Estimation Framework Research Articles

Robotic Framework for Requirement Management, Estimations and Project Proposals

Robotic Framework for Requirement Management, Estimations and Project Proposals

Estimation-based model predictive control with objective prioritization for mutually exclusive objectives: Application to a power plant

This work presents an algorithm for estimation-based model predictive control with objective prioritization such that distinct objectives may be defined for mutually exclusive operational regions. The objective prioritization algorithm is built by using logical conditions that define regions of operation which are incorporated into the objective function, thus allowing smooth transitions between a bank of objectives. The control objective prioritization is cast in the framework of model predictive control that is coupled with an extended Kalman filter for estimation of critical yet unmeasured state variables. The algorithm is applied to the challenging control problem of an industrial superheater (SH)-reheater (RH) system of a natural gas combined cycle plant under load following operation where smooth transitions among various control objectives is desired – operation under nominal conditions, avoidance of spraying to saturation at the inlet of the SH and RH systems, and avoidance of main steam temperature excursions. The results from the estimator framework are compared with the industrial data from an operating power plant. The control algorithm is evaluated by simulating a servo control problem and disturbance rejection scenarios as expected under load-following operation of the power plant. This algorithm is generic and can be applied to accomplish local control policies for safety, economics, quality control, state constraints, and others. Topical HeadingProcess Systems Engineering

Real-Time Deep Learning Framework for Accurate Speed Estimation of Surrounding Vehicles in Autonomous Driving

Accurate speed estimation of surrounding vehicles is of paramount importance for autonomous driving to prevent potential hazards. This paper emphasizes the critical role of precise speed estimation and presents a novel real-time framework based on deep learning to achieve this from images captured by an onboard camera. The system detects and tracks vehicles using convolutional neural networks and analyzes their trajectories with a tracking algorithm. Vehicle speeds are then accurately estimated using a regression model based on random sample consensus. A synthetic dataset using the CARLA simulator has been generated to validate the presented methodology. The system can simultaneously estimate the speed of multiple vehicles and can be easily integrated into onboard computer systems, providing a cost-effective solution for real-time speed estimation. This technology holds significant potential for enhancing vehicle safety systems, driver assistance, and autonomous driving.

Fast joint estimation of direction of arrival and towed array shape based on marginal likelihood maximization

This paper addresses the joint estimation problem of directions of arrival of sources and towed array shape. Ocean currents and the maneuvering of the towing platform often cause the towed array to bend rather than stay in a straight line, so the array shape must be estimated to avoid performance degradation due to array mismatch. Existing joint estimation methods suffer from high computational complexity or the need for external sensors installed on the array. Therefore, this paper proposes a fast joint estimation algorithm that solely utilizes received acoustic data. The joint estimation is transformed into a sparse reconstruction optimization problem within the Bayesian estimation framework. Firstly, by decomposing the marginal likelihood function, a closed-form solution for the signal variance in the spatial domain is obtained. Then, the towed array shape is introduced as a hyperparameter into the marginal likelihood function and optimized using the quasi-Newton method. In this way, we accomplish joint estimation and reduce computational complexity. Multi-source simulation results show that the proposed method achieves high resolution and fast computational speed. The robustness and efficiency of the proposed joint estimation method are demonstrated by experimental results in the South China Sea.

Better electrobiological markers and a improved automated diagnostic classifier for schizophrenia—based on a new EEG effective information estimation framework

Better electrobiological markers and a improved automated diagnostic classifier for schizophrenia—based on a new EEG effective information estimation framework

A physics-informed, data-driven framework for estimation and optimization of two-phase pressure drop of refrigerants in mini- and macro channels

