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.

Application of the N + 2 Transversal Network Method to the Study of a Coupled Resonator Filter

This paper presents a new approach to synthesize admittance function polynomials and coupling matrices for coupled resonator filters. The N + 2 transversal network method is applied to study a coupled resonator filter. This method allowed us to determine the polynomials of the reflection and transmission coefficients. A study is made for a 4 poles filter with 2 transmission zeros between the N + 2 transversal network method and the one found in the literature. A MATLAB code was designed for the numerical simulation of these coefficients for the 6, 8, and 10 pole filter with 4 transmission zeros.

A novel DPSS filter optimization scheme to reduce the intrinsic interference of FBMC-QAM systems

In order to reduce the intrinsic interference of the filter bank multicarrier-quadrature amplitude modulation(FBMC-QAM)system,a novel filter optimization scheme based on discrete prolate spheroidal sequences(DPSS)is proposed.Firstly,a prototype filter function based on DPSS is designed,since the eigenvalue can be used as an indicator of the energy concentration of DPSS,so a threshold is set,and the sequence with the most concentrated energy is selected under the threshold,that is,the sequence with the eigenvalue higher than the threshold,and the prototype filter function is rewritten as a weighted sum function of multiple eigenvectors.Under the energy constraints of the filter,the relationship between the eigenvectors and the intrinsic interference function is established,and the function problem is transformed into an optimization problem for the weighted coefficients.Through the interior point method,the most suitable weight is found to obtain the minimum intrinsic interference result.Theoretical analysis and simulation results show that compared with the prototype filters such as Type1 and CaseC,the DPSS filter applying the proposed optimization algorithm can effectively suppress the intrinsic interference of the system and obtain a better bit error rate(BER)performance.

基于改进Filter和随机森林的应急物资供应商评价方法研究

针对应急物资供应商评价时因主观因素影响较大、存在冗余指标等情况,导致供应商评价分类效果不佳的问题,提出基于改进Filter和随机森林的方法改善应急物资供应商评价分类预测效果的思路。利用Filter中拉普拉斯分值算法的高效计算能力进行初步特征优化,并在此过程中引入互信息法消除冗余特征,构建随机森林分类器,以期在消除主观因素影响的同时,降低冗余指标造成的不利后果。实验数据显示,该方法可以明显提升应急物资供应商评价时的分类预测效果,为应急供应链管理的快速构建和高效运作提供可靠依据。

Study of nonlinear filter methods: particle filter

Extended Kalman filter （EKF） is one of the most widely used methods for nonlinear system estimation. A new filtering algorithm, called particle filtering （PF） is introduced. PF can yield better performance than that of EKF, because PF does not involve the linearization approximating to nonlinear systems, that is required by the EKF. PF has been shown to be a superior alternative to the EKF in a variety of applications. The base idea of PF is the approximation of relevant probabifity distributions using the concepts of sequential importance sampling and approximation of probability distributions using a set of discrete random samples with associated weights. PF methods still need to be improved in the aspects of accuracy and calculating speed.

Hydraulic model with roughness coefficient updating method based on Kalman filter for channel flood forecast

A real-time channel flood forecast model was developed to simulate channel flow in plain rivers based on the dynamic wave theory. Taking into consideration channel shape differences along the channel, a roughness updating technique was developed using the Kalman filter method to update Manning＇s roughness coefficient at each time step of the calculation processes. Channel shapes were simplified as rectangles, triangles, and parabolas, and the relationships between hydraulic radius and water depth were developed for plain rivers. Based on the relationship between the Froude number and the inertia terms of the momentum equation in the Saint-Venant equations, the relationship between Manning＇s roughness coefficient and water depth was obtained. Using the channel of the Huaihe River from Wangjiaba to Lutaizi stations as a case, to test the performance and rationality of the present flood routing model, the original hydraulic model was compared with the developed model. Results show that the stage hydrographs calculated by the developed flood routing model with the updated Manning＇s roughness coefficient have a good agreement with the observed stage hydrographs. This model performs better than the original hydraulic model.

