Search results

The three periods of Landsat TM / ETM + data(1999, 2005,2010) of the LaoWangPo detention basin are used to analysis and calculate land use/cover change of the LaoWangPo detention basin from 1999 to 2010 , the single land use dynamic degree and the land use/cover change transition matrix, and to calculate the detention basin land use extent change indicators.
Research data selection and technical route Overview and the selection of RS data of the Laowangpo detention basin.The Laowangpo, located in the northeast of Xiping County where Xiaohong River, a branch of Honglv River in Huai River basin meets Yuni River, is an important project for flood detention in the midstream of Xiaohong, with the controlled basin area of 1555.Three time phases of 3-scene satellite image data were selected, with Landsat ETM+ in November, 1999 and Landsat TM in September, 2005 as well as Landsat TM in July, 2010.
The topographic map of the Laowangpo was used to perform the clipping of RS data and sort management of inspection results.
Research technique route.RS data was used to research the technical route(see Fig.1)of the LUCC in river flood storage and detention basin.
Unsupervised classification Post-classification process Extraction of dynamic information Laowangpo flood storage and detention basin Assessment and classification LandsatTM/ETM+ in 1999, 2005 and 2010 Data format conversion Image enhancement Geometric correction Image registration in different time phase Subset image framing clipping Precision analysis Fig.1 The technical route to study the LUCC Analyses of LUCC in river flood storage and detention basin Thematic information extraction.Unsupervised classification of each RS data was conducted to extract thematic information.

And the experimental data show that the measuring model is feasible.
It can calculate data according to the trapezoid.
The calculated data can be returned after calculate the processing reversing point.
And the data can back to control system.
The experiment data and answer are like Table 1.

Model selected 28 variables, through the test of history, obtained future data.
The main data are selected from the Statistical Yearbook of Shaanxi Province, and China Energy Statistical Yearbook.
In this paper, the spatial extent is Shaanxi Province, the time range from 1999 to 2030, through collected data from 1999 to 2009 to predict the related prospects for future energy demand and CO2 emissions in Shaanxi Province.
Forecast of relevant data.
The paper take the energy, economic and population system in 1999 as the initial state, the collection of the known data of the initial state, the simulation program using Vensim simulates the energy consumption dynamics model for the year in 2009-2030, output 1 secondary data every three years, the results are showed in the form of chart or table inTab2,Fig3 and Fig4[6].

Using software systems Python and TensorFlow, an artificial neural network was first set up to determine mechanical material parameters (output data) from punching force curves (input data).
Further Python libraries used for the presented investigations are Numpy for data preparation purposes, Matplotlib for plotting and pandas for reading data from measuring protocols.
Data augmentation represents a commonly used procedure to generate data with high diversity without special experimental effort and thus to improve the training process of neural networks [22].
Therefore the amount of data or the number of measurement curves was virtually expanded.
The remaining 5% finally were used as an evaluation data set.

The air quality monitoring data from 2010 to 2012 in Chengkou County of Chongqing City in Southwest China was analyzed.The results show that in recent years the air pollutans affecting the air quality in Chengkou County mainly is PM10 and secondly is SO2.
The status and trends of ambient air quality in Chengkou County in recent years were analyzed according to the air quality monitoring data from 2010 to 2012 which could be beneficial to further improve the air quality and develop ecotourism.
Data and methodology The data provided by Chengkou environment monitoring station includes the concentrations of PM10, SO2 as well as NO2 and the air pollution index(API) from 2010 to 2012 in Chengkou County.The station is located at the North street of Gecheng Town in Chengkou County.
Analysis of ambient air quality status in Chengkou County According to the monitoring data the daily average concentrations of PM10 generally range from 0.005 mg/m3 to 0.150mg/m3 and 96.4% of them satisfy the national secondary standard for ambient air quality in China; the daily average concentrations of SO2 generally range from 0.010mg/m3 to 0.150mg/m3 and 98.3% of them satisfy the national secondary standard for ambient air quality in China; the daily average concentrations of NO2 range from 0.004mg/m3 to 0.060mg/m3 and all of them satisfy the national secondary standard for ambient air quality in China.In terms of the annual average concentrations the values of PM10,SO2 and NO2 in Chengkou County satisfy the national secondary standard for ambient air quality in China except that of PM10 in 2010.
spring the temperature rises rapidly and the cold air is quite active;summer is characterized by high temperature, vigorous convective motion and abundant rainfall, which is beneficial to the air pollutant dispersion and clearance; in autumn the temperature falls sharply with frequent raining;winter is characterized by scarcity of precipitation, low temperature and weak air convective motion,and if there is no activity of cold air and the near-surface wind speed is small, then the meteorological condition is unfavorable to air pollutant dispersion.In the past, the heavy air pollution was easily caused by the dust and SO2 produced by burning coal for heating in winter.In recent years Chengkou County has conducted environmental protection and pollution control actively by launching the construction of no coal zone and gradually popularizing using clean energy.It also launched series of special action of environmental protection including dust control as well as energy saving and emission reduction

According to the analysis of on-site monitoring data of wall deflection, settlement, and strut load, some conclusion can be drawn.
This paper describes the conditions at the site, discusses aspects of the design of the retaining structures, summarizes construction procedures, presents field performance data, and draws conclusions.
The field performance data included wall deflection, settlement, and strut load.
Data were obtained daily during wall installation and excavation, and at least once a week after the excavation had reached its final depth.
Ltd. for facilitating access to the data.

In this paper, the electrical energy potential saving of consumption in college has been analyzed and evaluated based on data envelopment analysis (DEA) model method.
The DEA Evaluation of University Power Consumption The Data Envelopment Analysis (DEA) is proposed by A.Charens, W.W.Cooper and E.Rhodes in 1978 [5].
The main principle of DEA is keeping the input and output of Decision Making Unit (DMU) as the constants by mathematical programming and statistical data to determine the relative effective production frontier, each DMU is projected to DEA on the production frontier, and relative effectiveness could be evaluated by comparing the decision unit deviation degree of DEA front surface.
The university of input and output index data is as shown in table 1.
Empirical Study On The Potential Of Energy Saving And Emission Reduction In Colleges And Universities[J].

Supervisory Control and Data Acquisition (SCADA) systems data from a Turkish wind turbine were leveraged to develop a predictive model using the eXtreme gradient boosting (XGBoost) algorithm.
The use of SCADA data as a basis for model training is crucial in wind power prediction, as shown in the study utilizing deep learning models with high-resolution SCADA data [7].
The model development process begins with data preprocessing, and then normalization of the data.
The results demonstrate that data performed well in predicting power output.
SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends.

For an adequate description of the shaping processes implemented in such devices on the basis of certain mathematical models, experimental data on the energy-force parameters of deformation are required, as well as data for the nature of the distribution and intensity of deformations arising in the process during cyclic deformation of a metal product.
A complete revolution of the drive shafts consists of two stages: a reduction stage and an idle stage.
The stage of reduction begins from the moment the workpiece is captured by the vertical surfaces of the sidewalls and ends at the moment of their closest approach.
The obtained experimental data on the distribution and magnitude of irreversible strains of the strip under conditions of (plastic deformation) are essential both for a qualitative understanding of the mechanics of the process and the design of machines for continuous casting and metal deformation.

The non-tidal water level fluctuation at four locations in Lianyungang coastal area was analyzed by using tide data.
Materials and methods Data acquisition.
The meteorological data were obtained from the Marine Environment Monitoring Station.
Data analysis methods.
Then empirical mode decomposition (EMD) was applied to the non-tidal water level data.