基于网络环境下对日志系统攻击的机理分析与研究

日志系统的安全对计算机安全来说至关重要,分析了日志系统受到攻击的位置和不同类型。并从日志系统的机密性、完整性和可用性三个方面对日志系统受到攻击的机理进行了详细的分析。最后,对日志系统如何防止受到各种攻击进行了总结。对在网络环境下计算机主机避免其日志系统受到攻击具有较强的指导意义。     

移动P2P文件共享系统中基于用户偏好的激励机制

提出一种基于用户偏好的激励机制,鼓励用户通过提供共享文件和转发操作为系统做出贡献。IMBPC结合了虚拟价格机制和预期文件传输延时,基于用户对价格和延时的偏好度来选择最合适的节点进行文件下载。通过设置模拟实同仅通过虚拟价格机制来激励用户做出贡献的策略进行比较,显示应用IMBPC策略的系统,节点的贡献积极性明显增加。     

基于WinPcap与Jpcap的校园局域网监控系统的设计与实现

利用基于Win32平台的抓包和网络分析开源库WinPcap,结合Java类库Jpcap,设计并实现一个校园局域网安全监控系统。该系统不但可以捕获、分析并存储数据包,还可使捕获的数据包以表格的形式展现,从而方便网络管理员的管理。给出其软件的设计和实现细节,实践证明该系统是有效的。     

Truth-Telling Reservations

We present a mechanism for reservations of bursty resources that is both truthful and robust. It consists of option contracts whose pricing structure induces users to reveal the true likelihoods that they will purchase a given resource. Users are also allowed to adjust their options as their likelihood changes. This scheme helps users save cost and the providers to plan ahead so as to reduce the risk of under-utilization and overbooking. The mechanism extracts revenue similar to that of a monopoly provider practicing temporal pricing discrimination with a user population whose preference distribution is known in advance.     

机构位移仿真分析方法比较研究

机构位移分析是运动分析中最根本的问题,机构位移分析方程通常是非线性的,只有简单的二级机构才能列出输入变量和输出变量之间的显函数表达式,而在其他情况下,方程的求解就需要利用各种数值解法.以压床六杆机构为研究对象,推导了机构多位移矩阵迭代求解和单一位移精确求解的理论方程,并用上述两种方法对压床六杆机构进行了机构位移仿真计算比较分析,证实单一位移精确求解方法的计算比较简便,求解过程不用推导,一旦确定单一位移值后,可对其它位移进行精确求解,对机构位移仿真计算有一定的实用性,累积误差小,可用于机构运动学设计.     

Stabilization of geometrically nonlinear topology optimization by the Levenberg–Marquardt method

This paper deals with the design of compliant mechanisms in a continuum-based finite-element representation. Because the displacements of mechanisms are intrinsically large, the geometric nonlinearity is essential for designing such mechanisms. However, the consideration of the geometric nonlinearity may cause some instability in topology optimization. The problem is in the analysis part but not in the optimization part. To alleviate the analysis problem and eventually stabilize the optimization process, this paper proposes to apply the Levenberg–Marquardt method to the nonlinear analysis of compliant mechanisms.     

一种基于知识发现的拓展型竞争情报系统

目前,制约竞争情报发展的一个关键问题就是情报的收集与分析手段比较落后,远远不能胜任竞争情报系统所蕴含的特点和功效。本文将竞争情报作为一种特殊的知识,吸纳知识发现理论成果和方法,提升现有竞争情报系统的构造技术,真正做到快速、全面、准确和智能化的竞争情报生成和利用。     

斜盘式偏转机构方案仿真研究

偏转弹头控制是一种新型的快速响应控制技术.为了提高偏转机构设计的效率和可靠性,对一种斜盘式偏转机构初始设计方案进行了研究:通过坐标转换推导出弹头偏转规律;基于多体系统动力学理论,在ADAMS中建立了该机构的虚拟样机,采用修正的Newton-Paphson迭代算法,仿真研究了机构的静态特性;通过ADAMS与SIMULINK联合,采用Runge-Kutta算法,仿真研究了机构在阶跃信号作用下的动态特性,确定了关键设计参数.仿真结果表明:弹头偏转角只与电机角位移有关;机构方案具有精度高、响应速度快等优点.     

基于黑曲霉固态发酵的绿豆皮降解及理化特性提升机理

本试验研究了黑曲霉固态发酵对绿豆皮特性的影响,并对降解机理进行探究,结果表明黑曲霉固态发酵能有效提升绿豆皮制粉特性和功能特性,所得绿豆皮粉体积平均径减小8.53倍,比表面积增大32.46倍。阳离子交换能力、胆酸钠吸附能力及葡萄糖吸附能力均有明显提升,依次提升1.11倍、1.18倍和1.17倍。黑曲霉对绿豆皮的降解,先降解木质素与半纤维素,再降解纤维素。主要通过破坏纤维素、半纤维素及木质素的化学键,降解长链结构为短链结构,破坏结晶区结构,降低相对结晶度,进而破坏绿豆皮结构,使其变得疏松多孔,让绿豆皮易被粉碎为粒径更小、比表面积更大的粉体,具有更好的物质交换效率,提升绿豆皮的理化特性。     

Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5

Safety helmet-wearing detection is an essential part of the intelligent monitoring system. To improve the speed and accuracy of detection, especially small targets and occluded objects, it presents a novel and efficient detector model. The underlying core algorithm of this model adopts the YOLOv5 (You Only Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (Complete Intersection Over Union) Loss function, and the Mish activation function. First, it applies the attention mechanism in the feature extraction. The network can learn the weight of each channel independently and enhance the information dissemination between features. Second, it adopts CIoU loss function to achieve accurate bounding box regression. Third, it utilizes Mish activation function to improve detection accuracy and generalization ability. It builds a safety helmet-wearing detection data set containing more than 10,000 images collected from the Internet for preprocessing. On the self-made helmet wearing test data set, the average accuracy of the helmet detection of the proposed algorithm is 96.7%, which is 1.9% higher than that of the YOLOv5 algorithm. It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.