Selective Mapping Scheme for Universal Filtered Multicarrier

The next step in mobile communication technology,known as 5G,is set to go live in a number of countries in the near future.New wireless applica-tions have high data rates and mobility requirements,which have posed a chal-lenge to mobile communication technology researchers and designers.5G systems could benefit from the Universal Filtered Multicarrier(UFMC).UFMC is an alternate waveform to orthogonal frequency-division multiplexing(OFDM),infiltering process is performed for a sub-band of subcarriers rather than the entire band of subcarriers Inter Carrier Interference(ICI)between neighbouring users is reduced via the sub-bandfiltering process,which reduces out-of-band emissions.However,the UFMC system has a high Peak-to-Average Power Ratio(PAPR),which limits its capabilities.Metaheuristic optimization based Selective mapping(SLM)is used in this paper to optimise the UFMC-PAPR.Based on the cognitive behaviour of crows,the research study suggests an innovative metaheuristic opti-mization known as Crow Search Algorithm(CSA)for SLM optimization.Com-pared to the standard UFMC,SLM-UFMC system,and SLM-UFMC with conventional metaheuristic optimization techniques,the suggested technique sig-nificantly reduces PAPR.For the UFMC system,the suggested approach has a very low Bit Error Rate(BER).

A probability theory for filtered ghost imaging

Based on probability density functions,we present a theoretical model to explain filtered ghost imaging(FGI)we first proposed and experimentally demonstrated in 2017[Opt.Lett.425290(2017)].An analytic expression for the joint intensity probability density functions of filtered random speckle fields is derived according to their probability distributions.Moreover,the normalized second-order intensity correlation functions are calculated for the three cases of low-pass,bandpass and high-pass filterings to study the resolution and visibility in the FGI system.Numerical simulations show that the resolution and visibility predicted by our model agree well with the experimental results,which also explains why FGI can achieve a super-resolution image and better visibility than traditional ghost imaging.

Woody plant taxonomic,functional,and phylogenetic diversity decrease along elevational gradients in Andean tropical montane forests:Environmental filtering and arrival of temperate taxa

Mountains are paramount for exploring biodiversity patterns due to the mosaic of topographies and climates encompassed over short distances.Biodiversity research has traditionally focused on taxonomic diversity when investigating changes along elevational gradients,but other facets should be considered.For first time,we simultaneously assessed elevational trends in taxonomic,functional,and phylogenetic diversity of woody plants in Andean tropical montane forests and explored their underlying ecological and evolutionary causes.This investigation covered four transects(traversing ca.2200 m a.s.l.) encompassing 114 plots of 0.1 ha across a broad latitudinal range(ca.10°).Using Hill numbers to quantify abundance-based diversity among 37,869 individuals we observed a consistent decrease in taxonomic,functional,and phylogenetic diversity as elevation increased,although the decrease was less pronounced for higher Hill orders.The exception was a slight increase in phylogenetic diversity when dominant species were over-weighted.The decrease in taxonomic and functional diversity might be attributed to an environmental filtering process towards highlands,where the increasingly harsher conditions exclude species and functional strategies.Besides,the differences in steepness decrease between Hill orders suggest that rare species disproportionately contribute to functional diversity.For phylogenetic diversity the shifting elevational trend between Hill orders indicates a greater than previously considered influence in central Andean highlands of tropical lowlands originated species with strong niche conservatism relative to distantly related temperate lineages.This could be explained by a decreasing presence and abundance of temperate,extratropical taxa towards the central Andes relative to northern or southern Andes,where they are more prevalent.

Numerical Study of the Efficiency of Multi-Layer Membrane Filtration in Desalination Processes

Multi-layer membrane filtration is a widely used technology for separating and purifying different components ofa liquid mixture. This technique involves passing the liquid mixture through a series of membranes with decreasing pore sizes, which allows for the separation of different components according to their molecular size. Thisstudy investigates the filtration process of a fluid through a two-dimensional porous medium designed forseawater desalination. The focus is on understanding the impact of various parameters such as the coefficientof friction, velocity, and the number of layers on filtration efficiency. The results reveal that the number of layersplays a crucial role in desalination, with an increase in layers leading to enhanced filtration quality, following apower law relationship. The study explores the influence of the coefficient of friction on filtration performance,emphasizing its significant effect on the number of particles filtered over time. Additionally, the role of the initialvelocity in filtration efficiency is examined, showing distinct effects at both high and low velocities. Biofouling isidentified as a factor influencing filtration, with an initial increase in filtered particles followed by a decline due toparticle accumulation in pores.

