一种新型单层递归神经网络解决非光滑伪凸优化问题

非光滑伪凸优化问题是一类比较特殊的非凸优化问题,常出现在各类科学与工程应用中,因此具有很大的研究价值.针对现有神经网络模型解决非光滑伪凸优化问题存在的不足,该文基于微分包含理论,提出一种新型单层递归神经网络模型.通过理论分析,证明了神经网络状态解在有限时间内收敛到可行域,且永驻其中,最终神经网络状态解收敛于原优化问题的最优解.最后,通过数值实验,验证了所提理论的有效性.与现有的神经网络相比,该文所提神经网络模型结构简单仅为单层,不需要提前计算罚参数,且对初始点选取没有任何特殊的要求.     

设计语用学的范式研究

目的确定设计语用学研究范畴,建立设计语用学的研究范式,进而通过设计语用学理论发展对设计方法论提供新的理论依据。方法从设计符号传播语境视角出发,对设计方、用户和用户的社交对象之间的设计符号传播模式进行论述。结论结合整合营销传播理论和设计事理学理论,得出了设计语用学的研究范畴、研究意义、研究核心及整合产品设计营销传播与定义设计符号使用场景两种设计语用学的研究路径,完成了设计语用学的理论框架。     

纳米Fe_2O_3作为润滑油添加剂的摩擦磨损性能

采用烧结方法制备得到纳米α-Fe_2O_3,利用X射线衍射仪(XRD)和透射电镜(TEM)对样品进行了表征,研究了纳米α-Fe_2O_3作为液体石蜡添加剂的摩擦磨损性能。结果表明,添加纳米添加剂后,润滑油摩擦系数改变不明显,但磨损率显著降低。磨损率与纳米α-Fe_2O_3添加剂的表面积和添加量密切相关,当纳米材料的表面积为47m~2/g时磨损率最低,磨损率值降低为3.78×10~(-15) m~2/g;添加量为1.0wt%,润滑油磨损率最低。     

高效能空气净化器评价技术要求国家标准研究

本文论述了编制高效能空气净化器产品评价技术要求国家标准的意义和作用,介绍了标准研制的总体思路和原则,高效能空气净化器的定性和定量评价指标要求,测试、计算和评价方法等,通过国标的研制与实施,将引领并促进高效能空气净化器产品的生产与应用.     

Design and Application of Leather Replacement Tool for Belt Conveyor Seal

In order to solve the problem which may be generated in the case of manual replacement of the belt conveyor sealing leather,such as the operation space is small,the work efficiency is low and the finger is easy to be scratched,Using the principle of simulated bionics,through theoretical analysis and experimental verification,a tool was designed to replace the belt conveyor sealing leather. The tool includes a limit device,feeding device,U-shaped handle three parts,suitable for the replacement of various types and sizes of belt conveyor sealing leather. Use this tool to replace the belt conveyor sealing leather,the results show that: 1) The effect of replace belt conveyor sealing leather is good,belt conveyor sealing leather flat and solid. 2) The replacement process averaged 1.77 min,shorter than the original method of more than 8 min. 3) Direct contact of the fingers and the metal baffle edge was avoided,so as to reduce the chances of a finger injury.     

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.     

计算机信息管理系统课程的实践教学体系

本文结合我校教学的实际情况,探讨了计算机信息管理系统课程实践教学的改革,主要包括"三阶段"的技术要求、"企业化"的分组管理、"过程化"的项目运行和"合理化"的考核评价办法。     

Implementation and evaluation of parallel FFT on Engineering and Scientific Computation Accelerator (ESCA) architecture

The fast Fourier transform (FFT) is a fundamental kernel of many computation-intensive scientific applications.This paper deals with an implementation of the FFT on the accelerator system,a heterogeneous multi-core architecture to accelerate computation-intensive parallel computing in scientific and engineering applications.The Engineering and Scientific Computation Accelerator (ESCA) consists of a control unit and a single instruction multiple data (SIMD) processing element (PE) array,in which PEs communicate with each other via a hierarchical two-level network-on-chip (NoC) with high bandwidth and low latency.We exploit the architecture features of ESCA to implement a parallel FFT algorithm efficiently.Experimental results show that both the proposed parallel FFT algorithm and the ESCA architecture are scalable.The 16-bit fixed-point parallel FFT performance of ESCA is compared with a published work to prove the superiority of the mapping algorithm and the hardware architecture.The floating-point parallel FFT performances of ESCA are evaluated and compared with those of the IBM Cell processor and GPU to demonstrate the computing power of the ESCA system for high performance applications.     

Neville编解码问题的线性时间算法

讨论标号树的Neville编码的编解码算法.文献中常见的第2种Neville编解码算法需要O(n log n)时间.近期研究文献指出至今尚未找到第2种Neville编解码的线性时间算法.本文对第2种Neville编解码问题的本质特征进行较深入的分析,从简单算法出发,逐步简化,得到一个非常简单实用的O(n)时间Neville编解码算法.本文采用的解决问题的方法也具有一定的技巧,可供解决类似问题时借鉴.