Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Variational Mode Decomposition-Informed Empirical Wavelet Transform for Electric Vibrator Noise Analysis

Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) offer valuable support for studying signal components, they also present certain limitations. This article integrates the strengths of both methods and proposes an enhanced approach that integrates VMD into the frequency band division principle of EWT. Initially, the method decomposes the signal using VMD, determining the mode count based on residuals, and subsequently employs EWT decomposition based on this information. This addresses mode aliasing issues in the original method while capitalizing on VMD’s adaptability. Feasibility was confirmed through simulation signals and ultimately applied to noise signals from vibrators. Experimental results demonstrate that the improved method not only resolves EWT frequency band division challenges but also effectively decomposes signal components compared to the VMD method.

Source Separation of Diesel Engine Vibration Based on the Empirical Mode Decomposition and Independent Component Analysis

Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements,such as independent component analysis（ICA）.However,the source separation of vibration signal from single-channel is impossible.In order to study the source separation from single-channel signal for the purpose of source extraction,the combination method of empirical mode decomposition（EMD） and ICA is proposed in diesel engine signal processing.The performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared,and the results show that EMD-ICA method outperforms the other,and overcomes the drawback of ICA in the case of single-channel measurement.The independent source signal components can be separated and identified effectively from one-channel measurement by EMD-ICA.Hence,EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements.

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

In this paper, a new method to reduce noises within chaotic signals based on ICA （independent component analysis） and EMD （empirical mode decomposition） is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the indepen- dent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions （IMFs） of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor.

Analysis of the regional spectral properties in northwestern South China Sea based on an empirical orthogonal function

This study presents an analysis of the spectral characteristics of remote sensing reflectance(Rrs)in northwestern South China Sea based on the in situ optical and water quality data for August 2018.Rrswas initially divided into four classes,classes A to D,using the max-classification algorithm,and the spectral properties of whole Rrs were characterized using the empirical orthogonal function(EOF)analysis.Subsequently,the dominant factors in each EOF mode were determined.The results indicated that more than 95%of the variances of Rrs are partly driven by the back-scattering characteristics of the suspended matter.The initial two EOF modes were well correlated with the total suspended matter and back-scattering coefficient.Furthermore,the first EOF modes of the four classes of Rrs(A-D Rrs-EOF1)significantly contributed to the total variances of each Rrs class.In addition,the correlation coefficients between the amplitude factors of class A-D Rrs-EOF1 and the variances of the relevant water quality and optical parameters were better than those of the unclassified ones.The spectral shape of class ARrs-EOF1 was governed by the absorption characteristic of chlorophyll a and colored dissolved organic matter(CDOM).The spectral shape of class B Rrs-EOF1 was governed by the absorption characteristic of CDOM since it exhibited a high correlation with the absorption coefficient of CDOM(ag(λ)),whereas the spectral shape of class C Rrs-EOF1 was governed by the back-scattering characteristics but not affected by the suspended matter.The spectral shape of class D Rrs-EOF1 exhibited a relatively good correlation with all the water quality parameters,which played a significant role in deciding its spectral shape.

Computational Intelligence Prediction Model Integrating Empirical Mode Decomposition,Principal Component Analysis,and Weighted k-Nearest Neighbor

On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.

Empirical Analysis on Urbanization Patterns in Zhejiang Province Based on Spatial Economy and GIS

Regional core cities are the growth poles for regional economic development, thus the issue about efficient urbanization pattern has always become a hot spot among researchers and policy makers. The Spatial Economy can be employed to improve it. Nevertheless, there actually exists an obvious gap between its theoretical models and the empirical simulation. To do so, this paper modifies the measurement of initial geographical advantages Stelder gave, and implements the empirical analyses of urban distribution in Zhejiang during the period of 1980-1990 by means of computer simulations in GIS environment. There are several interesting results achieved in this process. Firstly, given the values of parameters （transport cost is r, substitution elasticity of manufacturing sector p, and income share of spending on manufacture δ）, initial geographical advantages have the different impacts on urban systems, namely, urban locations, numbers and sizes over time across space. Secondly, the cities were distributed without any overlay in geographical space, which makes the patterns of urbanization generate the largest possible economic efficiencies. Thirdly, the urban systems-the location, number and size of Cities-from the simulation of the study area are suitable for the actual social and economic situations in the real world during the testing periods. Such results may have substantial implications for the policy how to choose the way of urbanization in a region.

Empirical Analysis on Company＇s Competitiveness from Financial Index

Taking the enterprise survival ability and the development ability as a basis, this paper constructs the financial appraisal index system of enterprise competitiveness. Also, it draws the overall impression of the enterprise competitiveness through selecting listed f＇u＇ms＇ financial index of the equipment manufacturing industry, with the aid of factor analysis model, using the principal components analytic method, making rotation of varimax, and arranging the synthesizes enterprise competitiveness from the financial angle. After the empirical analysis, the paper completes appraisal and analysis which based on company＇s competitiveness from financial index.

