Hadoop Distributed File System (HDFS) is widely used in massive data storage. Because of the disadvantage of the multi-copy strategy, the hardware expansion of HDFS cannot keep up with the continuous volume of big dat...
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Hadoop Distributed File System (HDFS) is widely used in massive data storage. Because of the disadvantage of the multi-copy strategy, the hardware expansion of HDFS cannot keep up with the continuous volume of big data. Now, the traditional data replication strategy has been gradually replaced by Erasure Code due to its smaller redundancy rate and storage overhead. However, compared with replicas, Erasure Code needs to read a certain amount of data blocks during the process of data recovery, resulting in a large amount of overhead for I/O and network. Based on the Reed-Solomon (rs) algorithm, we propose a novel Completely Local Repairable Code (CLRC) algorithm. By grouping rs coded blocks and generating local check blocks, CLRC algorithm can optimize the locality of the rs algorithm, which can reduce the cost of data recovery. Evaluations show that the CLRC algorithm can reduce the bandwidth and I/O consumption during the process of data recovery when a single block is damaged. What's more, the cost of decoding time is only 59% of the rs algorithm.
In this work the Pulse Shape Analysis has been used to improve the time resolution of High Purity Germanium (HPGe) detectors. A set of time aligned signals was acquired in a coincidence measurement using a coaxial HPG...
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In this work the Pulse Shape Analysis has been used to improve the time resolution of High Purity Germanium (HPGe) detectors. A set of time aligned signals was acquired in a coincidence measurement using a coaxial HPGe and a cerium-doped lanthanum chloride (LaGl(3):Ce) scintillation detector. The analysis using a Constant Fraction Discriminator (CFD) time output versus the HPGe signal shape shows that time resolution ranges from 2 to 12 ns depending on the slope in the initial part of the signal. An optimization procedure of the CFD parameters gives the same final time resolution (8 ns) as the one achieved after a correction of the CFD output based on the current pulse maximum position. Finally, an algorithm based on Pulse Shape Analysis was applied to the experimental data and a time resolution between 3 and 4 ns was obtained, corresponding to a 50% improvement as compared with that given by standard CFDs. (C) 2010 Elsevier B.V. All rights reserved.
Dissolved gas in transformer oil is an important parameter to analyze the operating condition of transformers. Dissolved gas analysis (DGA) is also a commonly used method for transformer fault diagnosis. Compared to t...
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ISBN:
(纸本)9781665490542
Dissolved gas in transformer oil is an important parameter to analyze the operating condition of transformers. Dissolved gas analysis (DGA) is also a commonly used method for transformer fault diagnosis. Compared to the traditional IEC Ratios, Rogers, Duval Triangle and Pentagon methods, the artificial intelligence algorithms improve the efficiency of transformer fault diagnosis, and also reduce the requirements and reliance on application experience. In order to explore more feature information in the DGA data and the accuracy of diagnosis results, a transformer fault diagnosis model based on random search and classification regression tree is built in the paper. Based on the interrelationship of the dissolved gases, the paper expands the number of features of DGA. In addition, the random search algorithm is used to realize the parameter optimization of CART model so as to improve the accuracy of fault diagnosis results. Based on the collected DGA sample dataset in the paper, the improvement effect of the rs algorithm on the CART model is verified and discussed. It is found that the median accuracy rate exceeds 92.3% for the power transformer diagnosis, demonstrating the effectiveness of the proposed technique.
An improved method of detecting least significant bit (LSB) steganography was proposed on the basis of regular-singular (rs) algorithm, which is an effective method for detecting LSB embedding in digital images. With ...
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An improved method of detecting least significant bit (LSB) steganography was proposed on the basis of regular-singular (rs) algorithm, which is an effective method for detecting LSB embedding in digital images. With experiments and theoretical analysis, the initial bias of the cover image turned out to be the main cause of the estimated error of rs algorithm. And based on such analysis, the fitting model was improved to better fit the actual rs curves and therefore increased the estimated accuracy. Simulation results showed that the improved method was more accurate and less time-consuming than some other existing algorithms. This reliable method can be applied to fast detect LSB steganography in digital images.
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