Abstract Offshore wind energy conversion systems have an inherent control challenge due to the long transmission cables necessary to deliver the generated power to an energy station. Grid-tied power system with a transmission cable has dynamics that threaten system stability. In this sense, controlling this kind of system subject to exogenous disturbances is not trivial, requiring sophisticated control algorithms whose parameter tuning is not intuitive. Therefore, this work presents a robust adaptive current control approach optimized with a new improved chaotic stochastic fractal search algorithm with fitness-distance balance (SFS-FDB). This novel optimizer uses a stochastic selective strategy of ten chaotic maps to improve the SFS-FDB performance. The modification consists in replacing the random variables in the SFS-FDB algorithm with values generated by chaotic maps, independently, at each iteration, enhancing the diversity in the solution, which avoids premature convergence, stagnation, and biased solutions. Four scenarios are considered to evaluate the resulting optimized controller: current reference changes, two grid inductance variations (including up to ten times the nominal value of grid-side inductance), and grid frequency oscillations. Simulation results demonstrate the effectiveness of the developed intelligent control solution, making the tracking errors converge to residual values in steady state when the renewable energy system operates under weak grid conditions.

Despite the critical role of fertilizers in modern agriculture, the lack of properly labeled datasets has significantly hindered advancements in automated fertilizer analysis. To address this gap, this paper introduces three novel datasets tailored for the development and validation of fertilizer detection and classification systems. First, a synthetic dataset is generated using a surface simulator that combines images of individual fertilizer grains, providing a highly controlled yet diverse data source for preliminary algorithm testing. Second, a controlled environment dataset is created under optimal yet realistic conditions to offer a balance between controlled experiments and applicability in natural settings. Third, a real-environment dataset is compiled under challenging field conditions, which presents the complexities of real-world agricultural data collection. Together, these datasets not only enhance the training and testing of machine learning models but also pave the way for substantial improvements in precision agriculture by enabling more accurate and efficient fertilizer management. This paper details the creation, characteristics, and potential applications of these datasets, aiming to set a new standard for dataset quality and utility in agricultural research.

The corrosion inspection process in ship tanks used by the oil industry for the production, storage, and disposal of oil, which is known as Floating Production Storage and Offloading (FPSO), is predominantly manual. It requires a long production downtime, and is an unhealthy job for inspectors. In the literature, some works proposed methods for corrosion segmentation. However, none of them classifies the level of corrosion in accordance with the International Association of Classification Societies (IACS) standard. This work proposes the use of U-Net-based network for segmentation of pitting corrosion, and also provides a corrosion level analysis algorithm relating the identified pitting to the IACS standard. Furthermore, data augmentation methods are adopted to make the dataset more diversified, aiming to generalize the neural network learning. The results indicate a mean squared error of only 0.1639 using the proposed method, and an intersection-of-union of 0.9453. In addition, we compared our method with classical methods such as Canny, Laplacian, Otsu, and Sobel methods, where a relevant advantage is obtained with U-Net.

This paper presents a programming exercise recommender system based on the Student’s Multiple Abilities and Skills (SMAS) model, which is developed from Item Response Theory and Elo System Classification, for estimation of multiple student’s abilities. This model assumes that programming exercises have many ways to be solved (paths) and each path requires different abilities from the student. To evaluate the recommender system, an experiment was conducted in a class of Algorithms and Data Structures I. For this study case, the recommender was connected to an Online Judge system that had a programming problem base. The results show that the proposed recommender has the ability to indicate relevant problems according to the student’s abilities.

This article presents a multi-skills estimation model for students using Online Judge systems. It is understood that there is not only one way to solve programming problems; and, for each solution form, a skill set is needed for the solution to be successful. The proposed model is based on performance expectations and integrates the Elo model, to estimate student’s abilities and problems, to the Multidimensional Item Response Theory model, which estimates the probability of success for each solution path. To validate the proposed model, a case study was carried out with students from the computing area, who solved problems on the beecrowd Online Judge platform. The proposed model was applied to the generated database. According to these results, it is observed that, in cases where the students got the solution right, more than 60% of the paths chosen by students are in accordance with paths indicated by the proposed model.

Fertilizers are fundamental compounds to balance nutrients in the soil, ensuring its fertility for food production. In the industry of fertilizers, a common task is the overhaul of the pipelines that convey the material through production lines, which need to be performed periodically, to avoid duct blockages. Traditionally, this task is carried out manually, which requires interruption of production. Therefore, it implies time consumption and waste of money, in the case of unnecessary inspection. To avoid needless production stoppage, in this paper is presented a non-invasive overhaul method for sediment detection in the pipelines of fertilizer production lines based in neural networks. The proposed model uses thermal images to estimate the volume of sediments into pipelines. Furthermore, as it is difficult to obtain images of several pipeline blockage conditions, a methodology for artificial dataset creation is also provided. The results indicate the feasibility of the proposed methodology.

