Optimal operating conditions to maximize the net power of polymer electrolyte membrane fuel cells: The stack-system interface of a commercial 18 kW module

A new, experimental method based on air flow rate rather than current is presented to optimize operating parameters for the stacks and systems of proton exchange membrane fuel cells (PEMFCs) for maximizing their net power. This approach is illustrated for a commercial 18 kW PEMFC module. The impact of contamination pressure drop across the cathode air filter is also investigated on the compressor behavior. It is further shown that a 4V reduction in the compressor voltage reduces its power consumption by 9.1%. Using the 3D graphs of the power-pressure-flow data, it is found that the stack pressure of 180 kPaa is superior to the higher tested pressures as it enhances the net power by 7.0 and 13.7% at different conditions. Application of the present study will lead to the development of PEMFCs with higher power output by optimizing stack pressure, stoichiometry and air flow to properly deliver the system design specifications.     

Modeling and Verification of a Sentiment Analysis System Using Aspect-Oriented Petri Nets

An increasing number of social media and networking platforms have been widely used. People usually post the online comments to share their own opinions on the networking platforms with social media. Business companies are increasingly seeking effective ways to mine what people think and feel regarding their products and services. How to correctly understand the online customers’ reviews becomes an important issue. This study aims to propose a method with the aspect-oriented Petri nets (AOPN) to improve the examination correctness without changing any process and program. We collect those comments from the online reviews with Scrapy tools, perform sentiment analysis using SnowNLP, and examine the analysis results to improve the correctness. In this paper, we apply our method for a case of the online movie comments. The experimental results have shown that AOPN is helpful for the sentiment analysis and verifying its correctness.     

自适应融合层级特征的混合退化图像复原算法

多种退化类型混合的图像比单一类型的退化图像降质更严重,很难建立精确模型对其复原,研究端到端的神经网络算法是复原的关键.现有的基于操作选择注意力网络的算法(operation-wiseattentionnetwork,OWAN)虽然有一定的性能提升,但是其网络过于复杂,运行较慢,复原图像缺乏高频细节,整体效果也有提升的空间.针对这些问题,提出一种基于层级特征融合的自适应复原算法.该算法直接融合不同感受野分支的特征,增强复原图像的结构;用注意力机制对不同层级的特征进行动态融合,增加模型的自适应性,降低了模型冗余;另外,结合L1损失和感知损失,增强了复原图像的视觉感知效果.在DIV2K,BSD500等数据集上的实验结果表明,该算法无论是在峰值信噪比和结构相似性上的定量分析,还是在主观视觉质量方面,均优于OWAN算法,充分证明了该算法的有效性.     

Subdivision based isogeometric analysis for geometric flows

We present a new isogeometric analysis (IGA) approach based on extended Loop subdivision scheme for solving various geometric flows defined on subdivision surfaces. The studied flows include the second-order, fourth-order, and sixth-order geometric flows, such as averaged mean curvature flow, constant mean curvature flow, and minimal mean-curvature-variation flow, which are generally derived by minimizing the associate energy functionals with $L^2$-gradient flow respectively. The geometric flows are discretized by means of subdivision based IGA, where the finite element space is formulated by the limit form of the extended Loop subdivision for different initial control meshes. The basis functions, consisting of quartic box-splines corresponding to each subdivided control mesh, are utilized to represent the geometry exactly. For the cases of the evolution of open surfaces with any shape boundary, high-order continuous boundary conditions derived from the mixed variational forms of the geometric flows should be implemented to be consistent with the isogeometric concept. For time discretization, we adopt an adaptive semi-implicit Euler scheme. By several numerical experiments, we study the convergence behaviors of the proposed approach for solving the geometric flows with high-order boundary conditions. Moreover, the numerical results also show the accuracy and efficiency of the proposed method.     

基于Abaqus的双裂纹不同间距对裂纹扩展速率的研究

扩展有限元法是近年经过大量运用的,在传统有限元的范围中求解不连续问题一种有效计算方法,它是基于单位分解的思想,在计算不连续问题时加入跳跃函数。以ABAQUS为平台,基于扩展有限元方法 (XFEM),以含双穿透型裂纹的有限宽板受横向拉伸载荷为力学模型,建立相应的裂纹尖端应力的有限元模型,研究焊接接头区域不同间距双裂纹相互作用对裂纹扩展速率的影响。结果表明:双裂纹间距的的大小并没有对裂纹的扩展速率产生影响。     

采用随机Petri网的嵌入式机载软件可靠性检测

针对嵌入式机载软件设计中存在的典型缺陷问题，结合嵌入式机载软件任务调度特性，提出采用随机Petri网对嵌入式机载软件设计进行仿真验证的可靠性检测方法，以提高嵌入式机载软件设计的可靠性。该方法采用随机Petri网对嵌入式机载软件系统行为建模，并给出典型缺陷的检测策略和判定准则，然后通过对Petri网模型进行仿真验证，检测系统是否存在此类设计缺陷；并给出了软件设计的运行流程的仿真验证算法，以支持对相应设计的可靠性检测。通过与其他可靠性检测方法的比较，表明了该方法的有效性。     

Approaching Ultrastable High‐Rate Li–S Batteries through Hierarchically Porous Titanium Nitride Synthesized by Multiscale Phase Separation

Porous architectures are important in determining the performance of lithium–sulfur batteries (LSBs). Among them, multiscale porous architecutures are highly desired to tackle the limitations of single‐sized porous architectures, and to combine the advantages of different pore scales. Although a few carbonaceous materials with multiscale porosity are employed in LSBs, their nonpolar surface properties cause the severe dissolution of lithium polysulfides (LiPSs). In this context, multiscale porous structure design of noncarbonaceous materials is highly required, but has not been exploited in LSBs yet because of the absence of a facile method to control the multiscale porous inorganic materials. Here, a hierarchically porous titanium nitride (h‐TiN) is reported as a multifunctional sulfur host, integrating the advantages of multiscale porous architectures with intrinsic surface properties of TiN to achieve high‐rate and long‐life LSBs. The macropores accommodate the high amount of sulfur, facilitate the electrolyte penetration and transportation of Li⁺ ions, while the mesopores effectively prevent the LiPS dissolution. TiN strongly adsorbs LiPS, mitigates the shuttle effect, and promotes the redox kinetics. Therefore, h‐TiN/S shows a reversible capacity of 557 mA h g^?1 even after 1000 cycles at 5 C rate with only 0.016% of capacity decay per cycle.     

When Does Communication Learning Need Hierarchical Multi-Agent Deep Reinforcement Learning

Abstract

Multi-agent systems need to communicate to coordinate a shared task. We show that a recurrent neural network (RNN) can learn a communication protocol for coordination, even if the actions to coordinate are performed steps after the communication phase. We show that a separation of tasks with different temporal scale is necessary for successful learning. We contribute a hierarchical deep reinforcement learning model for multi-agent systems that separates the communication and coordination task from the action picking through a hierarchical policy. We further on show, that a separation of concerns in communication is beneficial but not necessary. As a testbed, we propose the Dungeon Lever Game and we extend the Differentiable Inter-Agent Learning (DIAL) framework. We present and compare results from different model variations on the Dungeon Lever Game.