Calibration method of meteorological sensor based on enhanced BP network

Wang, Y. M.; Jia, Ke; Liu, P. Y.; Zhang, W. J.; Yang, Jianbo

doi:10.1088/1748-0221/15/10/p10014

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…ANN models are widely used to enhance temperature sensors' accuracy because they capture complex nonlinearities and improve accuracy and stability [20][21][22]. The present study used an ANN model to enhance temperature sensor accuracy based on temperature data [23], and the results reveal its high accuracy of calibration.…”

Section: Introductionmentioning

confidence: 81%

Machine-Learning-Based Calibration of Temperature Sensors

Liu,

Zhao,

Wang

et al. 2023

Sensors

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Temperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial neural network (ANN) model for calibration and explored the feasibility and effectiveness of using ANNs to calibrate temperature sensors. The experiment collected multiple sets of temperature data from standard temperature sensors in different environments and compared the calibration results of the ANN model, linear regression, and polynomial regression. The experimental results show that calibration using the ANN improved the accuracy of the temperature sensors. Compared with traditional linear regression and polynomial regression, the ANN model produced more accurate calibration. However, overfitting may occur due to a small sample size or a large amount of noise. Therefore, the key to improving calibration using the ANN model is to design reasonable training samples and adjust the model parameters. The results of this study are important for practical applications and provide reliable technical support for industrial production and scientific research.

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Section: Introductionmentioning

confidence: 81%

Machine-Learning-Based Calibration of Temperature Sensors

Liu,

Zhao,

Wang

et al. 2023

Sensors

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“…In addition to traditional parameters such as light intensity, temperature, humidity, carbon dioxide concentration and soil moisture content [ 29 ], parameters such as aerosol accumulation and carbon loss have been detected by meteorological sensors [ 30 ]. With the application of wireless sensor networks [ 31 ], the intelligent calibration method [ 32 ] and multi-thread serial communication technology [ 33 ] on sensors, agrometeorological sensors are now more accurate and cheaper. At last, temperature, humidity, ammonia concentrations, hydrogen sulfide concentrations, carbon dioxide concentrations, sulfur dioxide concentrations and light intensity are the main parameters detected by environmental sensors integrated into breeding environments for poultry and livestock [ 34 ].…”

Section: State-of-the-art and Related Workmentioning

confidence: 99%

Analysis of the Development Status and Prospect of China’s Agricultural Sensor Market under Smart Agriculture

Sun

Fan

Ding

et al. 2023

Sensors

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Agricultural sensors are essential technologies for smart agriculture, which can transform non-electrical physical quantities such as environmental factors. The ecological elements inside and outside of plants and animals are converted into electrical signals for control system recognition, providing a basis for decision-making in smart agriculture. With the rapid development of smart agriculture in China, agricultural sensors have ushered in opportunities and challenges. Based on a literature review and data statistics, this paper analyzes the market prospects and market scale of agricultural sensors in China from four perspectives: field farming, facility farming, livestock and poultry farming and aquaculture. The study further predicts the demand for agricultural sensors in 2025 and 2035. The results reveal that China’s sensor market has a good development prospect. However, the paper garnered the key challenges of China’s agricultural sensor industry, including a weak technical foundation, poor enterprise research capacity, high importation of sensors and a lack of financial support. Given this, the agricultural sensor market should be comprehensively distributed in terms of policy, funding, expertise and innovative technology. In addition, this paper highlighted integrating the future development direction of China’s agricultural sensor technology with new technologies and China’s agricultural development needs.

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“…e evaluation problem is transformed into the problem of solving the objective function [14], that is, solving the consistency estimation value of English learning effectiveness μ to obtain the estimated value of English learning effectiveness evaluation index [15], which can be expressed using the following equation:…”

Section: Inquirementioning

confidence: 99%

Design of English Learning Effectiveness Evaluation System Based on K-Means Clustering Algorithm

Zhang

2021

Mobile Information Systems

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English is the universal language of the world. In the context of global economic integration, English learning is not only an essential course for business elites but also a required course for the general public. Currently, in colleges and universities across the world, English is presented as a compulsory first foreign language course. Therefore, how to improve the effect of English performance assessment in the context of smart teaching has become an important part of smart English teaching. Due to the influence of interference factors, human factors, or external factors, the traditional English language teaching evaluation system has the problems of high system sensitivity, long envelope delay jitter time, and short stationary state maintenance time. Therefore, this study develops an English learning effectiveness evaluation system based on a K-means clustering algorithm. The SQL Server 2005 database management software is used to develop the system database; various functional modules of the system are designed using ActiveX, with emphasis on the design of scoring functional modules; and different roles and permissions are given to administrators, teachers, and students. A student English learning effectiveness evaluation model based on BP neural network training and K-means clustering algorithm is designed to optimize the English learning effectiveness evaluation model and achieve effective English learning by solving the consistent estimate of the effectiveness of English learning assessment. The performance test results show that the proposed system has a lower sensitivity coefficient, a shorter envelope delay jitter time, and a longer period of steady-state maintenance, indicating that the system can achieve stable operation.

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Calibration method of meteorological sensor based on enhanced BP network

Cited by 7 publications

References 11 publications

Machine-Learning-Based Calibration of Temperature Sensors

Machine-Learning-Based Calibration of Temperature Sensors

Analysis of the Development Status and Prospect of China’s Agricultural Sensor Market under Smart Agriculture

Design of English Learning Effectiveness Evaluation System Based on K-Means Clustering Algorithm

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