Real-time Estimation Research Articles

Fluid viscosity prediction leveraging computer vision and robot interaction

Accurately determining fluid viscosity is crucial for various industrial and scientific applications. Traditional methods of viscosity measurement, though reliable, often require manual intervention and cannot easily adapt to real-time monitoring. With advancements in machine learning and computer vision, this work explores the feasibility of predicting fluid viscosity by analyzing fluid oscillations captured in video data. The pipeline employs a 3D convolutional autoencoder pretrained in a self-supervised manner to extract and learn features from semantic segmentation masks of oscillating fluids. Then, the latent representations of the input data, produced from the pretrained autoencoder, are processed with a distinct inference head to infer either the fluid category (classification) or the fluid viscosity (regression) in a time-resolved manner. When the latent representations generated by the pre-trained autoencoder are used for classification, the system achieves a 97.1% accuracy across a total of 4140 test datapoints. Similarly, for regression tasks, employing an additional fully-connected network as a regression head allows the pipeline to achieve a mean absolute error of 0.258 cP over 4416 test datapoints. This study represents an innovative contribution to both fluid characterization and the evolving landscape of Artificial Intelligence, demonstrating the potential of deep learning in achieving near real-time viscosity estimation and addressing practical challenges in fluid dynamics through the analysis of video data capturing oscillating fluid dynamics.

Edge compute algorithm enabled localized crop physiology sensing system for apple (Malus domestica Borkh.) crop water stress monitoring

Elevated air temperature (>35 ℃) combined with intense solar radiation can cause heat stress related damage to apple (Malus domestica Borkh.) fruits (e.g., sunburn) and increase tree evapotranspiration demand. Current heat stress mitigation techniques (e.g., evaporative cooling and netting) may protect fruits but can skew the tree evapotranspiration rates, preventing precision under-tree irrigation. A detailed understanding of heat stress mitigation techniques on tree fruit water status is critical for optimized irrigation scheduling and reduced crop losses. This study aimed to quantify water stress using a localized edge-compute-enabled crop physiology sensing system (CPSS), developed previously for fruit heat stress management. The CPSS is capable of acquiring thermal infrared and RGB images of the scene at predetermined interval. In this study, the edge compute algorithm on CPSS was amended to estimate crop water stress index (CWSI). Developed algorithm was validated for its accuracy in predicting the crop water stress under four different heat stress mitigation techniques namely: conventional overhead sprinklers, foggers, netting, and combinations of foggers and netting. A CPSS node was deployed in each treatment for acquiring thermal infrared and RGB images. Acquired imagery data were used to estimate CWSI using the modified algorithm. The algorithm-estimated CWSI showed significant negative correlation with stem water potential measurements (r = -0.8, p < 0.01). The heat stress mitigation techniques had varying effects on sensitivity of estimated CWSI. Algorithm estimated CWSI was most sensitive to changes in water stress under fogging (r = 0.76) and least sensitive under neeting (r = -0.65). Overall, the use of real-time CWSI estimates in conjunction with heat stress monitoring could help improve precision irrigation management, enabling timely actuation of the under tree drip irrigation in apple orchards.

A particle filter-based approach for real-time temperature estimation in a lithium-ion battery module during the cooling-down process

Temperature exerts a remarkable influence on the safety, performance, and lifespan of lithium-ion (Li-ion) batteries. Consequently, the real-time monitoring of this variable within battery cells emerges as both crucial and a significant challenge for battery thermal management systems. This paper introduces an approach for real-time temperature estimation in a Li-ion battery module during the cooling-down process by applying the particle filter (PF) technique. A battery module comprising fifteen cylindrical lithium cobalt oxide cells connected in series was employed for experimentation. The battery module was arranged into three rows, the PF employs real-time temperature measurements from the first cell of each string, based on a fractal-time model and an artificial evolution approach for parameter estimation, to estimate two unknown parameters and the temperature of the remaining twelve cells. Furthermore, the temperature measured on the surface of each cell in the module was compared both the PF and computational fluid dynamics (CFD) approach. The results exhibit a favorable agreement between the cell temperature estimates derived from the PF, the CFD simulations, and their respective experimental measurements. Finally, the proposed approach facilitates real-time temperature monitoring in battery modules with a reduced number of temperature sensors, thereby implying a cost reduction in the data acquisition system.