At present, there is no universally accepted approach for forecasting the flow boiling heat transfer in the scientific literature. The objective of this study is to predict and optimize the two-phase pressure drop (2ϕΔP) for different refrigerants in both mini and macro-sized channels under various operating conditions. The dataset is amassed from open literature and consists of 1954 points that include seven different refrigerants, including R12345yf, R1234ze, R134A, R410A, R22, R744, and R717 containing heat flux (Q) in the range 0–40 kW/m2, hydraulic diameters (Dh) in the range 0.509–14 mm, mass flux in the range 55 < G < 1800 kg/m2s, saturation temperature (Tsat) in the range -40 – 43 °C, and flow qualities (x) in the range 0.001–0959. The first step involves conducting feature engineering operations to select the most significant and impactful features for estimating the considered output. The interpretation by Pearson’s correlation highlighted that Dh, Tsat, x, Q, and G were the most impactful params. The Bayesian optimization incorporated with surrogacy was used to determine the required hyper-parameters, which were then deployed to form multi-layer deep neural network (DNN) for estimating 2ϕΔP. A metaheuristic algorithm known as differential evolution (GA) was used to estimate Dh, Tsat, x, Q, and G to minimize the pressure drop. The performance of the established model was determined to be highly accurate with an accuracy of 0.9985. Additionally, an appropriate 2ϕΔP value for each refrigerant was provided to obtain the optimized values of the different input features. The optimized ML (regression) models performed better than the existing semi-empirical pressure drop correlations. This approach can be beneficial for the development and improvement of various engineering systems, particularly those involving the 2ϕΔP of numerous refrigerants.

Modeling Multimodal Curbside Usage in Dynamic Networks

The proliferation of emerging mobility technology has led to a significant increase in demand for ride-hailing services, on-demand deliveries, and micromobility services, transforming curb spaces into valuable public infrastructure for which multimodal transportation competes. However, the increasing utilization of curbs by different traffic modes has substantial societal impacts, further altering travelers’ choices and polluting the urban environment. Integrating the spatiotemporal characteristics of various behaviors related to curb utilization into general dynamic networks and exploring mobility patterns with multisource data remain a challenge. To address this issue, this study proposes a comprehensive framework of modeling curbside usage by multimodal transportation in a general dynamic network. The framework encapsulates route choices, curb space competition, and interactive effects among different curb users, and it embeds the dynamics of curb usage into a mesoscopic dynamic network model. Furthermore, a curb-aware dynamic origin-destination demand estimation framework is proposed to reveal the network-level spatiotemporal mobility patterns associated with curb usage through a physics-informed data-driven approach. The framework integrates emerging real-world curb use data in conjunction with other mobility data represented on computational graphs, which can be solved efficiently using the forward-backward algorithm on large-scale networks. The framework is examined on a small network as well as a large-scale real-world network. The estimation results on both networks are satisfactory and compelling, demonstrating the capability of the framework to estimate the spatiotemporal curb usage by multimodal transportation. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25. Funding: This material is based upon work supported by the Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy [Award DE-EE0009659]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0522 .

Distributionally robust origin–destination demand estimation

Gaining a good understanding of the travel demands of a city or region is extremely important for many transportation applications. For stochastic origin–destination (OD) estimation problems, an accurate distribution assumption or observation of OD estimates or data is usually desired but not always available. In this paper, we establish a novel two-stage OD estimation framework based on distributionally robust optimization (DRO) and quasi-sparsity property of large-scale OD demand matrices. The proposed two-stage Distributionally Robust Quasi-Sparsity OD estimation (DR-QSOD) model does not require an accurate or complete distribution assumption of estimates/data. Numerical results demonstrate that DR-QSOD model outperforms stochastic QSOD model in estimating OD demands when the distribution assumption of data is biased. This paper also discusses two different approaches to solve the DR-QSOD model as well as compares their computational efficiency. In addition, DR-QSOD model is shown to keep relatively high quasi-sparsity consistency, which also brings lots of meaningful practical insights.

Assessment of Economic Policy Uncertainty spillovers: A cross-border analysis of global and BRIC economies

This study empirically assesses the dynamics of variation in economic policy uncertainty over the financial markets of BRIC countries, including Brazil, Russia, India, and China. Using monthly frequency data from January 2003 to June 2023 and employing Diebold and Yilmaz (2012) spillover estimation framework, we find a significant influence of global economic policy uncertainty on the BRIC economies that varies with the time horizons. The findings show a total spillover of 35.65% in the global and BRIC economic policy uncertainty. The highest gross directional spillover is reported globally and in China, while Brazil shows the lowest directional gross economic policy uncertainty spillover. However, the global economic policy uncertainty transmits the highest net directional spillover. The study provides practical policy implications in adopting a comprehensive, globally informed approach to economic policy decisions and risk mitigation strategies to navigate the evolving landscape of economic policy uncertainties effectively.

