65,681 research outputs found

    Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

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    Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques

    Online Dispute Resolution: Stinky, Repugnant, or Drab?

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    Probabilistic Human Mobility Model in Indoor Environment

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    Understanding human mobility is important for the development of intelligent mobile service robots as it can provide prior knowledge and predictions of human distribution for robot-assisted activities. In this paper, we propose a probabilistic method to model human motion behaviors which is determined by both internal and external factors in an indoor environment. While the internal factors are represented by the individual preferences, aims and interests, the external factors are indicated by the stimulation of the environment. We model the randomness of human macro-level movement, e.g., the probability of visiting a specific place and staying time, under the Bayesian framework, considering the influence of both internal and external variables. We use two case studies in a shopping mall and in a college student dorm building to show the effectiveness of our proposed probabilistic human mobility model. Real surveillance camera data are used to validate the proposed model together with survey data in the case study of student dorm.Comment: 8 pages, 9 figures, International Joint Conference on Neural Networks (IJCNN) 201

    Designing the venue logistics management operations for a World Exposition

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    World Expositions, due to their size and peculiar features, pose a number of logistics challenges. This paper aims at developing a design framework for the venue logistics management (VLM) operations to replenish food products to the event site, through a combination of qualitative and quantitative research approaches. First, an in-depth interview methodology, combined with the outcomes of a literature review, is adopted for defining the key variables for the tactical and operational set-up of the VLM system. Second, a quantitative approach is developed to define the necessary logistics resources. The framework is then applied to the case of Milan 2015 World Exposition. It is the first time that such a design framework for a World Exposition is presented: the originality of this research lies in the proposal of a systematic approach that adds to the experiential practices constituting the current body of knowledge on event logistics

    NASA SBIR abstracts of 1990 phase 1 projects

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    The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

    Generating Rembrandt: Artificial Intelligence, Copyright, and Accountability in the 3A Era--The Human-like Authors are Already Here- A New Model

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    Artificial intelligence (AI) systems are creative, unpredictable, independent, autonomous, rational, evolving, capable of data collection, communicative, efficient, accurate, and have free choice among alternatives. Similar to humans, AI systems can autonomously create and generate creative works. The use of AI systems in the production of works, either for personal or manufacturing purposes, has become common in the 3A era of automated, autonomous, and advanced technology. Despite this progress, there is a deep and common concern in modern society that AI technology will become uncontrollable. There is therefore a call for social and legal tools for controlling AI systems’ functions and outcomes. This Article addresses the questions of the copyrightability of artworks generated by AI systems: ownership and accountability. The Article debates who should enjoy the benefits of copyright protection and who should be responsible for the infringement of rights and damages caused by AI systems that independently produce creative works. Subsequently, this Article presents the AI Multi- Player paradigm, arguing against the imposition of these rights and responsibilities on the AI systems themselves or on the different stakeholders, mainly the programmers who develop such systems. Most importantly, this Article proposes the adoption of a new model of accountability for works generated by AI systems: the AI Work Made for Hire (WMFH) model, which views the AI system as a creative employee or independent contractor of the user. Under this proposed model, ownership, control, and responsibility would be imposed on the humans or legal entities that use AI systems and enjoy its benefits. This model accurately reflects the human-like features of AI systems; it is justified by the theories behind copyright protection; and it serves as a practical solution to assuage the fears behind AI systems. In addition, this model unveils the powers behind the operation of AI systems; hence, it efficiently imposes accountability on clearly identifiable persons or legal entities. Since AI systems are copyrightable algorithms, this Article reflects on the accountability for AI systems in other legal regimes, such as tort or criminal law and in various industries using these systems
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