Projection Error Research Articles

Sliding mode control for finite-time projective synchronisation in distinct Caputo fractional-order delayed neural networks with inconsistent orders

This manuscript delves into the realm of projective synchronisation in delayed neural networks governed by Caputo fractional-order dynamics, addressing the intricate challenges of attaining finite-time synchronisation in networks with distinct structures, functions, and orders. To start, the integration of a compensation controller within the control architecture is examined, aiming to formulate the projection error dynamics. By leveraging projection error system analysis alongside output observation and sliding mode control, a meticulously crafted derivative-based sliding mode surface along with a sliding mode controller are devised, ensuring both convergence and seamless sliding motion. Afterwards, the accessibility of the surface is assessed through relevant lemmas, Caputo derivative characteristics and the Lyapunov direct approach. The subsequent stability of sliding path is corroborated, and indispensable prerequisites for achieving finite-time projective synchronisation are outlined. Furthermore, the establishment of an upper bound for synchronisation time of distinct Caputo fractional-order delayed neural networks (CFDNNs) with inconsistent orders is carried out. Ultimately, simulations on distinct CFDNNs with consistent and inconsistent orders are conducted to exemplify our approach, employing the continuous frequency distribution model.

Real‐Time Nuclear Magnetic Resonance Detection Using Maximum Likelihood Estimation with Single‐Shallow‐Nitrogen‐Vacancy Centers in Quantum Heterodyne Measurements

Single‐nitrogen‐vacancy (NV) centers in diamond are highly promising quantum nuclear magnetic resonance (NMR) sensors. However, their exposure to many sources of noise, such as surface impurities, shot noise from avalanche photodiode overlaps, and spin‐state projection errors inherent in quantum systems, limits their usefulness. Often, long measurement durations are required to accumulate sufficient data for NMR signal detection via fast Fourier transform spectrometry. For practical reasons, methods to shorten the necessary accumulation time for NMR signal detection are greatly desired. In this article, an on‐line formulation of maximum likelihood estimation (MLE) signal processing for quantum heterodyne NMR measurements is presented as a step toward this goal. This MLE method reduces the required data accumulation time by orders of magnitude and provides good estimates of target frequency locales in real time. These results are significant to practitioners of NMR detection with single‐NV centers in diamond who require a quick litmus test for potential signals when probing a wide area.

Detecting and Explaining Python Name Errors

Python has become one of the most popular programming languages nowadays but has not received enough attention from the software engineering community. Many errors, either fixed or not yet, have been scattered in the lifetime of Python projects, including popular Python libraries that have already been reused. NameError is among one of those errors that are widespread in the Python community, as confirmed in our empirical study. Yet, our community has not put effort into helping developers mitigate its introductions. To fill this gap, we propose in this work a static analysis-based approach called DENE (short for Detecting and Explaining Name Errors) to automatically detect and explain name errors in Python projects. To this end, DENE builds control-flow graphs for Python projects and leverages a scope-aware reaching definition analysis to locate identifiers that may cause name errors at runtime and report their locations. Experimental results on carefully crafted ground truth demonstrate that DENE is effective in detecting name errors in real-world Python projects. The results also confirm that unknown name errors are still widely presented in popular Python projects and libraries, and the outputs of DENE can indeed help developers understand why the name errors are flagged as such.

NeRFtrinsic Four: An end-to-end trainable NeRF jointly optimizing diverse intrinsic and extrinsic camera parameters

