矩形大口径激光光束质量评价光学系统设计

自适应光学校正技术可有效提升固体板条激光器的光束质量,但随着激光器输出功率的提升,输出光束口径逐渐增加,系统体积逐渐增大,自适应光学校正系统的设计难度也增加了.因此,在满足自适应光学校正系统中共轭探测等需求的前提下,统筹优化系统的尺寸参数,同时实现波前相位、光束质量评估等多参数的检测具有一定的研究意义.本文在系统整体尺...     

青霉胺稳定的铜/银双金属纳米簇制备及其在银离子检测中的应用EI北大核心CSCD

以青霉胺(DPA)为还原剂和稳定剂,通过一锅法一步制备了青霉胺稳定的铜/银双金属纳米簇(DPA-Cu/Ag NCs),并将其作为一种传感器用于检测水样中的银离子。该银离子传感器具有价格低廉、分析速度快速、选择性高等特点。采用透射电子显微镜(TEM)等方法表征了DPA-Cu/Ag NCs的结构及其化学组成,并通过荧光光谱和紫外-可见光谱法研究了DPA-Cu/Ag NCs的光学性质。结果表明,该DPA-Cu/Ag NCs在激发波长为300 nm时的最大发射波长为555 nm,其溶液在可见光照射下呈现乳白色,在紫外灯照射下则呈现出明亮的黄色荧光。在DPA-Cu/Ag NCs的制备条件达到最优化的情况下,可以将其作为探针,用来高选择性、高灵敏性地检测银离子。该探针检测银离子的检测限为0.3μmol/L,线性范围为0~500μmol/L。该DPA-Cu/Ag NCs探针还可应用于自然环境水样(湖水、瓶装矿泉水和实验室自来水)中银离子浓度的检测,其检测性能十分优异且具有良好的准确度和重现性,表明DPA-Cu/Ag NCs探针在环境检测方面有非常高的应用价值。     

复杂气候系统和全球变暖

两位气候学家和一位理论物理学家共同荣获2021年诺贝尔物理学奖。诺贝尔物理学奖委员会在背景介绍材料中指出,他们在“理解复杂物理系统领域做出了开创性贡献”。相信很多人会对两位气候学家获奖而感到惊讶,物理学奖委员会进一步具体指出,人类活动导致全球变暖这一论断建立在坚实的科学基础之上,两位气候学家获奖是因为他们基于物理原理模拟地球气候、量化气候变率、可靠地预测全球变暖所做出的杰出贡献。物理学奖委员会将诺贝尔奖授予两位气候学家,表明对基于物理理论解决现实世界复杂物理问题的高度重视,尤其是对人类身处其中的气候环境问题的重视。文章将解读两位气候学家的学术贡献,尤其是如何基于基础物理理论建立预测全球变暖的模型以及检测和归因人类活动导致全球变暖的方法。     

全透射近红外光谱的苹果整果糖度在线检测模型优化

光谱质量、样本个体差异、检测系统和建模算法等多种因素共同决定水果糖度检测模型的预测精度和稳定性。采用自主研发的短积分全透射近红外在线检测系统以5 ms积分时间和0.5 m·s-1运行速度在线获取了“富士”苹果全透射光谱信号。不同姿态获取的透射光谱强度差异明显,但曲线走势相近,均在920 nm波段具有最大的光谱强度,在850 nm波段存在波谷。采用移动平均平滑、标准正态变量变换和多元散射校正等预处理方法有效去除原始光谱的随机噪声和基线偏差,减小了样本检测姿态引起的光谱差异。为分析不同检测姿态对苹果整果糖度预测模型的影响,构建了单一姿态局限模型和多姿态通用模型,结果表明基于全位点平均透射光谱构建的单一姿态局限模型对检测姿态具有很大的局限性,而多姿态通用模型预测能力较单一检测姿态相当,但却对不同的检测姿态具有更强的适用能力。为进一步提高光谱信号质量,优化模型预测能力,采用信号强度阈值优选方法实现了苹果整果糖度预测模型优化,发现移除中央位点获取的透射光谱信号,有利于提高苹果整果糖度预测模型精度。多姿态通用信号强度优化模型综合考虑不同姿态获取的光谱信息有效性,有效提升了通用信号强度优化模型的预测能力和稳定性,当多姿态通用模型中信号强度阈值为5 000时,模型预测性能最佳,其预测参数R_p,RMSEP和RPD分别为0.79,0.84%和1.58。表明短积分全透射近红外在线检测系统用于不同姿态苹果糖度预测是可行的,多姿态通用模型的建立,扩大了模型在不同姿态的预测稳健性,短积分光谱采集方式结合信号强度阈值优选方法提升了光谱信号的质量和模型的预测能力。     

