Sensor Model Research Articles

Comparative Analysis of Sinusoidal Oscillators with Surface Acoustic Wave Sensor in the Feedback Loop

Surface acoustic wave sensors can be implemented within the feedback loop of sinusoidal oscillators for detecting pollutants. This detection process involves analyzing the variation of oscillator output voltage parameters, such as amplitude and oscillation frequency. However, each oscillator topology has a distinct operating range concerning frequency and component values. Therefore, a comparative analysis is necessary to determine the oscillator with optimal performance to use with the sensor. To obtain results, a surface acoustic wave sensor model was chosen based on a piezoelectric crystal with a resonant frequency of 117.6 MHz, and three sinusoidal oscillator topologies were used: Pierce, Colpitts, and Clapp. The oscillators were simulated with the sensor model in the feedback loop using the Montecarlo method. The simulation   occurred in two steps: individually varying each component by +/- 20% with 250 iterations, and simultaneously varying all components by +/- 20% with 1000 iterations. The variables analyzed in the simulations were amplitude, oscillation frequency, and a new parameter indicating the percentage of time there is an oscillation, termed "robustness." As a result, it was determined that all three oscillators generated oscillations in the output voltage, making them viable for use with the sensor. Furthermore, it was discovered that the Clapp oscillator had a robustness of 84.5%, while the Colpitts and Pierce oscillators had a robustness of 56.3% and 49.9%, respectively. Therefore, based on the results presented in this study, the Clapp oscillator is the optimal choice for use with the surface acoustic wave sensor.

VAX

The use of audio and video modalities for Human Activity Recognition (HAR) is common, given the richness of the data and the availability of pre-trained ML models using a large corpus of labeled training data. However, audio and video sensors also lead to significant consumer privacy concerns. Researchers have thus explored alternate modalities that are less privacy-invasive such as mmWave doppler radars, IMUs, motion sensors. However, the key limitation of these approaches is that most of them do not readily generalize across environments and require significant in-situ training data. Recent work has proposed cross-modality transfer learning approaches to alleviate the lack of trained labeled data with some success. In this paper, we generalize this concept to create a novel system called VAX (Video/Audio to 'X'), where training labels acquired from existing Video/Audio ML models are used to train ML models for a wide range of 'X' privacy-sensitive sensors. Notably, in VAX, once the ML models for the privacy-sensitive sensors are trained, with little to no user involvement, the Audio/Video sensors can be removed altogether to protect the user's privacy better. We built and deployed VAX in ten participants' homes while they performed 17 common activities of daily living. Our evaluation results show that after training, VAX can use its onboard camera and microphone to detect approximately 15 out of 17 activities with an average accuracy of 90%. For these activities that can be detected using a camera and a microphone, VAX trains a per-home model for the privacy-preserving sensors. These models (average accuracy = 84%) require no in-situ user input. In addition, when VAX is augmented with just one labeled instance for the activities not detected by the VAX A/V pipeline (~2 out of 17), it can detect all 17 activities with an average accuracy of 84%. Our results show that VAX is significantly better than a baseline supervised-learning approach of using one labeled instance per activity in each home (average accuracy of 79%) since VAX reduces the user burden of providing activity labels by 8x (~2 labels vs. 17 labels).

Modelling of silicon ring resonator sensor for the detection of hormonal disorders in the human body

Modelling of silicon ring resonator sensor for the detection of hormonal disorders in the human body

Theoretical proposal of a sensor based on the comb-shaped coherent absorption realized by the magnetized plasma photonic crystals

The study presents a theoretical framework outlining a sensor capable of realizing comb-shaped coherent absorption peaks in magnetized plasma photonic crystals. The proposed model enables simultaneous measurement of alterations in the applied magnetic induction intensity, thickness of the dielectric layer, incident angle of electromagnetic waves, and the refractive index. The nascent stage of coherent absorption theory's application to photonic crystals is currently under scrutiny. Enabling interaction between both forward and backward propagating electromagnetic waves at either end of the photonic crystals creates comb-shaped absorption peaks for detecting variances in physical quantities. The sensor model utilized the transfer matrix method for computation of the transmission and reflection coefficients, linear range, sensitivity, and further linear fitting equations across all physical quantities when employed for multi-physics measurements. The present study entailed an analysis of linear range, the figure of merit, and the sensitivity of a sensor designed to detect magnetic induction intensity. Results demonstrate that the linear range from 0.7 T–1.3 T, while the figure of merit equals 50.2189 T⁻1 and sensitivity equals 4.3535 × 10⁻8 (ωd/2πc)/T. Conversely, when measuring thickness, the corresponding linear range extends from 0.4–0.85 mm, with figure of merit reaching 481.06 mm⁻1 and sensitivity equating to 3.5567 × 10⁻4 (ωd/2πc)/mm. Additionally, the sensor's application to the measurement of both incident angle and refractive index revealed parameters for linear range that ranged from 2 to 4.1, while figure of merit was calculated at 124.00 RIU⁻1 and sensitivity at 15.54952 (ωd/2πc)/RIU. Regarding incident angle, related values for linear range spanned from 15°–44°, with figure of merit established at 4.2157°⁻1 and sensitivity at 0.8661 (ωd/2πc)/degree.

