A Polishing Robot Force Control System Based on Time Series Data in Industrial Internet of Things

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Chen Zhang ◽  
Zhuo Tang ◽  
Kenli Li ◽  
Jianzhong Yang ◽  
Li Yang
Keyword(s):  
Time Series ◽  
Control System ◽  
Force Control ◽  
Time Series Data ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Series Data ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Force Control System

Installing a six-dimensional force/torque sensor on an industrial arm for force feedback is a common robotic force control strategy. However, because of the high price of force/torque sensors and the closedness of an industrial robot control system, this method is not convenient for industrial mass production applications. Various types of data generated by industrial robots during the polishing process can be saved, transmitted, and applied, benefiting from the growth of the industrial internet of things (IIoT). Therefore, we propose a constant force control system that combines an industrial robot control system and industrial robot offline programming software for a polishing robot based on IIoT time series data. The system mainly consists of four parts, which can achieve constant force polishing of industrial robots in mass production. (1) Data collection module. Install a six-dimensional force/torque sensor at a manipulator and collect the robot data (current series data, etc.) and sensor data (force/torque series data). (2) Data analysis module. Establish a relationship model based on variant long short-term memory which we propose between current time series data of the polishing manipulator and data of the force sensor. (3) Data prediction module. A large number of sensorless polishing robots of the same type can utilize that model to predict force time series. (4) Trajectory optimization module. The polishing trajectories can be adjusted according to the prediction sequences. The experiments verified that the relational model we proposed has an accurate prediction, small error, and a manipulator taking advantage of this method has a better polishing effect.

Theoretical Consideration on the Application of Continuous Wavelet Transform to Time-Series Processing for Magnetotelluric Data

2020 ◽  
Author(s):  
Hiroki Ogawa ◽  
Yuki Hama ◽  
Koichi Asamori ◽  
Takumi Ueda
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Data Processing ◽  
Time Series Data ◽  
Series Data ◽  
Short Time Fourier Transform ◽  
Numerical Errors ◽  
Spectral Transform ◽  
Frequency Components ◽  
Short Time

Abstract In the magnetotelluric (MT) method, the responses of the natural electromagnetic fields are evaluated by transforming time-series data into spectral data and calculating the apparent resistivity and phase. The continuous wavelet transform (CWT) can be an alternative to the short-time Fourier transform, and the applicability of CWT to MT data has been reported. There are, however, few cases of considering the effect of numerical errors derived from spectral transform on MT data processing. In general, it is desirable to adopt a window function narrow in the time domain for higher-frequency components and one in the frequency domain for lower-frequency components. In conducting the short-time Fourier transform, because the size of the window function is fixed unless the time-series data are decimated, there might be difference between the calculated MT responses and the true ones due to the numerical errors. Meanwhile, CWT can strike a balance between the resolution of the time and frequency domains by magnifying or reducing the wavelet, according to the value of frequency. Although the types of wavelet functions and their parameters influence the resolution of time and frequency, those calculation settings of CWT are often determined empirically. In this study, focusing on the frequency band between 0.001 Hz and 10 Hz, we demonstrated the superiority of utilizing CWT in MT data processing and determined its proper calculation settings in terms of restraining the numerical errors caused by the spectral transform of time-series data. The results obtained with the short-time Fourier transform accompanied with gradual decimation of the time-series data, called cascade decimation, were compared with those of CWT. The shape of the wavelet was changed by using different types of wavelet functions or their parameters, and the respective results of data processing were compared. Through these experiments, this study indicates that CWT with the complex Morlet function with its wavelet parameter k set to 6 ≤ k < 10 will be effective in restraining the numerical errors caused by the spectral transform.

scholarly journals A Comparison of ArcGIS and QGIS for Animation

2017 ◽  
pp. 23-32
Author(s):  
Owen Stuckey
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Open Source ◽  
Time Series Data ◽  
Series Data ◽  
Data Input ◽  
Input Processing ◽  
Similarities And Differences ◽  
Baseline Cost ◽  
Criteria For Evaluation

