Customized Optimization for Vehicle Acoustic Statistical Energy Analysis

Statistical Energy Analysis(SEA) is one of the conventional tools for predicting vehicle high-frequency acoustic responses.This study proposes a new method that can provide customized optimization solutions to meet NVH targets based on the specific needs of different project teams during the initial project stages.This approach innovatively integrates dynamic optimization,Radial Basis Function(RBF),and Fuzzy Design Variables Genetic Algorithm(FDVGA) into the optimization process of Statistical Energy Analysis(SEA),and also takes vehicle sheet metal into account in the optimization of sound packages.In the implementation process,a correlation model is established through Python scripts to link material density with acoustic parameters,weight,and cost.By combining Optimus and VaOne software,an optimization design workflow is constructed and the optimization design process is successfully executed.Under various constraints related to acoustic performance,weight and cost,a globally optimal design is achieved.This technology has been effectively applied in the field of Battery Electric Vehicle(BEV).

Combinatorial analysis on spatial information statistics for the karst water environment in Guiyang,China

The karst groundwater system is extremely vulnerable and easily contaminated by human activities.To understand the spatial distribution of contaminants in the groundwater of karst urban areas and contributors to the contamination,this paper employs the spatial information statistics analysis theory and method to analyze the karst groundwater environment in Guiyang City.Based on the karst ground water quality data detected in 61 detection points of the research area in the last three years,we made Kriging evaluation isoline map with some ions in the karst groundwater,such as SO4 2-,Fe 3+,Mn 2+and F -,analyzed and evaluated the spatial distribution,extension and variation of four types of ions on the basis of this isoline map.The results of the analysis show that the anomaly areas of SO4 2-,Fe 3+,Mn 2+,Fand other ions are mainly located in Baba’ao,Mawangmiao and Sanqiao in northwestern Gui- yang City as well as in its downtown area by reasons of the original non-point source pollution and the contamination caused by human activities(industrial and domestic pollution).

Statistics and Analysis on Papers Published on Journal ‘Advanced Technology of Electrical Engineering and Energy’

The paper presented the statistics and analysis on papers published on the journal 'Advanced Technology of Electrical Engineering and Energy' from 1996 to 2008: the paper acceptance rate,the paper category,the first author's affiliations,the top 7 first authors,the top 10 coauthors and also the journal evaluation indexes of the journal.It offers details of the journal to anyone interested,especially to our editorial board and our broad readers.

Microarray Analysis Using Rank Order Statistics for ARCH Residual Empirical Process

Statistical two-group comparisons are widely used to identify the significant differentially expressed (DE) signatures against a therapy response for microarray data analysis. We applied a rank order statistics based on an Autoregressive Conditional Heteroskedasticity (ARCH) residual empirical process to DE analysis. This approach was considered for simulation data and publicly available datasets, and was compared with two-group comparison by original data and Auto-regressive (AR) residual. The significant DE genes by the ARCH and AR residuals were reduced by about 20% - 30% to these genes by the original data. Almost 100% of the genes by ARCH are covered by the genes by the original data unlike the genes by AR residuals. GO enrichment and Pathway analyses indicate the consistent biological characteristics between genes by ARCH residuals and original data. ARCH residuals array data might contribute to refining the number of significant DE genes to detect the biological feature as well as ordinal microarray data.

Statistics Analysis:FDI Obsorbing in Jan-Jun

According to China's Customs' statistics,from January to May 2008,the import and export volume of foreign- invested enterprises totaled US$563.612 billion,an increase of 21.28 percent over the same period of last year,5.02 percent lower than the growth rate of the country (26.30 percent) in the same period,accounting for 55.69 percent of the total import and export of the country.(See Chart 1)

Cryo-EM Data Statistics and Theoretical Analysis of KaiC Hexamer

Cryo-electron microscopy(cryo-EM) provides a powerful tool to resolve the structure of biological macromolecules in natural state. One advantage of cryo-EM technology is that different conformation states of a protein complex structure can be simultaneously built, and the distribution of different states can be measured. This provides a tool to push cryo-EM technology beyond just to resolve protein structures, but to obtain the thermodynamic properties of protein machines. Here, we used a deep manifold learning framework to get the conformational landscape of Kai C proteins, and further obtained the thermodynamic properties of this central oscillator component in the circadian clock by means of statistical physics.

