Measurement System Research Articles

Fabrication of porcine acellular dermal matrix and oxidized hyaluronic acid conductive composite hydrogels for accelerating skin wound healing under electrical stimulation

Electrical stimulation (ES) of skin wounds can promote cell proliferation, protein synthesis, inflammatory response, and improve neurological function. In this study, the dynamical cross-linked conductive hydrogels have been developed by integration of poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) into porcine acellular dermal matrix (PADM) and oxidized hyaluronic acid (OHA) using dopamine-Fe complex (DA@Fe) as cross-linking agent. The as-obtained conductive composite hydrogels have the characteristics of rapid gelation, excellent deformation ability, high water absorption, and suitable degradation rate. Four-point probes resistivity measurement system has been used to test the electrical properties of the as-obtained hydrogels, and showing their conductivity at 0.207–0.322 S/m. In addition, the developed dynamical cross-linked conductive hydrogels exhibit excellent biocompatibility, thereby inducing cells proliferation and migration. In vivo results show that the resultant composite hydrogel can accelerate wound healing with combination of electrical stimulation (ES) by promoting expression of vascular factor (CD31) and decreasing the expression of tumor necrosis factor-α (TNF-α).

State feedback control of HPR1000 average coolant temperature based on dominant pole

Control of nuclear power plant is still based on the traditional PID control system, which is difficult to obtain high control quality in the process of a wide range of load changes. To effectively use the measurable information of the system and consider the constraints, state feedback control based on the dominant pole method is proposed for the average coolant temperature control of HPR1000. The control system is divided into two parts: one part is a feedback branch, which realizes the state feedback by using the measurable system state quantity including the core inlet temperature, the core outlet temperature and the reactor power, and at the same time introduces the integral link to reduce the steady-state error; the other part is a feedforward branch, which uses the nominal load change to make feedforward compensation to improve the control performance of load tracking. At the same time, Particle Swarm Optimization (PSO) method is used to optimize the controller parameters, and the dominant pole meeting the requirements is obtained. The control performance under different working conditions is verified on the HPR1000 model. The test results show that the state feedback control can effectively improve the setpoint tracking ability and anti-disturbance ability.

Design and validation of an automated and remote free space measurement system for nondestructive testing of fiber composites

Assessing electromagnetic constitutive parameters is crucial to prescribe the macroscopic properties of composites and their prospective applications. Free space methods are widely used for this purpose, due to their nondestructive/noncontact nature and their applicability on composites incorporating large inclusions or over-frequency bands where waveguide measurements are impractical. However, there still exists issues associated with automation, accurate calibration, remote controlling, and multifunctional characterization. Here, we designed and implemented a microwave-integrated laboratory including a test bench for permittivity/permeability and impedance measurements of individual inclusions and a free space setup for transmission/reflection measurements of fiber-based composites. Easy switching between the bench and antenna measurements was enabled by a homemade RF multiplexer. A three-stage calibration was applied: 2-port error correction (12-term model) of the vector network analyzer and the cables connecting it to the multiplexer, de-embedding of the cables connecting the multiplexer to the switches within the antenna pillars, and thru, reflect, and line (TRL) error correction for the antennas and free space. Exploiting robotics for precise antenna movement and TRL calibration enabled adjustment of the antenna distance to the test frame to a maximum of 2.5 m with a 100 μm accuracy. A multifunctional frame for external stimuli application was also designed. Apart from automation, remote control was realized through user-friendly graphical interfaces and remote access software allowing to swiftly respond to challenges faced during the global pandemic. The free space setup effectiveness was then validated by measuring the transmission/reflection of microwire-based composites from 0.5 to 20 GHz under various magnetic fields.

