21,681 research outputs found
Towards In-Transit Analytics for Industry 4.0
Industry 4.0, or Digital Manufacturing, is a vision of inter-connected
services to facilitate innovation in the manufacturing sector. A fundamental
requirement of innovation is the ability to be able to visualise manufacturing
data, in order to discover new insight for increased competitive advantage.
This article describes the enabling technologies that facilitate In-Transit
Analytics, which is a necessary precursor for Industrial Internet of Things
(IIoT) visualisation.Comment: 8 pages, 10th IEEE International Conference on Internet of Things
(iThings-2017), Exeter, UK, 201
Towards a Tool-based Development Methodology for Pervasive Computing Applications
Despite much progress, developing a pervasive computing application remains a
challenge because of a lack of conceptual frameworks and supporting tools. This
challenge involves coping with heterogeneous devices, overcoming the
intricacies of distributed systems technologies, working out an architecture
for the application, encoding it in a program, writing specific code to test
the application, and finally deploying it. This paper presents a design
language and a tool suite covering the development life-cycle of a pervasive
computing application. The design language allows to define a taxonomy of
area-specific building-blocks, abstracting over their heterogeneity. This
language also includes a layer to define the architecture of an application,
following an architectural pattern commonly used in the pervasive computing
domain. Our underlying methodology assigns roles to the stakeholders, providing
separation of concerns. Our tool suite includes a compiler that takes design
artifacts written in our language as input and generates a programming
framework that supports the subsequent development stages, namely
implementation, testing, and deployment. Our methodology has been applied on a
wide spectrum of areas. Based on these experiments, we assess our approach
through three criteria: expressiveness, usability, and productivity
Towards adaptive multi-robot systems: self-organization and self-adaptation
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugÀnglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible
Developing a distributed electronic health-record store for India
The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India
Software scaffolds to promote regulation during scientific inquiry learning
This research addresses issues in the design of online scaffolds for regulation within inquiry learning environments. The learning environment in this study included a physics simulation, data analysis tools, and a model editor for students to create runnable models. A regulative support tool called the Process Coordinator (PC) was designed to assist students in planning, monitoring, and evaluating their investigative efforts within this environment. In an empirical evaluation, 20 dyads received a âfullâ version of the PC with regulative assistance; dyads in the control group (nâ=â15) worked with an âemptyâ PC which contained minimal structures for regulative support. Results showed that both the frequency and duration of regulative tool use differed in favor of the PC+ dyads, who also wrote better lab reports. PCâ dyads viewed the content helpfiles more often and produced better domain models. Implications of these differential effects are discussed and suggestions for future research are advanced
Load flow studies on stand alone microgrid system in Ranau, Sabah
This paper presents the power flow or load flow analysis of Ranau microgrid, a
standalone microgrid in the district of Ranau,West Coast Division of Sabah. Power
flow for IEEE 9 bus also performed and analyzed. Power flow is define as an
important tool involving numerical analysis applied to power system. Power flow
uses simplified notation such as one line diagram and per-unit system focusing on
voltages, voltage angles, real power and reactive power. To achieved that purpose,
this research is done by analyzing the power flow analysis and calculation of all the
elements in the microgrid such as generators, buses, loads, transformers,
transmission lines using the Power Factory DIGSilent 14 software to calculate the
power flow. After the analysis and calculations, the results were analysed and
compared
Efficient Parallel Reinforcement Learning Framework using the Reactor Model
Parallel Reinforcement Learning (RL) frameworks are essential for mapping RL
workloads to multiple computational resources, allowing for faster generation
of samples, estimation of values, and policy improvement. These computational
paradigms require a seamless integration of training, serving, and simulation
workloads. Existing frameworks, such as Ray, are not managing this
orchestration efficiently, especially in RL tasks that demand intensive
input/output and synchronization between actors on a single node. In this
study, we have proposed a solution implementing the reactor model, which
enforces a set of actors to have a fixed communication pattern. This allows the
scheduler to eliminate work needed for synchronization, such as acquiring and
releasing locks for each actor or sending and processing coordination-related
messages. Our framework, Lingua Franca (LF), a coordination language based on
the reactor model, also supports true parallelism in Python and provides a
unified interface that allows users to automatically generate dataflow graphs
for RL tasks. In comparison to Ray on a single-node multi-core compute
platform, LF achieves 1.21x and 11.62x higher simulation throughput in OpenAI
Gym and Atari environments, reduces the average training time of synchronized
parallel Q-learning by 31.2%, and accelerates multi-agent RL inference by
5.12x.Comment: 10 pages, 11 figure
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