I compare two GIS programs which can be used to create cartographic animations—the commercial Esri ArcGIS and the free and open-source QGIS. ArcGIS implements animation through the “Time Slider” while QGIS uses a plugin called “TimeManager.” There are some key similarities and differences as well as functions unique to each plugin. This analysis examines each program’s capabilities in mapping time series data. Criteria for evaluation include the number of steps, the number of output formats, input of data, processing, output of a finished animation, and cost. The comparison indicates that ArcGIS has more control in input, processing, and output of animations than QGIS, but has a baseline cost of $100 per year for a personal license. In contrast, QGIS is free, uses fewer steps, and enables more output formats. The QGIS interface can make data input, processing, and output of an animation slower.

Time Series Data Processing for Classifying Wandering Patterns in People with Dementia

2021 ◽  
pp. 1-1
Author(s):  
Arnoldo Diaz-Ramirez ◽  
Jesus E. Miranda-Vega ◽  
Daniel Ramos-Rivera ◽  
Dalia A. Rodriguez ◽  
Wendy Flores-Fuentes ◽  
...  
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Time Series Data ◽  
Series Data ◽  
People With Dementia

scholarly journals Time Series Clustering Based on the K-Means Algorithm

2020 ◽  
Vol 1 (3) ◽  
pp. 1-7
Author(s):  
Oleg Kobylin ◽  
Vyacheslav Lyashenko
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Time Series Data ◽  
Real Data ◽  
Series Data ◽  
High Similarity ◽  
Time Series Clustering ◽  
Data Presentation

Time series is one of the forms of data presentation that is used in many studies. It is convenient, easy and informative. Clustering is one of the tasks of data processing. Thus, the most relevant currently are methods for clustering time series. Clustering time series data aims to create clusters with high similarity within a cluster and low similarity between clusters. This work is devoted to clustering time series. Various methods of time series clustering are considered. Examples are given for real data.

MULTIPLE‐CONSTRAINT ADAPTIVE FILTERING

Geophysics ◽  
1971 ◽  
Vol 36 (3) ◽  
pp. 498-509 ◽  
Author(s):  
Aaron Booker ◽  
Chung‐Yen Ong
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Adaptive Filtering ◽  
Time Series Data ◽  
Seismic Array ◽  
Wiener Filtering ◽  
Series Data ◽  
Noise Sources ◽  
Multiple Constraint

An algorithm is derived for multichannel time‐series data processing, which maintains specified initial multiple filter constraints for known signal or noise sources while simultaneously adapting the filter to minimize the effect of the unknown noise field. Problems of implementing the technique such as convergence, determination of a starting filter, and comparison of results with conventional filters are discussed and illustrated with data from a vertical seismic array. The procedure is shown to be stable and obtains approximately 3–4 db gain in S/N improvement over conventional Wiener filtering in the band 1 to 3 hz.

Effective Data Localization Using Consistent Hashing in Cloud Time-Series Databases

2013 ◽  
Vol 347-350 ◽  
pp. 2246-2251 ◽  
Author(s):  
Jia Kui Zhao ◽  
Ping Fei Zhu ◽  
Liang Huai Yang
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Time Series Data ◽  
Series Data ◽  
Localization Problem ◽  
Performance Study ◽  
Consistent Hashing ◽  
A Performance

The commercial time-series database is suitable for processing the time-series data. However, a single commercial time-series database can only accommodate the time-series data acquired by limited amount of sensors. In this paper, in order to cope with the challenge of massive time-series data processing, we first propose a cloud time-series database framework based on commercial time-series databases, and then propose an effective consistent hashing based algorithm for solving the key problem, i.e., the data localization problem, in cloud time-series databases. A performance study shows the superiority of the framework and the algorithm for processing massive time-series data acquired by large amount of sensors.

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