1,632 research outputs found

    Eras of electric vehicles: electric mobility on the Verge. Focus Attention Scale

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    Daily or casual passenger vehicles in cities have negative burden on our finite world. Transport sector has been one of the main contributors to air pollution and energy depletion. Providing alternative means of transport is a promising strategy perceived by motor manufacturers and researchers. The paper presents the battery electric vehicles-BEVs bibliography that starts with the early eras of invention up till 2015 outlook. It gives a broad overview of BEV market and its technology in a chronological classification while sheds light on the stakeholders’ focus attentions in each stage, the so called, Focus-Attention-Scale-FAS. The attention given in each era is projected and parsed in a scale graph, which varies between micro, meso, and macro-scale. BEV-system is on the verge of experiencing massive growth; however, the system entails a variety of substantial challenges. Observations show the main issues of BEVsystem that require more attention followed by the authors’ recommendations towards an emerging market

    MLaaS: Machine Learning as a Service

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    The demand for knowledge extraction has been increasing. With the growing amount of data being generated by global data sources (e.g., social media and mobile apps) and the popularization of context-specific data (e.g., the Internet of Things), companies and researchers need to connect all these data and extract valuable information. Machine learning has been gaining much attention in data mining, leveraging the birth of new solutions. This paper proposes an architecture to create a flexible and scalable machine learning as a service. An open source solution was implemented and presented. As a case study, a forecast of electricity demand was generated using real-world sensor and weather data by running different algorithms at the same time

    The DigiHome Service-Oriented Platform

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    International audienceNowadays, the computational devices are everywhere. In malls, offices, streets, cars, and even homes, we can find devices providing and consuming functionality to improve the user satisfaction. These devices include sensors that provide information about the environment state (e.g., temperature, occupancy, light levels), service providers (e.g., Internet TVs, GPS), smartphones (that contain user preferences), and actuators that act on the environment (e.g., closing the blinds, activating the alarm, changing the temperature). Although these devices exhibit communication capabilities, their integration into a larger monitoring system remains a challenging task, partly because of the strong heterogeneity of technologies and protocols. Therefore, in this article, we focus on home environments and propose a middleware solution, called DigiHome, that applies the Service Component Architecture (SCA) component model to integrate data and events generated by heterogeneous devices in this kind of environments. DigiHome exploits the SCA extensibility to incorporate the REpresentational State Transfer (REST) architectural style and, in this way, leverages on the integration of multiscale systems-of-systems (from wireless sensor networks to the Internet). Additionally, the platform applies Complex Event Processing technology that detects application-specific situations. We claim that the modularization of concerns fostered by DigiHome and materialized in a service-oriented architecture, makes it easier to incorporate new services and devices in smart home environments. The benefits of the DigiHome platform are demonstrated on smart home scenarios covering home automation, emergency detection, and energy saving situations

    The DigiHome Service-Oriented Platform

    Get PDF
    International audienceNowadays, the computational devices are everywhere. In malls, offices, streets, cars, and even homes, we can find devices providing and consuming functionality to improve the user satisfaction. These devices include sensors that provide information about the environment state (e.g., temperature, occupancy, light levels), service providers (e.g., Internet TVs, GPS), smartphones (that contain user preferences), and actuators that act on the environment (e.g., closing the blinds, activating the alarm, changing the temperature). Although these devices exhibit communication capabilities, their integration into a larger monitoring system remains a challenging task, partly because of the strong heterogeneity of technologies and protocols. Therefore, in this article, we focus on home environments and propose a middleware solution, called DigiHome, that applies the Service Component Architecture (SCA) component model to integrate data and events generated by heterogeneous devices in this kind of environments. DigiHome exploits the SCA extensibility to incorporate the REpresentational State Transfer (REST) architectural style and, in this way, leverages on the integration of multiscale systems-of-systems (from wireless sensor networks to the Internet). Additionally, the platform applies Complex Event Processing technology that detects application-specific situations. We claim that the modularization of concerns fostered by DigiHome and materialized in a service-oriented architecture, makes it easier to incorporate new services and devices in smart home environments. The benefits of the DigiHome platform are demonstrated on smart home scenarios covering home automation, emergency detection, and energy saving situations

    Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

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    The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic

    Strategies to Expand the U.S. Automated External Defibrillator Market

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    Despite defibrillation as the only effective treatment for sudden cardiac arrest (SCA), less than 15% of homes and public facilities have access to an automated external defibrillator (AED). In the United States, ineffective response to SCA cases occurring each year classifies it as a business problem for medical device manufacturing leaders, emergency responders, and bystanders. The purpose of this multicase study was to explore the marketing strategies AED manufacturing leaders use to expand their consumer customer base. Data were collected via in-depth interviews with a purposive sample of participants from 2 U.S. AED manufacturers on the east coast, 2 AED distributors, and 2 healthcare corporations in Texas, as well as a review of company materials. The framework for this study was product life cycle theory. Initial findings for expanding the U.S. AED market indicated that the market was not led by its manufacturers but by its distributors. This finding became an important theme noted from AED manufacturers in considering the consumer segment, an aftermarket from commercial marketing strategies. A common concern for the security of strategic marketing was evident across the AED manufacturer participants with reluctance to discuss business models and marketing plans. A congruent theme was the curtailment of open discussions regarding AED marketing strategies because of security and confidentiality risk. Also, limited number of approved AED manufacturers by the Federal Drug Administration minimizes AED access. Residual outcomes include improving the quality of life for the aging population while reducing the loss of the lives and costs of healthcare. Social implications include preventing sudden cardiac death by providing more accessibility of AEDs to baby boomers

    The Wooster Voice (Wooster, OH), 1962-03-23

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    This edition of the Wooster Voice from March 23, 1962, heads with an article regarding the six Color Day Queen candidates. The elections will occur on Monday. The Academic Honor Code is being presented to the faculty on the evening of the 26th. P. T. Raju from the University of Rajputana in India will be coming to teach at Wooster next year under the Gillespie Professorship. Bill Thompson is presenting an experimental theater production of Gerhart Hauptmann\u27s Hanneles Himmelfahrt. The students who have taken out petitions for the Student Senate positions thus far are listed in an article on the first page. 377 student names have been release for the Dean\u27s List. On April 12, Dean Emeritus William Taeusch will be speaking on C. P. Snow\u27s The Search. Anything related to sports can be discovered on the third page.https://openworks.wooster.edu/voice1961-1970/1032/thumbnail.jp
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