Model Imperfections Research Articles

Promises and challenges of generative artificial intelligence for human learning.

Generative artificial intelligence (GenAI) holds the potential to transform the delivery, cultivation and evaluation of human learning. Here the authors examine the integration of GenAI as a tool for human learning, addressing its promises and challenges from a holistic viewpoint that integrates insights from learning sciences, educational technology and human-computer interaction. GenAI promises to enhance learning experiences by scaling personalized support, diversifying learning materials, enabling timely feedback and innovating assessment methods. However, it also presents critical issues such as model imperfections, ethical dilemmas and the disruption of traditional assessments. Thus, cultivating AI literacy and adaptive skills is imperative for facilitating informed engagement with GenAI technologies. Rigorous research across learning contexts is essential to evaluate GenAI's effect on human cognition, metacognition and creativity. Humanity must learn with and about GenAI, ensuring that it becomes a powerful ally in the pursuit of knowledge and innovation, rather than a crutch that undermines our intellectual abilities.

Modeling and benchmarking XRF analysis using MCNP for applications in accident tolerant fuel and cladding

There is an interest in using nondestructive testing (NDT) methods for the preliminary investigation of accident-tolerant fuel cladding materials, such as chromium (Cr) coated Zircaloy-4 (Zr4). One promising application is X-ray fluorescence (XRF) analysis. Computational methods, such as Monte Carlo N-Particle Transport (MCNP) 6.2, can be used to expand algorithms based on XRF measurements, however, it has been demonstrated that MCNP is more sensitive to modeling imperfections at lower energies (≤ 80 keV). In this work, several MCNP models were developed to evaluate the XRF measurements given by a Niton XL-5 device to minimize deviations at low energies. The final model was benchmarked to an experimental XRF measurement of Cr-coated Zr4 taken by the XL-5. The percent error in the resulting XRF peak intensities was within ± 4.92% for the Kα1 peaks and within ± 16.0% for the Kβ1. The discrepancies in the magnitude of these errors are largely due to the Kβ1 peaks having far fewer counts in the spectra that were compared. Nonetheless, these results demonstrate the potential for MCNP 6.2 to accurately predict low-energy X-ray interactions such as XRF. The deviations observed were similar to those seen in the 0.1–1 MeV range in prior works, despite only being in the ∼ 5–30 keV range themselves.

Accelerated motion correction with deep generative diffusion models.

The aim of this work is to develop a method to solve the ill-posed inverse problem of accelerated image reconstruction while correcting forward model imperfections in the context of subject motion during MRI examinations. The proposed solution uses a Bayesian framework based on deep generative diffusion models to jointly estimate a motion-free image and rigid motion estimates from subsampled and motion-corrupt two-dimensional (2D) k-space data. We demonstrate the ability to reconstruct motion-free images from accelerated two-dimensional (2D) Cartesian and non-Cartesian scans without any external reference signal. We show that our method improves over existing correction techniques on both simulated and prospectively accelerated data. We propose a flexible framework for retrospective motion correction of accelerated MRI based on deep generative diffusion models, with potential application to other forward model corruptions.

Comprehensive design optimization of vertical seismic absorbers incorporating sensitivity and robust analysis: A case study of the KDamper

This study presents a comprehensive design optimization approach for building vibration control systems, demonstrated through a case study on the KDamper implemented as a vertical seismic absorber (VSA). The methodology encompasses a series of steps to identify the most suitable VSA design while maximizing the utilization of available resources and considering model and reality imperfections. The outcomes of this investigation include a Pareto front featuring optimal structures and a set of design guidelines specifically tailored for the development of a VSA system. Moreover, optimization analyses conducted on simplified systems have revealed their potential effectiveness even without certain components. Furthermore, a bi-objective robust optimization has demonstrated the system’s resilience to manufacturing uncertainties prevalent in real-world scenarios. Finally, this approach has yielded more consistent system performance than conventional optimization approaches. The proposed methodology holds broad applicability to diverse systems, offering valuable support in achieving widespread utilization and facilitating the design of optimal structures, such as the vibration-absorption system examined in this study.

