Filtragem adaptativa para a estimação da freqüência em sistemas elétricos de potência

Esta pesquisa apresenta um método para a estimação da freqüência em sistemas elétricos de potência utilizando filtros adaptativos baseados no método dos mínimos quadrados (MMQ). A análise do sistema de potência é realizada através da conversão das tensões trifásicas em um sinal complexo pela aplicação da transformada α β, sendo este direcionado ao algoritmo de filtragem adaptativa. As simulações computacionais, assim como a modelagem dos equipamentos, foram realizadas utilizando-se do software ATP (Alternative Transients Program). Este teve por objetivo, gerar dados das mais diversas e distintas situações para a verificação e análise da metodologia proposta, em comparação a resultados obtidos de um determinado relé comercial, habilitado à supervisão da freqüência do sistema.This research presents a method for frequency estimation in power system using adaptive filter based on the Least Mean Square algorithm (LMS). In power system analysis, the three-phase voltages are converted to a complex signal with the application of α β-transform whose complex form was submitted to the algorithm of adaptive filtering. The computational simulations were accomplished using the software ATP. This utilization had as objective to generate data for the most severe and different situations for the verification and analysis of the proposed methodology. The results were to those of a commercial relay for validation, showing the advantages of the new method.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Dissociation Between Users’ Explicit and Implicit Attitudes Toward Artificial Intelligence: An Experimental Study

The latest developments in the field of artificial intelligence (AI) have given rise to many ethical and socio-economic concerns. Nonetheless, the impact of AI technologies is evident and tangible in our everyday life. This dichotomy leads to mixed feelings toward AI: people recognize the positive impact of AI, but they also show concerns, especially about their privacy and security. In this article, we try to understand whether the implicit and explicit attitudes toward AI are coherent. We investigated explicit and implicit attitudes toward AI by combining a self-report measure and an implicit measure, i.e., the implicit association test. We analyzed the explicit and implicit responses of 829 participants. Results revealed that while most of the participants explicitly express a positive attitude toward AI, their implicit responses seem to point in the opposite direction. Results also show that, in both the explicit and implicit measures, females show a more negative attitude than males, and people who work in the field of AI are inclined to be positive toward AI

The Development of a Short Version of the SIMS Using Machine Learning to Detect Feigning in Forensic Assessment

In the present study, we applied machine learning techniques to evaluate whether the Structured Inventory of Malingered Symptomatology (SIMS) can be reduced in length yet maintain accurate discrimination between consistent participants (i.e., presumed truth tellers) and symptom producers. We applied machine learning item selection techniques on data from Mazza et al. (2019c) to identify the minimum number of original SIMS items that could accurately distinguish between consistent participants, symptom accentuators, and symptom producers in real personal injury cases. Subjects were personal injury claimants who had undergone forensic assessment, which is known to incentivize malingering and symptom accentuation. Item selection yielded short versions of the scale with as few as 8 items (to differentiate between consistent participants and symptom producers) and as many as 10 items (to differentiate between consistent and inconsistent participants). The scales had higher classification accuracy than the original SIMS and did not show the bias that was originally reported between false positives and false negatives

Distance protection algorithm for multiterminal HVDC systems using the Hilbert–Huang transform

Multiterminal high-voltage direct current (HVDC) systems still need advances in terms of protection in order to improve their reliability. In this context, the distance protection can play a major role by adding selectivity to the existing DC fault detection algorithms. Hence, the present work proposes a non-unit DC distance protection algorithm that uses the frequency of the DC voltage transient oscillation to estimate the distance of the fault. The DC voltage transient frequency is extracted using the Hilbert–Huang transform and compared with a pre-defined frequency/distance curve. The technique was evaluated by simulating faults in a four-terminal symmetric monopole multiterminal HVDC system. In the simulation environment the algorithm was fully selective for faults within the first protection zone and had a correct operation rate of 94% or more for faults located in the second protection zone. To further validate the presented technique, the proposed algorithm was embedded in a digital signal controller, running in real-time. In all performed tests in hardware, the faults were correctly detected and identified as being internal or external. The results indicate that the proposed algorithm could be used in real-world applications, in conjunction with fault detection techniques, adding selectivity to multiterminal DC protection schemes

How to improve compliance with protective health measures during the covid-19 outbreak. Testing a moderated mediation model and machine learning algorithms

In the wake of the sudden spread of COVID-19, a large amount of the Italian population practiced incongruous behaviors with the protective health measures. The present study aimed at examining psychological and psychosocial variables that could predict behavioral compliance. An online survey was administered from 18–22 March 2020 to 2766 participants. Paired sample t-tests were run to compare efficacy perception with behavioral compliance. Mediation and moderated mediation models were constructed to explore the association between perceived efficacy and compliance, mediated by self-efficacy and moderated by risk perception and civic attitudes. Machine learning algorithms were trained to predict which individuals would be more likely to comply with protective measures. Results indicated significantly lower scores in behavioral compliance than efficacy perception. Risk perception and civic attitudes as moderators rendered the mediating effect of self-efficacy insignificant. Perceived efficacy on the adoption of recommended behaviors varied in accordance with risk perception and civic engagement. The 14 collected variables, entered as predictors in machine learning models, produced an ROC area in the range of 0.82–0.91 classifying individuals as high versus low compliance. Overall, these findings could be helpful in guiding age-tailored information/advertising campaigns in countries affected by COVID-19 and directing further research on behavioral compliance