汤加火山喷发所产生的次声波

2022年1月15日汤加火山发生了剧烈的喷发。在火山喷发后的8 h 39 min,距离火山喷发位置10151 km的昆明次声台阵记录到很强的次声波信号。次声波波列前几个周期平均为443 s,传播速度约为321 m/s,其波形幅度与频率随时间变化与核爆炸产生的次声波形相似。采用相关检测方法估计次声信号的方位角为119°。在持续监测中,还观测到了火山喷发产生的次声信号沿反方向绕地球到达的信号和沿正方向绕地球一周后再次到达的信号。     

Injection attack detection using machine learning for smart IoT applications

Smart cities are a rapidly growing IoT application. These smart cities mainly rely on wireless sensors to connect their different components (smart devices) together. Smart cities rely on the integration of IoT and 5G technologies, and this has created a demand for a massive IoT network of connected devices. The data traffic coming from indoor wireless networks (e.g., smart homes, smart hospitals, smart factories , or smart school buildings) contributes to over 80% of the total data traffic of the current IoT network. As smart cities and their applications grow, security and privacy challenges have become a major concern for billions of IoT smart devices. One reason for this could be the oversight of handling security issues of IoT devices by their manufacturers, which enables attackers to exploit the vulnerabilities in these devices by performing different types of attacks, e.g., DDoS and injection attacks. Intrusion detection is one way to detect and mitigate the risk of such attacks. In this paper, an intrusion detection method was proposed to detect injection attacks in IoT applications (e.g. smart cities). In this method, two types of feature selection techniques (constant removal and recursive feature elimination) were used and tested by a number of machine learning classifiers (i.e., SVM, Random Forest, and Decision Tree). The T-Test was conducted to evaluate the quality of this proposed feature selection method. Using the public dataset, AWID, the evaluation results showed that the decision tree classifier can be used to detect injection attacks with an accuracy of 99% using only 8 features, which were selected using the proposed feature selection method. Also, the comparison with the most related work showed the advantages of the proposed intrusion detection method.     

An interpretable intrusion detection method based on few-shot learning in cloud-ground interconnection

An enterprise’s private cloud may be attacked by attackers when communicating with the public cloud. Although traffic detection methods based on deep learning have been widely used, these methods rely on a large amount of sample data and cannot quickly detect new attacks such as Zero-day Attacks. Moreover, deep learning has a black-box nature and cannot interpret the detection results, which has certain security risks. This paper proposes an interpretable abnormal traffic detection method, which can complete the detection task with only a few malicious traffic samples. Specifically, it uses the covariance matrix to characterize each traffic category and then calculates the similarity between the query traffic and each category according to the covariance metric function to realize the traffic detection based on few-shot learning. After that, the traffic images processed by the random masks are input into the model to obtain the predicted probability of the corresponding traffic category. Finally, the predicted probability is linearly summed with each mask to generate the final saliency map to interpret and analyze the model decision. In this paper, experiments are carried out by simulating only 15 and 25 malicious traffic samples. The results show that the proposed method can obtain good accuracy and recall, and the interpretation analysis shows that the model is reliable and interpretable.     

宽带腔增强吸收光谱技术应用于痕量气体探测及气溶胶消光系数测量

基于氙灯的非相干宽带腔增强吸收光谱系统, 并将其应用于痕量气体及气溶胶消光系数的测量. 该系统的探测灵敏度通过测量NO₂在520—560 nm波长范围内的吸收得到验证, 最小可探测灵敏度为1.8× 10^-7cm^-1 (1σ, 0.12 s积分时间, 50次平均), 对应的NO₂探测极限～33 nmol/mol. 结合标准气溶胶粒子发生系统, 测量了不同浓度的单分散硫酸铵气溶胶粒子在532 nm波长处的消光系数, 得到粒径为600 nm的硫酸铵气溶胶的消光截面为1.12× 10^-8cm², 与文献报道值1.167× 10^-8cm²相一致, 验证了气溶胶测量的可行性和准确性.     

改进的Zernike矩工业CT图像边缘检测

为提高工业计算机断层扫描(CT)图像亚像素边缘检测的精度和速度,研究了一种改进的Zernike矩边缘检测方法。该方法采用Sobel边缘算子快速检测出图像所有可能的边缘,通过Zernike矩算子对所有可能的边缘进行重新检测,最后,检测出图像的亚像素边缘并计算其精确位置。由于采用Sobel算子检测出可能的边缘使后续Zernike矩算子检测范围缩小,从而减小了运算量,提高了运算速度。对实际CT图像进行的实验结果表明:改进的Zernike矩工业CT图像边缘检测精度绝对误差<0.24 pixel,改进算法的运算速度提高了约70%。     

磁旋光反射腔交流调制输出倍频信号特性分析

根据法拉第效应的非互易性原理,分析了磁旋光反射腔交流调制输出倍频信号相关的特性;具体研究了倍频信号幅度及角度检测灵敏度与调制角幅度θ0和反射腔反射率R的关系.研究结果表明:当调制角幅度较小时,输出倍频信号幅度随反射率R的增加而增加,最大值是正交调制法的18倍左右;利用倍频信号检测角度的灵敏度与正交法相比也得到多倍提高,...