Self-Powered Force Sensor Based on Thinned Bulk PZT for Real-Time Cutaneous Activities Monitoring

2018 ◽  
Vol 39 (8) ◽  
pp. 1226-1229 ◽  
Author(s):  
Zhiran Yi ◽  
Hanjia Yang ◽  
Yingwei Tian ◽  
Xiaoxue Dong ◽  
Jingquan Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Force Sensor ◽  
Self Powered

Flexible High‐Resolution Triboelectric Sensor Array Based on Patterned Laser‐Induced Graphene for Self‐Powered Real‐Time Tactile Sensing

2021 ◽  
pp. 2100709 ◽  
Author(s):  
Zhengguang Yan ◽  
Liangliang Wang ◽  
Yifan Xia ◽  
Rendong Qiu ◽  
Wenquan Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Sensor Array ◽  
Tactile Sensing ◽  
Self Powered

scholarly journals Light-Weight, Self-Powered Sensor Based on Triboelectric Nanogenerator for Big Data Analytics in Sports

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2322
Author(s):  
Xiaofei Ma ◽  
Xuan Liu ◽  
Xinxing Li ◽  
Yunfei Ma
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Analytics ◽  
Mechanical Energy ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Triboelectric Nanogenerator ◽  
Light Weight ◽  
Table Tennis ◽  
Self Powered

With the rapid development of the Internet of Things (IoTs), big data analytics has been widely used in the sport field. In this paper, a light-weight, self-powered sensor based on a triboelectric nanogenerator for big data analytics in sports has been demonstrated. The weight of each sensing unit is ~0.4 g. The friction material consists of polyaniline (PANI) and polytetrafluoroethylene (PTFE). Based on the triboelectric nanogenerator (TENG), the device can convert small amounts of mechanical energy into the electrical signal, which contains information about the hitting position and hitting velocity of table tennis balls. By collecting data from daily table tennis training in real time, the personalized training program can be adjusted. A practical application has been exhibited for collecting table tennis information in real time and, according to these data, coaches can develop personalized training for an amateur to enhance the ability of hand control, which can improve their table tennis skills. This work opens up a new direction in intelligent athletic facilities and big data analytics.

A Novel Approach to Time Series Forecasting Using Model-Free Adaptive Control Framework

2020 ◽  
Author(s):  
Meenakshi Narayan ◽  
Ann Majewicz Fey
Keyword(s):  
Time Series ◽  
Adaptive Control ◽  
Real Time ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Force Sensor ◽  
Sensor Data ◽  
Series Data ◽  
Model Free ◽  
Control Framework

Abstract Sensor data predictions could significantly improve the accuracy and effectiveness of modern control systems; however, existing machine learning and advanced statistical techniques to forecast time series data require significant computational resources which is not ideal for real-time applications. In this paper, we propose a novel forecasting technique called Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) which is derived from the existing model-free adaptive control framework. This approach enables near real-time forecasts of seconds-worth of time-series data due to its basis as an optimal control problem. The performance of the CFDL-MFP algorithm was evaluated using four real datasets including: force sensor readings from surgical needle, ECG measurements for heart rate, and atmospheric temperature and Nile water level recordings. On average, the forecast accuracy of CFDL-MFP was 28% better than the benchmark Autoregressive Integrated Moving Average (ARIMA) algorithm. The maximum computation time of CFDL-MFP was 49.1ms which was 170 times faster than ARIMA. Forecasts were best for deterministic data patterns, such as the ECG data, with a minimum average root mean squared error of (0.2±0.2).

Triboelectric nanogenerator as self-powered impact force sensor for falling object

2020 ◽  
Vol 20 (1) ◽  
pp. 137-144 ◽  
Author(s):  
Aminullah ◽  
Ajab Khan Kasi ◽  
Jafar Khan Kasi ◽  
Moiz Uddin ◽  
Muzamil Bokhari
Keyword(s):  
Impact Force ◽  
Force Sensor ◽  
Triboelectric Nanogenerator ◽  
Self Powered

Deconvolution-based real-time neutron flux reconstruction for Self-Powered Neutron Detector

2018 ◽  
Vol 326 ◽  
pp. 261-267 ◽  
Author(s):  
Qingmin Zhang ◽  
Bangjie Deng ◽  
Xinxin Liu ◽  
Chengyuan Li ◽  
Yaodong Sang ◽  
...  
Keyword(s):  
Neutron Flux ◽  
Real Time ◽  
Neutron Detector ◽  
Flux Reconstruction ◽  
Self Powered

Errors Analysis and Compensation of Six-Axis Force Sensor Based on Stewart Platform

2011 ◽  
Vol 317-319 ◽  
pp. 1041-1044
Author(s):  
Zhi Jun Wang ◽  
Jian Tao Yao ◽  
Yu Lei Hou ◽  
Yong Sheng Zhao
Keyword(s):  
Numerical Simulation ◽  
Measurement Error ◽  
Dynamic Response ◽  
Real Time ◽  
External Force ◽  
Force Sensor ◽  
Stewart Platform ◽  
Force Sensors ◽  
Compensation Algorithm ◽  
Errors Analysis

Six-axis force sensors based on Stewart platform necessitate highly accurate, sensitivity and dynamic response. In response to this need, errors analysis and compensation of the force sensor are essential. In this paper, the measurement error generated by the upper platform deformation is discussed and evaluated. Furthermore, in order to improve the precision, a real-time compensation algorithm is proposed depending on the external force applied on the force sensor. Finally, a numerical simulation example is presented, which indicates that the precision is related to the stiffness of limbs directly and improved obviously by the compensation algorithm.

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