Diagnosis of the Heavy Rain near a Meiyu Front Using the Wet Q Vector Partitioning Method

A heavy rain process of the Changjiang-Huaihe Meiyu front (MYF) is diagnosed by the agency of the traditional Q vector partitioning (QVP) method to decompose the wet Q vector (Q) in a natural coordinate system that follows the isoentropes and by using the numerical simulation results of the revised MM4 meso-scale model. The technique shows that the partitioned wet Q vectors can lead to a significant scale separation of vertical motion related to the torrential rain. The results not only verify the existing conclusion that different scales interact throughout the rainstorm but also indicate the largely different roles of these scales during differing phases of the heavy ramfall on a quantitative basis. Specifically, during the developing stage, the large-scale plays a predominant role in forcing vertical motion, while frontal-scale forcing is secondary; during the intense stage, the frontal-scale evolves into the primary factor of forcing vertical motion, whereas the large-scale forcing is minor and plays a diminishing role and can even be ignored; and during the decaying stage, the large-scale once again serves as the main forcing of vertical motion in such a way that the forcing of the frontal-scale decays quickly and is of secondary importance. Furthermore, the partitioned wet Q vectors are suggested to be more suitable than the total wet Q vector for evaluating the potential physical mechanism of rainstorm genesis. The first step is that the forcing of large-scale $2?bla cdot {? Q}_s^*$ gives rise to the genesis of meso-scale $2?bla cdot {? Q}_n^*$ forcing; and then, accordingly as $2?bla cdot {? Q}_n^*$ forcing increases, whereby the secondary circulation is reinforced, the intensity of the rainfall is strengthened; and at last, the secondary circulation caused by $2?bla cdot {? Q}_n^*$ forcing is directly responsible for generation of the MYF heavy rainfall.

Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction

Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.

Massive Vector Particles Tunneling from the Neutral Rotating Anti-de Sitter Black Holes in Conformal Gravity

We investigate the massive vector particles' Hawking radiation from the neutral rotating Anti-de Sitter(AdS) black holes in conformal gravity by using the tunneling method.It is well known that the dynamics of massive vector particles are governed by the Proca field equation.Applying WKB approximation to the Proca equation,the tunneling probabilities and radiation spectrums of the emitted particles are derived.Hawking temperature of the neutral rotating AdS black holes in conformal gravity is recovered,which is consistent with the previous result in the literature.

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

According to statistic data,machinery faults contribute to largest proportion of High-voltage circuit breaker failures,and traditional maintenance methods exist some disadvantages for that issue.Therefore,based on the wavelet packet decomposition approach and support vector machines,a new diagnosis model is proposed for such fault diagnoses in this study.The vibration eigenvalue extraction is analyzed through wavelet packet decomposition,and a four-layer support vector machine is constituted as a fault classifier.The Gaussian radial basis function is employed as the kernel function for the classifier.The penalty parameter c and kernel parameterδof the support vector machine are vital for the diagnostic accuracy,and these parameters must be carefully predetermined.Thus,a particle swarm optimizationsupport vector machine model is developed in which the optimal parameters c andδfor the support vector machine in each layer are determined by the particle swarm algorithm.The validity of this fault diagnosis model is determined with a real dataset from the operation experiment.Moreover,comparative investigations of fault diagnosis experiments with a normal support vector machine and a particle swarm optimization back-propagation neural network are also implemented.The results indicate that the proposed fault diagnosis model yields better accuracy and e-ciency than these other models.

Identifcation of large-scale goaf instability in underground mine using particle swarm optimization and support vector machine

An approach which combines particle swarm optimization and support vector machine(PSO–SVM)is proposed to forecast large-scale goaf instability(LSGI).Firstly,influencing factors of goaf safety are analyzed,and following parameters were selected as evaluation indexes in the LSGI:uniaxial compressive strength(UCS)of rock,elastic modulus(E)of rock,rock quality designation(RQD),area ration of pillar(Sp),the ratio of width to height of the pillar(w/h),depth of ore body(H),volume of goaf(V),dip of ore body(a)and area of goaf(Sg).Then LSGI forecasting model by PSO-SVM was established according to the influencing factors.The performance of hybrid model(PSO+SVM=PSO–SVM)has been compared with the grid search method of support vector machine(GSM–SVM)model.The actual data of 40 goafs are applied to research the forecasting ability of the proposed method,and two cases of underground mine are also validated by the proposed model.The results indicated that the heuristic algorithm of PSO can speed up the SVM parameter optimization search,and the predictive ability of the PSO–SVM model with the RBF kernel function is acceptable and robust,which might hold a high potential to become a useful tool in goaf risky prediction research.