Novel view synthesis using neural radiance fields (NeRF) is the state-of-the-art technique for generating high-quality images from novel viewpoints. Existing methods require a priori knowledge about extrinsic and intrinsic camera parameters. This limits their applicability to synthetic scenes, or real-world scenarios with the necessity of a preprocessing step. Current research on the joint optimization of camera parameters and NeRF focuses on refining noisy extrinsic camera parameters and often relies on the preprocessing of intrinsic camera parameters. Further approaches are limited to cover only one single camera intrinsic. To address these limitations, we propose a novel end-to-end trainable approach called NeRFtrinsic Four. We utilize Gaussian Fourier features to estimate extrinsic camera parameters and dynamically predict varying intrinsic camera parameters through the supervision of the projection error. Our approach outperforms existing joint optimization methods on LLFF and BLEFF. In addition to these existing datasets, we introduce a new dataset called iFF with varying intrinsic camera parameters. NeRFtrinsic Four is a step forward in joint optimization NeRF-based view synthesis and enables more realistic and flexible rendering in real-world scenarios with varying camera parameters.

Decomposing high uncertainty in greenhouse gas mitigation pathways in emerging regions: An approach to evaluate risks toward carbon neutrality

In the context of ambitious greenhouse gas (GHG) mitigation strategies in emerging regions, addressing the prevailing uncertainty and its key influential factors is crucial. While uncertainty analysis of GHG mitigation pathways has been extensively explored, the systematic identification and quantification of pivotal factors has been notably absent. This study introduces a novel methodology that combines Quasi-Monte Carlo simulation with Sobol variance decomposition, which identifies key influential factors and traces the flow of uncertainties. Applied in Anhui Province, a rapidly developing region in China, our findings indicate an uncertain GHG emission proportion of 6.2 % by 2030, escalating to 68.6 % by 2060, with a 95 % confidence interval. This uncertainty results in a cumulative projection error of 2.1 billion tons of CO2e from 2020 to 2070. The per capita GDP factor emerges as the predominant influence, alongside the increasing impact of renewable energy factor and UHV import electricity factor. In our uncertainty flow analysis, the energy transformation sector is identified as the principal contributor to total uncertainty, driven significantly by economic and energy-related factors. These results underscore the critical need for policymakers in emerging regions to incorporate uncertainty decomposition analysis into their strategic planning to mitigate risks in GHG reduction efforts.

Augmented Reality-Based Chronic Wound Healing Assessment Using 3D Stereoscopic Imaging and Self-Organizing Maps

Background: Chronic wounds (CW) present significant public health challenges, impacting individuals' well-being and daily activities. Traditional wound measurement methods, such as the ruler technique, are time-consuming, imprecise, and pose infection risks. Advances in wound care technology, including artificial intelligence (AI) and augmented reality (AR), have improved wound healing (WH) assessment accuracy and enhanced clinician-patient communication. Methods: This study developed a novel CW evaluation device combining AR and a stereoscopic camera system. The device captures images using two Leopard Imaging LI-OV580 stereo cameras and a pico-projector to create a 3D wound model. The system calculates the wound area by processing stereoscopic images, utilizing structured-from-motion techniques and convolutional neural networks (CNNs). Additionally, the device measures epipolar and projection errors (PE) during AR-based wound evaluation. A Self-Organizing Map (SOM) was employed to refine the 3D reconstruction of the wound. Results: The AR-PE analysis demonstrated the device's accuracy, with the lowest PE recorded at 20 cm from the wound area. The projection error increased as the measurement distance moved toward the edges of the wound. The average epipolar error (EE) was 0.46 pixels, indicating high precision in stereoscopic measurements. Conclusion: The study introduced a non-invasive CW evaluation device that improves wound assessment accuracy and clinician-patient interaction. By utilizing 3D wound modeling and AR, the system enables more effective communication and progress tracking during WH. The results suggest this technology could enhance CW management, offering a reliable alternative to traditional methods.

