We propose a new approach to creating a subject-oriented distributedcomputing environment. Such an environment is used to support decision-making in solving relevant problems of ensuring energy systems resilience. Th...
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ISBN:
(纸本)9781728173863
We propose a new approach to creating a subject-oriented distributedcomputing environment. Such an environment is used to support decision-making in solving relevant problems of ensuring energy systems resilience. The proposed approach is based on the idea of advancing and integrating the following important capabilities in supercomputer engineering: continuous integration, delivery, and deployment of the system and applied software, high-performance computing in heterogeneous environments, multi-agent intelligent computation planning and resource allocation, big data processing and geo-information servicing for subject information, including weakly structured data, and decision-making support. This combination of capabilities and their advancing are unique to the subject domain under consideration, which is related to combinatorial studying critical objects of energy systems. Evaluation of decision-making alternatives is carrying out through applying combinatorial modeling and multi-criteria selection rules. The Orlando Tools framework is used as the basis for an integrated software environment. It implements a flexible modular approach to the development of scientific applications (distributed applied software packages).
The paper describes the sensor fusion for the newly developed omnidirectional mechatronic system. To that end, the kinematic model of the platform and the chosen configuration of omnidirectional Mecanum wheels is desc...
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Some graph analyses, such as social network and biological network, need large-scale graph construction and maintenance over distributed memory space. distributed data-streaming tools, including MapReduce and Spark, r...
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ISBN:
(纸本)9781728162515
Some graph analyses, such as social network and biological network, need large-scale graph construction and maintenance over distributed memory space. distributed data-streaming tools, including MapReduce and Spark, restrict some computational freedom of incremental graph modification and run-time graph visualization. Instead, we take an agent-based approach. We construct a graph from a scientific dataset in CSV, tab, and XML formats;dispatch many reactive agents on it;and analyze the graph in the form of their collective group behavior: propagation, flocking, and collision. The key to success is how to automate the run-time construction and visualization of agent-navigable graphs mapped over distributed memory. We implemented this distributed graph-computing support in the multi-agent spatial simulation (MASS) library, coupled with the Cytoscape graph visualization software. This paper presents the MASS implementation techniques and demonstrates its execution performance in comparison to MapReduce and Spark, using two benchmark programs: (1) an incremental construction of a complete graph and (2) a KD tree construction.
The distributed fault-tolerant computer technology of deterministic communication takes the needs of a new generation of IVMS as background in this paper, the system architecture design and software architecture of di...
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In the era of rapid development of Internet technology and increasing social needs of people, web crawlers have been maturely applied to major search engines and search fields. By using Spark's RDD elastic computi...
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ISBN:
(纸本)9781728161365
In the era of rapid development of Internet technology and increasing social needs of people, web crawlers have been maturely applied to major search engines and search fields. By using Spark's RDD elastic computing architecture and task assignment algorithm, this paper integrates the architecture of Spark-based distributed crawler system, gives the corresponding framework diagram, and introduces the distributed framework system in detail. Through this Spark-based distributed crawler system we can solve the problem of insufficient resource utilization and low collection efficiency, and then solve the contradiction between the current explosive growth of data scale and the speed of obtaining information.
This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. OTM-MPI represents the first open-source, dis...
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ISBN:
(纸本)9781728141497
This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. OTM-MPI represents the first open-source, distributed-memory, macroscopic simulation model developed for modern high performance parallel machines and large networks. Macroscopic simulations are appropriate for studying regional traffic scenarios when aggregate trends are of interest, rather than individual vehicle traces. They are also appropriate for studying the routing behavior of classes of vehicles, such as app-informed vehicles. The network partitioning was performed with METIS. Inter-process communication was done with MPI (message-passing interface). Results are provided for two networks: one realistic network which was obtained from Open Street Maps for Chattanooga, TN, and another larger synthetic grid network. The software recorded a speedups of 198x using 256 cores for Chattanooga, and 475x with 1,024 cores for the synthetic network.
The development of power systems has called higher requirements for sensor information platforms, and sensor information platforms have become one key supporting technology for efficient and stable operation of power ...
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The industrial world is amid a revolution, titled Industry 4.0, which entails the use of IoT technologies to enable the exchange of information between sensors, industrial machines and end users. A major issue in many...
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ISBN:
(纸本)9783030238872;9783030238865
The industrial world is amid a revolution, titled Industry 4.0, which entails the use of IoT technologies to enable the exchange of information between sensors, industrial machines and end users. A major issue in many industrial sectors is production inefficiency, with process downtime representing a loss for companies. Predictive maintenance, whereby maintenance is performed only when needed and before a failure occurs, has the potential to substantially reduce costs. This paper describes the fault detection mechanism of a predictive maintenance system developed for the metallurgic industry. Considering no previous information about faults is available, learning happens in an unsupervised manner. Imminent faults are predicted by estimating autoregressive integrated moving average models using real-world sensor data obtained from monitoring different machine components and parameters. The models' outputs are fused to assess the significance of an anomaly (or anomalies) along the time domain and determine how likely a fault is to occur, with alarms being issued when the prospect of a fault is high enough.
Building a new-type power system with renewable energy is the key to achieving the target of carbon peak and carbon neutrality, which has been the consensus of the clean, low-carbon, and safe energy transition. The co...
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Building a new-type power system with renewable energy is the key to achieving the target of carbon peak and carbon neutrality, which has been the consensus of the clean, low-carbon, and safe energy transition. The construction of a new-type power system at the industrial park level is the first demonstration of the coordinated optimization and intelligent control of power sources, grid, load, and energy storage to make overall planning to achieve diversified development. To sustain the ‘balance, security, diversity, and low cost’ construction goals of the new-type power system at the park level, the power grid dispatching and control system is transformed to the direction of intelligence and automation, which is reflected in the rapid emergence and construction of complex systems such as digital twin, simulation, reinforcement learning, model training and prediction, and intelligent decision-making of the EMS (Energy Manage System). This brings more complex data sharing, interaction, construction, and other technical requirements than ever before. At the same time, along with the simultaneous construction of new power systems such as micro-grids, virtual power plants, and distributed photovoltaic systems, it has exacerbated the problems of data divergence from business, low data utilization, and centralized architecture brought by the data lakes and data warehouses under the previous monolithic architecture, leading to the data model hard to deploy to meet the demand for efficient collection, interaction, and sharing of scattered and heterogeneous data under the new generation energy structure and greatly increases the work pressure of the data team, making the data team a bottleneck for business applications. Therefore, this paper proposes a novel design scheme of data processing framework with the data mesh concept, supports the federal computing and analysis engine by building a distributed metadata center, changes the previous centralized data modeling method t
Faulty nodes are expected to have a significant effect on the performance of Big Data systems. The cause of discharges, on the other hand, is a complex issue. The previous work has mostly focused on identifying Stragg...
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