A dataset to accompany the "Detrending and Characterizing System Frequency Oscillations Using an Adapted Zhou Algorithm" article submitted to IEEE Transaction on Power Systems
It contains the Single Freqneuy Model used to create data for the study involved in the paper, & a python based program that impliments the method propsed in said paper
This is an alarm management dataset based on the “Tennessee-Eastman-Process” (TEP). The presented dataset aims to provide a suitable benchmark for the development and validation of alarm management methods in complex industrial processes using both quantitative data and qualitative information from different sources. Unlike real industrial processes, the simulation of the TEP allows to design and generate abnormal situations, which can be repeated and varied without risking the loss of equipment or harming the environment.
This dataset includes a supplementary technical report. The report starts with a brief overview of the facility, implemented control loops and used simulation model. Two types of data, process and alarm data, were collected from the facility, their underlying data structure is included. The process of developing and implementing suitable alarm thresholds and alarm management techniques is described in detail. Furthermore, all initiated abnormal situations and the respective test design is presented thoroughly. The report concludes with a detailed description of the dataset structure and layout.
This data-set consists of 3-phase differential currents of internal faults and 4 other transients cases for Phase Angle Regulators (PAR). The transients other than faults include magnetizing inrush, sympathetic inrush, external faults with CT saturation, and overexcitation conditions. PSCAD/EMTDC software is used for simulation of the internal faults and the transients.
The files - ’fault_location_pha’, ’fault_location_phb’, and ’fault_location_phc’ have phase a,b,c differential currents for 46872 cases. Each row has 167 samples (one cycle). In fault_location_target’- the first 33480 are faults in series unit, next 13392 are faults in exciting unit. The files - ’transients_pha’, ’transients_phb’, and ’transients_phc’ have phase a,b,c differential currents for 13680 cases. Each row has 167 samples (one cycle). In fault_transient_target’- the first 720 - overexcitation, next 2520 - magnetizing inrush, next 2520 -sympathetic inrush, and next 7920 are external faults.
"The friction ridge pattern is a 3D structure which, in its natural state, is not deformed by contact with a surface''. Building upon this rather trivial observation, the present work constitutes a first solid step towards a paradigm shift in fingerprint recognition from its very foundations. We explore and evaluate the feasibility to move from current technology operating on 2D images of elastically deformed impressions of the ridge pattern, to a new generation of systems based on full-3D models of the natural nondeformed ridge pattern itself.
The present data release contains the data of 2 subjects of the 3D-FLARE DB.
These data is released as a sample of the complete database as these 2 subjects gave their specific consent for the distribution of their 3D fingerprint samples.
The acquisition system and the database are described in the article:
[ART1] J. Galbally, L. Beslay and G. Böstrom, "FLARE: A Touchless Full-3D Fingerprint Recognition System Based on Laser Sensing", IEEE ACCESS, vol. 8, pp. 145513-145534, 2020.
We refer the reader to this article for any further details on the data.
This sample release contains the next folders:
- 1_rawData: it contains the 3D fingerprint samples as they were captured by the sensor describe in [ART1], with no processing. This folder includes the same 3D fingerprints in two different formats:
* MATformat: 3D fingerprints in MATLAB format
* PLYformat: 3D fingerprints in PLY format
- 2_processedData: it contains the 3D fingerprint samples after the two initial processing steps carried out before using the samples for recognition purposes. These files are in MATLAB format. This folder includes:
* 2a_Segmented: 3D fingerprints after being segemented according to the process described in Sect. V of [ART1]
* 2b_Detached: 3D fingerprints after being detached according to the process described in Sect. VI of [ART1]
The naming convention of the files is as follows: XXXX_AAY_SZZ
XXXX: 4 digit identifier for the user in the database
AA: finger identifier, it can take values: LI (Left Index), LM (Left Middle), RI (Right Index), RM (Right middle)
Y: sample number, with values 0 to 4
ZZ: acquisition speed, it can take values 10, 30 or 50 mm/sec
With the data files we also provide a series of example MATLAB scripts to visualise the 3D fingerprints:
We cannot guarantee the correct functioning of these scripts depending on the MATLAB version you are running.