Semi-supervised learning for predicting multivariate attributes of process units from small labeled and large unlabeled data sets with application to detect properties of crude feed distillation unit

In a refinery, the accurate estimation of feed properties is crucial for precise real-time optimization (RTO), and thus, developing models for real-time estimation of crude feed properties from plant measurements remains a challenge. The unbalanced dataset arising from varying variable collection frequencies hinders estimation from plant data. To address these challenges, this study initially proposes two novel algorithms, the clustering co-training semi-supervised learning (CCoT-SSL) and stacked co-training semi-supervised learning (SCoT-SSL), to solve the unbalanced dataset problem. In contrast to prior work, this study tackles the estimation of multivariate attributes when available data comprises small labeled and large unlabeled data with a “cascade shape” structure and illustrates the proposed methods by estimating Crude Distillation Units (CDU). Results indicate that CCoT-SSL excels when the base learners are judiciously selected, which is essential for the real-time optimization of CDU and refinery operations.

Design of Small-Phase Time-Variant Low-pass Digital Fractional Differentiators and Integrators

The design method and the time-variant FIR architecture for real-time estimation of fractional and integer differentials and integrals are presented in this paper. The proposed FIR architecture is divided into two parts. Small-phase filtering, integer differentiation, and fractional differential and integration on the local data are performed by the first part, which is time-invariant. The second part, which is time-variant, handles fractional and global differentiation and integration. The separation of the two parts is necessary because real-time matrix inversion or an extensive analytical solution, which can be computationally intensive for high-order FIR architectures, would be required by a single time-variant FIR architecture. However, matrix inversion is used in the design method to achieve negligible delay in the filtered, differentiated, and integrated signals. The optimum output obtained by the method of least squares results in the negligible delay. The experimental results show that fractional and integer differentiation and integration can be performed by the proposed solution, although the fractional differentiation and integration process is sensitive to the noise and limited resolution of the measurements. In systems that require closed-loop control, disturbance observation, and real-time identification of model parameters, this solution can be implemented.

Intelligent Bayesian regularization backpropagation neuro computing paradigm for state features estimation of underwater passive object

In underwater environments, the accurate estimation of state features for passive object is a critical aspect of various applications, including underwater robotics, surveillance, and environmental monitoring. This study presents an innovative neuro computing approach for instantaneous state features reckoning of passive marine object following dynamic Markov chains. This paper introduces the potential of intelligent Bayesian regularization backpropagation neuro computing (IBRBNC) for the precise estimation of state features of underwater passive object. The proposed paradigm combines the power of artificial neural network with Bayesian regularization technique to address the challenges associated with noisy and limited underwater sensor data. The IBRBNC paradigm leverages deep neural networks with a focus on backpropagation to model complex relationships in the underwater environment. Furthermore, Bayesian regularization is introduced to incorporate prior knowledge and mitigate overfitting, enhancing the model’s robustness and generalization capabilities. This dual approach results in a highly adaptive and intelligent system capable of accurately estimating the state features of passive object in real-time. To evaluate the efficacy of this intelligent computing approach, a controlled supervised maneuvering trajectory for underwater passive object is constructed. Real-time estimations of location, velocity, and turn rate for dynamic target are scrutinized across five distinct scenarios by varying the Gaussian observed noise’s standard deviation, aiming to minimize mean square errors (MSEs) between real and estimated values. The effectiveness of the proposed IBRBNC paradigm is demonstrated through extensive simulations and experimental trials. Results showcase its superiority over traditional nonlinear filtering methods like interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model unscented Kalman filter (IMMUKF), especially in the presence of noise, incomplete measurements and sparse data.

