Real-time Measurement Method Research Articles

In situ real-time measurement for electron spin polarization in atomic spin gyroscopes.

Atomic spin gyroscopes (ASGs) based on spin-exchange relaxation-free (SERF) co-magnetometers represent a new generation of ultra-high-precision inertial sensors. However, their long-term stability is significantly constrained by the stability of electron spin polarization. Despite its critical importance, current research lacks effective methods for in situ and real-time measurement of electron spin polarization. This paper addresses this gap by developing a model for pump laser propagation within the vapor cell and proposing an Euler-particle swarm optimization (PSO) algorithm to estimate the model's unknown parameters. By utilizing artificial neural networks, we derive an output equation for electron spin polarization, using transmitted laser power and cell temperature as independent variables. Comparative experiments validate the accuracy of the proposed method, and perturbation experiments demonstrate its real-time capability. The proposed in situ real-time measurement method for electron spin polarization lays a solid foundation for improving closed-loop control and enhancing the long-term stability of ASGs.

Near Real-Time Measurement of Airborne Carbon Nanotubes with Metals Using Raman-Spark Emission Spectroscopy.

We present a near real-time measurement method that combines Raman and spark emission spectroscopy to quantitatively analyze the molecular structure of airborne single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs), as well as detect toxic metals within CNTs. A corona-based aerosol microconcentrator was used for airborne CNTs sampling to enhance the measurement accuracy and sensitivity. The intensity of the characteristic Raman bands of CNTs and atomic emission lines of metals exhibited a linear relationship with the analyte mass, yielding high coefficient R2 values. By carefully selecting appropriate signal peaks for calibration, we achieved a limit of detection (LOD) in terms of air concentration as low as 0.09 μg/m3 for SWCNT and 0.81 μg/m3 for MWCNT with a sampling time of 10 min. Additionally, our method exhibited excellent performance in measuring metals, with a mass LOD of 0.8-0.9 ng for Co and Ni and a mass LOD of 35.09 ng for Fe. The method performed well for the measurement of CNT and relevant metal composition with advantages of near real-time monitoring, low LOD, and portable use, making it a valuable tool for various applications in nanomaterial analysis.

Accuracy verification of protoacoustic measurements in a heterogeneous phantom by an optical hydrophone.

Protoacoustics has emerged as a promising real-time range measurement method for proton therapy. Optical hydrophones (OHs) are considered suitable to detect protoacoustic waves owing to their ultracompact size and high sensitivity. In our previous research, we demonstrated that the time-of-arrival (TOA) measured by an OH showed good agreement with the simulated ground truth in a homogeneous medium. The purpose of the study was to experimentally evaluate the accuracy of the TOA and compression peak pressures detected by the OH. Protoacoustic waves that undergo the typical distortions occurring in the human body were investigated. In such cases, the use of small detectors such as OHs is desirable to minimize the effects of detector size and directivity. A 100-MeV proton pencil beam emitted from a fixed-field alternating gradient accelerator was irradiated onto a homogeneous water phantom and a water phantom with a half- or full-sized silicone plate downstream of the Bragg peak (BP) or a bone plate that covered half of the beam cross-section in the beam path. The OH was shifted 70mm laterally across the beam axis downstream of the BP to measure the protoacoustic waves. The k-WAVE acoustic wave transport simulation was employed as the ground truth. The TOA and the first compression peak pressures were compared between the simulation and experiment. The TOA deviation against the ground truth was primarily attributed to alignment errors of the measurement devices and phantoms, with deviations of<1mm. The peak pressure distribution closely resembled the ground truth, with FWHM differences of 0.0%-3.0% for the tested geometries. The OH was able to determine the TOA and peak pressures with sufficient accuracy in heterogeneous phantoms, even without considering the effect of the size of the detector or directivity on the measurements.

Multiscale Cloud-Based Pipeline for Neuronal Electrophysiology Analysis and Visualization.