Comparison of Several Statistical Analysis Models for Genotypic Stability of Saccharum officinarum

[Objective] The study aimed to compare several statistical analysis models for estimating the sugarcane （Saccharum spp.） genotypic stability. [Method] The data of sugarcane regional trials in Guangdong, in 2009 was analyzed by three models respectively： Finlay and Wilkinson model： the additive main effects and multiplicative interaction （AMMI） model and linear regression-principal components analysis （LR- PCA） model, so as to compare the models. [Result] The Finlay and Wilkinson model was easier, but the analysis of the other two models was more comprehensive, and there was a bit difference between the additive main effects and multiplicative inter- action （AMMI） model and linear regression-principal components analysis （LR-PCA） model. [Conclusion] In practice, while the proper statistical method was usually con- sidered according to the different data, it should be also considered that the same data should be analyzed with different statistical methods in order to get a more reasonable result by comparison.

Statistical and causes analysis of storm surges along Tianjin coast during the past 20 years

Based on tidal data statistical analysis for 20 years of Tanggu Marine Environmental Monitoring Station from 1991 to 2010, we concluded that an average of nearly 10 days of 100 cm above water increase took place at Tianjin coast every year. The maximum high tide and average tide of Tianjin coast occurred in summer and autumn, and the maximum water increase also occurred in summer and autumn. Days with water increase more than 100 cm mostly occurred in spring, autumn and winter. Then we summarized the causes of coastal storm surge disaster in Tianjin based on astronomical tide factors, meteorological factors, sea level rise, land subsidence, and geographic factors, et al. Finally, we proposed storm surge disaster prevention measures.

A Survey of Statistical Analysis in a Selected M.A. Dissertation

The use of statistics has received increasing attention in the studies of linguistics and applied linguistics. However,due to its complexity and preciseness,there are many problems of application of statistics. The present paper tries to discuss the application of statistics in a M.A. dissertation and aims to explain how to apply statistics to linguistics research normatively,scientifically,and precisely.

STATISTICAL ANALYSIS AND NUMERICAL SIMULATION OF POYANG LAKE'S INFLUENCE ON TROPICAL CYCLONES

Based on the Tropical Cyclone(TC briefly thereafter)Yearbook 1980-2009,this paper first analyzes the number and intensity change of the TCs which passed directly over or by the side of Poyang Lake(the distance of TC center is less than 1°longitude or 1°latitude from the Lake)among all the landfalling TCs in China during the past 30 years.Two cases are examined in detail in this paper.One is severe typhoon Rananim with a speed of 3.26 m/s and a change of 1 hPa in intensity when it was passing the Lake.The other is super typhoon Saomai with a faster moving speed of 6.50 m/s and a larger change in intensity of 6 hPa.Through numerical simulation experiments,this paper analyzes how the change of underlying surface from water to land contributes to the differences in intensity,speed and mesoscale convection of the two TCs when they passed the Lake.Results show that the moisture and dynamic condition above the Lake were favorable for the maintenance of the intensity when Rananim was passing through Poyang Lake,despite the moisture supply from the ocean was cut off.As a result,there was strong convection around the lake which led to a rainfall spinning counter-clockwise as it was affected by the TC movement.However,little impact was seen in the Saomai case.These results indicate that for the TCs coming ashore on Poyang Lake with a slow speed,the large water body is conducive to the sustaining of the intensity and strengthening of the convection around the TC center and the subsequent heavy rainfall.On the contrary,a fast-moving TC is less likely to be influenced by the underlying surface in terms of intensity and speed.

