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Reformulating Input Data for Linear Regression of Demographic Data Xiaoheng Pan, Yangping Li∗, Huaqiang Yuan Dongguan University of Technology, China ∗Contact author.
Independent and identical distribution is a fundamental assumption often made in data sampling, but it is no longer valid for demographic data.
Neighborhood augmentation achieves the most significant MSE reduction on both datasets.
(f) plots MSE vs ρ on crime data.
Statistics for Spatial Data.

Anjos et al. [5] proposed a reduction in weight of the pick-up loading bed by replacing the metal pan-els with polyamide 66 composites with 40% glass fiber, with a weight reduction of 56% in comparison to the traditional structure.
According to Klein et al. [2], a promising technology to improve product performance by weight reduction are carbon fiber reinforced plastics (CFRP).
Table 3 shows the listing of some materials that met the requirements in place of the components evaluated, together with the materials to be replaced, listing the properties and costs according to data from the CES EduPack™ 2018 program.
The materials were compared using the Pugh matrix according to the data from the previous version (Table 3).
Weight reduction of the pickup cargo bed by replacing metal panels with polymer composite materials.

Data mining put more emphasis on discovering implicit knowledge in huge amounts of data and the scalability of the algorithm, and is a technology very close to the actual use, with high technical content, bigger implementation difficulty. 1.
The process of clustering: (1)Data preparation: including characteristics of standardization and dimension reduction
Description as follows: With N data points for a kind of {xi} (I = 1, 2...
[2] Boley D L．Principal direction divisive partitioning．Data Mining andKnowledge Discovery,1998，2(4)：325—344
(Eds．)Advances in Knowledge Discovery and Data Mining，AAAI Press／M IT Press，1996：95— 164 [4]Y.

As a new data analysis theory dealing with fuzzy and uncertain information, it's main idea is to make out decisions or classification rules of the issue through Knowledge Reduction Method with the classification ability unchanged.
Evaluate the three landscape areas respectively, so as to evaluate the general situation of Liuli Expressway Landscape quality. 4.2 The Sources of Data The index of highway landscape quality evaluation system includes two parts: quantitative and qualitative.
Table of attribute characteristic value U r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 x1 3 2 1 4 3 3 3 1 2 1 2 3 x2 1 3 2 3 2 3 3 2 3 2 1 4 x3 2 3 1 2 3 2 2 3 1 3 2 2 U r13 r14 r15 r16 r17 r18 r19 r20 r21 r22 r23 r24 x1 2 3 3 4 4 4 2 2 2 2 3 2 x2 1 3 3 3 4 3 1 3 4 2 2 2 x3 3 3 2 3 3 4 3 2 3 3 2 1 (2) Index collection Reduction After data reduction of the above form, 15 evaluation indexes are eventually retained after screening: r1, r2, r4, r5, r7, r8, r9, r11, r12, r13, r16, r20, r21, r22, r23.
Data of information system after reduction are shown in table 5.
The attribute reduction of information system [J].

ϕ2 = 45˚ ODF sections obtained by XRD for cold rolled and annealed sheet, (a) 66% cold rolling reduction, (b) 76% cold rolling reduction, (c) 86% cold rolling reduction.
Conventional rm (a) and ∆r (b) as a function of cold reduction ratio. 66 71 76 81 86 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 rm Cold reduction ratio (%) HACA HCA (a) 66 71 76 81 86 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 ∆∆∆∆r Cold reduction ratio (%) HACA HCA (b) Fig. 4.
Modified rm (a) and ∆r (b) as a function of cold reduction ratio.
But in case of HCA, the modified ∆r became higher after 76% cold rolling reduction.
Comparison of the experimental r-value data and the calculated values of r for the {554}<225> texture component.

Raw data of samples On the case of historic data in one hospital, part samples are extracted from lack data and normal data changed into the original data sheet involving 20 samples (as Table2).
In view that rough set supports merely discrete data, those data in Table need to be discretized.
Picking up original data According to minimum reduction {b,e,f,h} in Table 4, original data in sample is taken out and listed as table5.
Original data after attributes reduction 3.2.
Because SVM has higher ability of accurate forecasting based on normalized data, so data in Table4-5 will be normalized in advance.

