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Optimal Intermediate Pressure Investigation in a CO2 Transcritical distributed compression Refrigeration Cycle

INTERNATIONAL JOURNAL OF REFRIGERATION 2025年 169卷 405-417页

作者： Nie, Junrui Ma, Guoyuan Wang, Lei Beijing Univ Technol Dept Refrigerat & Cryogen Engn Beijing 100124 Peoples R China

The CO2 transcritical distributed compression cycle proposal provides a novel approach for refrigerant cooling in traditional transcritical cycles. This paper employed an exhaustive search method to derive the correlation formula for the optimal intermediate pressure. From the perspectives of thermodynamic performance and economics, a comparative analysis was conducted between the cycle established under the optimal intermediate pressure obtained in this study, the cycle based on equal compression ratios in traditional two-stage compression cycles, and the cycle established using the optimal secondary compression ratio method based on low-pressure stage discharge pressure mentioned in previous literature study. The research results indicate that the system's COP calculated using the obtained optimal intermediate pressure correlation method can be improved by up to 7.26% and 5.32%, respectively, compared to the traditional and literature-based methods. The exergy loss with the optimal intermediate pressure method is less than with the other two methods. The entransy dissipation rate of the system obtained using the optimal intermediate pressure correlation method is 24.61% and 50.14% lower than that of the traditional and literature-based methods, respectively. The investment cost of the main components using the optimal intermediate pressure correlation method is about 5% higher than that of the traditional method and about 1% higher than that of the optimal pressure ratio method. However, the total annual cost rate of the system is the lowest. The research enriches and improves the theory of the CO2 transcritical distributed compression cycle, thereby facilitating the advancement of practical applications based on this cycle theory.

关键词： distributed compression Transcritical cycle Optimal intermediate pressure Economic analysis CO2

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distributed compression for Condition Monitoring of Wind Farms

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2013年第1期4卷 174-181页

作者： Stankovic, Vladimir Stankovic, Lina Wang, Shuang Cheng, Samuel Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland Univ Oklahoma Sch Elect & Comp Engn Tulsa OK 74135 USA

A good understanding of individual and collective wind farm operation is necessary for improving the overall performance of the wind farm "grid," as well as estimating in real time the amount of energy that can be generated for effectively managing demand and supply over the smart grid. This paper proposes a scheme for compressing wind speed measurements exploiting both temporal and spatial correlation between the readings via distributed source coding. The proposed scheme relies on a correlation model based on true measurements. Two compression schemes are proposed, both of low encoding complexity, as well as a particle-filtering-based belief propagation decoder that adaptively estimates the nonstationary noise of the correlation model. Simulation results using realistic models show significant performance improvements compared to the scheme that does not dynamically refine correlation.

关键词： Adaptive decoding distributed compression distributed source coding wind farms

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distributed compression of Graphical Data

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第3期68卷 1412-1439页

作者： Delgosha, Payam Anantharam, Venkat Univ Illinois Dept Comp Sci Urbana IL 61801 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

In contrast to time series, graphical data is data indexed by the vertices and edges of a graph. Modern applications such as the internet, social networks, genomics and proteomics generate graphical data, often at large scale. The large scale argues for the need to compress such data for storage and subsequent processing. Since this data might have several components available in different locations, it is also important to study distributed compression of graphical data. In this paper, we derive a rate region for this problem which is a counterpart of the Slepian-Wolf theorem. We characterize the rate region when the statistical description of the distributed graphical data can be modeled as being one of two types - as a member of a sequence of marked sparse Erdos-Renyi ensembles or as a member of a sequence of marked configuration model ensembles. Our results are in terms of a generalization of the notion of entropy introduced by Bordenave and Caputo in the study of local weak limits of sparse graphs. Furthermore, we give a generalization of this result for Erdos-Renyi and configuration model ensembles with more than two sources.

关键词： Entropy distributed databases Time series analysis Data models US Government Social networking (online) Random variables distributed compression source coding graph compression

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distributed compression FOR CONDITION MONITORING OF WIND FARMS

DISTRIBUTED COMPRESSION FOR CONDITION MONITORING OF WIND FAR...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Wang, Shuang Cheng, Samuel Stankovic, Vladimir Stankovic, Lina Univ Oklahoma Sch Elect & Comp Engn Tulsa OK 74135 USA Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XQ Lanark Scotland

ISBN: (纸本)9781467300469

In order to estimate the amount of energy that will be generated by a wind farm and provide efficient power distribution planning, it is necessary to deliver information of wind speed at all wind turbines. This paper proposes a scheme for compressing wind speed measurements exploiting both temporal and spatial correlation between the turbine readings via distributed source coding. The proposed scheme relies on a correlation model based on true measurements. A compression scheme proposed is of low encoding complexity and uses a particle-filtering based belief propagation decoder that adaptively estimates the nonstationary noise of the correlation model. Simulation results using realistic models show significant performance improvements compared to the scheme that does not dynamically refine correlation.

