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The fleet of Electric Vehicles is part of the Smart Grid ecosystem. The EV recharge process and its mathematical modeling is an important component that impacts on the stability and the security of energy supply. Global Sensitivity Analysis support tools are used to measure the relative importance of the inputs contributing to produce the model’s outputs. Recently, the energy flows being exchanged between Smart Grid and an EV fleet are part of the Transactive Energy Control scenario in which an economic incentive becomes an impacting parameter to investigate further because it can produce a substantial behavioral change. In this article the authors discuss about the experimental workflows used to support the impact assessment studies and about the role of Sensitivity and Uncertainty Analysis tools helping to optimize the EV fleet recharge processes.

The paper focuses on the experimental and theoretical study of instability of plastic strain and its localization in materials subjected to high-velocity perforation. The online study of the specimen surface was made using the high-speed infrared camera CEDIP Silver 450M. The distribution of the temperature field obtained at different instants of time allows getting insight into the evolution of the process of plastic strain localization. After testing, the structure of the specimen material was examined by scanning electron microscopy. In our investigation, the mechanisms of plastic shear instability and localization of plastic strain (in the three-dimensional statement) have been simulated numerically, and the peculiarities of the kinetics of microshear accumulation in the material have been analyzed.

Recent developments in smart metering applications have led to the conceptualization and construction of a new type of energy meter, operating on the basis of event-driven principles. The event-driven metering concepts are applied to represent the information on the electrical load patterns, which have an integral value. This paper explains why these concepts are different from the ones used for event-based applications in other domains, discusses the principles used in the new type of electricity meter, presents the data formats structured in such a way to provide detailed knowledge representation, and shows a number of results on real-case applications. A specific index is defined in order to represent the effectiveness of the event-driven metering scheme illustrated to represent the details of the metered pattern, comparing the results with the ones that could be reached in the most favorable case through regular timer-driven metering. The presentation of specific applications based on real-life datasets highlights the advantages of the event-driven energy metering over the traditional timer-driven metering scheme.

The event-driven energy metering paradigm has been recently introduced to make the management of the metered data more effective while reducing the overall amount of data to be transmitted or stored. This paper addresses the specific advantages of reconstructing the results of the aggregation of a number of loads in order to obtain an effective representation of the load aggregation that maintains detailed information on the individual and aggregate power. Examples are provided on real data gathered from individual and aggregate residential users.

This paper discusses the conceptual and practical implications of adopting a new type of energy meter, based on the identification of events from measuring the power pattern and on sending information on the actual energy used in the time interval between two events. The event-driven metering (EDM) approach enables gathering data useful for setting up effective representations of the knowledge that can be extracted about the prosumers' energy usage during time. Specific comparisons between the EDM effectiveness in reconstructing the power patterns with respect to the classical time-driven metering at regular time intervals are introduced to show the remarkable advantages of the EDM approach. An enhanced average power pattern reconstruction mechanism is also presented, based on the direct reproduction of the average power at the fastest time step occurring before the event generation, and on averaging the power pattern for the remaining part of the corresponding time interval. The case study application is illustrated by using the highly variable power pattern of a residential user.

This work is about the in-process visualization of multi-dimensional datasets recorded by compressed event-driven energy metering methods. The main goal of this work is to provide easy visual form to better understand energy transformations caused by real-life processes.

Event-driven energy metering has emerged recently with conceptual and practical implications, as well as with new manufactured technological solutions. This paper recalls the underlying concepts that have led to these improvements. The event-driven energy metering paradigm is addressed in terms of the definition of the major aspects leading to its interpretation, the type of information provided, and the prospects for its use. The characteristics of the approach are shown through specific application cases.

This paper presents the results obtained from a new type of electricity meter, constructed as the industrial application of event-driven metering concepts. The underlying concepts of event-driven metering are applied to gather information on the electrical load patterns. The data formats used are presented and the details of the knowledge representation referring to the results coming from event-driven metering are discussed. The event-driven metering approach is illustrated by using data gathered from real-life applications.

This article presents software framework that integrates Automated Meter Reading (AMR) and Advanced Metering Infrastructure (AMI) components in the context of Cloud-based Service-Oriented Architecture made available by the Future Internet Public-Private-Partnership. Authors used standard DLMS-COSEM interoperability protocol to convey metering datasets to the FI-WARE cloud. Newly developed framework supports timer-driven metering schemes from the known art. Additionally, it is fully compatible with the new coarse-grained method of analytical accounting of energy.

This article reviews different approaches to electricity metering and proposes novel coarse-grained but cycle-accurate measurement method which preserves the existing Legacy billing applications but supplies historical series of true RMS measurements. New method traces energy variations with cycle-accuracy. To reduce the quantity of data but keep high precision, it uses geometric integration. It transmits accumulated time-series upon timer-driven events. The method is designed in a way to remain compliant with the open APIs developed by Future Internet PPP projects FINESCE and FI-WARE. The proposed approach bridges the Legacy approach and the service-oriented one.

