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This lock-in technique improves the signal-to-noise ratio, making extracting meaningful data from the noisy environment easier.
These data are obtained by fixing the diode current to 200 nA for all the different temperatures.
The data of panel (a) are averaged over 20 measurements at each temperature.
Data are taken at 500 °C.
The high-temperature data are based on work supported by the Defense Advanced Research Projects Agency (DARPA) under Agreement No.

Analysis on Lubricating and Viscosity-temperature characteristics of the Vegetable Oil Fazheng Zheng a, Shandan Zhou b, Feng Xie c, Xinnian Li d, Yumeng Yu e Air Force service  Institute, Xuzhou, 221000, China aylyyz103@sina.com, bJack1975@126.com, cchinaxiefeng@163.com, dlixinnian@sina.com, ekuaileyym@.126.com Keywords: Vegetable lubricating oil, Viscosity-temperature characteristics, lubrication property Abstract: The reduction of oil reserves will cause the exhaustion of mineral oil.
Product V40℃/（mm2/s） V100℃/（mm2/s） VI Coconut oil 6.55 2.05 107 Linseed oil 28.85 7.33 237 Grape seed oil 29.86 7.33 226 Soybean oil 31.34 7.61 226 Sunflower seed oil 31.58 7.65 226 Rice bran oil 31.83 8.08 244 Cottonseed oil 32.52 7.71 220 Castor oil 34.07 8.01 221 Corn oil 34.08 7.88 214 Canola oil 35.23 8.12 216 Rapeseed oil 35.30 8.14 216 Peanut oil 35.96 8.25 216 Sweet almond oil 38.51 8.50 207 Olive oil 38.54 8.24 197 Palm oil 39.79 8.41 195 500SN 102.20 11.10 93 It can be derived from the viscosity data in Table 1 that in addition to the coconut oil, the viscosity values of most vegetable oils are similar, the viscosity value at 100℃ is mostly in the range of 7.0 mm2/s to 9.0mm2/s at 40℃, and the viscosity value is from 28 mm2/s to 40 mm2/s.
The viscosity index is mostly over than 200, the test data in the table, except the viscosity index value of the coconut oil is 107, and the values of the remaining vegetable oil are from 195 to 244.
These data suggest that the vegetable oil has excellent viscosity-temperature characteristics.
Product PB(1450r/min)/N PD(1450r/min)/N WSD(30min,392N)/mm WSD(30min,588N)/mm Coconut oil 372 1235 0.779 - Linseed oil 784 1568 0.565 0.676 Grape seed oil 745 1568 0.570 0.756 Soybean oil 745 1568 0.569 0.728 Sunflower seed oil 745 1568 0.529 0.752 Rice bran oil 686 1568 0.584 0.731 Cottonseed oil 784 1568 0.557 0.711 Castor oil 745 1568 0.545 0.699 Corn oil 715 1568 0.588 0.724 Canola oil 745 1568 0.561 0.728 Rapeseed oil 715 1568 0.557 0.717 Peanut oil 745 1568 0.546 0.648 Sweet almond oil 686 1568 0.583 0.766 Olive oil 627 1568 0.553 0.736 Palm oil 657 1568 0.511 0.617 500SN 451 1235 0.640 0.922 Table 2 lists the bearing capacity data of the vegetable-based oil, it can be seen from the test results that except the coconut oil with small viscosity, the maximum non-seizure load of the vegetable-based oil is mostly about from 627N to 784N, which is higher than that of the contrast sample mineral base oil 500SN, reflecting that the vegetable-based oil generally has excellent oiliness and

The statistics collection can last for 5 days,totally getting nearly 305000 pieces of data.
This idea is also demonstrated in Day3 data, which is shown in Figure 6b.
Intrusion Detection Systems and Multi-Sensor Data Fusion：Creating Cyberspace Situational Awareness [J].
Multi-Sensor Data Fusion for Next Generation Distributed Intrusion Detection Systems[C]. 1999 IRIS National Symposium on Sensor and Data Fusion, Laurel，USA，1999 (1):24-27
[4] Llinas J，Hall DL．An Introduction to Multi-Sensor Data Fusion[C].Proceedings of the 2008 IEEE International Symposium on Circuits and Systems，Monterey, CA，USA．2008 (6):537-540 [5] Hall D L，Llinas J．An Introduction to Multisensor Data Fusion [J].

