atusa-gh Goto Github PK

bytetrack

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

cnc-rul-prediction

It aims to implement a real-time system to monitor the tool condition and predict the Remaining Useful Life (RUL) Computer Numerical Control (CNC) machine tools.

estimatinggripforcefromfingertippressure

Recording both gripping force and the applied pressure on the tips of the thumb, index and middle fingers simultaneously, followed by signal processing and prediction of the gripping force from pressure signals

fairmot

[IJCV-2021] FairMOT: On the Fairness of Detection and Re-Identification in Multi-Object Tracking

grippingforceprediction

Recording both EMG raw signals and gripping force simultaneously, followed by signal processing and prediction of the gripping force from EMG signals

handprosthesisforceprediction

Force prediction of a hand prosthesis from EMG signals

icaps_20_summerschool_rosplan

Source files for the 2020 ICAPS Online Summer School Lab on Plan Execution.

kalman_filter_multi_object_tracking

Multiple object tracking using Kalman Filter and Hungarian Algorithm - OpenCV

myo-python

Python bindings for the Myo SDK

predictive-failure-analysis-of-spindle-motor-cutting-oil-condition-monitoring-of-grinding-machine-

With the advent of Industry 4.0, manufacturing industries are competing to adopt intelligent machining systems into their processes to get on high-performance delivery of tasks in the least possible time limits. The implementation of condition-based monitoring systems to reduce machine downtime caused due to unavoidable machine breakdowns is the most cardinal step toward the smart manufacturing process. Such unplanned downtimes of machines cause hefty production loss and increased maintenance costs. Most of the breakdowns occur due to bearing failure or spindle misalignment due to wear and tear over time the service life of the motor. To predict the spindle failure various vibration analysis methodologies are present in the case to analyse the wear limit of the spindle through structural vibrations produced during the running condition. The objective of the article is to propose a vibration analysis of spindle using Learning Vector Quantization (LVQ) law and Artificial Intelligence models to predict the failure before wear limit of the spindle, thereby it will provide a self-maintenance capability to the machine, at a cheaper cost focusing on cutting fluids using smart sensors incorporated over Artificial Intelligence models & Machine Learning algorithm to predict the critical concentration of contamination of filtered oil used for grinding operation by various manufacturers. The algorithm is mathematically modelled and the simulated results are obtained for the realization of the model. The model is tested under various test conditions from best to worst and the results are found to be more promising than the current existing conventional system.

self-organizing-maps

Anomalies detection (fraud customers) and breast cancer detection using the self-organizing maps, an unsupervised deep learning technique

tnt