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Feature Extraction In the speech emotion recognition, a few seconds voice will produce large amount of data.
Endpoint detection accuracy is directly related to the effectiveness of feature parameter extraction, will affect the whole recognition system robustness, and can also reduce the amount of data for the identification, reduce the system running time.
The model can be expressed as: (4) The training of Gaussian Mixture Model is to determine the model parameters based on some criteria using a set of training data.
Test results Experiment training voice data are recorded an in acoustic room by using the handset microphone, sampled at 11025 Hz.
Comparing feature dimension reduction algorithms for GMM-SVM based speech emotion recognition, Signal and Information Processing Association Annual Summit and Conference, 2013 [8] Bhaykar, Manav ; Yadav, Jainath ; Rao, K.Sreenivasa .Speaker dependent, speaker independent and cross language emotion recognition from speech using GMM and HMM, 2013 National Conference on Communications (NCC), 2013 [9] Tsang-Long Pao,Chun-Hsiang Wang ,Yu-Ji Li.

Feature selection allows the reduction of feature space, which is crucial in reducing the training time and improving the prediction accuracy [3].
Experimental Study In our study, the bearing vibration signals of four types of bearing conditions were collected from the CWRU bearing test data center [8].
All signals were transformed to the signal records with 2048 data points in each record, then for all 2280 signal records representing four bearing conditions, we used 1520 signal records for training the PNN classifier, and 760 for testing the diagnostic performance.
Table 1 Diagnostic performance using the set of complete features and reduced subset of features Complete WPT Features Features Selected by the proposed Hybrid Method Training Data Testing Data Num. of Features Diagnostic Accuracy Num. of Features Diagnostic Accuracy 1520 760 32 95.5% 22 100% Conclusions In this paper, we presented a novel hybrid feature selection algorithm based on ACO and PNN.

Table 1 Clay mortar masonry wall, cement pointing model,the data watch of the experiment Unit discharge(L/s) 40.70 67.70 98.90 113.10 157.10 Average depth(cm) 20.80 26.30 34.30 37.30 45.30 Biggest velocity in moment (cm/s) 64.65 77.03 80.78 81.64 85.10 Center measured Strain（） Boundary measured Strain（） 0.40 0.94 0.95 0.98 -0.94 Figure 1: The characteristic points of the wall relate between pressure and flow curve in the program To some extent, the building can resist the larger flood flows through the existing clay mortar masonry residential construction reinforced by cement mortar plastering.However this kind of the town housings reinforced in the method, the phenomenon would appear that the soaked cement hang up to sew the parts will shed off in flood at long hours.Once the soaked cement mortar falls off in flood , the building may collapse.
Table 2 Clay mortar masonry wall, cement mortar plastering model, the data watch of the experiment unit discharge (L/s) 39.30 97.50 124.90 161.00 Average depth (cm) 17.00 32.00 38.00 47.50 Biggest velocity in moment (cm/s) 83.37 90.28 100.65 118.72 Center measured Strain（） -0.01 -0.13 -0.02 -0.07 Boundary measured Strain（） 0.00 0.22 3.08 3.08 Figure 3: The characteristic points of the wall relate between pressure and flow curve in the program To some extent,the town housings building plastered by cement mortar,they can protect the building in the flood.
Table 3 Cement mortar masonry construction model,the data watch of the experiment unit discharge (L/s) 72.0 104.8 130.7 172.2 226.9 Average depth (cm) 30 36 41 51 60 Biggest velocity in moment (cm/s) 91.72 120.52 132.04 130.6 90.28 Center measured Strain（） 0.4 0.7 0.9 1.03 1.2 Boundary measured Strain（） -3.0 6.5 14.0 16.16 19.0 Figure 4: The characteristic points of the wall relate between pressure and flow curve in the program After the town housings was constructed by the cement mortar, as the whole of the building is better, so it can withstand more pressure in the flood.
It is known from the test data in measured,For the whole building, the collapse of the building does not occur normally as long as the basis of the building immersed in water will not be damaged.
Disaster Reduction In China 2001,2,11 (1) :38-42, In Chinese [4] Zhonggui Hui, Liu Shuguang.

The highest data was got at 50mg/kg, about 18 times of the control.
The data of TR in the present study was higher than that of early report [10], in which the highest TR was 3.1 mmol H2O m-2 s-1.
The higher data of TR suggested the better ability in transporting inorganic salt to the aerial part.
In similar with TR, the high data of gs was given at the period of heading, which may be explained by the effect of anatomic features alternation of xerophyte leaves under Cd stress [9].
The reduction of net photosynthesis rate in the heading period was mainly caused by non-stomatal limitations.

