5,112 research outputs found

    Using formal methods to develop WS-BPEL applications

    Get PDF
    In recent years, WS-BPEL has become a de facto standard language for orchestration of Web Services. However, there are still some well-known difficulties that make programming in WS-BPEL a tricky task. In this paper, we firstly point out major loose points of the WS-BPEL specification by means of many examples, some of which are also exploited to test and compare the behaviour of three of the most known freely available WS-BPEL engines. We show that, as a matter of fact, these engines implement different semantics, which undermines portability of WS-BPEL programs over different platforms. Then we introduce Blite, a prototypical orchestration language equipped with a formal operational semantics, which is closely inspired by, but simpler than, WS-BPEL. Indeed, Blite is designed around some of WS-BPEL distinctive features like partner links, process termination, message correlation, long-running business transactions and compensation handlers. Finally, we present BliteC, a software tool supporting a rapid and easy development of WS-BPEL applications via translation of service orchestrations written in Blite into executable WS-BPEL programs. We illustrate our approach by means of a running example borrowed from the official specification of WS-BPEL

    Description and Experience of the Clinical Testbeds

    Get PDF
    This deliverable describes the up-to-date technical environment at three clinical testbed demonstrator sites of the 6WINIT Project, including the adapted clinical applications, project components and network transition technologies in use at these sites after 18 months of the Project. It also provides an interim description of early experiences with deployment and usage of these applications, components and technologies, and their clinical service impact

    Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

    Full text link
    Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

    An application of physical flexibility and software reconfigurability for the automation of battery module assembly

    Get PDF
    Batteries are a strategic technology to decarbonize conventional automotive powertrains and enable energy policy turnaround from fossil fuels to renewable energy. The demand for battery packs is rising, but they remain unable to compete with conventional technologies, primarily due to higher costs. Major sources of cost remain in manufacturing and assembly. These costs can be attributed to a need for high product quality, material handling complexity, uncertain and fluctuating production volumes, and an unpredictable breadth of product variants. This research paper applies the paradigms of flexibility from a mechanical engineering perspective, and reconfigurability from a software perspective to form a holistic, integrated manufacturing solution to better realize product variants. This allows manufacturers to de-risk investment as there is increased confidence that a facility can meet new requirements with reduced effort, and also shows how part of the vision of Industry 4.0 associated with the integration and exploitation of data can be fulfilled. A functional decomposition of battery packs is used to develop a foundational understanding of how changes in customer requirements can result in physical product changes. A Product, Process, and Resource (PPR) methodology is employed to link physical product characteristics to physical and logical characteristics of resources. This mapping is leveraged to enable the design of a gripper with focused flexibility by the Institute for Machine Tools and Industrial Management (iwb) at the Technical University of Munich, as it is acknowledged that mechanical changes are challenging to realize within industrial manufacturing facilities. Reconfigurability is realised through exploitation of data integration across the PPR domains, through the extension of the capabilities of a non-commercial virtual engineering toolset developed by the Automation Systems Group at the University of Warwick. The work shows an “end-to-end” approach that practically demonstrates the application of the flexibility and reconfigurability paradigms within an industrial engineering context

    An Application of Physical Flexibility and Software Reconfigurability for the Automation of Battery Module Assembly

    Get PDF
    Batteries are a strategic technology to decarbonize conventional automotive powertrains and enable energy policy turnaround from fossil fuels to renewable energy. The demand for battery packs is rising, but they remain unable to compete with conventional technologies, primarily due to higher costs. Major sources of cost remain in manufacturing and assembly. These costs can be attributed to a need for high product quality, material handling complexity, uncertain and fluctuating production volumes, and an unpredictable breadth of product variants. This research paper applies the paradigms of flexibility from a mechanical engineering perspective, and reconfigurability from a software perspective to form a holistic, integrated manufacturing solution to better realize product variants. This allows manufacturers to de-risk investment as there is increased confidence that a facility can meet new requirements with reduced effort, and also shows how part of the vision of Industry 4.0 associated with the integration and exploitation of data can be fulfilled. A functional decomposition of battery packs is used to develop a foundational understanding of how changes in customer requirements can result in physical product changes. A Product, Process, and Resource (PPR) methodology is employed to link physical product characteristics to physical and logical characteristics of resources. This mapping is leveraged to enable the design of a gripper with focused flexibility by the Institute for Machine Tools and Industrial Management (iwb) at the Technical University of Munich, as it is acknowledged that mechanical changes are challenging to realize within industrial manufacturing facilities. Reconfigurability is realised through exploitation of data integration across the PPR domains, through the extension of the capabilities of a non-commercial virtual engineering toolset developed by the Automation Systems Group at the University of Warwick. The work shows an “end-to-end” approach that practically demonstrates the application of the flexibility and reconfigurability paradigms within an industrial engineering context

    Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation

    Get PDF
    Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients

    Deep Learning for Face Anti-Spoofing: A Survey

    Full text link
    Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, traditional FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

    Open Source Big Data Platforms and Tools: An Analysis

    Get PDF
    Big data is attracting an excessive amount of interest in the IT and academic sectors. On a regular basis, computer and digital industries generate more data than they have space to store. In the current situation, five billion people have their own mobile phone, and over two billion people are linked globally to exchange various types of data. By 2020, it is estimated that about fifty billion people will be connected to the internet. During2020, data generation, use, and sharing would be forty-four times higher than in previous years. A variety of sectors and organizations are using big data to manage various operations. As a result, a thorough examination of big data's benefits, drawbacks, meaning, and characteristics is needed. The primary goal of this research is to gather information on the various open-source big data tools and platforms that are used by various organizations. In this paper we use a three perspective methodology to identify the strength and weaknesses of the workflow in a open source big data arena. This helps to establish a pipeline of workflow events for both researcher and entrepreneur decision making
    corecore