This paper presents the developments across a multi-year collaborative industry-academia R&D project designing and testing novel Augmented Reality (AR) solutions for differing maritime operations and work tasks. W...
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High volumes of a wide variety of valuable data can be easily collected and generated from a broad range of data sources of different veracities at a high velocity. In the current era of big data, many traditional dat...
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High volumes of a wide variety of valuable data can be easily collected and generated from a broad range of data sources of different veracities at a high velocity. In the current era of big data, many traditional data management and analytic approaches may not be suitable for handling the big data due to their well-known 5V's characteristics. Over the past few years, several systems and applications have developed to use cluster, cloud or grid computing to manage and analyze big data so as to support data science (e.g., knowledge discovery and data mining). In this paper, we present a knowledge-based system for social network analysis so as to support big data mining of interesting patterns from big social networks that are represented as graphs.
We introduce policy resolution (PR) for workflow management systems (WFMS) as service to assign work to agents. Policy resolution is a framework for defining arbitrary role and organization models together with operat...
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This paper argues that security design for Open Distributed Processing (ODP) would benefit from a shift of focus from the infrastructure to individual servers as the owners and enforcers of security policy. It debates...
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This paper presents dynamic behaviors of autonomous solid oxide fuel cells (SOFC) with AC bus control. Both voltage and frequency control are utilized to achieve load-sharing of the studied SOFC feeding isolated loads...
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This paper presents dynamic behaviors of autonomous solid oxide fuel cells (SOFC) with AC bus control. Both voltage and frequency control are utilized to achieve load-sharing of the studied SOFC feeding isolated loads. A DC-to-DC converter is connected to the output terminals of the studied SOFC for stabilizing output voltage and current fluctuations under different loading conditions. The PWM inverter connected to the output terminals of the DC-to-DC converter is operated under voltage-controlled mode to regulate the voltage profile across the connected loads. It can be concluded from the simulation results that the proposed voltage and frequency droop controllers of the PWM inverter may operate satisfactory under stand-alone mode.
Load imbalance is a challenge for parallel applications in High Performance Computing (HPC). It is caused by processes having different execution times or load values, leading to idle or wait times at synchronization ...
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ISBN:
(数字)9798350355543
ISBN:
(纸本)9798350355550
Load imbalance is a challenge for parallel applications in High Performance Computing (HPC). It is caused by processes having different execution times or load values, leading to idle or wait times at synchronization points, where faster processes must wait for the slowest process to catch up. To mitigate this issue, applications can employ load balancing (LB) strategies, which migrate load between processes to even out load. This is often referred to as the Load Rebalancing Problem (LRP). While many approaches solving the LRP exist, they can only be heuristics and hence further optimization potential exists. In our work, we turn to a novel approach by using hybrid classical-quantum approaches and present two versions of the constrained quadratic model for solving the LRP; the two differ in how they balance the number of qubits required with the types of applied constraints. We compare the quantum-based methods with classical methods using heuristic algorithms Greedy, Karmarkar–Karp, and ProactLB. We evaluate our approaches using imbalance ratio and speedup as metrics, as well as the number of migrated tasks to indicate overhead caused by migrations. Our results show that the quantum-based methods outperform the classic methods. For example, we need only 1/4 of the number of migrated tasks in a realistic use case compared with classical methods, particularly Greedy and KK, to balance the load.
The network model bases upon the energy hub concept, which was developed by the Vision of Future Energy Networks (VoFEN) research group at ETH Zurich in the last years. Keynote of the concept is a combined optimizatio...
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The advancing automation of the industrial production requires faster and more efficient programmable logic controllers. Today39;s controller architectures based on specialized processors to execute the STL applicat...
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The advancing automation of the industrial production requires faster and more efficient programmable logic controllers. Today's controller architectures based on specialized processors to execute the STL application are at their limits. For any further improvement the architecture of these processors needs to evolve from single core in order execution to a multicore out of order architecture. This step can not occur without additions to the PLC's instruction set. Therefore a PLC processor instruction set simulation is required that allows a quick evaluation of the effectiveness and efficiency of instruction set changes as well as changes to the processor's architecture. Current architecture description languages (ADL) allow the fast modeling of these changes with enough flexibility for the planned improvements and provide fast simulation speed. This work presents for the first time an instruction set simulation environment for STL compatible PLC processors using the ADL ArchC as a base for further research.
Neural networks have been used as an effective method for solving many problems in a wide range of application areas. As neural networks are being more and more widely used in recent years, the need for their more for...
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Neural networks have been used as an effective method for solving many problems in a wide range of application areas. As neural networks are being more and more widely used in recent years, the need for their more formal definition becomes increasingly apparent. This paper presents a novel architecture of neural network models using the functional graph. The network creates a graph representation by dynamically allocating nodes to code local form attributes and establishing arcs to link them. In this paper application of functional graph in the architecture of electronic neural network, opto-electronic neural network and genetic neural network are detailed with experimental results. Learning is defined in terms of functional graph. The proposed architectures are applied in evaluating 3G wireless network performance.
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