The dataset contains fundamental approaches regarding modeling individual photovoltaic (PV) solar cells, panels and combines into array and how to use experimental test data as typical curves to generate a mathematical model for a PV solar panel or array.

 

Instructions: 

This dataset contain a PV Arrays Models Pack with some models of PV Solar Arrays carried out in Matlab and Simulink. The PV Models are grouped in three ZIP files which correspond to the papers listed above.

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The work starts with a short overview of grid requirements for photovoltaic (PV) systems and control structures of grid-connected PV power systems. Advanced control strategies for PV power systems are presented next, to enhance the integration of this technology. The aim of this work is to investigate the response of the three-phase PV systems during symmetrical and asymmetrical grid faults.

Instructions: 

1. Open the "Banu_power_PVarray_grid_EPE2014_.slx" file with Matlab R2014a 64 bit version or a newer Matlab release. 2. To simulate various grid faults on PV System see the settings of the "Fault" variant subsystem block (Banu_power_PVarray_grid_EPE2014_/20kV Utility Grid/Fault) in Model Properties (File -> Model Properties -> Model Properties -> Callbacks -> PreLoadFcn* (Model pre-load function)):           MPPT_IncCond=Simulink.Variant('MPPT_MODE==1')           MPPT_PandO=Simulink.Variant('MPPT_MODE==2')           MPPT_IncCond_IR=Simulink.Variant('MPPT_MODE==3')           MPPT_MODE=1           Without_FAULT=Simulink.Variant('FAULT_MODE==1')           Single_phases_FAULT=Simulink.Variant('FAULT_MODE==2')           Double_phases_FAULT=Simulink.Variant('FAULT_MODE==3')           Double_phases_ground_FAULT=Simulink.Variant('FAULT_MODE==4')           Three_phases_FAULT=Simulink.Variant('FAULT_MODE==5')           Three_phases_ground_FAULT=Simulink.Variant('FAULT_MODE==6')           FAULT_MODE=1 3. For more details about the Variant Subsystems see the Matlab Documentation Center: https://www.mathworks.com/help/simulink/variant-systems.html or https://www.mathworks.com/help/simulink/examples/variant-subsystems.html

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Crowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots.

Instructions: 

Extract locally the zip files, read the readme file.

Instructions for dataset usage are included in the open access paper: Franzoni, V., Biondi, G., Milani, A., Emotional sounds of crowds: spectrogram-based analysis using deep learning (2020) Multimedia Tools and Applications, 79 (47-48), pp. 36063-36075. https://doi.org/10.1007/s11042-020-09428-x

File are released under Creative Commons Attribution-ShareAlike 4.0 International License

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This data set is the result of model test trained on the basis of the Stanford earthquake dataset (stead): a global data set of seismic signals for AI, which can effectively get the seismic signal and the arrival time of seismic phase from the image, so as to prove the effectiveness of this model

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This dataset contains a created ST annotations for all recordings of Abdominal and Direct Fetal ECG Database (ADFECGDB).

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In this dataset we provide the source code we used for the evaluation of the involution delay model. Analog simulations (SPICE) serve as golden reference for the digital predictions. For comparison also the commonly used inertial delay is included.

Due to non-disclosure agreements only the source files are included. The tools and the respective libraries have to be provided by the user.

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MATLAB code for test spectrum sensing algorithm based on statistical processing of instantaneous magnitude (SPIM). The associated SCRIPTs allow: Generating different signals to check the method, FHSS, LFM, CW Pulse, etc. Plot the generated signal, the detection threshold and compare it with the ideal detection. Determine the errors for the different hypotheses based on SNR. Calculate errors in the determination of the amplitude and frequency for different SNRs. Evaluate the probability of detection with different threshold control values A and U.

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This dataset contains one ideal fetal phonocardiography (fPCG) signal and twelve signals made by adding three types of interference having different amplitudes to the reference signal. 

- Ambient noise - this is any noise from the surroundings. It occurs commonly when recording the fPCG. Its spec-trum includes frequencies from 10 Hz.

- White Gaussian noise - a random signal with the same power in any band of the same width.

- Maternal and fetal movement artifacts - a noise caused by muscle movement or breathing. They occur at low frequencies, usually up to 100 Hz.

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The dataset is composed of digital signals obtained from a capacitive sensor electrodes that are immersed in water or in oil. Each signal, stored in one row, is composed of 10 consecutive intensity values and a label in the last column. The label is +1 for a water-immersed sensor electrode and -1 for an oil-immersed sensor electrode. This dataset should be used to train a classifier to infer the type of material in which an electrode is immersed in (water or oil), given a sample signal composed of 10 consecutive values.

Instructions: 

The dataset is acquired from a capacitive sensor array composed of a set of sensor electrodes immersed in three different phases: air, oil, and water. It is composed of digital signals obtained from one electrode while it was immersed in the oil and water phases at different times. 

## Experimental setup

The experimental setup is composed of a capacitive sensor array that holds a set of sensing cells (electrodes) distributed vertically along the sensor body (PCB). The electrodes are excited sequentially and the voltage (digital) of each electrode is measured and recorded. The voltages of each electrode are converted to intensity values by the following equation:

intensity = ( |Measured Voltage - Base Voltage| / Base Voltage ) x 100

Where the Base Voltage is the voltage of the electrode recorded while the electrode is immersed in air. The intensity values are stored in the dataset instead of the raw voltage values.

## Experimental procedure 

The aim of the experiments is to get fixed-size intensity signals from one electrode (target electrode) when being immersed in water and oil; labeled as +1 (water) or -1 (oil). For this purpose, the following procedure was applied:

- The linear actuator was programmed to move the sensor up and down at a constant speed (20 mm / second).

- The actuator stops when reaching the upper and bottom positions for a fixed duration of time (60 seconds).

- At the upper position, the target electrode is immersed in oil; intensity signals are labeled -1 and sent to the PC.

- At the bottom position, the target electrode is immersed in water; intensity signals are labeled +1 and sent to the PC.

- The sampling rate is 100 msec; since each intensity signal contains 10 values, it takes 1 second to record one intensity signal 

## Environmental conditions

The experiments were perfomed under indoors laboratory conditions with room temperature of around 23 degree Celsius. 

## Dataset structure 

The signals included in the dataset are composed of intensity signals each with 10 consecutive values and a label in the last column. The label is +1 for a water-immersed electrode and -1 for an oil-immersed electrode.

## Application

The dataset should be used to train a classifier to differentiate between electrodes immersed in water and oil phases given a sample signal.

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532 Views

Radar devices can be used to monitor vital signs, such as respiratory and cardiac rates. For this purpose, the phase of an echo signal received from the chest or back of a human is usually used; subsequently, respiratory rate detection (RRD) and cardiac rate detection (CRD) can be achieved by estimating two fundamental frequencies corresponding to the respiratory and cardiac rates, respectively.

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