Real-time monitoring and analysis of distributed grids require synchro-waveform measurements, which capture almost all high-frequency disturbances and transient phenomena. However, due to limitations in high-speed mea...
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The existent problem of the weather radar data lies in its too large data which is not conducive to its storage and transmission. This paper put forward a hybrid compression algorithm of the weather radar data that co...
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ISBN:
(纸本)9781509047437
The existent problem of the weather radar data lies in its too large data which is not conducive to its storage and transmission. This paper put forward a hybrid compression algorithm of the weather radar data that consists of three steps including pre-compression, lossy compression and lossless compression. Firstly, the paper introduced the radar data's types and characteristics, analyzing the pre-compressionalgorithm;Secondly, according to the practical requirements of the diverse users, it proposed the adoption of lossless compression and lossy compressionalgorithm for the data;Finally, the flow chart of mixed compressionalgorithm was presented and verified based on the actual data.
In the paper we introduce a hybrid compression algorithm, which is the co- mbination of Huffman algorithm and RLE algorithm, for compressing the GPS data. This algorithm acquires statistical characteristics of GPS dat...
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In the paper we introduce a hybrid compression algorithm, which is the co- mbination of Huffman algorithm and RLE algorithm, for compressing the GPS data. This algorithm acquires statistical characteristics of GPS data according to the NMEA0183 protocol, mixes Huffman algorithm and RLE algorithm to compress GPS data, to improve the coding efficiency and to restrain data expansion. Huffman algori- thm has high compression rate on duplicated single-byte data while RLE algorithm has high compression rate on duplicated code segment. The flag bit is added in the process of encoding for encoding for the classification processing on GPS data in order to effectively identify the outputs of two kinds of algorithm when decoding and to ensure the complete decoding of compressed data. This improved hybrid compression algorithm is applied to local storage and 3G remote transmission of vehicle terminal GPS data, results show that the algorithm has clear improvement in compression performance of GPS data.
Effective real-time monitoring and analysis of distributed grids necessitate the use of synchro-waveform measurements, which capture almost all high-frequency disturbances and transient phenomena. However, due to limi...
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Effective real-time monitoring and analysis of distributed grids necessitate the use of synchro-waveform measurements, which capture almost all high-frequency disturbances and transient phenomena. However, due to limitations in high-speed measurements and network bandwidth, it is challenging to transfer all high-fidelity synchro-waveforms losslessly and successfully. To cope with these challenges, a hybrid-based online multi-stage compressionalgorithm is proposed to significantly improve the compression efficiency for synchro-waveform measurements. Initially, the multiple discrete Wavelet transformation is deployed to deconstruct the waveform components. The delta encoding is further developed to decrease the magnitude. In conjunction with the Lempel-Ziv-Markov chain, the hybrid compression algorithm is implemented to achieve real-time compression for the synchro-waveform measurements. Moreover, an innovative error index that synergizes the time and frequency domain error and correlation is formulated to evaluate the waveform distortion. By integrating compression ratio, suitable parameters can be optimally selected. Finally, the simulation, laboratory experiments, as well as field tests across a spectrum of sampling frequencies and time intervals are conducted to substantiate the efficacy of the proposed method. The outcomes demonstrated that a compression ratio of approximately 15.5 and 17.83 can be reached for 0.5 s and 1 s data under both offline and online scenarios, which equates to a substantial 93.5% to 94.39% reduction in data storage requirements.
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