Abstracts:Robotics and LiDAR technology stand as a crucial cornerstone for the development of cutting-edge three-dimensional mapping systems. This study represents a significant advancement by addressing the development of an initial approach for a three-dimensional mapping system, utilizing a unique LiDAR translational mechanism. In pursuit of this objective, a comprehensive review of works exclusively dedicated to mechanisms employing two-dimensional LiDAR has been conducted. This selective approach results in a comprehensive understanding of the mechanism used for three-dimensional reconstruction and lays the groundwork for future endeavors. Furthermore, a robotic prototype has been implemented using the Robot Operating System (ROS), serving as an accessible tool for implementing our initial approach and engaging new researchers from our university in the application of robotics for three-dimensional reconstruction through LiDAR technology. The validation of our study is conducted through tests in both open and closed environments, revealing high data resolution and a correlation of over 98% with the real environment. The study suggests further research based on the identified errors and introduces new challenges for developing robust prototypes capable of handling changes in a robot's attitude.

Abstracts:This article proposes a novel Ultra Gain Cubic (UGC) DC-DC converter for integrating PV and Fuel cell into the grid. The proposed UGC has 12 components in total, including asingle switch. The steady-state operation of the converter in both modes were explained and necessary equations were derived. The impact of parasitic elements on the UGCs DC-voltage gain is investigated and finally the stability of the UGC converter is verified through state space averaging technique. Optimized number of components, Ultra voltage gain, input and output terminals are connected to same ground, Single switch, input current continuous are the key aspects of the UGC converter. A 300W prototype with a 325V output voltage is tested and validated using hardware results.

Abstracts:This work investigates the performance of residential microgeneration photovoltaic PV systems connected to the electrical grid. It considers the overall available energy for powering households and charging electric vehicles (EVs). The conducted assessments elucidate the developed methodology and criteria for sizing PV panels, utilizing calculations derived from PV-SOL software. Analysis of atmospheric emissions indicates a reduction in greenhouse gases, notably fossil carbon dioxide (CO2). These assessments have been compared to internal combustion vehicle (ICV) calculations, expressed in the annual equivalent number of trees required to neutralize emissions. Results from Rio de Janeiro, with ample annual sunlight availability, show a positive energy supply balance for such installations. Combining PV power with EV charging is promising, assuming an average daily journey of 84 km and nighttime charging occurring approximately 4 hours after peak hours.

Abstracts:A current trend in load modeling topic is to take advantage of ambient data from Phasor Measurement Units (PMU) to estimate the parameters of load models. In this context, the estimation algorithms or methodologies that are proposed or investigated need to be evaluated in a controlled environment, where, among other things, synthetic PMU measurements obtained from simulations are used. These synthetic measurements require the addition of noise to be like the real ones. The problem found in the literature is the large difference in noise magnitudes used by the authors in their research. These magnitudes in several cases are inconsistent with each other and even seem to be exaggerated. It is for this reason that the present work determines the noise contained in the ambient data reported by PMU. The reliability of the results of this work is based, among other things, on the use of real PMU measurements, located in two different countries, with diverse reporting rates, and located at high, medium, and low voltage. Moreover, this work quantifies the impact that noise has on load modeling with ambient PMU data. In conclusion, the main results of this work are two. The first one covers the noise magnitudes contained in ambient PMU data. The second one demonstrates that noise has a significant and negative impact on load modeling.

Abstracts:Modelling pedestrians and groups of people is a highly multidisciplinary technique, given the significant interest it attracts from various branches of science and engineering. This results in many different methodologies that may arise from diverse objectives. The model developed in this work is an agent-based model, in which pedestrian behaviour is defined by a set of forces. Each force models an aspect of pedestrian gait, with the objective of creating a virtual environment to train and test control systems for collaborative robots or autonomous vehicles. To meet the modelling requirements, the system employs various algorithms, such as "flocking", which simulates the coordination and formation of groups, "pathfinding", which enables agents to discover optimal routes within a given space, and algorithms specialized in avoiding walls and dynamic obstacles. These components collaborate to accurately depict how crowds move and react in different environments and situations. Thanks to the modularity of this approach, which facilitates the adjustment and expansion of the components, the developed system can be integrated into various applications, such as simulating non-playable characters (NPCs) in video games or modelling the evacuation of a building.

