks in the centre-south region of Colombia, with a global object detection accuracy of up to 80.4%. Initial results suggest that the proposed method significantly reduces human effort compared to two classic approaches. The benefits and shortcomings of the proposed method are discussed together with future work opportunities.
In recent years, both academia and industry have witnessed significant advancements in Large Language Models (LLMs) research, with models like ChatGPT garnering extensive attention from society. These advancements in LLM technology have exerted a profound influence on the entire AI community, potentially revolutionizing how we design and utilize AI systems. Among Natural Language Processing (NLP) tasks, Question Answering (QA) has gained increasing attention. In this paper, we provide an overview of the advancements in LLMs, covering background, major findings, and fine-tuning experiments conducted from BERT to XLNet models on the SQuAD v1.1 and SQuAD v2.0 datasets for the QA task. Our evaluation of various encoder-only models on SQuAD tasks reveals that RoBERTa consistently demonstrates the best performance, achieving the highest Exact Match (EM) and F1 scores on both the SQuAD 1.1 and SQuAD 2.0 datasets. Additionally, we find that Flan T5-base yields even better results, boasting an EM of 77.9% and an F1 Score of 81.2%.
Technological advancements are increasingly evident across various sectors, including automobiles, industry, and healthcare. In precision agriculture, significant progress has been made, with AgroTICs and Smart Agriculture gaining substantial traction in the market. However, a gap remains between cutting-edge technology and family farming, presenting a challenge from both social and applied research perspectives. However, there is still a gap between cutting-edge technology and family farming, which creates a challenge from a social and applied research point of view. In this context, this paper proposes a monitoring model based on Fuzzy Logic and sensor automation applied to estimate the health of a corn crop. The proposed Fuzzy inference system involves calculating an indicator of nutrients as well as the average color and area of corn plants. The nutrient indicator is automatically computed by an ESP32 microcontroller using sensor readings, while the average color and area inputs are manually entered via a mobile application. Additionally, the Fuzzy inference is integrated into the ESP32. The model underwent experimental validation on the health of the plantation, and the results were evaluated in four areas: one was designated for testing, and three were for validation. The model achieved an accuracy of 97.5% in Scenario 3, categorized as ’Very Favorable’, and an accuracy of 65% in Scenarios 2 and 4, categorized as ‘Unfavorable’. The implications of this research contribute to the advancement of AgroTICs among small producers, with the potential to enhance and automate the monitoring of their harvest production.
It has recently been shown that a typical implementation of Simulated Annealing in continuous domains is effectively the same as random search/Hill Climbing. This result is repeated for Dual Annealing that exists as a popular and highly used optimization method in SciPy. A simpler version of Hill Climbing is then developed, and a restart-based version with local search is able to achieve better overall results on the CEC2022 benchmark functions.
Current software engineering focuses on achieving higher quality and speed in development and generating value for the business. This article proposes combining scenario thinking from requirement analysis techniques, such as Use Case 2.0, with test design and automation using the approaches of "Behavior-Driven Development (BDD)" and "Acceptance Test-Driven Development (ATDD)." This approach involves designing automated functional tests supported by artificial intelligence before development, which provides benefits regarding the time and cost of test automation. Enjisst is a user-language test automation platform aimed at creating different options for functional testing, which improves the understanding of the problem and promotes more effective communication of requirements across the entire team. This article demonstrates how Enjisst implements the described approach, which, supported by artificial intelligence and other sophisticated aspects, improves development speed and quality by over 50%. In summary, applying the required quality concepts in current agile and DevOps methodologies is a practical way.