With practical data of the BF ironmaking from Jiuquan Iron&Steel Cooperation Ltd.
Among the three main processes-blast furnace-converter (BF-BOF), direct reduction-electric arc furnace and scrap steel-electric arc furnace, BF-BOF occupies the dominant degree [1].
Data collecting and pre-processing In this paper, the whole continuous data (one group data is recorded once an hour) in two days were collected from BF system of Jiuquan Iron&Steel Cooperation, and the output and input parameter data heading is respectively shown in Table 1.
After multiple correlation investigation of independent variable aggregation (only part of correlation data is list), altitudinal multiple correlation phenomenon of X can be seen and so multiple regression model has bad reliability.
The regression model was set up by partial least square regression method with practical data from BF system; the calculation and analysis process is shown as below.

The output voltage and current is recorded.The data is fit according to certain fitting algorithm and IU characteristic curve can be obtained.
Although the method is able to reflect the output characteristics of a solar cell accurately, we need test plenty of data if more than two curves are need in which case the cost and the test period increases.
The output characteristics of solar cells can be obtained according to the basic data from the manufacturers.
At the same temperature and light intensity, measure and record the value of output voltage and current,analyze and compare with the simulation results.The simulation results and the test data are shown in Fig.3.The results shows that: the error of simulation and test data results is 1% or less, and meet the design requirements; the model can meet the actual analysis of the solar cell output.
Fig.3 Tthe error of output data Conclusion This paper proposes a practical mathematical model of solar cells in connection with the complex mathematical model and low precision according to the national standards.

The experimental data have shown that the engine’s temperature has an influence on CO2, CO, HC emissions.
To report data determined in laboratory tests for a precise analysis or in research workshops obtained under arbitrary conditions of temperature and pressure requires the adjustment to the standard conditions..
The BEA Emission Analyser - Bosch The data resulted after the 18 tests were downloaded and a database was created in order to analyze them.
The results of collected data (excess air factor λ, CO, CO2, HC and O2 concentration) are shown in Table 1, Table 2 and Table 3 [6].
Results and discussion The centralized data were used to determine the dependence between the exhaust gas temperature and concentrations of three compounds: CO, HC and CO2.

S-N data in mid-fatigue life range and fatigue limit data of smooth small specimens are applied for material fatigue behavior.
The data of real axles reveal the difference between material and a special structure.
Totally, 4 pairs of "failure" vs "survival" data for real axle specimens, 7 pairs of the data for material specimens, 6 pairs of the data for similar specimens, 31 pairs of S-N data for material specimens are obtained for the present study.
The "failure" vs "survival" data are given in Table 3.
S-N data in mid-fatigue life range and fatigue limit data of smooth small specimens are applied for material fatigue behavior.

By means of factor analysis and logistic regression approaches, this paper predicted financial failures of Chinese listed companis in 2009 from the published data in 2006 of those companies.
But most of these financial risk analysis are based on accounting data and financial ratios.
Sample and Data Selection.
Then the financial failure analysis was based on those data of 2006 fiscal year, in order to achieve the purpose of prediction, i.e., risk analysis and control beforehands.
In rotating the factor loading matrix after extracting factor, and its economic significance to clear explanations, which not only can find out the influence of a company's financial condition main factors, but also for warning model dimension reduction processing, to simplify the regression model.

For the current situation and deficiency of the enterprise material management system: huge material data, species diversity, single management means, management behavior is not standard, people involved in most materials management activities, materials management standards is not unique, materiel maintenance and resource sharing is difficult, and so on.
Da Hong Shan Pipeline company has already built a website platform, integrated management and control systems, data acquisition and monitoring of SCADA systems, slurry characteristics data management system and production information such as video monitoring platform.
It is in the "unified planning, step by step implementation, the key breakthroughs" management philosophy, advocate and support the digital management of enterprise production, real-time monitoring of production site and dynamic analysis and decision of production data.
Materials department of use according to "material management regulation", fill in the scrap material disposal application, after approved by head of department, review by the competent department of company, put forward disposal opinions, after approved by the company in charge of the leadership, report to energy conservation and emissions reduction center. 2.6 Material of repair.

The fouling of the coil leads to poor airflow, reduction of heat transfer efficiency coupled with high energy use.
It starts with an actuator, regulating the height of the distribution nozzle in relation to proximity sensor data.
This is a huge reduction of 122 liters per operation, which indeed shows that the new system has outstanding efficiency in the use of water.
This reduction in manpower complements the overall reduction of labor costs and improves general operational efficiency.
Data in the graph indicates that the new method drastically cuts down the time required to perform the full cleaning operation, performing it within 13-16 minutes.