Mining Weights of Land Evaluation Factors Based on Cloud Model and Correlation Analysis

The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain factor conditions into quantitative values with the uncertain illation based on cloud model, and then, inte- grating correlation analysis, a new way of figuring out the weight of land evaluation factors is proposed. It may solve the limitations of the conventional ways.

ON USING NON-LINEAR CANONICAL CORRELATION ANALYSIS FOR VOICE CONVERSION BASED ON GAUSSIAN MIXTURE MODEL

Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation.

Correlation Analysis of Fiscal Revenue and Housing Sales Price Based on Multiple Linear Regression Model

This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis and regression analysis to comprehensively study the correlation between financial revenue and housing sales price in China,and establishes the relationship between financial revenue and housing sales price When the average selling price of commercial housing increases by one unit,the fiscal revenue will increase by 27.855 points.

The Establishment of Mathematical Models for the Composition Analysis and Identification of Ancient Glass Products

Glass is the precious material evidence of the trade of the early Silk Road. The ancient glass was easily affected by the environmental impact and weathering, and the change of composition ratios affected the correct judgment of its category. In this paper, mathematical models and methods such as Chi-square test, weighted average method, principal component analysis, cluster analysis, binary classification model and grey correlation analysis were used comprehensively to analyze the data of sample glass products combined with their categories. The results showed that the weathered high-potassium glass could be divided into 12, 9, 10 and 27, 7, 22 and so on.

Correlation failure analysis of an uncertain hysteretic vibration system

In this paper, a numerical method for correlation sensitivity analysis of a nonlinear random vibration system is presented. Based on the first passage failure model, the probability perturbation method is employed to determine the statistical characteristics of failure modes and the correlation between them. The sensitivity of correlation between failure modes with respect to random parameters characterizing the uncertainty of the hysteretic loop is discussed. In a numerical example, a two-DOF shear structure with uncertain hysteretic restoring force is considered. The statistical characteristics of response, failure modes and the sensitivity of random hysteretic loop parameters are provided, and also compared with a Monte Carlo simulation.

A multivariate partial least squares approach to joint association analysis for multiple correlated traits

Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more accurate estimation. To develop a statistical approach to joint association analysis that includes allele detection and genetic effect estimation, we combined multivariate partial least squares regression with variable selection strategies and selected the optimal model using the Bayesian Information Criterion(BIC). We then performed extensive simulations under varying heritabilities and sample sizes to compare the performance achieved using our method with those obtained by single-trait multilocus methods. Joint association analysis has measurable advantages over single-trait methods, as it exhibits superior gene detection power, especially for pleiotropic genes. Sample size, heritability,polymorphic information content(PIC), and magnitude of gene effects influence the statistical power, accuracy and precision of effect estimation by the joint association analysis.

Comprehensive Evaluation Model of Reservoir Operation Based on Improved Set Pair Analysis

A comprehensive evaluation model based on improved set pair analysis is established. Considering the complexity in decision-making process, the model combines the certainties and uncertainties in the schemes, i.e., identical degree, different degree and opposite degree. The relations among different schemes are studied, and the traditional way of solving uncertainty problem is improved. By using the gray correlation to determine the difference degree, the problem of less evaluation indexes and inapparent linear relationship is solved. The difference between the evaluation parameters is smaller in both the fuzzy comprehensive evaluation model and fuzzy matter-element method, and the dipartite degree of the evaluation result is unobvious. However, the difference between each integrated connection degree is distinct in the improved set pair analysis. Results show that the proposed method is feasible and it obtains better effects than the fuzzy comprehensive evaluation method and fuzzy matter-element method.