基于超声波信号的门式起重机无损快速探伤研究

目前常规的门式起重机无损快速探伤方法主要通过采用探测设备对检测区域进行扫描识别,从而获取损伤信息,由于存在扫描盲区,导致无损探伤精度较低。对此,本文提出基于超声波信号的门式起重机无损快速探伤方法。通过采用多个相互独立且角度不同的超声探头进行组合,缩小检测扫描盲区。并结合滤波算法对采集到的超声波信号数据进行去噪处理,同时提取超声信号的时域能量特征。最后通过计算超声波信号传输往返时间,获取起重机表面裂纹缺陷的具体位置,实现无损快速探伤检测。在实验中,对提出的方法进行了无损探伤性能的检验。经实验结果表明,采用提出的方法对不同类型的裂纹缺陷进行检测时,轴向定位误差值较低,具备较为理想的探伤检测精度。

Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

The use of programming online judges(POJs)has risen dramatically in recent years,owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming.Since POJs have greater number of pro-gramming problems in their repository,learners experience information overload.Recommender systems are a common solution to information overload.Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’current context,like learning goals and current skill level(topic knowledge and difficulty level).To overcome the issue,we propose a context-aware practice problem recommender system based on learners’skill level navigation patterns.Our system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and tofind learners’learning goals.Collaborativefiltering(CF)and con-tent-basedfiltering approaches are employed to recommend problems in the cur-rent and next skill levels based on frequent skill level navigation patterns.The sequence similarity measure is used tofind the top k neighbors based on the sequence of problems solved by the learners.The experiment results based on the real-world POJ dataset show that our approach considering the learners’cur-rent skill level and learning goals outperforms the other approaches in practice problem recommender systems.

A Mixed Method for Feature Extraction Based on Resonance Filtering

Machine learning tasks such as image classification need to select the features that can describe the image well.The image has individual features and common features,and they are interdependent.If only the individual features of the image are emphasized,the neural network is prone to overfitting.If only the common features of images are emphasized,neural networks will not be able to adapt to diversified learning environments.In order to better integrate individual features and common features,based on skeleton and edge individual features extraction,this paper designed a mixed feature extraction method based on reso-nancefiltering,named resonance layer.Resonance layer is in front of the neural network input layer,using K3M algorithm to extract image skeleton,using the Canny algorithm to extract image border,using resonancefiltering to reconstruct training image byfiltering image noise,through the common features of the images in the training set and efficient expression of individual characteristics to improve the efficiency of feature extraction of neural network,so as to improve the accuracy of neural network prediction.Taking the fully connected neural net-work and LeNet-5 neural networks for example,the experiment on handwritten digits database shows that the proposed mixed feature extraction method can improve the accuracy of training whilefiltering out part of image noise data.

Alternatingly stacked thin film electrodes-based compact aqueous hybrid electrochemical capacitors for hundred-volts AC line filtering

Filtering capacitor with compact configuration and a wide range of operating voltage has been attracting increasing attention for the smooth conversion of the electric signal in modern circuits.Lossless integration of capacitor units can be regarded as one of the efficient ways to achieve a wider voltage range,which has not yet been fully conquered due to the lack of rational designs of the electrode structure and integration technology.This study presents an alternatingly stacked assemble technology to conveniently fabricate compact aqueous hybrid integrated filtering capacitors on a large scale,in which a unit consists of rGO/MXene composite film as a negative electrode and PEDOT:PSS based film as a positive electrode.Benefiting from the synergistic effect of rGO and MXene components,and morphological characteristics of PEDOT:PSS,the capacitor unit exhibits outstanding AC line filtering with a large areal specific energy density of 1,015 μF V^(2)cm^(-2)(0.28 μW h cm^(-2)) at 120 Hz.After rational integration,the assembled capacitors present compact/lightweight configuration and lossless frequency response,as reflected by almost constant resistor-capacitor time constant of 0.2 ms and dissipation factor of 15% at120 Hz,identical to those of the single capacitor unit.Apart from standing alone steadily on a flower,a small volume(only 8.1 cm^(3)) of the integrated capacitor with 70 units connected in series achieves hundred-volts alternating current line filtering,which is superior to most reported filtering capacitors with sandwich configuration.This study provides insight into the fabrication and application of compact/ultralight filtering capacitors with lossless frequency response,and a wide range of operating voltage.

Hybrid Recommender System Using Systolic Tree for Pattern Mining

A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.

一种改进的slope one推荐算法研究

随着互联网信息的不断膨胀,互联网已经进入了大数据时代。为了解决人们当前面临的信息过载问题,个性化推荐系统应运而生,系统核心是其所使用的推荐算法。slope one算法是一种简单高效的典型协同过滤推荐算法,算法通过对用户——项目评分矩阵进行线性回归,预测用户对于未评分项目的可能评分。由于算法的输入只有用户评分矩阵,而实际情况中的评分矩阵通常较为稀疏,因此数据稀疏性是影响其推荐准确率的主要问题。为了克服该问题,文章基于现有研究提出了一种改进的slope one算法。该算法根据所有用户对项目的历史评分计算其项目相似度,然后将其加入评分公式予以修正,同时针对稀疏的评分矩阵使用奇异值分解技术降低矩阵维度,生成更加稠密的相似矩阵作为slope one核心计算部分的输入。项目相似度的引入增加了算法对于项目内在联系的考虑,推荐结果更加合理。而奇异值分解则可以使稀疏的评分矩阵转换为更适用于算法计算的形式。通过项目相似性和奇异值分解两种技术的融合,文中算法实现了更好的推荐准确性和适应性。

