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Despite these differences in system architecture, the common feature of these systems is to provide real time bus location data.
Usually, this kind of system has a component to collect the GPS data from the remote devices.
They can also be used to collect data for making maps for highway navigation systems.
The proposed procedure uses data collected by means of a GPS receiver placed in a standard vehicle; therefore, it has the advantage of not needing a specific vehicle for data collecting.
In order to improve the accuracy of these data, they have been post processed in order to introduce a differential correction.

Finally, after data transfer, call “close ()”to close the socket.
“Sockfd” is a Socket descriptor used to transmit data; “Msg ” is a pointer to send data; “Len” is the length of data with bytes used as unit; Under normal circumstances, “flags” is set to be zero.
“Sockfd” is the socket descriptor to accept data; “Buf” is a buffer to stored received data; “Len” means the length of the buffer; “flags” is also set to be zero.
“Sockfd” is used to transmit data Socket descriptor; “Msg” is a pointer to send data; “len” means the length of data bytes; “flags” under normal circumstances is set to be zero.
“Sockfd” is a data socket descriptor; “Buf” is stored to the receive data buffer; “Len” is the length of the buffer and “flags” is also set to zero.

This way PCA represents a simple procedure to process, compress and visualize data.
Transforming a set of data is equivalent with it rotating; in the case of PCA, original data rotation is ensured in such a way that will cover a maximum dispersion of it on the main axis (MA), which is orthogonal.
The above mentioned algorithm was used in the exemplification below were we gathered data and processed that data in order to clearly see how the engine behaves when certain malfunctions are simulated.
Analysis of Multivariate and Time Series Data.
Multivariate Data Analysis.

Create assembly-level parametric modeling template and parameter data sheet.
Saving data with Excel table can be more quickly and be modified more easily than traditional SQL, and there are a lot of highly efficient methods to convert the data.
Virtual assembly technology reduces data redundancy and makes assembly model relevant.
Part prototype is a really part model, which records all data required for the model.
Taking fixed pedestal as an example, the data of excel sheet is shown in Fig6.

Only consistent data integration provides the strategic information basis for the identification of effective, company-specific measures.
By assigning semantic meaning, linkage, context and experiences to the raw data, knowledge can be developed [37].
The next possibility is testing with test data (16.3 %).
Franke, “Semantic Meta Model for the Description of Resource and Energy Data in the Energy Data Management Cycle,” Applied Mechanics and Materials, vol. 871. pp. 69-76, 2017
Data Eng., vol. 11, no. 1, pp. 202–212, 1999

These include the definition of Non Linear Path Length, where the sub paths may not be the shortest path, having ‘k’ no of shortest paths in a node instead of having only the shortest path, then removing the path dominancy for state space reduction.
Fig. 1 The data flow diagram of the system When we talk about the exact requirement of the path dominancy we need a queue and some values that have been already loaded into the queue since the previous modules of main algorithm.
The data flow diagram (DFD) of the proposed work explains the state changes of modules when data flow take place.
For proper handling of these attributes we need the member functions to read and write data over these attributes.
Summaries and Conclusion The queue is packed as user – defined data in our work and all the modules are packed as member of user defined data types.

The obtained flow stress-strain data was used to develop the Arrhenius constitutive model of which material constants considered the compensation of strain.
Toward this end, isothermal hot compression tests were conducted and the stress−strain data were then employed to derive Arrhenius constitutive equations.
The slight variation in the slope of the lines can be attributed to scattering in the experimental data points.
The Arrhenius predicted data are not so satisfactory under the 1000ºC temperature condition, because the microstructure of Ti−6Al−4V alloy changes from α phase to β phase in this temperature zone.
In most regions of the forging, the effective stress is between 10 and 40 MPa, which is agreed with isothermal compression test data.

One-hot encoding is used to convert IP, categorical to numerical data.
IP being a nominal categorical factor, one-hot coding is necessary to convert categorical data to numerical data from Gyroid, Line, and Tri-hexagon to 1, 2, and 3 respectively.
This error represents how well the model fits the training data.
This reflects the model’s predictive performance on new data.
Plot distribution of (a) training, (b) testing, and (c) checking data vs prediction of FIS.

Such as the classic C4.5 algorithm which evaluated from ID3 algorithm .Its shortcoming is the demand for the tree data scanning and sorting of data sets, which lead to inefficient algorithms.
ID3 algorithm uses a method called window that randomness to select a subset from the data set to avoid access to the entire data set.
Data Mining Concepts and Techniques[M].Fan Ming, Meng Xiaofeng translation.
Principle and algorithm of data mining[M].
The attribute reduction algorithm based on attribute similarity [J].

A novel semi-supervised Model For a given m-dimension data set , , the main propose of semi supervised clustering algorithm is to find , the membership degree of belonging to the cluster , by integrating the cluster information of labeled data, and the structural information of unlabeled data.
is a weight factor indicating the importance of the cluster information of labeled data.
The performance of critically depends on the shape of data set.
Different percent of instances from the whole dataset are randomly selected as labeled data.
Data set after dimemsion reduction Figure 3.