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6th World Congress on Intelligent Control and Automation

作者： Huang, Wenjun Wang, Weimin Xu, Hui Zhejiang Univ Inst Adv Proc Control Natl Lab Ind Control Technol Yuquan Campus Hangzhou 310027 Peoples R China Zhejiang Supcon Technol Co Ltd Zhengzhou Peoples R China

ISBN: (纸本)1424403316

Real-time database applied in process industry requests the performance of mass data and high speed So the process data in database must be compressed effectively and reliably. According to process data characteristic, a lossless compression algorithm was designed based on LZW algorithm and RLE algorithm. The compression algorithm first classified process data by characteristic, and then different compression methods were designed for all kinds of data. In order to increase compression radio, pretreatment approaches were implemented before compression. The compression algorithm solved the difficulties of low compression radio and compression speed. Performance test shows that this algorithm obviously improved the real-time performance and efficiency when accessing the process database. It will be widely applied in MES and PCS.

关键词： historical data lossless compression algorithm real-time database

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A compression algorithm for Real-Time Distributed Nonlinear MPC

A Compression Algorithm for Real-Time Distributed Nonlinear ...

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European Control Conference (ECC)

作者： Quirynen, Rien Zanon, Mario Kozma, Attila Diehl, Moritz KU Leuven Univ Dept ESAT STADIUS OPTEC Kasteelpk Arenberg 10 B-3001 Leuven Belgium Univ Freiburg Dept Microsyst Engn IMTEK Georges Koehler Allee 102 D-79110 Freiburg Germany

ISBN: (纸本)9783952426937

Model Predictive Control (MPC) requires the online solution of an Optimal Control Problem (OCP) at each sampling time. Efficient online algorithms such as the Real-Time Iteration (RTI) scheme have been developed for real-time MPC implementations even for fast nonlinear dynamic systems. The RTI framework is based on direct Multiple Shooting (MS) for centralized systems. Distributed Multiple Shooting (DMS) is an MS-based OCP discretization strategy for distributed systems. Many fast dynamic systems can be described as connected subsystems and in order to exploit this structure, a DMS based RTI scheme has been developed and implemented in ACADO code generation. A novel technique called compression is proposed to reduce the dimensions of the convex subproblem, while exploiting the coupling structure. The performance of the presented scheme is illustrated on a nontrivial example from the literature, where a speedup of factor 11 in simulation time and factor 6 in the total computation time can be shown over the classical RTI scheme.

关键词： centralised control convex programming distributed control iterative methods nonlinear control systems nonlinear dynamical systems optimal control predictive control sampling methods ACADO code generation DMS based RTI scheme MS-based OCP discretization strategy centralized systems compression algorithm connected subsystems convex subproblem coupling structure direct multiple shooting distributed multiple shooting distributed systems fast nonlinear dynamic systems model predictive control online algorithms optimal control problem real-time MPC implementations real-time distributed nonlinear MPC real-time iteration sampling time Couplings Heuristic algorithms Nonlinear dynamical systems Optimal control Polynomials Real-time systems Sensitivity

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Dynamic Data compression algorithm Selection for Big Data Processing on Local File System 18

Dynamic Data Compression Algorithm Selection for Big Data Pr...

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2nd International Conference on Computer Science and Artificial Intelligence (CSAI) / 10th International Conference on Information and Multimedia Technology (ICIMT)

作者： Wang Fuzong Guo Helin Zhao Jian Shanghai Jiao Tong Univ Sch Software 800 Dongchuan Rd Shanghai Peoples R China

ISBN: (纸本)9781450366069

Data compression has become a commodity feature for space efficiency and performance by reducing reading and writing traffic and space capacity demand. This technology is particularly valuable for a file system to manage and server the big data processing tasks. However, the fixed data compression scheme cannot fit all the big data workloads and data-set which have complex internal data structure and compressibility. This paper investigates a dynamic and smart data compression algorithm selection scheme for different big data processing cases in the local file system. To this end, we propose a dynamic algorithm selection module in the Linux ZFS which is an open source file system. This module will select a high compression ratio algorithm for high compressibility data, and select a fast compression algorithm for low compressibility data, and skip all data compression process for incompressibility data. The comprehensive evaluations validate that dynamic algorithm selection module can achieve up to 2.69x response time improvement for reading and writing operation in file system and reduce about 32.12% storage space for a large amount data-set.

