How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

Iterative Learning Fault Diagnosis Algorithm for Non-uniform Sampling Hybrid System

For a class of non-uniform output sampling hybrid system with actuator faults and bounded disturbances,an iterative learning fault diagnosis algorithm is proposed.Firstly,in order to measure the impact of fault on system between every consecutive output sampling instants,the actual fault function is transformed to obtain an equivalent fault model by using the integral mean value theorem,then the non-uniform sampling hybrid system is converted to continuous systems with timevarying delay based on the output delay method.Afterwards,an observer-based fault diagnosis filter with virtual fault is designed to estimate the equivalent fault,and the iterative learning regulation algorithm is chosen to update the virtual fault repeatedly to make it approximate the actual equivalent fault after some iterative learning trials,so the algorithm can detect and estimate the system faults adaptively.Simulation results of an electro-mechanical control system model with different types of faults illustrate the feasibility and effectiveness of this algorithm.

Non-PLL high-precision synchronous sampling method among lots of acoustics acquisition channels for underwater multilinear array seismic exploration system

Synchronous sampling is very essential in underwater multilinear array seismic exploration system in which every acquisition node(AN)samples analog signals by its own analog-digital converter(ADC).Aiming at the problems of complex synchronous sampling method and long locking time after varying sampling rate in traditional underwater seismic exploration system,an improved synchronous sampling model based on the master-slave synchronous model and local clock asynchronous drive with non phase locked loop(PLL)is built,and a high-precision synchronous sampling method is proposed,which combines the short-term stability of local asynchronous driving clock with the master-slave synchronous calibration of local sampling clock.Based on the improved synchronous sampling model,the influence of clock stability,transmission delay and phase jitter on synchronous sampling error is analyzed,and a high-precision calibration method of synchronous sampling error based on step-by-step compensation of transmission delay is proposed.The model and method effectively realize the immunity of phase jitter on synchronous sampling error in principle,and compensate the influence of signal transmission delay on synchronous sampling error.At the same time,it greatly reduces the complexity of software and hardware implementation of synchronous sampling,and solves the problem of long locking time after changing the sampling rate in traditional methods.The experimental system of synchronous sampling for dual linear array is built,and the synchronous sampling accuracy is better than 5 ns.

Error Analysis of ERM Algorithm with Unbounded and Non-Identical Sampling

A standard assumption in the literature of learning theory is the samples which are drawn independently from an identical distribution with a uniform bounded output. This excludes the common case with Gaussian distribution. In this paper we extend these assumptions to a general case. To be precise, samples are drawn from a sequence of unbounded and non-identical probability distributions. By drift error analysis and Bennett inequality for the unbounded random variables, we derive a satisfactory learning rate for the ERM algorithm.

Compromise Allocation for Combined Ratio Estimates of Population Means of a Multivariate Stratified Population Using Double Sampling in Presence of Non-Response

This paper is an attempt to work out a compromise allocation to construct combined ratio estimates under multivariate double sampling design in presence of non-response when the population mean of the auxiliary variable is unknown. The problem has been formulated as a multi-objective integer non-linear programming problem. Two solution procedures are developed using goal programming and fuzzy programming techniques. A numerical example is also worked out to illustrate the computational details. A comparison of the two methods is also carried out.

Estimating a Finite Population Mean under Random Non-Response in Two Stage Cluster Sampling with Replacement

Non-response is a regular occurrence in Sample Surveys. Developing estimators when non-response exists may result in large biases when estimating population parameters. In this paper, a finite population mean is estimated when non-response exists randomly under two stage cluster sampling with replacement. It is assumed that non-response arises in the survey variable in the second stage of cluster sampling. Weighting method of compensating for non-response is applied. Asymptotic properties of the proposed estimator of the population mean are derived. Under mild assumptions, the estimator is shown to be asymptotically consistent.

Recursive State-space Model Identification of Non-uniformly Sampled Systems Using Singular Value Decomposition

In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition(SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.