IOT Based Distance Measuring Robot

The paper describes the development of a distance measurement system that uses ultrasonic waves and is interfaced with a microcontroller module ATmega328P and the ATMega 16U2 Processor. We know that the human audible range is 20 Hz to 20 kHz. We can use these frequency range waves with an ultrasonic sensor. The advantages of this sensor are that when it is connected to an Arduino, which is a control and sensing system, a pro per distance measurement can be done using new methodologies. As vast sums of money are spent on hundreds of inflexible circuit boards, the Arduino will enable businesses to introduce many more unique items. This distance measurement system has a wide range of industrial applications, including range meters and proximity detectors. The ultrasonic sensor's hardware is interfaced with the Arduino. This method of measurement is an effective way to accurately measure small distances. The distance between an obstruction and the sensor is measured using an ultrasonic sensor. After knowing the speed of sound, the distance can be computed. In this project, The Bluetooth sensor receives a code from the Arduino app, and the Arduino operates in accordance with the code. Additionally, an ultrasonic sensor is utilized, which transmits an ultrasonic wave towards the object. When the ultrasonic wave returns after hitting the item, the transmitter detects the obstacle's distance and displays it on the LCD in centimeters. Using the ESP32 camera module, the user may log in to a specific IP address and see the object from a remote location while also controlling the robot.

Traffic Flow Prediction Using Novel Spatial-Temporal Multi-Head Attention Graph Convolution Networks

For urban development, accurate traffic flow prediction is crucial. Traffic flow prediction data can be used for optimizing public transportation systems, reducing congestion, planning road networks, and improving overall city infrastructure management. Despite numerous spatiotemporal methods used to forecast traffic flow, information aggregation at each site neither produce optimal results, because of the use non-Euclidean distance data. For more accurate results of traffic flow prediction, we propose a spatial-temporal multi-head attention graph convolution network (STMAGCN). It extracts temporal features using temporal convolutional layers and enriches nodes with spatial attention and multi-head attention mechanisms. By combining them, we can extract space characteristics more effectively. We also introduce a novel graph structure construction method that employs graph-based transformations to convert non-connected data points into a comprehensive graph structure, facilitating the effective application of our STMAGCN. This approach improves the predictive efficiency and accuracy of our model by ensuring seamless integration of disparate data sources. Experimental results indicate that the preferential extraction of spatiotemporal data significantly enhances traffic flow prediction accuracy. Compared with the latest models, our performance on the California Department of Transportation Performance Measurement System (PeMS) PeMS04 and PeMS08 datasets shows a slight improvement, with an average increase of 16.8%. Additionally, the average improvement in time is approximately 445.95%.

Exploring the metabolic daylength measurement system: implications for photoperiodic growth.

Photoperiod is an environmental signal that varies predictably across the year. Therefore, the duration of sunlight available for photosynthesis and in turn the ability of plants to accumulate carbon resources also fluctuates across the year. To adapt to these variations in photoperiod, the metabolic daylength measurement (MDLM) system measures the photosynthetic period rather than the absolute photoperiod, translating it into seasonal gene expression changes linked to photoperiodic growth. In this Tansley Insight, we briefly summarize the current understanding of the MDLM system and highlight gaps in our knowledge. Given the system's critical role in seasonal growth, understanding the MDLM system is essential for enhancing plant adaptation to different photoperiods and optimizing agricultural production.

Anti-disturbance adaptive cruise control method based on H∞ robust controller with optimized safe velocity and following time headway

Abstract To alleviate driver workload, many vehicles are now equipped with adaptive cruise control (ACC) systems. However, ACC performance can significantly deteriorate in real-world conditions due to disturbances such as sensor measurement errors, system uncertainties, and modeling errors. Therefore, an innovative anti-disturbance ACC method is proposed based on H ∞ robust controller with optimized safe velocity and following time headway to overcome the above problems. Initially, we analyze the longitudinal following characteristics that are undergoing disturbances and establish the ACC system model. Then, the optimization models for the safe velocity and the following time headway during ACC are designed. Subsequently, based on H ∞ theory, the design criterion of the H ∞ robust controller for ACC is established by utilizing the linear matrix inequality technique. Finally, two typical experiment scenarios are given for hardware-in-the-loop simulation to demonstrate the effectiveness of the proposed method.

The Impact of Afterpulsing Effects in Single-Photon Detectors on the Performance Metrics of Single-Photon Detection Systems

A single-photon detection system based on InGaAs SPADs is a high-precision optical measurement system capable of detecting quantum-level optical signals. However, the afterpulsing effect when using InGaAs SPADs severely limits their practical utility. The impact of afterpulsing effects on the performance of systems based on this type of detector can no longer be ignored. Therefore, this paper provides a detailed analysis of the measurement errors induced by afterpulsing effects and proposes a correction method based on a power-law model. This method analyzes the probability distribution of afterpulsing effects using the power-law model and improves the expressions for the system’s average count rate and signal-to-noise ratio by calculating the average number of afterpulses within the average response time. The influence of afterpulse probability and dead time on the system’s average count rate is also analyzed. This afterpulse correction method mitigates the measurement errors caused by afterpulsing effects, thereby enhancing the system’s measurement accuracy.