Electrophysiology offers a high-resolution method for real-time measurement of neural activity. Longitudinal recordings from high-density microelectrode arrays (HD-MEAs) can be of considerable size for local storage and of substantial complexity for extracting neural features and network dynamics. Analysis is often demanding due to the need for multiple software tools with different runtime dependencies. To address these challenges, we developed an open-source cloud-based pipeline to store, analyze, and visualize neuronal electrophysiology recordings from HD-MEAs. This pipeline is dependency agnostic by utilizing cloud storage, cloud computing resources, and an Internet of Things messaging protocol. We containerized the services and algorithms to serve as scalable and flexible building blocks within the pipeline. In this paper, we applied this pipeline on two types of cultures, cortical organoids and ex vivo brain slice recordings to show that this pipeline simplifies the data analysis process and facilitates understanding neuronal activity.

Air Pollutant Emission Factors of Inland River Ships under Compliance

Inland river ships (IRSs) use diesel with a lower sulfur content and emit relatively low emissions, making it challenging to monitor their emissions. Sniffer monitoring equipment was installed from August 2020 to June 2022 at the Gezhou Dam of the Yangtze River and monitored emissions from 8,238 IRSs passing through the lock. We partnered with the maritime department to select 100 ships passing through the lock to extract fuel oil samples for direct fuel sulfur content (FSC) detection, which determined the actual FSC of the passing ships. The monitoring data from these 100 ships indicated that the relative error of the SO2 emission factors (EFs) and FSC results is significant at the 10-parts-per-million level. The monitoring data from the remaining 8,138 ships showed that the EFs of NO, NO2, PM2.5, and PM10 were 24.02 ± 16.92 g kg−1, 10.30 ± 18.08 g kg−1, 0.72 ± 0.60 g kg−1, and 0.92 ± 0.70 g kg−1, respectively. The NOx EFs of container ships are higher than those of other ship types, while the PM EFs for different ship types do not significantly differ. Based on these EFs, we calculated the average emission rates for different types of ships passing through locks, which is a real-time measurement method for estimating ship emissions. In addition, a comparison of ship EF measurements over the past 20 years revealed that EF values for SO2, NOx, and PM exhibited a downward trend, with the calculated results of the current study determined to be the lowest numerical level.

Near-Infrared-Based Measurement Method of Mass Flow Rate in Grain Vibration Feeding System

The radial distribution of material feeding onto a screen surface is an important factor affecting vibration screening performance, and it is also the main basis for the optimization of the operating parameters of a vibration screening system. In this paper, based on near-infrared properties, a real-time measurement method for the mass flow rate of grain vibration feeding was proposed. A laser emitter and a silicon photocell were used as the measuring components, and the corresponding signal processing circuit mainly composed of a T-type I/V convertor, a voltage follower, a low-pass filter, and a setting circuit in series was designed. Calibration test results showed that the relationship between grain mass flow rate and output voltage could be described using the Gaussian regression model, and the coefficient of determination was greater than 0.98. According to the working principle of the grain cleaning system of combine harvesters, the dynamic characteristics of grain vibration feeding were analyzed using discrete element method (DEM) simulations, and the monitoring range of the sensor was determined. Finally, grain mass flow rate measurement tests were carried out on a vibration feeding test rig. The results indicated that the grain mass measurement error could be controlled within 5.0% with the average grain mass flow rate in the range of 3.0–5.0 g/mm·s. The proposed measurement method has potential application value in the uniform feeding control systems of vibration feeders.