Analysis of continuous data assimilation with large (or even infinite) nudging parameters

This paper considers continuous data assimilation (CDA) in partial differential equation (PDE) discretizations where nudging parameters can be taken arbitrarily large. We prove that solutions are long-time optimally accurate for such parameters for the heat and Navier–Stokes equations (using implicit time stepping methods), with error bounds that do not grow as the nudging parameter gets large. Existing theoretical results either prove optimal accuracy but with the error scaled by the nudging parameter, or suboptimal accuracy that is independent of it. The key idea to the improved analysis is to decompose the error based on a weighted inner product that incorporates the (symmetric by construction) nudging term, and prove that the projection error from this weighted inner product is optimal and independent of the nudging parameter. We apply the idea to BDF2-finite element discretizations of the heat equation and Navier–Stokes equations to show that with CDA, they will admit optimal long-time accurate solutions independent of the nudging parameter, for nudging parameters large enough. Several numerical tests are given for the heat equation, fluid transport equation, Navier–Stokes, and Cahn–Hilliard that illustrate the theory.

Measurement-induced phase transitions by matrix product states scaling

We study the time evolution of long quantum spin chains subjected to continuous monitoring projected on matrix product states (MPS) with fixed bond dimension, by means of the Time-Dependent Variational Principle (TDVP) algorithm. The latter gives an effective unitary evolution which approximates the real quantum evolution up to the projection error. We show that such error displays, at large times, a phase transition in the monitoring strength, which can be well detected by scaling analysis with relatively low values of bond dimensions. Moreover, in the presence of U(1) global spin charge, we show the existence of a charge-sharpening transition well separated from the entanglement transition which we detect by studying the charge fluctuations of a local subpart of the system at large times. Our work shows that quantum monitored dynamics projected on MPS manifolds contains relevant information on measurement-induced phase transitions and provides a new method to identify measured-induced phase transitions in systems of arbitrary dimensions and sizes. Published by the American Physical Society 2024

Bias and bottlenecks study in outdoor long term thermal monitoring by infrared thermography: Leveraging opportunistic data for temperature estimation

This study aims to assess the impact of the targeted scene and environmental variables on in situ long-term thermal monitoring by infrared thermography. It also addresses projection errors arising from camera perspectives and digitization. Over a span of 3 years, extensive measurements were conducted at two instrumented test sites, yielding a valuable dataset for analysis. The paper begins by introducing and discussing the model used to convert the collected data into surface temperature estimations and its associated parameters’ sensitivities. Subsequently, it delves into the effects of camera resectioning on infrared measurements. Finally, the study investigates the influence of environmental parameters and proposes a strategy to correct for these effects, with and without additional sensors (opportunistic data) and additional in-situ measurements. Finally, a discussion is proposed and perspectives are addressed.

Theory of errors in innovation failure and strategic management of winning by failing

ABSTRACT The risk of failure in innovation projects affects organisational behaviour and the performance of firms. This study develops a theory of errors in innovation project-level failure. Error is a set of faults that generates deviations in phases of the R&D process in projects, decreasing the expected results. From a systemic perspective, innovation failure in a project is due to a set of errors that generates a negative outcome. The proposed theory clarifies the general determinants of innovation failure in projects that are due to errors in design, execution and market orientation. Case studies in the drug discovery industry and pharmaceuticals validate the consistency of the proposed theory, giving examples of errors in projects leading to innovation failures. The payoff matrix of success/failure occurrences predicts that the probability of failure in innovation projects can double by setting a difficult goal compared to an easy one. Inductive implications of the proposed theory are that organisations reduce innovation failure in projects with better adaptedness to changes that minimise errors and learn from them. The proposed theory can guide R&D managers, designers, etc. in detecting critical errors in the R&D process to apply strategies of problem solving based on a winning approach by learning from errors that lead to innovation failure in projects to enable the organisation to adapt, take advantage of important opportunities and cope with consequential environmental threats for achieving goals and sustaining the competitive advantage in turbulent markets.