Simulation on a Car Interior Aerodynamic Noise Control Based on Statistical Energy Analysis

How to simulate interior aerodynamic noise accurately is an important question of a car interior noise reduction. The unsteady aerodynamic pressure on body surfaces is proved to be the key effect factor of car interior aerodynamic noise control in high frequency on high speed. In this paper, a detail statistical energy analysis （SEA） model is built. And the vibra-acoustic power inputs are loaded on the model for the valid result of car interior noise analysis. The model is the solid foundation for further optimization on car interior noise control. After the most sensitive subsystems for the power contribution to car interior noise are pointed by SEA comprehensive analysis, the sound pressure level of car interior aerodynamic noise can be reduced by improving their sound and damping characteristics. The further vehicle testing results show that it is available to improve the interior acoustic performance by using detailed SEA model, which comprised by more than 80 subsystems, with the unsteady aerodynamic pressure calculation on body surfaces and the materials improvement of sound/damping properties. It is able to acquire more than 2 dB reduction on the central frequency in the spectrum over 800 Hz. The proposed optimization method can be looked as a reference of car interior aerodynamic noise control by the detail SEA model integrated unsteady computational fluid dynamics （CFD） and sensitivity analysis of acoustic contribution.

Application of Integration of Spatial Statistical Analysis with GIS to Regional Economic Analysis

This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of global Moran Coefficient, Local Moran and Local Geary. Furthermore, a user-friendly statistical module, combining spatial statistical analysis methods with GIS visual techniques, is developed in Arcview using Avenue. An example is also given to show the usefulness of this module in identifying and quantifying the underlying spatial association patterns between economic units.

Statistical Analysis on Field Efficacy Trial of Pesticide Using Visual Basic

[Objective] To introduce a convenient and easy way for the statistical anal- ysis on field efficacy trials of pesticide by using Visual Basic. [Method] The calcula- tion procedure of using Visual Basic to conduct statistical analysis on the field efficacy of pesticides was introduced, and an example was used to illustrate the usage and skill of the program. [Result] The procedure could quickly and accurately con- duct statistical analysis on the field efficacy of pesticide by only inputting initial data of the test, and it could compare the significance of differences between various fac- tors. Its calculated results were consistent with the results of the specialized statisti- cal software DPS. [Conclusion] It is a quick and simple method with high accuracy and reliability, which can greatly improve the efficiency of pesticide formulation opti- mization.

GREY RELATIONAL ANALYSIS: A NEW STATISTICAL METHOD OF MULTIFACTORIAL ANALYSIS IN MEDICINE

The concept, fundamental theory, analytical steps and formulae of grey relational analysis (GRA)-a new statistical method or multifactorial analysis in the field of medicine were introduced. GRA of grouping sequence that is applied to medical study was built by the authors. An example was given to demonstrate it. The superiority of GRA was recounted briefly.

Multivariate Statistical Process Monitoring of an Industrial Polypropylene Catalyzer Reactor with Component Analysis and Kernel Density Estimation

Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.

Application of Statistical Tools for Data Analysis and Interpretation in Rice Plant Pathology

There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets.

Construction of Inorganic Elemental Fingerprint and Multivariate Statistical Analysis of Marine Traditional Chinese Medicine Meretricis concha from Rushan Bay

Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, the elemental contents of M. concha from five sampling points in Rushan Bay have been determined by means of inductively coupled plasma optical emission spectrometry(ICP-OES). Based on the contents of 14 inorganic elements(Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, and Zn), the inorganic elemental fingerprint which well reflects the elemental characteristics was constructed. All the data from the five sampling points were discriminated with accuracy through hierarchical cluster analysis(HCA) and principle component analysis(PCA), indicating that a four-factor model which could explain approximately 80% of the detection data was established, and the elements Al, As, Cd, Cu, Ni and Pb could be viewed as the characteristic elements. This investigation suggests that the inorganic elemental fingerprint combined with multivariate statistical analysis is a promising method for verifying the geographical origin of M. concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM.