Design and Verification of Spacecraft Pose Estimation Algorithm using Deep Learning

This study developed a real-time spacecraft pose estimation algorithm that combined a deep learning model and the least-squares method. Pose estimation in space is crucial for automatic rendezvous docking and inter-spacecraft communication. Owing to the difficulty in training deep learning models in space, we showed that actual experimental results could be predicted through software simulations on the ground. We integrated deep learning with nonlinear least squares (NLS) to predict the pose from a single spacecraft image in real time. We constructed a virtual environment capable of mass-producing synthetic images to train a deep learning model. This study proposed a method for training a deep learning model using pure synthetic images. Further, a visual-based real-time estimation system suitable for use in a flight testbed was constructed. Consequently, it was verified that the hardware experimental results could be predicted from software simulations with the same environment and relative distance. This study showed that a deep learning model trained using only synthetic images can be sufficiently applied to real images. Thus, this study proposed a real-time pose estimation software for automatic docking and demonstrated that the method constructed with only synthetic data was applicable in space.

Real-Time Speed-of-Sound Estimation In Vivo via Steered Plane Wave Ultrasound.

Speed-of-sound (SoS) is an intrinsic acoustic property of human tissues and has been regarded as a potential biomarker of tissue health. To foster the clinical use of this emerging biomarker in medical diagnostics, it is important for SoS estimates to be derived and displayed in real time. Here, we demonstrate that concurrent global SoS estimation and B-mode imaging can be achieved live on a portable ultrasound scanner. Our innovation is hinged upon the design of a novel pulse-echo SoS estimation framework that is based on steered plane wave imaging. It has accounted for the effects of refraction and imaging depth when the medium SoS differs from the nominal value of 1540 m/s that is conventionally used in medical imaging. The accuracy of our SoS estimation framework was comparatively analyzed with through-transmit time-of-flight measurements in vitro on 15 custom agar phantoms with different SoS values (1508-1682 m/s) and in vivo on human calf muscles ( N = 9 ; SoS range: 1560-1586 m/s). Our SoS estimation framework has a mean signed difference (MSD) of - 0.6 ± 2.3 m/s in vitro and - 2.2 ± 11.2 m/s in vivo relative to the reference measurements. In addition, our real-time system prototype has yielded simultaneous SoS estimates and B-mode imaging at an average frame rate of 18.1 fps. Overall, by realizing real-time tissue SoS estimation with B-mode imaging, our innovation can foster the use of tissue SoS as a biomarker in medical ultrasound diagnostics.

A rapid tool for quantification of latent infection of wheat leaves by powdery mildew1

Wheat powdery mildew caused by Blumeria graminis f. sp. tritici (Bgt) is an important disease worldwide. Detection of latent infection of leaves by the pathogen in late autumn is valuable for estimating the inoculum potential to assess disease risks in the spring. We developed a new tool for rapid detection and quantification of latent infection of seedlings by the pathogen. The method was based on recombinase polymerase amplification (RPA) coupled with an end-point detection via lateral flow device (LFD). The limit of detection is 100 ag μL-1 of Bgt DNA, without noticeable interference from either other common wheat pathogens or wheat material (Triticum aestivum). It was evaluated on wheat seedlings for this accuracy and sensitivity in detecting latent infection of Bgt. We further extended this RPA-LFD assay to estimate the level of latent infection by Bgt based on imaging analysis. There was a strong correlation between the image-based and real-time PCR assay estimates of Bgt DNA. The present results suggested that this new tool can provide rapid and accurate quantification of Bgt in latently infected leaves and can be further development as an on-site monitoring tool.

