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Meanwhile Liang adopted CDFG (Control Data Flow Graph) as IR.
The way of communication may facilitate data sharing between embedded processor and RU.
In third stage, select unique BC set to overlap original data-path of CI candidates.
In addition, we use 32 as factor because we assume that data width of RU is 4 bytes.
Summary In the paper, we proposed a strategy to strategy to quickly enumerate a set of basic cells from data-path of CI candidates, and optimally select least number of basic cells to overlap original data-path.

In order to research the effects of every factor of evaluation system of accounting, method of Data Envelopment Analysis (DEA) in operational research is suggested to evaluate the economic effectiveness of resources invested in developing nanomaterials.
The Data Envelopment Analysis (DEA) evaluates Decision Making Units (DMU) by using the mathematical programming pattern comparing relative efficiency between DMUs [3].
The necessary data for method of Data Envelopment Analysis (DEA) comes from the financial data chose carefully from accounting information.
Fig. 1 Flow chart of DEA method for accounting system of economical assessment Suggestion of System of Accounting Data for Models.
There are I kinds of input factors of accounting data and J kinds of outputs in every decision-making unit.

Therefore, effective energy management of these ACs is an essential task to energy saving and carbon reduction.
Computer GUI for supervisory control and data acquisition (SCADA) and energy management is also developed.
It can record the electrical load data so that the user can seize the electricity usage at any time.
The presented system can be divided into two modules: ”Demand controller” and ”RFID energy billing module”, wherein RFID energy billing module is responsible for reading the power meter data and the pre-paid card data on the card reader so as to use them as the basis for controlling relay and payment deduction.
(d) When the reading temperature button is pressed, the energy billing system will read back the temperature data.

Replication, or repeated tests at the same stress amplitude, is used to provide statistical confidence in life data during the development of S-N curves.
However, with the increasing interest in gigacycle fatigue and the development of high frequency fatigue testing machines capable of tests carried out up to 1012 cycles, methods of further data reduction are desirable in order to be able to economically gather adequate data for design purposes.
The parameters A and B estimated from various combinations of the S-N data measured on the Mg specimens.
The recorded S-N data are plotted in the semi-log system of stress amplitude (sam) versus fatigue life (Nf), and are analysed in terms of replication.
In the present study, the two parameters were estimated by fitting the data using Eq. (2).

Even though the FCG tests have generated a large number of data, the data deviation may still be found.
Furthermore, the probabilistic assessment of FCG data was evaluated by generating a large number of FCG data with the MCM.
The LSFM tends to give the lower C, and higher m values than the experimental data since the FCG data distribution influences the LSFM.
About 5% of data lies below the lower bound, and 90% data lies above the upper bound.
All predicted of FCGR data lay on an 85% confidence interval.

Therefore, ontologies improve upon data models, which are mainly used for single applications.
Unlike knowledge representation with lists or databases, ontologies imply hierarchies and are able to describe characteristics and features as well as relations among concepts or data sets.
Phase 2: Data acquisition, situation analysis, problem description and target analysis (UK).
Therefore, the user has to determine the objectives, e.g. the reduction of energy intake per unit produced, the overall energy intake, the reduction of current peaks, etc.
Fensel, “Knowledge engineering: Principles and methods,” Data & Knowledge Engineering, vol. 25, no. 1-2, pp. 161–197, 1998

Fast access to data and manufacturers resources is desirable.
Ease of manufacturing is possible by products complexity reduction.
Data and groups of data are organized in fields as entities (objects, events) and records (attributes of objects or events) on datasheets.
Entities represent elements for which data should be stored.
Data for all tools, inserts and machines are stored.

This layer is made up of data acquisition and control module of various types of composition, main function is to complete the IOT application information, data acquisition and control facilities, is an important foundation for the Internet of things.
The data acquisition layer, data through the Internet of things Internet technology information exchange layer and upper layer of the transfer and exchange, for hair, transmission, distribution side application to electricity to provide data to support side application, make more strong and smart grid.
Real time data acquisition, measurement, analysis of user of electricity in support of the system, and through the communication layer allows data upload and send.
Then the temperature data through CC2530, sent to the sink node by the wireless transmission, the sink node will pack all temperature data information which monitor region, then the packet data send to the remote asset monitoring and control system by Ethernet, the system sends the data packet information is split, and then evaluation and analysis of temperature data, when found abnormal or equipment failure, maintenance personnel can use mobile operation equipment carry on receiving the abnormal equipment data through the GPRS network, the rapid response, the repair efficiency is improved, the power cost is reduced.
Monitoring data information of each tower through a wireless network and multi hop transmission mode the system collected data is transmitted to the monitoring center, the monitoring center for data processing and analysis.

Data Analysis.
For details of data analysis, see Xiaofang and Pengfei (2011).
While the use of PLS scores into NN regression models (PLS-NN) has not been as widespread as that of PCA, Honigs discussed the merits of PLS-NN [13], the advantages reported include the data reduction from the use of the PLS scores, easy identification and elimination of outliers and regression curvature reduction and improved regression slope and offset.
Detailed data [15] used in network training are shown as Tab.1.
The model outputs with the test data are plotted versus the targets as stars.

This paper states the basic theory and algorithm of the model, and provides the experimental results by using the data of China railway passenger volume in 2006 to 2010.
The formulae of calculating the initial value: (5) (6) (7) The equations gives the arithmetic mode of initial value of level, trend and seasonal. is the average of the observational data in one cycle; is the initial value of increment, is the average of the value gets from the second cycle data minus the first cycle corresponding data; is the seasonal change of the first cycle.
The data comes from National Bureau of Statistics of China.
Table 1 shows the factual data of railway passenger volume Table 1 National railway passenger volume in 2006-2010 Jan Feb.
Multiplicative Holt-Winters model is quite suitable for this kind of data.