Abstract
Storing energy efficiently is one of the main factors of a more sustainable world. The battey management system in energy storage plays an extremely important role in ensuring these systems' efficiency, safety, and performance. This battery management system is capable of estimating the battery states, which are used to give better efficiency, a long life cycle, and safety. However, these states cannot be measured directly and must be estimated indirectly using battery models. Therefore, accurate battery models are essential for battery management systems implementation. One of these models is the nonlinear grey box model, which is easy to implement in embedded systems and has good accuracy when used with a good parameter identification method. Regarding the parameter identification methods, the nonlinear least square optimization is the most used method. However, to have accurate results, it is necessary to define the system's initial states, which is not an easy task. This paper presents a two-outputs nonlinear grey box battery model. The first output is the battery voltage, and the second output is the battery state of charge. The second output was added to improve the system's initial states identification and consequently improve the identified parameter accuracy. The model was estimated with the best experiment design, which was defined considering a comparison between seven different experiment designs regarding the fit to validation data, the parameter standard deviation, and the output variance. This paper also presents a method for defining a weight between the outputs, considering a greater weight in the output with greater model confidence. With this approach, it was possible to reach a value 1000 times smaller in the parameter standard deviation with a non-biased and little model prediction error when compared to the commonly used one-output nonlinear grey box model.

Abstract
This paper describes a robotic system to detect and estimate the volume of sediments in underwater wall corners, in scenarios with zero visibility. All detection and positioning is based on data from a scanning sonar. The main idea is to scan the walls and the bottom of the structure to detect the corner, and then use data obtained in the direction of the corner to estimate the presence of sediment accumulation and its volume. Our approach implements an image segmentation to extract range from the surfaces of interest. The resulting data is then employed for relative localization and estimate of the sediment accumulation. The paper provides information about the methodologies developed and data from practical experiments.

Abstract
In this paper we explain how to compute gradients of functions of the form G = 1/2 Sigma(m)(i=1) (Ey(i) - C-i )(2), which often appear in the calibration of stochastic models, using automatic adjoint differentiation and parallelization. We expand on the work of Goloubentsev and Lakshtanov and give approaches that are faster and easier to implement. We also provide an implementation of our methods and apply the technique to calibrate European options.

Abstract
This paper proposes a tool that extracts data from computational tomography (CT) scans of long bones, applies filters to allow a distinction between cortical and cancellous tissue, and converts the tissues into a three-dimensional (3D) model that can be used to generate finite element meshes. In order to identify the best segmentation technique for the problem under study, cortical, cancellous and medulla tissue segmentation was tested based on image histogram information, simple Hounsfield scale (HU) information, HU scale information with morphological operator filters, and active contour methods (active contour, random walker segmentation and findContours). These segmentations were evaluated qualitatively through a visual comparison and quantitatively through the calculation of the Dice Coefficient (DICE) and Mean-Squared Error (MSE) parameters. The developed algorithm presents a Dice higher than 0.95 and a MSE lower than 0.01 for cortical tissue segmentation, which allows it to be used as a bone characterization method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
Semantic segmentation consists of classifying each pixel according to a set of classes. Conventional models spend as much effort classifying easy-to-segment pixels as they do classifying hard-to-segment pixels. This is inefficient, especially when deploying to situations with computational constraints. In this work, we propose a framework wherein the model first produces a rough segmentation of the image, and then patches of the image estimated as hard to segment are refined. The framework is evaluated in four datasets (autonomous driving and biomedical), across four state-of-the-art architectures. Our method accelerates inference time by four, with additional gains for training time, at the cost of some output quality.

Abstract
The use of graphene oxide (GO) as a saturable absorber for short pulses generation in an Erbium-doped fiber laser was studied and demonstrated. The saturable absorber consisted of a thin GO film, with a high concentration of monolayer GO flakes, spray-coated on the end face of a ferrule-connected fiber. By including the saturable absorber in the laser cavity and controlling the intra-cavity polarization, the generation of shortpulsed light was achieved under mode-locking and Q-switching operations. Under mode-locking operation, it was observed a pulse train with a fundamental repetition rate of 1.48 MHz, with a working wavelength centered at 1564.4 nm. In the Q-switch operation, a pulse train with a 12.7 kHz repetition rate and a 14.3 µs pulse duration was attained for a 230-mA pump current. Further investigation showed a linear dependence of the repetition rate with the pump power, attaining frequencies between 12.7 and 14.4 kHz. © 2023 by SCITEPRESS - Science and Technology Publications, Lda.