恶意URL多层过滤检测模型的设计与实现

近年来,恶意网站危害到用户的方方面面,对恶意网站URL的检测越来越重要。目前对恶意URL的检测主要有黑白名单技术和机器学习分类算法,黑白名单技术对于没有标记集的URL无能为力,每种机器学习分类算法也有各自不擅长的数据。文章结合黑白名单技术和机器学习算法提出了恶意URL多级过滤检测模型,训练每层过滤器的阈值,过滤器达到阈值的能够直接对URL进行判定,否则过滤到下一层过滤器。本模型能够充分发挥不同分类器针对所擅长数据类型的作用。文章用实例验证了多级过滤检测模型能够提高URL检测的准确率。

供水管井允许出水量的合理确定

每一眼管井在井结构确定的条件下,有一个最大允许出水量,而盲目追求增大出水量,势必造成管井涌砂,过滤管腐蚀和堵塞的加剧。松散含水层供水管井允许出水量既要满足地下水允许开采量“宏观”定义要求,从供水管井使用寿命考虑又要满足管井两个流速参数:井壁允许进水流速和滤管允许进水流速。本文给出了管井允许出水量确定方法和计算框图,并以实例说明之,在具体应用时某些细节也予以了说明。

交互多模型在高动态导航定位中的应用

文章将高动态目标的运动状态分为匀速运动、匀加速运动、转弯模型等状态模型,采用估计目标当前运动状态进而在多个模型中自适应切换运动状态模型的形式实现高动态目标的导航定位,提升卫星导航终端在高动态条件下的导航定位性能,仿真结果也验证了该算法在高动态条件下具备较好的导航定位性能。

多重模型自适应滤波的故障诊断与容错

对多重模型自适应滤波（ＭＭＡＦ）进行了改进并将它用于控制系统的传感器的故障诊断与容错。数值仿真表明，其故障诊断准确度高，容错效果良好。

局部放电信号处理相关方法的研究

分析了FFT阈值滤波和基于能量比预处理的FFT阈值滤波法,分别采用这两种方法对实际监测信号进行处理,并对比分析了两种信号处理结果。结果表明,FFT阈值滤波法采用不同的滤波系数时,信号处理结果会呈现很大的差异;基于能量比预处理法能找到能量突变的点,信号处理更具针对性,但采用能量比预处理阈值滤波过程中应当注意选取合适的窗口长度,且在信号处理的时候应当尽可能地减少局部放电信号能量的损失。

三段并流洗涤过滤机计算机控制研究

水平胶(履)带式三段并流洗涤真空过滤机生产效率高,可靠性好。通过计算机控制系统的应用研究,扩大了该设备的适用范围。对比传统设备,它具有能耗低等很多优点,已成功应用于多种物料的过滤。

基于残差正交判别的UPF容错算法

从改进提议分布的成片野值容错能力入手,提出了基于残差正交判别的UPF容错滤波算法.该算法将残差正交判别法UKF的野值自适应性和粒子滤波的"适者生存性"有机地结合起来.通过非线性状态估计的实验,证实了这种新的自适应粒子滤波对成片野值处理的有效性.

扩展卡尔曼滤波在受到恶意攻击系统中的状态估计

设计了一种分布式扩展卡尔曼滤波器(Extended Kalman lter,EKF),对非线性目标状态进行估计.在设计过程中,对滤波误差上界进行优化,获得了最优滤波增益.此外,在通信过程中,考虑恶意攻击信号的同时引入了分布式事件触发机制,使得系统在保持一定的估计精度的情况下节省通信资源.最后,以室内的机器人定位问题为例,验证了提出的滤波器的有效性.

线形痕迹三维点云去噪技术研究

目的通过对采集到的线形痕迹的三维点云数据进行去噪处理研究,为后续点云数据的特征提取和匹配提供良好的依据。方法针对共聚焦显微镜采集的10种常见工具痕迹的点云数据,实验了均值滤波、中值滤波、曲率滤波三种点云数据去噪的滤波算法,通过对这三种算法处理结果的分析,提出了融合这三种滤波的线形痕迹三维点云数据去噪算法。结果测试样本在去噪后,点云特征更加显著,经过计算滤波后点云、带噪声点云与标准点云的误差值,可以发现最大误差平均减少了79.6%。结论融合后的去噪算法利用了三种滤波器各自的优势,在针对工具的线形痕迹点云的去噪实验中取得了比单独采用三种滤波方法更好的效果。

煤矿电网谐波治理

电压偏离正压波形 ,会产生谐波电流 ,对电网产生不利影响 ,介绍了谐波对电网造成危害 ,对谐波采取抑制措施 ,并提出合理的设计方案 ,说明其应用效果。