Theoretical analysis of low GWP mixture R600a/R1234ze as a possible alternative to R600a in domestic refrigerators

In this study, a thermodynamic analysis of R600a and R600a/R134ze mixture at three compositions of 0%, 20% and 50% R1234ze is measured in a domestic refrigerator. The main purpose of this study is to theoretically verify the possibility of applying the mixture R600a/R1234ze in large capacity refrigerator. The performance has been assessed for different condensing temperatures between 30 and 50? with constant -20? evaporating temperature .The performance of the refrigerator was compared in terms of volumetric cooling capacity, COP (coefficient of performance), compression ratio and compressor discharge temperature. The results show that the volumetric cooling capacity, COP, compressor power consumption and compressor discharge temperature of R600a/R1234ze mixture are similar to those of pure R600a,so that R600a compressor can be used for R600a/R1234ze mixture without any modifications. The amount charge of the mixture R600a/R1234ze is slight lower than that of R600a in the same equipment. Flammability decreases in R600a/R1234ze mixtures with increasing fractions of R1234ze. This is an desirable characteristic because of the large charge requirement of large refrigeration systems

Low-PMEPR Preamble Sequence Design for Dynamic Spectrum Allocation in OFDMA Systems

Orthogonal Frequency Division Multiple Access (OFDMA) with Dynamic spectrum allocation (DSA) is able to provide a wide range of data rate requirements. This paper is focused on the design of preamble sequences in OFDMA systems with low peak-to-mean envelope power ratio (PMEPR) property in the context of DSA. We propose a systematic preamble sequence design which gives rise to low PMEPR for possibly non-contiguous spectrum allocations. With the aid of Golay-Davis-Jedwab (GDJ) sequences, two classes of preamble sequences are presented. We prove that their PMEPRs are upper bounded by 4 for any DSA over a chunk of four contiguous resource blocks

High-Resolution Deep Image Matting

Image matting is a key technique for image and video editing and composition. Conventionally, deep learning approaches take the whole input image and an associated trimap to infer the alpha matte using convolutional neural networks. Such approaches set state-of-the-arts in image matting; however, they may fail in real-world matting applications due to hardware limitations, since real-world input images for matting are mostly of very high resolution. In this paper, we propose HDMatt, a first deep learning based image matting approach for high-resolution inputs. More concretely, HDMatt runs matting in a patch-based crop-and-stitch manner for high-resolution inputs with a novel module design to address the contextual dependency and consistency issues between different patches. Compared with vanilla patch-based inference which computes each patch independently, we explicitly model the cross-patch contextual dependency with a newly-proposed Cross-Patch Contextual module (CPC) guided by the given trimap. Extensive experiments demonstrate the effectiveness of the proposed method and its necessity for high-resolution inputs. Our HDMatt approach also sets new state-of-the-art performance on Adobe Image Matting and AlphaMatting benchmarks and produce impressive visual results on more real-world high-resolution images.Comment: AAAI 202

Communication-Efficient Cluster Federated Learning in Large-scale Peer-to-Peer Networks

A traditional federated learning (FL) allows clients to collaboratively train a global model under the coordination of a central server, which sparks great interests in exploiting the private data distributed on clients. However, once the central server suffers from a single point of failure, it will lead to system crash. In addition, FL usually involves a large number of clients, which requires expensive communication costs. These challenges inspire a communication-efficient design of decentralized FL. In this paper, we propose an efficient and privacy-preserving global model training protocol in the context of FL in large-scale peer-to-peer networks, CFL. The proposed CFL protocol aggregates local contributions hierarchically by a cluster-based aggregation mode, as well as a leverged authenticated encryption scheme to ensure the security communication, whose key is distributed by a modified secure communication key establishment protocol. Theoretical analyses show that CFL guarantees the privacy of local model update parameters, as well as integrity and authenticity under the widespread internal semi-honest and external malicious threat models. In particular, the proposed key revocation based on public voting can effectively defense against external adversaries hijacking honest participants to ensure the confidentiality of the communication keys. Moreover, the modified secure communication key establishment protocol indeed achieves high network connectivity probability to ensure transmission security of the system

A Benchmark for Structured Extractions from Complex Documents

Understanding visually-rich business documents to extract structured data and automate business workflows has been receiving attention both in academia and industry. Although recent multi-modal language models have achieved impressive results, we find that existing benchmarks do not reflect the complexity of real documents seen in industry. In this work, we identify the desiderata for a more comprehensive benchmark and propose one we call Visually Rich Document Understanding (VRDU). VRDU contains two datasets that represent several challenges: rich schema including diverse data types as well as nested entities, complex templates including tables and multi-column layouts, and diversity of different layouts (templates) within a single document type. We design few-shot and conventional experiment settings along with a carefully designed matching algorithm to evaluate extraction results. We report the performance of strong baselines and three observations: (1) generalizing to new document templates is very challenging, (2) few-shot performance has a lot of headroom, and (3) models struggle with nested fields such as line-items in an invoice. We plan to open source the benchmark and the evaluation toolkit. We hope this helps the community make progress on these challenging tasks in extracting structured data from visually rich documents

Sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit

In this paper, we use the block orthogonal matching pursuit (BOMP) algorithm to recover block sparse signals x from measurements y = Ax + v, where v is an ℓ2-bounded noise vector (i.e., kvk2 ≤ ǫ for some constant ǫ). We investigate some sufficient conditions based on the block restricted isometry property (block-RIP) for exact (when v = 0) and stable (when v , 0) recovery of block sparse signals x. First, on the one hand, we show that if A satisfies the block-RIP with δK+1 1 and √2/2 ≤ δ < 1, the recovery of x may fail in K iterations for a sensingmatrix A which satisfies the block-RIP with δK+1 = δ. Finally, we study some sufficient conditions for partial recovery of block sparse signals. Specifically, if A satisfies the block-RIP with δK+1 < √2/2, then BOMP is guaranteed to recover some blocks of x if these blocks satisfy a sufficient condition. We further show that this condition is also sharp

Numerical Simulation of the Rock SHPB Test with a Special Shape Striker Based on the Discrete Element Method

A split Hopkinson pressure bar (SHPB) system with a special shape striker has been suggested as the test method by the International Society for Rock Mechanics (ISRM) to determine the dynamic characteristics of rock materials. In order to further verify this testing technique and microscopically reveal the dynamic responses of specimens in SHPB tests, a numerical SHPB test system was established based on particle flow code (PFC). Numerical dynamic tests under different impact velocities were conducted. Investigation of the stresses at the ends of a specimen showed that the specimen could reach stress equilibrium after several wave reverberations, and this balance could be maintained well for a certain time period after the peak stress. In addition, analyses of the reflected waves showed that there was a clear relationship between the variation of the reflected wave and the stress equilibrium state in the specimen, and the turning point of the reflected wave corresponded well with the peak stress in the specimen. Furthermore, the reflected waves can be classified into three types according to their patterns. Under certain impact velocities, the specimen deforms at a constant strain rate during the whole loading process. Finally, the influence of the micro-strength ratio ( $${{\tau_{\text{c}} } \mathord{\left/ {\vphantom {{\tau_{\text{c}} } {\sigma_{\text{c}} }}} \right. \kern-0pt} {\sigma_{\text{c}} }}$$

Numerical Simulation of the Rock SHPB Test with a Special Shape Striker Based on the Discrete Element Method

A split Hopkinson pressure bar system with a special shape striker has been suggested as the test method by the International Society for Rock Mechanics (ISRM) to determine the dynamic characteristics of rock materials. In order to further verify this testing technique and microscopically reveal the dynamic responses of specimens in SHPB tests, a numerical SHPB test system was established based on particle flow code (PFC). Numerical dynamic tests under different impact velocities were conducted. Investigation of the stresses at the ends of a specimen showed that the specimen could reach stress equilibrium after several wave reverberations, and this balance could be maintained well for a certain time period after peak stress. In addition, analyses of the reflected waves showed that there was a clear relationship between the variation of the reflected wave and the stress equilibrium state in the specimen, and the turning point of the reflected wave corresponded well with the peak stress in the specimen. Furthermore, the reflected waves can be classified into three types according to their patterns. Under certain impact velocities, the specimen deforms at a constant strain rate during the whole loading process. Finally, the influence of the micro-strength ratio and distribution pattern on the dynamic increase factor of strength DIF were studied, and the lateral inertia confinement and heterogeneity were found to be two important factors causing the strain-rate effect for rock materials

HpGAN: Sequence Search with Generative Adversarial Networks

Sequences play an important role in many engineering applications and systems. Searching sequences with desired properties has long been an interesting but also challenging research topic. This article proposes a novel method, called HpGAN, to search desired sequences algorithmically using generative adversarial networks (GAN). HpGAN is based on the idea of zero-sum game to train a generative model, which can generate sequences with characteristics similar to the training sequences. In HpGAN, we design the Hopfield network as an encoder to avoid the limitations of GAN in generating discrete data. Compared with traditional sequence construction by algebraic tools, HpGAN is particularly suitable for intractable problems with complex objectives which prevent mathematical analysis. We demonstrate the search capabilities of HpGAN in two applications: 1) HpGAN successfully found many different mutually orthogonal complementary code sets (MOCCS) and optimal odd-length Z-complementary pairs (OB-ZCPs) which are not part of the training set. In the literature, both MOCSSs and OB-ZCPs have found wide applications in wireless communications. 2) HpGAN found new sequences which achieve four-times increase of signal-to-interference ratio--benchmarked against the well-known Legendre sequence--of a mismatched filter (MMF) estimator in pulse compression radar systems. These sequences outperform those found by AlphaSeq.Comment: 12 pages, 16 figure