Estimating the subsolar magnetopause position from soft X-ray images using a low-pass image filter

The Lunar Environment heliospheric X-ray Imager(LEXI)and Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)missions will image the Earth’s dayside magneto pause and cusps in soft X-rays after their respective launches in the near future,to specify glo bal magnetic reconnection modes for varying solar wind conditions.To suppo rt the success of these scientific missions,it is critical to develop techniques that extract the magnetopause locations from the observed soft X-ray images.In this research,we introduce a new geometric equation that calculates the subsolar magnetopause position(RS)from a satellite position,the look direction of the instrument,and the angle at which the X-ray emission is maximized.Two assumptions are used in this method:(1)The look direction where soft X-ray emissions are maximized lies tangent to the magnetopause,and(2)the magnetopause surface near the subsolar point is almost spherical and thus RSis nea rly equal to the radius of the magneto pause curvature.We create synthetic soft X-ray images by using the Open Geospace General Circulation Model(OpenGGCM)global magnetohydrodynamic model,the galactic background,the instrument point spread function,and Poisson noise.We then apply the fast Fourier transform and Gaussian low-pass filte rs to the synthetic images to re move noise and obtain accurate look angles for the soft X-ray pea ks.From the filte red images,we calculate RS and its accuracy for different LEXI locations,look directions,and solar wind densities by using the OpenGGCM subsolar magnetopause location as ground truth.Our method estimates RS with an accuracy of<0.3 RE when the solar wind density exceeds>10 cm-3.The accuracy improves for greater solar wind densities and during southward interplanetary magnetic fields.The method ca ptures the magnetopause motion during southwa rd interplaneta ry magnetic field turnings.Consequently,the technique will enable quantitative analysis of the magnetopause motion and help reveal the dayside reconnection modes for dynamic solar wind conditions.This technique will suppo rt the LEXI and SMILE missions in achieving their scientific o bjectives.

On low-pass digital filters in oceanography

-Two types of filters are widely used to remove semidirunal and diurnal tidal signals and other high frequency noises in oceanography. The first type of filters uses moving average with weights in time domain, and can be easily operated. Some data will be lost at each end of the time series, especially for the low low-pass filters. The second type of filters uses the discrete Fourier transform filter (DFTF) which operates in the frequency domain, and there are no data loss at the ends for the forward transform. However, owing to the Gibbs phenomenon and the discrete sampling (Nyquist effect) , ringing appears in the inverse transformed data, which is especially serious at each end. Thus some data at the ends are also discarded. The present study tries to find out what causes the ringing and then to seek for methods to overcome the ringing. We have found that there are two kinds of ringings, one is the Gibbs phenomenon, as defined before. The other is the 'Nyquist'ringing due to sampling Nyquist critical frequency. The former is due to the abrupt transition in frequency band. The Gibbs and Nyquist effects show the ringing at each end of the filtered time series. Thus, the use of a cosine taper or a linear taper on the window in the frequency domain makes the transition band smooth, so that the Gibbs phenomenon will be minimized. Before applying the Fast Fourier Transform (FFT), the original time series at each end is properly tapered by a split cosine bell that reduces significant ringing since this method limits the energy transfer from outside of the Nyquist frequency. Thus, the DFTF can be a powerful tool to suppress the signals in which we are not interested, with sharp peaks in low frequency variation and less data loss at each end.

Feature Matching via Topology-Aware Graph Interaction Model

Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Joint learning based on multi-shaped filters for knowledge graph completion

To solve the problem of missing many valid triples in knowledge graphs(KGs),a novel model based on a convolutional neural network(CNN)called ConvKG is proposed,which employs a joint learning strategy for knowledge graph completion(KGC).Related research work has shown the superiority of convolutional neural networks(CNNs)in extracting semantic features of triple embeddings.However,these researches use only one single-shaped filter and fail to extract semantic features of different granularity.To solve this problem,ConvKG exploits multi-shaped filters to co-convolute on the triple embeddings,joint learning semantic features of different granularity.Different shaped filters cover different sizes on the triple embeddings and capture pairwise interactions of different granularity among triple elements.Experimental results confirm the strength of joint learning,and compared with state-of-the-art CNN-based KGC models,ConvKG achieves the better mean rank(MR)and Hits@10 metrics on dataset WN18 RR,and the better MR on dataset FB15k-237.