A Nonmonotone Filter Method for Minimax Problems

In this paper, we propose a modified trust-region filter method algorithm for Minimax problems, which based on the framework of SQP-filter method and associated with the technique of nonmonotone method. We use the SQP subproblem to acquire an attempt step, and use the filter to weigh the effect of the attempt step so as to avoid using penalty function. The algorithm uses the Lagrange function as a merit function and the nonmonotone filter to improve the effect of the algorithm. Under some mild conditions, we prove the global convergence.

Adaptability of n-γdiscrimination and filtering methods based on plastic scintillation

Neutrons have been extensively used in many fields,such as nuclear physics,biology,geology,medical science,and national defense,owing to their unique penetration characteristics.Gamma rays are usually accompanied by the detection of neutrons.The capability to discriminate neutrons from gamma rays is important for evaluating plastic scintillator neutron detectors because similar pulse shapes are generated from both forms of radiation in the detection system.The pulse signals measured by plastic scintillators contain noise,which decreases the accuracy of n-y discrimination.To improve the performance of n-y discrimination,the noise of the pulse signals should be filtered before the n-y discrimination process.In this study,the influences of the Fourier transform,wavelet transform,moving-average filter,and Kalman algorithm on the charge comparison method,fractal spectrum method,and back-propagation neural network methods were studied.It was found that the Fourier transform filtering algorithm exhibits better adaptability to the charge comparison method than others,with an increasing accuracy of 6.87%compared to that without the filtering process.Meanwhile,the Kalman filter offers an improvement of 3.04%over the fractal spectrum method,and the adaptability of the moving-average filter in backpropagation neural network discrimination is better than that in other methods,with an increase in 8.48%.The Kalman filtering algorithm has a significant impact on the peak value of the pulse,reaching 4.49%,and it has an insignificant impact on the energy resolution of the spectrum measurement after discrimination.

Ultravilolet-radiation-induced graft polymerization of acrylamide onto the melt-blown polypropylene filter element by dynamic method

By dynamic method under UV irradiation, commercial melt-blown polypropylene （PPMB） filter element was modified with acrylamide （AAm） using benzophenone （BP） as initiator. Attenuated total reflection-Fourier transform infrared spectroscopy and scanning electron microscope verified that polyacrylamide chain was grafted on the fiber surface of PPMB filter element. Elemental content analysis with energy dispersive X-ray of fibers revealed that the polymerization content in the inner part of filter element was relatively higher than that in the outer. Degree of grafting changed with initiator concentration, monomer concentration, reaction temperature and reached 2.6% at the reaction condition： CBp=0.06 mol/L, CAAm=2.0 mol/L, irradiation time： 80 min, temperature： 60℃. Relative water flux altered with the hydrophilicity and pore size of filter element. In the antifouling test, the modified filter gave greater flux recovery （approximately 70%） after filtration of the water extract of Liuweidihuang, suggesting that the fouling layer was more easily reversible due to the hydrophilic nature of the modified filter.

Filtering Function Method for the Cauchy Problem of a Semi-Linear Elliptic Equation

A Cauchy problem for the semi-linear elliptic equation is investigated. We use a filtering function method to define a regularization solution for this ill-posed problem. The existence, uniqueness and stability of the regularization solution are proven;a convergence estimate of H?lder type for the regularization method is obtained under the a-priori bound assumption for the exact solution. An iterative scheme is proposed to calculate the regularization solution;some numerical results show that this method works well.

A FILTER-TRUST-REGION METHOD FOR LC^1 UNCONSTRAINED OPTIMIZATION AND ITS GLOBAL CONVERGENCE

In this paper we present a filter-trust-region algorithm for solving LC1 unconstrained optimization problems which uses the second Dini upper directional derivative. We establish the global convergence of the algorithm under reasonable assumptions.

EFFECTS OF A CLOUD FILTERING METHOD FOR FENGYUN-3C MICROWAVE HUMIDITY AND TEMPERATURE SOUNDER MEASUREMENTS OVER OCEAN ON RETRIEVALS OF TEMPERATURE AND HUMIDITY