Linear Inferential Modeling: Theoretical Perspectives, Extensions, and Comparative Analysis

Inferential models are widely used in the chemical industry to infer key process variables, which are challenging or expensive to measure, from other more easily measured variables. The aim of this paper is three-fold: to present a theoretical review of some of the well known linear inferential modeling techniques, to enhance the predictive ability of the regularized canonical correlation analysis (RCCA) method, and finally to compare the performances of these techniques and highlight some of the practical issues that can affect their predictive abilities. The inferential modeling techniques considered in this study include full rank modeling techniques, such as ordinary least square (OLS) regression and ridge regression (RR), and latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least squares (PLS) regression, and regularized canonical correlation analysis (RCCA). The theoretical analysis shows that the loading vectors used in LVR modeling can be computed by solving eigenvalue problems. Also, for the RCCA method, we show that by optimizing the regularization parameter, an improvement in prediction accuracy can be achieved over other modeling techniques. To illustrate the performances of all inferential modeling techniques, a comparative analysis was performed through two simulated examples, one using synthetic data and the other using simulated distillation column data. All techniques are optimized and compared by computing the cross validation mean square error using unseen testing data. The results of this comparative analysis show that scaling the data helps improve the performances of all modeling techniques, and that the LVR techniques outperform the full rank ones. One reason for this advantage is that the LVR techniques improve the conditioning of the model by discarding the latent variables (or principal components) with small eigenvalues, which also reduce the effect of the noise on the model prediction. The results also show that PCR and PLS have comparable performances, and that RCCA can provide an advantage by optimizing its regularization parameter.

Energy consumption hierarchical analysis based on interpretative structural model for ethylene production

Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.

App Store Analysis: Using Regression Model for App Downloads Prediction

App store provides rich information for software vendors and customers to understand the market of mobile applications. However, app store analysis don’t consider some vital factors such as version number, app description and app name currently. In this paper we propose an approach that App Store Analysis can be used to predict app downloads. We use data mining to extract app name and description and app rank information etc. from the Wandoujia App Store and AppCha App Store. We use questionnaire and sentimentanalysis to quantify some app nonnumeric information. We revealed strong correlations app name score, app rank, app rating with app downloads by Spearman’s rank correlation analysis respectively. Finally, we establish a multiple nonlinear regression model which app downloads defined as dependent variable and three relevant attributes defined as independent variable. On average, 59.28 % of apps in Wandoujia App Store and 66.68 % of apps in AppCha App Store can be predicted accurately within threshold which error rate is 25 %. One can observe the more detailed classification of app store, the more accurate for regression modeling to predict app downloads. Our approach can help app developers to notice and optimize the vital factors which influence app downloads.

Strength Degradation of Wood Members Based on the Correlation of Natural and Accelerated Decay Experiments

An accelerated decay test and a natural decay test were conducted synchronically to explore the strength degradation of decaying wood members under long-term exposure to natural environment.A natural decay test was carried out to measure the bending strength,compressive strength parallel to grain and modulus of elasticity of the wood members,with 6 groups of specimens decayed in natural environment for 3 to 18 months respectively.To compare with corresponding decay test,in which 6 other groups of specimens were measured under accelerated conditions.The experimental data collected were evaluated by Pearson productmoment for the correlation.The results indicate that the mechanical properties of the accelerated decay were highly correlated with those in natural environment,both of which decreased in the same trend.Under the given test conditions,the mean value of the accelerated decay test data were curve-fitted to achieve the time-dependent degradation model of the bending strength,the compressive strength parallel to grain,as well as the modulus of elasticity.Due to the high correlation,the acceleration shift factors(ASF)of the two tests were derived,where the bending strength of 2.934,the compressive strength parallel to grain of 2.519 and the elastic modulus of 2.346 were employed to formulate the strength degradation models in the long-term natural environment.The results verify that the exponential function
σ=σ0e^(-βt)enables to exactly capture the degradation of the mechanical properties of wood members decayed in natural environment.

