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Afterwards, the intrinsic TDDB model (used in the simulations) has been calibrated and validated against experimental data.
Block diagram illustrating simulations workflow and its interplay with the experimental data.
In addition, TDDB data were collected using constant-current stress (CCS) method at three different current densities of 100, 120 and 150 mA/cm2.
Kinetic Monte-Carlo (kMC) engine and discrete nature of the defects enable reproducing the data with the statistical significance.
Conducting defect spectroscopy on IV data, time-zero electronic defects properties were obtained.

We use statistics methods to obtain original data matrix
Principal component play the greater role in the sample data if it can get greater variance value.
The figure chooses ten sample data as analysis.
Summary This paper proposes improved PCA model to eliminate dimensional inconsistency and the difference of magnitude of the sample data.
Weltz (2008), Vegetation data, Walnut Gulch Experimental Watershed, Arizona, USA, Water Resour.

If we can measure several spatial coherence of different baselines using amplitude interferometry array, the image can be obtained by the Fourier transform of measured data.
But due to the low signal to noise ratio of measured data and sparse sampling to the spatial frequency of object, the satellite’s imaging didn’t obtained [8].
In the process of AII, the high imaging resolution can be obtained using smaller aperture optical system, the aperture’s size only affect SNR of measured data and is nocorrelated to the resolution.
Due to the brightness of high-orbit satellite is mostly around 16 Mv, the SNR of measured data is much lower than that of high brightness stars, so bigger optical collector should be used to improve the SNR of measured data.
But the SNR of measured data is much lower than that of other methods and the phase information is lost in observing process.

According to the daily rainfall, evaporation and discharge data of Wuding River Basin during 1980～2000, the parameters of the model were calibrated and verified.
With the Chinese academy of sciences institute of geographical sciences and nature resources data center provides land use data as the foundation in 1990, 1995 and 2000 (1:100,000).
Based on GIS platform, first respectively get the three-time land use map, and then superimposed analysis for each unit of land use change space and attribute data (Table 2).
The calibration and validation with runoff data of 1980~2000 years in Wuding river basin (results see Table 3), from the results, R is 1.004 and DR is 86.67% in calibration (1986~2000), and R is 0.938 and DR is 66.67% in validation (1980~1987).
Appraisal and code conversion of land use/cover classification systems based on re-motely sensed data [J].

achenjing2003@126.com, bfumeichen@163.com, cweijing810201@126.com Key words: Bohai Rim in Hebei Province; transfer matrix; barycenter transfer model Abstract: Based on the remote sensing image data of 1970, 1980, 1990, 2000 and 2008, the transfer matrix of land use/cover change were extracted using ArcGIS software, and the paper analyzed the spatial and temporal change of land use/cover change using barycenter transfer model.
This paper, taken the Hebei Bohai rim for the study area, using ArcGIS and digital image data to make spatial and temporal dynamic change of land use/cover change.
In 2012, the economy increased rapidly, with the GDP of 47 billion yuan, fixed assets investment of 50.7 billion yuan, 8.8 billion yuan of all revenues. 1.2 Data sources and data processing The data mainly included remote sensing image data (1970, 1980, 1990, 2000 and 2008), as well as topographic map, soil map, land use map, land survey statistical yearbooks and other information.
Using Erdas Imagine to analyze the remote sensing image data, including coordinating transformations, geometric correction and image enhancement processing.
So the barycenter transfer of cultivated land moved toward the northwest in the map(Figure 7). 3.3 Overall area of forest land was rising, especially increased considerably in the southwest, and it mainly converted from cultivated land(Figure 8). 3.4 The barycenter transfer of grass land moved southeast, mainly because grass land decreased in the northeast and part of cultivated land converted into grass land in the southeast(Figure 9). 3.5 The barycenter transfer of water area moved northwest, because the water area decreased in the northwest and southeast, which was related to climate drought year after year and surface runoff reduction during a study period in the study area.

The results show that the variation trends of both the maximum stress of targets and system total energy loss are in a good agreement with experiment data,which increaes with increasing impact angle.
Thease results are compared with the erosion rate data from experiments as shown in Fig.4.
This trend of curve appears a good agreement with experiment data, which increase with impact angle and reach its maximum at normal incidence .
The system total energy losses at different impact angles are compared with the erosion rate data obtained in tests as shown in Fig.6.
This trend is in a good agreement with experiment data.

The principal objective of this study is to make use of medium and strong motion data from instrumented shaking table tests to evaluate the effects of kinematic soil-structure interaction on foundation input motion (FIM).
Ishii et al (1984) used the observed data investigated the kinematic interaction of soil-structure system.
Shaking table model tests were performed using MTS shaking table facility at the state key laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China.
Measurement of accelerations in the soil away from the foundation was unaffected by the presence of the piles and can be considered free field data.
Kinematic Interaction of Soil-Structure System based on Observed Data: Proc. 8th World Conf.

Several tests were carried out by varying bending conditions and collecting load-displacement data.
In addition, a study based on foam density showed a remarkable data scatter that can be considered as a characteristic of the state of the art in foamed panels manufacturing (in terms of process control and product variability).
Several tests were carried out by varying bending geometry and collecting data about the deformed geometry and about the punch load-displacement.
When observing Fig.4-F, evaluated from load-displacement data, some remarks can be made.
Material flow stress data were selected from Deform database.

Buffer layer addition between the hard alloys cladding shows a reduction in crack formation and the dilution rates.
The temperature data is recorded at a 20 Hz sampling rate using LabVIEW software through National Instruments (NI) data acquisition system (Model 9213).
The online temperature data for samples S1 and S4 are shown in Fig. 3, which observes to be of similar trends in both cases.
Hence, the data acquired from P5 is considered to be the maximum temperature generated in the substrate, shown in Fig. 4.

The basic principles of cloud computing is that computing is distributed in a large number of distributed computers, rather than the local computer or a remote server and the running of enterprise data centers is more like the Internet, which allows companies to be able to switch resources to the applications needed and access to a computer and storage systems on demand.
Cloud workflow has the multi-tenant characteristics of cloud computing, a number of different tenants can simultaneously design, deploy and run their own workflows, and achieve three kinds of isolations: data isolation, performance isolation and execution isolation. 4) Real-time Monitoring.
The topology of cloud workflow model can be divided into DAG (Directed Acyclic Graph) and Non-DAG (Non-Directed Acyclic Graph), that there is a directed acyclic graph with non-directed acyclic graph. 2) Model type: Cloud workflow model is abstract model, which means that no transactions are mapped to specific computing resources in the definition phase. 3) Focus on describing model: According to the different focuses of control flow and data flow in the model definition, it can be divided into: focusing on controlling, focusing on data and both.
Instance generation of cloud workflow have two dynamic and static ways. 4) Decision Making: When a transaction is mapped to a specific computing node in a workflow management system, the data to support making decision are from two sources: the running status of the current transaction, as well as the overall running status of the workflow.
Virtualization means that the specific computing resources is abstracted and encapsulated to make the process to be transparent for users, API or Service is only exposed to users. 4) Programming Model: In terms of data management, different workflow systems adapt different programming model, the Map-Reduce programming model is used cloud workflow.