Estimation of the Number of Collapsed Houses Damaged by Typhoon Based on Principal Components Analysis and Support Vector Machine

The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of buildings were processed by Principal Component Analysis.The key factor was extracted to support input of vector machine model and to build an evaluation model;the historical fitting result kept in line with the fact.In the real evaluation of two typhoons landed in Zhejiang Province in 2008 and 2009,the coincidence of evaluating result and actual value proved the feasibility of this model.

Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

Laser-induced breakdown spectroscopy（LIBS） is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis（PCA） and support vector machine（SVM） is applied to rock analysis.Fourteen emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy（91.38% for the real rock） is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA.It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and SVM.By this method,the ability of LIBS to classify rock is validated.

Support vector classifier based on principal component analysis

Support vector classifier （SVC） has the superior advantages for small sample learning problems with high dimensions, with especially better generalization ability. However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC. A principal component analysis （PCA） is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently, and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC. Furthermore, a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines. Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically, but also improves the identify rates effectively.

Anomaly Detection System Based on Principal Component Analysis and Support Vector Machine

This article presents an anomaly detection system based on principal component analysis （PCA） and support vector machine （SVM）. The system first creates a profile defining a normal behavior by frequency-based scheme, and then compares the similarity of a current behavior with the created profile to decide whether the input instance is norreal or anomaly. In order to avoid overfitting and reduce the computational burden, normal behavior principal features are extracted by the PCA method. SVM is used to distinguish normal or anomaly for user behavior after training procedure has been completed by learning. In the experiments for performance evaluation the system achieved a correct detection rate equal to 92.2% and a false detection rate equal to 2.8%.

Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

Predicting configuration performance of modular product family using principal component analysis and support vector machine

A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.

Lung Cancer Prediction from Elvira Biomedical Dataset Using Ensemble Classifier with Principal Component Analysis

Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal epithelium, lung cancer has the highest mortality and morbidity among cancer types, threatening health and life of patients suffering from the disease. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) have been used for lung cancer prediction. However they still face challenges such as high dimensionality of the feature space, over-fitting, high computational complexity, noise and missing data, low accuracies, low precision and high error rates. Ensemble learning, which combines classifiers, may be helpful to boost prediction on new data. However, current ensemble ML techniques rarely consider comprehensive evaluation metrics to evaluate the performance of individual classifiers. The main purpose of this study was to develop an ensemble classifier that improves lung cancer prediction. An ensemble machine learning algorithm is developed based on RF, SVM, NB, and KNN. Feature selection is done based on Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This algorithm is then executed on lung cancer data and evaluated using execution time, true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), recall (R), precision (P) and F-measure (FM). Experimental results show that the proposed ensemble classifier has the best classification of 0.9825% with the lowest error rate of 0.0193. This is followed by SVM in which the probability of having the best classification is 0.9652% at an error rate of 0.0206. On the other hand, NB had the worst performance of 0.8475% classification at 0.0738 error rate.

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.

Fast Tensor Principal Component Analysis via Proximal Alternating Direction Method with Vectorized Technique

This paper studies the problem of tensor principal component analysis (PCA). Usually the tensor PCA is viewed as a low-rank matrix completion problem via matrix factorization technique, and nuclear norm is used as a convex approximation of the rank operator under mild condition. However, most nuclear norm minimization approaches are based on SVD operations. Given a matrix , the time complexity of SVD operation is O(mn2), which brings prohibitive computational complexity in large-scale problems. In this paper, an efficient and scalable algorithm for tensor principal component analysis is proposed which is called Linearized Alternating Direction Method with Vectorized technique for Tensor Principal Component Analysis (LADMVTPCA). Different from traditional matrix factorization methods, LADMVTPCA utilizes the vectorized technique to formulate the tensor as an outer product of vectors, which greatly improves the computational efficacy compared to matrix factorization method. In the experiment part, synthetic tensor data with different orders are used to empirically evaluate the proposed algorithm LADMVTPCA. Results have shown that LADMVTPCA outperforms matrix factorization based method.

Short-Term Financial Time Series Forecasting Integrating Principal Component Analysis and Independent Component Analysis with Support Vector Regression

Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. Experiments were carried out based on 16 years’ historical data of three prominent stocks from three different sectors listed in Dhaka Stock Exchange (DSE), Bangladesh. The predictions were made for 1 to 4 days in advance targeting the short term prediction. For comparison, the integration of PCA with SVR (PCA-SVR), ICA with SVR (ICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Experimental results show that the proposed model (PCA-ICA-SVR) outperforms the PCA-SVR, ICA-SVR and single SVR methods.

