1,720,980 research outputs found
Shaping the future of automotive design: the automotive experience design lab.
No full textAs technology continues to reshape the automotive landscape, a radical transformation is underway in the design of automobiles. The focus is shifting from the vehicle itself to the onboard experience, marking a pivotal moment where driving becomes less central, and the «passenger» experience takes center stage. The proliferation of multimedia entertainment systems, ambient lighting, interactive sounds, and other non-driving elements reflects this shift, especially in anticipation of the impending era of autonomous driving.
This paper presents a thoughtful exploration of this evolving paradigm, culminating in the establishment of the Automotive eXperience Design Lab (AXD): a dedicated space and simulator for testing new interactive systems both in physical and digital dimensions. Leveraging the lab’s expertise, prototypes can be efficiently developed and tested, providing valuable insights into the complex and multidimensional nature of the onboard experience.
The Automotive eXperience Design Lab represents an initiative offering new tools and operational methods tailored for emerging professional roles in the realm of automotive design
Fractional surface doping by topological neutral wall intersections on Ge(111)
No full textWe show that a fluctuating incommensurate domain wall structure such as that hypothesized for clean Ge(111) above 540 K and the observed weakly metallic behavior can be mutually related in an unexpected manner. The wall structure implies a liquid of defects-adatom trimers at the intersection of three concurrent topological walls-that carry fractional charge, one half extra electron each. These electrons are delocalized among defects, giving rise to a narrow band 2D metal, whose Fermi level density of states grows with the density of walls and thus with temperature. This model agrees strikingly with new photoemission measurements carded out on Ge(111) across the 540 K transition and beyond
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Deep reinforcement learning based on proximal policy optimization for the maintenance of a wind farm with multiple crews
Full text linkThe life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible to significantly optimize operation and maintenance (O&M) by combining the (uncertain) information provided by PHM with the other factors influencing O&M activities, including the limited availability of maintenance crews, the variability of energy demand and corresponding production requests, and the long-time horizons of energy systems operation. In this work, we consider the operation and maintenance optimization of wind turbines in wind farms woth multiple crews. A new formulation of the problem as a sequential decision problem over a long-time horizon is proposed and solved by deep reinforcement learning based on proximal policy optimization. The proposed method is applied to a wind farm of 50 turbines, considering the availability of multiple maintenance crews. The optimal O&M policy found outperforms other state-of-the-art strategies, regardless of the number of available maintenance crews
Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning
Full text linkEquipment of renewable energy systems are being supported by Prognostics & Health Management (PHM) capabilities to estimate their current health state and predict their Remaining Useful Life (RUL). The PHM health state estimates and RUL predictions can be used for the optimization of the systems Operation and Maintenance (O&M). This is an ambitious and challenging task, which requires to consider many factors, including the availability of maintenance crews, the variability of energy demand and production, the influence of the operating conditions on equipment performance and degradation and the long time horizons of renewable energy systems usage. We develop a novel formulation of the O&M optimization as a sequential decision problem and we resort to Deep Reinforcement Learning (DRL) to solve it. The proposed solution approach combines proximal policy optimization, imitation learning, for pre-training the learning agent, and a model of the environment which describes the renewable energy system behavior. The solution approach is tested by its application to a wind farm O&M problem. The optimal solution found is shown to outperform those provided by other DRL algorithms. Also, the approach does not require to select a-priori a maintenance strategy, but, rather, it discovers the best performing policy by itself
Deep reinforcement learning for optimizing operation and maintenance of energy systems equipped with phm capabilities
Full text linkThe Life Cycle Cost (LCC) of energy systems including Renewable Energy Sources (RES) strongly depends on the Operation and Maintenance (O&M) costs. Nowadays, many components of these energy systems are equipped with Prognostics & Health Management (PHM) capabilities, for estimating their current and future health states. This information is intended to be used for the optimization of O&M. It is an ambitious and challenging objective as the uncertain information brought by PHM must be combined with other factors influencing O&M, such as the limited availability of maintenance crews, the variability of energy demand and production, the long-time horizons of energy systems. In this work, we formalize the O&M optimization of RES-based energy systems equipped with PHM as a sequential decision problem over a long-time horizon and we solve it by Deep Reinforcement Learning (DRL). The proposed methodology is applied to a small wind farm. Strengths and weaknesses are analyzed by means of a comparison with state-of-the-art O&M policies
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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