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 bench of experiments is on the premises of the School of Engineeringof São Carlos (EESC) of the University of São Paulo (USP), Brazil, more specifically in theLaboratory of Intelligent Automation of Processes and Systems (LAIPS) and Laboratory ofIntelligent Control of Electrical Machines (LACIME).

The three-phase induction motor is a model of the W22 standard line from manufacturer WEG, 1 cv, 220V / 380V, 3.02A / 1.75A, 4 poles, 60 Hz, with a nominal torque of 4.1 N.m and nominal speed of 1715 rpm. The rotor is a squirrel cage type made up of 34 bars. It is driven by means of a control panel that allows the selection of the type of drive, star or triangle, and the type of supply, direct mains voltage or via a three-phase inverter.

The rotary torque wrench used in the research is the Transtec model MT-103, with a maximum rotation of 2000 rpm, based on Wheatstone bridge technology and with a sensitivity of 2 mV / V. Its main function is to allow visualization of the torque present in the shaft, which will be varied simulating various operating conditions of the induction motor.

Manual adjustment of the resistant torque is done by varying the field winding voltage of the direct current generator. Therefore, to reduce the magnitude of the grid voltage, a 1800W single-phase voltage variation is used by Variac, and to convert the alternating voltage to continuous, a single-phase rectifier is used which feeds the field winding.

The vibration sensors used were Vibrocontrol uniaxial accelerometers, model PU 2001, with sensitivity of 10 mV / mm / s, frequency range 5 to 2000 Hz and stainless-steel housing, which provides the integrated acceleration signal over time. , ie provides the measure of vibration velocity. In total five accelerometers were used simultaneously, located non-drive end side motor, drive end side motor, housing, in the axial direction of the motor, and on the support desk. Therefore, these monitoring points allow the measurement of axial, tangential and radial velocity.

The currents were measured using alternating current probes, which correspond to precision meters, with a capacity of up to 50 A RMS, with an output voltage of 10 mV / A, corresponding to the Yokogawa model 96033. The voltages were measured directly at the MIT terminals using oscilloscope voltage tips also from the manufacturer Yokogawa.

To simulate the failure of broken bars in the squirrel cage rotor of the three-phase induction motor it was necessary to drill the rotor. Drilling was carried out by means of a bench drill mounted with a 6 mm diameter drill to ensure that the diameter of the hole exceeds the width of a rotor bar, with the tip centered at half the longitudinal length of the rotor.

Since in a real situation the breaking rotor bars are usually adjacent to the first broken bar, 4 rotors were tested, the first with one broken bar, the second with two adjacent broken bars, and so on to the rotor containing four adjacent bars. broken . It is worth mentioning that the hub depth of all tested rotors was the same, corresponding to 20 mm.

Thus, a rotor without a hole was tested first, that is, a healthy rotor, and then it was successively replaced in order to obtain a database of monitored variables.

Experiments were carried out using the bench mentioned above for the construction of the database. Tests were carried out on healthy motors and motors with defects in direct start with balanced three-phase supply voltage and 60 Hz frequency.

For the preparation of a reliable database, enabling future work were applied 0.5nm shipments, 1,0Nm, 1,5Nm, 2,0Nm, 2,5Nm, 3,0Nm, 3,5Nm, and 4.0Nm to the axis of the three-phase induction motor. For each loading condition of the motor shaft, ten repetitions were performed.

In this way, using the data acquisition system, for each experiment of each loading, the following variables were acquired:

·         voltages in phases A, B, and C;

·         currents in phases A, B, and C;

·         mechanical vibration speeds tangential in the housing, tangential in the base, axial on the driven side, radial on the driven side, and radial on the non-drive side.

This experimental process was performed for the detection and diagnosis of failures for healthy engines and engines with rotors containing 1, 2, 3, and 4 bars broken adjacent.

The database is organized as a structure of the Matlab application. The “struct_rs_R1” structure presents the experimental data referring to the defectless induction motor, “struct_r1b_R1” referring to the rotor with one broken bar, “struct_r2b_R1” referring to the rotor with two broken bars, “struct_r3b_R1” referring to the rotor with three broken bars and “Struct_r4b_R1” for the rotor with four broken bars.

When loading the files containing the experimental data for each structure in the Matlab application, it will be possible to view the experimental data for each of the mechanical loads imposed on the motor shaft. Then, it will be possible to observe the experimental data for each monitored variable.