A real-time estimation method of carbon emission for cement corporations based on load identification

Abstract Accurate carbon emission estimation is the basis for assessing the carbon emissions of various regions and industries and formulating reasonable and feasible emission reduction programs. This paper presents a generalized real-time corporate carbon footprint measurement method. The total carbon emissions are categorized into direct emissions and indirect emissions. For the former, a load identification method based on CNN-BLSTM is proposed to monitor the state of the devices, and the carbon emissions are directly calculated in combination with the carbon emission intensity of the devices; for the latter, accurate estimation of carbon emissions is achieved through the application of marginal carbon emission factor. By utilizing this analytical model, the estimated carbon emissions are more accurate than those of other methods, which can better grasp the carbon emissions of enterprises and promote energy saving and emission reduction.

Evaluation of agreement between a noninvasive method for real-time measurement of critical blood values with a standard point-of-care device.

The purpose of this work was to investigate the degree of agreement between two distinct approaches for measuring a set of blood values and to compare comfort levels reported by participants when utilizing these two disparate measurement methods. Radial arterial blood was collected for the comparator analysis using the Abbott i-STAT® POCT device. In contrast, the non-invasive proprietary DBC methodology is used to calculate sodium, potassium, chloride, ionized calcium, total carbon dioxide, pH, bicarbonate, and oxygen saturation using four input parameters (temperature, hemoglobin, pO2, and pCO2). Agreement between the measurement for a set of blood values obtained using i-STAT and DBC methodology was compared using intraclass correlation coefficients, Passing and Bablok regression analyses, and Bland Altman plots. A p-value of <0.05 was considered statistically significant. A total of 37 participants were included in this study. The mean age of the participants was 42.4 ± 13 years, most were male (65%), predominantly Caucasian/White (75%), and of Hispanic ethnicity (40%). The Intraclass Correlation Coefficients (ICC) analyses indicated agreement levels ranging from poor to moderate between i-STAT and the DBC's algorithm for Hb, pCO2, HCO3, TCO2, and Na, and weak agreement for pO2, HSO2, pH, K, Ca, and Cl. The Passing and Bablok regression analyses demonstrated that values for Hb, pO2, pCO2, TCO2, Cl, and Na obtained from the i-STAT did not differ significantly from that of the DBC's algorithm suggesting good agreement. The values for Hb, K, and Na measured by the DBC algorithm were slightly higher than those obtained by the i-STAT, indicating some systematic differences between these two methods on Bland Altman Plots. The non-invasive DBC methodology was found to be reliable and robust for most of the measured blood values compared to invasive POCT i-STAT device in healthy participants. These findings need further validation in larger samples and among individuals afflicted with various medical conditions.

Utilizing COVID-19 as a Model for Diagnostics Using an Electrochemical Sensor.

This paper reports a rapid and sensitive sensor for the detection and quantification of the COVID-19 N-protein (N-PROT) via an electrochemical mechanism. Single-frequency electrochemical impedance spectroscopy was used as a transduction method for real-time measurement of the N-PROT in an immunosensor system based on gold-conjugate-modified carbon screen-printed electrodes (Cov-Ag-SPE). The system presents high selectivity attained through an optimal stimulation signal composed of a 0.0 V DC potential and 10 mV RMS-1 AC signal at 100 Hz over 300 s. The Cov-Ag-SPE showed a log response toward N-PROT detection at concentrations from 1.0 ng mL-1 to 10.0 μg mL-1, with a 0.977 correlation coefficient for the phase (θ) variation. An ML-based approach could be created using some aspects observed from the positive and negative samples; hence, it was possible to classify 252 samples, reaching 83.0, 96.2 and 91.3% sensitivity, specificity, and accuracy, respectively, with confidence intervals (CI) ranging from 73.0 to 100.0%. Because impedance spectroscopy measurements can be performed with low-cost portable instruments, the immunosensor proposed here can be applied in point-of-care diagnostics for mass testing, even in places with limited resources, as an alternative to the common diagnostics methods.

Real-time measurement of pump beam polarization through the transmitted light intensity in a polarized alkali metal vapor cell.

