Search results

Based on GARCH model to catch the financial data of auto correlation and volatility clustering, while the use of expert predictive value and the short term newer data using the Bootstrap method for Vary estimation.
First of all, is the financial market data is always certain timeliness, long historical data may not reflect the current and predicted future situation, so usually not take too long history data.
Below we use prior to 8 August data of SSE index to estimate the day the stock market risk.
Use (2) (3) type reduction, that is 　　　　　　　　　　　　　　　　　　 (4) (4) Type that we use Bootstrap method to estimate the confidence level for VaR.
At the same time, this method, combined with long-term historical data using the GARCH model better catch the financial data of auto correlation and volatility clustering, and then use the Bootstrap method only use a short period of a new data for VaR estimation, avoid the old data invalid information, to improve the estimation accuracy.

A special application of MCDM is the estimation of the number of clusters for a data set.
Principal Component Analysis (PCA) method, a standard technique to reduce multivariate data sets in a subspace of small dimension, uses new factors, but artificial ones, instead of realer attributes, so that the subspace yields the minimum deformations of the original cloud of data, preserving the variance in the high dimensional space as much as possible [8,9].
The results of the PCA application for the materials data from Table 1 are concisely presented bellow.
Many authors have developed with structured of data objects into clusters that have high intra-groups similarities and inter-groups dissimilarities [20].
Shi, A Multi criteria Decision Making Approach for Estimating the Number of Clusters in a Data Set, PLoS ONE, v.7, is. 7 (2012) 1-8.

Reflectance data were stored as log (1/R) (R= reflectance) at 1nm intervals (751 spectra data points).
Furthermore, it can also reduce repetition and redundancy of the input spectra data.
PCA is a method of data reduction that constructs new uncorrelated variables, known as principal components (PCs).
In addition, PCs can also eliminate noises and random errors in the original data.
As an effective approach to the separation of blind signal, ICA has recently attracted broad attention and has been successfully used in many fields. e.g., medical signal analysis, image processing, dimension reduction, fault detection and near-infrared spectral data analysis [12]-[14].

Heat transfer coefficient of the external wall and thermal inertia factor are selected as data for evaluating external wall.
Steps Data Collection and Preparation.
The data of function index on the same level can be determined by the method of 0-4 Two-two Comparison.
Value engineering team needs collect data on the following aspects:①Historical cost data of similar project.
According to the collected data, details and cost accounting were shown in table 1.

In OBS network, to construct a good data structure is very important.
The OBS incoming data are assembled into basic units, referred to as data bursts, which are then transported over the optical core network.
OBS Network and Its Data Structure In OBS, the bandwidth for a data burst is generally reserved in a one-way process.
The OBS data structure is shown in Fig. 3.
Fig. 3 OBS data structure An important issue in OBS networks is data burst assembly.

Representative corrosion rate data.
Representative corrosion imbalance data.
Representative pH data Figure 6.
Representative oxidation-reduction potential (ORP) data.
Mass loss data for LPR electrodes.

The proposed strategy enables the QOSTBC to achieve ML performance with significant reduction in computational load for any high-level modulation scheme.
Introduction Multiple transmit and receive antenna systems have been a promising approach to achieve diversity in wireless communications, because of their ability to support higher data rates [1][2].
The four transmit antennas launch the signals as the following matrix shows: (1) where each row ofrepresents the data launched by the four transmitting antennas in one moment of transmission time.
The four columns of the matrix present the four complex data need four transmission moments.

Based on the encoded data, that is functioned from length of data and for this purpose padding is arranged to reduce the noise and unused data.
Those attacks can be of any security issues such as services, data bytes and so on.
The AE layer performs well in recognizing the patterns of intruders, diagnosing the faults caused by attackers, and learning features fast using dimensionality reduction and data denoising techniques in the IDS.
The encoder can compress the original data from the input layer and the decoder can decompress back to relatively close original data.
Step d: Data extraction from the input features Step e: Create the encoding from the length of embedded information Step f: Convert the unsupervised data into proper document form to decode the data Step g: Optimize the data using genetic algorithm Step h: Set the rate for the learning process using the low detection value Step i: Convert the input to sequence of vector Step j: Document classification using the NRNN-AE techniques Step k: Stop.

The alloy TiNb can be obtained by conventional melting route or powder metallurgy, where the powders can be obtained by spray drying, chemical reactions and the process of hydrogenation and dehydrogenation (HDH).The hydrogenation is carried out by inserting hydrogen in the structure of Ti and Nb in vacuum at high temperatures, forming a phase extremely fragile called hydride of Ti and Nb, thus enabling the reduction of particle size by milling.
Comparing the values of the elasticity modulus in the composition of Ti-10 wt (%) Nb obtained by arc melting and PM, we can observe a large reduction in elasticity modulus from 96GPa to 2.7 GPa [13].
With the increase of the Nb concentration in the alloy, occurs the reduction of the elasticity modulus that is related to the appearance of β phase.
[11] Powder Diffraction File, International Centre for Diffraction Data, 1998

It transmits air pressure data by the means of IIC
The Samsung's S3C2440A based on the ARM920T architecture is selected as the control core of the embedded gateway, together with all other unit, which is used for data acquisition, data preprocessing, data storage, and data forwarding of the atmospheric information and meteorological parameters.
The sensor node will send the acquisition data to the embedded gateway when the capacity of environmental data collected up to the set value.
“Data_Collection” thread can be used to complete the task of data collection.
It implements several functions, such as the analysis of environmental data, the more limited control alarm, data display and query, etc.