Reconstructing individual responses to direct questions: a new method for reconstructing malingered responses

Introduction: The false consensus effect consists of an overestimation of how common a subject opinion is among other people. This research demonstrates that individual endorsement of questions may be predicted by estimating peers’ responses to the same question. Moreover, we aim to demonstrate how this prediction can be used to reconstruct the individual’s response to a single item as well as the overall response to all of the items, making the technique suitable and effective for malingering detection. Method: We have validated the procedure of reconstructing individual responses from peers’ estimation in two separate studies, one addressing anxiety-related questions and the other to the Dark Triad. The questionnaires, adapted to our scopes, were submitted to the groups of participants for a total of 187 subjects across both studies. Machine learning models were used to estimate the results. Results: According to the results, individual responses to a single question requiring a “yes” or “no” response are predicted with 70–80% accuracy. The overall participant-predicted score on all questions (total test score) is predicted with a correlation of 0.7–0.77 with actual results. Discussion: The application of the false consensus effect format is a promising procedure for reconstructing truthful responses in forensic settings when the respondent is highly likely to alter his true (genuine) response and true responses to the tests are missing

A 2-month follow-up study of psychological distress among italian people during the covid-19 lockdown

The spread of coronavirus disease 2019 (COVID-19) has called for unprecedented measures, including a national lockdown in Italy. The present study aimed at identifying psychological changes (e.g., changes in depression, stress, and anxiety levels) among the Italian public during the lockdown period, in addition to factors associated with these changes. An online follow-up survey was administered to 439 participants (original sample = 2766), between 28 April and 3 May 2020. A paired sample t-test tested for differences in stress, anxiety, and depression over the period. Multivariate regression models examined associations between sociodemographic variables, personality traits, coping strategies, depression, and stress. Results showed an increase in stress and depression over the lockdown, but not anxiety. Negative affect and detachment were associated with higher levels of depression and stress. Higher levels of depression at the start of the lockdown, as well as fewer coping strategies and childlessness, were associated with increased depression at follow-up, whereas higher levels of stress at the start of the lockdown and younger age were associated with higher stress at follow-up. These results may help us to identify persons at greater risk of suffering from psychological distress as a result lockdown conditions, and inform psychological interventions targeting post-traumatic symptoms

An approximated analytical model for pole-to-ground faults in symmetrical monopole MMC-HVDC systems

Developing pole-to-ground (PG) fault models for Modular Multilevel Converters (MMC) is not straightforward due to the fault asymmetry and converter switching concerning blocking characteristics. Various studies have been carried out regarding transient simulation of PG faults. However, there is a lack of analytical models for the first stage of the fault. Therefore, this work proposes an approximated analytical model for PG faults in half-bridge MMCs. Closed-form expressions for the MMC contribution to the fault and the fault current are derived. We show that separating the solutions in different resonant frequencies represents the system dynamics and facilitates the interpretation of the phenomena. When compared to system calculated by Ordinary Differential Equations (ODEs), the proposed model provided a good approximation for a wide range of parameters. When compared to the full PSCAD solution, the analytical model was able to precisely calculate the peak fault current value, which confirmed its validity

The detection of malingering amnesia: an approach involving multiple strategies in a mock crime

The nature of amnesia in the context of crime has been the subject of a prolonged debate. It is not uncommon that after committing a violent crime, the offender either does not have any memory of the event or recalls it with some gaps in its recollection. A number of studies have been conducted in order to differentiate between simulated and genuine amnesia. The recognition of probable malingering requires several inferential methods. For instance, it typically involves the defendant\u2019s medical records, self-reports, the observed behavior, and the results of a comprehensive neuropsychological examination. In addition, a variety of procedures that may detect very specific malingered amnesia in crime have been developed. In this paper, we investigated the efficacy of three techniques, facial thermography, kinematic analysis, and symptom validity testing in detecting malingering of amnesia in crime. Participants were randomly assigned to two different experimental conditions: a group was instructed to simulate amnesia after a mock homicide, and a second group was simply asked to behave honestly after committing the mock homicide. The outcomes show that kinematic analysis and symptom validity testing achieve significant accuracy in detecting feigned amnesia, while thermal imaging does not provide converging evidence. Results are encouraging and may provide a first step towards the application of these procedures in a multimethod approach on crime-specific cases of amnesia

現代朝鮮語の特殊助詞‘-도’について

Objective: Here we report an investigation on the accuracy of the b Test, a measure to identify malingering of cognitive symptoms, in detecting malingerers of mild cognitive impairment. Method: Three groups of participants, patients with Mild Neurocognitive Disorder (n=21), healthy elders (controls, n=21) and healthy elders instructed to simulate mild cognitive disorder (malingerers, n=21) were administered two background neuropsychological tests (MMSE, FAB) as well as the b Test. Results: Malingerers performed significantly worse on all error scores as compared to patients and controls, and scored poorly than controls, but comparably to patients, on the time score. Patients scored significantly worse than controls on all scores, but both groups showed the same pattern of more omission than commission errors. By contrast, malingerers exhibited the opposite pattern with more commission errors than omission errors. Machine Learning models achieve an overall accuracy higher than 90% in distinguishing patients from malingerers on the basis of b Test results alone. Conclusions: our findings suggest that b Test error scores accurately distinguish patients with Mild Neurocognitive Disorder from malingerers and may complement other validated procedures such as the Medical Symptom Validity Test