Real-Time Sequential Security-Constrained Optimal Power Flow: A Hybrid Knowledge-Data-Driven Reinforcement Learning Approach

To confront the inaccuracy and imperfection of the environmental model, this article proposes a hybrid knowledge-data-driven reinforcement learning (KDD-RL) approach to solve the sequential optimal power flow problem during real-time operation. An improved soft actor-critic algorithm is proposed to train the control policy and formulate the sequential dispatch commands to the generators. To promote the safe exploration of the reinforcement learning algorithm, a hybrid knowledge-data-driven safety layer is developed to convert the unsafe actions into the safety region. Furthermore, a security-constrained linear projection model with an inactive constraint identification process is proposed to accelerate the computation efficiency of the safety layer. Numerical simulation results verify the superiority and scalability of the proposed approach in improving the decision-making efficiency and promoting the security operation of the power systems.

Robust MPC with Support Vector Clustering-based Parametric Uncertainty Set for Building Thermal Control

Control systems are essential to support the use of building structures as short-term thermal energy storage (TES). Due to modeling and forecast imperfections, the controller must be able to deal with uncertainties. This paper proposes a robust model predictive controller (MPC) with a new uncertainty set construction technique to regulate the heat supply in a building envelope. We extend the Support Vector Clustering-based set construction technique to estimate modeling and forecast uncertainty sets. Subsequently, we integrate the sets into a Min-Max MPC framework to ensure robust feasibility by tightening the constraints. The resulting controller successfully deals with modeling and forecast uncertainties. The quality of the presented framework is compared with a nominal MPC and a robust MPC with different uncertainty set estimates. On the basis of a numerical simulation, we demonstrate that the proposed controller successfully maintains the room temperature within the comfort limits. The result also shows that our MPC is less conservative than the controller designed using a box-shaped non-falsifed parametric uncertainty set.

Study of a Low-Cost Method for Estimating Energy Fuel Resources in Anthropogenic Sediments

Abstract In 2021, at the request of PJSC “Dnepropetrovsk Coke Plant”, city Kamianske, we conducted research at the facility where coke-chemical waste is stored on one of the coal waste collectors. The study area was 9.37 ha. The research subject was anthropogenic formations, which were formed due to the storage of coal-floated sediments on the company’s premises. The research aimed to determine the characteristics and analysis of the quantity and quality of coal accumulations, determine the retention of the insulating layer and its composition, and create maps of the quality and capacity of accumulated anthropogenic sediments. The ordering company decided to use both – the latest ground penetration technology and the traditional methods of drilling and laboratory, thanks to which an unusually large number of parameters were obtained and processed during the research process. Similar research was conducted on other industrial facilities, but to a much lesser extent; since we have implemented a combined, comprehensive methodology for the assessment of anthropogenic deposits, because of which the effectiveness of geological research and economic assessment increased, the time of conducting research shortened to 40-45 calendar days, and the cost of such tests decreased five times. At this stage, some things could be improved in the methodology, such as imperfection of the mathematical model and methodology of statistical data processing. These errors will be eliminated during further modelling work under the prepared implementation plan for the tested methodology.

Extended Sliding Mode Observer-Based Output Feedback Control for Motion Tracking of Electro-Hydrostatic Actuators

This paper develops a novel output feedback control scheme for the motion-tracking problem of an electro-hydrostatic actuator (EHA) in the presence of model uncertainties and external disturbances. Firstly, a simplified third-order system model of the studied EHA is established using theoretical methods. For the first time, an extended sliding mode observer (ESMO) is introduced to simultaneously account for the shortage of unknown system states and modeling imperfections. Based on this, a robust nonlinear controller is developed using the backstepping control framework to stabilize the closed-loop system. This controller integrates estimates of immeasurable system states and lumped disturbances to deal with their adverse impacts. Moreover, the dynamic surface control (DSC) technique is employed to effectively mitigate the computational burden of the traditional backstepping framework. An ultimately uniformly bounded (UUB) performance is assured by using the recommended method. Furthermore, the stability of not only the observer but also the closed-loop system is concretely analyzed by using the Lyapunov theory. Finally, experiment results under various working scenarios are given to convincingly demonstrate the advantage of the suggested method in comparison with some reference control approaches.

Making allyship visible: evaluation of a faculty development DEI curriculum

ABSTRACT Undergraduate medical learners from historically marginalized groups face significant barriers, which was made concrete at our institution when a student presented her research indicating that Black students felt unsure about which faculty members to approach. To better support our students, we used Kern’s model for curriculum development and a critical pedagogy approach to create a Faculty Allyship Curriculum (FAC). A total of 790 individuals attended 90 workshops across 16 months and 20 individuals have completed the FAC. A majority of participants reported they felt at least moderately confident in their ability to teach learners who are underrepresented in medicine, mentor learners who are different than they are, and teach allyship topics. An informal content analysis of open-ended responses indicated changes in awareness, attitude, insight, and use of language and being more likely to display advocacy. For others considering creating a similar program, partnering with an existing program allows for rapid implementation and reach to a wide audience. We also recommend: beginning with a coalition of willing learners to quickly build community and culture change; ensuring that the curriculum supports ongoing personal commitment and change for the learners; and supporting facilitators in modeling imperfection and upstanding, ‘calling in’ rather than ‘calling out’ learners.

