In the Energy Conversion for Next-Generation Smart Cities, intelligent substation plays an important role in the power conversion. As an important guarantee for the stable operation of intelligent substation, the rese...
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In the Energy Conversion for Next-Generation Smart Cities, intelligent substation plays an important role in the power conversion. As an important guarantee for the stable operation of intelligent substation, the research on fault diagnosis technology is particularly important. In this paper, the acoustic characteristic diagnosis of substation equipment (take transformers for example) is researched and the application of "Voice Recognition + artificial neural network (ANN) " technology in substation fault diagnosis is analyzed. At the same time, the continuous online monitoring of the intelligent substation equipment will produce a large amount of monitoring data, which needs to be analyzed timely and effectively to understand the operating status of the equipment accurately. Because of this, this paper adopts distributed computing by establishing a real-time distributed computing platform, using open source technology to store the online monitoring of sound data into the computing platform for data processing to achieve the purpose of automatic fault detection and analysis. The results show that distributed computing can realize the intelligent analysis, storage, and visualization of equipment data in the substation, which provides data support for fault diagnosis. Besides, the fitting accuracy rates of ANN model are 95.123% for training process and the fitting accuracy rates of ANN model are 99.353% for training process and the overall fitting accuracy rates of ANN model are 95.478% and the error between the predicted value and the actual value of the 5 sound signals is within 5% in the fault diagnosis process. Consequently, the ANN model can accurately identify each fault sound of substation and achieve the purpose of fault diagnosis.
This work considers the optimal design of MapReduce-based coded distributed computing (CDC) with nonuniform input file sizes. We propose an efficient heterogeneous CDC (HetCDC) scheme capable of handling an arbitrary ...
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ISBN:
(数字)9798350367942
ISBN:
(纸本)9798350367959
This work considers the optimal design of MapReduce-based coded distributed computing (CDC) with nonuniform input file sizes. We propose an efficient heterogeneous CDC (HetCDC) scheme capable of handling an arbitrary number of files of nonuniform sizes. We further jointly optimize the file placement and coded shuffling strategies of the proposed HetCDC by formulating an integer linear programming (ILP) problem. Finally, the performance of the optimized HetCDC is verified by numerical studies.
As quantum computing confronts scalability challenges, distributed hybrid QPU-CPU techniques emerge as a crucial solution. These techniques distribute quantum algorithms across quantum and classical computing resource...
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As quantum computing confronts scalability challenges, distributed hybrid QPU-CPU techniques emerge as a crucial solution. These techniques distribute quantum algorithms across quantum and classical computing resources to surpass the computational reach of either one alone.
This paper describes an Information-Centric Dataflow system that is based on name-based access to computation results, NDN PSync dataset synchronization for enabling consuming compute functions to learn about updates ...
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ISBN:
(纸本)9781450384605
This paper describes an Information-Centric Dataflow system that is based on name-based access to computation results, NDN PSync dataset synchronization for enabling consuming compute functions to learn about updates and for coordinating the set of compute functions in a distributed Dataflow pipeline. We describe how relevant Dataflow concepts can be mapped to ICN and how data-sharing, data availability and scalability can be improved compared to stateof-the-art systems. We also provide a specification of an applicationindependent namespace design and report on our experience with a first prototype implementation.
In the last few years, the exponential rise in urban population and allied demands have alarmed governing agencies as well as industries to achieve more quality-of-service (QoS) oriented solutions to meet up-surging d...
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ISBN:
(纸本)9781728185293
In the last few years, the exponential rise in urban population and allied demands have alarmed governing agencies as well as industries to achieve more quality-of-service (QoS) oriented solutions to meet up-surging demands, especially towards real-time decision making, information exchange and knowledge-driven decisions. To achieve it, smart city concept which employs Internet-of-Things (IoT), distributed software computing, and BigData analytics has gained widespread attention. Though, inclusion of QoS-sensitive routing has helped enabling better and efficient sensory or node's data collection and dissemination;however, ensuring optimal query-driven knowledge mining and information exchange has remained a challenge. Considering it as motivation, in this paper an evolutionary computing assisted K-Means clustering algorithm is developed for MapReduce computation in Hadoop distributed framework. The proposed method employs genetic algorithm to enhance centroid estimation as well as clustering, which as a result helped in achieving better clustering to support MapReduce. The proposed GA based K-Means clustering has been applied over Hadoop-MapReduce, where to achieve aforesaid centroid estimation and clustering enhancement Silhouette coefficient was used as the objective function. Here, GA-K Means was applied in such manner that it estimates optimized centroid and clusters simultaneously over Mapper and Reducer, which makes overall computation faster and more accurate.
Studying distributed computing through the lens of algebraic topology has been the source of many significant breakthroughs during the last two decades, especially in the design of lower bounds or impossibility result...
