Magnetic domain transition controlled by distributed normal magnetic field for stepped magneto-impedance sensor

2019 ◽  
Vol 59 (1) ◽  
pp. 105-114 ◽  
Author(s):  
Tomoo Nakai
Keyword(s):  
Magnetic Field ◽  
Magnetic Domain ◽  
Impedance Sensor ◽  
Normal Magnetic Field

scholarly journals Magnetic Domain Transition of Adjacent Narrow Thin Film Strips with Inclined Uniaxial Magnetic Anisotropy

Micromachines ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 279
Author(s):  
Tomoo Nakai
Keyword(s):  
Magnetic Field ◽  
Easy Axis ◽  
Magnetic Domain ◽  
Magnetic Interaction ◽  
Individual Element ◽  
Typical Characteristic ◽  
Many Body ◽  
Uniaxial Magnetic Anisotropy ◽  
Line Arrangement ◽  
Normal Magnetic Field

This study deals a phenomenon of magnetic domain transition for the stepped magneto-impedance element. Our previous research shows that an element with 70° inclined easy axis has a typical characteristic of the domain transition, and the transition can be controlled by the normal magnetic field. In this paper, we apply this phenomenon and controlling method to the line arrangement adjacent to many body elements, in which mutual magnetic interaction exists. The result shows that the hidden inclined Landau–Lifshitz domain appears by applying a distributed normal field the same as an individual element.

scholarly journals Nondestructive Detection of Magnetic Contaminant in Aluminum Casting Using Thin Film Magnetic Sensor

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4063
Author(s):  
Tomoo Nakai
Keyword(s):  
Magnetic Field ◽  
Thin Film ◽  
Single Layer ◽  
Static Field ◽  
Inspection System ◽  
Aluminum Casting ◽  
Logarithmic Amplifier ◽  
Impedance Sensor ◽  
Driving Circuit ◽  
Normal Magnetic Field

The thin film magneto-impedance sensor is useful for detecting a magnetic material nondestructively. The sensor made by single layer uniaxial amorphous thin film has a tolerance against surface normal magnetic field because of its demagnetizing force in the thickness direction. Our previous study proposed the sensitive driving circuit using 400 MHz high frequency current running through the sensor to detect the logarithmic amplifier. We also confirmed the sensitivity of the sensor within 0.3 T static normal magnetic field, which resulted in detection of 5 × 10−8 T of 5 Hz signal. This paper proposes a nondestructive inspection system for how detecting a contaminant of small tool steel chipping in aluminum casting specimen would be carried out. Three channel array sensors installed in the 30 mT static field detecting area were fabricated and experimentally showed a detection of low remanence magnetic contaminant in a bulk aluminum casing specimen.

scholarly journals Neural Network for Metal Detection Based on Magnetic Impedance Sensor

Sensors ◽  
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.

Human Brain Like Memory Behavior in the Magnetic Domain Expansion Type Magneto-Optical Disk

2001 ◽  
Vol 674 ◽  
Author(s):  
Norio Ota ◽  
Hiroyuki Awano ◽  
Manabu Tani ◽  
Susumu Imai
Keyword(s):  
Magnetic Field ◽  
Human Brain ◽  
Magnetic Domain ◽  
Field Pattern ◽  
Human Response ◽  
External Stimulation ◽  
Stored Information ◽  
The Magnetic Field ◽  
Timing Phase ◽  
Coherent Amplification

ABSTRACTMagnetic Amplifying Magneto-Optical System (MAMMOS) shows human brain like memory behavior. Magnetic field and laser power have threshold to recover the stored memory like the human response of remembering. MAMMOS also has a feature to amplify very small recorded signals like our recovery of memory, e.g. fifty years ago episode.By adding the meaningful information on the magnetic field pattern, we can get some correlation between our memory and external stimulation. Such scheme is named as “the Active readout MAMMOS” which is analogues to the human process of remembering the memory.If the applied field pattern and timing phase just coincide with stored information, there occurs the coherent amplification of MAMMOS signal. We can utilize such phenomena as the trigger of “Memory Association”.

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