基于SWMM和LISFLOOD-FP的城市内涝耦合模型研究

以柳州市箭盘山流域为例,构建SWMM一维管道模型与LISFLOOD-FP二维地面模型并将其耦合,基于实测降雨“20180818”24 h暴雨资料,将得到的暴雨内涝淹没水深和淹没面积与该场次降雨情况下记录淹没点的范围相比较,验证耦合模型具有较好的适用性。进而基于耦合模型,对柳州市箭盘山流域2、5、10、20年一遇下设计降雨进行模拟,得到不同重现期下研究区的溢流节点、淹没水深和淹没面积,并于ARCGIS平台将结果可视化。结果表明,重现期由2年上升到20年过程中,溢流节点比例从9.03%增加至25.99%,溢流面积从0.473 km^(2)增加至2.114 km^(2);重点淹没区域分布在屏山大道、炮团路、西江路和东堤路。

基于FP-growth的老年行人交通事故损伤致因研究

为研究老年行人交通事故相关因素及其对损伤程度的影响,以中国交通事故深度调查数据库中2013—2023年710起机动车-老年行人事故为分析对象,利用FP-growth算法挖掘出4类影响因素28个类型变量共5594项关联规则。研究结果表明:行人年龄、碰撞速度、事故时间段及事故地点是影响老年行人事故死亡率的显著因素。特别是,行人年龄和碰撞速度对死亡率有显著影响,随着碰撞速度的增加,死亡率显著上升;60岁及以上老年行人年龄每增加1岁,其死亡率提升0.037倍。此外,事故发生在夜间、郊区或村庄的死亡率更高。研究结果可为提高老年行人交通安全,制定相关安全措施提供一定参考。

用于高压测量的MEMS硅-玻光纤FP压力传感器

研究了一种基于微机电系统(MEMS)技术用于高压测量的硅-玻光纤法布里-珀罗(FP)压力传感器。该传感器以硅材料作为敏感元件,将电感耦合等离子体(ICP)干法刻蚀后的单晶硅膜片和高硼硅玻璃阳极键合构成FP腔。传感头使用MEMS技术批量制造,结构稳定、抗过载能力强、在高压环境下不容易失效。实验结果表明,该传感器能够实现30 MPa的高压压力测量,灵敏度为46.94 nm/MPa,线性拟合度为0.99897,测量结果具有较好的一致性和可靠性,所设计的压力传感器在高压检测方面有很强的应用前景。

基于CFD的FPSO风载荷规范计算适用性研究

[目的]目前,设计人员多使用针对大型油轮的OCIMF规范和针对海上平台的API规范对FPSO风载荷进行计算,但因FPSO船舶上层建筑更为复杂,需进一步研究这2种规范对于FPSO风载荷计算的适用性。[方法]建立具有通用型上部模块的某30万吨级大型FPSO数值模型,对恶劣海况下不同风向角、横倾角下的FPSO所受风载荷进行数值模拟,分析其中存在的遮蔽效应;与规范计算结果进行对比分析,讨论在风载荷作用下FPSO受到的横倾力矩。[结果]结果显示,船舶正浮状态受到的最大风载荷和横倾力矩出现在270°风向角;船舶横倾状态下受到的风载荷和横倾力矩比正浮状态更大,最大横倾力矩出现在10.5°横倾角280°风向角;采用API规范和OCIMF规范得到的FPSO风载荷计算结果与CFD计算结果相差较大,二者在270°风向角的结果与CFD分别相差13.6%和24.5%。[结论]数值仿真给出的流场细节有利于分析上部模块间的遮蔽效应,能够较为准确地预报船舶所受到的风载荷,可以为考虑遮蔽效应的FPSO稳性设计提供参考。

