RESUMO
Global sea levels, having risen by approximately 20 cm since the mid-19th century, necessitate a critical examination of their impacts on shoreline dynamics. This research evaluates the historical (1985-2022) and future shoreline changes in Conde County, Paraíba State, Brazil, an area of significant touristic interest. Employing Landsat satellite imagery, the study utilized the Digital Shoreline Analysis System (DSAS) and a Kalman filter algorithm for cloud removal, while also assessing land use and land cover changes using data from the MapBiomas Project for 2000, 2010, and 2020. These analyses informed projections of potential inundation under various sea-level rise (SLR) scenarios: 1, 2, 5, and 10 m. Key findings revealed a negative average coastline change rate of -0.27 m/year from 1985 to 2022, indicative of erosive trends likely accelerated by human activities. Long-term projections for 2032 and 2042 anticipate continued erosion in areas identified as highly vulnerable. The SLR scenario analysis underscores the urgent need for adaptive climate measures; while a 1- or 2-meter SLR presents limited immediate effects, a 5-meter rise could lead to significant inundation across key sectors, including urban and agricultural landscapes. The projected severity of a 10-meter SLR necessitates immediate, comprehensive interventions to safeguard both natural and human systems.
RESUMO
Some advantages of using cameras as sensor devices on feedback systems are the flexibility of the data it represents, the possibility to extract real-time information, and the fact that it does not require contact to operate. However, in unstructured scenarios, Image-Based Visual Servoing (IBVS) robot tasks are challenging. Camera calibration and robot kinematics can approximate a jacobian that maps the image features space to the robot actuation space, but they can become error-prone or require online changes. Uncalibrated visual servoing (UVS) aims at executing visual servoing tasks without previous camera calibration or through camera model uncertainties. One way to accomplish that is through jacobian identification using environment information in an estimator, such as the Kalman filter. The Kalman filter is optimal with Gaussian noise, but unstructured environments may present target occlusion, reflection, and other characteristics that confuse feature extraction algorithms, generating outliers. This work proposes RMCKF, a correntropy-induced estimator based on the Kalman Filter and the Maximum Correntropy Criterion that can handle non-Gaussian feature extraction noise. Unlike other approaches, we designed RMCKF for particularities in UVS, to deal with independent features, the IBVS control action, and simulated annealing. We designed Monte Carlo experiments to test RMCKF with non-Gaussian Kalman Filter-based techniques. The results showed that the proposed technique could outperform its relatives, especially in impulsive noise scenarios and various starting configurations.
RESUMO
This paper presents a computational model based on interval type-2 fuzzy systems for analysis and forecasting of COVID-19 dynamic spreading behavior. The proposed methodology is related to interval type-2 fuzzy Kalman filters design from experimental data of daily deaths reports. Initially, a recursive spectral decomposition is performed on the experimental dataset to extract relevant unobservable components for parametric estimation of the interval type-2 fuzzy Kalman filter. The antecedent propositions of fuzzy rules are obtained by formulating a type-2 fuzzy clustering algorithm. The state space submodels and the interval Kalman gains in consequent propositions of fuzzy rules are recursively updated by a proposed interval type-2 fuzzy Observer/Kalman Filter Identification (OKID) algorithm, taking into account the unobservable components obtained by recursive spectral decomposition of epidemiological experimental data of COVID-19. For validation purposes, through a comparative analysis with relevant references of literature, the proposed methodology is evaluated from the adaptive tracking and forecasting of COVID-19 dynamic spreading behavior, in Brazil, with the better results for RMSE of 1.24×10-5, MAE of 2.62×10-6, R2 of 0.99976, and MAPE of 6.33×10-6.