This article describes geometrical numeric integration applied to coarse-grained cycle-precise event-driven measurements of electrical energy. The proposed simplectic method draws from Stormer-Verlet and leapfrog algorithms in order to provide operational intelligence tools to analyze the amounts of analytical energy data. The proposed tool is extremely quick. Being very simple in computational terms, it suites real-time usages. This work extends the event-driven measurement approaches.

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “New Methodologies for Patients Rehabilitation”.Background: The article presents the approach in which the rehabilitative exercise prepared by healthcare professional is encoded as formal knowledge and used by humanoid robot to assist patients without involving other care actors.Objectives: The main objective is the use of humanoids in rehabilitative care. An example is pulmonary rehabilitation in COPD patients. Another goal is the automated judgment functionality to determine how the rehabilitation exercise matches the pre-programmed correct sequence.Methods: We use the Aldebaran Robotics’ NAO humanoid to set up artificial cognitive application. Pre-programmed NAO induces elderly patient to undertake humanoid-driven rehabilitation exercise, but needs to evaluate the human actions against the correct template. Patient is observed using NAO’s eyes. We use the Microsoft Kinect SDK to extract motion path from the humanoid’s recorded video. We compare human- and humanoid-operated process sequences by using the Dynamic Time Warping (DTW) and test the prototype.Results: This artificial cognitive software showcases the use of DTW algorithm to enable humanoids to judge in near real-time about the correctness of rehabilitative exercises performed by patients following the robot’s indications.Conclusion: One could enable better sustainable rehabilitative care services in remote residential settings by combining intelligent applications piloting humanoids with the DTW pattern matching algorithm applied at run time to compare humanoid- and human-operated process sequences. In turn, it will lower the need of human care.

Electric CAR Recharge Optimization Toolkit is an integrated system for modeling and optimization of Electric Vehicle recharge processes. It uses semantic technology to conceptualize, model, and optimize battery recharge processes occurring in smart grid. The new tool attempts the time shifting postponing the demand exceeding the available energy flows. It is useful for operational planning of saturated electricity grid when the electricity demand exceeds the available resources during some time slots.

In this paper, we propose a new efficient technique for forecasting the bandwidth of digital access links to the Internet. This technique can be used for calculating the throughput of access links in implementing electronic document management systems and in organizing information portals. The main idea is to estimate the bandwidth of digital access links to the Internet using the statistics of the service load. The main result is the derivation of analytical expressions to estimate the required bandwidth.

Event-driven energy metering brings the possibility to detect and understand the non-unique electrical events. Events, preceding the use of the vehicle, give the anticipatory knowledge relevant to the electrical grid balancing and energy optimization. Author discusses about the anticipatory knowledge exchanged between the stakeholders improving the electric vehicle's integration into smart grid.

Since energy use is a type of consumer behavior reflecting the interests to maximize some objective function, the human being activities seen in energy terms might be used to create the social aggregations or groups. Electric energy generated from ecologic sources brings some unpredictability. Authors model the unpredictability of the distributed generation in order to create a tool for minimization. Authors propose the novel method to build real life smart micro grids in the distributed generation context characterized by zero emissions. The proposed tool becomes an instrument to create the social aggregation of users and negotiate locally the “social” energy in real time, strengthening and mastering a virtual neighborhood of the local community.

This work suggests an effective approach for information management in smart power grids based on the introduction of a suitable theory of digital energy. It shows a possible way to effectively manage energy dynamics in real life systems in real time. Power grids hold real time information flows already, but the control systems currently adopted use other information sources. We discuss the use of the information and semantic technologies in order to balance the loads in storage-less electric energy domain, and the changes brought by Future Internet and its entities.

New technologies have improved the ability of electronically storing, transferring and sharing medical data; they also create serious questions about who has access to this cross-media content and how it is protected and distributed. Our aim is to support healthcare professionals in this changing environment, by providing a set of software tools that help reducing the time and associated costs to collect the information and knowledge required, and in making the best use of it for a more informed decision making (diagnoses, therapies, protocols). An appropriate and consistent level of information security consists of protecting information both in written, spoken, electronically recorded, or printed form from accidental or malicious modification, destruction, or disclosure by applying the strictest requirements for data security and privacy and complying with ethical standards. By Doc@Hand project we experiment the distribution of computer-based patient record, natural language queries and the contained knowledge in a secured way.

The Event-Driven Metering (EDM) method for electricity enabled the detection and better understanding of variable energy-transforming processes from real life. This article generalizes the above-mentioned Lebesgue-based approach by translating it to the accounting of other energy vectors in the form of gases or liquids. Given the behavioral difference between the physical forms of vectors of energy, authors made an effort to keep newly proposed method compatible with the process knowledge being extracted and shared grid-wide by the EDM method previously declined to electricity. Authors exploit the symplectic properties deriving from the energy conservation law. Authors set up new compressive and sparse sensing method by moving from the point-like data to the segment-like entities remaining valid over longer finite time intervals.