Design of low-pass filter based on a novel defected ground structure

A novel defected ground structure （DGS） for the microstrip line is proposed in this paper. The DGS lattice has more defect parameters so that it can provide better performance than the conventional dumbbell-shaped DGS. Selectivity is improved by 97.2% with a sharpness factor of 24.6%. The method is applied to the design of a low-pass filter to confirm validity of the proposed DGS.

Guided Intra-Patch Smoothing Graph Filtering for Single-Image Denoising

Graph filtering is an important part of graph signal processing and a useful tool for image denoising.Existing graph filtering methods,such as adaptive weighted graph filtering(AWGF),focus on coefficient shrinkage strategies in a graph-frequency domain.However,they seldom consider the image attributes in their graph-filtering procedure.Consequently,the denoising performance of graph filtering is barely comparable with that of other state-of-the-art denoising methods.To fully exploit the image attributes,we propose a guided intra-patch smoothing AWGF(AWGF-GPS)method for single-image denoising.Unlike AWGF,which employs graph topology on patches,AWGF-GPS learns the topology of superpixels by introducing the pixel smoothing attribute of a patch.This operation forces the restored pixels to smoothly evolve in local areas,where both intra-and inter-patch relationships of the image are utilized during patch restoration.Meanwhile,a guided-patch regularizer is incorporated into AWGF-GPS.The guided patch is obtained in advance using a maximum-a-posteriori probability estimator.Because the guided patch is considered as a sketch of a denoised patch,AWGF-GPS can effectively supervise patch restoration during graph filtering to increase the reliability of the denoised patch.Experiments demonstrate that the AWGF-GPS method suitably rebuilds denoising images.It outperforms most state-of-the-art single-image denoising methods and is competitive with certain deep-learning methods.In particular,it has the advantage of managing images with significant noise.

Image Denoising with Adaptive Weighted Graph Filtering

Graph filtering,which is founded on the theory of graph signal processing,is proved as a useful tool for image denoising.Most graph filtering methods focus on learning an ideal lowpass filter to remove noise,where clean images are restored from noisy ones by retaining the image components in low graph frequency bands.However,this lowpass filter has limited ability to separate the low-frequency noise from clean images such that it makes the denoising procedure less effective.To address this issue,we propose an adaptive weighted graph filtering(AWGF)method to replace the design of traditional ideal lowpass filter.In detail,we reassess the existing low-rank denoising method with adaptive regularizer learning(ARLLR)from the view of graph filtering.A shrinkage approach subsequently is presented on the graph frequency domain,where the components of noisy image are adaptively decreased in each band by calculating their component significances.As a result,it makes the proposed graph filtering more explainable and suitable for denoising.Meanwhile,we demonstrate a graph filter under the constraint of subspace representation is employed in the ARLLR method.Therefore,ARLLR can be treated as a special form of graph filtering.It not only enriches the theory of graph filtering,but also builds a bridge from the low-rank methods to the graph filtering methods.In the experiments,we perform the AWGF method with a graph filter generated by the classical graph Laplacian matrix.The results show our method can achieve a comparable denoising performance with several state-of-the-art denoising methods.

Compact Metamaterial Microstrip Low-Pass Filter

Complimentary hexagonal-omega structures are used to design compact, low insertion loss (IL), low pass filter with sharp cut-off. It has been designed for improvement of roll-off performance based on both μ and ε negative property of the complimentary hex-omega structure while maintaining the filter pass-band performance. By properly designing and loading the hexagonal-omega structure in the ground of microstrip line not only improve the roll-off of the low pass filter, but also reduced the filter size. The simulated results indicate that the proposed filter achieves a flat pass band with no ripples as well as selectivity of 19.68 dB/GHz, corresponding to 5-unit cells hex-omega structures. This significantly exceeds the 5.6 dB/GHz selectivity of the conventional low pass filter design, due to sub-lambda dimensions of the hex-omega structure. A prototype filter implementing area is: 0.712λg x 0.263λg, λg being the guided wavelength at 3-dB cut-off frequency (fc). The proposed filter has a size smaller by 36.2%.

Microstrip Low-Pass Elliptic Filter Design Based on Implicit Space Mapping Optimization

It is a time-consuming and often iterative procedure to determine design parameters based on fine, accurate but expensive, models. To decrease the number of fine model evaluations, space mapping techniques may be employed. In this approach, it is assumed both fine model and coarse, fast but inaccurate, one are available. First, the coarse model is optimized to obtain design parameters satisfying design objectives. Next, auxiliary parameters are calibrated to match coarse and fine models’ responses. Then, the improved coarse model is re-optimized to obtain new design parameters. The design procedure is stopped when a satisfactory solution is reached. In this paper, an implicit space mapping method is used to design a microstrip low-pass elliptic filter. Simulation results show that only two fine model evaluations are sufficient to get satisfactory results.