Study on Correlation Characteristics of Static and Dynamic Explosion Temperature Fields

The warheads such as missiles and artillery shells have a certain speed of motion during the explosion.Therefore,it is more practical to study the explosion damage of ammunition under motion.The different speeds of the projectiles have a certain influence on the temperature field generated by the explosion.In this paper,AUTODYN is used to simulate the process of projectile dynamic explosion.In the experiment,the TNT spherical bare charges with the TNT equivalent of 9.53kg and the projectile attack speed of 0,421,675,1020m/s were simulated in the infinite air domain.The temperature field temperature peaks and temperature decay laws at different charge rates and the multi-function regression fitting method were used to quantitatively study the functional relationship between the temperature and peak temperature correlation calculations of static and dynamic explosion temperature fields.The results show that the temperature distribution of the dynamic explosion temperature field is affected by the velocity of the charge,and the temperature distribution of the temperature field is different with the change of the charge velocity.Through the analysis and fitting of the simulation data,the temperature calculation formula of the static and dynamic explosion temperature field is obtained,which can better establish the relationship between the temperature peak of the static and dynamic explosion temperature field and various influencing factors,and use this function.Relational calculations can yield better results and meet the accuracy requirements of actual tests.

Metallogenic Correlations for the Fe-Nb-REE Mineralization in the West Mine of the Bayan Obo Deposit, Inner Mongolia, China

The West Mine of the Bayan Obo deposit, located in the northern-central part of Inner Mongolia, China, is enriched in Nb, rare earth elements and iron （Nb-REE-Fe） mineral resources. This paper presents a combined method to explore metallogenic correlation of the Nb-REE-Fe mineralization at the Bayan Obo West Mine. The method integrates factor analysis and Back Propagation （BP） neural network technology into processing and modeling of geological data. In this study, the Nb and REE contents of samples were transformed into discrete values to analyze the correlations among the metallogenic elements. The results show weak mineralization correlations between Nb and REEs. Nb and U are closely related in the geochemical patterns, while Fe is closely related to both Th and Mn. LREEs are an important factor for the mineralization of the Bayan Obo deposit, while Fe and Nb can be considered as the results of passive mineralization. On the basis of a metallogenic correlation analysis, the factors affecting the Fe-REE-Nb mineralization were extracted, and the Nb mineralization model was established by the BP neural network. Based on the BP neural network data computing, the variability of the Nb concentration displays a coupled multi-factor nonlinear relationship, which can be used to reveal the inherent metallogenic elemental regularities and predict the degree of element mineralization enrichment in the mining area.

Exploiting multi-context analysis in semantic image classification

As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image classification methods do not overcome the so-called semantic gap problem in which low-level visual features cannot represent the high-level semantic content of images. Image classification using visual and textual information often performs poorly since the extracted textual features are often too limited to accurately represent the images. In this paper, we propose a semantic image classification ap- proach using multi-context analysis. For a given image, we model the relevant textual information as its multi-modal context, and regard the related images connected by hyperlinks as its link context. Two kinds of context analysis models, i.e., cross-modal correlation analysis and link-based correlation model, are used to capture the correlation among different modals of features and the topical dependency among images induced by the link structure. We propose a new collective classification model called relational support vector classifier (RSVC) based on the well-known Support Vector Machines (SVMs) and the link-based cor- relation model. Experiments showed that the proposed approach significantly improved classification accuracy over that of SVM classifiers using visual and/or textual features.

Decomposition Method of Genetic Correlation Coefficient Based on NC Ⅱ Mating Design

There are different degrees of correlation between crop traits. The phenotypic correlation is decomposed into genetic and environmental correlation in quantitative genetics. In this paper,according to stochastic model of variance and covariance analysis,we calculate different genetic components,bring up a decomposition method of genetic correlation coefficient based on NC II mating design,and use examples to show analytic steps and interpret results.

New Trends in Corrosion Analysis of Al-Sn Alloy Duplex System

The corrosion characterization of binary Al – Sn alloy systems has been statistically analyzed in the light of developed model equations. It was observed that the modeled corrosion penetration rate values generated using the developed model equations are in tandem with the experimental values.