A Heterogeneous Sampling Strategy to Model Earthquake‑Triggered Landslides

Regional modeling of landslide hazards is an essential tool for the assessment and management of risk in mountain environments.Previous studies that have focused on modeling earthquake-triggered landslides report high prediction accuracies.However,it is common to use a validation strategy with an equal number of landslide and non-landslide samples,scattered homogeneously across the study area.Consequently,there are overestimations in the epicenter area,and the spatial pattern of modeled locations does not agree well with real events.In order to improve landslide hazard mapping,we proposed a spatially heterogeneous non-landslide sampling strategy by considering local ratios of landslide to non-landslide area.Coseismic landslides triggered by the 2008 Wenchuan Earthquake on the eastern Tibetan Plateau were used as an example.To assess the performance of the new strategy,we trained two random forest models that shared the same hyperparameters.The frst was trained using samples from the new heterogeneous strategy,and the second used the traditional approach.In each case the spatial match between modeled and measured(interpreted)landslides was examined by scatterplot,with a 2 km-by-2 km fshnet.Although the traditional approach achieved higher AUC_(ROC)(0.95)accuracy than the proposed one(0.85),the coefcient of determination(R^(2))for the new strategy(0.88)was much higher than for the traditional strategy(0.55).Our results indicate that the proposed strategy outperforms the traditional one when comparing against landslide inventory data.Our work demonstrates that higher prediction accuracies in landslide hazard modeling may be deceptive,and validation of the modeled spatial pattern should be prioritized.The proposed method may also be used to improve the mapping of precipitation-induced landslides.Application of the proposed strategy could beneft precise assessment of landslide risks in mountain environments.

Post-failure analysis of landslides in spatially varying soil deposits using stochastic material point method

This paper presents the probabilistic analysis of landslides in spatially variable soil deposits, modeled by a stochastic framework which integrates the random field theory with generalized interpolation material point method(GIMP). Random fields are simulated using Cholesky matrix decomposition(CMD) method and Latin hypercube sampling(LHS) method, which represent material properties discretized into sets of random soil shear strength variables with statistical properties. The approach is applied to landslides in clayey deposits under undrained conditions with random fields of undrained shear strength parameters, in order to quantify the uncertainties of post-failure behavior at different scales of fluctuation(SOF) and coefficients of variation(COV). Results show that the employed approach can reliably simulate the whole landslide process and assess the uncertainties of runout motions. It is demonstrated that the natural heterogeneity of shear strength in landslides notably influences their post-failure behavior. Compared with a homogeneous landslide model which yields conservative results and underestimation of the risks, consideration of heterogeneity shows larger landslide influence zones. With SOF values increasing, the variances of influence zones also increase, and with higher values of COV, the mean values of the influence zone also increase, resulting in higher uncertainties of post-failure behavior.

Non-integrable Movement Equations in Landsliding System

The answer of the question was considered as non-integrable basically. The integrable system was thought in the exceptional.Since Poincare had presented in a theorem and proved that non-integrable system is normal, and yet integrable system is abuormal. In the landsliding system, discussed question was regarded to be true.Sortie fundamentals were considered; other systems and example with engineering control are concerned and discussed in details.

On a Class of Generalized Sampling Functions

In this note, we discuss a class of so-called generalized sampling functions. These functions are defined to be the inverse Fourier transform of a family of piecewise constant functions that are either square integrable or Lebegue integrable on the real number line. They are in fact the generalization of the classic sinc function. Two approaches of constructing the generalized sampling functions are reviewed. Their properties such as cardinality, orthogonality, and decaying properties are discussed. The interactions of those functions and Hilbert transformer are also discussed.

Efficiency of Two-Stage Adaptive Cluster Sampling Design in Estimating Fringe-Eared Oryx

Two-stage adaptive cluster sampling and two-stage conventional sampling designs were used to estimate population total of Fringe-Eared Oryx that are clustered and sparsely distributed. The study region was Amboseli-West Kilimanjaro and Magadi-Natron cross boarder landscape between Kenya and Tanzania. The study region was partitioned into different primary sampling units with different secondary sampling units that were of different sizes. Results show that two-stage adaptive cluster sampling design is efficient compared to simple random sampling and the conventional two- stage sampling design. The design is less variable compared to the conventional two-stage sampling design.

FAST ALGORITHM FOR NON-UNIFORMLY SAMPLED SIGNAL SPECTRUM RECONSTRUCTION

In this paper, a fast algorithm to reconstruct the spectrum of non-uniformly sampled signals is proposed. Compared with the original algorithm, the fast algorithm has a higher computational efficiency, especially when sampling sequence is long. Particularly, a transformation matrix is built, and the reconstructed spectrum is perfectly synthesized from the spectrum of every sampling channel. The fast algorithm has solved efficiency issues of spectrum reconstruction algorithm, and making it possible for the actual application of spectrum reconstruction algorithm in multi-channel Synthetic Aperture Radar (SAR).