Event Vision-based Corner Detection with Count-normalized Multi-Layer Perceptron and Throughput Indicator

The advancement of event cameras has sparked a revolution in imaging technology, presenting exciting opportunities for vision-based measurement tasks. Event cameras operate on an innovative asynchronous imaging principle, which offers several advantages over traditional cameras, including ultra-high dynamic range and exceptional temporal resolution. As a result, event cameras excel in challenging environments characterized by motion blur, overexposure, or underexposure, outperforming conventional frame-based cameras. However, the asynchronous signal streams generated by event cameras pose compatibility challenges with existing vision-based measurement algorithms. This paper focuses specifically on corner detection, a critical vision-based measurement task, tailored for event cameras. To address this challenge, we propose novel corner detectors that leverage advanced optimization techniques, including enhanced time surface representations, multi-layer perceptron classifiers, and an innovative throughput mechanism. Through rigorous experimentation, our method consistently shows lower projection errors compared to state-of-the-art methods across all datasets while also maintaining longer tracking times in low-textured scenarios. Specifically, our CMLP and CMLP-T methods achieve an average valid tracking rate of 83.38% and 84.65%, respectively, on the DAVIS240C dataset collection, surpassing all existing methods. We validate the effectiveness of our proposed corner detectors by demonstrating their enhanced performance compared to state-of-the-art methods. Furthermore, our work contributes to the application of machine learning in event signal processing for vision-based measurement tasks, providing insights into optimizing models for the unique characteristics of event cameras.

Time-Dependent Variational Principle with Controlled Bond Expansion for Matrix Product States.

We present a controlled bond expansion (CBE) approach to simulate quantum dynamics based on the time-dependent variational principle (TDVP) for matrix product states. Our method alleviates the numerical difficulties of the standard, fixed-rank one-site TDVP integrator by increasing bond dimensions on the fly to reduce the projection error. This is achieved in an economical, local fashion, requiring only minor modifications of standard one-site TDVP implementations. We illustrate the performance and accuracy of CBE-TDVP with several numerical examples on finite quantum lattices, including new results on bipolaron formation in the Peierls-Hubbard model and spin pumping via adiabatic flux insertion in a chiral spin liquid.

Modeling and compensation of phase error in projection moiré

The phase error caused by the non-sinusoidal characteristics of the projector and reference grating seriously reduces the measurement accuracy of projection moiré, yet relevant research is lacking. This work presents a phase error model and a phase compensation method to address this issue. The model is established by considering the multiple error sources and phase-solving principles. By employing the phase error model difference, the method accurately fits the model parameters from the non-ideal phase error. With these parameters, the phase error is iteratively extracted and eliminated from the measured phase. Due to its comprehensive consideration of the impacting factors, the model can clarify the phase error mechanism. The method not only significantly enhances the measurement accuracy but also requires only relatively few images for calibration. Experimental results demonstrate an improvement in the morphology measurement accuracy by up to 50%.

Similarities in the US Department of Agriculture international baseline projections

AbstractThe US Department of Agriculture (USDA) international baseline projections are crucial for US agricultural policy. Using a dynamic time warping algorithm, we examine whether corn, soybean, and wheat baselines exhibit a lack of significant distance indicating similarity in projections across countries. We then examine whether projections for countries with more similarity with the United States projections exhibit higher projection errors. We discover evidence of projections being similar to the United States, which is associated with lower errors for corn yield, harvested area, and exports and wheat import projections, but not for other variables. These findings offer valuable insights for USDA and stakeholders.

Determining the significant contributing factors to the occurrence of human errors in the urban construction projects: A Delphi-SWARA study approach