An End-to-End Deep Learning Framework for Cyclone Intensity Estimation in North Indian Ocean Region Using Satellite Imagery

An End-to-End Deep Learning Framework for Cyclone Intensity Estimation in North Indian Ocean Region Using Satellite Imagery

MonoLoT: Self-Supervised Monocular Depth Estimation in Low-Texture Scenes for Automatic Robotic Endoscopy.

The self-supervised monocular depth estimation framework is well-suited for medical images that lack ground-truth depth, such as those from digestive endoscopes, facilitating navigation and 3D reconstruction in the gastrointestinal tract. However, this framework faces several limitations, including poor performance in low-texture environments, limited generalisation to real-world datasets, and unclear applicability in downstream tasks like visual servoing. To tackle these challenges, we propose MonoLoT, a self-supervised monocular depth estimation framework featuring two key innovations: point matching loss and batch image shuffle. Extensive ablation studies on two publicly available datasets, namely C3VD and SimCol, have shown that methods enabled by MonoLoT achieve substantial improvements, with accuracies of 0.944 on C3VD and 0.959 on SimCol, surpassing both depth-supervised and self-supervised baselines on C3VD. Qualitative evaluations on real-world endoscopic data underscore the generalisation capabilities of our methods, outperforming both depth-supervised and self-supervised baselines. To demonstrate the feasibility of using monocular depth estimation for visual servoing, we have successfully integrated our method into a proof-of-concept robotic platform, enabling real-time automatic intervention and control in digestive endoscopy. In summary, our method represents a significant advancement in monocular depth estimation for digestive endoscopy, overcoming key challenges and opening promising avenues for medical applications.

Reimagining Accessibility: Leveraging Deep Learning in Smartphone Applications to Assist Visually Impaired People Indoor Object Distance Estimation

Every other aspect of life is organized around the sight. A person with vision impairment suffers severely from independent mobility and quality of life. The proposed framework combines mobile deep learning with distance estimation algorithms to detect and classify indoor objects with estimated distance in real-time during indoor navigation. The user, wearing the device with a lanyard or holding it in a way that positions the camera forward, identifies in real-time surroundings indoor objects with estimated distance and voice commentary. Moreover, the mobile framework provides an estimated distance to the obstacles and suggests a safe navigational path through voice-guided feedback. By harnessing the power of deep learning in a mobile setting, this framework aims to improve the independence of visually impaired individuals by facilitating them a higher degree of independence in indoor navigation. This study's proposed mobile object detection and distance estimation framework achieved 99.75% accuracy. This research contributes by leveraging mobile deep learning with identifying objects in real-time, classification and distance estimation, a state-of-the-art approach to use the latest technologies to enhance indoor mobility challenges faced by visually impaired people.

Integrated Framework for Accurate State Estimation of Lithium-Ion Batteries Subject to Measurement Uncertainties

Integrated Framework for Accurate State Estimation of Lithium-Ion Batteries Subject to Measurement Uncertainties

Self-immunity study of quadrotor UAV based on Modelica system modeling and disturbance feed-forward compensation

For addressing the challenges of decreased attitude and trajectory tracking accuracy and a delayed response in the flight control operations of quadcopter unmanned aerial vehicles (UAVs) under the uncertainties of model parameters and external disturbances, this study leverages the advantages of the non-causal declarative modeling language Modelica in system modeling and simulation. In addition, it incorporates the nonlinear Active Disturbance Rejection Control (ADRC) framework for disturbance observation, estimation, and compensation. A state observer is designed to mitigate the impact of external disturbances and model uncertainties through feed-forward compensation, and stability analysis is conducted. Numerical simulations for hover resistance demonstrate that, compared to the cascade proportional integral differential (PID) control strategy, PID-NLADRC reduces the maximum deviation induced by wind disturbances by ∼50% and shortens the disturbance influence time by around 40%. Simulations for different trajectories, such as planar or spatial, smooth or abrupt changes, indicate that under the PID-NLADRC control strategy, the real-time spatial distance deviation mean is reduced by 69.5%, and the peak time is shortened by 75.7%. Validation through multi-objective applications and physical experiments highlights the advantages of PID-NLADRC in terms of positioning accuracy, rapid tracking, and disturbance suppression, aligning well with the fast, precise, and robust flight control requirements of quadcopter UAVs.