Two videos of the 3D fingerprint scanner can be checked at:
See our next papers.
The dataset consists of echo data collected at the Matre research station (61°N) of the Institute of Marine Research (IMR), Norway. Six square sea cages (12 × 12 m and 15 m depth; approximately 2000 m^3) were used. The fish's vertical distribution and density were observed continuously by a PC-based echo integration system (CageEye MK IV, software version 1.1.1., CageEye AS, Steinkjer, Norway) connected to an upward facing transducer which multiplexes between 50 kHz (42° acoustic beam angle) and 200 kHz (14° beam angle).
The 1-6 are named 15.1-15.6 respectively. There are some header columns indicating date and time, which can be removed. The depth is along the x-axis in the .csv files, thus the data need to be rotated to get a proper visualization.
1. Unzip data to a folder
2. Import data using pandas (python) or equivalent.
3. Remove the first header columns.
4. Log scale the data.
5. Rotate the data.
The data set contains electrical and mechanical signals from experiments on three-phase induction motors. The experimental tests were carried out for different mechanical loads on the induction motor axis and different severities of broken bar defects in the motor rotor, including data regarding the rotor without defects. Ten repetitions were performed for each experimental condition.
The experimental workbench consists of a three-phase induction motor coupled with a direct-current machine, which works as a generator simulating the load torque, connected by a shaft containing a rotary torque wrench.
- Induction motor: 1hp, 220V/380V, 3.02A/1.75A, 4 poles, 60 Hz, with a nominal torque of 4.1 Nm and a rated speed of 1715 rpm. The rotor is of the squirrel cage type composed of 34 bars.
- Load torque: is adjusted by varying the field winding voltage of direct current generator. A single-phase voltage variator with a filtered full-bridge rectifier is used for the purpose. An induction motor was tested under 12.5, 25, 37.5, 50, 62.5, 75, 87.5 and 100% of full load.
- Broken rotor bar: to simulate the failure on the three-phase induction motor's rotor, it was necessary to drill the rotor. The rupture rotor bars are generally adjacent to the first rotor bar, 4 rotors have been tested, the first with a break bar, the second with two adjacent broken bars, and so on rotor containing four bars adjacent broken.
All signals were sampled at the same time for 18 seconds for each loading condition and ten repetitions were performed from transient to steady state of the induction motor.
- mechanical signals: five axial accelerometers were used simultaneously, with a sensitivity of 10 mV/mm/s, frequency range from 5 to 2000Hz and stainless steel housing, allowing vibration measurements in both drive end (DE) and non-drive end (NDE) sides of the motor, axially or radially, in the horizontal or vertical directions.
- electrical signals: the currents were measured by alternating current probes, which correspond to precision meters, with a capacity of up to 50ARMS, with an output voltage of 10 mV/A, corresponding to the Yokogawa 96033 model. The voltages were measured directly at the induction terminals using voltage points of the oscilloscope and the manufacturer Yokogawa.
Data Set Overview:
- Three-phase Voltage
- Three-phase Current
- Five Vibration Signals
The database was acquired in the Laboratory of Intelligent Automation of Processes and Systems and Laboratory of Intelligent Control of Electrical Machines, School of Engineering of São Carlos of the University of São Paulo (USP), Brazil.
1. The complex noises underwater leads to more errors for the velocities measurements of AUV so that it is difficult to determine the accurate navigation and positioning information.
The novel ariational Bayesian (VB) -based filter (VBF) is proposed and these data is used.
2. The format of data is ".mat".
The color fractal images with correlated RGB color components were generated using the midpoint displacement alogrithm, using vectorial increments in the RGB color space, according to a multivariate Gaussian distribution specified by the variance-covariance matrix. This data set contains two sets of 25 color fractal images with two color components, of varying complexity expressed as the color fractal dimension, as a function of (i) the Hurst coefficient that was varied from 0.1 to 0.9 in steps of 0.2 and (ii) the correlation coefficient between the red and green color channels.
This data set is for research purposes only. Please consider citing the paper entitled "Fractal Dimension of Color Fractal Images with Correlated Color Components", IEEE Transactions on Image Processing, 2020: https://doi.org/10.1109/TIP.2020.3011283