Wind-field characterization using synthetic lidar measurements and proper orthogonal decomposition

This paper presents a simple least-square method combined with Proper Orthogonal Decomposition to reconstruct full-rotor flow, using synthetic measurements from a pulsed lidar mounted on the turbine hub. The proposed lidar effectively overcomes blade blockage effects, enhancing data availability. Conducted at a wind speed of 11.4 m/s with 10% turbulence intensity, the study assesses wind-field reconstruction accuracy with the proposed method by examining the influence of mode count and measurement range selection. Comparisons with a baseline, derived from averaging line-of-sight across the rotor plane, reveal that including more modes generally improves reconstruction performance, achieving up to 57% error reduction in the wind-field reconstruction over the baseline. However, this benefit is constrained by the availability of measurements at each time step; limited data coupled with an increased number of modes can lead to overfitting, escalating errors. The method demonstrated here offers advantages in characterizing turbine responses, particularly in capturing low-frequency content in the wind-flow. Yet, channels like tower base moment necessitate a substantially higher number of modes for accurate characterization. Overall, this approach shows potential for real-time wind-flow estimation in lidar-assisted control applications.

Accurate and Real-Time Variant Hand Pose Estimation Based on Gray Code Bounding Box Representation

Accurate and Real-Time Variant Hand Pose Estimation Based on Gray Code Bounding Box Representation

AM-HNN based real-time voltage estimation method of distribution network using joint PMU and AMI data

A large amount of distributed energy access increases the risk of voltage fluctuation and off-limit voltage. Accurate voltage estimation is more and more important to ensure the safety of distribution networks. With the introduction of phasor measurement unit (PMU), multi-source measuring data with different time scales and measuring precision exist in distribution networks. How to fully integrate multi-source measurement data to achieve fast and accurate state estimation of distribution networks is an important prerequisite for efficient operation decision making. In this paper, a distribution network voltage state estimation method based on AM-HNN model is proposed. The attentional mechanism (AM) is introduced to mine the influence of PMU measurement data on voltage state estimation, and the feature weight is modified adaptively. The highway neural network (HNN) was used to fit the mapping relationship between the injected power of grid nodes and the voltage of key nodes. Taking the distribution network data of different scenes as an example, the superiority of the proposed optimal measurement model is verified by comparing with various voltage estimation methods.

Cluster-based Bayesian approach for noisy and sparse data: application to flow-state estimation

This study presents a cluster-based Bayesian methodology for state estimation under realistic conditions including noisy data from sparse sensors. The proposed approach is interpretable and, building upon previous work on transition networks, explicitly accounts for experimental noise within the data-driven framework by means of data clustering. Experimental measurements are exploited, beyond model training, to quantify the degree of uncertainty (noise) for each trained state. Such noise levels are eventually associated with probability distributions that, when combined with Bayes’ theorem, allow us to perform real-time state estimation. The proposed methodology is tested on two cases of challenging flows generated by an accelerating elliptical plate and also a delta wing experiencing gusts. Results specifically indicate that the proposed approach is robust against the number of clusters, enabling state estimation with a significant order reduction, notably decreasing the computational cost while preserving estimation accuracy. Based on the present findings, the proposed data-driven approach can be employed for realistic state estimation in nonlinear systems where noise, sensor sparsity and nonlinearities represent a challenging scenario.

Beyond margins: TIME-infused insights into recurrence in surgically treated oral squamous cell carcinoma (OSCC).

