Universal Compressor Research Articles

Process model design for positive displacement compressors and their experimental validation: Comparison of Optimal Experimental Design and Machine Learning

Valid simulation models play a critical role in enhancing efficiency in development processes and minimizing experimental efforts. As a result, ensuring accurate predictions through model discrimination, Model Calibration, and Validation (MoCaVal) has become increasingly important and is a necessary step to analyze the behavior and evaluate the effectiveness of systems during their initial design stages. Additionally, to achieve the climate objectives, it is imperative that heat pumps predominantly perform the building sector’s heating. The effectiveness of heat pumps hinges on the performance of the compressor. Therefore, there is a requirement for valid compressor models. However, currently, there is no universal compressor model that applies to all refrigerants and designs. Therefore, every refrigerant or design modification requires the development or adaptation of models. Since creating new models is a time-consuming procedure, automated techniques are advantageous.This paper compares two automated model development methods, Optimal Experimental Design (OED) and Machine Learning (ML), to create valid simulation models within the MoCaVal framework. OED is used to calibrate existing simulation models, while ML is employed to develop new models. Our comparison of the two methods focuses on a fixed-speed scroll compressor of 4 kW nominal power, using R410A and featuring 51 measurement points. Using the Full Factorial Plan (FFP) and the D-Optimal Experimental Design (D-OED), predefined models for mass flow rate, electrical power, isentropic and volumetric efficiency were calibrated. Employing the FFP, Machine Learning (ML) was also applied through support vector regression.The OED reduces experimental effort by 75–90 % compared to the FFP while only slightly increasing the average uncertainty by 0.30–0.79 %. For the FFP, MoCaVal achieved uncertainties of 0.52–3.9 % for calibrated models and 0.34–0.56 % with ML models. Therefore, within the design space, ML outperforms calibrated models based on the FFP and OED regarding uncertainty. However, since machine learning models were trained on the FFP, additional research is needed to demonstrate their potential for reducing test time and extrapolating results.

GLEAN: Generalized-Deduplication-Enabled Approximate Edge Analytics

The Internet of Things (IoT) has brought about exponential growth in sensor data. This has led to increasing demands for efficient and novel data transmission, storage, and analytics solutions for sustainable IoT ecosystems. It has been shown that the generalized deduplication (GD) compression algorithm offers not only competitive compression ratio and throughput but also random access properties that enable direct analytics of compressed data. In this article, we thoroughly stress test existing methods for direct analytics of GD compressed data with a diverse collection of 103 data sets, identify the need to optimize GD for analytics, and develop a new version of GD to this end. We also propose the generalized deduplication-enabled approximate edge analytics (GLEAN) framework. This framework applies the aforementioned analytics techniques at the Edge server to deliver end-to-end lossless data compression and high-quality Edge analytics in the IoT, thereby addressing challenges related to data transmission, storage, and analytics. Impressive analytics performance was achieved using this framework, with a median increase in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering error of just 2% relative to analytics performed on uncompressed data, while running <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.5\times $ </tex-math></inline-formula> faster and requiring <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.9\times $ </tex-math></inline-formula> less storage at the Edge server compared to universal compressors.

Survey of Grammar-Based Data Structure Compression

A data string can be represented with the help of a context-free grammar such that the string is the unique string belonging to the language of the grammar. One can then losslessly compress the string indirectly by encoding the grammar into a unique binary codeword. This approach to data compression, called grammar-based data compression, can also be employed to losslessly compress graphical data structures, which are graphs in which every vertex carries a data label. Under mild restrictions, grammar-based data compression schemes are universal compressors, meaning that they perform at least as well as any finite-state compression scheme. Some of the theory of universal grammar-based compressors is surveyed. Applications of grammar-based compressors to various areas such as bioinformatics and data networks are discussed. Future directions for grammar-based compression research are outlined, including compression issues arising in highly repetitive databases and issues concerning the compression of sparse graphical data.

АЛГОРИТМ ПОТОКОВОЙ КОМПРЕССИИ ДАННЫХ С ПЛАВАЮЩЕЙ ЗАПЯТОЙ В ИНФОРМАЦИОННЫХ СИСТЕМАХ ОБЕСПЕЧЕНИЯ НАУЧНЫХ ЭКСПЕРИМЕНТОВ

The paper presents an original algorithm and its implementation for single pass real-timecompression of streams of numeric floating-point data. The purpose of the research is to developand formalize a single-pass algorithm of stream floating-point data compression in order to increaseperformance of both encoding and decoding, because a use of existing implementationsprovides insufficient speed of compression, are too restrictive on hardware resources and limitedin applicability to real-time stream data compression when it comes to floating-point data.For that, the following issues have been addressed. The developed mathematical model and thealgorithm for compression of scalar floating-point data are described together with results of experimentalresearch of the compression method applied to single-dimensional and twodimensionalscientific data. The model is based upon the commonly-used binary_64 representation,of the IEEE-754 standard, onto which extended real-line values are mapped. The algorithmcan be implemented as part of high-performance distributed systems in which performance ofinput-output operations, as well as internetwork communication, are critical to overall efficiency.The performance and applicability of the algorithm in data stream compression result from itssingle-pass behaviour, relatively low requirements to a priori known and statically defined size ofmemory required to implement history of compression, which the predictor, used in compressionand decompression, is based on. Indeed, the measured compression ratios are comparable to oneswhich result from using more resource-intensive universal coders but providing significantly lowerlatency. Provided synchronization of parameters of both compressor and decompressor applied toa stream of vector values and assuming a correlation between absolute values of scalars of thesame dimension within the vectors, further improvement of the predictor performance can be attainedby means of SIMD-class parallelism which, in turn, is beneficial for overall performance ofcompression and decompression, provided that the underlying hardware is capable of addressingrandom-access memory based on offsets in a vector register, such as by employment of theVGATHER class instructions of Intel processors. In order to reduce the bottlenecks associatedwith input-output, an implementation of the algorithm is employed by the authors as part of acomputing system used for parallel simulation of wave fields which is distributed via a network.The experiments described in the paper demonstrate significant performance increase of the proposedcoder compared to well-known universal compressors, RAR, ZIP and 7Z, while the achievedcompression factors remain comparable.

