Innovative Method for Measuring Tensile Force Based on the Notch Frequency Property of Longitudinal Guided Wave in Steel Strand

2016 ◽  
Vol 52 (12) ◽  
pp. 9 ◽  
Author(s):  
Bin WU
Keyword(s):  
Tensile Force ◽  
Guided Wave ◽  
Frequency Property ◽  
Innovative Method ◽  
Steel Strand

scholarly journals Research on ultrasonic guided wave dispersion compensation for steel strand defect detection

2020 ◽  
Vol 740 ◽  
pp. 012101
Author(s):  
Molin Zhao ◽  
Haisheng Wang ◽  
Bin Xue ◽  
Yonggang Yue ◽  
Pengfei Zhang ◽  
...  
Keyword(s):  
Defect Detection ◽  
Dispersion Compensation ◽  
Wave Dispersion ◽  
Guided Wave ◽  
Ultrasonic Guided Wave ◽  
Steel Strand

Comparison of AC and pulsed magnetization-based elasto-magnetic methods for tensile force measurement in steel strand

Measurement ◽  
2018 ◽  
Vol 117 ◽  
pp. 410-418 ◽  
Author(s):  
Xiucheng Liu ◽  
Donghang Wu ◽  
Cunfu He ◽  
Huan Feng ◽  
Bin Wu
Keyword(s):  
Force Measurement ◽  
Tensile Force ◽  
Magnetic Methods ◽  
Steel Strand

A compact guided-wave EMAT with pulsed electromagnet for ferromagnetic tube inspection

2020 ◽  
Vol 64 (1-4) ◽  
pp. 951-958
Author(s):  
Tianhao Liu ◽  
Yu Jin ◽  
Cuixiang Pei ◽  
Jie Han ◽  
Zhenmao Chen
Keyword(s):  
Power Plants ◽  
Signal To Noise Ratio ◽  
Small Diameter ◽  
Performance Testing ◽  
Guided Wave ◽  
High Signal ◽  
Receiver Coil ◽  
Signal To Noise ◽  
Corrosion Defects

Small-diameter tubes that are widely used in petroleum industries and power plants experience corrosion during long-term services. In this paper, a compact inserted guided-wave EMAT with a pulsed electromagnet is proposed for small-diameter tube inspection. The proposed transducer is noncontact, compact with high signal-to-noise ratio and unattractive to ferromagnetic tubes. The proposed EMAT is designed with coils-only configuration, which consists of a pulsed electromagnet and a meander pulser/receiver coil. Both the numerical simulation and experimental results validate its feasibility on generating and receiving L(0,2) mode guided wave. The parameters for driving the proposed EMAT are optimized by performance testing. Finally, feasibility on quantification evaluation for corrosion defects was verified by experiments.

REALIZATION OF IDEAS OF ARTIFICIAL INTELLIGENCE FOR THE FINDING OF HIERARCHY OF MOTIVES OF TRAINING

2018 ◽  
pp. 12-19
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Hierarchical System ◽  
Productive Activity ◽  
Large Role ◽  
Educational Work ◽  
Intelligent Search ◽  
Dynamic Mechanisms ◽  
Innovative Method ◽  
Effectiveness Of Training ◽  
Probabilistic Nature

The hierarchy of learning motives plays an extremely important role for a management of productive activity of learners, their activity and purposefulness. In the process of educational work, such a motivational hierarchy is formed, where some motives are dynamic mechanisms of other motives that are very difficult to identify at the intuitive level, especially considering the influence of each of them. Therefore, to determine the most significant hierarchical sequence of motives, an innovative method was proposed which is based on the ideas of artificial intelligence. As an example, the search was implemented based on the so-called algorithm of imitation roasting, which is capable to take into account the probabilistic nature of motivational indicators. The article highlights the main leading educational motives of students, on the basis of which the “mechanism” of finding their optimal hierarchical system is shown, and one that simultaneously takes into account the multifactorial influence of their driving causes, taking into account their interconnection, interaction and dynamism. A step-by-step realization of construction of such a hierarchical system of main educational motives in combination with casual, minor motives which are difficult for expecting or providing in advance is shown. Given their unpredictability and probabilistic nature of occurrence, the proposed system of intelligent search allows you to select exactly those sequences of motives that provide the highest productivity and effectiveness of training. The value of the proposed algorithm of imitation roasting is that the accuracy of the result is sacrificed, but the number of iteration cycles decreases, which plays a large role in processing a significant number of motivational indicators.

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