The process of size analysis of grains in the fertilizer industry is slow, because it is performed by sieves. As an alternative to this mechanized process, digital image techniques have been used to segment and analyze particles in the quality analysis of the grains. However, most deterministic methods for image segmentation do not present high performance when there is no background in the scene, which provides the contrast with the object to be segmented. Furthermore, these methods only ensure its accuracy for segmentation of the objects class considered in the algorithm calibration. Therefore, taking into account this constraint and the great variety of grain size in the fertilizer production process, this paper proposes to use a neural network, U-net, for generalization of grain segmentation, considering a fully covered surface scene, where there is no background. Besides, to show the advantages of proposed solution, a comparison of neural network with deterministic methods is also provided.

In this work, it is presented a contribution for backlash compensation in robotic systems. For that, a direct model and a inverse model of the backlash are presented. The first model is used to simulate the system, while the second model is applied as the PID (Proportional-Integral-Derivative) control input for backlash compensation. A case study is presented to demonstrate the performance of the proposed control system using a linear welding robot BUGO MDS system. Simulations and experimental results are shown and discussed.

In this paper, a study on nonlinear state estimation methods for Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs) is presented. Based on a detailed dynamic model simulation, we analyse and elect the best nonlinear algorithm among those presented in the state-of-the-art literature addressing local derivative-free nonlinear Kalman Filters (KFs): the Unscented Kalman Filter (UKF), the Cubature Kalman Filter (CKF) and the Transformed Unscented Kalman Filter (TUKF). Here, these three nonlinear probabilistic estimators were compared in terms of the Root Mean Square Error (RMSE) and the average execution time over Monte Carlo simulations. We simulated real-world conditions for our in-production HUAUV prototype using Inertial Measurement Unit (IMU) data and state augmentation for sensor data filtering and trajectory estimation. We have concluded that the CKF proved to be the most interesting KF to low-cost on-board applications for high dimensional state spaces.

The robotic welding processes have been highly widespread in manufacturing industries due to large-scale production. An area where these processes are widely applied are shipyards, where there are necessary hundreds to thousands of kilograms of weld by hour. However, systems that operate in open-air environments are vulnerable to sundry disturbances, noises in measures, as well as possible unavailability of measurement of some system states, required for the controller. Taking it into account, in this work, a Robust Model Reference Adaptive Control is proposed to regulate the velocity of a nonlinear motor of a linear welding robot. Furthermore, an Extended Kalman Filter is implemented to estimate the system states and attenuate measurement noises. The proposed control system demonstrated a very good performance with fast convergence and small error.

Welding is an important operation in manufacturing processes and it can represent a highly relevant amount of production costs, depending on material workpieces and required weld quantity. In mechanised and robotised welding processes, robot's travel velocity is an important parameter, which requires a proper regulation to obtain a good quality for weld beam. Then, in this work, a nonlinear model of a robot motor with their identified parameters is presented and a sliding mode control is proposed to regulate robot velocity. Furthermore, an Unscented Kalman Filter and a Cubature Kalman Filter are implemented, separately, for output system estimation. The control system with both estimators presented satisfactory tracking performances, in simulations, converging fast and with very small chattering.

Welding is an important process in the industrial scenario, especially in the shipbuilding industry. This process is recognized by the laborious work and the hazardous work environment. The use of robots to automate the welding process can reduce the human interference and improve the productivity. This paper proposes a system for automated seam tracking based on passive monocular vision. The vision provides a data feedback to the automated robotic welding system allowing quality and productivity gains. A trajectory controller is developed to correct the robot's movement over the seam reference. The controller and a visual algorithm to find the seam reference in a real Gas Metal Arc Welding (GMAW) process are presented. The proposed system allows the automated seam tracking, the trajectory control of the welding torch, and a higher automation level in linear robotic welding. The capabilities of the method is evaluated using a commercial linear welding robot showing its viability.

This article presents a performance comparison between two state observers in a pole placement control (PPC). The first is a classical full order states observer and the second observer is proposed in this work, which is based on a State Variable Filter (SVF). A inverted pendulum on movable base was the system chosen to accomplish performance comparison of the state observers. Simulation results are presented and results are detailed.