scholarly journals A CMOS Hall sensor modeling with readout circuitry and microcontroller processing for magnetic detection

2021 ◽  
Vol 92 (3) ◽  
pp. 034707
Author(s):  
Hua Fan ◽  
Jiayi Zhang ◽  
Siming Zuo ◽  
Qiang Hu ◽  
Quanyuan Feng ◽  
...  
Keyword(s):  
Hall Sensor ◽  
Magnetic Detection ◽  
Sensor Modeling

Magnetic detection of cracks by fatigue in mild steels using a scanning Hall-sensor microscope

1999 ◽  
Vol 70 (1) ◽  
pp. 184-186 ◽  
Author(s):  
A. Oota ◽  
T. Ito ◽  
K. Kawano ◽  
D. Sugiyama ◽  
H. Aoki
Keyword(s):  
Hall Sensor ◽  
Magnetic Detection

Research of Linear Differential Hall Sensor Modeling and Output Characteristics Experiment

2011 ◽  
Vol 383-390 ◽  
pp. 1488-1494 ◽  
Author(s):  
Zhao Yun Qiu ◽  
Zong Bao Zhang ◽  
Qi Tao Liu ◽  
Guang Dong Jiang
Keyword(s):  
Signal Amplitude ◽  
Hall Sensor ◽  
Current Sensor ◽  
Sensor Model ◽  
Differential Voltage ◽  
Linearity Error ◽  
Linear Differential ◽  
Output Characteristics ◽  
Sensor Modeling ◽  
Mode Interference

The purpose of this paper is to model and study linear differential Hall sensor. A component for linear differential Hall sensor model was constructed, then a number of experiments were performed to check its output characteristics and temperature characteristics.Two Hall-components formed a linear differential Hall model,which had two independent outputs outputing differential voltage. The results show that the model significantly reduces quiescent output voltage, the signal amplitude increased 99.5%, sensitivity ≥ 40mV/mT, linearity error ≤ 0.5%, zero drift coefficient ≤0.023mV/°C.It is concluded that outputing differential voltage can prohibit common-mode interference and zero shift.The model will has self temperature compensation and nonlinear correctiion.In the future ,this model will practicaly in the current sensor.

Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy

2020 ◽  
Vol 16 (6) ◽  
pp. 3721-3730 ◽  
Author(s):  
Xiaofeng Yuan ◽  
Jiao Zhou ◽  
Biao Huang ◽  
Yalin Wang ◽  
Chunhua Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Strategy ◽  
Soft Sensor ◽  
Feature Representation ◽  
Relevant Feature ◽  
Sensor Modeling

Ultrasound sensor modeling in Gazebo simulator for diagnostics of abdomen pathologies

2021 ◽  
Author(s):  
Bulat Abbyasov ◽  
Alexandra Dobrokvashina ◽  
Roman Lavrenov ◽  
Enzhe Kharisova ◽  
Tatyana Tsoy ◽  
...  
Keyword(s):  
Ultrasound Sensor ◽  
Sensor Modeling

Study of a modified non-contact Hall sensor-based flow transducer using centrifugal and momentum force principle

2021 ◽  
Author(s):  
Sirshendu Saha ◽  
Saikat Kumar Bera ◽  
Saurabh Pal ◽  
Satish Chandra Bera
Keyword(s):  
Hall Sensor ◽  
Flow Transducer

scholarly journals Industrial Semi-Supervised Dynamic Soft-Sensor Modeling Approach Based on Deep Relevant Representation Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3430
Author(s):  
Jean Mário Moreira de Lima ◽  
Fábio Meneghetti Ugulino de Araújo
Keyword(s):  
Short Term Memory ◽  
Soft Sensor ◽  
Fine Tuning ◽  
Current Layer ◽  
Layer By Layer ◽  
Linear Processes ◽  
Method Performance ◽  
Modeling Approach ◽  
Ensemble Strategy ◽  
Sensor Modeling

Soft sensors based on deep learning have been growing in industrial process applications, inferring hard-to-measure but crucial quality-related variables. However, applications may present strong non-linearity, dynamicity, and a lack of labeled data. To deal with the above-cited problems, the extraction of relevant features is becoming a field of interest in soft-sensing. A novel deep representative learning soft-sensor modeling approach is proposed based on stacked autoencoder (SAE), mutual information (MI), and long-short term memory (LSTM). SAE is trained layer by layer with MI evaluation performed between extracted features and targeted output to evaluate the relevance of learned representation in each layer. This approach highlights relevant information and eliminates irrelevant information from the current layer. Thus, deep output-related representative features are retrieved. In the supervised fine-tuning stage, an LSTM is coupled to the tail of the SAE to address system inherent dynamic behavior. Also, a k-fold cross-validation ensemble strategy is applied to enhance the soft-sensor reliability. Two real-world industrial non-linear processes are employed to evaluate the proposed method performance. The obtained results show improved prediction performance in comparison to other traditional and state-of-art methods. Compared to the other methods, the proposed model can generate more than 38.6% and 39.4% improvement of RMSE for the two analyzed industrial cases.

scholarly journals Sensor Modeling for Underwater Localization Using a Particle Filter

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1549
Author(s):  
Humberto Martínez-Barberá ◽  
Pablo Bernal-Polo ◽  
David Herrero-Pérez
Keyword(s):  
Sequential Monte Carlo ◽  
Sensory Information ◽  
Dead Reckoning ◽  
Underwater Vehicle ◽  
Position Estimation ◽  
Sources Of Uncertainty ◽  
Sensor Signals ◽  
Underwater Navigation ◽  
Underwater Localization ◽  
Sensor Modeling

This paper presents a framework for processing, modeling, and fusing underwater sensor signals to provide a reliable perception for underwater localization in structured environments. Submerged sensory information is often affected by diverse sources of uncertainty that can deteriorate the positioning and tracking. By adopting uncertain modeling and multi-sensor fusion techniques, the framework can maintain a coherent representation of the environment, filtering outliers, inconsistencies in sequential observations, and useless information for positioning purposes. We evaluate the framework using cameras and range sensors for modeling uncertain features that represent the environment around the vehicle. We locate the underwater vehicle using a Sequential Monte Carlo (SMC) method initialized from the GPS location obtained on the surface. The experimental results show that the framework provides a reliable environment representation during the underwater navigation to the localization system in real-world scenarios. Besides, they evaluate the improvement of localization compared to the position estimation using reliable dead-reckoning systems.

Detecting the Total Stray Fields of Ferrogel Nanoparticles Using a Prototype Magnetoimpedance Sensor: Modeling and Experiment

2019 ◽  
Vol 83 (7) ◽  
pp. 906-908
Author(s):  
A. A. Chlenova ◽  
N. A. Buznikov ◽  
A. P. Safronov ◽  
E. V. Golubeva ◽  
V. N. Lepalovskii ◽  
...  
Keyword(s):  
Sensor Modeling
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