基于位移-速度输入法结构多点激励FPS支座隔震效果研究

受行波效应、相干效应、场地效应的影响,大跨度结构的多点激励地震反应分析非常复杂,且考虑多点激励下对大跨空间网格结构隔震性能的影响是结构工程抗震方向中有重大意义的研究课题。以单层圆柱面大跨度空间网架结构为研究对象,基于直接位移法,应用SAP2000有限元软件建立了多点激励动力反应时程分析模型,进行结构在地震动一致激励和多点激励作用下的动力反应分析计算。结果表明:水平地震动多点激励效应对大跨度空间结构的影响十分显著,需采取使用多点激励输入方式。不设置隔震支座时,多点激励下,支撑柱弯矩与剪力随波速的减小而明显增加,柱顶位移变小,中柱轴力略微上升。基础隔震时,各支撑柱柱顶内力与位移的分配较无隔震时存在差异。边柱的弯矩、剪力及轴力均小于中柱的相应值,但柱顶位移增加明显。柱弯矩减震效率保持在45%~75%,柱剪力减震效率保持在60%~85%;柱顶隔震时,能得到比基础隔震更高的隔震效率,且柱弯矩隔震效率高达90%左右,柱剪力隔震效率保持在85%~90%。柱顶位移显著减小,柱轴力比基础隔震时略有增加。

基于双金属和FP干涉结构的光纤温度传感器

为了提高现有光纤温度传感器的灵敏度和测温范围,提出了一种基于双金属和法布里—珀罗(FP)干涉结构的传光型光纤温度传感器。以双金属片作为感温元件,利用部分反射镜和双金属片作为上、下表面,构成FP腔,采用调频激光干涉原理测量感温元件受热产生的FP腔腔长变化量,实现对温度的传感。实验结果表明:制成的光纤温度传感器的腔长变化量与温度呈较好的线性关系,线性度为99.96%,温度测量灵敏度达到了3885nm/℃,重复性高。

Geographical origin identification of winter jujube(Ziziphus jujuba Dongzao')by using multi-element fingerprinting with chemometrics

Winter jujube(Ziziphus jujuba'Dongzao')is greatly appreciated by consumers for its excellent quality,but brand infringement frequently occurs in the market.Here,we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer(ICP-MS).As a result,16 elements(Mg,K,Mn,Cu,Zn,Mo,Ba,Be,As,Se,Cd,Sb,Ce,Er,Tl,and Pb)exhibited significant differences in samples from different producing areas.Supervised linear discriminant analysis(LDA)and orthogonal projection to latent structures discriminant analysis(OPLS-DA)showed better performance in identifying the origin of samples than unsupervised principal component analysis(PCA).LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64%in the testing set,respectively.By using the multilayer perceptron(MLP)and C5.0,the prediction accuracy of the models could reach 96.36 and 91.06%,respectively.Based on the above four chemometric methods,Cd,Tl,Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube.Overall,this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics,and may also provide reference for establishing the origin traceability system of other fruits.

An Image Fingerprint and Attention Mechanism Based Load Estimation Algorithm for Electric Power System

With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.

A UWB/IMU-Assisted Fingerprinting Localization Framework with Low Human Efforts

With the rapid development of smart phone,the location-based services(LBS)have received great attention in the past decades.Owing to the widespread use of WiFi and Bluetooth devices,Received Signal Strength Indication(RSSI)fingerprintbased localization method has obtained much development in both academia and industries.In this work,we introduce an efficient way to reduce the labor-intensive site survey process,which uses an UWB/IMU-assisted fingerprint construction(UAFC)and localization framework based on the principle of Automatic radio map generation scheme(ARMGS)is proposed to replace the traditional manual measurement.To be specific,UWB devices are employed to estimate the coordinates when the collector is moved in a reference point(RP).An anchor self-localization method is investigated to further reduce manual measurement work in a wide and complex environment,which is also a grueling,time-consuming process that is lead to artificial errors.Moreover,the measurements of IMU are incorporated into the UWB localization algorithm and improve the label accuracy in fingerprint.In addition,the weighted k-nearest neighbor(WKNN)algorithm is applied to online localization phase.Finally,filed experiments are carried out and the results confirm the effectiveness of the proposed approach.

CMAES-WFD:Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy

Website fingerprinting,also known asWF,is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination,even when using the Tor anonymity network.While advanced attacks based on deep neural network(DNN)can performfeature engineering and attain accuracy rates of over 98%,research has demonstrated thatDNNis vulnerable to adversarial samples.As a result,many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success.However,these methods suffer from high bandwidth overhead or require access to the target model,which is unrealistic.This paper proposes CMAES-WFD,a black-box WF defense based on adversarial samples.The process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy(CMAES)optimization algorithm.Perturbations are injected into the local parts of the original traffic to control bandwidth overhead.According to the experiment results,CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting(DF)and VarCnn to below 8.3%and the bandwidth overhead to a maximum of only 14.6%and 20.5%,respectively.Specially,for Automated Website Fingerprinting(AWF)with simple structure,CMAES-WFD reduced the classification accuracy to only 6.7%and the bandwidth overhead to less than 7.4%.Moreover,it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.