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ISBN:
(纸本)9781450385480
Studying distributed computing through the lens of algebraic topology has been the source of many significant breakthroughs during the last two decades, especially in the design of lower bounds or impossibility results for deterministic algorithms. In a nutshell, this approach consists of capturing all the possible states of a distributed system at a certain time as a simplicial complex called protocol complex, and viewing computation as a simplicial map from that complex to the so-called output complex, that captures all possible legal output states of the system. This paper aims at studying randomized synchronous distributed computing through the lens of algebraic topology. We do so by studying the wide class of (input-free) symmetry-breaking tasks, e.g., leader election, in synchronous fault-free anonymous systems. We show that it is possible to redefine solvability of a task "locally", i.e., for each simplex of the protocol complex individually, without requiring any global consistency. However, this approach has a drawback: it eliminates the topological aspect of the computation, since a single facet has a trivial topological structure. To overcome this issue, we introduce a "projection" pi of both protocol and output complexes, where every simplex.. is mapped to a complex pi (sigma);the later has a rich structure that replaces the structure we lost by considering one single facet at a time. To show the significance and applicability of our topological approach, we derive necessary and sufficient conditions for solving leader election in synchronous fault-free anonymous sharedmemory and message-passing models. In both models, we consider scenarios in which there might be correlations between the random values provided to the nodes. In particular, different parties might have access to the same randomness source so their randomness is not independent but equal. Interestingly, we find that solvability of leader election relates to the number of parties that posse
distributed computing is known for its high efficiency of processing large amounts of data in parallel, at the expense of communication load between different servers. Coding was introduced to minimize the communicati...
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ISBN:
(纸本)9781728182988
distributed computing is known for its high efficiency of processing large amounts of data in parallel, at the expense of communication load between different servers. Coding was introduced to minimize the communication load by exploiting the repetitive computing, thus drawing great attention within the academia. Most existing works assume that all servers are identical in computational capability, which is inconsistent with practical scenarios. In this paper, we investigate a distributed computing system that consists of two types of servers, i.e., fast servers and slow servers. Due to the heterogeneous computational capabilities within the system, the overall computation time will be delayed by the slow servers, which is called the straggling effect. To this end, we develop a novel framework of coding-based distributed computing to alleviate the straggling effect. Specifically, for a given number of fast servers and slow servers with their corresponding computational capabilities, we aim to minimize the overall computation time by assigning different amounts of workloads to different servers. Further, we derive the information-theoretic lower hound of the communication load of the system, which is shown to be within a constant multiplicative gap to the achievable communication load by our scheme.
Esparza and Reiter have recently conducted a systematic comparative study of models of distributed computing consisting of a network of identical finite-state automata that cooperate to decide if the underlying graph ...
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ISBN:
(纸本)9781450385480
Esparza and Reiter have recently conducted a systematic comparative study of models of distributed computing consisting of a network of identical finite-state automata that cooperate to decide if the underlying graph of the network satisfies a given property. The study classifies models according to four criteria, and shows that twenty-four initially possible combinations collapse into seven equivalence classes with respect to their decision power, i.e. the properties that the automata of each class can decide. However, Esparza and Reiter only show (proper) inclusions between the classes, and so do not characterise their decision power. In this paper we do so for labelling properties, i.e. properties that depend only on the labels of the nodes, but not on the structure of the graph. In particular, majority (whether more nodes carry label a than b) is a labelling property. Our results show that only one of the seven equivalence classes identified by Esparza and Reiter can decide majority for arbitrary networks. We then study the expressive power of the classes on bounded-degree networks, and show that three classes can. In particular, we present an algorithm for majority that works for all bounded-degree networks under adversarial schedulers, i.e. even if the scheduler must only satisfy that every node makes a move infinitely often, and prove that no such algorithm can work for arbitrary networks.
We consider a MapReduce-type task running in a distributed computing model which consists of K edge computing nodes distributed across the edge of the network and a Master node that assists the edge nodes to compute o...
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ISBN:
(纸本)9781728159621
We consider a MapReduce-type task running in a distributed computing model which consists of K edge computing nodes distributed across the edge of the network and a Master node that assists the edge nodes to compute output functions. The Master node and the edge nodes, both equipped with some storage memories and computing capabilities, are connected through a multicast network. We define the communication time spent during the transmission for the sequential implementation (all nodes send symbols sequentially) and parallel implementation (the Master node can send symbols during the edge nodes' transmission), respectively. We propose a mixed coded distributed computing scheme that divides the system into two subsystems where the coded distributed computing (CDC) strategy proposed by Songze Li et al. is applied into the first subsystem and a novel master-aided CDC strategy is applied into the second subsystem. We prove that this scheme is optimal, i.e., achieves the minimum communication time for both the sequential and parallel implementation, and establish an optimal information-theoretic tradeoff between the overall communication time, computation load, and the Master node's storage capacity. It demonstrates that incorporating a Master node with storage and computing capabilities can further reduce the communication time. For the sequential implementation, we deduce the approximately optimal file allocation between the two subsystems, which shows that the Master node should map as many files as possible in order to achieve smaller communication time. For the parallel implementation, if the Master node's storage and computing capabilities are sufficiently large (not necessary to store and map all files), then the proposed scheme requires at most 1/2 of the minimum communication time of system without the help of the Master node.
The content and structure of the news text are relatively complex and cannot effectively capture the core content. Existing supervision models cannot achieve good results in areas with less annotated data. To this end...
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The content and structure of the news text are relatively complex and cannot effectively capture the core content. Existing supervision models cannot achieve good results in areas with less annotated data. To this end, we propose a new Chinese keyword extraction model with a distributed computing method. Precisely, we first fused the Bidirectional Encoder Representation from Transformers (BERT) and Conditional Random Fields (CRF) so that each word learns its relationship with the context while reducing errors;secondly, the adversarial training encourages the model to retain a small amount of annotations Sample knowledge to help extract keywords from unannotated samples;and because the model contains a large number of time-consuming components, it creatively uses distributed computing to save overall computing time. The results show that our model can steadily improve the performance of keyword phrase extraction in areas with insufficient labeled samples.
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