A laser beam with left-/right-handed circular polarization is generally used to create the oriented atomic spins for precision measurements in a spin-exchange relaxation-free (SERF) co-magnetometer. The fluctuation of laser polarization interferes with the spin polarization of alkali metal atoms, leading to the system performance degradation. Here, we report a method for real-time polarization state measurement by using the transmitted light intensity of the pump beam passing through the vapor cell. Based on the principle of circular dichroism, the optical absorption model of polarized alkali metal atoms is established. The simulation results of the transmittance of the pump laser with different polarization states through the alkali metal vapor cell are given and verified by experiments. The experimental results show that the circularly polarized beam has the weakest absorption, while the linearly polarized laser beam is absorbed the strongest. The achieved measurement accuracy stands at an impressive 98.83 %. This work provides a simple and easy-to-use way to measure the polarization state of the laser beam used in the vapor cell devices, particularly the microfabricated prototypes with limited space.

Measurement of void fraction of zeotropic refrigerant R454C using capacitance-based sensor in horizontal flow configuration

This study measures and analyzes the void fraction of R454C, a zeotropic refrigerant, using a capacitance-based sensor. Capacitance-based sensors offer an attractive method for real-time void fraction measurement. Given that zeotropic refrigerants undergo compositional changes in their gas and liquid phases during phase change, it is essential to account for variations in relative permittivity due to these compositional changes when using a capacitance-based sensor for void fraction measurement. In this study, Landau and Lifshitz’s mixing rule is employed to predict the relative permittivity based on compositional variations of a bi-component refrigerant, along with a calibration strategy for the capacitance-based sensor based on flow pattern transitions. The void fraction measurement results obtained from the capacitance-based sensor for R454C are validated by comparison with results obtained using the quick-closing valve-based (QCV) method under identical conditions. The measurement uncertainty of the developed sensor is 4.13 %, and compared to the measurement results from the QCV method, it is found to have a relative error of −0.955 % over the entire range of experimental conditions. The measurements are conducted in a smooth tube with an inner diameter of 7.1 mm, mass flow rates ranging from 100 to 400 kg m−2 s−1, pressures from 0.789 to 1.724 MPa, and inlet vapor quality from 0.025 to 0.900. The measurement results are then compared with existing prediction correlations, and those correlations offering acceptable predictions of the void fraction of R454C are presented.

Real-Time Deformation Measurement of Long-Span Bridge using Multi-Inertial Camera System

Abstract. Monitoring the deformation of long-span bridges is essential for assessing structural health and safety. The existing single-camera deformation measurement method is unable to meet the high-precision measurement requirement for the deformation of large-span bridges. This paper proposes a real-time deformation measurement method for the long-span bridge using multi-inertial camera system. The method is based on visual measurement which the target deformation is measured by multiple cameras. Meanwhile, inertial measurement is used to estimate the relative pose of the camera to compensate for camera motion errors. It is applied to the health monitoring system of a long-span bridge. The experimental results show that the method proposed in this paper is highly consistent with the hydrostatic leveling measurement, with a RMSE of 4.83mm. The method can accurately and real-time measure the deformation of large-span bridges with a promising engineering value.

A Sensitive Technique Unravels the Kinetics of Activation and Trans-Cleavage of CRISPR-Cas Systems.

Activation of the CRISPR-Cas13a system requires the formation of a crRNA-Cas13a ribonucleoprotein (RNP) complex and the binding of an RNA activator to the RNP. These two binding processes play a crucial role in the performance of the CRISPR-Cas13a system. However, the binding kinetics remain poorly understood, and a main challenge is the lack of a sensitive method for real-time measurements of the dynamically formed active CRISPR-Cas13a enzyme. We describe here a new method to study the binding kinetics and report the rate constants (kon and koff) and dissociation constant (Kd) for the binding between Cas13a and its activator. The method is able to unravel and quantify the kinetics of binding and cleavage separately, on the basis of measuring the real-time trans-cleavage rates of the CRISPR-Cas system and obtaining the real-time concentrations of the active CRISPR-Cas ternary complex. We further discovered that once activated, the Cas13a system operates at a wide range of temperatures (7-37 °C) with fast trans-cleavage kinetics. The new method and findings are important for diverse applications of the Cas13a system, such as the demonstrated quantification of microRNA at ambient temperatures (e.g., 25 °C).

