The crime rate is increasing at a high rate in India. Terrorist attacks like Mumbai 26/11, Pulwama attack, Pune German Beckary attacks have created terrific fear amongst Indian Society. Video analytics plays a significant role in detecting and predicting such suspicious human activities using deep learning models It will help in reducing the increasing crime rate by preventing treacherous actions. Video analytics analyzes the video content and adds brains to eyes i.e. analytics to the camera. It extracts contents from the video by monitoring the video in real-time.
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this is a test
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Any work using this dataset should cite the following paper:
Nirmalya Thakur, Saumick Pradhan, and Chia Y. Han, “Investigating the impact of COVID-19 on Online Learning-based Web Behavior”, Proceedings of the 7th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications (IHIET-AI 2022), Lausanne, Switzerland, April 21-23, 2022 (Submitted)
Abstract
For details on instructions on how to use the dataset, the above mentioned paper may be studied.
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Dataset for handwriting digit layout generation。
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Any work using this dataset should cite the following paper:
Nirmalya Thakur, Isabella Hall, and Chia Y. Han, “Investigating the Emergence of Online Learning in Different Countries using the 5 W’s and 1 H Approach”, Proceedings of the 7th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications (IHIET-AI 2022), Lausanne, Switzerland, April 21-23, 2022
Abstract
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The video data set was obtained from the Mental Health Center of Renmin Hospital of Wuhan University, it includes 128 children and the video records the behavior of each subject (mainly upper body movements) during clinical intelligence evaluation. The ratio of training set, validation set and test set is 7:1:2.
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The video data set was obtained from the Mental Health Center of Renmin Hospital of Wuhan University, it includes 128 children and the video records the behavior of each subject (mainly upper body movements) during clinical intelligence evaluation. The ratio of training set, validation set and test set is 7:1:2.
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This dataset contains measurements of TPC-C benchmark executions in MySQL server deployed in Google Cloud Platform.
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The SiCWell Dataset contains data of battery electric vehicle lithium-ion batteries for modeling and diagnosis purposes. In this experiment, automotive-grade lithium-ion pouch bag cells are cycled with current profiles plausible for electric vehicles.
The analysis of current ripples in electric vehicles and the corresponding aging experiments of the battery cells result in a dataset, which is composed of the following parts:
Cell Aging Scenarios
The battery cells are cycled in groups of three cells in series. The scenarios for each cell are the following:
- Ka01, Ka02: Calendar test 35°C 80% SoC
- Ka03, Ka04: Calendar test 35°C 45% SoC
- Ka05, Ka06: Calendar test 45°C 80% SoC
- Ka07, Ka08: Calendar test 45°C 20% SoC
- Ka09, Ka10: Calendar test 45°C 45% SoC
- Ka11, Ka12: Calendar test 45°C 60% SoC
- DC01, DC02, DC03: DC cycling
- AC01, AC02, AC03: Sinusoidal cycling 10 kHz, 12.5 A
- AC04, AC05, AC06: Sinusoidal cycling 10 kHz, 25.0 A
- AC07, AC08, AC09: Sinusoidal cycling 10 kHz, 6.25 A
- AC10, AC11, AC12: Sinusoidal cycling 40 kHz, 12.5 A
- AC13, AC14, AC15: Sinusoidal cycling 20 kHz, 12.5 A
- AC16, AC17, AC18: Sinusoidal cycling 40 kHz, 6.25 A
- AC19, AC20, AC21: Artificial ripple cycling
- AC22, AC23, AC24: Realistic ripple cycling
- AC25, AC26, AC27: Realistic ripple cycling
Current Ripple Evaluation
The evaluation results of current ripples in a battery-electric vehicle are stored in the “current_ripple_evaluation” directory. It contains the following files:
- input_sWLTP.csv/input_UDDS.csv: The speed, torque, and power of the sWLTP and UDDS cycles at every second of the simulated battery-electric vehicle.
- cycler_sWLTP.csv/cycler_UDDS.csv: The compressed current values for every second of the sWLTP and UDDS cycles. More details about the compression can be found at [1].
- parameters.csv: The parameters for simulation of the battery-electric vehicle and the drivetrain.
- sWLTP.h5/UDDS.h5: The simulated current and voltage of the battery-electric vehicle in the time-domain sampled with 500 kHz. Every second has its operating point, which is simulated for a second.
Cell Cycles
The raw current, voltage, and temperature measurements of the cycled battery cells. The results are stored in the “cell_cycling_*” directory with the following files:
- cell_cycling_sinusoidal/[cell id]/[cell id]_cycle[cycle id].hdf5: The raw measurements of the sinusoidal cycling experiments.
- cell_cycling_artificial_ripple/[cell id]/[cell id]_cycle[cycle id].hdf5: The raw measurements of the artifical ripple cycling experiments.
- cell_cycling_realistic_ripple/[cell id]/[cell id]_cycle[cycle id].hdf5: The raw measurements of the realistic ripple cycling experiments.
The measurements have been taken periodically by an external 2MHz measurement system. Each .hdf5 file contains the measurements of a specific cycle number of the experiment. In the sinusoidal and artificial ripple tests, a measurement has been taken for every 1 % State-of-Charge. In the realistic ripple tests, a measurement has been taken every 5 seconds. Every measurement has a duration of 100 ms and a sampling rate of 2 MS/s.
Each measurement is a group in the hdf5 file, with the voltage and current as 1d 32bit floating-point arrays. Each measurement also has a Unix UTC timestamp of the time of the measurement, the cell temperature, capacity, and resistance[10s] stored as attributes. The capacity and resistance are synchronized with the checkups and interpolated linearly over the number of cycles.
Cell Checkups
The periodic checkups of the battery cells are composed of capacity, internal resistance, EIS, OCV, and qOCV measurements. Measurements that take longer, such as EIS and OCV, are not taken at every checkup. The results are stored in the “cell_checkups” directory with the following files:
- Overview.csv: List of every checkup of every battery cell. For every checkup, the date, number of cycles, capacity, 10s resistance, and references to the more detailed checkup files are stored.
- EIS/[cell id]_CheckUp[checkup id]_[date]_EIS.csv: The results of the electrochemical impedance spectroscopy from 0.001 to 50,000 Hz using an EIS-meter.
- OCV/[cell id]_CheckUp[checkup id]_[date]_OCV.csv: Results of the open-circuit voltage measurement between 0 and 100 % State of Charge in 5 % steps.
- qOCV/[cell id]_CheckUp[checkup id]_[date]_qOCV.csv: Results of the quasi-open-circuit voltage measurement between 0 and 100 % State of Charge in 1 % steps. It contains the quasi-open-circuit voltage, 1s resistance, and 10s resistance.
- OCV_raw/[cell id]_CheckUp[checkup id]_[date]_OCV_raw.csv: Raw current, voltage, and temperature values of the OCV measurement.
- qOCV_raw/[cell id]_CheckUp[checkup id]_[date]_qOCV_raw.csv: Raw current, voltage, and temperature values of the qOCV measurement.
- Capacity_raw/[cell id]_CheckUp[checkup id]_[date]_Cap_raw.csv: Raw current, voltage, and temperature values of the capacity measurement.
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