Design Index and Empirical Analysis of the Evaluation Index System for the Transformation of Scientific and Technological Achievements of Universities in Beijing, Tianjin and Hebei

Promoting the transformation of scientific and technological achievements of universities and colleges in Beijing, Tianjin, and Hebei is an important measure to enhance the level of scientific and technological development in universities, enhance the scientific and technological synergy of Beijing-Tianjiii-Hebei urban agglomeration, practice the coordinated development strategy of Beijing, Tianjin and Hebei, and promote the construction of Xiong'an New District. Based on the scientific and technological input of colleges and universities, the development of science and technology and the output of science and technology, this paper uses Delphi and AHP to construct a Beijing, Tianjin, Hebei University Science and Technology Achievement Transformation Performance Evaluation System from the perspectives of transformation potentials, scientific research activities and achievements transformation of university scientific and technological achievements. An empirical analysis was carried out to provide reference for the government's efficient decision-making and improvement of strategies for transforming scientific and technological achievements in universities.

Understanding Human Development Potentials and Demands for Greenhouse Gas Emissions:with Empirical Analysis using time series and cross-sectional data

Based on an investigation of the meaning of development, the neo-classical economic approach to development, and the post-welfarist theory of development, this paper proposes a conceptual framework for understanding human development potentials, while undertaking empirical analysis using cross-sectional and time series data on human development. Human development is associated with basic necessities for subsistence, the quality of life, and political and civil rights, in addition to income indicators. Our analysis suggests that the concept of human development potentials has two dimensions: the rights of development and limits to human development. Both are largely ignored in the neoclassical theory of development. However, human development is not unbounded, which approaches to a relatively fixed constant at given economic, technological and institutional conditions. This conceptual understanding is supported by results from the empirical examination of the relationships between demands for carbon

Empirical Analysis of China's Food Policy Impact on Grain Production

As to China with the characteristic of huge population and limited arable land,grain production has always been the most fundamental issue to national interests and people's livelihood,so China's government constantly enact food policy to promote and secure grain production. Based on the viewpoint of macro perspective,the paper firstly summarized the historical changes of food policy in China,secondly elaborated the status quo of the food policy,and thirdly made Granger Causality Test for food policy impact on grain production. Granger Causality Test result shows that fiscal expenditure for agriculture is granger cause for growth of grain production,so that it proves the positive role of the food policy on grain production. At last,the paper put forward suggestions for improving food policy in the future.

Empirical Study on Growth of Evil Forces in Land Requisition and Relocation in City G of Hubei Province Based on Social Network Analysis

Using social network analysis method,this paper made an empirical study on growth of evil forces in land requisition and relocation in City G of Hubei Province. It obtained following results:(i) lawless developers and inefficient public security organs form interested parties of evil forces. Besides,the inward closeness centrality of evil forces is high,manifesting that evil forces independently possess decentralized power of network and have unscrupulous behavior in land requisition and relocation to a certain extent.(ii) Activities of evil forces have complicated spatial correlation and their geographical distribution is uneven,taking on irregular characteristics. In the field of land requisition and relocation,some evil forces are expandi Xng and spreading,while other forces are relatively weak. In conclusion,growth of evil forces comes from premeditation and collaboration of lawless developers,lack of functions and weak attack of public security organs; growth of evil forces has periodic changes,when in power,they will take opportunity to expand,while losing power,they will hide or even disappear.

An Empirical Study of Downstream Analysis Effects of Model Pre-Processing Choices

This study uses an empirical analysis to quantify the downstream analysis effects of data pre-processing choices. Bootstrap data simulation is used to measure the bias-variance decomposition of an empirical risk function, mean square error (MSE). Results of the risk function decomposition are used to measure the effects of model development choices on model bias, variance, and irreducible error. Measurements of bias and variance are then applied as diagnostic procedures for model pre-processing and development. Best performing model-normalization-data structure combinations were found to illustrate the downstream analysis effects of these model development choices. In additions, results found from simulations were verified and expanded to include additional data characteristics (imbalanced, sparse) by testing on benchmark datasets available from the UCI Machine Learning Library. Normalization results on benchmark data were consistent with those found using simulations, while also illustrating that more complex and/or non-linear models provide better performance on datasets with additional complexities. Finally, applying the findings from simulation experiments to previously tested applications led to equivalent or improved results with less model development overhead and processing time.

Estimating the Empirical Null Distribution of Maxmean Statistics in Gene Set Analysis

Gene Set Analysis (GSA) is a framework for testing the association of a set of genes and the outcome, e.g. disease status or treatment group. The method replies on computing a maxmean statistic and estimating the null distribution of the maxmean statistics via a restandardization procedure. In practice, the pre-determined gene sets have stronger intra-correlation than genes across sets. This may result in biases in the estimated null distribution. We derive an asymptotic null distribution of the maxmean statistics based on sparsity assumption. We propose a flexible two group mixture model for the maxmean statistics. The mixture model allows us to estimate the null parameters empirically via maximum likelihood approach. Our empirical method is compared with the restandardization procedure of GSA in simulations. We show that our method is more accurate in null density estimation when the genes are strongly correlated within gene sets.