Design of a Low-Cost and High-Precision Measurement System Suitable for Organic Transistors

Design of a Low-Cost and High-Precision Measurement System Suitable for Organic Transistors

Validation of Multi-Frequency Inductive-Loop Measurement System for Parameters of Moving Vehicle Based on Laboratory Model

The paper presents research on a system for measuring the parameters of a vehicle in motion and the process of validating it under laboratory conditions. The measurement system uses four inductive-loop (IL) sensors, two slim ILs and two wide ILs. The vehicle speed, wheelbase, length, and overhangs are all determined on the basis of a vehicle magnetic profile (VMP) waveform. VMPs are captured from the continuous IL-based impedance measurement. The impedance measurement for a single IL is performed simultaneously at three carrier frequencies. The uncontrolled measurement conditions in the field on a real road test bed (RTB), including the speed of passing vehicles, motivated the development of a laboratory test bed (LTB). This LTB serves as a model of an existing measurement setup installed on the road, i.e., the RTB. The LTB includes IL sensors and a movable model of the vehicle made in 1:50 scale. The LTB enables validation of the whole measurement system in the vehicle speed range from 10 km/h up to 150 km/h in 5 km/h increments in fully controlled conditions. The measurement results are presented in the distance domain, calculated from the VMPs and the measured speed. The largest errors in estimating vehicle-model body parameters, on a natural scale, do not exceed 4.3 cm.

Measurement of government performance in literature review

Government performance means the results of government work. Its use is to evaluate government accountability, decision-making, efficiency, effectiveness, transparency, and achievement of goals. Purpose: This paper aims to explore the understanding of performance measurement tools commonly used in government, the reasons for using them, and the implementation of performance measurement in Indonesia. Method: This study uses a meta-synthesis method, an integrative review approach from 2000–2021, in the Scopus database using the keywords measurement system, performance measurement, performance measurement government, measurement system government. Results and Discussion: The final sample consisted of 23 studies, and the results showed that the most commonly used performance measurement was the balanced scorecard. This is because the balanced scorecard is able to explain the vision, mission, strategy, results, and operational actions, so that it can achieve local government goals. Research implications: Insight into government performance measurement can be used to determine the strengths and weaknesses of various performance measurement tools so that the government can implement performance measurement tools that are more appropriate for its government. Originality/Value: This study offers an adaptation of existing methods to measure government performance more effectively. In addition, this study focuses on the context of developing countries, which can provide new contributions to the literature.

Multi-Frequency Microwave Sensing System with Frequency Selection Method for Pulverized Coal Concentration

The accurate measurement of pulverized coal concentration (PCC) is crucial for optimizing the production efficiency and safety of coal-fired power plants. Traditional microwave attenuation methods typically rely on a single frequency for analysis while neglecting valuable information in the frequency domain, making them susceptible to the varying sensitivity of the signal at different frequencies. To address this issue, we proposed an innovative frequency selection method based on principal component analysis (PCA) and orthogonal matching pursuit (OMP) algorithms and implemented a multi-frequency microwave sensing system for PCC measurement. This method transcended the constraints of single-frequency analysis by employing a developed hardware system to control multiple working frequencies and signal paths. It measured insertion loss data across the sensor cross-section at various frequencies and utilized PCA to reduce the dimensionality of high-dimensional full-path insertion loss data. Subsequently, the OMP algorithm was applied to select the optimal frequency signal combination based on the contribution rates of the eigenvectors, enhancing the measurement accuracy through multi-dimensional fusion. The experimental results demonstrated that the multi-frequency microwave sensing system effectively extracted features from the high-dimensional PCC samples and selected the optimal frequency combination. Filed experiments conducted on five coal mills showed that, within a common PCC range of 0–0.5 kg/kg, the system achieved a minimum mean absolute error (MAE) of 1.41% and a correlation coefficient of 0.85. These results indicate that the system could quantitatively predict PCC and promptly detect PCC fluctuations, highlighting its immediacy and reliability.

