ASP.NET中数据库的连接方法

网站建设中的核心技术是数据库技术,掌握数据库连接方法是掌握数据库操作的重要内容。对ASP.NET中的数据库的连接进行研究,并针对常用数据库给出相应的连接实例,使用该方法能较好地解决ASP.NET中数据库连接建立的问题     

无刷直流电机网络控制系统的丢包研究

将无刷直流电机作为网络控制系统的控制对象,从而实现电机远程控制的目的。将具有数据包丢失的无刷直流电机网络控制系统建模为具有事件率约束的异步动态系统,并对其进行稳定性分析。利用MatLab/Simulink对无刷直流电机网络控制系统搭建仿真模型,并对不同数据包丢失率进行仿真研究。     

浅谈计算机网络教学系统的应用

详细介绍了计算机网络教学系统的发展现状,并对计算机网络教学系统的设计进行了概述,包括系统设计所采用的软件体系架构——B/S模式和系统划分的功能模块,探讨了计算机网络教学的优势。     

任务多变情况下技术团队隐性知识管理策略

很多以技术性活动为主的团队所执行的任务环境多变,无论是地形、气候或者是人员、设备的变化非常规问题地多发,会对其知识管理尤其是隐性知识管理带来一定影响。本文提出将承载着隐性知识的非结构化文档以数列集合的方式表达,能够便于管理和决策。     

相位可控双路同频信号源的FPGA部分设计与实现

介绍了直接数字频率合成(DDS)技术的基本原理,给出了基于Altera公司FPGA器件的相位可调双路同频正弦信号发生器的设计方案,同时给出了其软件程序和试验结果。试验结果表明:该方法生成的双路同频正弦信号具有波形失真小、频率和相位精度高,且输出频率与相位可调等优点。     

超低功耗手持式检波器测试仪

设计了手持式检波器测试仪,选取超低功耗的16位单片机MSP430系列为主控MCU,模拟部分硬件采用最新设计思路的低压供电,软件采用低功耗的智能处理策略,以实现超长待机。     

煤矿井下电磁干扰对通信和监控系统的影响分析

文章从电磁波在矩形巷道中的传输特性出发,分析了电磁波低于和高于截止频率的衰减特性,总结了井下各种电磁辐射干扰源产生干扰的情况。通过对井下电磁干扰实测数据的分析,得出井下电磁干扰的频段主要为500 MHz以下;当干扰源与通信和监控系统的距离为几米远时,其对系统基本不会产生影响。     

白光LEDs用(Ba/Sr/Ca)2.9Ce(PO4)3:0.1Eu2+的发光特性

采用高温固相法制备了(Ba/Sr/Ca)_2.9Ce(PO₄)₃:0.1Eu²⁺系列荧光粉,研究了材料的发光特性。在400 nm光激发下,增大x值,Ba_2.9-xSr_xCe(PO₄)₃:0.1Eu²⁺的主发射峰由515 nm红移至540 nm,色坐标从(0.312,0.607)变到(0.376,0.580);随着y值的增大,Ba_2.9-yCa_yCe(PO₄)₃:0.1Eu²⁺的主发射峰由515 nm红移到553 nm,色坐标从(0.312,0.607)变到(0.427,0.535),随z值的增大,Sr_2.9-zCa_zCe(PO₄)₃:0.1Eu²⁺的主发射峰值由540 nm红移...     

非线性规划在工业产品设计中的应用

目的判断非线性规划方法能否在满足用户使用要求的前提下节约生产加工成本。方法以平板折叠桌作为研究对象,以加工方便作为目标函数,以桌面和桌腿之间的几何关系作为约束条件,建立非线性规划模型,通过MATLAB软件求解得到最优加工方案。结论在给定高为70 cm和圆形桌面直径为80 cm时,平板折叠桌最优加工方案为平板长度158 cm,宽度79.9 cm,厚度3 cm,木条宽度4.7 cm,单侧木条数为17条。     

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.