Abstracts:5G mobile communication systems have increasing demands related to Quality of Service (QoS) parameters integrated with high user densification in heterogeneous network scenarios. In this sense, 5G networks are expected to handle a wide range of applications and services. Therefore, scheduling algorithms that can benefit users of real-time (RT) and non-real-time (NRT) applications are studied. System-level simulations are carried out to analyze the performance of a new proposed Channel and Quality of Service Aware scheduler (CQAS) and compare it to Round Robin (RR), Best Channel Quality Indicator (CQI), and QoS Aware Scheduler (QAS) in a heterogeneous network with multiple traffic models while varying the number of users to stress test the network. The results show that CQAS has significant gains in overall throughput and latency while performing well in the reliability and fairness index.

Abstracts:Recent developments in the field of Additive Manufacturing have been improving the capabilities of the technique not only to be able to build complex geometry parts layer by layer with different materials, but also including the so-called Industry 4.0 technologies, namely Internet of Things (IoT), big data (BD) and Digital Twins (DT). The combination of these technologies with Additive Manufacturing allows online process monitoring and simulation, along with the cloud storage of the process and geometry data collected during the material deposition. The analysis of such data allows online and post-deposition identification of eventual process instabilities that can lead to quality problems. Considering the above-mentioned concepts, this work presents a DT architecture based on the ISO 23247-Digital Twin Framework for Manufacturing standard. In this sense, an approach of a Digital Twin framework for metal additive manufacturing process in a robotic cell composed of a robotic arm, positioning table and welding machine is presented and validated, focusing on the collection and cloud storage of both geometrical and process data along with near real-time process simulation.

Abstracts:Renal carcinoma stands prominently as a significant contributor to global cancer-related mortality rates, highlighting the critical importance of early detection and diagnosis in the management of this ailment. Moreover, the rising incidence of kidney tumors poses a challenge in differentiating between malignant and benign lesions using radiographic methods. Therefore, we present CPP-UNet, an innovative convolutional neural network-based architecture designed for the segmentation of renal structures, including the kidneys themselves and renal masses (cysts and tumors), in a computed tomography (CT) scan. Particularly, we investigate the fusion of the Pyramid Pooling Module (PPM) and Atrous Spatial Pyramid Pooling (ASPP) for improving the UNet network by integrating contextual information across multiple scales. Our proposed method yielded promising outcomes in the Kidney and Kidney Tumor Segmentation challenge (KiTS21 and KiTS23) datasets, exhibiting Dice indices of 93.51% and 92.84% for Kidneys and Masses, 90.33% and 92.08% for Renal Masses, and 85.69% and 88.17% for Tumors, respectively.

Abstracts:Overhead cranes are industrial machines that can cause user accidents due to deficiencies in inspecting their various components. One of the activities consists of verifying that the load hook does not exceed the limit deformation. However, the lack of historical records and negligence in visual inspection is a problem to ensure a correct maintenance service. Therefore, the objective of the research was to develop a vision system capable of inspecting, recording, and verifying the hook parameters. The method involved designing a vision module on an integral crane scanner system. A five-state deformation experiment of a cargo hook was modeled with ANSYS and fabricated in 3D printing to validate the proposed module by measuring the parameters manually with a commercial and proprietary vision system. The comparison between the two systems was satisfactory in detecting the latch and the correct position. Finally, the proprietary system improved the resolution up to 0.0435 mm on the hook deformation. With the systems contribution, it will be possible to know the valuable and safe life of the crane hook by automatic constant inspection.