炼油厂污水处理升级改造工艺选择及分析

某炼油厂300 m3/h污水处理装置受进水水质COD、氨氮波动大影响,污水总排无法满足《石油炼制工业污染物排放标准》(GB31570-2015)的要求,针对排放标准及总排排放不合格影响因素,通过对原工艺预处理单元、生化氧化单元升级改造,改造后污水总排水满足《石油炼制工业污染物排放标准》(GB31570-2015)要求并...     

压电半导体纳米材料在声动力疗法中的应用进展

随着纳米医学的发展, 利用纳米材料在外源超声波的刺激下催化产生过量的活性氧物种(Reactive Oxygen Species, ROS)以治疗疾病的方法, 被称为声动力疗法(Sonodynamic Therapy, SDT), 已引起人们的广泛关注。目前, 开发可用于SDT的高效声敏剂用于提高ROS产率, 仍然是当前研究和未来临床转化的最大挑战之一。近年来, 得益于压电电子学和压电光电子学的兴起, 基于压电半导体纳米材料的新型声敏剂在SDT中崭露头角, 显示出良好的应用前景。本文从压电半导体的结构出发, 介绍了压电半导体纳米材料应用于SDT的机理研究, 以及利用压电半导体纳米材料作为声敏剂在声动力学癌症治疗及相关抗菌性能方面所取得的研究进展。最后, 本文对该领域存在的问题以及未来的发展趋势进行了展望。     

晓南煤矿通风系统改造方案设计优化

针对晓南煤矿矿井通风存在的问题,提出了对矿井通风系统进行改造的必要性,并对改造的方案进行经济和技术比较。论证了一种经济合理、技术可行的优化改造方案,为改造方案的实施提供了依据。     

High-precision deformation monitoring algorithm for GBSAR system: rail determination phase error compensation

In recent years, the repeat-pass GBSAR (ground based synthetic aperture radar) system has demonstrated its capacity to acquire deformation. Nevertheless, in a variety of applications, it needs to measure the deformation with the precision up to 0.1 mm, which could not be reached by utilizing the traditional PS (permanent scatterer) algorithm in most cases. Generally, one of the main reasons could be summarized into the phase error caused by the rail determination error, because the precision of rail determination might degrade during long working hours. However, the traditional PS algorithm could not compensate for the phase error caused by the rail determination error. In order to solve the problems, we modify the conventional PS algorithm. Firstly, we deduced the transformation relationship between the rail determination error and its corresponding interferometric phase error. Then, the phase errors caused by the atmosphere and the rail determination error were jointly compensated. The experimental data, which were obtained in Fangshan District in Beijing (China), were used to test and verify the performance of the new algorithm. After the comparison between the results processed by the new algorithm and those processed by the traditional algorithm, the proposed method demonstrated its ability to obtain high-precision deformation.     

基于群体适应度均值商的自适应遗传算法

在系统分析和研究自适应遗传算法特点的基础上,提出一种基于群体适应度均值商的自适应遗传算法.一方面,算法设计了群体早熟收敛的定量计算公式和引入了早熟收敛的判定阈值;另一方面,算法采用了改进的交叉和变异策略,即对陷入局部收敛的群体,依据平均适应度值将其一分为二:对性能优于平均适应度者实施先变异后交叉,而对性能劣于平均适应度者实施先交叉后变异.该算法应用在函数优化中,仿真结果表明其能有效提高全局寻优的性能,且鲁棒性好.     

基于稀疏表示及正则约束的图像去噪方法综述

数据去噪声是信号和图像处理领域的一个经典问题,广泛应用于各类工程实践中。由于噪声源的多样性,去噪一直是富有挑战性且十分活跃的研究课题,发展了多种经典去噪方法。近年来,随着压缩感知理论的发展,基于稀疏表示及正则化约束反问题求解方法成为图像去噪领域的重要发展方向和技术途径。本文首先回顾和总结图像噪声的来源和类型,然后针对不同类型的图像噪声,重点围绕基于稀疏表示及正则化约束的图像去噪技术进行全面综述,分析和描述了几种主要去噪方法的原理及优缺点。最后,对去噪算法的性能评价指标进行总结。     

IPv6与隧道多地址性的DoS攻击放大问题研究

DoS攻击是威胁IPv4网络安全的重要问题之一.随着IPv6的发展,相关安全问题也逐步体现并影响IPv6网络的正常运行.该文指出利用IPv6和隧道主机的多地址性,攻击者可获得大量合法IPv6地址,通过伪装成多个虚拟主机实施对目标设备的DoS攻击.这种攻击具有大量的可用地址范围,且受控于同一真实主机,通过不断使用新地址和多地址间配合,可避开以IP为单位的传统检测与防御策略,并可有效放大攻击节点数目或减少实际攻击节点数量.为此提出了基于地址特征分类的防御框架(defense framework based on addresses classification,DFAC).通过分类不同地址特征,构造特征子集,在特征子集基础上实施对虚拟主机攻击的检测和防御,解决虚拟主机引发的放大问题.原型系统实验结果表明,DFAC有效地降低了上述DoS攻击对系统负载的影响.     

Progress in the Development of National Knowledge Infrastructure

This paper presents the recent process in a long-term research project,called National Knowledge Infrastructure(or NKI).Initiated in the early 2000,the project aims to develop a multi-domain shareable knowledge base for knowledge-intensive applications.To develop NKI,we have used domain-specific ontologies as a solid basis,and have built more than 600 ontologies.Using these ontologies and our knowledge acquisition methods,we have extracted about 1.1 millions of domain assertions.For users to access our NKI knowledge,we have developed a uniform multi-modal human-knowledge interface.We have also implemented a knowledge application programming interface for various applications to share the NKI knowledge.     

关于风险检测安全阀失效的分析系统

安全阀是保障石油化工设备正常运行的重要绀成部分,本文以目前广泛采用的AP1581标准为基础,建市安全阀的失效分析系统.该系统在计算机理和界向交互方式上改进原标准,可以根据工厂设备和安全阀管理数据,分析安伞阀的开启失效和泄漏频率,为提高石油化工牛产的安全性和可靠性提供重要的分析依据.     

CVSkSA: cross-architecture vulnerability search in firmware based on kNN-SVM and attributed control flow graph

To prevent the same known vulnerabilities from affecting different firmware, searching known vulnerabilities in binary firmware across different architectures is crucial. Because the accuracy of existing cross-architecture vulnerability search methods is not high, we propose a staged approach based on support vector machine (SVM) and attributed control flow graph (ACFG) at the function level to improve the accuracy using prior knowledge. Furthermore, for efficiency, we utilize the k-nearest neighbor (kNN) algorithm to prune and SVM to refine in the function prefilter stage. Although the accuracy of the proposed method using kNN-SVM approach is slightly lower than the accuracy of the method using only SVM, its efficiency is significantly enhanced. We have implemented our approach CVSkSA to search several vulnerabilities in real-world firmware images. The experimental results show that the accuracy of the proposed method using kNN-SVM approach is close to the accuracy of the method using only SVM in most cases, while the former is approximately four times faster than the latter.