e18036 Background: Surgical margins are considered negative if the tumor is 3-5mm away from margins. At times patients with clear margins report back with a recurrence. There are factors beyond the margins that needs to be analyzed. The tumor immune microenvironment (TIME) plays a critical role in cancer development, progression, &amp; control. Aim: To assess the TIME at different surgical margins and derive scoring criteria for the prognostication of recurrence in OSCC. Objective: 1) Derive a TIME score that prognosticates the recurrence-free survival (RFS) and overall survival (OS) of the patients 2) Determine the most significant margin at which TIME score can be estimated. Methods: A retrospective cohort study between 2016 to 2023. Slides of the OSCC patients who had local recurrence were evaluated for TIME parameters: a) Tumor infiltrating lymphocytes (TILS)-(CD3, CD4, CD8, FOXP3, CD57) b) Tumor associated macrophages (TAMS)-(CD68, CD163) c) Programmed death Ligand(PDL-1) d) P53 and d) Desmoplasia at - Interphase, 1mm, 3mm, 5mm, &amp; periphery of the surgical specimen. 30 patients were included. Logistic Regression Equation (LRE) was derived for all the variables of TIME at 5 different margins. Significant margin and average RFS &amp; OS obtained. TIME score was derived. A standard equation for RFS &amp; OS was obtained using the TIME score. Results: Total = 30 patients. 19 were 40-60 years. Tongue was common site(36.66%). The predicted RFS = 23+/- 7 Months (M). OS = 47+/-8M. Regression model with the highest accuracy was found at 3mm margins (83.33%) for all the variables. LRE were fitted for all the variables to find the RFS &amp; OS. LRE for RFS for each parameter at 3mm: TILS:12.4211-0.0190*CD3-0.1222*CD4-0.0979*CD8-0.0744*FXOP3-0.1809*CD57 TAMS: -0.6635+0.0768*CD68 +0.0241*CD163 PDL1: -0.4055+0.5878*PDL1+TC +2.1972*PDL1+2.1972*PDL1 DESMOPLASIA: 0.4055+0.4418* DESMOPLASIA GRADE P53: 0.4700+0.4055*P53 LRE for OS for each parameter at 3mm: TILS:0.5784+0.0262*CD3-0.0233*CD4-0.0132*CD8-0.0233*FXOP3-0.0036*CD57 TAMS: -2.8158+0.1307*CD68 + 0.0451*CD163 PDL-1: -1.3860+1.9460*PDL1 + 1.0990*PDL1 + 0.4700*PDL1 DESMOPLASIA: -0.4055* DESMOPLASIA P53: -0.8109+0.9287*P53 Value (V) From the above equations substituted in : Probability=1/1+exp(-V)), &gt;0.5 the score is 0 : RFS (&lt; 23+/-7 M), OS (&lt;47+/-8 M) &lt;0.5 the score is 1 : RFS (&gt;23+/-7 M), OS (&gt;47+/-8 M) Summing the score from each equation= TIME score (Max. score =5) RFS = 15.621+3.030*TIME score (p Value significant) OS = 11.481+10.699*TIME score(p Value significant) Conclusions: The TIME score can be the real-time estimator of the recurrence in surgically treated OSCC. 3mm margins have best accuracy in evaluating the RFS and OS. Average RFS &amp; OS are 23+/-6 &amp; 47+/-8 M respectively.

P112 Real-time estimation of individual long- and short-term lung functions trends in persons with cystic fibrosis within the Swedish cystic fibrosis registry

P112 Real-time estimation of individual long- and short-term lung functions trends in persons with cystic fibrosis within the Swedish cystic fibrosis registry

Three-dimensional stochastic dynamical modeling for wind farm flow estimation

Modifying turbine blade pitch, generator torque, and nacelle direction (yaw) are conventional approaches for enhancing energy output and alleviating structural loads. However, the efficacy of such methods is challenged by the lag in adjusting such settings after atmospheric variations are detected. Without reliable short-term wind forecasting tools, current practice, which mostly relies on data collected at or just behind turbines, can result in sub-optimal performance. Data-assimilation strategies can achieve real-time wind forecasting capabilities by correcting model-based predictions of the incoming wind using various field measurements. In this paper, we revisit the development of a class of prior models for real-time estimation via Kalman filtering algorithms that track atmospheric variations using ground-level pressure sensors. This class of models is given by the stochastically forced linearized Navier-Stokes equations around the three-dimensional waked velocity profile defined by a curled wake model. The stochastic input to these models is devised using convex optimization to achieve statistical consistency with high-fidelity large-eddy simulations. We demonstrate the ability of such models in reproducing the second-order statistical signatures of the turbulent velocity field. In support of assimilating ground-level pressure measurements with the predictions of said models, we also highlight the significance of the wall-normal dimension in enhancing two-point correlations of the pressure field between the ground and the computational domain.

