Under the conditions of low flow rate and strong noise, the current electromagnetic flowmeter (EMF) cannot satisfy the requirement for measurement or separate the actual flow signal and interference signal accurately. Correlation detection technology can reduce the bandwidth and suppress noise effectively using the periodic transmission of signal and noise randomness. As for the problem that the current anti-interference technology cannot suppress noise effectively, the noise and interference of the electromagnetic flowmeter were analyzed in this paper, and a design of the electromagnetic flowmeter based on differential correlation detection was proposed. Then, in order to verify the feasibility of the electromagnetic flow measurement system based on differential correlation, an experimental platform for the comparison between standard flow and measured flow was established and a verification experiment was carried out under special conditions and with flow calibration measurements. Finally, the data obtained in the experiment were analyzed. The research result showed that an electromagnetic flowmeter based on differential correlation detection satisfies the need for measurement completely. The lower limit of the flow rate of the electromagnetic flowmeter based on the differential correlation principle could reach 0.084 m/s. Under strong external interferences, the electromagnetic flowmeter based on differential correlation had a fluctuation range in output value of only 10 mV. This shows that the electromagnetic flowmeter based on the differential correlation principle has unique advantages in measurements taken under the conditions of strong noise, slurry flow, and low flow rate.

Downhole annulus multiphase fluid monitoring during drilling is technically tricky, and the existing downhole overflow monitoring means the specific overflow type and state cannot be determined. For the monitoring of downhole multiphase fluids, this paper proposes the downhole annulus electromagnetic tomography (AEMT) detection technology and focuses on the extreme learning machine (ELM) algorithm to realise downhole annulus image reconstruction. For the special characteristics of EMT in downhole annulus, this paper firstly establishes a theoretical model of AEMT mathematics and proposes the ELM image reconstruction algorithm for downhole annulus fluids. Secondly, the 3D simulation model of the AEMT is established. The 3D simulation model of the AEMT random phase distribution multiphase flow measurement is constructed by co-simulation of MATLAB with COMSOL, and 12000 groups of simulation data samples are established. Finally, this paper builds an annulus fluid multiphase flow test platform for static experimental comparison and analysis. The results show that the average correlation coefficients of the test set of the downhole ELM image reconstruction algorithm are increased by 139.27%, 138.65% and 725.15%, and the average relative errors are reduced by 66.5%. The average relative errors are reduced by 66.5%, and the average relative errors are decreased by 66%. The average relative error decreased by 66.34%, 68.41%, and 81.18% compared with the three classical algorithms, LBP, Landweber and Tikhonov. This paper realises the EMT based on the ELM algorithm for the first time in downhole annulus flow channel, which provides the theoretical basis for the future real-time image monitoring of downhole annulus multiphase fluids.

High-sensitivity early fire detection is an essential prerequisite to intelligent building safety. However, due to the small changes and erratic fluctuations in environmental parameters in the initial combustion phase, it is always a challenging task. To address this challenge, this paper proposes a hybrid feature fusion-based high-sensitivity early fire detection and warning method for in-building environments. More specifically, the temperature, smoke concentration, and carbon monoxide concentration were first selected as the main distinguishing attributes to indicate an in-building fire. Secondly, the propagation neural network (BPNN) and the least squares support vector machine (LSSVM) were employed to achieve the hybrid feature fusion. In addition, the genetic algorithm (GA) and particle swarm optimization (PSO) were also introduced to optimize the BPNN and the LSSVM, respectively. After that, the outputs of the GA-BPNN and the PSO-LSSVM were fused to make a final decision by means of the D-S evidence theory, achieving a highly sensitive and reliable early fire detection and warning system. Finally, an early fire warning system was developed, and the experimental results show that the proposed method can effectively detect an early fire with an accuracy of more than 96% for different types and regions of fire, including polyurethane foam fire, alcohol fire, beech wood smolder, and cotton woven fabric smolder.

Using the downhole annular flow measurement system to get real-time information of downhole annular flow is the core and foundation of downhole microflux control drilling technology. The research work of electromagnetic flowmeter in recent years creates a challenge to the design of downhole annular flow measurement. This paper proposes a design and optimization of annular flow electromagnetic measurement system for drilling engineering based on the finite element method. Firstly, the annular flow measuring and optimization principle are described. Secondly, a simulation model of an annular flow electromagnetic measurement system with two pairs of coil is built based on the fundamental equation of electromagnetic flowmeter by COMSOL. Thirdly, simulations of the structure of excitation system of the measurement system are carried out, and simulations of the size of the electrode’s radius are also carried out based on the optimized structure, and then all the simulation results are analyzed to evaluate the optimization effect based on the evaluation indexes. The simulation results show that optimized shapes of the excitation system and electrode size can yield a better performance in the annular flow measurement.

Purpose: The purpose of this study is to deal largely with the influence of temperature variation on the measurement accuracy of transit-time ultrasonic flowmeter. Design/methodology/approach: The causes of measurement error due to temperature are qualitatively and quantitatively analyzed, and a mathematical model is established. The experimental data are processed and analyzed, and the temperature compensation coefficient of flow measurement is obtained. Findings: The experimental results show that the flow measurement results by temperature compensation are helpful in improving the measurement accuracy of the ultrasonic flowmeter. Practical implications: This study has certain application value, which can provide theoretical support for the design of high-precision ultrasonic flowmeters and design guidance. Originality/value: It is worth emphasizing that there are few research studies on the influence factors of temperature. This paper focuses on the influence of the temperature change on the flowmeter that is modeled, and the high precision flow parameter test system is designed based on the established model.

Since drunk driving poses a significant threat to road traffic safety, there is an increasing demand for the performance and dependability of online drunk driving detection devices for automobiles. However, the majority of current detection devices only contain a single sensor, resulting in a low degree of detection accuracy, erroneous judgments, and car locking. In order to solve the problem, this study firstly designed a sensor array based on the gas diffusion model and the characteristics of a car steering wheel. Secondly, the data fusion algorithm is proposed according to the data characteristics of the sensor array on the steering wheel. The support matrix is used to improve the data consistency of the single sensor data, and then the adaptive weighted fusion algorithm is used for multiple sensors. Finally, in order to verify the reliability of the system, an online intelligent detection device for drunk driving based on multi-sensor fusion was developed, and three people using different combinations of drunk driving simulation experiments were conducted. According to the test results, a drunk person in the passenger seat will not cause the system to make a drunk driving determination. When more than 50 mL of alcohol is consumed and the driver is seated in the driver’s seat, the online intelligent detection of drunk driving can accurately identify drunk driving, and the car will lock itself as soon as a real-time online voice prompt is heard. This study enhances and complements theories relating to data fusion for online automobile drunk driving detection, allowing for the online identification of drivers who have been drinking and the locking of their vehicles to prevent drunk driving. It provides technical support for enhancing the accuracy of online systems that detect drunk driving in automobiles.