Modelling the Interaction of Illuminations with a Sensor Matrix Used in Vision Systems

The article discusses a method of modelling the interaction of industrial illuminators with sensor arrays used in industrial vision systems cameras. The research used a model containing a light source and a sensor matrix. As part of the research, the average intensity of electromagnetic radiation in the visible range on the surface of the sensor matrix was measured, as well as its analysis and interpretation using a model of illuminators. The light source is described based on the measurement of an authentic industrial illuminator, for which a photometric solid was determined using a photogoniometer and spectral irradiance with a spectroradiometer. A theoretical model of the matrix was prepared, enabling the selection and control of the parameters of its work. As part of the conducted research, the impact of changing the lighting model parameters, such as the photometric solid, spectral irradiance, and number of rays, and the effect of changing the matrix parameters, such as the dimensions, number of pixels, on the irradiance measurement, were checked. The results of simulation tests are presented for selected sets of models of the light source and for the model of sensors’ matrix. The summary presents the impact of changes in parameters adopted for models on the simulation results.

Realistic sensor simulations for the digital twin

Digital twins use actual sensor data to replicate the current state of a plant in a virtual model. They can be used to evaluate the current state, predict future behavior, and thus allow to refine control or optimize operation, enable predictive maintenance as well as detection of anomalies and failures.The model of a digital twin includes models of the components, behaviors and dynamics of a system. With the ability to simulate real scenarios, such models can therefore also be used before a plant is actually implemented, e.g., to predict the actual performance, identify potential issues for the implementation and to develop optimal operation strategy and algorithms. Furthermore, interfaces may be defined, implemented, and tested with such models allowing fast and easy commissioning of the physical implementation.Accurate digital twins therefore also need to include realistic sensor models, considering adverse effects that impact their output signals. The proposed work presents approaches for accurate sensor simulations allowing researchers and industries to assess sensor performance, optimize algorithms, and evaluate system-level integration. We address Frequency Modulated Continuous Wave (FMCW) radar sensors and time-of-flight cameras as examples for far-field sensors and capacitive sensors as an example for near-field sensors. The approaches can be transferred to other sensors, e.g., ultrasound sensors, LiDAR sensors and inductive or magnetic sensors so that a wide range of industrial sensors can be covered.The proposed simulations are benchmarked with different tests, including real-world experiments and compared with the corresponding real sensors.

A new type of velocity averaging tube vortex flow sensor and measurement model of mass flow rate

Accurate mass flow rate measurement of fluid is one of the key technologies to ensure product quality and utilization of energy resources. However, the accuracy of traditional combined measuring devices is limited. In this paper, a new type of velocity averaging tube vortex flow sensor (VATV) is designed, and the mass flow rate measurement model is proposed and analyzed to achieve the high precision measurement of mass flow rate. The size, shape and structure of the VATV are designed in this study and there are no moving parts inside the integrated VATV. The method of double differential pressure is proposed to achieve the measurement of the average differential pressure signal and differential pressure fluctuation signal. Then, the method of Empirical Wavelet Transform (EWT) is used to extract the vortex shedding frequency from the differential pressure fluctuation signal. It indicated that the proposed VATV achieves an accuracy of ±0.50%, and the repeatability is also lower than 0.07%. A remarkable improvement of accuracy is achieved compared to the original combined measurement devices. The proposed sensor shows good prospect for the detection of gas-liquid two-phase flow such as wet stream due to the feature of strong anti-interference, high stability and reliability.

Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference

Strong electromagnetic interference, which has a significant impact on the performance and safety of the lithium-ion battery, usually affects the accurate state of charge (SOC). Different optimization strategies are used to estimate the model parameters and the SOC due to the unknown nonlinear characteristics caused by noise. However, the impact of sensor and model errors is treated separately. To express the sensor and model uncertainties, a noise bias compensation-equivalent circuit model (NBC-ECM) is proposed, in which sensor noise and model error voltages are employed in the model structure and the SOC estimation process of the lithium-ion battery. For parameter identification, a singular value decomposition-bias compensation recursive least squares (SVD-BCRLS) algorithm is proposed to identify the characteristic micro-parameters of the battery. Then, a moving window adaptive extended Kalman filtering (MWAEKF) algorithm based on window functions is proposed for accurate SOC estimation of lithium-ion batteries. The stability of the model parameters and the reliability of the proposed algorithm in estimating the SOC are evaluated using different noise factors: current and voltage sensor noises of 10 and 50 mA. Using the proposed SVD-BCRLS-MWAEKF algorithm, the maximum SOC error is 1.3 %, the root mean square error (RMSE) is 0.3972 %, and the mean absolute error (MAE) is 0.2316 % using the noise of 0.05 V/A under the hybrid power pulse characterization (HPPC) operating condition. With the same noise value under the Beijing bus dynamic stress test (BBDST) operating condition, the proposed algorithm SOC has a maximum SOC error of 1.57 %, an RMSE of 0.5638 %, and an MAE of 0.4475 %. Under noise interference conditions, estimation is more accurate compared to static conditions, proving that the proposed algorithm can overcome the uncertainties encountered by lithium-ion batteries for real-time BMS applications.

Intraurban NO2 hotspot detection across multiple air quality products

High-resolution air quality data products have the potential to help quantify inequitable environmental exposures over space and across time by enabling the identification of hotspots, or areas that consistently experience elevated pollution levels relative to their surroundings. However, when different high-resolution data products identify different hotspots, the spatial sparsity of ‘gold-standard’ regulatory observations leaves researchers, regulators, and concerned citizens without a means to differentiate signal from noise. This study compares NO2 hotspots detected within the city of Chicago, IL, USA using three distinct high-resolution (1.3 km) air quality products: (1) an interpolated product from Microsoft Research’s Project Eclipse—a dense network of over 100 low-cost sensors; (2) a two-way coupled WRF-CMAQ simulation; and (3) a down-sampled product using TropOMI satellite instrument observations. We use the Getis-Ord Gi * statistic to identify hotspots of NO2 and stratify results into high-, medium-, and low-agreement hotspots, including one consensus hotspot detected in all three datasets. Interrogating medium- and low-agreement hotspots offers insights into dataset discrepancies, such as sensor placement and model physics considerations, data retrieval caveats, and the potential for missing emission inventories. When treated as complements rather than substitutes, our work demonstrates that novel air quality products can enable researchers to address discrepancies in data products and can help regulators evaluate confidence in policy-relevant insights.

Monte Carlo analyses and experimental investigation of the vanadium self-powered neutron detector in 60Co source and research pulsed reactor

As the online monitoring apparatus in the reactor, self-powered neutron detectors (SPNDs) have a great potential for development in nuclear energy. To further characterize the SPND response in the actual radiation field, some irradiation experiments and Monte Carlo analyses based on the 60Co gamma-rays source and a research pulsed reactor were performed. During the experiments, the current signals were measured with various reactor irradiation facilities. A series of experimental phenomena, such as the negative current effect of the gamma rays, shutdown attenuation current, and the difference in detector sensitivity, are described and theoretically explained by using a numerical model GEANT4-SPND, an in-house Monte Carlo toolkit for SPND simulation based on Geant4. The numerical model proves to be highly effective in restoring the experimental results for the detector response current, the signal prompt proportion, and the current trend. This work has not only validated the existing calculation method but also provided reference radiation field parameters and detector experimental data for the future development and verification of sensor models. Besides, the analyses results have provided some insights into the study of the mechanism and optimization of detector design.

Development of a sensor layout for a dynamic indentation device

Issues related to the design of a dynamic indentation sensor are presented in this paper. The use of a magnetic induction method for recording the motion parameters of a striker with a differential circuit of inductors is proposed. A prototype of the primary transducer and a sensor model with a new mechanism for the striker reset system are developed.