Support Vector Regression for Bluetooth Ranging in Multipath Environments

Ranging solutions for Internet of Things (IoT) localization applications seek to provide high accuracy with low cost of implementation. Among candidate IoT technologies that may fit this criterion, Bluetooth is a desirable choice as Bluetooth Low Energy (BLE) support is ubiquitous in modern smartphones, providing low implementation costs and low power consumption. Recent advancements in BLE ranging technology employ the Multi-Carrier Phase Difference technique which takes two-way channel frequency response (CFR) measurements. However, accurate ranging with these measurements is challenging due to many closely spaced multipath components from squaring the one-way CFR, a single or low number of snapshots, and model imperfections that arise in practical scenarios. To overcome these challenges, we propose a data-driven support vector regression (SVR) approach. Using real-world BLE measurements, our proposed SVR method demonstrates decimeter-level accuracy with single antenna devices, whereas Multiple Signal Classification (MUSIC), a popular model-based method, requires multiple antennas to obtain comparable performance. Moreover, we show robustness in different multipath environments including indoor, outdoor, and non-line-of-sight conditions, we determine generalization capabilities with training size, and we analytically establish the reduction in computational complexity compared to MUSIC.

Arts and culture engagement and mortality: A population-based prospective cohort study.

The aim of this study was to investigate associations between having visited the theatre/cinema and an arts exhibition during the past year and all-cause, cardiovascular disease (CVD), cancer and other-cause mortality. The 2008 public health postal survey in Scania, Sweden, was distributed to a stratified random sample of the adult population (18-80 years old). The participation rate was 54.1%, and 25,420 participants were included in the present study. The baseline 2008 survey data were linked to cause-of-death register data to create a prospective cohort with 8.3-year follow-up. Associations between visit to the theatre/cinema, visit to an arts exhibition and mortality were investigated in survival (Cox) regression models. Just over a quarter (26.5%) had visited both the theatre/cinema and an arts exhibition during the past year, 36.6% only the theatre/cinema, 4.9% only an arts exhibition and 32% neither of the two. Not visiting the theatre/cinema during the past year was associated with higher all-cause and CVD mortality. Not visiting an arts exhibition was associated with higher all-cause and other-cause mortality. The combination of having visited neither the theatre/cinema nor an arts exhibition during the past year was associated with higher all-cause, CVD and other-cause mortality. There is an association between attending arts and culture activities and a reduced risk of CVD and other-cause mortality but not cancer mortality, although model imperfections are possible.

Supporting Information Quality Management in Information and Communications Technology Systems with Uncertainty Modelling

Information quality management (IQM) is crucial in critical Information and Communications Technology (ICT) systems since our lives and health often depend on the quality of information that the systems provide. Especially in the field of transport, we can speak about critical systems. Worldwide, a small number of scientists take on these problems. This is because information quality (IQ) measurement is a very complex process, and IQM is impossible without IQ measurement. In the article, the authors used a method for IQ measurement created by one of them. This method for IQ measurement makes it possible to create IQM and IQ assessment (IQA). This article proposes a method for IQA that makes IQM possible. The authors demonstrated the possibility of searching for IQ modelling imperfections and offered a computation example for a road traffic light power system. In the final section authors present simulation results for two models. The results of this research can be used to optimise information flow. Employing IQA to evaluate ICT systems will enable controlling the maintenance of adequate IQ, among others, after changes to an ICT system or locating the quality models’ weakest or improper elements. The novelty of the presented IQM method is using multi-layer IQA based on uncertainty modelling.