An Active Deception Defense Model Based on Address Mutation and Fingerprint Camouflage

The static and predictable characteristics of cyber systems give attackers an asymmetric advantage in gathering useful information and launching attacks.To reverse this asymmetric advantage,a new defense idea,called Moving Target Defense(MTD),has been proposed to provide additional selectable measures to complement traditional defense.However,MTD is unable to defeat the sophisticated attacker with fingerprint tracking ability.To overcome this limitation,we go one step beyond and show that the combination of MTD and Deception-based Cyber Defense(DCD)can achieve higher performance than either of them.In particular,we first introduce and formalize a novel attacker model named Scan and Foothold Attack(SFA)based on cyber kill chain.Afterwards,we develop probabilistic models for SFA defenses to provide a deeper analysis of the theoretical effect under different defense strategies.These models quantify attack success probability and the probability that the attacker will be deceived under various conditions,such as the size of address space,and the number of hosts,attack analysis time.Finally,the experimental results show that the actual defense effect of each strategy almost perfectly follows its probabilistic model.Also,the defense strategy of combining address mutation and fingerprint camouflage can achieve a better defense effect than the single address mutation.

Certis: Cloud Asset Management & Threat Evaluation Using Behavioral Fingerprinting at Application Layer

This paper introduces Certis, a powerful framework that addresses the challenges of cloud asset tracking, management, and threat detection in modern cybersecurity landscapes. It enhances asset identification and anomaly detection through SSL certificate parsing, cloud service provider integration, and advanced fingerprinting techniques like JARM at the application layer. Current work will focus on cross-layer malicious behavior identification to further enhance its capabilities, including minimizing false positives through AI-based learning techniques. Certis promises to offer a powerful solution for organizations seeking proactive cybersecurity defenses in the face of evolving threats.

AWeb Application Fingerprint Recognition Method Based on Machine Learning

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

Fingerprint Study of Polygonati Rhizoma with Steaming and Exposing to the Sun Alternatively for Different Times

[Objectives]To explore the influence of different times of steaming and exposing to the sun on the fingerprint of Polygonati Rhizoma by studying the HPLC fingerprint of Polygonati Rhizoma processed products with different times of steaming and exposing to the sun,and to provide a basis for the determination of the best processing technology of Polygonati Rhizoma.[Methods]SETSAIL II AQ-C 18(5μm×250 mm×4.6 mm)was used as the column,the column temperature was 30℃,pure water(A)and acetonitrile(B)were eluted gradually,0-10 min,B(5%-10%),10-30 min,B(10%-35%),30-40 min,B(35%-60%),40-45 min,B(60%-100%),flow rate 1 mL/min,absorption wavelength 200 nm.[Results]The relative retained peak area RSDs of the common peaks in the precision,reproducibility and stability tests were all less than 5%.There were 17 common peaks in the fingerprint of nine batches of samples,and the retention time of Peak 2 was basically the same as that of the reference peak of 5-HMF.Peak 4 mainly existed in the chromatogram of Sample 3 to Sample 5,peaks 5 and 11 mainly existed after Sample 3,peaks 9,14 and 16 mainly existed after Sample 6,and peaks 12 and 17 mainly existed after Sample 4.[Conclusions]A total of 17 common peaks were obtained,and the Peak 2 was the designated peak,and the chemical components of each processed product were different.

UV,five wavelengths fusion and electrochemical fingerprints combined with antioxidant activity for quality control of antiviral mixture

Aiming to ensure the consistency of quality control of Traditional Chinese Medicines(TCMs),a combination method of high-performance liquid chromatography(HPLC),ultraviolet(UV),electrochemical(EC)was developed in this study to comprehensively evaluate the quality of Antiviral Mixture(AM),and Comprehensive Linear Quantification Fingerprint Method(CLQFM)was used to process the data.Quantitative analysis of three active substances in TCM was conducted.A fivewavelength fusion fingerprint(FWFF)was developed,using second-order derivatives of UV spectral data to differentiate sample levels effectively.The combination of HPLC and UV spectrophotometry,along with electrochemical fingerprinting(ECFP),successfully evaluated total active substances.Ultimately,a multidimensional profiling analytical system for TCM was developed.

