With the gradual increase in the number of vehicles owned by people, the problem of urban traffic congestion is becoming more and more serious. Timely and accurate traffic flow prediction helps travel vehicles to plan...
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With the gradual increase in the number of vehicles owned by people, the problem of urban traffic congestion is becoming more and more serious. Timely and accurate traffic flow prediction helps travel vehicles to plan their routes reasonably, avoid congested roads and reduce environmental pollution. In order to take full advantage of the daily periodicity of the traffic flow of a road section, K-means++ with improved initial clustering centroids is applied to analyze the similarity of the daily traffic flow patterns and classify the traffic flow patterns with day as the time interval. One can separate the traffic patterns affected by different factors, and the other can exclude individual days containing abnormal data trends formed by unexpected road conditions, which can save the deep learning model from the interference of other irrelevant data features. Finally, a Long Short-Term Memory (LSTM) model is combined to predict the traffic flow value at the next time point based on the data of the previous hour. In the experiment, the K-means-LSTM model can significantly reduce the prediction error of short-time traffic flow and has better prediction performance by comparing with the prediction results of the support vector machine (SVR) model optimized by genetic algorithm (GA) and the LSTM model.
This paper proposes a robust approach based on geographic and statistical data to distribute national projections for distributed generation and for consumption into five distinct distribution grid types (‘City’, ‘...
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This paper proposes a robust approach based on geographic and statistical data to distribute national projections for distributed generation and for consumption into five distinct distribution grid types (‘City’, ‘Suburban’, ‘Rural’, ‘Holiday cottages’ and ‘Industry’). First, an approach based on geographic data is presented, which allows to categorize areas into these five grid types. Second, national projections for consumption from EVs are used to determine the needed number of EV chargers (home, fast and rapid chargers) and how these will be distributed on the five grid types. Similar approaches are presented for distributing roof-top and large-scale PVs as well as individual heat pumps. The proposed method will help network planers to assess the impact of these new consumers and producers on the distribution grid. The approach is illustrated using national projections for Denmark.
Computational methods are nowadays ubiquitous in the field of bioinformatics and biomedicine. Besides established fields like molecular dynamics, genomics or neuroimaging, new emerging methods rely heavily on large sc...
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Computational methods are nowadays ubiquitous in the field of bioinformatics and biomedicine. Besides established fields like molecular dynamics, genomics or neuroimaging, new emerging methods rely heavily on large scale computational resources. These new methods need to manage Tbytes or Pbytes of data with large-scale structural and functional relationships, TFlops or PFlops of computing power for simulating highly complex models, or many-task processes and workflows for processing and analyzing data. Today, many areas in Life Sciences are facing these challenges. This special issue contains papers showing existing solutions and latest developments in Life Sciences and computing Sciences to collaboratively explore new ideas and approaches to successfully apply distributed IT-systems in translational research, clinical intervention, and decision-making. (C) 2020 Published by Elsevier B.V.
Checkpoint/Restart (C/R) is widely used to provide fault tolerance on High-Performance computing (HPC) systems. However, parallel File System (PFS) overhead and failure uncertainty cause significant application overhe...
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ISBN:
(纸本)9781450370523
Checkpoint/Restart (C/R) is widely used to provide fault tolerance on High-Performance computing (HPC) systems. However, parallel File System (PFS) overhead and failure uncertainty cause significant application overhead. This paper develops an adaptive multi-level C/R model that incorporates a failure prediction and analysis model, which orchestrates failure prediction, checkpointing, checkpoint frequency, and proactive live migration along with the additional benefit of Burst Buffers (BB). It effectively reduces the overheads due to failures, checkpointing, and recovery. Simulation results for the Summit supercomputer yield a reduction of approximate to 20%-86% in application overhead due to BBs, orchestrated failure prediction, and migration. We also observe a approximate to 29% decrease in checkpoint writes to BBs, which can increase the longevity of the BB storage devices.
With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support th...
With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t
For robots using motion planning algorithms such as RRT and RRT*, the computational load can vary by orders of magnitude as the complexity of the local environment changes. To adaptively provide such computation, we p...
