A Wavelet Based Multiscale Weighted Permutation Entropy Method for Sensor Fault Feature Extraction and Identification

Sensor is the core module in signal perception and measurement applications. Due to the harsh external environment, aging, and so forth, sensor easily causes failure and unreliability. In this paper, three kinds of common faults of single sensor, bias, drift, and stuck-at, are investigated. And a fault diagnosis method based on wavelet permutation entropy is proposed. It takes advantage of the multiresolution ability of wavelet and the internal structure complexity measure of permutation entropy to extract fault feature. Multicluster feature selection (MCFS) is used to reduce the dimension of feature vector, and a three-layer back-propagation neural network classifier is designed for fault recognition. The experimental results show that the proposed method can effectively identify the different sensor faults and has good classification and recognition performance

Expectations and Trust in Automated Vehicles

A lack of trust is a major barrier to the adoptions of Automated Vehicles (AVs). Given the ties between expectation and trust, this study employs the expectation-confirmation theory to investigate in trust in AVs. An online survey was used to collect data including expectation, perceived performance, and trust in AVs from 443 participants which represent U.S. driver population. Using the polynomial regression and response surface methodology, we found that higher trust is engendered when perceived performance is higher than expectation, and perceived risk can moderate the relationship between expectation confirmation and trust in AVs. Results have important theoretical and practical implicationsUniversity of Michigan McityPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153795/1/Zhang et al. 2020.pdfDescription of Zhang et al. 2020.pdf : Main fil

Resource utilization of microalgae from biological soil crusts::biodiesel production associated with desertification control

With the continuing consumption of resources and increasingly prominent environmental issues, microalgal resource utilization has received extensive attention. In this study, based on the microalgal investigation in desert biological soil crusts (BSCs) using pyrosequencing technology, the cultivated crust microalgae were further isolated in order to obtain high quality microalgae for resource utilization. The results showed that with crust development and succession, microalgal diversity gradually decreased, including the number of operational taxonomic units (OTUs) and genus, although Microcokus always was the dominant genera. Pyrosequencing obtained 630 OTUs of cyanobacteria, 25 OTUs of green algae and 9 OTUs of diatom; however, part of cultivated microalgae still could not yet be detected due to the DNA extraction preferences and errors caused by PCR amplification. After isolation, four strains were purified and cultivated, including two filamentous cyanobacteria Microcoleus vaginatus BSC-06 and Scytonema javanicum BSC-39, and two unicellular green algae Chlorella sp. BSC-24 and Monoraphidium dybowskii BSC-81. The two green algae grew fast (> 250 mg L-1 d(-1)), and achieved high lipid productivity up to 75-85 mg L-1 d(-1), with lipid content of 28.7-39.0%, thus was considered as promising feedstock for biodiesel production. In addition, the two crust cyanobacteria could be used to construct artificial cyanobacterial soil crusts in desertification control, although their biomass accumulation was not as high as that in the green algae. Ultimately, combining biodiesel production with desertification control would not only improve desert environments, but also provide ideal places for the local microalgal resource exploitation, further promoting desert socioeconomic development

Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workload

Explanations given by automation are often used to promote automation adoption. However, it remains unclear whether explanations promote acceptance of automated vehicles (AVs). In this study, we conducted a within-subject experiment in a driving simulator with 32 participants, using four different conditions. The four conditions included: (1) no explanation, (2) explanation given before or (3) after the AV acted and (4) the option for the driver to approve or disapprove the AV's action after hearing the explanation. We examined four AV outcomes: trust, preference for AV, anxiety and mental workload. Results suggest that explanations provided before an AV acted were associated with higher trust in and preference for the AV, but there was no difference in anxiety and workload. These results have important implications for the adoption of AVs.Comment: 42 pages, 5 figures, 3 Table

What and when to explain? A survey of the impact of explanation on attitudes towards adopting automated vehicles

Automated vehicles (AV) have the potential to decrease driving-related accidents and traffic congestion and to reduce fuel consumption and carbon emissions. However, because of a lack of trust and acceptance, their widespread adoption is far from certain. One approach researchers have taken to promote trust and acceptance of AVs is to decrease the uncertainty associated with their actions by providing explanations. AV explanations are the reasons the AV provides to make its actions easier to understand. There is now a nascent but rapidly growing body of research on AV explanations. Yet, answers to basic questions like whether or when AV explanations are effective still elude us. To better understand what has been done and what should be done with regard to AV explanations, we present a review of the literature, discuss the findings and identify several important future research directions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/170928/1/Zhang et al. 2021 (online).pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/170928/3/Zhang et al. 2021 (published).pdfDescription of Zhang et al. 2021 (online).pdf : PreprintSEL