A Self-Powered Sensor Network Data Acquisition, Modeling, and Analysis Method for Cold Chain Logistics Quality Perception

It is of great practical significance to design a cold chain logistics temperature monitoring terminal with high performance, low power consumption, low cost, safety and reliability. Based on the theory of self-powered sensor network, this paper designs data acquisition, modeling and analysis methods for the actual needs of cold chain logistics transportation management and temperature monitoring problems, applying embedded technology, sensor technology, and wireless transmission technology to solve the problem of cold chain logistics. The logistics model is designed for cold chain logistics tracking, and D-Bus technology is introduced to solve the problem of communication between design data communication methods between different nodes, and filter redundant sensor data. During the simulation process, we first initialize the monitoring software, set the serial port parameter m_ctrlComm, then select the serial port through the m_choosecom serial port selection parameter and m_DataList serial port baud rate selection parameter, the experiment proves that the road matching can be well carried out, thus improving the accuracy and value of the trajectory. The experimental results show that about 15066 times of temperature data can be collected and transmitted, and the frequency of collecting and uploading temperature data reaches 900 seconds, which verifies the effectiveness of the cold chain logistics middleware and the practicability of the data error correction algorithm.

Package Design Thermal Optimization for Metal-Oxide Gas Sensors by Finite Element Modeling and Infra-Red Imaging Characterization.

We designed a 3D geometrical model of a metal-oxide gas sensor and its custom packaging and used it in finite element modeling (FEM) analysis for obtaining temperature and heat flux distribution. The 3D computer simulation, performed with GetDP software (version 3.5.0, 13 May 2022), accurately predicted the temperature distribution variation across the entire assembly. Knowing the temperature variation and the location of the hot spots allowed us to select the best electrical interconnect method and to choose the optimal materials combination and optimal geometry. The thermal modeling also confirmed the need to use a low thermal conductivity material to insulate the MOX sensor since the latter is heated to its operational temperature of 250 °C. For that purpose, we used the in-house formulated xerogel-epoxy composite of thermal conductivity of 0.108 W m-1 K-1, which is at least 30% less compared to the best-in-class among commercially available materials. Based on the 3D FEM outputs, we designed, assembled, and characterized a fully functional packaged MOX gas sensor in several configurations. We measured the temperature distribution on all parts of the MOX gas sensor assembly using a thermal imaging infrared (IR) microscope. The results of 3D FEM are in good agreement with the temperature distribution obtained by the non-contact IR thermal characterization.

Miniaturized fiber Bragg grating accelerator sensors for low-frequency vibration monitoring.

Acceleration monitoring is an important technical means of seismic monitoring, oil exploration, deep well observation, etc. A miniaturized fiber Bragg grating (FBG) acceleration sensor with three cantilever beams is proposed against the fact that it is difficult for fiber-optic sensors to meet the requirements for low-frequency vibration monitoring. First, the model of the FBG acceleration sensor was built and theoretically analyzed; second, the effect of structural parameters on sensor sensitivity and natural frequency was analyzed, and the sensors were subjected to static stress analysis and modal simulation analysis through the ANSYS finite element analysis software; finally, the real sensors were developed and subjected to performance tests with a low-frequency vibration test system. According to the result, the natural frequency of the sensor is about 64Hz, and its sensitivity is 201.3pm/g; favorable linearity is observed at the working frequency band of 0.1-40Hz, and the transverse interference is less than 2.51%. The research findings offer a reference for the development of like sensors and the further exploration of the lower limit of low frequency.

Phase-insensitive versus phase-sensitive ultrasound absorption tomography in the frequency domain

ABSTRACT The sensitivity of phase-sensitive detectors, such as piezoelectric detectors, becomes increasingly directional as the detector element size increases. In contrast, pyroelectric sensors, which are phase-insensitive, retain their omni-directionality even for large element sizes, although they have significantly poorer temporal resolution. This study uses numerical models to examine whether phase-insensitive detectors can be used advantageously in ultrasound tomography, specifically absorption tomography, when the number of detectors is sparse. We present measurement models for phase-sensitive and phase-insensitive sensors and compare the quality of the absorption reconstructions between these sensor types based on relative error and image contrast metrics. We perform the inversion using synthetic data with a Jacobian-based linearized matrix inversion approach.

A novel surface temperature sensor and random forest-based welding quality prediction model

A novel surface temperature sensor and random forest-based welding quality prediction model

Soft Sensing of LPG Processes Using Deep Learning.