Filter Graph技术在语音处理系统中的应用

随着Internet的普及和VoIP技术的发展,语音媒体流的处理显得越来越重要.因此,如何将语音流媒体的处理变得简单而高效逐渐成为人们研究的重点问题.本文以ISDN媒体网关为例,探讨如何使用Fiter Graph技术构建语音处理模块,完成复杂多变的语音信号的处理.在本人开发的ISDN语音媒体网关中,应用Filter Graph技术,使我们在短期内实现了抖动消除、语音压扩、RTP打包等复杂的功能,而且编制的语音媒体处理软件易于维护和升级.

Comparative Study of Low-Pass Filter and Phase-Locked Loop Type Speed Filters for Sensorless Control of AC Drives

High quality speed information is one of the key issues in machine sensorless drives,which often requires proper filtering of the estimated speed.This paper comparatively studies typical low-pass filters(LPF)and phase-locked loop(PLL)type filters with respect to ramp speed reference tracking and steady-state performances,as well as the achievement of adaptive cutoff frequency control.An improved LPF-based filter structure with no ramping and steady-state errors caused by filter parameter quantization effects is proposed,which is suitable for applying LPF for sensorless drives of AC machines,especially when fixed-point digital signal processor is selected e.g.in mass production.Furthermore,the potential of adopting PLL for speed filtering is explored.It is demonstrated that PLL type filters can well maintain the advantages offered by the improved LPF.Moreover,it is found that the PLL type filters exhibit almost linear relationship between the cutoff frequency of the PLL filter and its proportional-integral(PI)gains,which can ease the realization of speed filters with adaptive cutoff frequency for improving the speed transient performance.The proposed filters are verified experimentally.The PLL type filter with adaptive cutoff frequency can provide satisfactory performances under various operating conditions and is therefore recommended.

基于邻域采样的多任务图推荐算法

近年来,图神经网络(GNN)成为解决协同过滤的主流方法之一。它通过构建用户-物品图,模拟用户与物品的交互关系,并用GNN学习它们的特征表示。尽管现有在模型结构上的研究已取得了较大进展,但如何在图结构上更有效地进行负采样仍未有效解决。为此,提出一种基于邻域采样的多任务图推荐算法。该算法提出了一种基于GNN的邻域采样策略,该策略以每个用户为中心构建子图,将次高阶物品作为用户邻域采样的负样本,可以更有效地挖掘强负样本并提高采样质量。通过GNN对图结点进行信息聚合与特征提取,得到结点的最终嵌入表示。设计一种余弦边际损失来过滤部分冗余负样本,以有效减少采样过程中的噪声数据。同时,该算法引入了多任务策略对模型进行联合优化,以增强模型的泛化能力。在3个公开数据集上进行的大量实验表明,该算法在大多数情况下明显优于其他主流算法。

多任务联合学习的图卷积神经网络推荐

基于图神经网络的协同过滤推荐可以更有效地挖掘用户项目之间的交互信息,但其性能依然受到数据稀疏和表征学习质量不高问题的影响,因此提出一种多任务联合学习的图卷积神经网络推荐(multi-task joint learning for graph convolutional neural network recommendations,MTJL-GCN)模型。利用图神经网络在用户-项目交互图上所聚集到的同质结构信息与初始嵌入信息形成结构邻居关系,设计节点邻居关系的对比学习辅助任务来缓解数据稀疏问题;向节点的原始表征添加随机的统一噪声进行表征级数据增强,构建节点表征关系的对比学习辅助任务,并提出直接优化对齐性和均匀性两个属性的学习目标来提高表征学习质量;将图协同过滤推荐任务与对比学习辅助任务和直接优化学习目标进行联合训练,从而提升推荐性能。在Amazon-books和Yelp2018两个公开数据集上进行实验,该模型在Recall@k和NDCG@k两个推荐性能指标上的表现均优于基线模型,证明了MTJL-GCN模型的有效性。

考虑学科交叉需求的学术交流资源推荐方法

针对学术交流资源数量与日俱增,学者自身研究方向多样性、学者与资源的交互行为稀疏等问题,提出考虑学科交叉需求的学术交流资源推荐方法。融合协同过滤与知识图谱算法,通过优化的TransE模型对学术交流资源知识进行表示学习,基于资源语义向量计算学术交流资源语义相似度,分析学者对学科交叉研究的需求,基于交互行为计算学者相似度,形成学者对学术交流资源的感兴趣程度,进而得出最终推荐结果。结果表明,相比传统推荐协同过滤推荐算法,该算法拥有较高的性能。