A real-time measurement and analysis method for gas holdup in a wet scrubber with the use of image information entropy

Wet scrubbers are often used in various types of industrial workshops to separate dust particles from the air. Non-uniform gas–liquid flow patterns above the sieve plate can result in significant losses in purification efficiency for particles. To improve the flow pattern via sieve plate design and to more accurately predict purification efficiency, a better understanding of the gas holdup is required. Various methods have been developed to measure and analyze the gas holdup from local and global perspectives. However, for guiding the operation and design of reactors, it is obviously insufficient to obtain the phase holdup characteristics under a steady state. The spatial and temporal characteristics of gas holdup in a wet scrubber are rather difficult to measure in real time with the current techniques. In this study, a fast real-time method for measurement of gas holdup characteristics was proposed, based on image information entropy analysis. The axial and radial profiles of gas holdup under different bubble washing operation statuses were obtained and are discussed here. More detailed information about the gas holdup above the sieve plate under various operation conditions has been attained. Sieve plate resistance and bubbling state were analyzed with the use of image information entropy.

A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals

In the Czochralski silicon single crystal growth process, the tail diameter is a key parameter that cannot be directly measured. In this paper, we propose a real-time soft measurement method that combines a deep belief network (DBN) and a support vector regression (SVR) network based on system identification to accurately predict the crystal diameter. The main steps of the proposed method are as follows: First, we address the delay problem of the effects of the temperature and crystal pulling speed on the tail diameter growth by using a back propagation (BP) neural network based on the mean impact value (MIV) method to determine the optimal delay time. Second, we construct a prediction model of the tail diameter by using the DBN network with the temperature and crystal pulling speed as input variables in the crystal growth process. Third, we improve the DBN network by using the SVR network to enhance its linear regression capability. We also employ the ant colony optimization (ACO) algorithm to obtain the optimal parameters of the SVR network. Finally, we compare the performance of the DBN-ACO-SVR network based on system identification with the DBN and SVR networks, and the results show that our method can effectively deal with the delay problem and achieve the accurate prediction of the tail diameter in the Czochralski silicon single crystal growth process.

Real-time 3D shape measurement of dynamic scenes using fringe projection profilometry: lightweight NAS-optimized dual frequency deep learning approach.

Achieving real-time and high-accuracy 3D reconstruction of dynamic scenes is a fundamental challenge in many fields, including online monitoring, augmented reality, and so on. On one hand, traditional methods, such as Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP), are struggling to balance measuring efficiency and accuracy. On the other hand, deep learning-based approaches, which offer the potential for improved accuracy, are hindered by large parameter amounts and complex structures less amenable to real-time requirements. To solve this problem, we proposed a network architecture search (NAS)-based method for real-time processing and 3D measurement of dynamic scenes with rate equivalent to single-shot. A NAS-optimized lightweight neural network was designed for efficient phase demodulation, while an improved dual-frequency strategy was employed coordinately for flexible absolute phase unwrapping. The experiment results demonstrate that our method can effectively perform 3D reconstruction with a reconstruction speed of 58fps, and realize high-accuracy measurement of dynamic scenes based on deep learning for what we believe to be the first time with the average RMS error of about 0.08 mm.

