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They are specified in a veryhigh-dimensional tensor space and recognition methods operatingdirectly on this space suffer from the curse of dimensionality[3].Dimensionality reduction is commonly used to transform a high-dimensional data set to a low-dimensional subspace while retaining most of the underlying structure in the data [4].
In this paper, we use Kernel Principal Component Analysis (KPCA) for dimensionality reduction and feature extraction in railway tunnel deformation data analysis.
KPCA reduce dimensionality of original data.
The projected data could basically represent the original railway tunnel deformation data information, but its distribution in Eigen space is not compact.
Jain, "Incremental nonlinear dimensionality reduction by manifold learning," IEEE Trans.

The size and number of carbide become smaller and lesser with decreasing the rolling reduction.
It is because of the reduction of dislocation density [2].
Stress-strain curves plain low carbon steel (a) cold-rolled to a reduction of 25%, 50% and 75%.
The data of the as-received (ferrite-pearlite) and the as-quenched (dual-phase ferrite-martensite) specimen are also shown, (b) cold-rolled to a various reduction and subsequently annealed at 500°C for 30 min.
The strength increased, but the elongation decreased with increasing the reduction. 3.

Fault diagnosis methods are researched in this paper according to NCS with long-time delay and data packet loss.
Secondly, make the state estimation error equation equivalent to an asynchronous dynamical system having event incidence constraint according to whether the system having data packets loss.
NCS fault diagnosis methods are studied in this paper according to NCS with long-time delay and data packet loss.
The time-delay only exists between controller and actuator .In addition, suppose that the data packet loss rate is fixed.
Different from the previous studies, long- time delay and data packet loss are considered in this paper ,then the state estimation error equation is equivalent to an asynchronous dynamical system having event incidence constraint according to whether the system having data packets loss .

The application research on the data protection of the typical CNC machine Qiujun Wu1,a, Jianjun wang1,b 1HeBei institute of mechatronic technology, Xingtai, China 054048 awqj12san@126.com , bwabj2025@126.com Keywords: Machine data, data backup, data recovery Abstract.
Therefore, a good data backup solution is the most effective mean to solve the data fault.
Such as machine data, tool data, zero offsets, setting data, pitch compensation, part program and so on.
Data reduction is the reverse process of data backup.
FANUC data backup includes user data backup and PMC data backup.

This analytical model can explain almost all experimental data: oxidation-temperature dependence, oxidation-time dependence, and initial-Z1/2-concentration dependence of the defect reduction.
Based on these data, an analytical model of defect reduction is proposed.
Each symbol indicates the experimental data and each line indicates the calculated nI curve obtained from Eq. (1)-(3).
Each symbol indicates the experimental data and each line indicates the calculated nV curve.
Each symbol indicates the experimental data and each line indicates the calculated nV curve.

RDF inference component, according to the data generated RDF CardOnto and description information semantic closure.
The combination of RDF and XML, not only can the concept system of concepts linked with real world knowledge, to realize the data based on semantic description, also give full play to the respective advantages of XML and RDF, facilitate Web data retrieval and knowledge discovery, and then defined and build the clinical ontology CardOnto according to the medical classification principles of traffic organization, get some clinical diseases, the concept of ontology system, this structure can clearly and accurately represent the knowledge structure of the disease.
Concept lattice redundant relations reduction algorithm achieving Ontology integration In response to the above definition of distributed ontology integration adapter, the project puts forward one of distributed ontology integration based on concept lattice and reduction method, this method doesn’t rely on artificial participation, and it has a high degree of formalization and can be directly on the computer, taking into the quality and efficiency of ontology integration.
Redundant relations reduction algorithm Set up a stack, all the concept of into degree 0 in the concept lattice will be pressed into stack.
To this end, the steps of redundant relations reduction algorithm are: (1) Press the concept of no precursor (count domain is 0) into stack S1

In cork stopper production SMED resulted in a 43% overall reduction in changeover time.
Step 1 was done together with step 6, and step 3 was done together with step 4, which resulted in a time reduction of 9.35 minutes.
Design and create a new bypass system to reduce the time required to bypass PVC, resulting in a reduction in work time.
Data was collected and timing was recorded for five instances after the improvements were made.
By analyzing the data and implementing a system to sustain positive outcomes, the standardized procedures and implementation methods that have proven effective are adopted as work standards by the management team and provide the standard documents for training the relevant personnel.

When reduced, boundary region of neighborhood-based rough set narrows, on the contrary, open out, indicated that the neighborhood-based rough set has a certain degree of tolerance to the data noise, which can enhance the robustness of the produced rules.
The results show that the model can not only deal directly with the continuous attributes, but also has a certain degree of noise tolerance, it can enhance the robustness of data analysis and processing and is better adapted to practical problems in engineering.
References [1] Berka P, Bruha I., “Discretization and grouping: preprocessing steps for data mining”.
The 6th European Conference on Principles of Data Mining and Knowledge Discovery, Helsinki, Finland, 1998, pp.239-245
[4] Jensen R., Shen Q., “Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches”, IEEE Transactions on Knowledge and Data Engineering 16(12), 2004, pp. 1457-1471

This text studied the affection of the cold reduction ratio and annealing temperature to the high strength IF steel on microstructure and property, and assured the optimal process parameter which has supplied the theoretical guidance and reference data to the industry.
The A80 gradually reduce under 85% cold reduction ratio, and the A80 gradually increase under others cold reduction ratio.
The Ae gradually increase under 75% cold reduction ratio, and the Ae gradually reduces first increases again under others cold reduction ratio.
The A80 gradually reduce under 75% cold reduction ratio, and the A80 had no obvious change under others cold reduction ratio.
The Ae reduces first and increases again under 85% cold reduction ratio, and the Ae g had no obvious change under others cold reduction ratio.

Hierarchical Modeling makes the data dimensionality reduction and interpretation much easier by principal component analysis (PCA).
Metabolomics research generates a large number and complex samples data, the chemometric tools were proved to be powerful for metabonomic data analysis.
Hierarchical Modeling was generating to make the data dimensionality reduction, this method was investigated for data processing.
The data set contains 244 variables.
The data of all the groups was imported into the SIMCA-P 12.0.