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 Kalman Filter Method in the Forecast of Temperature in Nanchang

A temperature forecasting model was created firstly based on the Kalman filter method,and then used to predict the highest and lowest temperature in Nanchang station from October 27 to November 1,2017.Finally,according to the empirical forecasting method,guidance forecasts were established for the northern,central,and southern parts of Nanchang City.After inspection,it was found that the temperature prediction model established based on the Kalman filter method in Nanchang station had good prediction performance,and especially in the 24-hour forecast,it had advantages over the European Center.The accuracy of low temperature forecast was better than that of high temperature forecast.

A New Time-Aware Collaborative Filtering Intelligent Recommendation System

Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy,this paper introduces project attribute fuzzy matrix,measures the project relevance through fuzzy clustering method,and classifies all project attributes.Then,the weight of the project relevance is introduced in the user similarity calculation,so that the nearest neighbor search is more accurate.In the prediction scoring section,considering the change of user interest with time,it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation,so that the newer evaluation information in the system has a relatively large weight.The experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.

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.

ixed Cadzow filtering method in fractional Fourier domain

Conventional frequency domain method used in random noise attenuation singular value decomposition （SVD） filtering processing causes bending event damage. To mitigate this problem, we present a mixed Cadzow filtering method based on fractional Fourier transform to suppress random noise in 3D seismic data. First, the seismic data is transformed to the time-frequency plane via the fractional Fourier transform. Second, based on the Eigenimage filtering method and Cadzow filtering method, the mixed high-dimensional Hankel matrix is built; then, SVD is performed. Finally, random noise is eliminated effectively by reducing the rank of the matrix. The theoretical model and real applications of the mixed filtering method in a region of Sichuan show that our method can not only suppress noise effectively but also preserve the frequency and phase of effective signals quite well and significantly improve the signal-to-noise ratio of 3D post-stack seismic data.

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,

A New Hybrid Feature Selection Sequence for Predicting Breast Cancer Survivability Using Clinical Datasets

This paper proposes a hybrid feature selection sequence comple-mented with filter and wrapper concepts to improve the accuracy of Machine Learning(ML)based supervised classifiers for classifying the survivability of breast cancer patients into classes,living and deceased using METABRIC and Surveillance,Epidemiology and End Results(SEER)datasets.The ML-based classifiers used in the analysis are:Multiple Logistic Regression,K-Nearest Neighbors,Decision Tree,Random Forest,Support Vector Machine and Multilayer Perceptron.The workflow of the proposed ML algorithm sequence comprises the following stages:data cleaning,data balancing,feature selection via a filter and wrapper sequence,cross validation-based training,testing and performance evaluation.The results obtained are compared in terms of the following classification metrics:Accuracy,Precision,F1 score,True Positive Rate,True Negative Rate,False Positive Rate,False Negative Rate,Area under the Receiver Operating Characteristics curve,Area under the Precision-Recall curve and Mathews Correlation Coefficient.The comparison shows that the proposed feature selection sequence produces better results from all supervised classifiers than all other feature selection sequences considered in the analysis.

Paradigm of Numerical Simulation of SpatialWind Field for Disaster Prevention of Transmission Tower Lines

Numerical simulation of the spatial wind field plays a very important role in the study of wind-induced response law of transmission tower structures.A reasonable construction of a numerical simulation method of the wind field is conducive to the study of wind-induced response law under the action of an actual wind field.Currently,many research studies rely on simulating spatial wind fields as Gaussian wind,often overlooking the basic non-Gaussian characteristics.This paper aims to provide a comprehensive overview of the historical development and current state of spatial wind field simulations,along with a detailed introduction to standard simulation methods.Furthermore,it delves into the composition and unique characteristics of spatial winds.The process of fluctuating wind simulation based on the linear filter AR method is improved by introducing spatial correlation and non-Gaussian distribution characteristics.The numerical simulation method of the wind field is verified by taking the actual transmission tower as a calculation case.The results show that the method summarized in this paper has a broader application range and can effectively simulate the actual spatial wind field under various conditions,which provides a valuable data basis for the subsequent research on the wind-induced response of transmission tower lines.

Research on Distribution Regularities of Dust Concentration and Granularity in Large Mining Height Working Face

The key for dust control of coal mine is to clarify the dust concentration distribution and sedimentation in different areas. Both similarity experiment and numerical simulation method have certain restrictions and are quite different from the actual situation on site. In order to study the dust sedimentation regularity of coal mine in large mining height, “filter membrane method” is adopted in this paper, i.e., to dry and weigh the filter membrane before and after sampling, collect the dust of respirable zone on mining face and calculate the dust concentration based on a main airway of 100 m. The result shows that: A large amount of dust will be produced during coal mining, wherein the maximum dust concentration from 6 m upstream to 100 m downstream of coal cutter is 121 mg/m3, while the minimum dust concentration is 61 mg/m3;The dust concentration in return airway is reduced with the distance increases, while the dust concentration at the entrance is 91 mg/m3;A large amount of dust may fall from roof during section advancing and improves the dust concentration of hydraulic support in walking area obviously;The dust granularity of mining face and return airway is 0 - 100 μm, but the amount of respirable dust is higher than 80%, the larger the dust particle size, the higher the dust concentration. Besides, dust in small particle size can be suspended in air flow for longer, but that in large particle size may subside under the action of gravity;To reduce dust exposure, the mining position shall be located in the windward direction of advancing or coal cutter. This research can provide guidance for taking dust prevention measures of working face in large mining height.

