Performance analysis of a new deep super-cooling two-stage organic Rankine cycle

This document is the Accepted Manuscript version of the following article: Y. Yuan, G. Xu, Y. Quan, H. Wu, G. Song, W. Gong, and X. Luo, ‘Performance analysis of a new deep super-cooling two-stage organic Rankine cycle’, Energy Conversion and Management, Vol. 148: 305-316, September 2017. The final, definitive version is available online at doi:https://doi.org/10.1016/j.enconman.2017.06.006. Published by Elsevier.In this article, a new deep super-cooling two-stage organic Rankine cycle (DTORC) is proposed and evaluated at high temperature waste heat recovery in order to increase the power output. A thermodynamic model of recuperative organic rankine cycle (ORC) is also established for the purpose of comparison. Furthermore, a new evaluation index, effective heat source utilization, is proposed to reflect the relationship among the heat source, power output and consumption of the waste heat carrier. A simulation model is formulated and analysed under a wide range of operating conditions with the heat resource temperature fixed at 300℃. Hexamethyldisiloxane (MM) and R245fa are used as the working fluid for DTORC, and MM for ORC. In the current work, the comparisons of heat source utilization, net thermal efficiency as well as the total surface area of the heat exchangers between DTORC and RC are discussed in detail. Results show that the DTORC performs better than ORC at high temperature waste heat recovery and it could increase the power output by 150%. Moreover, the maximum net thermal efficiency of DTORC can reach to 23.5% and increased by 30.5% compared with that using ORC, whereas the total surface areas of the heat exchangers are nearly the same.Peer reviewe

Outage Performance Based on Optimal Relay Location in Multi-node Relay Network

In this paper, we investigate the outage performance of multi-node relay network under equal power allocation (EPA) scheme. Due to the mobility of relay nodes, we proposed to design a method that considers random relay location based on random model. We conduct deep experiments which demonstrate that there exists optimal relay location to minimize the outage probability of multi-node relay networks. Finally, simulation results show that the outage performance of the multi-node relay network is closely related to the relay location. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.274

High-order BDF convolution quadrature for stochastic fractional evolution equations driven by integrated additive noise

The numerical analysis of stochastic time fractional evolution equations presents considerable challenges due to the limited regularity of the model caused by the nonlocal operator and the presence of noise. The existing time-stepping methods exhibit a significantly low order convergence rate. In this work, we introduce a smoothing technique and develop the novel high-order schemes for solving the linear stochastic fractional evolution equations driven by integrated additive noise. Our approach involves regularizing the additive noise through an $m$-fold integral-differential calculus, and discretizing the equation using the $k$-step BDF convolution quadrature. This novel method, which we refer to as the ID$m$-BDF$k$ method, is able to achieve higher-order convergence in solving the stochastic models. Our theoretical analysis reveals that the convergence rate of the ID$2$-BDF2 method is $O(\tau^{\alpha + \gamma -1/2})$ for $1< \alpha + \gamma \leq 5/2$, and $O(\tau^{2})$ for $5/2< \alpha + \gamma <3$, where $\alpha \in (1, 2)$ and $\gamma \in (0, 1)$ denote the time fractional order and the order of the integrated noise, respectively. Furthermore, this convergence rate could be improved to $O(\tau^{\alpha + \gamma -1/2})$ for any $\alpha \in (1, 2)$ and $\gamma \in (0, 1)$, if we employ the ID$3$-BDF3 method. The argument could be easily extended to the subdiffusion model with $\alpha \in (0, 1)$. Numerical examples are provided to support and complement the theoretical findings.Comment: 22page

Energy Efficiency and Throughput Optimization of Cognitive Relay Networks

In this paper, we investigate the energy efficiency and throughput optimization problem of cognitive relay networks. We propose to design sensing time and signal to noise ratio (SNR) to maximize the energy efficiency and throughput, since analytical and empirical studies have shown that sensing time and SNR are key factors for energy efficiency and throughput. We design a method that simultaneously considers the parameters of spectrum sensing time and SNR to optimize the energy efficiency of cognitive radio networks. Furthermore, we conduct deep experiments which show that there exists the optimal sensing time to maximize energy efficiency and throughput. In addition, optimal sensing time and optimal SNR can be jointly designed to maximize energy efficiency. Finally, we provide simulation results to show that energy efficiency of cognitive relay transmission scheme can be significantly improved compared with that of direct transmission scheme in cognitive radio networks

Progress in animal model research on obstructive sleep apnea

Obstructive sleep apnea (OSA) is a common sleep disorder, and its pathophysiological mechanism complex and not fully understood. This article elaborately explores three categories of OSA animal models: natural, direct and indirect, emphasizing their advantages and disadvantages in simulating OSA pathophysiological processes. Natural OSA models primarily focus on spontaneous upper airway obstructions. Direct OSA models induce OSA through direct obstruction of the airway, while indirect OSA models mainly investigate the impacts of chronic intermittent hypoxia (IH) and sleep deprivation (SD) on the organism. Although these models have played a pivotal role in studying the pathophysiological mechanisms of OSA and developing new therapeutic methods, they also present certain limitations and challenges. Future research directions include the development of non-invasive monitoring technologies, establishing OSA-combined models, and the application of gene-editing technologies, aiming to more comprehensively and accurately simulate the complexity and diversity of human OSA, providing more insights into its mechanisms and developing new therapeutic methods

Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences

Cloth-Changing Person Re-Identification (CC-ReID) is a common and realistic problem since fashion constantly changes over time and people's aesthetic preferences are not set in stone. While most existing cloth-changing ReID methods focus on learning cloth-agnostic identity representations from coarse semantic cues (e.g. silhouettes and part segmentation maps), they neglect the continuous shape distributions at the pixel level. In this paper, we propose Continuous Surface Correspondence Learning (CSCL), a new shape embedding paradigm for cloth-changing ReID. CSCL establishes continuous correspondences between a 2D image plane and a canonical 3D body surface via pixel-to-vertex classification, which naturally aligns a person image to the surface of a 3D human model and simultaneously obtains pixel-wise surface embeddings. We further extract fine-grained shape features from the learned surface embeddings and then integrate them with global RGB features via a carefully designed cross-modality fusion module. The shape embedding paradigm based on 2D-3D correspondences remarkably enhances the model's global understanding of human body shape. To promote the study of ReID under clothing change, we construct 3D Dense Persons (DP3D), which is the first large-scale cloth-changing ReID dataset that provides densely annotated 2D-3D correspondences and a precise 3D mesh for each person image, while containing diverse cloth-changing cases over all four seasons. Experiments on both cloth-changing and cloth-consistent ReID benchmarks validate the effectiveness of our method.Comment: Accepted by ACM MM 202