A Modified Three-Dimensional Negative-Poisson-Ratio Metal Metamaterial Lattice Structure.

Mechanical metamaterials are of interest to researchers because of their unique mechanical properties, including a negative Poisson structure. Here, we study a three-dimensional (3D) negative-Poisson-ratio (NPR) metal metamaterial lattice structure by adding a star structure to the traditional 3D concave structure, thus designing three different angles with a modified NPR structure and control structure. We further study the mechanical properties via finite element numerical simulations and show that the stability and stiffness of the modified structures are improved relative to the control structure; the stability decreases with increasing star body angle. The star angle has the best relative energy absorption effect at 70.9°. The experimental model is made by selective laser melting (SLM) technology (3D printing), and the compression experiment verification used an MTS universal compressor. The experimental results are consistent with the changing trend in finite element simulation.

RDF-TR: Exploiting structural redundancies to boost RDF compression

The number and volume of semantic data have grown impressively over the last decade, promoting compression as an essential tool for RDF preservation, sharing and management. In contrast to universal compressors, RDF compression techniques are able to detect and exploit specific forms of redundancy in RDF data. Thus, state-of-the-art RDF compressors excel at exploiting syntactic and semantic redundancies, i.e., repetitions in the serialization format and information that can be inferred implicitly. However, little attention has been paid to the existence of structural patterns within the RDF dataset; i.e. structural redundancy. In this paper, we analyze structural regularities in real-world datasets, and show three schema-based sources of redundancies that underpin the schema-relaxed nature of RDF. Then, we propose RDF-Tr(RDF Triples Reorganizer), a preprocessing technique that discovers and removes this kind of redundancy before the RDF dataset is effectively compressed. In particular, RDF-Tr groups subjects that are described by the same predicates, and locally re-codes the objects related to these predicates. Finally, we integrate RDF-Tr with two RDF compressors, HDT and k2-triples. Our experiments show that using RDF-Tr with these compressors improves by up to 2.3 times their original effectiveness, outperforming the most prominent state-of-the-art techniques.

Implementation of CCSDS Standards for Lossless Multispectral and Hyperspectral Satellite Image Compression

This paper presents the modeling, design, and implementation of two intellectual property (IP) cores that are compliant with the consultative committee for space data systems (CCSDS) 121.0-B-2 and CCSDS 123.0-B-1 lossless satellite image compression standards. The CCSDS 121.0-B-2 describes a lossless universal compressor based on a Rice adaptive encoding. The CCSDS 123.0-B-1 standard describes a lossless algorithm specifically designed for efficient on-board compression of hyperspectral and multispectral images, and it is based on a prediction and entropy-based encoding structure. Two options are offered for the latter: the sample-adaptive and the block-adaptive encoder, which corresponds to the CCSDS 121.0-B-2 algorithm. These IP cores have been designed as independent compressors, but they can be easily combined in a plug-and-play fashion to be used together thanks to a dedicated interface. Additionally, standard interfaces are provided for configuration and external memory access. The design process encompasses the consideration of several different hardware architectures in order to maximize throughput and optimize the requirements of on-board resources at the same time. Both IPs are compliant with the high degree of configurability considered in the standard. The obtained VHDL code is completely technology independent, so it can be used to target any field-programmable gate array (FPGA) or ASIC of interest in the space environment, aiming to perform efficiently compression in satellites despite the inherent constraints of these devices. Results are reported as implementation results for the FPGA devices that are currently considered most representative and suited for on-board use from Xilinx and Microsemi families. In particular, the CCSDS123 IP core reaches for the Virtex5 FX130 a throughput of 153.5 Msamples per second, comprising only 5% of the look-up table (LUTs) in the device.

Lossless compression of color-mapped images

In a color-mapped (pseudo-color) image, pixel values repre- sent indices that point to color values in a look-up table. Well-known linear predictive schemes, such as JPEG and CALIC, perform poorly when used with pseudo-color images, while universal compressors, such as Gzip, Pkzip and Compress, yield better compression gain. Recently, Burrows and Wheeler introduced the Block Sorting Lossless Data Com- pression Algorithm (BWA). The BWA algorithm received considerable attention. It achieves compression rates as good as context-based meth- ods, such as PPM, but at execution speeds closer to Ziv-Lempel tech- niques. The BWA algorithm is mainly composed of a block-sorting trans- formation which is known as Burrows-Wheeler Transformation (BWT), followed by Move-To-Front (MTF) coding. We introduce a new block transformation, Linear Order Transformation (LOT). We delineate its re- lationship to Burrows-Wheeler Transformation and show that LOT is faster than BWT transformation. We then show that when MTF coder is employed after the LOT, the compression gain obtained is better than the well-known compression techniques, such as GIF, JPEG, CALIC, Gzip, LZW (Unix Compress) and the BWA for pseudo-color images.