Quantitative analysis of urban intelligence and ranking the potential of individual initiatives within a designed smart growth plan

Smart growth has been gaining increasing attention among academia and practitioners as a new technology-based solution to meet the city disease challenges.In the research,we mainly accomplish two tasks.One builds an evaluation system to measure the smart growth of a city.And the other develops a growth plan.Firstly,coordination coefficient(C value) model is applied to measure the smart degree.To begin with,we divide the indicators into four aspects which involve five parameters.Then,entropy method is used to calculate the weight of every parameter.After normalizing data of indicators,we set up a smart growth indicator evaluation system.Aiming to assessing the detailed performances,we rank the eight cities according to the score of C value which corresponds to our normal cognition.Secondly,based on Salvo combat model and dynamic trend analysis model,We draw up a 20-year growth plan with a period of 5 years for the two cities we choose.The Salvo model is adopted to describe the dynamic process.Dynamic trend analysis model is introduced to gain the optimum solution and the optimal point in every stage.In addition,compared with the point of every stage,we can obtain the proportion of investment in different stages.Thirdly,to evaluate the sensitivity of our model with the OFAT Method,we adjust the parameters k_1,k_2 and O_(ij) approximately.It comes out that the change of k_1,k_2 and O_(ij) has an impact on the C value.But the sensitivity of k_1,k_2 is higher.Lastly,we analyze the influence caused by population growth.To a certain extent,it can be concluded that the plan we made can alleviate the negative impact of population growth through the analysis of the chart.

Employment Quality EvaluationModel Based on Hybrid Intelligent Algorithm

In order to solve the defect of large error in current employment quality evaluation,an employment quality evaluation model based on grey correlation degree method and fuzzy C-means(FCM)is proposed.Firstly,it analyzes the related research work of employment quality evaluation,establishes the employment quality evaluation index system,collects the index data,and normalizes the index data;Then,the weight value of employment quality evaluation index is determined by Grey relational analysis method,and some unimportant indexes are removed;Finally,the employment quality evaluation model is established by using fuzzy cluster analysis algorithm,and compared with other employment quality evaluation models.The test results show that the employment quality evaluation accuracy of the design model exceeds 93%,the employment quality evaluation error can meet the requirements of practical application,and the employment quality evaluation effect is much better than the comparison model.The comparison test verifies the superiority of the model.

Influence of Online Learning Attitude in the Blended Teaching Model on the Final Grades

The combination of online teaching and traditional offline teaching can maximize the advantages of both.Based on the blended teaching of English Reading course,39 students were selected as the research subjects to study the relationship between their online learning attitudes and their grades in the final examination.Judged from the number of times for each student to download teaching resources,the number of assignments submitted online,and the quality of the submitted assignments,each student’s attitude toward online learning was examined comprehensively,and a correlation analysis was conducted through SPSS Statistics 21.0 to explore the influence of online learning attitude on English reading performance.Through data collection and analysis of the online learning attitudes over a 16-week period,a significant positive correlation was found between the online learning attitudes and the English reading grades,indicating that the online learning attitude in the blended learning model plays a crucial role in improving the English reading skill,and students should maintain a positive attitude toward online teaching in blended learning.

2000-2020年黄河流域水-能源-粮食系统耦合协调时空演变特征

[目的]探讨黄河流域水—能源—粮食系统的耦合协调时空演化特征,可为定量评估流域高质量发展和区域可持续发展提供科学依据。[方法]通过耦合协调度模型分析水—能源—粮食系统的综合发展水平和耦合协调特征,并利用灰色关联度对各子系统内部指标进行排序,进而判断对水—能源—粮食系统耦合协调度的影响程度。[结果](1)黄河流域水—能源—粮食系统的综合发展指数呈上升趋势,水资源子系统波动幅度最大。(2) 2000—2020年黄河流域水—能源—粮食系统耦合协调度从初级协调向良好协调阶段发展;不同省区间的系统耦合协调发展水平差异较为明显,整体上可表现为上游地区>中下游地区。(3)各子系统与系统耦合协调度的影响程度排名可表示为水资源子系统>粮食子系统>能源子系统,其中水资源子系统内部指标与系统耦合协调度关联度均在85%以上,水资源子系统相对其他子系统的严格管理尤为重要。[结论]水—能源—粮食系统的耦合协调发展水平有待提高,为促进系统协调发展和流域高质量发展应综合考虑各子系统的相互作用关系。