For Microwave Humidity and Temperature sounder(MWHTS) measurements over the ocean, a cloud filtering method is presented to filter out cloud-and precipitation-affected observations by analyzing the sensitivity of the simulated brightness temperatures of MWHTS to cloud liquid water, and using the root mean square error(RMSE)between observation and simulation in clear sky as a reference standard. The atmospheric temperature and humidity profiles are retrieved using MWHTS measurements with and without filtering by multiple linear regression(MLR),artificial neural networks(ANN) and one-dimensional variational(1DVAR) retrieval methods, respectively, and the effects of the filtering method on the retrieval accuracies are analyzed. The numerical results show that the filtering method can improve the retrieval accuracies of the MLR and the 1DVAR retrieval methods, but have little influence on that of the ANN. In addition, the dependencies of the retrieval methods upon the testing samples of brightness temperature are studied, and the results show that the 1DVAR retrieval method has great stability due to that the testing samples have great impact on the retrieval accuracies of the MLR and the ANN, but have little impact on that of the 1DVAR.

A Novel Adaptive Kalman Filter Based on Credibility Measure

It is quite often that the theoretic model used in the Kalman filtering may not be sufficiently accurate for practical applications,due to the fact that the covariances of noises are not exactly known.Our previous work reveals that in such scenario the filter calculated mean square errors(FMSE)and the true mean square errors(TMSE)become inconsistent,while FMSE and TMSE are consistent in the Kalman filter with accurate models.This can lead to low credibility of state estimation regardless of using Kalman filters or adaptive Kalman filters.Obviously,it is important to study the inconsistency issue since it is vital to understand the quantitative influence induced by the inaccurate models.Aiming at this,the concept of credibility is adopted to discuss the inconsistency problem in this paper.In order to formulate the degree of the credibility,a trust factor is constructed based on the FMSE and the TMSE.However,the trust factor can not be directly computed since the TMSE cannot be found for practical applications.Based on the definition of trust factor,the estimation of the trust factor is successfully modified to online estimation of the TMSE.More importantly,a necessary and sufficient condition is found,which turns out to be the basis for better design of Kalman filters with high performance.Accordingly,beyond trust factor estimation with Sage-Husa technique(TFE-SHT),three novel trust factor estimation methods,which are directly numerical solving method(TFE-DNS),the particle swarm optimization method(PSO)and expectation maximization-particle swarm optimization method(EM-PSO)are proposed.The analysis and simulation results both show that the proposed TFE-DNS is better than the TFE-SHT for the case of single unknown noise covariance.Meanwhile,the proposed EMPSO performs completely better than the EM and PSO on the estimation of the credibility degree and state when both noise covariances should be estimated online.

Removing Random-Valued Impulse Noises by a Two-Staged Nonlinear Filtering Method

Digital images are frequently contaminated by impulse noise(IN)during acquisition and transmission.The removal of this noise from images is essential for their further processing.In this paper,a two-staged nonlinear filtering algorithm is proposed for removing random-valued impulse noise(RVIN)from digital images.Noisy pixels are identified and corrected in two cascaded stages.The statistics of two subsets of nearest neighbors are employed as the criterion for detecting noisy pixels in the first stage,while directional differences are adopted as the detector criterion in the second stage.The respective adaptive median values are taken as the replacement values for noisy pixels in each stage.The performance of the proposed method was compared with that of several existing methods.The experimental results show that the performance of the suggested algorithm is superior to those of the compared methods in terms of noise removal,edge preservation,and processing time.

How to Design a Generic Accuracy-Enhancing Filter for Discontinuous Galerkin Methods

Higher order accuracy is one of the well-known beneficial properties of the discontinu-ous Galerkin(DG)method.Furthermore,many studies have demonstrated the supercon-vergence property of the semi-discrete DG method.One can take advantage of this super-convergence property by post-processing techniques to enhance the accuracy of the DG solution.The smoothness-increasing accuracy-conserving(SIAC)filter is a popular post-processing technique introduced by Cockburn et al.(Math.Comput.72(242):577-606,2003).It can raise the convergence rate of the DG solution(with a polynomial of degree k)from order k+1 to order 2k+1 in the L2 norm.This paper first investigates general basis functions used to construct the SIAC filter for superconvergence extraction.The generic basis function framework relaxes the SIAC filter structure and provides flexibility for more intricate features,such as extra smoothness.Second,we study the distribution of the basis functions and propose a new SIAC filter called compact SIAC filter that significantly reduces the support size of the original SIAC filter while preserving(or even improving)its ability to enhance the accuracy of the DG solution.We prove the superconvergence error estimate of the new SIAC filters.Numerical results are presented to confirm the theoretical results and demonstrate the performance of the new SIAC filters.