基于分区过滤-增量验证的图编辑相似查询

图编辑相似查询问题是指从图集G中查询出所有与查询图q的图编辑距离(Graph Edit Distance,GED)在给定阈值τ内的数据图.由于GED计算是NP-Hard问题,现有的研究多采用过滤-验证框架进行查询,对未能过滤掉的图采用A*-GED算法验证.本文提出了分区过滤-增量验证框架PFIV来处理图相似查询问题,在增强过滤效果的同时,还能加快验证速度.首先,在过滤阶段提出了2种分区策略,用来加快分区速度.(1)映射顶点顺序策略:在分区过程中,基于图的特征信息和结构信息提出分区时顶点的映射顺序,尽快过滤掉不相似的图,减少计算量;(2)分区结束条件策略:在分区过程中,设置分区结束条件,加快不相似图的过滤速度.其次,在验证阶段提出了增量验证策略,利用过滤阶段保留的映射结果,设计状态空间树,进行增量验证,加快验证阶段的计算.最后,通过大量实验验证了PFIV能够高效地处理图编辑相似查询问题,对比原有算法,查询效率提高8%~17%,并证明了所提出策略的有效性.

两阶段文档筛选和异步多粒度图多跳问答

多跳问答旨在通过对多篇文档内容进行推理,来预测问题答案以及针对答案的支撑事实。然而当前的多跳问答方法在文档筛选任务中旨在找到与问题相关的所有文档,未考虑到这些文档是否都对找到答案有所帮助。因此,该文提出一种两阶段的文档筛选方法。第一阶段通过对文档进行评分且设置较小的阈值来获取尽可能多的与问题相关文档,保证文档的高召回率;第二阶段对问题答案的推理路径进行建模,在第一阶段的基础上再次提取文档,保证文档的高精确率。此外,针对由文档构成的多粒度图,提出一种新颖的异步更新机制来进行答案预测以及支撑事实预测。提出的异步更新机制将多粒度图分为异质图和同质图来进行异步更新以更好地进行多跳推理。该方法在性能上优于目前主流的多跳问答方法,验证了该方法的有效性。

基于图滤波与自表示的无监督特征选择算法

针对现有方法未考虑数据的高阶邻域信息而不能完全捕捉数据内在结构的问题,提出一种基于图滤波与自表示的无监督特征选择算法.首先,将高阶图滤波器应用于数据获得其平滑表示,并设计一个正则化器联合高阶图信息进行自表示矩阵学习以捕捉数据的内在结构;其次,应用l_(2,1)范数重建误差项和特征选择矩阵,以增强模型的鲁棒性与稀疏性选择判别的特征;最后,用一个迭代算法有效地求解所提出的目标函数,并进行仿真实验以验证该算法的有效性.

基于图优化参数辨识的船体变形测量方法

针对船体变形惯性匹配测量中模型参数准确性影响测量精度的问题,本文提出了一种基于图优化的船体变形模型参数辨识方法并应用于惯性匹配测量。通过分析船体变形模型预设参数对惯性匹配测量的Kalman滤波影响,得到参数对惯性匹配精度的影响机制;利用船体变形历史数据,结合待辨识的船体变形模型参数组成图优化超图,建立船体变形参数辨识的图优化模型,实现船体变形惯性匹配预设模型参数的辨识,最后将参数辨识结果代入惯性匹配方程以完成准确测量。仿真实验验证了该方法可以有效地完成船体变形参数辨识,保障船体变形惯性匹配测量的准确度。

基于门控图游走网络的推荐多样性研究

近年来,纯粹追求准确性的推荐算法已不再符合用户日益增长的多元化需求.因为该类算法将所有用户同等对待,导致推荐结果趋于单一化.从推荐系统的多样性角度出发,提出由两路图游走网络和门控网络组成的门控图游走网络.图游走网络在原有邻域上扩展一类新邻域,聚合两类邻域的信息,从而生成偏向准确性或多样性的推荐结果.门控网络对两个不同偏好推荐结果进行选择,得到最终推荐结果.不同于其他推荐多样性算法,门控图游走网络的推荐结果准确性-多样性比例可由超参数λ调整,而不是完全由算法决定.3个真实数据集的实验结果验证了门控图游走网络在多样化整体协作推荐方面的有效性.