On Performance Evaluation of Inertial Navigation Systems: The Case of Stochastic Calibration

In this work we address the problem of rigorously evaluating the performances of an inertial navigation system during its design phase in presence of multiple alternative choices. We introduce a framework based on Monte-Carlo simulations in which a standard extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms to recover a reference trajectory from noisy measurements. The evaluation of several statistical metrics of the solution, aggregated over hundreds of simulated realizations, provides reasonable estimates of the expected performances of the system in real-world conditions. This framework allows the user to make a choice between alternative setups. To show the generality of our approach, we consider an example application to the problem of stochastic calibration. Two competing stochastic modeling techniques, namely, the widely popular Allan variance linear regression, and the emerging generalised method of wavelet moments, are rigorously compared in terms of the framework’s defined metrics and in multiple scenarios. We find that the latter provides substantial advantages for certain classes of inertial sensors. Our framework allows to consider a wide range of problems related to the quantification of navigation system performances, such as the robustness of integrated navigation systems (such as INS/GNSS) with respect to outliers or other modeling imperfections. While real world experiments are essential to assess to performance of new methods, they tend to be costly and are typically unable to lead to a sufficient number of replicates to provide suitable estimates of, for example, the correctness of the estimated uncertainty. Therefore, our method can contribute in bridging the gap between these experiments and pure statistical consideration as usually found in the stochastic calibration literature.

Finite Element Modeling of Cold-Formed Steel Bolted Moment Connection

This paper describes the finite element procedure for modeling cold-formed steel bolted moment connection to simulate hysteretic moment-rotation behavior and failure mode. The connection element consists of CFS curved flange beams, double-lipped channel columns, and trough plates. Abaqus software is used in this paper. The modeling procedure includes material properties, bolt modeling, boundary conditions, mesh, loading, and geometrical imperfections. The results of the finite element modeling were compared with the experimental test results in the form of a back-bone of the moment-rotation curve and a comparison of failure deformation. It was found that the finite element results had fairly good accuracy in predicting the hysteretic moment–rotation behavior. In the elastic region, the result shows that the finite element model successfully simulates the initial stiffness of the referenced beam-column connection. Meanwhile, the peak moment of the finite element model occurs at the same rotation as the experimental test but the magnitude of the peak moment is lower than the experimental result, which indicates that the finite element model produces a more conservative design. The comparison of failure deformation between finite element model and experimental test shows a very good agreement. The numerical model can simulate well the rotational behavior of the beam-column connection and can predict the general shape and location of local/distortional buckling at the beam-column connection.

Neural network-based sensor fault estimation and active fault-tolerant control for uncertain nonlinear systems

In this work, we developed a novel active fault-tolerant control (FTC) design scheme for a class of nonlinear dynamic systems subjected simultaneously to modelling imperfections, parametric uncertainties and sensor faults. Modelling imperfections and parametric uncertainties are dealt with using an adaptive radial basis function neural network (RBFNN) that estimates the uncertain part of the system dynamics. For sensor fault estimation (FE), a nonlinear observer based on the estimated dynamics is designed. A scheme to estimate sensor faults in real-time using the nonlinear observer and an additional RBFNN is developed. The convergence properties of the RBFNN, used in the fault FE part, are improved by using a sliding surface function. For FTC design, a sliding surface is designed that incorporates the real-time sensor FE. The resulting sliding mode control (SMC) technique-based FTC law uses the estimated dynamics and real-time sensor FE. A double power-reaching law is adopted to design the switching part of the control law to improve the convergence and mitigate the chattering associated with the SMC. The FTC works well in the presence and absence of sensor faults without the requirement for controller reconfiguration. The stability of the proposed active FTC law is proved using the Lyapunov method. The developed scheme is implemented on a nonlinear simulation of an unmanned aerial vehicle (UAV). The results show good performance of the proposed unified FE and the FTC framework.

Modern problems of the state subsurface management system and possible solutions

Aim. The presented study aims to investigate the existing models and problems of subsurface management regulation in Russia in the context of economic development priorities. Tasks. The authors describe subsurface as an object of state regulation and control; identify problems in subsurface management; define theoretical models of subsurface management; substantiate the need to reorganize the current Russian subsurface management model; provide recommendations for the formation of mechanisms to support economic entities that implement environmentally friendly subsurface management systems and digital technologies; justify the need for strategic subsurface management. Methods. This study uses general scientific research methods (analysis, synthesis, tabular method, grouping) and methods of strategic analysis. Results. Several problems in Russia’s existing state subsurface management system are considered and recommendations for overcoming them are given. It is established that the current licensing model of subsurface management needs to be enhanced by strengthening the contractual component (which includes creating a mixed model). The problem of developing an environmentally friendly subsurface management system is revealed and issues in the industry’s digital transformation are investigated. Conclusions. Nowadays, subsurface management is one of the key complex objects in the public administration system. It is based on an extensive system of legal relations between the government and subsurface users. The main problems in the field of subsurface management include imperfection of the current model of state regulation, incomplete formation of a system of environmentally friendly subsurface management, insufficient development of digital platforms and technologies within the industry, and lack of strategic management. These problems can be solved by implementing the following recommendations: developing mixed models of subsurface management (a combination of concession/contract and concession/license models); implementing the concept of circular economy in subsurface management; conducting an environmental modernization of the technologies used; making a transition towards digital management of the mineral resource base; enhancing the production of domestic geological and mining equipment; conducting a strategic exploration and strategic management of subsurface resources.