FP电压值范围和匀整粗节机理分析

为验证乌斯特理论推荐的FP电压值范围,采用明显偏大、甚至超出理论推荐范围的FP电压值,对比不同号数FP喇叭口输出熟条的质量变化,说明FP电压值的绝对值偏大掌握时,从号数较小的FP喇叭口出来的熟条外观质量更好,并分析棉条定量和喇叭口大小的匹配依据、自调匀整并条机的匀整机理。指出:FP电压值的范围为-4000 mV~-6000 mV,FP喇叭口的号数偏小掌握,棉条的紧密度更好,TP-N棉结也更少,更能够发挥自调匀整系统的作用。

The significance of metabolic fingerprinting in carcinogenesis

Carcinogenesis describes the process through which normal cells transform into malignant cells(cancer).There were an estimated 18.1 million new cases of cancer(all cancers combined excluding non-melanoma skin cancer)worldwide in 2020:8.8 million(48%)in females and 9.3 million(52%)in males,giving a male:female ratio of 10:9.5.It may be initiated by the action of biological,physical,or chemical agents that cause a non-lethal,permanent,DNA error on the cell with a consequence of altered cell metabolism.This altered cell metabolism include the Warburg effect,altered lipid and amino acid metabolism and production of various metabolites.It also results in unique metabolic dependencies that,in some cases,can be targeted with precision medicine and nutrition,including drugs that selectively target metabolic enzymes.Metabolic fingerprinting has been applied to the study of carcinogenesis and is particularly helpful in early diagnosis,staging and choice of treatment,thus improving health outcomes.This technology could therefore be harnessed effectively while combining with other omics technologies.

基于FP-Growth算法的运毒嫌疑车辆智能推荐研究

毒品运输是毒品犯罪的重要环节,虽然毒品运输的手段越来越多样化,但公路运输仍然是主要的运输方式之一,而运毒人员有着各自经典的运毒模式。文中对运毒模式进行特征挖掘,发现存在前后车伴随的规律,根据实际业务中前后车行为以半小时为时间间隔导向,建模时选择PostgreSQL数据库。在数据库中建立过往车辆前半小时中间表、后半小时中间表、中间跨度表,运用人工智能数据挖掘技术实现从大量的通行车辆中抽取车辆伴随信息,采用FP-Growth算法挖掘频繁项集,查找高频出现车牌号,通过设定阈值并找到对应的关联规则,经过缉毒民警提供的黑名单进行过滤并排序,最后进行车辆嫌疑度的推荐,为民警拦截嫌疑车辆提供支持,能够在一定程度上提高对嫌疑车辆排查的针对性、准确性和有效性。

基于FP-growth算法的高维混合属性数据挖掘方法

常规高维混合属性数据挖掘方法多采用云平台技术,无法完整保留数据的结构相似性,使得数据挖掘效率较低。为此,提出了基于FP-growth算法的高维混合属性数据挖掘方法。为了改善数据质量,根据高维混合属性数据在数据库中的存储结构,采用了一种固定算法实现数据去噪,并依据数据类型计算分类型和数值型相似度,结合FP-growth算法对频繁项样本分支进行筛选生成项表头,保证数据结构相似性的完整性,通过搜索项表头输出有效关联规则,实现数据挖掘过程。实验结果表明,所提方法具有较高的挖掘效率。

FPS游戏地图的著作权属性探析

网络游戏行业迅速崛起的同时,游戏相关的著作权问题也随之增加,其中有关游戏地图的侵权纠纷也在司法实践中逐渐增加,其核心问题在于游戏地图作品属性的认定。在FPS游戏中,游戏地图具有不可否认的重要意义,从其制作过程和本质功能出发能够为判定其作品属性提供有益思路。场景地图发挥指示玩家位置的实用性功能和呈现美术效果的艺术性功能,具备图形作品和美术作品的双重属性,而平面缩略图在满足独创性要求的前提下能够构成图形作品。