Event-triggered predefined-time control for full-state constrained nonlinear systems: A novel command filtering error compensation method

In this paper, a command filter-based adaptive fuzzy predefined-time event-triggered tracking control problem is investigated for uncertain nonlinear systems with time-varying full-state constraints. By designing a sliding mode differentiator, the inherent computational complexity problem within the predefined-time backstepping framework is solved. Different from the existing command filter-based finite-time and fixed-time control strategies that the convergence time of the filtering error is adjusted through the system initial value or numerous parameters, a novel command filtering error compensation method is presented,which tunes one control parameter to make the filtering error converge in the predefined time, thereby reducing the complexity of design and analysis of processing the filtering error. Then, an improved event-triggered mechanism(ETM) that builds upon the switching threshold strategy, in which an inverse cotangent function is designed to replace the residual term of the ETM,is proposed to gradually release the controller's dependence on the residual term with increasing time. Furthermore, a tan-type nonlinear mapping technique is applied to tackle the time-varying full-state constraints problem. By the predefined-time stability theory, all signals in the uncertain nonlinear systems exhibit predefined-time stability. Finally, the feasibility of the proposed algorithm is substantiated through two simulation results.

DIGITAL FILTER FOR SINGLE FREQUENCY SINUSOID SERIES

The sinusoid curve fit is widely applied in the evaluation of digitized measurement equipment, such as data acquisition system, digital storage oscilloscope, waveform recorder and A/D converter,etc. Because of the distortion and noise of sinusoid signal generator, the digitizing and the non linearity errors in measurement, it is impossible to avoid the distortion and the noise in sinusoid sampling series. The distortion and the noise limit the accuracy of curve fit results. Therefore, it is desirable to find a filter that can filter out both distortion and noise of the sinusoid sampling series, and in the meantime, the filter doesn′t influence the amplitude, the frequency, the phase and DC bias of fitting curve of the sine wave. And then, the uncertainty of fitting parameter can be reduced. This filter is designed and realized. Its realization in time domain is described and its transfer function in frequency domain is presented.

Study on the Acute Toxicity of Rare Earth Yttrium to Earthworms under the Stress of Leaching Agent Ammonium Sulfate

This study aimed to investigate the toxicity of rare earth ion yttrium under the stress of leaching agent ammonium sulfate （NH4）2SO4. [Method] By using earthworms as indicator organisms of environmental pol ution, acute toxic ef-fects of rare earth yttrium on earthworms under the stress of ammonium sulfate were investigated with filter paper contact method. [Result] Under single stress of rare earth yttrium, the semi-lethal concentration after 48 and 24 h was LC50=213.41 mg/L and LC50=322.63 mg/L, respectively. ② Under single stress of ammonium sul-fate, the semi-lethal concentration after 48 h and 24 h was LC50=13.89 g/L and LC50=15.05 g/L, respectively. ③ In combined treatment of low concentration （10 g/L） of ammonium sulfate and different doses of rare earth yttrium, the semi-lethal con-centration after 48 and 24 h was LC50=198.65 g/L and LC50=399.85 g/L, respective-ly; in combined treatment of middle concentration （14 g/L） of ammonium sulfate and different doses of rare earth yttrium, the semi-lethal concentration after 48 and 24 h was LC50=167.3 mg/L and LC50=256.73 mg/L, respectively; in combined treatment of high concentration （20 g/L） of ammonium sulfate and different doses of rare earth yttrium, the semi-lethal concentration after 48 h and 24 h was LC50=31.03 mg/L and LC50=127.65 mg/L, respectively. [Conclusion] Low concentration of ammonium sulfate could reduce the toxicity of rare earth yttrium to earthworms and produce certain antagonism against rare earth yttrium; middle concentration ammonium sulfate in-creased the toxicity of rare earth yttrium to earthworms and produced relatively sig-nificant synergistic effects; high concentration ammonium sulfate significantly in-creased the toxicity of rare earth yttrium to earthworms. Compared with ammonium sulfate, dead earthworms exposed to rare earth yttrium were more easily fractured, and living earthworms showed insensitive response to acupuncture.

