Search results

The purpose of this paper is to reduce the problem of temperature drift causing output errors in such sensors, three hardware compensation schemes are proposed in this paper, and three compensation schemes are designed and implemented.

In response to the problem of temperature drift causing output errors in this type of sensor, this paper proposes three hardware compensation schemes and carries out the design and implementation of the three compensation schemes. Finally, the advantages and disadvantages of the three compensation schemes are discussed through the analysis of the experimental results. The three hardware compensation methods are series-parallel resistance network compensation, digital signal processor (DSP) compensation and the joint compensation of resistance network and DSP. Series parallel resistance network compensation is to connect the low-temperature drift resistance and the sensor in series and parallel; DSP compensation is based on the combination of cubic spline interpolation and linear fitting algorithm, which uses DSP to process the data. Joint compensation is a new compensation method composed of the above two compensation methods.

The experimental results show that the relative error of the output is reduced to a certain extent after the three compensation methods, and the relative error of the output after the joint compensation is reduced to about 0.2%, which proves that the three compensation methods are feasible.

This paper presents three novel hardware compensation methods to reduce temperature drift in silicon on insulator (SOI) high-temperature pressure sensors. The joint compensation method, combining resistance network and DSP compensation, is particularly innovative and significantly improves output accuracy, reducing relative error to about 0.2%.

Accurately identifying the risk of poverty-returning is a complex and critical challenge in current poverty alleviation efforts. However, there is currently no study on evaluation methods for the risk of poverty-returning. This study aims to establish a robust and systematic approach for an evaluation framework for the risk of poverty-returning.

Based on relevant assessment criteria, a maximum deviation method was established to identify the weights of the indicators. A complex evaluation methodology using prospect theory (PT), a q-rung orthopair fuzzy set (QrOFS) and evaluation relying on distance from average solution [EDAS] (QrOFS-PT-EDAS) was developed to evaluate the poverty-returning risks. Some policy recommendations to reduce the risk of poverty-returning have also been put forward.

His study identifies the risk factors of poverty relapse from nine aspects, including natural disasters, accidents and policy-driven poverty relapse. In addressing the evaluation challenge arising from uncertain decision-making, the QrOFS aligns more with people’s thinking habits and expression methods in complex environments. The proposed hybrid evaluation framework accurately measures the poverty-returning risk, which is beneficial for the formulation of policy recommendations.

A scientific and comprehensive assessment system index for poverty-returning is constructed. A hybrid QrOFS-PT-EDAS framework is presented to make the evaluation results more scientific and objective. Several strategic recommendations for reducing the poverty-returning risk are presented. This study offers a novel framework for assessing poverty-returning issues that can be extended to many other areas.