Including Code & Result of the paper "Unsupervised Paraphrasing via Sentence Reconstruction and Back-translation"


Including Code & Result of the paper "Unsupervised Paraphrasing via Sentence Reconstruction and Back-translation"


Our complex street scene(CSS) containing strong light and heavy shadow scenes mainly comes from the Kitti dataset.  Our datasets are captured by driving around Karlsruhe's mid-size city, in rural areas, and on highways. We equipped a standard station wagon with two high-resolution color and grayscale video cameras. Up to 15 cars and 30 pedestrians are visible per image. We aim to verify the performance of the algorithm in specific and complex street scenes.


We propose a driver pattern dataset consists of 51 features extracted from CAN (Controller Area Network) of Hyundai YF Sonata while four drivers drove city roads of Seoul, Republic of Korea. Under the belief that different driving patterns implicitly exist at CAN data, we collected CAN diagnosis data from four drivers in pursuit of research on driver identification, driver profiling, and abnormal driving behavior detection. Four drivers are named A, B, C, and D.



The dataset contains 51 features extracted from CAN along with numerous trips performed by four drivers. The four drivers drove along city roads of Seoul, the Republic of Korea. The recorded 51 features can be employed for driver identification, driver profiling, abnormal driving pattern identification, and any related tasks. Please check the abstract for a more detailed description.

CSV Files

Directory A, B, C and D contains .csv files of CAN data. Each .csv file represents a trip.


The names of 51 features are described in the features.pkl file. Please check the file for detailed information.


Park, Kyung Ho, and Huy Kang Kim. "This Car is Mine!: Automobile Theft Countermeasure Leveraging Driver Identification with Generative Adversarial Networks." arXiv preprint arXiv:1911.09870 (2019).

Park, Kyung Ho, and Huy Kang Kim. "This Car is Mine!: Automobile Theft Countermeasure Leveraging Driver Identification with Generative Adversarial Networks.", ESCAR Asia (2019)



Recently, the coronavirus pandemic has made the use of facial masks and respirators common, the former to reduce the likelihood of spreading saliva droplets and the latter as Personal Protective Equipment (PPE). As a result, this caused problems for the existing face detection algorithms. For this reason, and for the implementation of other more sophisticated systems, able to recognize the type of facial mask or respirator and to react given this information, we created the Facial Masks and Respirators Database (FMR-DB).


For reasons related to the copyright of the images, we cannot publish the entire database here. If you are a student, a professor, or a researcher and you want to use it for research purposes, send an email to attaching the license, duly completed, which you can find here on IEEE DataPort.



The CHU Surveillance Violence Dataset (CSVD) is a collection of CCTV footage of violent and non-violent actions aiming to characterize the composition of violent actions into more specific actions. We produced several simple action classes for violent and non-violent actions do add variety and better distribution among simple and complex action classes for RGB and Action Silhouette Videos (enhanced Optical Flow Images) with their localized actions.


This dataset includes the labeled grade by the XRF analyzer and its related visual features and feed grades.

It can be used for traing the performance monitoring model  in the froth flotation.


The dataset contains 2,400 vehicle images for license plate detection purposes. Images are taken from actively operating commercial cameras which are installed on a highway and in an entrance of a shopping mall. Images

contain generally one vehicle, but sometimes can contain two or more vehicles. For each image in pixel domain there exists two different images generated from encoded High Efficiency Video Coding (HEVC) stream using our method. 



•2,400 Pixel Domain Images

•2,400 HEVC Domain Images Generated from Our Block Partition Method

•2,400 HEVC Domain Images Generated from Our Prediction Based Method


•Each train test set contains 1,800 images.

•Each test set contains 600 images.


Images are given numeral names starting from 100,001 to 102,400 for each method. The same numbers are used to represent HEVC domain representations of pixel domain images. 


For each image there exists another file which contains plate annotation information in YOLO format.



|   +---HEVCDomain_BlockPartition

|   |   +---Test

|   |   \---Train

|   +---HEVCDomain_PredictionUnit

|   |   +---Test

|   |   \---Train

|   \---PixelDomain

|       +---Test

|       \---Train



E-learning is a type of learning by using electronic technologies to access an educational program outside of a traditional classroom. As conventional classrooms continue to be transformed into digital, teachers must deliver lectures through multiple learning modes. Digitally enriched content and personalized learning, should be the primary way of teaching, as well as collaborative and interactive learning.