With the increasing human population and requirements, the current electric power grid is becoming complex and expanding rapidly. Increasing integration of renewable energy sources and other advanced metering technolo...
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ISBN:
(纸本)9781728193847
With the increasing human population and requirements, the current electric power grid is becoming complex and expanding rapidly. Increasing integration of renewable energy sources and other advanced metering technologies introduces a complex, expanded and distributed power system infrastructure. real-time operation and control of large-scale power system networks have become a challenging task due to these reasons. Power system clustering is a feasible solution to overcome these challenges. Clustering utilizes parallel processing capabilities of control methods and provides adequate methods for real-time power system control. In this paper, a graph theory-based clustering approach is presented for large interconnected power system networks. The algorithm is tested on multiple power system networks from small to large scale and the performance is analyzed. Typical results indicate that the method can provide clusters with uniform sizes within a short time limit for any type of power system network. The clusters observed are applied in solving optimal power flow (OPF) problem.
The proceedings contain 32 papers. The topics discussed include: functional QoS metric for LoRaWAN applications in challenging industrial environment;work-in-progress: voting framework for distributedreal-time Ethern...
ISBN:
(纸本)9781728152974
The proceedings contain 32 papers. The topics discussed include: functional QoS metric for LoRaWAN applications in challenging industrial environment;work-in-progress: voting framework for distributedreal-time Ethernet based dependable systems;observers and predictors for wireless time-sensitive control loops;work-in-progress: layering concerns for the analysis of credit-based shaping in IEEE 802.1 TSN;machine learning-aided classification of LoS/NLoS Radio links in industrial IoT;work-in-progress: modeling of real-time communication for industrial distributed automation systems;window-based schedule synthesis for industrial IEEE 802.1Qbv TSN networks;scaling TSN scheduling for factory automation networks;and plug play retrofitting approach for data integration to the cloud.
An algorithm for synthesizing a diagnostic model of a distributedreal-time computing system was proposed that is embedded in the system, runs in parallel with the main software of the system and allows simplifying th...
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An algorithm for synthesizing a diagnostic model of a distributedreal-time computing system was proposed that is embedded in the system, runs in parallel with the main software of the system and allows simplifying the process of testing it. Sufficient conditions for observability and controllability are formulated for models in a field of real numbers.
Fully peer-to-peer application software promises many benefits over cloud software, in particular, being able to function indefinitely without requiring servers. Research on distributed consistency mechanisms such as ...
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ISBN:
(纸本)9781450375245
Fully peer-to-peer application software promises many benefits over cloud software, in particular, being able to function indefinitely without requiring servers. Research on distributed consistency mechanisms such as CRDTs has laid the foundation for P2P data synchronisation and collaboration. In this paper we report on our experience in taking these technologies beyond research prototypes, and working towards commercial-grade P2P collaboration software. We identify approaches that work well in our experience, such as the functional reactive programming paradigm, and highlight areas in need of further research, such as the reliability of NAT traversal and usability challenges.
In the emerging industry of deep-sea mining for minerals and deposits (e.g., polymetallic nodules for nickel, cobalt, copper, and manganese), more data is required to understand the effects of sediment plume generatio...
