Search results

(RLB2) 12) Protection of the financial and personal data of customers.
(ETM3) Sample Design and Data Collection Data were collected using self-administered questionnaires according to service quality scale from the college students (167 of 175 copies are valid).
Factor analysis is a technique for data reduction.
It offers mathematical tools to discover patterns hidden in data.
Information-preserving hybrid data reduction based on fuzzy-rough techniques.

The method can be divided into 4 parts: dimension reduction, data segmentation, feature extraction, and feature cluster.
Methodology The cluster analysis model based on RSF is composed of 4 parts: dimension reduction, data segmentation, feature extraction, feature cluster.
Step1 Dimension Reduction In general, there are only several dimensions of the high-dimensional data are important to the underlying information.
Step2 Data Segmentation The second step is to segment the data set after dimension reduction.
Step2 After data dimension reduction, we execute the data segmentation to the 701×601×2 data set with the improved high-dimension RSF model based on level set formulation.

Finite Element Data Reduction Based Energy Release Rate for Delamination Tests S.
This is shown through two relevant aspects in delamination toughness measuring, say: data reduction and Iso-G delamination front design.
Many experimental data reductions are based on beam theories and thus assumes straight delamination front during propagation, which is not true when investigating laminates with general anisotropy.
Data Reduction Based Energy Release Rate Experimental Approaches.
This last expression (3) can be used as data reduction scheme more efficiently in delamination toughness measuring.

Another one for dimension reduction trains a subspace projection matrix to project original data space into some low-dimensional subspaces which have deep architecture, so that the low-dimensional codes would be learned.
Unfortunately, most effective visual features are at high-dimensional data space [2].
Thus, it is difficult for computers to process such a huge data.
It tries to represent the data by the linear combination of a small number of basic elements, and the combination coefficients will be used as low dimensional data.
Kim: Data Mining and Knowledge Discovery, Vol. 26 (2013) No.3, p. 512-32 [3] J.Y.

· Extracting knowledge from data.
Roadmap on technologies of model order reduction, data-sciences and hybridation.
The first concerns data analysis and more particularly data-reduction.
By combining both, physics-based models, calibrated by using data assimilation, and operating in real time by using advanced model order reduction techniques, with a data-driven model for describing the gap between the measures and the physics-based model prediction.
Model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction.

The evaluation from the data objects based on key attributes can reduce the data size and algorithm complexity.
After Clustering analysis of customers, then the evaluation analysis will process to the clustering data.
There are a lot of uncertain data of customer cluster, so the traditional method of classification and evaluation to the incomplete data is very difficult.
Tested by actual data analysis, cluster analysis can reduce the size of customer data and data noise, and the key class evaluation analysis can improve the evaluation efficiency of e-commerce customers.
Data Analysis Approaches Of Soft Sets Under Incomplete Information.

Without reactive power data, the system assumes that the power factor is fixed; without voltage data, the system makes use of the rated voltage.
In addition, with GPRS module installed in it, the meter can achieve wireless data transmission.
Therefore, decision support system for comprehensive power loss reduction of rural power network is designed with a common data interface.
In the process of data input and theoretical line loss calculation, the system can obtain existing data from other software systems avoiding manual input.
It ensures the accuracy of the data and improves efficiency of inputting data.

The article reviews, summarizes and concludes the existing literature researches at home and abroad, and mainly from the meaning and the present situation of energy conservation and emissions reduction, the relationship with economy, society and environment, index system of energy conservation and emissions reduction, and efficiency evaluation methods of energy conservation and emissions reduction.
To sum up, the researches mainly focused on the significance and the status of energy saving and emission reduction, and the relationship with economic, social, environmental, the index system design, policy advice and evaluation method of energy saving and emission reduction. 1.The Significance and Present Situation of Energy Conservation and Emissions Reduction The formulation and implementation of energy saving and emission reduction is of great strategic significance for social sustainable development.
Suqin Chen(2012) designed the chemical enterprise index system from the energy saving and the effect of emission reduction, economic benefit and social benefit of the pollution treatment. 4.Research Method on Energy Conservation and Emissions Reduction Efficiency Evaluation Data Envelopment (DEA) evaluation method is one of the most widely used evaluation method.
Zhonghua Wang, Huiting Liang (2012) using data envelopment analysis (DEA) method, regarded the various industries in Hei Longjiang province as the basic unit of energy saving and emission reduction, established the industrial efficiency evaluation model of energy saving and emission reduction in Hei Longjiang province.
Jie Lu (2010) established DEA energy conservation and emissions reduction model of Qingdao city, and made a comparative study on the partition efficiency and potential of energy saving and emission reduction in Qingdao city, then summarized the optimal energy saving and emission reduction index distribution of Qingdao city.

According to the distribution of SMEs in Ningbo, Our group hands out 1200 questionnaires in total and collected back 1035, 957 of which are valid except the questionnaires whose data are clearly abnormal.
By means of analyzing and comparing information and data which has been collected, our group find out the current SMEs’ actual needs and the problems existed in implementation of relevant policy during the process of energy conservation and emission reduction.
The Main Features of Energy Conservation and Emission Reduction of SMEs in Ningbo 3.1 Basic Situation of SMEs Surveyed Table 1 Industry Distribution of SMEs Surveyed Industry Distributed (A) Machinery &Industrial Products (B ) Clothing & Textile (C) Hardware & Tools (D) House- hold appliances (E) Auto Parts & Accessories (F) Building Materials (G) Office Equipment & Supplies (H) Service (I) Others 13.99% 20.99% 12.39% 7.45% 6.65% 11.01% 7.11% 8.03% 11.93% From: Data of Survey 3.2 Situation of Energy Conservation and Emission Reduction of SMEs Surveyed 3.2.1 High cost and low utilization rate of the SMEs’ energy utilization Table 2 Energy Cost of SMEs Surveyed Energy cost /Total cost of production More than 50% 30-50% 15-30% Less than 15% 5.83% 26.66% 48.33% 19.16% From: Data of Survey As is shown from the above table, the energy cost of the SMEs is high and utilization rate of the energy is low.
Compared with the number of SMEs and their employees, valid sample size of the survey 957 is not enough, as well as the objectivity of some responses to the questionnaire. 3.Limited statistical analysis of the survey data.
Due to the limitations of our relevant professional skills, our group failed to dig out the deeper problems hidden behind the data.

The data of water also shown in this figure.
The data show that coefficient of friction fibers at Re > 25.000 is lower that water data and Blasius equation.
Drag reduction occured if the data is higher than curve A.
The data is greater that curve A.
After Reynolds number about 35.000, the data shows constant.