Fuzzy Geometric Programming in Multivariate Stratified Sample Surveys in Presence of Non-Response with Quadratic Cost Function

In this paper, the problem of non-response with significant travel costs in multivariate stratified sample surveys has been formulated of as a Multi-Objective Geometric Programming Problem (MOGPP). The fuzzy programming approach has been described for solving the formulated MOGPP. The formulated MOGPP has been solved with the help of LINGO Software and the dual solution is obtained. The optimum allocations of sample sizes of respondents and non respondents are obtained with the help of dual solutions and primal-dual relationship theorem. A numerical example is given to illustrate the procedure.

Modeling of Non-uniformly Sampled Systems by Support Vector Regression with Applications

This paper presents a modeling method for a non-uniformly sampled system bused on support vector regression （ SVR ）. First, a lifted discrete-time state-space model for a non-uniformly sampled system is derived by using the lifting technique to reduce the modeling difficulty caused by multirate sampling. Then, the system is divided into several parallel subsystems and their input-output model is presented to satisfy the SVR model. Finally, an on-line SVR technique is utilized to establish the models of all subsystems to deal with uncertainty. Furthermore, the presented method is applied in a multichannel electrohydraulic force servo synchronous loading system to predict the system outputs over the control sample interval and the prediction mean absolute percentage error reaches 0. 092%. The results demonstrate that the presented method has a high modeling precision and the subsystems have the same level of prediction error.

基于径流事件的农业面源监测采样间隔分析

选择丹江口库区的五龙池小流域为对象,以典型降雨径流事件的水质水量加密监测数据作为径流过程真实值,通过不同时间间隔和水量间隔模拟采样,分析污染负荷通量估算误差,并确定有效采样间隔.结果显示,五龙池小流域降雨径流事件具有明显的峰值特征(初始流量55m^(3)/h,峰值流量977.3m^(3)/h),TP浓度与径流高度同步(初始浓度为0.05mg/L,峰值浓度为0.25mg/L),TN随径流达到峰值后下降不明显(初始浓度为2mg/L,峰值浓度为8mg/L).模拟采样表明,时间间隔5,10,15,20,30,60,120,240,480,720min,采样点数量从1957个减少到14个;水量间隔16,32,64,128,256,384,576,800,1600,3200m^(3),采样点数量从2893个减少到14个;水量间隔采样对径流峰值的捕捉能力明显高于时间间隔采样.负荷估算误差显示,TP误差波动大于TN;以±5%作为允许的误差范围,降雨径流事件采样监测的有效时间间隔为120min,有效水量间隔为1600m^(3).

基于环境因子优化TSES法选择负样本及其在滑坡易发性评价中的应用

滑坡易发性评价是滑坡灾害防治的重要手段之一,而不合理的滑坡负样本会影响滑坡易发性评价,从而影响到滑坡灾害的防治,因此提供一种合理的负样本选取方法变得尤为关键。以西藏米林市的古滑坡为例,选择高程、坡度、坡向、坡位、距道路距离、距断层距离、距水系距离、地形起伏度、地层岩性、土地利用类型10类环境因子,使用Relief算法计算环境因子的贡献值并依据贡献值优化选择环境因子;基于环境因子优化的目标空间外向化采样法(target space exteriorization sampling,简称TSES)选择负样本,作为性能优异的随机森林模型的输入变量;之后结合优化的环境因子和正或负样本预测米林市的滑坡易发性,并用混淆矩阵和ROC曲线评价构建模型的性能。为检验环境因子优化的TSES法的有效性和先进性,采用耦合信息量法和TSES法选择滑坡负样本并构建随机森林模型,与环境因子优化的TSES法构建的随机森林模型进行对比研究。结果表明,环境因子优化的TSES法构建的随机森林模型的评价效果较好,其ACC为93.7%、AUC为0.987,均高于耦合信息量、TSES法构成的模型。环境因子优化的TSES法能够提高模型的精度,解决多因子作为约束条件取样中因子选取的问题,为滑坡易发性评价采集负样本提供了新的思路。

