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As measurement noise will interfere with the test data, test data is get by the theoretical calculations with the addition of random noise: .
Where l is the standard gauss distributing random data, and e is level of random noise.
There are a total of 96 groups training data.
Some training data are presented in Table 3.
And other 100 groups testing data will be used for reviewing the validity of this method.

The result can provide data reference to improve environmental-friendly for manufacturing process [3].
(2) Data processing of scatter degree combination evaluation method The evaluation conclusions of types of evaluation methods are regarded as the value of indexes.
Combined with the evaluation system of resource and environmental index in manufacturing process, the input and output data table of valve body production process can be established based on IPO analysis and the data analysis results are listed, as shown in Table 1.
Based on the data above, data envelopment analysis [6], analytic hierarchy process [7] and grey comprehensive evaluation [8] are respectively applied to make an evaluation of resources and environment attribute of the valve body’s manufacturing process.
In the meanwhile, some other methods, such as data envelopment analysis, analytic hierarchy process and grey comprehensive evaluation, are respectively applied to make the evaluation.

The average values of TWR for each parameter at levels 1, 2 and 3 for S/N data are plotted in figure 1 and raw data is plotted in figure 2.
Table 3 and 4 shows the pooled ANOVA table for S/N data and raw data respectively.
Table 5 and 6 shows the Taguchi response table for S/N data and raw data respectively.
) Figure 2 Effect of input on TWR (raw data) From figure 1 and 2 it can be seen that to get minimized value of tool wear rate the optimized value of tool electrical conductivity is 26316 S/m, gap current is 8 A and pulse on time is 63 µs.
Table 3 Pooled ANOVA for TWR (S/N data) Table 4 Pooled ANOVA for TWR (raw data) Table 5 Response table for TWR (S/N data) Table 6 Response table for TWR (raw data) Based on optimal set of parameters confirmatory experiments have been performed to validate the obtained results.

Network packet capture is the first step in the process of network forensics, and then the preservation and analysis of captured network data streams in which the network packets are displayed in transmission order and organized to establish connection in the transport layer between two hosts, which is called “Sessionizing”.
The correlation of network flow -removing irrelevant data with filter as capturing network flow in certain circumstances, the integrality of data-demanding data streams continuously monitored rather than retransmitted with extravagant hopes for network forensics tools, the rate of packet capture, the above are the primary factor considered in network forensics and analysis.
Firstly, electronic evidence can be preserved according to the requirement of secrecy of data on the server of Intrusion Tolerant System, which guarantees its legitimacy.
However, this system is imperfect in mechanism of simultaneous collection of host data and network data; in addition, it lacks connection with access control, access authentication, data encryption and some other network security mechanisms.
Removing meaningless and useless characteristic points or noise by the discovery of point characteristics of information behavior contributes to the reduction of information storage volume,the enhancement of veracity of detection,the exaltation of computing speed.

