The long-term operation planning problem is generally solved by a chain of computational models based on Stochastic Dual Dynamic Programming considering a period of 5 to 10 years ahead. A recent issue that has arisen concerning this problem is to define a convergence criterion when using Conditional Value-at-Risk with Stochastic Dual Dynamic Programming due to the lack of good upper bound estimators. The main contribution of this paper is to propose a convergence criterion in which including CVaR does not hinder convergence analysis. Also, the proposed method does not increase the computational burden. Moreover, this paper provides a method that allows forward paths to be individually assessed, which can then either be discarded to reduce computational time, or even replaced in an alternative resampling scheme in the SDDP. Based on aggregate reservoir representation, the proposed method of convergence was applied on Long Term Operation Planning Problems related to the Brazilian Power System. Results showed improvements in both the SDDP technique and the effectiveness of the proposed convergence criterion when CVaR was used.

Autonomous Surface Vessels (ASVs) are reliable and robust vehicles. They perform autonomous missions in lakes, rivers and even open waters. Those are dangerous environments that requires precise and secure navigation. Under these conditions, the knowledge of a robust and accurate mathematical model is a fundamental aspect for adjusting the control system for reaching safety and performance. Moreover, traditional mathematical models disregard assimetries and coupling between the degrees of freedom. While those models work fine for bigger vessels, to ignore these characteristics in small ASVs compromises the model's quality. In this context, this work presents a new methodology for modeling and identifying the dynamics of ASVs along with the uncertainties arising from disturbances and non-modeled dynamics. As for the uncertainties and disturbances, this work considers the coupling parameters into the mathematical modeling, which synthesizes the divergences between the model and the real application, allowing to incorporate asymmetries and model deficiencies. Regarding the parameter identification, the proposal is based on (i) the design of optimal input excitation signals from a double layer optimization methodology and (ii) a parametric estimation concept in two steps, dividing the original set of parameters into two partially coupled sub-problems. Finally, this work also presents a full discussion and analysis about the importance to manage the trade-off between precision and complexity of mathematical models, its respective solution spaces and the impact over the optimization algorithms. To validate the approach, a real 3 Degree of Freedom ASV with aerial holonomic propulsion system is used. The results show that it is possible to successfully capture the complex set of parameters and identify physical characteristics not considered by the model.

The image stitching process is based on the alignment and composition of multiple images that represent parts of a 3D scene. The automatic construction of panoramas from multiple digital images is a technique of great importance, finding applications in different areas such as remote sensing and inspection and maintenance in many work environments. In traditional automatic image stitching, image alignment is generally performed by the Levenberg–Marquardt numerical-based method. Although these traditional approaches only present minor flaws in the final reconstruction, the final result is not appropriate for industrial grade applications. To improve the final stitching quality, this work uses a RGBD robot capable of precise image positing. To optimize the final adjustment, this paper proposes the use of bio-inspired algorithms such as Bat Algorithm, Grey Wolf Optimizer, Arithmetic Optimization Algorithm, Salp Swarm Algorithm and Particle Swarm Optimization in order verify the efficiency and competitiveness of metaheuristics against the classical Levenberg–Marquardt method. The obtained results showed that metaheuristcs have found better solutions than the traditional approach.

According to the International Electrotechnical Commission (IEC) 61853 standard, the power rating of photovoltaic (PV) modules must be done on a measurement matrix that broadly covers the ranges of operating conditions encountered in the field. These results are becoming more frequent in recent module datasheets. This paper investigates the effectiveness of applying existing empirical PV performance models while using a matrix with 18 operational records, similar to the 22 that were recommended by IEC 61853, as an alternative to the thousands of records that are conventionally used to determine their coefficients. A review of fifteen empirical models is presented and the procedures for determining their coefficients are discussed. In order to validate them, they were applied to data from fourteen PV modules, which remained installed outdoors for about one year, in three locations with distinct climate types. Although the uncertainties that were obtained with the proposed approach, as compared to the conventional one, are about 1% higher for xSi and CdTe modules, and somewhat higher for mSi and CIGS modules, the total uncertainties were only around 5%, a value that is quite adequate for evaluating module performance. Moreover, these uncertainties were from two to five times smaller than those that were obtained by the method that was recommended by IEC 61853 for this purpose.