The construction industry is believed to be more susceptible to human errors than other industries because of its unique characteristics, particularly when it comes to urban construction projects (UCP). Despite the considerable attention given to human errors in construction sector, there has been a lack of emphasis on analysing these errors in specific projects like construction in urban environments with distinct complexities. Hence, this paper seeks to determine and assess the critical factors influencing human errors associated with the UCP. In this vein, Three rounds of Delphi surveys were done with 17 specialists in safety and construction management. According to the Delphi survey results, 35 substantial factors that contribute to the incidence of human errors in the UCP were discovered. Then, an empirical questionnaire based on the 5-point Likert scale of measurement was developed and distributed among 37 construction experts to assess the level of impact that each factor on occurring human error in the UCP. The questionnaire had 35 influential factors related to human errors, categorised into five primary divisions (environmental, technological/information systems, individual (permanently related), individual (temporarily related), and organisational). Before the distribution, the reliability and validity of the questionnaire were evaluated and confirmed. The factors were ranked using the Step-wise Weight Assessment Ratio Analysis (SWARA) technique at this point. The research findings indicated that the criterion of “technological factors/information systems” is the most crucial, with the criterion of “individual factors (permanently related)” coming in second and the criterion of “environmental factors” coming in third. The sub-criterion “weak maintenance management systems” scores first in the general ranking of sub-criteria, indicating traditional network systems, the absence of appropriate tools and equipment, and a lack of understanding of required resources. The sub-criterion “defects in details and information and lack of design dynamism” is placed second, while the sub-criterion “violation of safety regulations (use of drugs, etc.)” is ranked third. The study results can help industry practitioners make more educated judgements to minimise and manage human errors in the UCP.

Augmented‐reality‐based surgical navigation for endoscope retrograde cholangiopancreatography: A phantom study

AbstractBackgroundEndoscope retrograde cholangiopancreatography is a standard surgical treatment for gallbladder and pancreatic diseases. However, surgeons is at high risk and require sufficient surgical experience and skills.Methods(1) The simultaneous localisation and mapping technique to reconstruct the surgical environment. (2) The preoperative 3D model is transformed into the intraoperative video environment to implement the multi‐modal fusion. (3) A framework for virtual‐to‐real projection based on hand‐eye alignment. For the purpose of projecting the 3D model onto the imaging plane of the camera, it uses position data from electromagnetic sensors.ResultsOur AR‐assisted navigation system can accurately guide physicians, which means a distance of registration error to be restricted to under 5 mm and a projection error of 5.76 ± 2.13, and the intubation procedure is done at 30 frames per second.ConclusionsCoupled with clinical validation and user studies, both the quantitative and qualitative results indicate that our navigation system has the potential to be highly useful in clinical practice.

A data-driven bit projection system with motor yield prediction and advisory for directional drilling and well trajectory control

In the evolving of the oil and gas industry, where innovation and technological advancement are paramount, directional drilling stands as a pioneering technique that has revolutionized the extraction of hydrocarbon resources from beneath the Earth's surface. For drilling with mud motor, accurate projection of bit position from the latest survey and comprehensive evaluation of the current motor yield (MY) are essential to avoiding early well trajectory deviation and optimizing future directional plans. Currently, these responsibilities primarily rest on the shoulders of directional drillers (DDs), consuming valuable drilling operation time and being subject to biased human judgments. In this study, a data-driven bit projection system for directional drilling optimization and well trajectory control is developed. A kinematic model is first constructed for the projection of bit inclination (INC), azimuth (AZI), and north-east-vertical (NEV) in drilling with mud motors. Moreover, in scenarios involving curve section slide drilling, a machine learning model is developed to predict instant MYs observed between surveys, which is trained and tested on a dataset comprising drilling over 350 Delaware Basin curve section stands. Ultimately, a low MY reasoning algorithm is proposed to identify the dominant factor that causes the suboptimal MY output and provides recommendations for upcoming drilling plans. The proposed system is validated using 1189 vertical stands, 240 curve stands, and 1837 lateral stands from 17 different wells. The validation results illustrate that the bit NEV projection error averages below 0.82 ft for 90-foot surveys in vertical/lateral sections. In curve sections, the average MY prediction error is 0.96° one hundred feet or 7.6% in the relative sense and the average 30-foot bit NEV projection error is 0.228 ft, with the predicted MY as input. Compared with the physics-based bottomhole assembly (BHA) bending model and other data-driven approaches in the literature, the proposed system sees a large improvement in MY prediction and the bit NEV projection accuracies. With the minimal input data requirements, fast computation speed, and accurate projection/prediction results, the proposed bit projection system holds the opportunity of application in the autonomous directional drilling system, real-time drilling job evaluations, and well trajectory optimization.

