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The results of computation showed that the use of buckling restrained braces has led to several functional improvements, such as the increase in structure stiffness, reduction of story displacement, and improved energy dissipation ability when compared with the frame without such braces.
The basic data calculated for the original structure under the above condition was listed in Table 1.
Basic data calculated for the structure Story NO.
From the data list in Table 1, the lateral stiffness of the structure was insufficient.

For efficient acquisition of data at each exposure site, test specimens were set at angles of 0°, 45°, and 90° against the ground surface and, for the 0° and 45° angle settings, data acquisition was performed for the skyward- and groundward-facing surfaces.
The solid line refers to the regression relationship taking into account all the data plots.
Data points for the groundward surface at an inclination angle of 0° for test specimens at the University of the Ryukyus and Kyushu University and those for the skyward surface at the University of the Ryukyus, Kyushu University, and Kyoda are also plotted in Fig.5 as the reference data.
The regression line is in good agreement with the data plots.
Fig.4　Conversion method for reduction of ACM output during a period of rainfall.

It can provide basis for manufacturing readiness level and risk management (plans, and confirmation, and analysis, and reduction, and implementation and track).
Using BP neural network to evaluate the project Input the associated data of project into evaluation model and calculate and evaluate based on the model.
The top 13 groups of data in table1 are used as training data, with remaining 2 as a test data set.
Set the 14th, 15th data into the test, value assess is shown in table2.
Beijing: National Defense Industry Press. 2010:3-6,74-192 [8] Li Tingjun, The Phonetic Complex Data Based on FPGA Key Engineering Materials.

The variogram of image reflects the randomness and structuredness of image data.
The range of variogram describes spatial scope of image data.
Apparently image noise data has strong randomness but the influenced scope of data space is small.
Conversely, image edge has strong structuredness but the influenced scope of data space is large.
For image data, range is small in strong noise area but large in scenery area.

Many approaches employ k-means clustering because of its computational simplicity, which allows to apply it to very large data sets.
It is the reduction in the uncertainty of one random variable due to the knowledge of the other.
We first explain the experimental setup, then show the results on different test data and compare with other methods.
Test Data.
Data Mining: Concepts and Techniques, Second Edition.

The online application of domestic state estimation in Supervisory Control and Data Acquisition (SCADA) has gone through from the bad data check to the real-time cross-section data services.
(1)Telemetry value incorrect There are three main causes that lead to telemetry incorrect data of dispatching system, data acquisition error and error, data conversion and transmission error and data processing
The change of power grid operation will result in the data of rapid mutation and even the whole network data, and data transmission delay will lead to poor consistency of data used in state estimation.
The main steps are as follows: Step 1) Simulation computer simulate the daily operation data of power grid; Step 2) Data emulation layer to get the whole network operational data, simulate disturbance scenario and form various types of disturbance data; Step 3) Send disturbance data, which is formed in data simulation layer, to the testing platform and the SCADA application; Step 4) State estimation calculated according to the real-time data in SCADA.
Different ramp rate of load make the data obtained in state estimation is not consistent with the real-time data.

The NHD (National Hydrography Dataset) [21] was used as hydrography data, SRTM [22] was used to obtain elevations inside a study area and the hydrological data such as annual mean stream reaches flow rates was derived from gauge stations and precipitation data (using regional equations).
Remote sensing data was used for interpreting vegetation and surface data.
ASTER GDEM v2 [27] was used as elevation data source.
B Brief Overview of Methodology, Initial Data and Data Sources Used in Several Hydropower Potential Studies Tab. 1-4 represent initial data, data sources, criteria and equations used in described above studies.
Data sources for gross hydropower potential estimation Initial data Usage Saint-Lucia and Dominica USA Norway Russia Digital elevation model Obtaining synthetic river network, delineating watersheds, calculating hydraulic head on each stream reach ASTER GDEM v2 SRTM, EDNA SRTM SRTM or digital maps with 1:100000 scale Synthetic streams network All GIS calculations are performed on synthetic streams basis Derived from DEM by authors NHD ELVIS Derived from DEM by authors Precipitation data Obtaining rivers flow rates and other hydrological data Gauge stations and weather stations data Gauge stations and weather stations data Gauge stations and weather stations data The Map of flow rate distribution Surface and vegetation data Obtaining rivers flow rates and other hydrological data Spectral analysis of IKONOS satellite images Not taken into account Not taken into account Not taken into account Soil data Obtaining rivers flow rates and other hydrological data CEHI [31] Not taken

Chemistry data and lithology are fundamental in fractionation analysis.
The increase in the Ni grade of the saprolite ore fraction is calculated using the weighted average method, where the average value is calculated by considering weight data.
Each weighing is a pair of data (weight and assay data).
The reduction reaction in the processing depends on particle size and mineral composition.
Ceria Nugraha Indotama who contributed to the field data collection and sample analysis.

This paper researches the stochastic distribution feature and statistical parameter of geometrical imperfections for the data sources of above position deviation of joints by “abnormal data test method”.
By this contrast method, the above observation data can be taken as the important data source for studying the distribution feature and statistical parameter of geometrical imperfections for latticed shell structure.
The abnormal data eliminating method of t test rule will be mainly introduced as follows.
To study the stochastic distribution rule of joint position deviation for latticed shell structure, it needs a certain amount of raw data.
In particular, the sample average value of critical load Pμincreases more quickly according to the reduction of Cd. 4.

The data were logged at the frequency of 1000 Hz which means 60 data one second.
The force data at point A and B were recorded in the same experiments.
The data formed the red and black lines are chosen from one single experiment.
They are original data not the average data.
It is inferred from the present force data that larger amplitude can make penetration force lower.