The real-time vision measurement of multi geometric information of the rolled ring in the hot ring rolling process based on deep learning

Hot ring rolling (HRR) is an advanced incremental metal-forming technology to manufacture high-performance rings. Nowadays, the mechanical or laser-sensors measurement methods can only obtain one value of the dimensions on the ring circumference per time unit, the measurement data is single and the information is limited. This paper presents a real-time vision measurement method for multi geometric information of the rolled ring such as ring’s diameter, center position, circularity and growth speed in HRR process. Firstly, the deep learning model is constructed to intelligently and quickly identify rolled ring targets from complex image backgrounds, then the adaptive linear gray scale transformation algorithm is proposed to adjust the gray scale contrast of the image according to changes in ring temperature, the interference of oxide scales and flying chips are eliminated by morphology operation. Finally, the least square method is used to fit the contour points, and further the rolled ring’s geometric information and corresponding variation trend are calculated. Several experiments are conducted on a vertical HRR mill, the geometric information of the rolled ring during the HRR is measured in real-time under the interference of harsh working conditions. For the ring with outer diameter of 350 mm, the measurement error of ring’s outer and inner diameter is less than 0.8 mm, the average processing time per image takes about 80 ms. The research provides a basis for accurate measurement of HRR process.

Real-time measurement and compensation based on iGPS for industrial robotic pose accuracy

In order to satisfy the requirements of industrial robotic pose accuracy in the operation process, measurement and compensation methods based on iGPS (indoor Global Positioning System) are proposed. An iGPS measuring network is established by solving transformation relations between robotic coordinates and iGPS coordinate, setting up measuring field and calibrating external parameters of transmitters. According to the measured values of iGPS, pose accuracies are calculated, and robotic poses are compensated by modifying command pose and secondary motion fine-tuning respectively. Experiments are designed to verify the measurement and compensation methods. The robotic poses of pre and post compensation are depicted based on 30 successive motion cycles. The pose accuracies are calculated and the effects of the two compensation methods are compared. Experimental results show that the robotic pose accuracies are greatly improved by using the proposed methods, and the real-time compensation method of secondary motion fine-tuning has better compensation effect, which proves the feasibility and effectiveness of the real-time measurement and compensation method based on iGPS.

RESEARCH ON WIDE SPECTRUM FULL-COLOR NIGHT VISION ELDERLY FACIAL VIDEO HEART RATE AUTOMATIC MEASUREMENT AND WARNING SYSTEM

In recent years, the prevalence of cardiovascular diseases has been growing geometrically, especially among the elderly. Real-time heart rate monitoring becomes an effective measure for preventing cardiovascular diseases. Traditional contact-based real-time heart rate measurement methods require long-term wearing of contact electrodes, which can cause inconvenience to the elderly population. In this study, a wide spectrum full color night vision elderly face video heart rate automatic measurement and early warning system was designed and developed by combining imaging photoplethysmography (IPPG), video acquisition equipment based on starlight wide spectrum camera and high-speed face recognition signal processing algorithm based on deep learning. By constructing a software and hardware platform, a comprehensive evaluation of the system’s application in elderly facial video heart rate measurement experiments was conducted.

A Real-Time Measurement Method and System for the Harvesting Area of a Grain Combine Harvester

A real-time system for measuring harvesting area is crucial for cross-regional operation. In response to the current situation where significant errors often occur in measuring the areas of small and irregular fields, we designed a low-cost and high-precision online real-time measurement system for the harvesting area of grain combines. The real-time measurement hardware system for measuring area was designed using an STM32 microcontroller as the core unit. The system was developed using three modes: intelligent measurement, circular measurement, and fusion measurement. Simulation and field experiments were conducted to verify the performance of the system. The experimental results showed that in the field simulation experiment with four differently shaped operating areas, the average accuracy of the intelligent mode reached 93.76%, and the average accuracy of the circular mode reached 95.48%. In the field experiments, the average accuracy of the intelligent mode reached 95.09%, the average accuracy of the circular mode reached 95.74%, and the average accuracy of the fusion measurement mode reached 98.01%. Therefore, the real-time measurement system for the harvesting area of a grain combine harvester designed in this study has high accuracy when measuring the area of small and irregular farmland, hence meeting the requirements of the design objectives.