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An analysis of the field traffic data shows that the phenomenon of speed transition happens frequently once occupancy reaches to the critical occupancy (30%).
Data The traffic data used for this paper was detected by microwave detectors on 2th and 3th Ring in Beijing, including traffic volume, lane occupancy, speed heavy vehicle, time, location ID and identifier ID which links the detector or station to a specific urban expressway location and direction of travel.
And all data is aggregated in 2 minutes and detected by the leftmost lane microwave detectors.
On travel, speed reduction and volume reduction will lead a portion of travelers trip to be stranded and causing congestion.
Data collection and analysis methods to support congestion management systems.

A method, which could be used in the areas of short of data, to determine the retention coefficients of TN and TP was put forward in this case.
In addition, a method to determine the retention coefficients of TN and TP in the areas of short of data was put forward in this paper.
Methods and Models Process of Calculating Water Environmental Capacity of Uutrophicated Reservoirs The process of calculating water environmental capacity of eutrophicated reservoirs can be divided into the following several stages: (a) Collect and analyze the related data of the reservoir, including hydrological data, water quality data and geometric data.
The designed water stage is 332.59m which occurred in August, 2006, and this data is obtained from the materials which are provided by Shizitan Hydropower General Station.
(c)In the case, a method, which could be used in the areas of short of data, to determine the retention coefficients of TN and TP was put forward.

Asama et al. proposed the Intelligent Data Carrier (IDC) with the aim of sharing knowledge among the robots [4]-[6].
In the simulation (Fig.6), 1000 of sample testing data for the simulation was generated by randomly using normal distribution under the condition of standard deviation 0.035 cm and average 1.00 cm.
The generated data indicate the index of the quality.
This is caused by the process of random data generation by the simulator.
Kurabayashi, H.Asama et.al;”Autonomous Knowledge Acquisition and Revision of Dynamic Environment by Intelligent Data Carriers”ROBMEC2000, 2PI-33-035.

The classification rules are extracted by attribute reduction.
The data is classified into two sections.
(3) The unstructured data, particularly strength points and weakness points are classified by using text mining technique and then stored into the data warehouse
This experiment is done to classify strength and weakness points of unstructured data.
Then data were examined and cleaned.

The equivalent stress-equivalent strain input data, required to perform the FE analysis, were obtained by means of axisymmetric compression tests carried out over an extended range of strain rate [7].
Results and discussion Fig. 3 shows typical hollow specimens compressed at different height reductions.
Such behavior is overturned by the data at a strain rate of 0.1 s-1, obtained by lubricating the tool-specimen interfaces, that show as the DID% is lower than 0 and decreases with increasing DH% until reaching a minimum value.
Irrespective of the height reduction and lubrication condition investigated, the DID% values at 2000 s-1 are higher than those at 0.1 s-1.
Experimental data of ring compression tests of AA7075-O, obtained using different lubrication conditions, under (a) quasi-static (0.1 s-1) and (b) dynamic (2000 s-1) loading conditions.

In particular, there are no data on the influence of a modifier based on ultrafine powders of refractory metal oxides on the mechanism of structural phase change and mechanical properties of wear-resistant cast iron.
The data analysis have shown that the microstructure of unmodified and modified cast irons in longitudinal (Fig. 1, a, b) and transverse (Fig. 1, c, d) directions demonstrates the anisotropy of carbide phase associated with the dendrite growth orientation.
The obtained data show that the corrosion resistance of the modified sample is higher by a factor of 2–3 in comparison with an unmodified sample.
It has to be noted, that experimental error of the obtained data doesn't exceed 0,1%.
The size reduction of (Cr,Fe)7C3 carbide phase is accompanied by the increase of its total amount per volume unit, which ensures the growth of matrix microhardness.

Thus the problem of high-dimensional data classification can be solved by first mapping the original data into a lower dimensional feature space that reflects the inherent structure of the original data and holds the useful information as much as possible by dimensionality reduction method and then applying a suitable classifier to achieve fault classification in the lower dimensional space.
The locally linear embedding (LLE) algorithm is one of such representative manifold learning methods, to tackle the nonlinear dimensionality reduction problem, which unravels the underlying data manifold in its reduced space based on the assumption that each data point and its neighbors lie on a locally linear patch of the manifold.
The underlying idea of LLE is based on the assumption that data lying on a nonlinear manifold can be viewed as distributing linearly in a local patch if the data are well sampled and lying on a smooth manifold.
So each signal sample is transformed into a data point in a 1024-dimensional signal space.
The ball bearing data and rotor bed data both are used to validate the proposed approach.

In available literature, the formulae recommended for load bearing capacity prediction and design are based on empirical approach, mostly resulting from statistically processed experimental data [1,2].
The proposed reduction coefficient obviously has to be related to the reinforcement ratio, as for slightly reinforced sections the reduction in moment capacity induced by member curvature is negligible and for sections with high reinforcement ration, even at the threshold of concrete crushing failure, the reduction may reach levels higher than 15 %.
As the reduction was found to be strongly non-linear, a function with more than one parameter was required to approximate it.
For reinforcement ratios ρ lower than 0.15 %, the reduction should be considered zero.
Still, the proposed moment capacity reduction formula (Eq. 8) is quite simple and comprehensible.

Based on the survey data, the slope body formation is mainly made up of four parts: the first one was the Holocene Quaternary artificial fill soil, the second one was the Quaternary slope diluvia silty clay layer, the third one was the Quaternary residual talus silty clay and the last one was the Tertiary Pliocene silty clay layer.
According to the overall area of slope body and the above survey data, three representative profiles have been divided.
Parameters selection of soil mass According to Interior geotechnical testing and relative prospecting data, through converting parameters and experience-reduction, getting physical and mechanical parameters of soil mass in different states(see table 1).As elasticity modulus and Poisson’s ratio in the light of strength reduction in the paper just have a soft impact on coefficient of safety, therefore, according to experiential values, the value of elasticity of modulus was set as 2~5 times of modulus of compressibility, and each soil-layer set the values as 4 times of correspondent modulus of compressibility, Poisson’s ratio value taking account of both laboratory values and experiential values[2-4].
(3) (4) Description: cF: cohesion after reduction, φF: frictional angle after reduction, Ft: coefficient of reduction.
In the case of considering the effect of water, according to the engineering drilling data, getting the position of ground water line in profile, and simulating and calculating on this basis, the coefficient of safety in profile is 0.93.

Isotherm data were analyzed using the Langmuir and Freundlich isotherms.
The data fitted well to Langmuir isotherm with derived adsorption capacity of 20.1 mg/g.
Kinetics data were analyzed using intra-particle model, Elovich equation, pseudo first-, and pseudo second-order models.
The kinetics data fitted well to pseudo second-order suggesting that the mechanism is the adsorption process.
The experimental data had good fit to the pseudo second-order model with adsorption rate of 61.2 x 10-4 g/mg-min.