用户生成内容场景下角色导向图神经推荐方法

近年来互联网的飞速发展不断改变着信息的生产和传递方式,随之出现了用户使用互联网的新方式——用户生成内容(User-Generated Content,UGC).该场景中内容以传播速度快、获取成本低等优势迅速占据互联网信息传播的重要地位.不同于传统推荐场景,UGC场景下用户同时扮演生产者和消费者双重角色,这使得在构建推荐模型时既需要考虑消费者与内容之间的交互信息,也需关注内容生产者对于消费者决策的影响.因此,UGC场景下个性化推荐算法研究的关键在于如何充分挖掘消费者-内容和消费者-生产者之间的关联关系.在面向UGC场景的推荐研究中,比较有代表性的模型为CPRec,该模型虽取得一定进展,但仍存在两点不足之处.其一,在模型构建层面,未能显式建模消费者-内容与消费者-生产者之间的高阶连通关系,难以学习出高质量的节点表征.其二,在模型优化层面,无法区分每个观测数据在不同训练阶段的贡献度,将影响推荐结果的质量.为此,本文提出一种新颖的角色导向图神经推荐方法RGNRec(Role-Guided Graph Neural Recommendation)用于UGC场景的个性化排序任务.特别地,基于用户的历史行为数据与内容的创作者信息分别构建了消费者-内容交互图和消费者-生产者交互图.进一步,为了显式捕获两种交互图中的高阶连通信息,构建一种双通道线性传播模块,同时刻画了消费者兴趣与内容生产者影响的扩散过程.最终,提出设计一种自适应的正样本权重生成策略,将其融入非采样损失函数,并建立双层优化机制来学习模型的参数.本文的核心贡献包括:(1)引入双通道线性传播模块,以显式解耦出自身兴趣与内容生产者效应对于用户偏好建模的不同贡献度;(2)提出权重自适应的非采样损失函数,以解决不同观测样例在模型不同训练阶段贡献不同的问题.本文分别采用经典的和最先进的图神经网络方法作为基准,在3个UGC场景Pinterest、Recipes和Reddit下进行了实验对比.在整体推荐性能方面,无论模型精度亦或训练效率上均优于各基准方法,尤其在Precision@10指标上获得了 4.31%~17.83%的提升;然后通过消融实验验证了双通道线性传播模块和权重自适应优化机制的合理性与必要性;最后通过实验验证了本文方法在缓解数据稀疏和用户冷启动方面的优越性.

经济林种植户有机肥施用行为及影响因素分析——基于陕西省589户种植户调查数据

在积极倡导环境保护的时代背景下,探讨经济林种植户有机肥施用行为的影响因素,对林业绿色可持续发展具有重要影响。以陕西省589户经济林种植户的调查数据为基础,运用Heckman样本选择模型,着重分析电商参与和社会网络对经济林种植户有机肥施用行为的影响。结果表明,74%的种植户选择施用有机肥,但有机肥投入资金占肥料总投入较低。种植户电商参与、社会网络正向影响其有机肥施用决策和施用程度,并且社会网络在电商参与影响有机肥施用程度的过程中起正向调节作用。此外,家中农用机械数量、经济林种植面积以及是否被评选为科技示范户种植户也会对种植户有机肥施用行为产生影响。

滑坡易发性预测建模的不确定性:不同“非滑坡样本”选择方式的影响

滑坡易发性预测建模中如何选择非滑坡是影响建模结果的重要不确定因素。为研究不同非滑坡选择方式的影响规律,拟用5种方式,即全区随机、坡度低于5°区域、滑坡缓冲300 m外区域、信息量(IV)法、半监督法来选择出与滑坡等比例的非滑坡样本;进一步将各选择方式与随机森林(RF)耦合构建随机RF、低坡度RF、缓冲区RF、IV–RF及半监督RF等模型。以江西南康区为例,获取高程、岩性、公路密度等19种环境因子和233个滑坡编录,将滑坡编录划分为2598个滑坡栅格单元构建上述耦合模型的输入–输出数据集。再采用预测精度和易发性指数分布等指标分析其建模不确定性。进一步针对耦合模型预测的滑坡易发性指数分布不合理等问题,在半监督RF建模时采用滑坡与非滑坡比例为1∶2的样本集开展建模并与1∶1等比例样本集工况作对比。结果表明:1)低坡度RF、缓冲区RF、IV–RF和半监督RF等模型的预测精度均大幅优于随机RF模型,可见准确选择非滑坡样本对易发性建模至关重要;2)半监督RF模型选择非滑坡样本的建模性能最优,且半监督RF在滑坡∶非滑坡=1∶2比其在1∶1时预测的易发性指数分布规律更准确可信。后续研究中有必要更深入探索滑坡与非滑坡样本的比例问题。