关键词： Data compression Big Data File system compression algorithm

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Chinese text compression algorithm based on LZW

Chinese text compression algorithm based on LZW

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International Conference on Computer Application and System Modeling

作者： Chen, Wei Xu, Dewu Fu, Minfeng Math Physics and Information Engineering College Zhejiang Normal University Jinhua China

ISBN: (纸本)9781424472369

This paper introduced an improved LZW algorithm of data compression. In view of Chinese text characteristic, in this paper, the expansion to the dictionary was carried on, and the first level database of Chinese was added, with some second level of Chinese characters space reserved to improve the dictionary memory method as well as the renewal way which was improved by experiments to the improvements of compression ratio. © 2010 IEEE.

关键词： Chinese text compression algorithm LZW compression algorithms LZW Text compression Chinese Chinese Chinese philosophy Chinese art Dictionaries as Topic compression ratio

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Recording and replaying of battle simulation situation data based on serialization and compression algorithm

Recording and replaying of battle simulation situation data ...

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2011 International Conference on System Science, Engineering Design and Manufacturing Informatization, ICSEM 2011

作者： Liu, Shuo Li, Zenghua Yang, Xuehui Battle Simulation Center Academy of Armored Forces Engineering 21st Mail Box Dujiakan Beijing 100072 China Department of Equipment Command and Administration Academy of Armored Forces Engineering 21st Mail Box Dujiakan Beijing 100072 China

ISBN: (纸本)9781457702471

Recording and replaying of situation data is critical in the battle simulation system, which is the foundation of the subsequent analysis and evaluation. First of all, the demand for recording and replaying of situation data in the simulation system was expatiated, and then the realizing methods, that is, integrated federation member and embedded programs were analyzed. The situation data object was defined according to the requirements of serialization, and the process of data recording and replaying was analyzed in detail. Moreover, the Zlib function library which supports memory compression and uncompression was referred to specifically. © 2011 IEEE.

关键词： Zlib function library battle simulation situation data compression compression algorithm data compression data recording data replay memory compression memory uncompression military computing recording replaying serialization serialization algorithm seria

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Study on compression algorithm for Long-term Continuous Driving Behavior Data Based on Fractal Interpolation

Study on Compression Algorithm for Long-term Continuous Driv...

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IEEE International Conference on Intelligent Transportation Engineering (ICITR)

作者： Zhang Liangli Yang Yi Yu Min Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9781467390484

In this paper, we explore a compression algorithm for long-term continuous data based on fractal interpolation, and apply it to driving behavior data as types of respiration rate, skin conductance and blood volume plus. On basis of affine transform and compression mapping, iteration function system parameters of continuous data regarded as fractal and their data coordinates are acquired and available to be reproduced. Then, by dividing the fractal as data set into sections with the box-counting dimension value, the best compressing ratio can be speculated gradually. Before those calculations, digital filter on those electrocardiogram data such as finite impulse response (FIR) is introduced to get general outlines of those curves, and the actual unit impulse response of FIR filter is applied. Experimental data were derived from some biological signal collecting devices worn on a male test driver while he was driving a real vehicle on highways. Partial time series curves of those data are used to test the validity of the proposal algorithm. Results show that it makes the original data be compressed at least 51 times, and make sure the normed root mean square (NRMS) error is less than 0.23%. Compared with the traditional method of data compression, the algorithm based on fractal interpolation proposed in this paper is proved to be more accurate and efficient.

关键词： compression algorithm driving behavior data fractal interpolation affine transform

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Detecting Determinism in Time Series with Complex Networks Constructed Using a compression algorithm

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INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS 2018年第13期28卷

作者： Correa, Debora C. Walker, David M. Small, Michael Univ Western Australia Dept Math & Stat Nedlands WA 6009 Australia CSIRO Mineral Resources Perth NSW 6151 Australia

The properties of complex networks derived from applying a compression algorithm to time series subject to symbolic ordinal-based encoding is explored. The information content of compression codewords can be used to detect forbidden symbolic patterns indicative of nonlinear determinism. The connectivity structure of ordinal-based compression networks summarized by their minimal cycle basis structure can also be used in tests for nonlinear determinism, in particular, detection of time irreversibility in a signal.

