Based on the requirements of large-scale and high-resolution remote sensing image data processing, this paper proposes a distributed parallelprocessing model based on sea and land segmentation tasks. Based on the tra...
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ISBN:
(数字)9781510650435
ISBN:
(纸本)9781510650435;9781510650428
Based on the requirements of large-scale and high-resolution remote sensing image data processing, this paper proposes a distributed parallelprocessing model based on sea and land segmentation tasks. Based on the trained DeepUnet model, the mpi4py function library is used for parallel algorithm design to realize multi-process synchronization processing. Increase the number of processors and reduce the processing time of large-scale and high-resolution remote sensing image data. The experimental results show that on the basis of ensuring the detection accuracy, the parallel sea- land segmentation technology can significantly shorten the image processing time compared with the traditional serial sea-land segmentation technology, and has strong scalability.
Development of the parallel processing technology is necessary to solve problems created by programs with complex structures that are computation- and data-intensive. In the parallelization process, the detection of p...
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Development of the parallel processing technology is necessary to solve problems created by programs with complex structures that are computation- and data-intensive. In the parallelization process, the detection of parallelism is an important task. Automatic parallelism analysis tools help programmers in finding parallelism. However, these tools have limitations in analyzing complex programs. Herein, we propose a data-driven method that can be applied to parallelism detection. The proposed framework combines contextual flow graphs and a deep graph convolution neural network, which leverages the latest and most popular techniques for code embedding and graph classification. Further, we present a novel generator to solve the problem of existing dataset inadequacies. We use this generator to build a generic dataset for parallelism detection. The experimental results of our framework using the generated dataset demonstrate that using a graph representation of the code can capture domain-specific information, and our framework can accurately detect potential parallelism in sequential programs. This framework will enable the exploration of new tools to address the parallelism detection problem. (C) 2021 Elsevier B.V. All rights reserved.
In this paper we presented a new intrusion detection system framework. By using 2-stage processingtechnology, parallel processing technology, and data backup technology it can provide functions of catching network da...
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ISBN:
(纸本)0889864926
In this paper we presented a new intrusion detection system framework. By using 2-stage processingtechnology, parallel processing technology, and data backup technology it can provide functions of catching network data package, detecting the integrity of the files, network data analysis. The work includes the implementation of the examination rule databases and argument detectors of the various agreement datagram, load balancing etc.
The traditional IDS, lots of work should be done on the computer where the event analyzer had been established. It made this computer become bottleneck in system, and had affected the performance of IDS. In order to s...
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ISBN:
(纸本)0889864926
The traditional IDS, lots of work should be done on the computer where the event analyzer had been established. It made this computer become bottleneck in system, and had affected the performance of IDS. In order to solve this problem, we present a new CIDF_Based IDS. In our IDS, we had improved the event analyzer by using 2-stage processingtechnology and parallel processing technology. The improved IDS accelerated the data analysis speed, improved the ability of intrusion tolerance, and reduced the false positives rate and false negatives rate.
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