Effects of Addition of Bee Pollen on Water Holding Capacity of Pork

[ Objective] To explore the effects of different proportion of bee pollen on the water holding capacity of pork in Duroc Landrace x Yorkshire growing-finishing pigs in order to determine the optimal proportion. [ Method] A total of 80 Duroc x Landrace x Yorkshire growing-finishing pigs, weighing （10.0 ± 1.0） kg, were randomly divided into five groups. The experimental period was 120 d including the 5-day pre-feecling period. The corn-soybean meal was not replaced during the whole period. All pigs had free access to feed and water. The pigs were fasting for 24 h but not prohibited from feed before the beginning. Group I was the control group reared with the diet not supplemented bee pollen. The pigs in group II, III ,IV and V were reared with the diet supplemented bee pollen at the concentration of 1%, 3%, 5% and 7%, respectively. After slaughtering, eye muscle was used for determination of water holding capacity through fast filter paper method, drip loss method, cooking loss method and Na- pole yield determination method. [ Resultl With the increase of the proportion of bee pollen, the water holding capacity of pork was first good and then poor. The addition of bee pollen at the concentration of 5% significantly reduced the drip loss of pork and the water holding capacity which was detected by fast filter paper method, but the cooking loss and Napole yield were not significantly influenced. [ Condmion] The bee pollen can effectively improve the water holding capacity of pork in Duroc x Landrace x Yorkshire growing-finishing pigs, and the optimal proportion is 5%.

NEW FEATURE SELECTION METHOD IN MACHINE FAULT DIAGNOSIS

Aiming to deficiency of the filter and wrapper feature selection methods, anew method based on composite method of filter and wrapper method is proposed. First the methodfilters original features to form a feature subset which can meet classification correctness rate,then applies wrapper feature selection method select optimal feature subset. A successful techniquefor solving optimization problems is given by genetic algorithm (GA). GA is applied to the problemof optimal feature selection. The composite method saves computing time several times of the wrappermethod with holding the classification accuracy in data simulation and experiment on bearing faultfeature selection. So this method possesses excellent optimization property, can save more selectiontime, and has the characteristics of high accuracy and high efficiency.

Choice of reference surfaces for machined surface roughness in milling of SiC_p/Al composites

In order to choose the appropriate reference surface on the machined surface roughness of Si Cp/Al composites, the cutting experiments of Si Cp/Al composites were carried out, and the machined surface topography was measured by OLS3000 Confocal laser scanning microscope. The 3D measured data of machined surface topography were analyzed by the area power spectrum density. The result shows that the texture of machined surface topography in milling of Si Cp/Al composites is almost isotropic. This is the reason that the values of Rq at different locations on the same machined surface are obviously different. Through the comparison of performance of different filtering methods, the robust least squares reference surface can be used to extract the surface roughness of SiC p/Al composites effectively.

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.

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.

Frequency Weighting Filter Design for Automotive Ride Comfort Evaluation

Few study gives guidance to design weighting filters according to the frequency weighting factors,and the additional evaluation method of automotive ride comfort is not made good use of in some countries.Based on the regularities of the weighting factors,a method is proposed and the vertical and horizontal weighting filters are developed.The whole frequency range is divided several times into two parts with respective regularity.For each division,a parallel filter constituted by a low-and a high-pass filter with the same cutoff frequency and the quality factor is utilized to achieve section factors.The cascading of these parallel filters obtains entire factors.These filters own a high order.But,low order filters are preferred in some applications.The bilinear transformation method and the least P-norm optimal infinite impulse response（IIR） filter design method are employed to develop low order filters to approximate the weightings in the standard.In addition,with the window method,the linear phase finite impulse response（FIR） filter is designed to keep the signal from distorting and to obtain the staircase weighting.For the same case,the traditional method produces 0.330 7 m · s^–2 weighted root mean square（r.m.s.） acceleration and the filtering method gives 0.311 9 m · s^–2 r.m.s.The fourth order filter for approximation of vertical weighting obtains 0.313 9 m · s^–2 r.m.s.Crest factors of the acceleration signal weighted by the weighting filter and the fourth order filter are 3.002 7 and 3.011 1,respectively.This paper proposes several methods to design frequency weighting filters for automotive ride comfort evaluation,and these developed weighting filters are effective.

SMC-PHD based multi-target track-before-detect with nonstandard point observations model

Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.

Elimination of Wind Wave Effects on Sub-Bottom Profiler Record

With the presence of wind waves, the swaying of survey vessel may effect the quality of sub-bottom profiler records and, therefore, it is necessary to correct the distortions induced by wave action. A major issue is to distinguish wind wave effect and real bedforms such as sand waves. In this paper, a bandstop filter is designed according to the frequency features of wind wave effect to treat the distortion of seabed topography by wind waves. The technique is used to correct the sub-bottom profile in order to eliminate the wave-induced distortions for the sub-bottom profile records from the Yangtze Estuary. This study shows that the undulate seabed record is resulted from wave action, rather than the presence of sand waves, and the filtration technique helps to eliminate the wave effect and recover the real morphology of seabed and the sediment sequence underneath. In addition, a method for data processing is proposed for the case that the record indeed represents a combination of wave effects and real bedforms.