Approach Lactate was assumed to be the dominant limiting substrate and the steady-state experimental data were used to determine the kinetic constants.
The steady-state data were analysed using the Chen and Hashimoto (Eq. 3), Contois (Eq. 4) and Monod (Eq. 5) kinetic expressions to determine the kinetic constants
Data from the experiment using 1.0 g l-1 feed sulphate were used to describe lactate utilisation under conditions where the biological sulphate reduction reaction and concomitant lactate oxidation reaction were dominant, in accordance with the stoichiometric analysis presented by Oyekola et al
Using these data, the relative growth rates of lactate oxidation and lactate fermentation were compared as a function of operating conditions.
Experimental data studying BSR kinetics on varying feed sulphate concentration in chemostat STRs, shown in Fig. 3, agree well with the simulation presented.

Φ11mm Punch Base steel Al layer Interface Al layer Base steel 1mm Load 62.5mm 65mm Al layer Base steel Tensile test Tension shear test Compression shear test ND RD ND RD RD ND Φ13mm Fig.1 Schematic diagram of tensile, tension shear and compression shear specimen The tests were performed using a tensile testing machine (CMT7540) at a constant crosshead speed of 5mm/min, and force and displacement data was recorded to calculate the bond strength of the Al clad steel strips.
(a) Curves from different tests (reduction: 40%); (b) Curves from different reductions Fig.2 Strength-displacement curves under three tests and/or different reduction Discussions Fracture morphology by three methods.After being broken, the fracture morphology of clad materials at different tests with the same reduction of 40% has been shown in Fig.3.
It is noted that residual Al could be found in almost all samples with reduction from 25-40%, which represents a fact that a bonding has been obtained at that reduction.
Fig.4 Bond strength-reduction curves obtained by different tests From the experimental data given in Fig.4, the results show that strength by tensile test is the highest and it is the lowest obtained by tension shear test.
Summary (1) Bond strength significantly increases at reduction less than 40%, but it slightly changes when deformation increases from 40% to 60% in reduction

The N antibodies constitute a antibody group and data structure of each antibody can only have two value "1" and "0".
The reciprocal of the mean square error of test set data is selected as affinity function of immune algorithm[5]
In this paper randomly selected 500 groups of data as a training set, the remaining 69 cases as a test set.
The set of data of each case have total of 30 data including the average value, standard deviation and the worst value of 10 characteristic quantities of each cell nucleus in the sampling.
The 30 data of each sample are selected to build the BP prediction model.

Based on the reduction, obtain the optimal decision support system.
Introduction VPRS[1] (Variable Precision Rough Sets) is characterized by data analysis methods do not require any prior knowledge of the data itself, only using the information provided can be achieved on the data attribute reduction and have the access to the minimum expression of knowledge.
But it fault tolerance and generalization ability is weak, can only deal with quantized data.
Bearing fault diagnosis model based on variable precision rough set theory and neural network technology 2.1 The building of a fault diagnosis model Bearing fault diagnosis model based on Variable Precision Rough Set Theory has the following main steps: ① The collected bearing failure data as the domain U, determine the condition attribute set C and decision attribute set D;② Discrete attribute values ​​for continuous processing to form a decision table ;③ Calculation condition beta attribute reduction of decision table, to obtain the relative minimal set of attributes ;④ Build sub neural networks for each selected reduction, using the simplified decision table to train sub-networks ; ⑤ Merge output of each subnet, and get effectively converged network architecture;⑥ Predict new samples and achieve the final result . 2.2 Specific steps ( 1 ) determine the condition attribute set and decision attribute set Perform statistical analysis on the collected data of bearing failure ,
table 3 , in this paper, in order to identify the strong attributes and patterns in the data, β is set to become a higher value , orderβ=0.95,this step to achieve 6-reduction as shown in table 3.