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ISBN:
(纸本)9781728173955
For robots using motion planning algorithms such as RRT and RRT*, the computational load can vary by orders of magnitude as the complexity of the local environment changes. To adaptively provide such computation, we propose Fog Robotics algorithms in which cloud-based serverless lambda computing provides parallel computation on demand. To use this parallelism, we propose novel motion planning algorithms that scale effectively with an increasing number of serverless computers. However, given that the allocation of computing is typically bounded by both monetary and time constraints, we show how prior learning can be used to efficiently allocate resources at runtime. We demonstrate the algorithms and application of learned parallel allocation in both simulation and with the Fetch commercial mobile manipulator using Amazon Lambda to complete a sequence of sporadically computationally intensive motion planning tasks.
Scientific applications can benefit greatly from getting deployed on a cloud computing platform, but such deployments require skills and expertise that are beyond the reach of many scientists. We address this issue wi...
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ISBN:
(纸本)9781665408790
Scientific applications can benefit greatly from getting deployed on a cloud computing platform, but such deployments require skills and expertise that are beyond the reach of many scientists. We address this issue with a framework that simplifies the process of writing cloud-ready scientific applications, and that automates their deployment and execution on top of cloud infrastructures. This paper presents (1) our domain-specific language whose syntax is simple to learn and use, and (2) our compiler that exploits potential data parallelism opportunities and handles load balancing automatically for the users. Our framework prototype demonstrates the feasibility of our approach, and our scalability analysis looks promising.
Smart grid systems are designed to enable the efficient capture and intelligent distribution of electricity across a distributed set of utilities. They are an essential component of increasingly important renewable en...
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ISBN:
(纸本)9781728160344
Smart grid systems are designed to enable the efficient capture and intelligent distribution of electricity across a distributed set of utilities. They are an essential component of increasingly important renewable energy sources, where it is vital to forecast the levels of energy being fed into and drawn from the grid. However, because of the high levels of uncertainty affecting real-world environments, accurate forecasting for example of wind power generation - being directly dependent on meteorological parameters and climatic conditions - is extremely challenging. Fuzzy Logic systems are frequently used in control systems to leverage their capacity for handling varying levels of uncertainty. In most cases, while uncertainty affecting the systems is captured in fuzzy sets (FSs), the final output of such systems is reduced to a crisp number (e.g. a control output). The latter process, while providing an efficient pathway to generating a specific control output, at the same time implies substantial information loss, as the uncertainty information captured in the FS outputs of these systems is effectively discarded. In this paper, we explore the potential of Mamdani fuzzy logic system based forecasting in order to generate not only a numeric forecast of the energy generated, but to also generate uncertainty intervals around said forecast indicating the level of uncertainty associated with the prediction. The proposed model is explored using both synthetic and smart-grid specific real-world (wind power) time series datasets. The results of the study indicate that utilising the `complete' FS output can provide valuable additional information in terms of the reliability of the forecast without any extra computational cost. At a general level, the approach indicates strong potential for leveraging the uncertainty information in fuzzy system outputs - which is commonly discarded - in real world applications.
Decentralized parallel SGD (D-PSGD) and its asynchronous variant Asynchronous parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to perform well for large-scale deep lear...
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ISBN:
(纸本)9781509066315
Decentralized parallel SGD (D-PSGD) and its asynchronous variant Asynchronous parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to perform well for large-scale deep learning tasks. One drawback of (A)D-PSGD is that the spectral gap of the mixing matrix decreases when the number of learners in the system increases, which hampers convergence. In this paper, we investigate techniques to accelerate (A)D-PSGD based training by improving the spectral gap while minimizing the communication cost. We demonstrate the effectiveness of our proposed techniques by running experiments on the 2000-hour Switch-board speech recognition task and the ImageNet computer vision task. On an IBM P9 supercomputer, our system is able to train an LSTM acoustic model in 2.28 hours with 7.5% WER on the Hub5-2000 Switchboard (SWB) test set and 13.3% WER on the CallHome (CH) test set using 64 V100 GPUs and in 1.98 hours with 7.7% WER on SWB and 13.3% WER on CH using 128 V100 GPUs, the fastest training time reported to date.
With the large amount configuration of distributed energy storage (DES), the randomness of its output and access point will challenge the traditional operation mode of power grid. If DES is connected to the power syst...
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