Design and evaluation of an AR-based thermal imaging system for planning reconstructive surgeries.

Thermal imaging can be used for the non-invasive detection of blood vessels of the skin. However, mapping the results to the patient currently lacks user-friendliness. Augmented reality may provide a useful tool to superimpose thermal information on the patient. A system to support planning in reconstructive surgery using a thermal camera was designed. The obtained information was superimposed on the physical object using a Microsoft HoloLens. An RGB, depth, and thermal camera were combined to capture a scene of different modalities and reconstruct a virtual scene in real time. To register the different cameras and the AR device, an active calibration target was developed and evaluated. A Vuforia marker was used to register the hologram in the virtual space. The accuracy of the projected hologram was evaluated in a laboratory setting with participants by measuring the error between the physical object and the hologram. The AR-based system was evaluated by 21 participants in a laboratory setting. The mean projection error is 10.3 ± 9.4mm. The system is able to stream a three-dimensional scene with augmented thermal information in real time at 5 frames per second. The active calibration target can be used independently of the environment. The calibration target provides an easy-to-use method for the registration of cameras capturing the visible to long-infrared spectral range. The inside-out tracking of the HoloLens in combination with a Vuforia marker is not accurate enough for the intended clinical use.

Exploring visual quality of multidimensional time series projections

Dimensionality reduction is often used to project time series data from multidimensional to two-dimensional space to generate visual representations of the temporal evolution. In this context, we address the problem of multidimensional time series visualization by presenting a new method to show and handle projection errors introduced by dimensionality reduction techniques on multidimensional temporal data. For visualization, subsequent time instances are rendered as dots that are connected by lines or curves to indicate the temporal dependencies. However, inevitable projection artifacts may lead to poor visualization quality and misinterpretation of the temporal information. Wrongly projected data points, inaccurate variations in the distances between projected time instances, and intersections of connecting lines could lead to wrong assumptions about the original data. We adapt local and global quality metrics to measure the visual quality along the projected time series, and we introduce a model to assess the projection error at intersecting lines. These serve as a basis for our new uncertainty visualization techniques that use different visual encodings and interactions to indicate, communicate, and work with the visualization uncertainty from projection errors and artifacts along the timeline of data points, their connections, and intersections. Our approach is agnostic to the projection method and works for linear and non-linear dimensionality reduction methods alike.

Practical Multi-Mesh Registration for Few-View Poly-Chromatic X-Ray Inspection

Accurate 3D mesh registration is essential in many industrial applications of X-ray imaging, as it allows quality assessment and inspection of manufactured objects. Conventional methods rely mainly on time-consuming and expensive X-ray computed tomography (X-CT) or ancillary camera systems. Instead, we propose a novel approach for efficient 3D multi-mesh registration in few-view industrial X-ray imaging scenarios. Our approach harnesses the capabilities of CAD-ASTRA, an X-ray mesh projector, compatible with the ASTRA toolbox and popular GPU libraries such as CuPy and PyTorch, for the simulation of X-ray projec tions from a known object surface mesh. As a differentiable program, CAD-ASTRA allows iterative improvement of the objects’ position in space by back-propagation of a differentiable measure of the projection error. The potential of this approach is demonstrated through tests on simultaneous multiple object registration in a poly-chromatic imaging, even in cases where the spectral characteristics of the imaging system are unknown. Results from a diverse set of real experiments highlight the efficacy of mesh registration, achieving successful registrations even when only two projections at a 10∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ $$\\end{document} angle relative to the scanning system center are available. The mesh projector facilitates resource-efficient registration in industrial applications with few viewpoints, thereby reducing the demand for resources and eliminating the need for X-CT reconstruction.