Secure Path Cycle Selection Method Using Fuzzy Logic System for Improving Energy Efficiency in Statistical En-Route Filtering Based WSNs

Sensor nodes are easily compromised to malicious attackers due to an open environment. A false injected attack which takes place on application layer is elected by the compromised node. If the false report arrives in a base station, a false alarm is occurred, and the energy of the nodes is consumed. To detect the false report, statistical en-route filtering method is proposed. In this paper, we proposed the secure path cycle selection method using fuzzy rule-based system to consume effective energy. The method makes balanced energy consumption of each node. Moreover, the lifetime of the whole network will be increased. The base station determines the path cycle using the fuzzy rule-based system. The performance of the proposed method is demonstrated using simulation studies with the three methods.

An iterative Wiener filtering method based on the gravity gradient invariants

How to deal with colored noises of GOCE （Gravity field and steady - state Ocean Circulation Explorer） satellite has been the key to data processing. This paper focused on colored noises of GOCE gradient data and the frequency spectrum analysis. According to the analysis results, gravity field model of the optima] degrees 90-240 is given, which is recovered by COCE gradient data. This paper presents an iterative Wiener filtering method based on the gravity gradient invariants. By this method a degree-220 model was calculated from GOCE SGG （Satellite Gravity Gradient） data. The degrees above 90 of ITG2010 were taken as the prior gravity field model, replacing the low degree gravity field model calculated by GOCE orbit data. GOCE gradient colored noises was processed by Wiener filtering. Finally by Wiener filtering iterative calculation, the gravity field model was restored by space-wise harmonic analysis method. The results show that the model＇s accuracy matched well with the ESA＇s （European Space Agency） results by using the same data,

Filtering-efficiency measurement of Chinese-made filter 1by double-filter method

Filtering efficiency of the Chinese-made filter 1 has been measured by the double-filter method with only one set of measurement equipment. The β counts of the two filters are measured in turn for every 3 minutes, from which the decay constants are extracted to be 0.0131 min-1 and 0.0129 min-1 for the first and second filter respectively. After correcting the β counts of the second filter, the filtering efficiency of the filter is obtained to be (87.4±0.7)% in average.

Combining unscented Kalman filter and wavelet neural network for anti-slug

The stability of the subsea oil and gas production system is heavily influenced by slug flow. One successful method of managing slug flow is to use top valve control based on subsea pipeline pressure. However, the complexity of production makes it difficult to measure the pressure of subsea pipelines, and measured values are not always accessible in real-time. The research introduces a technique for integrating Unscented Kalman Filter (UKF) and Wavelet Neural Network (WNN) to estimate the state of subsea pipeline pressure using historical data and a state model. The proposed method treats multiphase flow transport as a nonlinear model, with a dynamic WNN serving as the state observer. To achieve real-time state estimation, the WNN is included into the UKF algorithm to create a WNN-based UKF state equation. Integrate WNN and UKF in a novel way to predict system state accurately. The simulated results show that the approach can efficiently predict the inlet pressure and manage the slug flow in real-time using the riser's top pressure, outlet flow and valve opening. This method of estimate can significantly increase the control effect.

Vibration Diagnosis and Optimization of Industrial Robot Based on TPA and EMD Methods

This paper proposedmethod that combined transmission path analysis(TPA)and empirical mode decomposition(EMD)envelope analysis to solve the vibration problemof an industrial robot.Firstly,the deconvolution filter timedomain TPA method is proposed to trace the source along with the time variation.Secondly,the TPA method positioned themain source of robotic vibration under typically different working conditions.Thirdly,independent vibration testing of the Rotate Vector(RV)reducer is conducted under different loads and speeds,which are key components of an industrial robot.The method of EMD and Hilbert envelope was used to extract the fault feature of the RV reducer.Finally,the structural problems of the RV reducer were summarized.The vibration performance of industrial robots was improved through the RV reducer optimization.From the whole industrial robot to the local RV Reducer and then to the internal microstructure of the reducer,the source of defect information is traced accurately.Experimental results showed that the TPA and EMD hybrid methods were more accurate and efficient than traditional time-frequency analysis methods to solve industrial robot vibration problems.