关键词： Wind Farms distributed compression Adaptive Decoding

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distributed compression of Linear Functions: Partial Sum-Rate Tightness and Gap to Optimal Sum-Rate

Distributed Compression of Linear Functions: Partial Sum-Rat...

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IEEE International Symposium on Information Theory (ISIT)

作者： Yang, Yang Xiong, Zixiang Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

ISBN: (纸本)9781457705953

We consider the problem of distributed compression of the difference Z = Y-1 - cY(2) of two jointly Gaussian sources Y-1 and Y-2 (with positive correlation coefficient rho and positive c) under an MSE distortion constraint D on Z. The rate region for this problem is unknown. We provide a new lower bound on the minimum sum-rate by utilizing the connection of the above problem with the two-terminal source coding problem with matrix-distortion constraint. Our lower bound not only improves existing bounds in many cases, but also allows us to prove sum-rate tightness of the Berger-Tung scheme when c is either relatively small or large and D is larger than some threshold. Furthermore, our lower bound enables us to show that the improved lattice-based scheme recently introduced in [1] (with the smallest achievable sum-rate) performs within 1.18 b/s from the optimal sum-rate for all values of rho, c, and D.

关键词： Berger-ÎRing scheme Correlation Covariance matrix Gaussian sources MSE distortion constraint Optimization Quantization Source coding Upper bound data compression distributed compression improved lattice-based scheme linear functions lower bound matrix algebra matrix-distortion constraint mean square error methods minimum sum-rate optimal sum-rate source coding sum-rate tightness two-terminal source coding problem

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distributed compression and the Blind Zone Recognition 12

Distributed Compression and the Blind Zone Recognition

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12th International Wireless Communications & Mobile Computing Conference (IWCMC)

作者： Wu, Shaochuan Pan, Siqi Ma, Kangjian Wei, Yuming Harbin Inst Technol Sch Elect & Informat Engn Harbin Peoples R China

ISBN: (纸本)9781509003044

Wireless sensor network (WSN) technology provides support infrastructure to solve blind zone coverage. Previous studies mainly focus on deployment and management of sensor nodes for enhancing signal coverage on a specific zone. But few of them are on coordinating several nodes to recognize blind zone. Moreover, it is a great challenge how to extract, transmit and process information energy efficiently in a decentralized way. In this paper, we propose a distributed algorithm for convenient and efficient blind zone recognition based on the early work on the unbiased broadcast gossip algorithm (UBGA) and the compressed sensing. Using UBGA, a faster consensus algorithm, the system can simultaneously compute the compressed data based on compressed sensing technique and disseminate them throughout the network. From these compressed data one can reconstruct the original data with approximate accuracy by querying any sensor values using the gradient projection strategy. Simulation results illustrate that our solution, proposed in this paper, can effectively recognize blind zone.

关键词： distributed compression Blind Zone Recognition Gossip Algorithm

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Robust distributed compression of Symmetrically Correlated Gaussian Sources

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IEEE TRANSACTIONS ON COMMUNICATIONS 2019年第3期67卷 2343-2354页

作者： Wang, Yizhong Xie, Li Zhang, Xuan Chen, Jun Tianjin Univ Sci & Technol Coll Elect Informat & Automat Tianjin 300222 Peoples R China Shanghai Acad Spaceflight Technol Inst Aerosp Syst Engn Shanghai Dept Elect Syst Launch Vehicle Shanghai 201109 Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada

Consider a lossy compression system with l-distributed encoders and a centralized decoder. Each encoder compresses its observed source and forwards the compressed data to the decoder for joint reconstruction of the target signals under the mean-squared-error distortion constraint. It is assumed that the observed sources can be expressed as the sum of the target signals and the corruptive noises, which are generated independently from two symmetric multivariate Gaussian distributions. Depending on the parameters of such distributions, the rate-distortion limit of this system is characterized either completely or at least for sufficiently low distortions. The results are further extended to the robust distributed compression setting, where the outputs of a subset of encoders may also be used to produce a non-trivial reconstruction of the corresponding target signals. In particular, we obtain in the high-resolution regime a precise characterization of the minimum achievable reconstruction distortion based on the outputs of k + 1 or more encoders when every k out of all l encoders are operated collectively in the same mode that is greedy in the sense of minimizing the distortion incurred by the reconstruction of the corresponding k target signals with respect to the average rate of these k encoders.