Chromatic confocal displacement measurement using a double-sided phase diffractive lens

A chromatic confocal displacement measurement (CCDM) system using a double-sided phase diffractive lens (DPDL) is demonstrated. The DPDL is designed to produce a strong dispersion, minimize the optical structure and reduce the difficulty of fabrication and assembly, in addition to reducing the weight and size of the probe of the CCDM. A CCDM experimental system was set up based on the DPDL, and the results of the measurement experiments show that the axial measurement range of the CCDM system reaches 9.463 mm in the working wavelength range (500-700 nm), the relative error is less than 0.022% and the axial resolution near the designed focal length is better than 0.5 µm.

Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage

This paper is devoted to the real-time monitoring of close transportation devices, namely, belt conveyors. It presents a novel measurement system based on the linear strain gauges placed on the tail pulley surface. These gauges enable the monitoring and continuous collection and processing of data related to the process. An initial assessment of the machine learning application to the load identification was made. Among the tested algorithms that utilized machine learning, some exhibited a classification accuracy as high as 100% when identifying the load placed on the moving belt. Similarly, identification of the preset damage was possible using machine learning algorithms, demonstrating the feasibility of the system for fault diagnosis and predictive maintenance.

Measuring Changes in Upper Body Movement Due to Fasting Using a Camera

Understanding activity levels during fasting is important for promoting healthy fasting practices. While most existing studies focus on step counts to objectively assess the impact of fasting on activity levels and behavioral changes, the results have been mixed. Despite evidence showing that individuals spend a significant amount of time sitting while fasting, there has been no objective measurement of body movement or activity levels during sitting and fasting. This research employs a video-based, unobtrusive human body movement measurement system to monitor upper body movements during fasting and non-fasting periods over several days. Key movement features, such as inactivity, movement speed, and movement scale, were automatically extracted from the video monitoring data using a computer vision pipeline. These features were then statistically compared using t tests between fasting and non-fasting periods, analyzed by hour of the day and across different days. The results of the monitoring of five participants during typical daily sitting office work and fasting for 3–5 days indicate no consistent pattern of upper body movement changes due to fasting among the participants.

Alternative Analyzers for the Measurement of Gaseous Compounds During Type-Approval of Heavy-Duty Vehicles

Emissions standards describe the fuels, the procedures, and, among others, the analyzers to be used for the measurement of the different compounds during the type-approval of heavy-duty engines and vehicles. Traditionally, NOx, CO, hydrocarbons, and CO2 were the gaseous compounds measured within the Euro standard, with the later addition of CH4 and NH3. Euro 7, introduced in early 2024, expanded those compounds, requiring the measurement of N2O and HCHO. With an increasing number of molecules that need to be measured and introducing carbonless fuels, such as hydrogen, that present different requirements compared to carbon-based fuels, the test procedure needs to be updated. The performances of three laboratory-grade instruments and three portable emissions measurement systems based on Fourier-transformed infrared (FTIR) or quantum cascade laser infrared (QCL-IR) technologies were investigated while measuring from the tailpipe of a Diesel engine and a compressed natural gas (CNG) vehicle. All instruments presented good agreement when emissions of NOx, CO, CH4, NH3, N2O, HCHO, and CO2 were compared using: Z-score, F-test and two tail t-test of student. Water concentration measured by the four FTIRs was also in good agreement. Moreover, the dry emissions of CO2 and CO measured by the laboratory non-dispersive infrared (NDIR) and corrected using water were a few percentages different from those obtained using the regulated carbon-based approach. The results indicate that all the investigated systems are suitable for the measurement of the investigated gaseous compounds, including CO2 and H2O.

Construction of a Wi-Fi System with a Tethered Balloon in a Mountainous Region for the Teleoperation of Vehicular Forestry Machines

In this study, a Wi-Fi system with a tethered balloon is proposed for the teleoperation of vehicular forestry machines. This system was developed to establish a Wi-Fi communication for stable teleoperation in a timber harvesting site. This system consisted of a helium balloon, Wi-Fi nodes, a measurement system, a global navigation satellite system (GNSS) antenna, and a wind speed sensor. The measurement system included a GNSS module, an inertial measurement unit (IMU), a data logger, and an altitude sensor. While the helium balloon with the Wi-Fi system was 60 m in the air, the received signal strength indicator (RSSI) was measured by moving a Wi-Fi receiver on the ground. Another GNSS set was also utilized to collect the latitude and longitude data from the Wi-Fi receiver as it traveled. The developed Wi-Fi system with a tethered balloon can create a Wi-Fi zone of up to 1.9 ha within an average wind speed range of 2.2 m/s. It is also capable of performing the teleoperation of vehicular forestry machines with a maximum latency of 185.7 ms.

