High-precision smart calibration system for temperature sensors

Wei, Rongshan; Ouyang, Kui; Bao, Xiaotian; Gao, Xiong; Chen, Chuandong

doi:10.1016/j.sna.2019.111561

Cited by 20 publications

(6 citation statements)

References 7 publications

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“…They can solve highly complex problems on mathematical calculations or other classical procedures without needing to explicitly define the model structure [36]. They reduce the modeling process to network training, which is useful, especially for non-linear sensor calibrations when sensor array signals are used to calculate the parameters [37]. In the literature, ANN-based soft sensors are usually employed to find the relationship between inputs and outputs by minimizing the mean square error.…”

Section: Methodsmentioning

confidence: 99%

Large-Area and Low-Cost Force/Tactile Capacitive Sensor for Soft Robotic Applications

Pagoli

Chapelle

Ramón

et al. 2022

Sensors

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This paper presents a novel design and development of a low-cost and multi-touch sensor based on capacitive variations. This new sensor is very flexible and easy to fabricate, making it an appropriate choice for soft robot applications. Materials (conductive ink, silicone, and control boards) used in this sensor are inexpensive and easily found in the market. The proposed sensor is made of a wafer of different layers, silicone layers with electrically conductive ink, and a pressure-sensitive conductive paper sheet. Previous approaches like e-skin can measure the contact point or pressure of conductive objects like the human body or finger, while the proposed design enables the sensor to detect the object’s contact point and the applied force without considering the material conductivity of the object. The sensor can detect five multi-touch points at the same time. A neural network architecture is used to calibrate the applied force with acceptable accuracy in the presence of noise, variation in gains, and non-linearity. The force measured in real time by a commercial precise force sensor (ATI) is mapped with the produced voltage obtained by changing the layers’ capacitance between two electrode layers. Finally, the soft robot gripper embedding the suggested tactile sensor is utilized to grasp an object with position and force feedback signals.

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Section: Methodsmentioning

confidence: 99%

Large-Area and Low-Cost Force/Tactile Capacitive Sensor for Soft Robotic Applications

Pagoli

Chapelle

Ramón

et al. 2022

Sensors

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“…On the contrary, if the error decreases, the algorithm is in the stage of convergence. In this case, u becomes smaller and LM algorithm is approximated to Gauss-Newton method to accelerate convergence [19]. LM algorithm can solve local minimum and slow convergence in BP network.…”

Section: Enhanced Bp Neural Networkmentioning

confidence: 99%

Calibration method of meteorological sensor based on enhanced BP network

Wang¹,

Jia²,

Liu³

et al. 2020

J. Inst.

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Meteorological observation plays an important role in establishing atmospheric theory and improving the accuracy of weather forecasts. Sensors are the foundation and the core in the meteorological observation. Only the accurate calibration of the sensor can ensure the validity of measurement data. In most current methods of sensor calibration, the least squares method is used for calibration which results in low calibration accuracy. In this paper, a sensor calibration model is implemented by using artificial intelligence technology. By combining a back propagation (BP) neural network, a Gaussian function and the Levenberg-Marquardt (LM) algorithm, an enhanced BP network is realized for sensor calibration. The calibration model is transplanted to a Microcontroller unit (MCU). The Gaussian function is fitted by piecewise polynomials, which effectively reduces the computing resources and time use of the MCU. The experimental results show that: the traditional BP network reduces the mean squared error (MSE) of the atmospheric pressure sensor from 5.93 to 2.83. This reduces the measurement error by 52.28%. The enhanced BP network reduces the MSE to 0.77, which further reduces the measurement error by 34.74%. By fitting a Gaussian function to a polynomial, the computing time of MCU is reduced from 40 us to 0.1 us, which significantly reduces the complexity of the algorithm.

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“…Researchers made efforts on reduction of sensing errors caused by these interferences. Some algorithms were developed by previous researchers [29], such as least squares [30], polynomial fitting [31], and interpolation [32], etc., but these methods do not reflect real-time output data and cannot be used for real-time monitoring tasks. This disadvantage limits their usage scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Iteration Enhanced 2P-SMA Method for Improved Error Reduction on a WP-SAW Water Temperature and Pressure Sensor

Tang

Gao

et al. 2021

IEEE Access

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Due to the instability of the characteristics of materials, fabrication processes and user handling, newly designed and fabricated wireless passive surface acoustic wave (WP-SAW) sensor nodes have inconsistent sensing performance. Furthermore, ambient environmental interferences aggravate inconsistences under complex working conditions. In this paper, a multi-iteration enhanced two-point simple moving average (MI-2P-SMA) method is proposed for sensing error reduction of a WP-SAW reflective delay line water temperature and pressure sensor. This method is improved from the traditional 2P-SMA method for better performance on error reduction. The results show: the MI-2P-SMA method does not change the original characteristics of experimental data; it can reduce relative errors of the WP-SAW reflective delay line water temperature and pressure sensor and has better performance than a traditional 2P-SMA method; it reduces the number of data points and the extent of this reduction is dependent on iteration time.

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High-precision smart calibration system for temperature sensors

Cited by 20 publications

References 7 publications

Large-Area and Low-Cost Force/Tactile Capacitive Sensor for Soft Robotic Applications

Large-Area and Low-Cost Force/Tactile Capacitive Sensor for Soft Robotic Applications

Calibration method of meteorological sensor based on enhanced BP network

A Multi-Iteration Enhanced 2P-SMA Method for Improved Error Reduction on a WP-SAW Water Temperature and Pressure Sensor

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