关键词： Chaos complex networks compression algorithm time irreversibility ordinal encoding

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ACTF: An efficient lossless compression algorithm for time series floating point data

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2024年第10期36卷

作者： Wang, Weijie Chen, Wenhui Lei, Qinhon Li, Zhe Zhao, Huihuang Hengyang Normal Univ Coll Comp Sci & Technol Hengyang 421002 Peoples R China Hunan Prov Key Lab Intelligent Informat Proc & App Hengyang 421002 Peoples R China

The volume of time series data across various fields is steadily increasing. However, this unprocessed massive data challenges transmission efficiency, computational arithmetic, and storage capacity. Therefore, the compression of time series data is essential for improving transmission, computation, and storage. Currently, improving time series floating-point coding rules is the primary method for enhancing compression algorithms efficiency and ratio. This paper presents an efficient lossless compression algorithm for time series floating point data, designed based on existing compression algorithms. We employ three optimization strategies data preprocessing, coding category expansion, and feature refinement representation to enhance the compression ratio and efficiency of compressing time-series floating-point numbers. Through experimental comparisons and validations, we demonstrate that our algorithm outperforms Chimp, Chimp128, Gorilla, and other compression algorithms across multiple datasets. The experimental results on 30 datasets show that our algorithm improves the compression ratio of time series algorithms by an average of 12.25% and compression and decompression efficiencies by an average of 27.21%. Notably, it achieves a 24.06% compression ratio improvement on the IOT1 dataset and a 42.96% compression and decompression efficiency improvement on the IOT4 dataset.

关键词： Time series floating point data Lossless compression Internet of things compression algorithm

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Cocv: A compression algorithm for time-series data with continuous constant values in IoT-based monitoring systems

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INTERNET OF THINGS 2024年 25卷

作者： Lin, Shengsheng Lin, Weiwei Wu, Keyi Wang, Songbo Xu, Minxian Wang, James Z. South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China South China Normal Univ Guangzhou 510631 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Clemson Univ Sch Comp Clemson SC USA

Sensor-generated time-series data now constitutes a significant and growing portion of the world's data due to the rapid proliferation of the Internet of Things (IoT). The transmission and storage of such voluminous data have emerged as enormous challenges. Data compression and reduction strategies have been instrumental in mitigating these challenges to some extent. However, they have exhibited limitations when applied to real-time IoT-based monitoring systems. This stems from their failure to adequately consider the stringent requirements of real-time data transmission and the continuous constant-value redundancy within periodic monitoring data. Consequently, we introduce a dedicated compression algorithm tailored specifically for time-series data within periodic IoT-based monitoring systems, namely Cocv. It takes advantage of the continuous constant-value repetition of the time-series data to compress data by discarding redundant data points. It can not only compress static batches of data but also dynamically compress data streams to improve system performance in real-time IoT-based monitoring systems. The offline Cocv outperforms traditional compressors on gas-leak monitoring data with a compression ratio of 98.5%, maintaining a decent speed for both compression and decompression. In an actual IoT-based gas-leak monitoring system, the online Cocv improves handling capacity by 255%, reading speed by 728%, reduces bandwidth consumption by 94%, and storage space consumption by 98% compared to the original scheme.

关键词： compression algorithm Internet of things Time-series data Continuous constant values Gas-leak monitoring systems

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Improved compression inference algorithm for reliability analysis of complex multistate satellite system based on multilevel Bayesian network

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2019年第0期189卷 123-142页

作者： Zheng, Xiaohu Yao, Wen Xu, Yingchun Chen, Xianqi Natl Univ Def Technol Coll Aerosp Sci & Engn 109 Deya Rd Changsha 410073 Hunan Peoples R China Chinese Acad Mil Sci Natl Innovat Inst Def Technol 53 East Main St Beijing 100071 Peoples R China

Bayesian Network (BN) is a powerful tool for analyzing system reliability. However, for the complex multistate satellite system, the state combination explosion makes the reliability analysis based on BN computationally unaffordable. To solve this problem, compression algorithm and inference algorithm have been proposed. The original compression algorithm classifies all the phrases with same composition into the same type. The original compression algorithm classifies all the phrases with same composition into the same type. However, cases tested in this paper show that the results of inference can be incorrect when some node probability tables have special formulations. To solve the above defects, the compression algorithm and sequential inference algorithm are improved in this paper. Based on improved compression algorithm and improved inference algorithm, the improved compression inference algorithm (ICIA) is formed and extended to the application of multistate nodes with independent binary parent nodes. Besides, the multilevel BN (MBN) models are used to construct the BN models of complex multistate satellite systems so as to further improve inference efficiency. Cases tested in this paper show that the proposed algorithms are more efficient than the Bayes Net Toolbox and the AgenaRisk software for the reliability analysis of complex multistate satellite systems.

关键词： compression algorithm Reliability analysis Multistate satellite system Bayesian network

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