Influence of polarization on demodulation methods for determining the SRI value for a single TFBG and an orthogonal grating system

This paper presents a comparison of the spectra of single and cascade of two tilted fiber Bragg grating (TFBG) sensors. These sensors are characterized by high sensitivity to the refractive index. The disadvantage of a single TFBG is cross-sensitivity to changes in the polarization of the light introduced to the sensor, which is visible both when analyzing individual modes and when analyzing a wide spectral range with global demodulation algorithms. In response, a cascade structure was created consisting of a set of two TFBGs connected in series with 90 degrees of mutual rotation between them. This work presents a comparison of the polarization cross-sensitivity of sensor spectral demodulation algorithms for measuring the surrounding refractive index (SRI). While maintaining the high accuracy of the SRI measurement typical of TFBG gratings, a cascade of two such gratings properly oriented toward each other allows for a significant reduction in the sensitivity of the SRI measurement to polarization changes. Importantly, the reduction in the polarization cross-sensitivity is significantly greater for the demodulation methods that determine the cutoff wavelength shift; owing to this, a perpendicular TFBG(P-TFBG) measurement system without polarization control remains highly reliable. The application of the proposed P-TFBG system also allows greater SRI sensitivity to be obtained for a single tilted grating with the same tilt angle.

International intercomparison of Radon 222 activity concentration calibration facilities

The Coalition of International Radon Associations (COIRA) organised an inter-comparison of Rn-222 (radon) activity concentrations reported by calibration laboratories. A set of three AlphaGUARDs were used as transfer reference instruments against which to compare reported Rn-222 activity concentrations. Rn-222 activity concentration calibration facilities (sometimes termed chambers) from seven countries (Australia, US, Czech Republic, Spain, England, Sweden, Canada), and three continents participated in this project. The objective of the study was to provide information useful to calibration chamber operators and public health officials in the improvement of measurement and control systems, the maintenance of performance standards for measurements, and regulatory requirements for calibrations. This work builds upon and expands previous interlaboratory comparisons and provides data for estimating and using calibration uncertainty values as part of overall field error estimates and limits. A simple proportional difference between laboratories is presented here, calculated as the average for N hours of each hour’s difference between the laboratory’s concentration and the average of the three reference instruments. This percent difference ranged from less than 0.5% to just less than 8%. This work demonstrates that the ANSI/AARST standards limit of 8% for the estimated unexpanded (one sigma) individual calibration estimated uncertainty for continuous radon monitor calibration facilities in the US is achievable. However, given the few standards regarding calibration of Rn-222 activity concentration measurement instruments that themselves are often used to calibrate secondary and tertiary Rn-222 calibration facilities, there is a great need for continued interlaboratory comparisons to harmonise and document the calibration of Rn-222 activity concentrations.

Deep Learning‐Based Anomaly Diagnosis Method for Capacitive Voltage Transformer Measurement Error in PIoT

ABSTRACTIn the era of power Internet of Things (PIoT), the accuracy of capacitive voltage transformers (CVTs) is crucial for maintaining the reliability of voltage measurement and protection systems in smart grids, thereby contributing to overall grid stability and efficiency. Accurate and timely detection of anomalies in CVT measurement errors is essential for preventing equipment failures, reducing maintenance costs, and improving overall system reliability. However, existing anomaly diagnosis methods often rely on statistical analysis and rule‐based approaches, which have limitations in capturing complex patterns and adapting to evolving anomaly types. This paper proposes a novel deep learning‐based anomaly diagnosis method for CVT measurement error in PIoT, called LSTM‐CVT. The proposed method leverages a long short‐term memory (LSTM) neural network architecture with three key strategies: bidirectional temporal dependency capture, hierarchical feature learning, and joint anomaly diagnosis and error estimation. The experimental results demonstrate the superior performance of LSTM‐CVT compared to state‐of‐the‐art baseline methods.