Real-time ammonia estimation in recirculating aquaculture systems: A data assimilation approach

Recirculating aquaculture systems offer sustainable fish production but face challenges related to ammonia levels. Ammonia, particularly the un-ionized fraction, NH3, can negatively impact fish growth and health. Traditional ammonia estimation methods, including offline analyses and affordable, yet inaccurate, in-situ measurements, lack the ability to provide reliable real-time insights. Data assimilation combines simulation models and in-situ measurements to provide more accurate estimations. In this study, we demonstrate a novel approach using data assimilation to enhance real-time ammonia estimation in RAS. First, ammonia dynamics are described by forming equations that constitute a simulation model, based on the feeding amounts and the biofilter removal rate parameters. Then, an extended Kalman filter is presented and customized to integrate the simulation model and total ammonia nitrogen measurements for estimating NH3, NH4+, and biofilter parameters. We validated our method through synthetic and laboratory case studies and demonstrated its superior estimation capability as compared to in situ measurements or simulation models. Furthermore, improved ammonia estimation led to improved current and future fish weight estimations, which can be essential for reliable RAS management. The proposed approach facilitates wider adoption of DA in challenging estimations in aquaculture.

HDA-pose: a real-time 2D human pose estimation method based on modified YOLOv8

HDA-pose: a real-time 2D human pose estimation method based on modified YOLOv8

Responsiveness and Precision of Digital IMUs under Linear and Curvilinear Motion Conditions for Local Navigation and Positioning in Advanced Smart Mobility.

Sensors based on MEMS technology, in particular Inertial Measurement Units (IMUs), when installed on vehicles, provide a real-time full estimation of vehicles' state vector (e.g., position, velocity, yaw angle, angular rate, acceleration), which is required for the planning and control of cars' trajectories, as well as managing the in-car local navigation and positioning tasks. Moreover, data provided by the IMUs, integrated with the data of multiple inputs from other sensing systems (such as Lidar, cameras, and GPS) within the vehicle, and with the surrounding information exchanged in real time (vehicle to vehicle, vehicle to infrastructure, or vehicle to other entities), can be exploited to actualize the full implementation of "smart mobility" on a large scale. On the other hand, "smart mobility" (which is expected to improve road safety, reduce traffic congestion and environmental burden, and enhance the sustainability of mobility as a whole), to be safe and functional on a large scale, should be supported by highly accurate and trustworthy technologies based on precise and reliable sensors and systems. It is known that the accuracy and precision of data supplied by appropriately in-lab-calibrated IMUs (with respect to the primary or secondary standard in order to provide traceability to the International System of Units) allow guaranteeing high quality, reliable information managed by processing systems, since they are reproducible, repeatable, and traceable. In this work, the effective responsiveness and the related precision of digital IMUs, under sinusoidal linear and curvilinear motion conditions at 5 Hz, 10 Hz, and 20 Hz, are investigated on the basis of metrological approaches in laboratory standard conditions only. As a first step, in-lab calibrations allow one to reduce the variables of uncontrolled boundary conditions (e.g., occurring in vehicles in on-site tests) in order to identify the IMUs' sensitivity in a stable and reproducible environment. For this purpose, a new calibration system, based on an oscillating rotating table was developed to reproduce the dynamic conditions of use in the field, and the results are compared with calibration data obtained on linear calibration benches.

Integrating Traditional and Social Media Data to Predict Bilateral Migrant Stocks in the European Union

Although up-to-date information on the nature and extent of migration within the European Union (EU) is important for policymaking, timely and reliable statistics on the number of EU citizens residing in or moving across other member states are difficult to obtain. In this paper, we develop a statistical model that integrates data on EU migrant stocks using traditional sources such as census, population registers and Labour Force Survey, with novel data sources, primarily from the Facebook Advertising Platform. Findings suggest that combining different data sources provides near real-time estimates that can serve as early warnings about shifts in EU mobility patterns. Estimated migrant stocks match relatively well to the observed data, despite some overestimation of smaller migrant populations and underestimation for larger migrant populations in Germany and the United Kingdom. In addition, the model estimates missing stocks for migrant corridors and years where no data are available, offering timely now-casted estimates.