关键词： distributed compression Gaussian source Karush-Kuhn-Tucker conditions mean squared error rate-distortion

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Signal-processing-aided distributed compression in virtual MIMO-Based wireless sensor networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2007年第5期56卷 2630-2640页

作者： Jayaweera, Sudharman K. Chebolu, Madhavi L. Donapati, Rakesh K. Wichita State Univ Dept Elect & Comp Engn Wichita KS 67260 USA Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Wichita State Univ Dept Elect & Comp Engn Wichita KS 67260 USA

An adaptive signal-processing-aided distributed source coding scheme for virtual multiple-input-multiple-output communication-based wireless sensor networks (WSNs) is proposed. A computationally inexpensive distributed compression scheme that exploits the spatiotemporal correlations of sensor data is implemented with the aid of a recursive least squares (RLS)-based adaptive correlation tracking algorithm. The tracked correlation is used to compute side information that assists in distributed source compression. The proposed virtual space-time block coding and RLS-based compression side information are shown to improve energy efficiency at distributed nodes compared to previously proposed schemes With single-input-single-output communication. A semi-analytical approach is developed for energy efficiency analysis over different channel conditions and transmission distances. The energy efficiency performance of the proposed design is evaluated on real WSN data. The results show that the proposed integrated system outperforms conventional designs beyond certain transmission distance thresholds and leads to lower decoding errors, which makes it a good candidate for energy-aware WSNs.

关键词： adaptive signal processing distributed compression energy efficiency virtual multiple-input-multiple-output (V-MIMO) wireless sensor networks (WSNs)

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On distributed compression of Linear Functions

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IEEE TRANSACTIONS ON INFORMATION THEORY 2011年第1期57卷 79-94页

作者： Wagner, Aaron B. Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

distributed compression of a pair of Gaussian sources in which the goal is to reproduce a linear function of the sources at the decoder is considered. It has recently been noted that lattice codes can provide improved compression rates for this problem compared to conventional, unstructured codes. It is first shown that the state-of-the-art lattice scheme can be improved by including an additional linear binning stage. An outer bound on the rate-distortion region and a separate lower bound on the optimal sum rate are then established. The outer bound implies that for the special case of communicating the difference of two positively correlated Gaussian sources, the unimproved lattice scheme achieves within one bit of the rate region at any distortion level. The sum rate lower bound implies that unstructured codes achieve within one bit of the optimal sum rate whenever the weights of the two sources in the linear combination differ by more than a factor of two.

关键词： distributed compression Gaussian source lattice coding lossy compression rate-distortion rate region vector quantization

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Learning Progressive distributed compression Strategies From Local Channel State Information

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2022年第3期16卷 573-584页

作者： Sohrabi, Foad Jiang, Tao Yu, Wei Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

This paper proposes a deep learning framework to design distributed compression strategies in which distributed agents need to compress high-dimensional observations of a source, then send the compressed bits via bandwidth limited links to a fusion center for source reconstruction. Further, we require the compression strategy to be progressive so that it can adapt to the varying link bandwidths between the agents and the fusion center. Moreover, to ensure scalability, we investigate strategies that depend only on the local channel state information (CSI) at each agent. Toward this end, we use a data-driven approach in which the progressive linear combination and uniform quantization strategy at each agent are trained as a function of its local CSI. To deal with the challenges of modeling the quantization operations (which always produce zero gradients in the training of neural networks), we propose a novel approach of exploiting the statistics of the batch training data to set the dynamic ranges of the uniform quantizers. Numerically, we show that the proposed distributed estimation strategy designed with only local CSI can significantly reduce the signaling overhead and can achieve a lower mean-squared error distortion for source reconstruction than state-of-the-art designs that require global CSI at comparable overall communication cost.

关键词： Quantization (signal) Estimation Training Deep learning Bandwidth Wireless communication Signal processing algorithms Deep learning distributed compression distributed estimation progressive transmission quantization

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