Statistic Analysis on Quantitative Characteristics for Developing the DUS Test Guideline of Ranunculus asiaticus L.

Selection of quantitative characteristics, division of their expression ranges, and selection of example varieties are key issues on developing DUS Test Guidelines, which are more crucial for quantitative characteristics since their expressions vary in different degrees. Taking the development of DUS Test Guideline of Ranunculus asiaticus L. as an example, this paper applied statistic-based approaches for the analyses of quantitative characteristics. We selected 9 quantitative characteristics from 18 pre-selected characteristics, based on within-variety uniformity, stability between different growing cycles, and correlation among characteristics, by the analyses of coefficient of variation, paired-samples t-test and partial correlation. The expression ranges of the 9 selected quantitative characteristics were divided into different states using descriptive statistics and distribution frequency of varieties. Eight of the 9 selected quantitative characteristics were categorized as standard characteristics as they showed one peak in distribution frequency of 120 varieties in various expressions of the characteristics, whereas, plant height can be categorized as grouping characteristic since it gave two peaks, and can group the varieties into pot and cut varieties. Finally, box-plot was applied to visually select the example varieties, and varieties 7, 12, and 28 were determined as the example varieties for plant height. The methods described in this paper are effective for the selection of quantitative characteristics, division of expression ranges, and selection of example varieties in Ranunculus asiaticus L. for DUS test, and may also be interest for other plant genera.

Statistical analysis of nondestructive testing results of cast steel joints in civil engineering structures

To study the distribution law and random characteristics of casting defects in steel castings,24 civil engineering structural cast steel joints were divided into two groups:simple cast steel joints and complex cast steel joints.Three kinds of nondestructive testing(NDT)methods,namely,visual,magnetic particle,and ultrasonic inspections,were used to detect the macroscopic defects in joints.The NDT results were then statistically analyzed.The results show that the unfused core support is a common defect of complex cast steel joints,and the defect can be seen visually,so excavation and repair welding should be carried out before castings leave factories.Casting cracks are extremely likely to occur in the intersection area of tubes,which is called the ultrasonic inspection blind zone.The occurrence probability of gas pores on simple cast steel joints is the largest,and the occurrence probability of core support incomplete fusions on complex cast steel joints is the largest.However,when cast steel joints are counted as a whole sample,the occurrence probability of gas pores is larger than that of core support incomplete fusions.Therefore,it is the most common defect in cast steel joints.

Quantitative analysis and prediction of the sound field convergence zone in mesoscale eddy environment based on data mining methods

The mesoscale eddy(ME)has a significant influence on the convergence effect in deep-sea acoustic propagation.This paper use statistical approaches to express quantitative relationships between the ME conditions and convergence zone(CZ)characteristics.Based on the Gaussian vortex model,we construct various sound propagation scenarios under different eddy conditions,and carry out sound propagation experiments to obtain simulation samples.With a large number of samples,we first adopt the unified regression to set up analytic relationships between eddy conditions and CZ parameters.The sensitivity of eddy indicators to the CZ is quantitatively analyzed.Then,we adopt the machine learning(ML)algorithms to establish prediction models of CZ parameters by exploring the nonlinear relationships between multiple ME indicators and CZ parameters.Through the research,we can express the influence of ME on the CZ quantitatively,and achieve the rapid prediction of CZ parameters in ocean eddies.The prediction accuracy(R)of the CZ distance(mean R:0.9815)is obviously better than that of the CZ width(mean R:0.8728).Among the three ML algorithms,Gradient Boosting Decision Tree has the best prediction ability(root mean square error(RMSE):0.136),followed by Random Forest(RMSE:0.441)and Extreme Learning Machine(RMSE:0.518).