Saravanan 2,b 1 Research Scholar – Mechanical Engineering, Karpagam University, Coimbatore, Tamil Nadu State, India 2 Dean - Mechanical Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu State, India a manojkumar@scmsgroup.org b r.saravanan@skct.edu.in Keywords: Reclaimed rubber, Reverse logistics, Network design, used tires, Tire Recycling, Matlab, Material recovery Abstract- Managing the products at the end of its intended use and recovery of such used products from the market is gaining significant importance these days due to global environmental concerns, resource reduction, government regulations and economic factors.
Due to increasing environmental deterioration, green manufacturing, government regulations, corporate social responsibilities, resource reduction, and economic factors, many companies are now engaged in the material recovery business.
· Transportation cost of BP inside the IRPS considered and accommodated in the overheads The objective function of the reverse logistics model is given by the following equation: Minimise Cost=TSLC+CALC+SULC+RCLC+SURC-SUSP+SUSC+SUTC (1) Subjected to constraints ni , q CBPA= i=1nni*q*SA (2) CBPC= i=1n ni* q*CTcWT + ni* q* IRPSO - ni* qs* Ssp (3) CBPD= i=1n ni* q*CTDWT + ni* q* SSRPS + ni* q* SSRPO - ni* qs* Ssp + ni* qBP * SSRPT (4) The mathematical model has been validated using the data collected as per the Table 2 from the case study and plotted in the graph (Fig. 4) Table 2: Data collected from the case study Item Cost SA 15 [Rs/kg] CTc 520 [Rs/piece] IRPSO 7.70 [Rs/kg] WT 62 [Kg/tire] qs 3548.39 [Kg/10 ton] qBP 6451.61 [Kg/10 ton] Ssp 20
[Rs/kg] ni 30 [Days] q 10 [Ton] The following assumptions have also been used based on the data collected from personal interviews with the concerned parties CTD = Average landing cost of truck tire per piece in SSRP is 400 rupees SSRPS=Sorting cost of SSRP is o.25 Rs per kg SSRPO=Overheads of SSRP is 8.45 per kg SSRPT=Transportation cost of SSRP to RP is 1 Rs per kg Figure 4.

The generation of measurement data from a point cloud is even more demanding.
The previously known image processing algorithms are programmed only sequentially and the usage of increasingly complex data results in more computing time to process image data.
One is used as an MPI (Message Passing Interface) network and the other as a general data network.
An adjustment of the local luminance and the resolution of the pattern are performed based on these data.
The data processing problems that lead to a slow data processing speed for production purposes were discussed.

Artificial neural network (ANN) is basically a data-driven black-box model capable of solving highly non-linear complex problems [6].
With a set of training data, the network is able to learn by adjusting the interconnection weights between the layers.
To consider the effect of temperature and variation of strain rate, the input data were primarily corrected [12].
Before training, all data were normalized within the interval [0, 1].
On the contrary, SVM is free from literature of the data dimension which is unnecessary to use any data pretreatment technologies such as data compression, input data reduction dimension and so on [14].

Secondly, the analysis of water supply, data statistics of the collected, combined with the hotel's water consumption characteristics, through studying problems of reclaimed water source, water use and water balance related, analyzing every factor interactions and dependencies, in search of water system of water balance factors influence in.
The amount of water in Suwon design specifications due to fixed water used to water the displacement reduction coefficient and hotel percentage of the regional breakdown of water supply are some differences with the measured data, resulting in the hotel water reuse project design is too large or too small water issue
(2) Data Standardization Using the formula, the various indicators of the raw data were normalized, the specific data standardization results briefly
Furthermore X2, X3, X5, X14, entropy values ​​for these factors were 0.055 comprehensive content, data accuracy, comprehend-siveness of seasonal change.
Obtained through questionnaires raw data to determine the impact of the water balance the weight of each factor binding entropy method.

The XRD data reveals broad humps in range of 25-35 degree representing the amorphous nature of the glass materials.
Other possibility for the reduction in the band gap is due to the formation of greater number of non-bridging oxygen.
El-mallawany, Tellurite Glasses Handbook: Physical Properties and Data (CRC Press, 2002)

When carries on the main body structure modeling, the user must pay attention to the determination of design datum.
The design datum will decide the design the mentality usually, the good datum can help the simplification modeling process, and facilitates the design revision in the later period.
Usually,the majority start of modeling process is starts from the datum design. 1.3 UG permit users establish the parameter relations between the components after completes the model establishment Some more direct method is to quote the related parameter directly in the modeling.
Based on the UG modeling step, we carry on the overall organization design regarding the robot walking mechanism structure first(as shown in Figure 1), the entire robot walking mechanism design mainly to be composed by 13 parts, including the chassis design, four pendulum arm axis system, four electrical machinery reduction gear system and three crank mechanisms. 3 Ease of Use 3.1 Chassis design Robot shifting mechanism chassis is load bearing platform of the entire robot various parts, also is the major component assembly design datum.