This study investigates the integration of soft sensors and deep learning in the oil-refinery industry to improve monitoring efficiency and predictive accuracy in complex industrial processes, particularly de-ethanization and debutanization. Soft sensor models were developed to estimate critical variables such as the C2 and C5 contents in liquefied petroleum gas (LPG) after distillation and the energy consumption of distillation columns. The refinery's LPG purification process relies on periodic sampling and laboratory analysis to maintain product specifications. The models were tested using data from actual refinery operations, addressing challenges such as scalability and handling dirty data. Two deep learning models, an artificial neural network (ANN) soft sensor model and an ensemble random forest regressor (RFR) model, were developed. This study emphasizes model interpretability and the potential for real-time updating or online learning. The study also proposes a comprehensive, iterative solution for predicting and optimizing component concentrations within a dual-column distillation system, highlighting its high applicability and potential for replication in similar industrial scenarios.

Electromagnetic field detectors based on spintronics devices

The paper proposes a model of an electromagnetic radiation sensor that uses the precession of the magnetization vector in a ferromagnet (ferromagnetic resonance) as a result of absorbing the energy of an incident electromagnetic wave, the generation of a spin current as a result of this precession, the generation of a spin-polarized current as a result of the passage of a spin current in a non-magnetic metal, and a change in the direction of magnetization of a ferromagnetic layer with a low coercive force (free layer) due to the passage of a spin-polarized current. Then the radiation will be detected by its effect on the electrical resistance of the entire structure, which depends on the mutual directions (parallel or antiparallel) of magnetization of the free and fixed (with a large coercive force) ferromagnetic layers (phenomenon of giant magnetic resistance). The dependence of the spin-polarized current in the device on the frequency and amplitude of the incident electromagnetic wave with linear polarization was calculated. A method of calculating the range of amplitude and frequency values of radiation that can be detected by the sensor has been developed. The parameters of this model are the detection time and the number of spin gates in one sensor. Calculations are given for a ferromagnetic layer made of permalloy and for spin valves with four different critical current values that determine the process of remagnetization of the free layer: 20, 50, 100, and 200 microamps.

A virtual sample generation method based on manifold learning and a generative adversarial network for soft sensor models with limited data

BackgroundIn the modern industrial process, data-driven-based soft sensor technology plays a key role in intelligent measurement. However, due to the strong dependence of the prediction accuracy on the training data, it can be challenging to apply this technology to particular industrial processes for which obtaining enough training samples is difficult. MethodsTo solve the problem of insufficient training data for soft sensor models, this paper proposes the Potential of Heat-diffusion for Affinity-based Trajectory Embedding and generative adversarial network based virtual sample generation (PGAN-VSG) method, which combines Potential of Heat-diffusion for Affinity-based Trajectory Embedding (PHATE) based feature extraction, virtual feature generation (VFG) techniques, and PHATE based generative adversarial network (PGAN) to generate virtual samples. The methodology of the work consists of several phases. First, the PHATE is deployed to embed the original data into a low-dimensional space to obtain the original feature set, which preserves the local and global structure of the original data. Second, a number of candidate features are randomly generated in the neighborhood space of each original feature, and the features located in the sparse feature region are selected as virtual features. Furthermore, the PGAN with L1 distance loss is used to map the virtual features to the corresponding high-dimensional space to obtain virtual samples. To validate the performance of the proposed PGAN-VSG method, a multilayer perceptron (MLP) model was employed as a soft sensor model to showcase the effectiveness of the proposed approach. This was achieved through both a numerical example and an industrial case study involving the penicillin fermentation process. Significant findingsBy filling the sample scarcity region while ensuring the distribution of generated samples, the virtual sample generation (VSG) method proposed in this paper improves the prediction accuracy of the soft sensor model while also outperforming the classical VSG method.

Gaussian mixture model sample selection strategy–based active semi-supervised soft sensor for industrial processes

Soft sensors have become reliable tools for estimating difficult-to-measure target variables in modern industrial processes. In order to make full use of labeled and unlabeled samples, an active semi-supervised soft sensor modeling method is proposed, which combines active learning and semi-supervised learning to maximize model performance and minimize the laboratory analysis cost of expanding the labeled sample data set. First, manifold regularization is introduced into the deep extreme learning machine (DELM) algorithm to form a semi-supervised DELM that improves the performance of a model trained with unlabeled samples. Then, considering non-Gaussian processes and the error information between the predicted and true values, an active sample selection strategy based on error Gaussian mixture model is developed. Using this strategy, the most uncertain and representative unlabeled samples are selected for labeling, and thereby expanding the labeled sample data set. Finally, the effectiveness of the proposed method is verified using industrial debutanizer process data.