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ISBN:
(纸本)9781613998526
In the emerging industry of deep-sea mining for minerals and deposits (e.g., polymetallic nodules for nickel, cobalt, copper, and manganese), more data is required to understand the effects of sediment plume generation and predict the distribution of disturbed sediments. There are two main sources of plume generation, the first being at the active mining site where the "collector" directly removes the top layer of the sea floor. The other is the "midwater plume" consisting of unwanted sediment that was collected during extraction that is pumped back into the aphotic zone. The vast majority of plume generation is caused by the collector, causing detrimental and long-lasting impacts on sea floor ecosystems due to the lack of wave activity or strong currents at the sea floor. Therefore, it is crucial to invest in the infrastructure to support the study and constant monitoring over a large area of the sea floor where plume generation is present. Due to the limited number of usable channels and power requirements, current subsea wireless communications technologies are not well suited to instrumenting the large areas of the sea floor needed to monitor plume migration. The scope of this effort is to transition experimental demonstrations of high-bandwidth, full-duplex scalable underwater laser communications to the sea floor in an open ocean environment. Specifically tackling challenges associated with the dynamic nature of the subsea world, including but not limited to, deployment logistics, sustainability, and range. The goal is to enable the internet of underwater things for deep sea industries by broadening the capabilities of subsea communications. By using high-precision laser transmitters, many of the challenges facing current subsea optical systems can be circumvented, such as power consumption, interference, and bandwidth limitations. This approach lends itself to wireless interlinking multi-node networks, in series or parallel, facilitating the implementation of
Containers are light, numerous, and interdependent, which are prone to cascading fault, increasing the probability of fault and the difficulty of detection. Existing detection methods are usually based on a cascade fa...
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Containers are light, numerous, and interdependent, which are prone to cascading fault, increasing the probability of fault and the difficulty of detection. Existing detection methods are usually based on a cascade fault model with traditional association analysis. The tradition model lacks consideration of the fault cascade history dimension and container space correlation dimension which results in a lower detection effect. And the imbalance of fault data in the cloud environment to the detection method to bring interference. Instead, this paper proposes a cascade fault detection method based on spatial-temporal correlation in cloud environment. First, the container cascade fault relationship model is constructed by extracting the spatial-temporal correlation from historical container faults. Second, based on dynamic feedback data sampling combined with ensemble learning, a container fault model learning method is designed to solve the imbalance of fault data. Then, a real-time container cascade fault detection mechanism for container cascade failure is proposed. The experimental results show that compared with the existing fault detection methods, the proposed method can effectively improve the detection accuracy, recall rate, and F-1 value.
distributed Stream Processing systems have become an essential part of big data processing platforms. They are characterized by the high-throughput processing of near to real-time event streams with the goal of delive...
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This work presents a dynamic parallel distribution scheme for the Hartree-Fock exchange (HFX) calculations based on the real-space NAO2GTO framework. The most time-consuming electron repulsion integrals (ERIs) calcula...
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This work presents a dynamic parallel distribution scheme for the Hartree-Fock exchange (HFX) calculations based on the real-space NAO2GTO framework. The most time-consuming electron repulsion integrals (ERIs) calculation is perfectly load-balanced with 2-level master-worker dynamic parallel scheme, the density matrix and the HFX matrix are both stored in the sparse format, the network communication time is minimized via only communicating the index of the batched ERIs and the final sparse matrix form of the HFX matrix. The performance of this dynamic scalable distributed algorithm has been demonstrated by several examples of large scale hybrid density-functional calculations on Tianhe-2 supercomputers, including both molecular and solid states systems with multiple dimensions, and illustrates good scalability. (C) 2020 Elsevier B.V. All rights reserved.
Most of the traditional collaborative filtering (CF) recommendation models mainly consider the users' ratings on items, often ignore the time context information of users. However this information is non-trivial t...
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Most of the traditional collaborative filtering (CF) recommendation models mainly consider the users' ratings on items, often ignore the time context information of users. However this information is non-trivial to improve the effectiveness of recommender system. A time-aware parallel CF movie recommendation based on Spark is proposed in this paper. The CF algorithm based on matrix factorisation can associate users' interests with items through implicit features and solve the sparse matrix problem. The time-aware CF algorithm considers the dynamic features associated with the items and users, which improves the recommendation accuracy by introducing discrete time parameter into the matrix factorisation model. To solve the problem of the slow processing speed of high volume data, distributed computing based on Spark is used to achieve the parallelisation of the algorithm. The experimental results on real dataset MovieLens show that the proposed method performs significantly better than traditional CF recommendation, which can alleviate the problem of data sparsity and significantly improve the processing speed and recommendation accuracy.
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