ALBERT

Description: This paper addresses the problem of vibrations produced by switched reluctance actuators, focusing on the linear configuration of this type of machines, aiming at its characterization regarding the structural vibrations. The complexity of the mechanical system and the number of parts used put serious restrictions on the effectiveness of analytical approaches. We build the 3D model of the actuator and use finite element method (FEM) to find its natural frequencies. The focus is on frequencies within the range up to nearly 1.2 kHz which is considered relevant, based on preliminary simulations and experiments. Spectral analysis results of audio signals from experimental modal excitation are also shown and discussed. The obtained data support the characterization of the linear actuator regarding the excited modes, its vibration frequencies, and mode shapes, with high potential of excitation due to the regular operation regimes of the machine. The results reveal abundant modes and harmonics and the symmetry characteristics of the actuator, showing that the vibration modes can be excited for different configurations of the actuator. The identification of the most critical modes is of great significance for the actuator’s control strategies. This analysis also provides significant information to adopt solutions to reduce the vibrations at the design.

Description: This paper addresses the problem of vibrations produced by switched reluctance actuators, focusing on the linear configuration of this type of machines, aiming at its characterization regarding the structural vibrations. The complexity of the mechanical system and the number of parts used put serious restrictions on the effectiveness of analytical approaches. We build the 3D model of the actuator and use finite element method (FEM) to find its natural frequencies. The focus is on frequencies within the range up to nearly 1.2 kHz which is considered relevant, based on preliminary simulations and experiments. Spectral analysis results of audio signals from experimental modal excitation are also shown and discussed. The obtained data support the characterization of the linear actuator regarding the excited modes, its vibration frequencies, and mode shapes, with high potential of excitation due to the regular operation regimes of the machine. The results reveal abundant modes and harmonics and the symmetry characteristics of the actuator, showing that the vibration modes can be excited for different configurations of the actuator. The identification of the most critical modes is of great significance for the actuator’s control strategies. This analysis also provides significant information to adopt solutions to reduce the vibrations at the design.

Description: HSimulator is a multithread simulator for mass-action biochemical reaction systems placed in a well-mixed environment. HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm (HRSSA). HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. Benchmarks show that HSimulator is often considerably faster than the other considered simulators. The software, running on Java v6.0 or higher, offers a simulation GUI for modeling and visually exploring biological processes and a Javadoc-documented Java library to support the development of custom applications. HSimulator is released under the COSBI Shared Source license agreement (COSBI-SSLA).

Description: The goal of this study was to characterize the degree of structuring of verbal and motor behaviours, unfolded during the application of an procedure called the Strange Situation. This procedure is used for assessing children’s attachment quality during early stages of their development. Many studies have demonstrated that communicative interactions share features with complex dynamic systems. In such studies, estimations of degree of structure have been used to characterize the system’s synchronization. Thus, assuming that processes of communicative interaction occur in the Strange Situation procedure, it was expected to find traces of synchronization. The metrics were estimated through a Categorical Cross-Recurrence Quantification Analysis applied to the behaviours of individuals and dyads. Two applications of the Strange Situation were implemented and recorded. Verbal and motor interactions among children, caregivers, and strangers were transcribed, categorized, and organized as time series. From each time series of original behaviours, randomized time series were created. Measures of recurrence extracted from Recurrent Plots, such as determinism, entropy, maximum line, laminarity, and trapping time, were calculated. Original and randomized time series were compared in terms of these measures. Results indicated that communicative interaction during the Strange Situation had a structure that mimics properties observed in social interactions where synchronization emerges. In our case, verbal behaviours were more prone to synchronization than motor behaviours, in both individuals and dyads, even though this pattern was more salient among caregivers and strangers than children. The relevance of having measures that can capture synchronization during the administration of the Strange Situation is discussed. Our preliminary findings allow us to point out that the application of RQA and C-RQA to the Strange Situation could not only contribute to methodology, but also contribute to emphasizing the role of coupling in communicative interaction generated by the application of this procedure to measure attachment patterns.

Description: The uncertainty of demand has led production systems to become increasingly complex; this can affect the availability of the machines and thus their maintenance. Therefore, it is necessary to adequately manage the information that facilitates decision-making. This paper presents a system for making decisions related to the design of customized maintenance plans in a production plant. This paper addresses this tactical goal and aims to provide greater knowledge and better predictions by projecting reliable behavior in the medium-term, integrating this new functionality into classic Balance Scorecards, and making it possible to extend their current measuring function to a new aptitude: predicting evolution based on historical data. In the proposed Custom Balance Scorecard design, an exploratory data phase is integrated with another analysis and prediction phase using Principal Component Analysis algorithms and Machine Learning that uses Artificial Neural Network algorithms. This new extension allows better control over the maintenance function of an industrial plant in the medium-term with a yearly horizon taken over monthly intervals which allows the measurement of the indicators of strategic productive areas and the discovery of hidden behavior patterns in work orders. In addition, this extension enables the prediction of indicator outcomes such as overall equipment efficiency and mean time to failure.