From the Head or the Heart? An Experimental Design on the Impact of Explanation on Cognitive and Affective Trust

Automated vehicles (AVs) are social robots that can potentially benefit our society. According to the existing literature, AV explanations can promote passengers’ trust by reducing the uncertainty associated with the AV’s reasoning and actions. However, the literature on AV explanations and trust has failed to consider how the type of trust—cognitive versus affective—might alter this relationship. Yet, the existing literature has shown that the implications associated with trust vary widely depending on whether it is cognitive or affective. To address this shortcoming and better understand the impacts of explanations on trust in AVs, we designed a study to investigate the effectiveness of explanations on both cognitive and affective trust. We expect these results to be of great significance in designing AV explanations to promote AV trust.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/170376/1/Zhang et al. 2021 (Upload Fall 2021 AAAI).pdfDescription of Zhang et al. 2021 (Upload Fall 2021 AAAI).pdf : ArticleSEL

Individual Differences and Expectations of Automated Vehicles

Despite the benefits of automated vehicles (AVs), there are still barriers to their widespread adoption. Expectations about AVs have been identified as one of the most important factors in understanding AV adoption. Therefore, by understanding the public's expectations of AVs, we can better understand whether or when AVs are likely to be adopted on a wide scale. Individual differences, including demographics and personality, have been identified as factors that impact technology expectations and adoption. However, it is not clear whether and how individual differences can influence expectations of AVs. To examine this, we conducted an online survey with 443 U.S. drivers who were recruited and divided into subpopulations by age, gender, ethnicity, census region, educational level, marital status, income, driving frequency, driving experience, and personality traits. Results revealed that drivers' expectations of AVs differ significantly by age, gender, ethnicity, education levels, marital status, drive frequency, drive experience, and personality. More specifically, higher expectations are more often generated by drivers who are younger, men, White non-Hispanic, more highly educated, never married, with a higher frequency of driving, with less driving experience, and who are high in extraversion, agreeableness, conscientiousness, and emotional stability. The results of this study provide a foundation for future research related to expectations and have important implications on future design and development of AVs.McityPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/168566/1/Zhang et al. 2021 IJHCI.pdfDescription of Zhang et al. 2021 IJHCI.pdf : PreprintSEL

Driver’s Age and Automated Vehicle Explanations

Automated Vehicles (AV) have the potential to benefit our society. However, a lack of trust is a major barrier to the adoption of AVs. Providing explanations is one approach to facilitating AV trust by decreasing uncertainty about AV's decision-making and action. However, explanations might increase drivers’ cognitive effort and anxiety. Because of differences in cognitive ability across age groups, it is not clear whether explanations are equally beneficial for drivers across age groups in terms of trust, effort, and anxiety. To examine this, we conducted a mixed-design experiment with 40 participants divided into three age groups (i.e., younger, middle-age, and older). Participants were presented with: (1) no explanation, or (2) explanation given before or (3) after the AV took action, or (4) explanation along with a request for permission to take action. Results suggest that the explanations provided before AV take actions produced the highest trust and lowest effort for all drivers regardless of age group. The request-for-permission condition led to the highest trust and lowest effort only for older drivers. Younger drivers had the lowest anxiety and effort under the AV-explanation-after-action condition; however, this condition produced the highest level of anxiety and effort in middle-age and older drivers, respectively. These results have important implications in designing AV explanations and promoting trust.University of Michigan McityPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166295/1/Zhang et al. 2021 [Final paper]-sustainability-0202.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/166295/3/Zhang et al. 2021.pdfDescription of Zhang et al. 2021 [Final paper]-sustainability-0202.pdf : PreprintSEL

Look who\u27s talking now: Implications of AV\u27s explanations on driver\u27s trust, AV preference, anxiety and mental workload

Explanations given by automation are often used to promote automation adoption. However, it remains unclear whether explanations promote acceptance of automated vehicles (AVs). In this study, we conducted a within-subject experiment in a driving simulator with 32 participants, using four different conditions. The four conditions included: (1) no explanation, (2) explanation given before or (3) after the AV acted and (4) the option for the driver to approve or disapprove the AV’s action after hearing the explanation. We examined four AV outcomes: trust, preference for AV, anxiety and mental workload. Results suggest that explanations provided before an AV acted were associated with higher trust in and preference for the AV, but there was no difference in anxiety and workload. These results have important implications for the adoption of AVs