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Abstract: Category level object discovery is important for a number of applications such as remote sensing image classification, and data mining in images and video sequences.
An efficient alternative is provided by object detection method [2], where the characteristics of objects are learned from labeled data.
However, the dependence on annotated training data inevitably limits the scalability of these methods.
Video patches are extracted from the video data by simultaneous generation of both spatial and temporal context for image regions in each frame.
Note that the video data can be divided by time slice.

Thus, this technique provides an assessment of strength characteristics of PFPCM based on data on its granulometric composition, filling ratio and type of binder.
Algorithm for calculating the specific energy characteristics of PFPCM The input data of the algorithm are data on the granulometric composition of the filler (the diameter of the filler particles d1..dn and their volume ratios ϕ1..ϕn), the filling ratio ν, the form of the representative volume and the type of binder.
Based on the data obtained, the numerical value of the work of adhesion Wа is determined according to Eq.2.
A comparative analysis of the experimental and calculated data obtained showed that the higher the value of the specific adhesion energy that the system possesses, the higher its strength characteristics.
Thus, a significant reduction in time and material costs at the stage of preparation of production of products from dispersed-reinforced polymeric composite materials is possible.

The experimental data were fitted by mathematical models available in the literature.
In order to select a suitable drying curve, twelve models in Table 1 were fitted to experimental MR data.
The suitability of the models was validated by the R², RMSE and χ2, which were calculated using the observed data and predictive model.
The Weibull distribution model showed good fit to observed data and also could represent the drying behavior of MW dried sweet potato slices.
The models of Page, Two terms and Verma also presented high R2 values and low RMSE and χ2, which showed good fit to the experimental data.

As a result the NCO structure has been efficiently simplified and the memory reduction has resulted in noticeable logic element (LE) saving.
Periodically the ROM data bus holds an ith segment initial amplitude coefficient for a certain clock cycle equal to (3) Where the (2M–2–log2s), represent the number of bit per segment.
During the present segment interval Tseqi, The Register continuously latches the previous sample ci–1, so, the inputs of the subtracter provide by ci which is directed from ROM data bus and ci–1from the output of register.
The output remains unchanged during the new present segment interval. , consequently the data inputs of subtracter will be updated with the new coefficients ci and ci+1.
The En signal generated by the digital comparator which is simultaneously examines the ROM address bus for detecting the changes in data select inputs.

The various data used for developing the tire design are mentioned by the industry as per their requirements.
“Integrated Assessment Of Noise Reduction Measures In The Road Transport Sector”, Project Report PR SE/652/03, TRL Limited

The brake pedal original date is received from data register ATDODR1of modulus conversion channel ATDO of MCU, which sampling 3 times in process to get brake pedal minimum data according to the figure of the process[5].
This algorithm in practical application have certain effect, but if the driver is handling the brake pedal when power supplied or brake pedal is not on the original position, thus will lead to this situation: research data will be very large, which causes little pedal range change when Hybrid cars running, which results in a large part of the energy lost.
Test Comparative Analysis To verify the algorithm feasibility in practical application, we collect the test data on the same sample car on Rotating Hub according to NEDC circulation conditions and analyze the test data.
Start brake= BPS-BPSmin |brake|>Cdelt BPSmin=BPS BPSmax=BPSmin+BPSRANGE END PPS>30% Y Y Fig.3 NEDC cycling condition Flg.2 The self-study process step of driving The original data of capacitance voltage is 37V for the two groups of data collected.
It has very important practical significance for the development of energy saving and emission reduction.

The external interference cancellation model in long-distance communication 2.1 Data transmission principle in long-distance communication Figure 1 A typical cluster structure diagram Assuming the square area with approximate side length of A is a data acquisition wireless sensor network, and immovable M nodes randomly, independently and uniformly distributed within the plane region.
In a data acquisition cycle, energy consumption of a multi-hop critical node is （2） The formula above shows that if two nodes are far from each other, directly transfer needs lots of energy while multi-hop communication consumes less. 2.2 Interference elimination during transmission The elimination principles are shown in Figure 2. x(k) is immeasurable information signal is not measured, n(k) is measurable noise source signal. d(k) is the distortion signal generated by noise source and is added to y(k).
Figure 2 Data transmission principle in long-distance communications Dynamic characteristics of the channel is located as F(·), n(k) goes through channel f and turns to delay and distort form d(k).
[3] Santhanaraman Gopalakrishnan,Wu Jiesheng,Huang Wei,et al.Designing zero-copy message passing interface derived data type communication over InfiniBand:alternative approaches and performance evaluation[J].International Journal of High Performance Computing Applications,2005,19(2):129-142
Reduction of instrument-dependent noise in hyperspectral image data using the principal component analysis: Applications to Galileo NIMS data[J].Planetary and Space Science,2008, 56(3): 406-419

In addition, Principal Components Analysis (PCA) is used to extract the main features of the wavelet order spectrum and reduce the volume of data.
Using few main components, it can keep most the original data set intact without mutual relationships to achieve the simplifying data
The wavelet order spectrum method with PCA, through the selection of principal components as principal features for the fault diagnosis, reduces data dimensions of the wavelet order spectrum.
The SOM neural network learning algorithm can automatically find it’s similarities between input data, with the configuration of input which close to the output layer neurons, and thus forms the input data which can be selectively provided to the reaction grid.
In addition, through the PCA data reduction process, training time can be reduced to 85 seconds.

This study surveyed and collected the urban trunk traffic flow data of Bayi South/North Street in Jinhua.
Bayi South/ North Street Traffic Flow Data Analysis.
(1) The traffic flow data convert.
The traffic data of whole sections in Bayi South /North St. at weekday evening peak hour (5:00 to 6:00 pm) was showed in Table 3.
Summary This study research surveyed and collected the urban trunk traffic flow data of Bayi South/North Street in Jinhua.

Theoretical analysis and data processing were conducted based on the theories of fracture mechanics and general rock mechanics, relations between the feature strength of three stages and the stress state were obtained, and a method was put forward for analyzing the brittle shear failure of rock material.
Researches showed that strength will be proportional reduction for the existing of double or three cracks.
Mine-by tunnel experiment data were applied for the calculation and analysis of mixing strength.
According to the data from Mine-by tunnel test, Hoek-Brown strength and tested values existed a certain degree of deviation in strength computation under low confining pressure, and returned to be coincided in high confining pressure [8].
Strength analysis of mixed granite of Shuichang is carried out by the brittle shear failure mode analysis with the method of data fitting, and the correlation coefficient is 0.9836.