The Inductor as a Smart Sensor for Material Identification in Domestic Induction Cooking

Bono-Nuez, Antonio; Martin-del-Brio, B.; Bernal-Ruíz, Carlos; Pérez-Cebolla, Francisco Jose; Martínez, A.; Sanz-Gorrachategui, Iván

doi:10.1109/jsen.2018.2795739

Cited by 14 publications

(8 citation statements)

References 22 publications

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“…Using the direction of the earth's gravity measured by the accelerometer, the direction of the earth's magnetic field is measured by the magnetic sensor to correct the acceleration information of the hands and feet measured by the gyroscope. Using Kalman filter Fusion [22] to calculate the algorithm of human body three-dimensional posture data (3-axis acceleration, 3-axis angular velocity, 3-axis magnetic field strength), the posture information in the form of Euler angle, quota power, or rotation matrix was the output. Figure 5 is a flow chart of VM-i sensor data collection.…”

Section: R E T R a C T E D R E T R A C T E D R E T R A C T E Dmentioning

confidence: 99%

Perception and Recognition of Upper Limb Movement Trajectory of Aerobics Based on Multi-Intelligent Sensors

2022

Wireless Communications and Mobile Computing

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Aerobics is an indispensable part of school teaching. Aerobics can exercise the human body’s flexibility, muscle extension, and cardiopulmonary function. However, there are many details in the aerobics movements that need attention, so this article mainly studies the perception and recognition of the upper limb movement trajectory of aerobics. This article is mainly based on multi-intelligent sensors to perform motion capture and recognition of upper limb movements in aerobics. Therefore, in order to design a multismart sensor-based aerobics upper limb movement perception recognition system, this paper proposes to combine the VM-i three-axis magnetic sensor and MEMS smart sensor to capture the upper limb movement trajectory. Then, the aerobics upper limb movement trajectory and posture mathematical description method to obtain the model of the action perception recognition system are written. In order to verify the system, this paper also designed the actual effect of attitude calculation and A-RRM algorithm verification experiment. Finally, it is optimized with the data obtained from the experiment, and the accuracy and efficiency comparison experiment with the traditional motion perception recognition system is carried out. Experimental results show that the motion recognition accuracy of the aerobics upper limb movement trajectory recognition system based on multi-smart sensors is improved by 13%-23% compared with the traditional motion recognition system. The recognition efficiency of the action perception recognition system based on multismart sensors is 9%-15% higher than that of the traditional action recognition system.

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Section: R E T R a C T E D R E T R A C T E D R E T R A C T E Dmentioning

confidence: 99%

Perception and Recognition of Upper Limb Movement Trajectory of Aerobics Based on Multi-Intelligent Sensors

2022

Wireless Communications and Mobile Computing

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“…First, the harmonic impedance is calculated from the spectrum estimation of the current and voltage waveforms recorded at the inductor. Then, machine learning algorithms are used to process the feature set including four harmonic impedances and power factors to identify pot materials [ 4 ]. In the recognition of people's emotions, smart sensors have also played an important role.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Effect of Badminton on Physical Health and Emotion Recognition on the account of Smart Sensors

Chang

Chen

Cao

et al. 2022

Applied Bionics and Biomechanics

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Emotional ability is an important symbol of human intelligence. Human’s understanding of emotions, from subjective consciousness to continuous or discrete emotional dimensions, and then to physiological separability, has shown a trend of gradually diverging from psychological research to the field of intelligent human-computer interaction. This article is aimed at studying the effects of smart sensor-based emotion recognition technology and badminton on physical health. It proposes a method of using smart sensor technology to recognize badminton movements and emotions during the movement. And the impact of emotion recognition based on smart sensors and badminton sports on physical health is carried out in this article. Experimental results show that the emotion recognition technology based on smart sensors can well recognize the changes in people’s emotions during badminton sports, and the accuracy of emotion recognition is higher than 70%. At the same time, experiments show that badminton can greatly improve people’s physical fitness and strengthen people’s physique.

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“…In [11], off-line FEM simulations are correlated with experimental measurements to obtain the material magnetic properties. In [19], a neural network-based system was used to perform the classification of the pot material for a fixed single-inductor structure. Only in [16], off-line impedance variation is used to obtain an estimation of the covered area.…”

Section: B Review Of Identification and Detection Systemsmentioning

confidence: 99%

Deep Learning-Based Magnetic Coupling Detection for Advanced Induction Heating Appliances

et al. 2019

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Induction heating has become the reference technology for domestic heating applications due to its benefits in terms of performance, efficiency and safety, among others. In this context, recent design trends aim at providing highly flexible cooking surfaces composed of multi-coil structures. As in many other wireless power transfer systems, one of the main challenges to face is the proper detection of the magnetic coupling with the induction heating load in order to provide improved thermal performance and safe power electronic converter operation. This is specially challenging due to the high variability in the materials used in cookware as well as the random pot placement in flexible induction heating appliances. This paper proposes the use of deep learning techniques in order to provide accurate area overlap estimation regardless of the used pot and its position. An experimental test-bench composed of a complete power converter, multi-coil system and real-time measurement system has been implemented and used in this study to characterize the parameter variation with overlapped area. Convolutional neural networks are then proposed as an effective method to estimate the covered area, and several implementations are studied and compared according to their computational cost and accuracy. As a conclusion, the presented deep learning-based technique is proposed as an effective tool to estimate the magnetic coupling between the coil and the induction heating load in advanced induction heating appliances.INDEX TERMS Inductive heating, resonant power conversion, wireless power transfer, deep learning, neural network, electromagnetic design, induction heating, home appliances.

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The Inductor as a Smart Sensor for Material Identification in Domestic Induction Cooking

Cited by 14 publications

References 22 publications

Perception and Recognition of Upper Limb Movement Trajectory of Aerobics Based on Multi-Intelligent Sensors

Perception and Recognition of Upper Limb Movement Trajectory of Aerobics Based on Multi-Intelligent Sensors

Analyzing the Effect of Badminton on Physical Health and Emotion Recognition on the account of Smart Sensors

Deep Learning-Based Magnetic Coupling Detection for Advanced Induction Heating Appliances

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