Storing natural gas in wet active carbon is a recently proposed method. The research progress shows that this method considerably decreases the storage pressure while maintaining the storage capacity equal to or even ...Storing natural gas in wet active carbon is a recently proposed method. The research progress shows that this method considerably decreases the storage pressure while maintaining the storage capacity equal to or even higher than compressed natural gas （CNG）. There is no requirement of pre-sifting any component out of natural gas for the storage, and the thermal effect on fast charging/discharging has almost no effect on the storage capacity. The charging and discharging processes are reversible and show good dynamic behavior. Although the storage temperature is a little lower than the ambient, the new method seems technically and costly more competitive than the available methods.展开更多
Modem storage systems incorporate data compressors to improve their performance and capacity. As a result, data content can significantly influence the result of a storage system benchmark. Because real-world propriet...Modem storage systems incorporate data compressors to improve their performance and capacity. As a result, data content can significantly influence the result of a storage system benchmark. Because real-world proprietary datasets are too large to be copied onto a test storage system, and most data cannot be shared due to privacy issues, a benchmark needs to generate data synthetically. To ensure that the result is accurate, it is necessary to generate data content based on the characterization of real-world data properties that influence the storage system performance during the execution of a benchmark. The existing approach, called SDGen, cannot guarantee that the benchmark result is accurate in storage systems that have built-in word-based compressors. The reason is that SDGen characterizes the properties that influence compression performance only at the byte level, and no properties are characterized at the word level. To address this problem, we present TextGen, a realistic text data content generation method for modem storage system benchmarks. TextGen builds the word corpus by segmenting real-world text datasets, and creates a word-frequency distribution by counting each word in the corpus. To improve data generation performance, the word-frequency distribution is fitted to a lognormal distribution by maximum likelihood estimation. The Monte Carlo approach is used to generate synthetic data. The running time of TextGen generation depends only on the expected data size, which means that the time complexity of TextGen is O（n）. To evaluate TextGen, four real-world datasets were used to perform an experiment. The experimental results show that, compared with SDGen, the compression performance and compression ratio of the datasets generated by TextGen deviate less from real-world datasets when end-tagged dense code, a representative of word-based compressors, is evaluated.展开更多
基金Presented at the International Conference on Carbon 2005 (Korea) as an invited keynote lecture, and supported by the National Natural Science Foundation of China (No. 20336020, No. 50376047).
文摘Storing natural gas in wet active carbon is a recently proposed method. The research progress shows that this method considerably decreases the storage pressure while maintaining the storage capacity equal to or even higher than compressed natural gas （CNG）. There is no requirement of pre-sifting any component out of natural gas for the storage, and the thermal effect on fast charging/discharging has almost no effect on the storage capacity. The charging and discharging processes are reversible and show good dynamic behavior. Although the storage temperature is a little lower than the ambient, the new method seems technically and costly more competitive than the available methods.
基金Project supported by the National Natural Science Foundation of China （Nos. 61572394 and 61272098）, the Shenzhen Funda mental Research Plan （Nos. JCYJ20120615101127404 and JSGG20140519141854753）, and thc National Kcy Technologies R＆D Program of China （No. 2011BAH04B03）
文摘Modem storage systems incorporate data compressors to improve their performance and capacity. As a result, data content can significantly influence the result of a storage system benchmark. Because real-world proprietary datasets are too large to be copied onto a test storage system, and most data cannot be shared due to privacy issues, a benchmark needs to generate data synthetically. To ensure that the result is accurate, it is necessary to generate data content based on the characterization of real-world data properties that influence the storage system performance during the execution of a benchmark. The existing approach, called SDGen, cannot guarantee that the benchmark result is accurate in storage systems that have built-in word-based compressors. The reason is that SDGen characterizes the properties that influence compression performance only at the byte level, and no properties are characterized at the word level. To address this problem, we present TextGen, a realistic text data content generation method for modem storage system benchmarks. TextGen builds the word corpus by segmenting real-world text datasets, and creates a word-frequency distribution by counting each word in the corpus. To improve data generation performance, the word-frequency distribution is fitted to a lognormal distribution by maximum likelihood estimation. The Monte Carlo approach is used to generate synthetic data. The running time of TextGen generation depends only on the expected data size, which means that the time complexity of TextGen is O（n）. To evaluate TextGen, four real-world datasets were used to perform an experiment. The experimental results show that, compared with SDGen, the compression performance and compression ratio of the datasets generated by TextGen deviate less from real-world datasets when end-tagged dense code, a representative of word-based compressors, is evaluated.