Support vector machine forecasting method improved by chaotic particle swarm optimization and its application

By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.

A STUDY OF PARTITIONING Q VECTOR ON BACKGROUND CONDITIONS OF A TORRENTIAL RAINFALL OVER SHANGHAI,CHINA ON 25 AUGUST 2008

A rainfall that occurred during 0200–1400 Beijing Standard Time(BST)25 August 2008 shows the rapid development of a convective system,a short life span,and a record rate of 117.5 mm h-1for Xujiahui station since 1872.To study this torrential rainfall process,the partitioning method of Q vector is developed,in which a moist Q vector is first separated into a dry ageostrophic Q vector(DQ)and a diabatic-heating component.The dry ageostrophic Q vector is further partitioned along isothermal lines in the natural coordinate to identify different scale forcing in adiabatic atmosphere,and the large-scale and convective condensational heating in non-uniform saturated atmosphere,convective condensational heating, and Laplace of diabatic heating that includes radiative heating and other heating and cooling processes,are calculated to study the forcing from diabatic heating.The effects of the environmental conditions on the development of the rainfall processes are diagnosed by performing the partitioning of Q vector based on 6-hourly NCEP/NCAR Final Analysis(FNL)data with the horizontal resolution of 1°×1°.The results include the following:(1)a low-pressure inverted trough associated with the landfall of Typhoon Nuri (2008),a strong southwesterly jet along the western side of the subtropical high,and an eastward-propagating westerly low-pressure trough provide favorable synoptic conditions for the development of torrential rainfall;(2)the analysis of DQ vector showed that the upward motions forced by the convergence of DQ vector in the lower troposphere(1000–600 hPa)favor the development of torrential rainfall.When DQ vector converges in the upper troposphere(500–100 hPa),upward motions in the whole air column intensify significantly to accelerate the development of torrential rainfall;(3)the partitioning analysis of DQ vector reveals that large-scale forcing persistently favors the development of torrential rainfall whereas the mesoscale forcing speeds up the torrential rainfall;(4)the calculations of large-scale condensational heating in non-uniform saturated atmosphere,convective condensational heating, and Laplace of diabatic heating showed that the forcing related to diabatic heating has the positive feedback on the convective development,and such positive feedback decays and dissipates when the convective system propagates eastward and weakens.

Effect of Tb/Mg doping on composition and physical properties of hydroxyapatite nanoparticles for gene vector application

Transfection efficiency of hydroxyapatite nanoparticles(HAnps)is relative to the particle size,morphology,surface charge,surface modifier and so on.This study prepared HAnps with doped Tb/Mg by hydrothermal synthesis method(HTSM)and investigated the effects of different Tb/Mg contents on the morphology,particle size,surface charge,composition and cellular endocytosis of HAnps.The results showed that Mg-HAnps possessed better dispersion ability than Tb-HAnps.With increasing doping content of Tb/Mg-HAnps,the granularity of Tb-HAnps increased,while that of Mg-HAnps declined.Both particle size and zeta potential of Mg-HAnps were lower than those of Tb-HAnps.7.5%Mg-doping HAnps presented relatively uniform slender rod morphology with average size of30nm,while10%Mg-doping HAnps were prone to agglomeration.Moreover,Mg-HAnps-GFP(green fluorescent protein)endocytosed by MG63cells was dotted in the perinuclear region,while Tb-HAnps were more likely to aggregate.In conclusion,as gene vectors,Mg-HAnps showed enhanced properties compared to Tb-HAnps.

A new support vector machine optimized by improved particle swarm optimization and its application

A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.