A systematic review of the integration between occupational and process safety risk analysis methodologies

Safety considerations in process industries primarily revolve around process and occupational safety. In recent times, there has been a growing emphasis on the development and application of risk estimation frameworks to provide a more holistic evaluation of these critical risk types. While previous studies have offered valuable insights into the concurrent assessment and management of occupational and process hazards, a comprehensive review of frameworks and models incorporating risk estimation dimensions is lacking. Accordingly, this review aims to systematically examine studies that have devised methods to integrate the estimation of both occupational and process risks, with a focus on categorizing applied criteria for risk estimation. A thorough literature review was conducted using reputable databases, including Web of Science and Scopus, resulting in the identification of 16 studies meeting the predetermined inclusion criteria. Subsequently, the selected studies were classified into four categories: (1) New Framework Developments, (2) Existing Framework Adjustments, (3) Hybrid Frameworks, and (4) Management System/Risk Management Integration. Furthermore, employing a relevant risk dimension perspective, the identified studies were categorized based on four primary dimensions: (a) Scenario Probability, (b) Scenario Consequences, and (c) Economical Damage. It is crucial to note that the existing literature on occupational-process hazards and associated risk dimensions lacks reliable and robust studies. Therefore, further research efforts are imperative to effectively evaluate, quantify, and precisely delineate diverse risk dimensions within the integrated domain of occupational-process safety.

An algorithm for decoy-free false discovery rate estimation in XL-MS/MS proteomics.

Cross-linking tandem mass spectrometry (XL-MS/MS) is an established analytical platform used to determine distance constraints between residues within a protein or from physically interacting proteins, thus improving our understanding of protein structure and function. To aid biological discovery with XL-MS/MS, it is essential that pairs of chemically linked peptides be accurately identified, a process that requires: (i) database search, that creates a ranked list of candidate peptide pairs for each experimental spectrum and (ii) false discovery rate (FDR) estimation, that determines the probability of a false match in a group of top-ranked peptide pairs with scores above a given threshold. Currently, the only available FDR estimation mechanism in XL-MS/MS is the target-decoy approach (TDA). However, despite its simplicity, TDA has both theoretical and practical limitations that impact the estimation accuracy and increase run time over potential decoy-free approaches (DFAs). We introduce a novel decoy-free framework for FDR estimation in XL-MS/MS. Our approach relies on multi-sample mixtures of skew normal distributions, where the latent components correspond to the scores of correct peptide pairs (both peptides identified correctly), partially incorrect peptide pairs (one peptide identified correctly, the other incorrectly), and incorrect peptide pairs (both peptides identified incorrectly). To learn these components, we exploit the score distributions of first- and second-ranked peptide-spectrum matches for each experimental spectrum and subsequently estimate FDR using a novel expectation-maximization algorithm with constraints. We evaluate the method on ten datasets and provide evidence that the proposed DFA is theoretically sound and a viable alternative to TDA owing to its good performance in terms of accuracy, variance of estimation, and run time. https://github.com/shawn-peng/xlms.

A dynamic scale-mixture model of motion in natural scenes.

Some of the most important tasks of visual and motor systems involve estimating the motion of objects and tracking them over time. Such systems evolved to meet the behavioral needs of the organism in its natural environment, and may therefore be adapted to the statistics of motion it is likely to encounter. By tracking the movement of individual points in movies of natural scenes, we begin to identify common properties of natural motion across scenes. As expected, objects in natural scenes move in a persistent fashion, with velocity correlations lasting hundreds of milliseconds. More subtly, but crucially, we find that the observed velocity distributions are heavy-tailed and can be modeled as a Gaussian scale-mixture. Extending this model to the time domain leads to a dynamic scale-mixture model, consisting of a Gaussian process multiplied by a positive scalar quantity with its own independent dynamics. Dynamic scaling of velocity arises naturally as a consequence of changes in object distance from the observer, and may approximate the effects of changes in other parameters governing the motion in a given scene. This modeling and estimation framework has implications for the neurobiology of sensory and motor systems, which need to cope with these fluctuations in scale in order to represent motion efficiently and drive fast and accurate tracking behavior.

