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Abstract

The non-contact current measurement method with magnetic sensors has become a subject of research. Unfortunately, magnetic sensors fail to distinguish the interested magnetic field from nearby interference and suffer from gauss white noise due to the intrinsic noise of the sensor and external disturbance. In this paper, a novel adaptive filtering-based current reconstruction method with a magnetic sensor array is proposed. Interference-rejection methods based on two classic algorithms, the least-mean-square (LMS) and recursive-least-square (RLS) algorithms, are compared when used in the parallel structure and regular triangle structure of three-phase system. Consequently, the measurement range of RLS-based algorithm is wider than that of LMS-based algorithm. The results of carried out simulations and experiments show that RLS-based algorithms can measure currents with an error of around 1%. Additionally, the RLS-based algorithm can filter the gauss white noise whose magnitude is within 10% of the linear magnetic field range of the sensor.

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Authors and Affiliations

Yafeng Chen
Qi Huang
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Abstract

An array consisting of four commercial gas sensors with target specifications for hydrocarbons, ammonia, alcohol, explosive gases has been constructed and tested. The sensors in the array operate in the dynamic mode upon the temperature modulation from 350°C to 500°C. Changes in the sensor operating temperature lead to distinct resistance responses affected by the gas type, its concentration and the humidity level. The measurements are performed upon various hydrogen (17-3000 ppm), methane (167-3000 ppm) and propane (167-3000 ppm) concentrations at relative humidity levels of 0-75%RH. The measured dynamic response signals are further processed with the Discrete Fourier Transform. Absolute values of the dc component and the first five harmonics of each sensor are analysed by a feed-forward back-propagation neural network. The ultimate aim of this research is to achieve a reliable hydrogen detection despite an interference of the humidity and residual gases.
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Authors and Affiliations

Patryk Gwiżdż
Andrzej Brudnik
Katarzyna Zakrzewska
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Abstract

Low-cost sensor arrays are an economical and efficient solution for large-scale networked monitoring of atmospheric pollutants. These sensors need to be calibrated in situ before use, and existing data-driven calibration models have been widely used, but require large amounts of co-location data with reference stations for training, while performing poorly across domains. To address this problem, a meta-learningbased calibration network for air sensors is proposed, which has been tested on ozone datasets. The tests have proved that it outperforms five other conventional methods in important metrics such as mean absolute error, root mean square error and correlation coefficient. Taking Manlleu and Tona as the source domain and Vic as the target domain, the proposed method reduces MAE and RMSE by 17.06% and 6.71% on average, and improves R2 by an average of 4.21%, compared with the suboptimal pre-trained multi-source transfer calibration. The method can provide a new idea and direction to solve the problem of cross-domain and reliance on a large amount of co-location data in the calibration of sensors.
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Authors and Affiliations

Feng Tianliang
1
Xiong Xingchuang
2
Jin Shangzhong
1

  1. College of Optical and Electronic Technology, China Jiliang University, Hangzhou, Zhejiang 310018, China
  2. National Institute of Metrology, Beijing 100029, China

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