Cicada(Tibicen linnei) steers by force vectoring

To change flight direction, flying animals modulate aerodynamic force either relative to their bodies to generate torque about the center of mass, or relative to the flight path to produce centripetal force that curves the trajectory. In employing the latter, the direction of aerodynamic force remains fixed in the body flame and rotations of the body redirect the force. While both aforementioned techniques are essential for flight, it is critical to investigate how an animal balances the two to achieve aerial locomotion. Here, we measured wing and body kinematics of cicada （Tibicen linnei） in flee flight, including flight periods of both little and substantial body reorientations. It is found that cicadas employ a common force vectoring technique to execute all these flights. We show that the direction of the half-stroke averaged aerodynamic force relative to the body is independent of the body orientation, varying in a range of merely 20 deg. Despite directional limitation of the aerodynamic force, pitch and roll torque are generated by altering wing angle of attack and its mean position relative to the center of mass. This results in body rotations which redirect the wing force in the global flame and consequently change the flight traiectorv.

On arable land changes in Shandong Province and their driving forces

The decrease of total cultivated area and the lower per capita available arable land resource are now serious problems in Shandong Province, a major agricultural province in China. These problems will become more serious along with the further development of economy. In this paper, based on the statistical information at provincial and county levels, the changes of arable land in Shandong Province and their driving forces during the last 50 years are analyzed. The general changing trends of arable land and per capita available arable land are reducing, and the trends of decrease will continue when the economy is developing. The result of GIS spatial analysis shows that the change of the arable land use in Shandong Province has a regional difference. Eight variables having influences on cultivated land change are analyzed by principal component analysis. The results show that the dynamic development of economy, pressure of social system and progress of scientific techniques in agriculture are the main causes for cultivated land reduction. The principal factors which can be considered as driving forces for arable land change include per capita net living space, total population and per ha grain yield. By using regressive equation, along with analysis on population growth and economic development, cultivated areas in Shandong Province in 2005 and 2010 are predicted respectively. The predicted cultivated areas in Shandong will be 6435.47 thousand hain 2005 and 6336.23 thousand ha in 2010 respectively.

Entropy generation analysis of thermally developing forced convection in fluid-saturated porous medium

Entropy generation for thermally developing forced convection in a porous medium bounded by two isothermal parallel plates is investigated analytically on the basis of the Darcy flow model where the viscous dissipation effects had also been taken into account. A parametric study showed that decreasing the group parameter and the Peclet number increases the entropy generation while for the Brinkman number the converse is true. Heatline visualization technique is applied with an emphasis on the Br 〈 0 case where there is somewhere that heat transfer changes direction at some streamwise location to the wall instead of its original direction, i.e., from the wall.

Analysis to driving forces of land use change in Lu’an mining area

By selecting impact factors of driving force and formulating evaluation criteria of the impacts, the evaluation system of corresponding driving force impact of land use change was established. Taking Lu'an mining area as an example, the specific impact factors of coal mine were comprehensively evaluated and analyzed in order to carry out qualitative and quantitative analysis for the driving force of mining-land use change. The principal component analysis shows that the social and economic development in mining area from 2000 to 2007 demonstrates continuous accelerate trends, and the impacts of its overall driving force to land use change are increased gradually. The socio-economic factors have more impacts to mining-land use change than those of the natural resources. The main driving force of mining-land use change also include population, technological progress and policy.

Grey Relational Analysis Coupled with Principal Component Analysis Method For Optimization Design of Novel Crash Box Structure

Crashworthiness and lightweight optimization design of the crash box are studied in this paper. For the initial model, a physical test was performed to verify the model. Then, a parametric model using mesh morphing technology is used to optimize and decrease the maximum collision force (MCF) and increase specific energy absorption (SEA) while ensure mass is not increased. Because MCF and SEA are two conflicting objectives, grey relational analysis (GRA) and principal component analysis (PCA) are employed for design optimization of the crash box. Furthermore, multi-objective analysis can convert to a single objective using the grey relational grade (GRG) simultaneously, hence, the proposed method can obtain the optimal combination of design parameters for the crash box. It can be concluded that the proposed method decreases the MCF and weight to 16.7% and 29.4% respectively, while increasing SEA to 16.4%. Meanwhile, the proposed method in comparison to the conventional NSGA-Ⅱ method, reduces the time cost by 103%. Hence, the proposed method can be properly applied to the optimization of the crash box.

