Search results for "vector [form factor]"
showing 10 items of 770 documents
Early detection and classification of bearing faults using support vector machine algorithm
2017
Bearings are one of the most critical elements in rotating machinery systems. Bearing faults are the main reason for failures in electrical motors and generators. Therefore, early bearing fault detection is very important to prevent critical system failures in the industry. In this paper, the support vector machine algorithm is used for early detection and classification of bearing faults. Both time and frequency domain features are used for training the support vector machine learning algorithm. The trained classier can be employed for real-time bearing fault detection and classification. By using the proposed method, the bearing faults can be detected at early stages, and the machine oper…
Permanent magnet system to guide superparamagnetic particles
2017
A new concept of permanent magnet systems for guiding superparamagnetic particles on arbitrary trajectories is proposed. The basic concept is to use one magnet system with a strong and homogeneous (dipolar) magnetic field to magnetize and orient the particles. A second constantly graded field (quadrupolar) is superimposed to the first to generate a force. In this configuration the motion of the particles is driven solely by the component of the gradient field which is parallel to the direction of the homogeneous field. Then the particles are guided with constant force in a single direction over the entire volume. The direction can be adjusted by varying the angle between quadrupole and dipo…
Magnetic field control of gas-liquid mass transfer in ferrofluids
2020
Abstract Gas-liquid mass transfer plays a key role in a broad range of industrial processes. The magnetic field control over the morphology of the gas-liquid interface and solute transport is an attractive feature if it can be realized efficiently. However, the magnetic properties of typical liquids and gases are rather weak. The experimental investigation is carried out to evaluate the effect of the magnetic field, which is mediated by magnetic nanoparticles, on the gas-liquid mass exchange during the sparging run through a hydrocarbon ferrofluid. The results indicate that the gradient field is especially effective at controlling the gas-liquid contact volume: the foaming of the liquid dur…
Data-driven Fault Diagnosis of Induction Motors Using a Stacked Autoencoder Network
2019
Current signatures from an induction motor are normally used to detect anomalies in the condition of the motor based on signal processing techniques. However, false alarms might occur if using signal processing analysis alone since missing frequencies associated with faults in spectral analyses does not guarantee that a motor is fully healthy. To enhance fault diagnosis performance, this paper proposes a machinelearning based method using in-built motor currents to detect common faults in induction motors, namely inter-turn stator winding-, bearing- and broken rotor bar faults. This approach utilizes single-phase current data, being pre-processed using Welch’s method for spectral density es…
FOC with Resolver Implementation for PMSM Drives by Using a Low Cost Atmel SAM3X8E Microcontroller
2020
The aim of this paper is the low-cost experimental implementation of a field oriented control strategy for a Permanent Magnet Synchronous Motor (PMSM) by using an Atmel SAM3X8E microcontroller, mounted on an Arduino DUE board. In this electrical drive for PMSM, a resolver is used in order to measure the rotor position and speed: Therefore, the low-cost Arduino DUE performs not only FOC algorithm and phase currents data acquisition, but also a resolver-To-digital converter process, rotor position and speed data acquisition, and resolver signals management. The code has been implemented in the open source Arduino IDE, using C language, whereas the control and plot visualization interfaces hav…
Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3
2012
Abstract ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity for Landsat 5/7, SPOT-5, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products with a high accuracy for land monitoring. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods. Machine learning regression algorithms may be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. By using data from …
Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples
2016
Abstract. Image processing of X-ray-computed polychromatic cone-beam micro-tomography (μXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squar…
SVM-based classification of High resolution Urban Satellites Images using Dense SURF and Spectral Information
2018
Remote-sensing focusing on image classification knows a large progress and receives the attention of the remote-sensing community day by day. Combining many kinds of extracted features has been successfully applied to High resolution urban satellite images using support vector machine (SVM). In this paper, we present a methodology that is promoting a performed classification by using pixel-wise SURF description features combined with spectral information in Cielab space for the first time on common scenes of urban imagery. The proposed method gives a promising classification accuracy when compared with the two types of features used separately.
Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe
2021
Abstract Soil moisture (SM) is a key variable that plays an important role in land-atmosphere interactions. Monitoring SM is crucial for many applications and can help to determine the impact of climate change. Therefore, it is essential to have continuous and long-term databases for this variable. Satellite missions have contributed to this; however, the continuity of the series is compromised due to the data gaps derived by different factors, including revisit time, presence of seasonal ice or Radio Frequency Interference (RFI) contamination. In this work, the applicability of different gap-filling techniques is evaluated on the ESA Climate Change Initiative (CCI) SM combined product, whi…
Crop specific algorithms trained over ground measurements provide the best performance for GAI and fAPAR estimates from Landsat-8 observations
2021
Abstract Estimation of Green Area Index (GAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from decametric satellites was investigated in this study using a large database of ground measurements over croplands. It covers six main crop types including rice, corn, wheat and barley, sunflower, soybean and other types of crops. Ground measurements were completed using either digital hemispherical cameras, LAI-2000 or AccuPAR devices over sites representative of a decametric pixel. Sites were spread over the globe and the data collected at several growth stages concurrently to the acquisition of Landsat-8 images. Several machine learning techniques were investigated to re…