Dispersion of the Mechanical Parts Performance Indicators Based on the Concept of Random Vector

To solve the precision and reliability problem of various machinery equipments and military vehicles, some military organisations, the industrial sector and the academia at home and abroad begin to pay attention to the statistical distribution of machining dimensions, material properties and service loads, and the system reliability optimization design with constraints and reliability optimization design of various mechanical parts is studied in this way. However, the above researches focus on solving the strength and the life problem, and no studies have been done on the discrete degree and discrete pattern of other performance indicators. The concept of using a random vector to describe the mechanical parts performance indicators is presented; characteristics between the value of the vector variance matrix determinant and the sum of the diagonal covariance matrix in describing the performance indicators of vector dispersion are studied and compared. A clutch diaphragm spring is set as an example, the geometric dimension indicator is described with random vector, and the applicability of using variance matrix determinant and variance matrix trace of geometric dimension vector to describe discrete degree of random vector is studied by using Monte-Carlo simulation method and component discrete degree perturbation method. Also, the effects of different components of diaphragm spring geometric dimension vector on the value of covariance matrix determinant and the sum of covariance matrix diagonal of diaphragm spring performance indicators vector are analyzed. The present study shows that the impacts of the dispersion of diaphragm spring cone angle on every performance dispersion are all ranked first, and far exceed that of other dimension dispersion. So it must be strictly controlled in the production process. The result of the research work provides a reference for the design of diaphragm spring, and also it presents a proper method for researching the performance of other mechanical parts.

Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.

Use of PEI-coated Magnetic Iron Oxide Nanoparticles as Gene Vectors

Summary: To evaluate the feasibility of using polyethyleneimine (PEI) coated magnetic iron oxide nanoparticles (polyMAG-1000) as gene vectors. The surface characteristics of the nanoparticles were observed with scanning electron microscopy. The ability of the nanoparticles to combine with and protect DNA was investigated at different PH values after polyMAG-1000 and DNA were combined in different ratios. The nanoparticles were tested as gene vectors with in vitro transfection models. Under the scanning electron microscope the nanoparticles were about 100 nm in diameter. The nanoparticles could bind and condense DNA under acid, neutral and alkaline conditions, and they could transfer genes into cells and express green fluorescent proteins (GFP). The transfection efficiency was highest (51 %) when the ratio of nanoparticles to DNA was 1:1 (v:w). In that ratio, the difference in transfection efficiency was marked depending on whether a magnetic field was present or not: about 10 % when it was absent but 51 % when it was present. The magnetic iron oxide nanoparticles coated with PEI may potentially be used as gene vectors.

Improve Neural Machine Translation by Building Word Vector with Part of Speech

Neural Machine Translation(NMT)based system is an important technology for translation applications.However,there is plenty of rooms for the improvement of NMT.In the process of NMT,traditional word vector cannot distinguish the same words under different parts of speech(POS).Aiming to alleviate this problem,this paper proposed a new word vector training method based on POS feature.It can efficiently improve the quality of translation by adding POS feature to the training process of word vectors.In the experiments,we conducted extensive experiments to evaluate our methods.The experimental result shows that the proposed method is beneficial to improve the quality of translation from English into Chinese.

Development of a particle swarm optimization based support vector regression model for titanium dioxide band gap characterization

Energy band gap of titanium dioxide(TiO_2) semiconductor plays significant roles in many practical applications of the semiconductor and determines its appropriateness in technological and industrial applications such as UV absorption, pigment,photo-catalysis, pollution control systems and solar cells among others. Substitution of impurities into crystal lattice structure is the most commonly used method of tuning the band gap of TiO_2 for specific application and eventually leads to lattice distortion. This work utilizes the distortion in the lattice structure to estimate the band gap of doped TiO_2, for the first time, through hybridization of a particle swarm optimization algorithm(PSO) with a support vector regression(SVR) algorithm for developing a PSO-SVR model. The precision and accuracy of the developed PSO-SVR model was further justified by applying the model for estimating the effect of cobalt-sulfur co-doping, nickel-iodine co-doping, tungsten and indium doping on the band gap of TiO_2 and excellent agreement with the experimentally reported values was achieved. Practical implementation of the proposed PSO-SVR model would further widen the applications of the semiconductor and reduce the experimental stress involved in band gap determination of TiO_2.

NOTES ON A STUDY OF VECTOR BUNDLE DYNAMICAL SYSTEMS(Ⅱ)──PART 1

The study of linear and global. properties of linear dynamical systems on vector bundles appeared rather extensive already in the past.Presently we propose to study perturbations of this linear dynamics The perturbed dynamical system which we shallconsider is no longer linear.while the properties to be studied will be still global in general.Moreover.we are interested in the nonuniformly hyperbolic properties.In this paper,we set an appropriate definition for such perturbations.Though it appearssome what not quite usual yet has deeper root in standard systens of differential equations in the theory of differentiable dynamical systens The general problen is to see which property of the original given by the dynamical system is persistent when a perturbation takes place.The whole contenl of the paper is deyoted to establishinga theorem of this sort.

