An isotropic magnetic-field transducer based on the giant magnetic impedance effect

Based on the developed methods of hysteresis interference, the calculated dependences U(x) of the electric voltage taken from the magnetic field transducer on the x coordinate were obtained. A magnetic carrier with an arctangent characteristic was exposed to a series of bipolar pulses of the magnetic field of a linear inductor of one, two, three, four, five and fifteen pulses. An algorithm is presented for the sequence of changes in the magnitude of the total strength of the magnetic field pulses on the surface of an aluminum plate, which provides the same amplitude of hysteresis oscillations of the electric voltage and makes it possible to obtain a linear difference dependence U(x) for wedge-shaped and flat aluminum samples. The results obtained make it possible to increase the accuracy and efficiency of control of the thickness of the object and its thickness variation in the given directions, as well as the defects of the object.

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Neural Network for Metal Detection Based on Magnetic Impedance Sensor

Sensors ◽

10.3390/s21134456 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4456

Author(s):

Sungjae Ha ◽

Dongwoo Lee ◽

Hoijun Kim ◽

Soonchul Kwon ◽

EungJo Kim ◽

...

Keyword(s):

Neural Network ◽

Magnetic Field ◽

Deep Learning ◽

Short Term Memory ◽

Magnetic Impedance ◽

Detection Technology ◽

Metal Detection ◽

Long Short Term Memory ◽

Impedance Sensor ◽

The Magnetic Field

The efficiency of the metal detection method using deep learning with data obtained from multiple magnetic impedance (MI) sensors was investigated. The MI sensor is a passive sensor that detects metal objects and magnetic field changes. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small and unstable with noise. Consequently, there is a limit to the detectable distance. To effectively detect and analyze this distance, a method using deep learning was applied. The detection performances of a convolutional neural network (CNN) and a recurrent neural network (RNN) were compared from the data extracted from a self-impedance sensor. The RNN model showed better performance than the CNN model. However, in the shallow stage, the CNN model was superior compared to the RNN model. The performance of a deep-learning-based (DLB) metal detection network using multiple MI sensors was compared and analyzed. The network was detected using long short-term memory and CNN. The performance was compared according to the number of layers and the size of the metal sheet. The results are expected to contribute to sensor-based DLB detection technology.

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CA transductor-based magnetic field transducer: transduction function analysis

10.1117/12.517137 ◽

2003 ◽

Author(s):

Stanislaw Moskowicz

Keyword(s):

Magnetic Field ◽

Function Analysis ◽

Magnetic Field Transducer

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Theoretical Investigation of the Shubnikov-de Haas Magnetoresistance Oscillations in a Quantum well under the Influence of Confined Acoustic Phonons

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.783.1 ◽

2018 ◽

Vol 783 ◽

pp. 1-11

Author(s):

Le Thai Hung ◽

Pham Ngoc Thang ◽

Nguyen Quang Bau

Keyword(s):

Magnetic Field ◽

Quantum Well ◽

Phonon Confinement ◽

Acoustic Phonons ◽

Magnetic Impedance ◽

Magnetoresistance Oscillations ◽

The Magnetic Field ◽

Amplitude Decrease ◽

Theoretical Results ◽

Phonon Confinement Effect

The Shubnikov – de Haas magnetoresistance oscillations in the Quantum well (QW) under the influence of confined acoustic phonons, The theoretical results show that the conductivity tensor, the complex magnetic impedance of the magnetic field, the frequency, the amplitude of the laser radiation, the QW width, the temperature of the system and especially the quantum index m characterizes the confinement of the phonon. The amplitude of the oscillations of the Shubnikov-de Haas impedance decreases with the increase of the influence of the confined acoustic phonons. The results for bulk phonons in a QW could be achieved, when m goes to zero. We has been compared with other studies when perform the numerical calculations are also achieved for the GaAs/AlGaAs in the QW. Results show that The Shubnikov-de Haas magnetoresistance oscillations amplitude decrease when phonon confinement effect increasing and when width L of the QW increases to a certain value, The Shubnikov – de Haas magnetoresistance oscillations amplitude completely disappears can not be observed.

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