Search results

The proposed system incorporates an obstacle detection mechanism, an Arduino microcontroller to process sensor data, and a stepper motor to activate the rear brake autonomously.
Prototype Testing and Data Analysis The prototype was tested in a controlled environment to evaluate its performance in real-world scenarios.
Data Analysis Table 2.
Data Analysis.
Data analysis.

A reduction in minimum temperature was observed to promote a decrease in TMF life by as much as a factor of ten for all TMF experiments.
Arrel et al. [5] and Kupkovits and Neu [6] showed for the Ni-base superalloys tested that through a reduction in the Tmin from 400 to 100◦C and 500 to 100◦C, respectively, exhibited a factor of four to five reduction in life for OP TMF conditions.
A reduction in the Tmin was observed to result on average in a 65% net reduction in life across all conditions tested.
Fig. 2: Influence of minimum cycle temperature on OP TMF life. 45% reduction in life.
To protect proprietary information, the data is normalized by reference values.

Wojska Polskiego 28 60-637 Poznan *corresponding author e-mail: jdach@up.poznan.pl Keywords: Biogas, Polish market, Data acquisition, Internet, Modelling.
The paper presents the internet tool for decision support and data acquisition for Polish biogas market.
The aim of this study is to check the potential of data collecting from Polish biogas market with usage of BWP as a free internet tool.
It is important that users are aware of that background process of gathering the data for scientific purposes.
From the data we were able to define what are the key regions interested in the Biogas industry (Fig. 2.).

The validation of the used model with the experimental data was done by predicting the stiffness reduction as a function of crack density.
Firstly, validation and comparison of the used model and the experimental data [15] were done by predicting the stiffness reduction due to transverse cracking for glass/epoxy [θ/90]s angle ply laminate.
First, A comparison with experimental data is done for a symmetric [θ/90]s glass/epoxy laminate [15] which is subjected to uniaxial loads.
These figures exhibit the prediction on axial modulus reduction using the present model and the experimental data published by Joffe et al. [15].
The obtained results show that the predicted model is in good agreement with the experimental data.

The conventional OSD method assumes a unique value of activation energy for all the data points.
The stress ruptures data of alloy MS3 containing 9% Cr is plotted in Fig. 1.
The multi region analysis could represent very well all the creep rupture data points via dividing them into several data sets.
Each data set has a unique value of activation energy.
The multi region analysis method examines can describe the creep rupture data points well.

Results obtained from this model are compared with experimental data and FEM results and good agreement is observed between them.
Results obtained from this model are compared with experimental data and FEM results of reference [6] and good agreement is observed between them.
Experimental data and FEM results are obtained from reference [6].
Fig.1 Deformed mesh and contours of effective strain at 28% and 57% reduction in height.
Results obtained from this model for disk upsetting, are compared with experimental data and FEM results and a good agreement is seen between them.

Meanwhile, data packets are cached in the nodes of the routing path.
Otherwise, the Data packet is unsolicited and discarded.
Named data networking (ndn) project.
A data-oriented (and beyond) network architecture.
LANES: an inter-domain data-oriented routing architecture.

Manifold learning is able to recover low-dimensional manifold structure from the high-dimensional sampling data in order to achieve dimensionality reduction or data visualization.
Incremental LLE LLE lacks generalization to new data.
Suppose thereis given already processed data,corresponding projected points, and a new point, which is sampled from the same data manifold as X.
First extract 70% fault data from each fault category as the original sample and the remaining 30% of the fault data are as additional test samples.
Laplacianeigenmaps for dimensionality reduction and data representation [J].

As the mass law was usually employed to predict the sound insulation performance of a wall in the design phase for its simplicity, but its predictive results are often higher than the measured data, especially in the frequency range of the coincidence effect.
Meanwhile their prediction on sound insulation of plate will be compared with the measured data [4], the comparison between sound insulation prediction and the measured data are shown from Fig. 2 to Fig. 4.
By considering comprehensively radiation coefficient and damping loss factor of a single-leaf wall, the reliable sound insulation prediction developed in this paper has a good agreement with the measured data.
The difference between the prediction of this paper and the measured data is less than 3dB, which is an acceptable deviation in engineering practice.
This calculation method in this paper can predict coincidence effect of a single-leaf wall, meanwhile the difference between the predicted sound insulation and measured data agrees better than it used to be, and it is able to meet the requirement of the engineering practice.

In this paper, a new load forecasting approach is proposed based on big data technologies using smart meter data.
Big data architecture can handle large amount of data and computation efforts.
Traditional data architecture have been unable to assume such a large amount of data calculation work.
Dimension reduction mapping from input space (n-d) to output plane (2-d) is achieved.
All data removed noise (some sampling point to null) treats as experimental data.