Recognition model and algorithm of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain

In order to improve the recognition rate and accuracy rate of projectiles in six sky-screens intersection test system,this work proposes a new recognition method of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain of six sky-screens detection sensor.Based on the measurement principle of the six sky-screens intersection test system and the characteristics of the output signal of the sky-screen,we analyze the existing problems regarding the recognition of projectiles.In order to optimize the projectile recognition effect,we use the support vector machine and basic particle swarm algorithm to form a new recognition algorithm.We set up the particle swarm algorithm optimization support vector projectile information recognition model that conforms to the six sky-screens intersection test system.We also construct a spatial-temporal constrain matching model based on the spatial geometric relationship of six sky-screen intersection,and form a new projectile signal recognition algorithm with six sky-screens spatial-temporal information constraints under the signal classification mechanism of particle swarm optimization algorithm support vector machine.Based on experiments,we obtain the optimal penalty and kernel function radius parameters in the PSO-SVM algorithm;we adjust the parameters of the support vector machine model,train the test signal data of every sky-screen,and gain the projectile signal classification results.Afterwards,according to the signal classification results,we calculate the coordinate parameters of the real projectile by using the spatial-temporal constrain of six sky-screens detection sensor,which verifies the feasibility of the proposed algorithm.

Analysis of Water Pollution Causes and Control Countermeasures in Liaohe Estuary via Support Vector Machine Particle Swarm Optimization under Deep Learning

This study explores the loss or degradation of the ecosystem and its service function in the Liaohe estuary coastal zone due to the deterioration ofwater quality.Aprediction systembased on support vectormachine(SVM)-particle swarm optimization(PSO)(SVM-PSO)algorithm is proposed under the background of deep learning.SVM-PSO algorithm is employed to analyze the pollution status of the Liaohe estuary,so is the difference in water pollution of different sea consuming types.Based on the analysis results for causes of pollution,the control countermeasures of water pollution in Liaohe estuary are put forward.The results suggest that the water pollution index prediction model based on SVM-PSO algorithm shows the maximum error of 2.41%,the average error of 1.24%in predicting the samples,the root mean square error(RMSE)of 5.36×10^(−4),and the square of correlation coefficient of 0.91.Therefore,the prediction system in this study is feasible.At present,the water pollution status of Liaohe estuary is of moderate and severe levels of eutrophication,and the water pollution status basically remains at the level of mild pollution.The general trend is fromphosphorus moderate restricted eutrophication to phosphorus restricted potential eutrophication.To sumup,the SVM-PSO algorithm shows good sewage prediction ability,which can be applied and promoted in water pollution control and has reliable reference significance.

Design of Ballistic Consistency Based on Least Squares Support Vector Machine and Particle Swarm Optimization

In order to improve the firing efficiency of projectiles,it is required to use the universal firing table for gun weapon system equipped with a variety of projectiles.Moreover,the foundation of sharing the universal firing table is the ballistic matching for two types of projectiles.Therefore,a method is proposed in the process of designing new type of projectile.The least squares support vector machine is utilized to build the ballistic trajectory model of the original projectile,thus it is viable to compare the two trajectories.Then the particle swarm optimization is applied to find the combination of trajectory parameters which meet the criterion of ballistic matching best.Finally,examples show the proposed method is valid and feasible.

Improving word vector model with part-of-speech and dependency grammar information

Part-of-speech(POS)and dependency grammar(DG)are the basic components of natural language processing.However,current word vector models have not made full use of both POS information and DG information,and hence the models’performances are limited to some extent.The authors first put forward the concept of POS vector,and then,based on continuous bag-of-words(CBOW),constructed four models:CBOW+P,CBOW+PW,CBOW+G,and CBOW+G+P to incorporate POS information and DG information into word vectors.The CBOW+P and CBOW+PW models are based on POS tagging,the CBOW+G model is based on DG parsing,and the CBOW+G+P model is based on POS tagging and DG parsing.POS information is integrated into the training process of word vectors through the POS vector to solve the problem of the POS similarity being difficult to measure.The POS vector correlation coefficient and distance weighting function are used to train the POS vector as well as the word vector.DG information is used to correct the information loss caused by fixed context windows.Dependency relations weight is used to measure the difference of dependency relations.Experiments demonstrated the superior performance of their models while the time complexity is still kept the same as the base model of CBOW.