Numerical and experimental studies of packed bed thermal energy storage system based on a novel transient energy model

AbstractPacked bed thermal energy storage (PBTES) is an essential means to solve the temporal difference and continuity between energy supply and utilization in the fields of concentrating solar power, compressed‐air energy storage, and waste heat recovery. In this paper, to solve the imperfection and inaccuracy of the current energy model, a precise transient two‐dimensional multiequation model is developed. The model comprehensively considers thermal dispersion, wall effects, thermophysical property changes, and other factors. Experiments are performed to compare and validate the simulation results, and the simulation error is −4.5% to 1.9%, achieving excellent simulation accuracy. Also, the wall effect on the PBTES is observed, and the gas‐solid temperature fields exhibit an evident nonuniform oscillation distribution in the radial direction. The effects of storage tank length, thermophysical properties, particle diameter, and gas‐solid initial temperature on the heat release rate are discussed in detail. Results indicated that decreasing the tank length, increasing the gas‐solid temperature difference, and particle materials with high thermal diffusivities can increase the heat release rate, and a clear linear change was found. Particularly because a larger range of particle diameter variation is considered, the heat release rate is shown to vary nonlinearly with particle diameter, and the selection of the optimal diameter must comprehensively consider the effects of the heat capacity and the heat transfer rate. This study provides a theoretical basis for the design of thermal energy storage systems.

Singularity-Free Continuous Adaptive Control of Uncertain Underactuated Surface Vessels With Prescribed Performance

This article is dealt with the problem of trajectory tracking with prescribed performance for a family of underactuated surface vessels (USVs) under model uncertainties and disturbances. The prescribed performance means that the USV tracks a given trajectory with the arbitrarily predefined speed of response and accuracy. The existing prescribed performance control (PPC) solutions and the traditional robust control approaches for USVs may have the singularity issue or cause a discontinuous control signal. Thereby, a new-type adaptive PPC strategy is put forward in this article. The adaptive technique is devoted to tackling model imperfections as usual, whereas the constraint-handling technique is adopted in a novel way. Herein, we first construct an auxiliary variable instead of using the approach angle or azimuth angle. Then, we impose constraints on the position error, not the tracking error, and the auxiliary variable, simultaneously. In this way, the predefined performance is achieved by moreover a singularity-free continuous control action. These theoretical findings are illustrated via a comparative simulation study.

Initialization and dynamical stabilization of a cloud-resolving model

A multiscale cloud-resolving model (CRM) composed of the anelastic grid equations coupled to viscous turbulence subgrid equations for the wind velocity is presented along with an initialization and dynamical stabilization method named Insertion & Brownian Motion (IBM). The subgrid model avoids the saturation of small scales. Its closure was driven by the assumption that the equilibrium law of energy production holds at small scales. The IBM method is shown to be effective in a 10-day long imperfect run with the CRM where the horizontal components of the grid velocity and the potential temperature are set to their reference value at the initial time. The model imperfection consists in a 30% raise in the heat transfer coefficient at surface, which directly impacts the potential temperature which is a control variable for the vertical component of the grid velocity. Future work will test the viability of the viscous turbulence equations as a subgrid model for global models that can be properly initialized and is less computationally demanding than 3D super-parametrization.

Positive-pressure ventilation: Modeling, validation, and stochastic control of mean-field type

We study the control design for a clinical positive-pressure ventilation device. We present a non-linear and linearized models of the positive-pressure ventilator, which are validated by means of clinical tests performed with four human patients. We propose a unified stochastic differential equation (SDE) with switching-regime-dependent drift and diffusion terms to combine both inspiratory and expiratory cycles into a unique dynamical system. The switching is determined by time-varying jump intensities letting the in-charge physician to establish the appropriate frequency of the patient breathing. We semi-explicitly design a stochastic optimal control of mean-field type that guarantees convergence of the pressure while minimizing risk terms associated to the uncertainty measurements and model imperfections.