Sport specificity background affects the principal component structure of vertical squat jump performance of young adult female athletes

Purpose： Long-term training specificity is thought to alter performance in tests evaluating strength and power production capability. The aim of the present study was to provide additional information to the limited existing knowledge concerning the possible differences of the force/time profile of squat jumping among different groups of young female athletes. Methods： One hundred and seventy-three adult women （20.1 ± 2.8 years, 1.71 ± 0.09 m, 65.6 ± 10.3 kg, mean± SD for age, height, and mass, respectively） engaged in track and field （TF）, volleyball （VO）, handball （HA）, basketball （BA）, and physical education students （PE） executed maximal squat jumps （SQJ） on a force plate. Pearson＇s correlation was used to identify the relationship between SQJ performance, the anthropometric characteristics and the biomechanical parameters. Differences concerning the biomechanical parameters among groups were investigated with analysis of variance, while the force- （FPD） or time- （TPD） dependency of SQJ execution was examined using principal components analysis （PCA）. Results： SQJ was unrelated to body height but significantly correlated with body mass （r = -0.26, p = 0.001）. TF jumped higher and produced larger peak body power output compared to all the other groups （p 〈 0.05）. All athletes were superior to PE since they performed the SQJ with a longer （p 〈 0.05） vertical body center of mass trajectory during the propulsion phase. PCA results revealed that TF significantly differentiated than the other groups by relying on FPD. Conclusion： Various different profiles of FPD and TPD were detected due to different sporting background in young female athletes. Since TF superiority in SQJ was relied on the larger power production and a greater FPD, female indoor team sport athletes are suggested to execute jumping exercises adopting the jumping strategies utilized by TE

Study on Farmland Use Change and Driving Force in the High and Cold Areas in Northwest Yunnan——A Case Study of Ninglang Yi Autonomous County

On the basis of overview of the study area,by analyzing the dynamic change of farmland in Ninglang County,we can find that the farmland area in this county tended to decrease from 1996 to 2008.According to the investigation data concerning land change provided by Bureau of Land and Resources in Ninglang County and socio-economic data provided by Bureau of Statistics in Ninglang County,we select 11 indices,such as total population,GDP,total output value of county and so on,coupled with SPSS statistical method,we adopt principal component analysis method to analyze driving force factors of farmland use change in the high and cold areas in Northwest Yunnan.The results show that the two factors of economic development and population growth are the dominant driving factors impacting farmland use change,and the policy factors,such as "returning farmland to forests",are also the important driving factors impacting Ninglang County.

Influence of Regular Wave and Ship Characteristics on Mooring Force Prediction by Data-Driven Model

The study of mooring forces is an important issue in marine engineering and offshore structures.Although being widely applied in mooring system,numerical simulations suffer from difficulties in their multivariate and nonlinear modeling.Data-driven model is employed in this paper to predict the mooring forces in different lines,which is a new attempt to study the mooring forces.The height and period of regular wave,length of berth,ship load,draft and rolling period are considered as potential influencing factors.Input variables are determined using mutual information(MI)and principal component analysis(PCA),and imported to an artificial neural network(NN)model for prediction.With study case of 200 and 300 thousand tons ships experimental data obtained in Dalian University of Technology,MI is found to be more appropriate to provide effective input variables than PCA.Although the three factors regarding ship characteristics are highly correlated,it is recommended to input all of them to the NN model.The accuracy of predicting aft spring line force attains as high as 91.2%.The present paper demonstrates the feasibility of MI-NN model in mapping the mooring forces and their influencing factors.

Relationship between Creasing-Recovery Force and Crease-Recovery Angle

To find out the reason of resulting in the crease recovery of a fabric and provide theoretical guidance for designing a new material with good creasing-recovery property,the relationship between the creasing-recovery force and the crease-recovery angle of a woven fabric was investigated by self-setup experimental device.The results show that the crease-recovery angle of a woven fabric is correlated with the creasing-recovery force of the fabric in a linear relation.Furthermore,it is found that the internal stress is the principal force of affecting the creasing-recovery property of a woven fabric.In addition,the relationship between the tensile property of a woven fabric and the creasing-recovery property of the fabric has also been investigated,showing that the lower relaxation velocity of tensile stress of a fabric is,the better creasing-recovery property of the fabric has.

Three-dimensional atomic force microscopy based on tailored cantilever probe with flared tip

In order to meet the requirements of nondestructive testing of true 3D topography of micro-nano structures,a novel three-dimensional atomic force microscope(3D-AFM)based on flared tip is developed.A high-precision scanning platform is designed to achieve fast servo through moving probe and sample simultaneously,and several combined nanopositioning stages are used to guarantee linearity and orthogonality of displacement.To eliminate the signal deviation caused by AFM-head movement,a traceable optical lever system is designed for cantilever deformation detection.In addition,a method of tailoring the cantilever of commercial probe with flared tip is proposed to reduce the lateral force applied on the tip in measurement.The tailored probe is mounted on the 3D-AFM,and 3D imaging experiments are conducted on different samples by use of adaptive-angle scanning strategy.The results show the roob-mean-square value of the vertical displacement noise(RMS)of the prototype is less than 0.1 nm and the high/width measurement repeatability(peak-to-peak)is less than 2.5 nm.