Empirical Analysis on Dominant Agriculture in Agricultural Districts and Counties of Tianjin

At present,developing the dominant agriculture plays an essential role in promoting the agricultural modernization of Tianjin. Based on the theory of location quotient,this paper made a quantitative analysis of the dominant agriculture in the agricultural districts and counties of Tianjin. According to the analysis,it made a qualitative analysis of the factors. Finally,it determined the dominant agriculture of each agricultural district and county of Tianjin.

Empirical Analysis of Agricultural Cultural Resources Value Evaluation under DEA Model

Agricultural culture is a productive activity about education and management.It aims at high efficiency and high quality,uses technology as its means,and takes nature as its carrier.Agricultural cultural resources are the product of the rapid development of modern economy.It promotes the development of the national economy and profoundly affects people's production and life.DEA model,also known as data envelope analysis method,is an algorithm that uses multiple data decision units for input and output training to obtain the final model.This article explains the concept and basic characteristics of agricultural culture.Through questionnaire surveys and expert interviews,we collected development data,screened human,material,and financial data,and calculated information on economic and social resources.On this basis,this paper establishes the evaluation index of agricultural culture based on DEA model.Then,through empirical analysis from a specific perspective,it can be concluded that increasing human,material and financial input can achieve economic and social benefits.Generally speaking,cultural investment can promote the development of the industry.The research results of this paper laid a theoretical foundation for the development of agricultural culture,and put forward a development model focusing on technology development,improving investment efficiency,and investing in material resources.

Application of Empirical Mode Energy to the Analysis of Fluctuating Signals

After an aerial object enters the water, physical changes to sounds in the water caused by the accompanying bubbles are quite complex. As a result, traditional signal analyzing methods cannot identify the real physical object. In view of this situation, a novel method for analyzing the sounds caused by an aerial object’s entry into water was proposed. This method analyzes the vibrational mode of the bubbles by using empitical mode decomposition. Experimental results showed that this method can efficiently remove noise and extract the broadband pulse signal and low-frequency fluctuating signal, producing an accurate resolution of entry time and frequency. This shows the improved performance of the proposed method.

Empirical Analysis of Object-Oriented Design Metrics for Predicting Unit Testing Effort of Classes

In this paper, we investigate empirically the relationship between object-oriented design metrics and testability of classes. We address testability from the point of view of unit testing effort. We collected data from three open source Java software systems for which JUnit test cases exist. To capture the testing effort of classes, we used metrics to quantify the corresponding JUnit test cases. Classes were classified, according to the required unit testing effort, in two categories: high and low. In order to evaluate the relationship between object-oriented design metrics and unit testing effort of classes, we used logistic regression methods. We used the univariate logistic regression analysis to evaluate the individual effect of each metric on the unit testing effort of classes. The multivariate logistic regression analysis was used to explore the combined effect of the metrics. The performance of the prediction models was evaluated using Receiver Operating Characteristic analysis. The results indicate that: 1) complexity, size, cohesion and (to some extent) coupling were found significant predictors of the unit testing effort of classes and 2) multivariate regression models based on object-oriented design metrics are able to accurately predict the unit testing effort of classes.

Empirical Analysis on Relationship between Apple Futures and Spot Prices in China

Taking a total of 903 apple futures data from Zhengzhou Futures Exchange as research samples, and through unit root test, cointegration test, error correction, Granger causality test and variance decomposition with the aid of Eviews 8.0, this paper made an empirical analysis on the relationship between Zhengzhou apple futures market and Chinese apple website(pingguo7.cn) spot market price. It concluded that the main continuity of apple futures has an absolute advantage over the reference value of the futures index, and apple market participants can achieve hedging through the futures and spot market.

Empirical Analysis of Chinese Industries＇ International Competitiveness after China＇s WTO Entry

Based on recent statistical data, this paper presents an empirical analysis on the current status and evolution of international competitiveness of Chinese industries after China＇s accession a decade ago into the World Trade Organization （WTO）. Our findings conclude that international competiveness of Chinese industries has increased during the decade. Although comparative advantage of traditional labor-intensive industries is on the decline and that of technology- and capital-intensive industries is on the rise, labor-intensive industries remain the most advantageous. Competitive advantages of labor-, technology- and capital-intensive industries have all improved. Growing competitive advantage of Chinese manufacturing is a major force behind the international competiveness of Chinese industries, and traditional labor-intensive industries maintain strong international eompetiveness. Meanwhile, rapid developments of some technology- and capital-intensive industries have strengthened the international eompetiveness of Chinese industries. China is now on the eve of a second round of rapid improvement in its industrial international competitiveness. In the short- and medium-term, acquiring ＂market economy status＂ in 2016 will further release advantages of Chinese industries. Further over the next two decades, China will usher in a new era of strategic opportunities which will serve as an additional source of sustainable improvement in its industrial international competiveness.