A PC based data acquisition correlated bath particle levels for various wafer processing operations (batch entered, batch extracted, and bath draining) and are reported as number of particles [0.065 to 0.1µm] per ml & as a sliding average number (on 5 values) to monitor the trend.
The data provided by the HSLIS particle counter validate the performance of the 0.05µm QuickChange® ATX filter from a hardware point of view: better baseline, stable performance and reduced time of filtration.
The better particle removal performance is manifested by a reduction in the average number of defects on production lots.
Raghavan, Effect of surface charge and fluid properties on particle removal characteristics of a surface-optimized REB filter, Diffusion and Defect Data - Solid State Data, Part B: Solid State Phenomena 76-77 (2000) 271-274

Colorimetric data of dyeing (K/S) were determined using a spectrophotometer (Hunter Lab Color Quest XE, USA).
Results and Discussion UV protection property The UPF values and protection data of silk fabrics dyed with and without a metal mordants agent are shown in Table 1.
From the UPF data, it can be observed that all metal mordants used in this study caused a reduction in UVR transmission through the silk fabric.
Therefore, it was proven that these results agree with previous data reported by Sarkar [8], who showed that a pale-coloured cotton fabric gives less protection against intense UV radiation.

A challenge in such systems concerns the lack of the available data to outline the complete profile of growth kinetics.
Yet, the reduction of the component number in alloy systems would decrease liquid stability.
The line is plotted from the fit to the data in terms of VFT equation .
A big discrepancy between the calculated and the experimental data is visible, albeit a shift by a undercooling of 96 K is applied (dash line).
The accessible crystal growth theories met with limited success when interpreting the experimental data in the whole undercooling range.

In the case of image demising, result the image must have sharp edge, but low noise, you must like observation data and more usually must have the same statistical properties as natural images Fig. (2) shows a sample to noise.
From Bayesian inference theory, the conditional probability can be written as (13) Where is the prior probability of u (i.e. image prior) and is the conditional probability (likelihood or data model), describing how well the observed data f can be explained by the solution.
It is possible to compute the best solution by modeling and accurately is a constant with respect to The noise reduction problem (with independently distributed additive Gaussian noise) can be modeled as the following: (14) Where D is the discrete set of image pixels and is a tuning factor related to the variance of the noise.
As seen in section (IV B), the Sublet model is given by Where the first term derived from the prior, is the regularization term and the second term is the data fidelity term.

Thus, it causes repair, maintenance, property degradation, and life reduction.
The following empirical equations were derived from the experimental data: 550°C ; 0log128.0960.2log ε σ += (1) 600°C ; 0log194.0975.2log ε σ += (2) 650°C ; 0log281.0915.2log ε σ += (3) 700°C ; 0log414.0899.2log ε σ += (4) In general, the relationship between creep stress and initial strain can be modeled as follows.
But, it is necessary to investigate the comparison through more experimental data in future. 100 101 10 2 103 500 600 700 800 900 1000 2000 550°C/Experimental 600°C/Experimental 650°C/Experimental 700°C/Experimental LMP/Calculated ISPT/Calculated Creep stress, σ (MPa) Creep life (Rupture time), tr (h) 100 101 10 2 103 500 600 700 800 900 1000 2000 101 10 2 103 101 102 103 Range of "Factor of 2" 550°C 600°C 650°C 700°C Experimental rupture time, tr (h) Calculated rupture time, tr (h) Fig. 3 Comparison by ISPT and the LMP Fig. 4 Comparison by ISPT data Summary The creep properties such as creep stress and rupture time had quantitative relationship with the initial strain.
But, it is necessary to investigate more the comparison through more experimental data of long time in future.

Fig. 2 The linked list data structure of dexel volume model Fig. 3 5-axis simulator interface and screenshot of impeller machining simulation The discrete mechanistic milling model can be used to estimate instantaneous force magnitude and direction.
Force measurements were made with a Kistler dynamometer and a PC data acquisition board.
Although the profiles are not an exact match, the magnitudes and trends of the direction forces are very similar between the estimated and measured force data.
Fig. 5 Machining propeller part: (a) part geometry, (b) machining tests Table 1 Cutting and simulation conditions for ball end milling of propeller surface Cutting and simulation conditions for ball end milling Cutting tool Tool length Spindle speed Feedrate Stock material Flutes Coolant 10 [mm] 52 [mm] 2500 [rpm] 1200 [mm/s] Aluminum 4 yes KTC KRC KAC P1 P2 P3 Discs 532 120 250 0.23 0.25 0.22 18 Fig. 6 Comparison between the measured and predicted cutting forces 0 30 60 90 120 150 400 200 0 600 800 1000 Machining time (min) 180 Reference force 650N After feedrate scheduling Before feedrate scheduling 35% reduction After scheduling Before scheduling Fig. 7 Cutting forces comparison before and after feedrate scheduling Conclusions This work demonstrates the feasibility of combining a geometric model with a discrete mechanistic model for the purpose of five-axis force prediction and feedrate selection.
The combined models of the software system are tied together in an integrated modeling approach, where the system is integrated such that the only links between components are passing data.