Novel estimation framework for short-circuit current contribution of type IV wind turbines at transient and steady-state of the faults

Given the increasing penetration of converter-interfaced resources in power systems, properly estimating the short-circuit current (SCC) contribution in large networks has become a growing challenge and necessity to ensure the security and stability of the systems and assets. This paper presents two novel methods to estimate the SCC contribution of type IV wind turbines at both the transient and steady-state stages of unbalanced and balanced faults: (1) a machine learning-based method trained with electromagnetic transient (EMT) simulations and capable of estimating some of the initial peak and transient current magnitudes; (2) an analytical approach to estimate the steady-state SCC based on the voltage and grid code dependency of the converter during the fault. The methods are coupled into a single framework and compared to field-validated EMT models of a real turbine. The results show that the majority of the estimated currents in the transient stages present errors below 5%. In steady-state, the errors are not greater than 1.21%. Given the complexity of the problem, these margins may be deemed acceptable for short-circuit studies.

Assessing the extinction risk of the spontaneous flora in urban tree bases.

As the spatial arrangement of trees planted along streets in cities makes their bases potential ecological corridors for the flora, urban tree bases may be a key contributor to the overall connectivity of the urban ecosystem. However, these tree bases are also a highly fragmented environment in which extinctions are frequent. The goal of this study was to assess the plant species' ability to survive and spread through urban tree bases. To do so, we developed a Bayesian framework to assess the extinction risk of a plant metapopulation using presence/absence data, assuming that the occupancy dynamics was described by a Hidden Markov Model. The novelty of our approach is to take into account the combined effect of low-distance dispersal and the potential presence of a seed bank on the extinction risk. We introduced a metric of the extinction risk and examined its performance over a wide range of metapopulation parameters. We applied our framework to yearly floristic inventories carried out in 1324 tree bases in Paris, France. While local extinction risks were generally high, extinction risks at the street scale varied greatly from one species to another. We identified 10 plant species that could survive and spread through urban tree bases, and three plant traits correlated with the extinction risk at the metapopulation scale: the maximal height, and the beginning and end of the flowering period. Our results suggest that some plant species can use urban tree bases as ecological corridors despite high local extinction risks by forming a seed bank. We also identified other plant traits correlated with the ability to survive in tree bases, related to the action of gardeners. Moreover, our findings demonstrate that our Bayesian estimation framework based on percolation theory has the potential to be extended to more general metapopulations.

Vessel-targeted compensation of deformable motion in interventional cone-beam CT

The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior information or external tracking and for user interaction. Motion estimation is performed in two stages: (i) a target identification stage that segments arteries and catheters in the projection domain using a multi-view convolutional neural network to construct a coarse 3D vascular mask; and (ii) a targeted motion estimation stage that iteratively solves for the time-varying motion field via optimization of a vessel-enhancing objective function computed over the target vascular mask. The vessel-enhancing objective is derived through eigenvalues of the local image Hessian to emphasize bright tubular structures. Motion compensation is achieved via spatial transformer operators that apply time-dependent deformations to partial angle reconstructions, allowing efficient minimization via gradient backpropagation. The framework was trained and evaluated in anatomically realistic simulated motion-corrupted CBCTs mimicking TACE of hepatic tumors, at intermediate (3.0 mm) and large (6.0 mm) motion magnitudes. Motion compensation substantially improved median vascular DICE score (from 0.30 to 0.59 for large motion), image SSIM (from 0.77 to 0.93 for large motion), and vessel sharpness (0.189 mm−1 to 0.233 mm−1 for large motion) in simulated cases. Motion compensation also demonstrated increased vessel sharpness (0.188 mm−1 before to 0.205 mm−1 after) and reconstructed vessel length (median increased from 37.37 to 41.00 mm) on a clinical interventional CBCT. The proposed anatomy-aware motion compensation framework presented a promising approach for improving the utility of CBCT for intra-procedural vascular imaging, facilitating selective embolization procedures.