Dimensional Space Research Articles

Fault Detection and Isolation in Simulated Batch Operation of Fine Motion Control Rod Drives

Control rods and elements manage the power distribution in nuclear reactors through the motion of banks of rods distributed throughout the core. These positional changes are achieved through the actuation of fine motion control rod drive (FMCRD) mechanisms. In the BWRX-300 design by GE-Hitachi, this mechanism is electrically driven by a servomotor that allows for high-precision control of power outputs. Under operational transients, such as load-following, accurate and precise operation of these servomotors is necessary over long periods of time, so they are key maintenance targets to maintain availability and operational flexibility. Swiftly and precisely identifying faults in the drive mechanisms will support predictive maintenance and reduced costs. This paper used three different types of simulated faults to test the fault detectability of principal component analysis (PCA) when considering the simulated operations of banks of control rods and their associated servomotors. These faults were stator short-circuit faults, ball screw jam faults, and ball screw wear faults. Torque and position were monitored in the simulation. The position signal was insufficient to detect mechanical faults. Torque signals for each servomotor in the bank of rods undergoing multiple position demand changes were projected to a reduced dimensional space via PCA. Q and T2 statistics were employed for anomaly detection. Using this approach, all faults were detected, and the anomalies were isolated to the faulty FMCRD mechanism.

Controllability and observability of conformable fractional finite dimensional linear systems

In this research paper, we investigate the controllability and observability of continuous-time fractional dynamical systems. The characterisation of all fractional continuous one-parameter semi groups on a finite dimensional vector space as fractional matrix-valued exponential functions is given. This characterisation is used to express the solution of continuous-time fractional dynamical system. Furthermore, we analyse controllability and observability of linear conformable fractional systems in the mathematical context of linear operators on a finite dimensional vector space. By defining the controllability and observability maps, we establish necessary and sufficient conditions for controllability and observability of this type of systems. We also present the Hautus test for controllability and observability, and introduce controllability and observability Gramians, both for finite and infinite time interval. Finally, we present several illustrative applications and examples to validate all the obtained results.

Deep Learning PCA Correlation Analysis of Teacher’s Educational Technology Quality and Teacher’s Professional Competence

In contemporary education, the integration of educational technology has become crucial for enhancing teaching quality and fostering student learning outcomes. This study employs a novel approach by combining deep learning techniques with Principal Component Analysis (PCA) to explore the relationship between teachers' educational technology proficiency and their professional competence. The research leverages a dataset comprising various metrics related to teachers' usage of educational technology tools and their performance in professional development programs. Initially, the study preprocesses the data and applies deep learning models to extract high-level features from the complex and heterogeneous dataset. Subsequently, PCA is employed to reduce the dimensionality of the feature space while preserving the underlying structure and variability of the data. Through this process, latent factors representing different aspects of educational technology proficiency and professional competence are identified. The correlation analysis conducted on the reduced feature space reveals intricate relationships between teachers' competency in educational technology and their overall professional capabilities. The findings indicate that certain dimensions of educational technology quality, such as adaptability to new tools and innovative pedagogical approaches, positively correlate with measures of professional competence, including classroom management skills, student engagement, and instructional effectiveness.

Simulation and parameter optimization of 6-dimensional phase space injection scheme based on HIAF-BRing

In high-intensity heavy ion synchrotrons such as HIAF-BRing, mitigating vacuum degradation and consequent beam lifetime reduction caused by injected beam losses remain a key challenge. BRing aims to inject particles on the order of 1011 with a beam loss not exceeding 5% when the collimation system is inactive. This paper introduces a 6-dimensional phase space painting injection (6-D injection) scheme designed to address both the high intensity and low loss challenges. To optimize numerous parameters in the injection schemes, a multi-parameter optimized injection model is constructed. The simulation example without space charge confirms the feasibility of the model, demonstrating the advantage of 6-D injection over multi-turn injection and two-plane painting injection. Subsequent simulations implement multi-parameter optimization under space charge, resulting in a 6-D injection scheme with a beam loss rate of 4%, meeting the desired performance requirements.

Physics-informed neural networks in groundwater flow modeling: Advantages and future directions

In recent years, there has been enormous development in soft computing, especially artificial intelligence (AI), which has developed robust methods for solving complex engineering problems. Researchers in the field of water resources engineering have applied these AI methods to solve a variety of hydrological problems. Despite their widespread use in the surface and atmospheric hydrology fields, groundwater hydrologists have not widely used AI methods in their routine field-scale modeling efforts. This is because AI models have been primarily considered black box models that lack physical meaning. Furthermore, using AI models to generate the space-time distribution of transient groundwater level variations is challenging and requires further flux balance and mass transport analyses. More recently, a new type of physics-informed neural network (PINN) model has been developed to address several limitations by integrating governing physics (groundwater flow equations) into the AI tools. This study presents the systematic advantages of the PINN algorithm for solving groundwater problems using a set of classic test problems. As discussed in detail in the article, these advantages and potentials are associated with the meshless nature of PINN, its continuous time and space dimensions, its independence from time-stepping and incremental marching in space, and its efficiency in running time. However, despite PINN's promising attributes, it is important to acknowledge its nascent stage of development and the inherent limitations of all neural network models, such as training challenges and hyperparameter selection. Thus, collaborative efforts between groundwater modelers and computer scientists are imperative to explore and exploit the full potential of PINN in tackling increasingly complex groundwater problems and nurturing PINN into a dependable modeling tool in industry and academia.

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is constructed using continuously differentiable obstacle representations. However, state-of-the-art approaches may suffer from local minima induced by non-convex obstacles, thus failing to scale to complex, high-dimensional joint spaces. On the other hand, sampling-based Model Predictive Control (MPC) techniques provide feasible collision-free paths in joint-space, yet are limited to quasi-reactive scenarios due to computational complexity that grows cubically with space dimensionality and horizon length. To control the robot in the cluttered environment with moving obstacles, and to generate feasible and highly reactive collision-free motion in robots’ joint space, we present an approach for modulating joint-space DS using sampling-based MPC. Specifically, a nominal DS representing an unconstrained desired joint space motion to a target is locally deflected with obstacle-tangential velocity components navigating the robot around obstacles and avoiding local minima. Such tangential velocity components are constructed from receding horizon collision-free paths generated asynchronously by the sampling-based MPC. Notably, the MPC is not required to run constantly, but only activated when the local minima is detected. The approach is validated in simulation and real-world experiments on a 7-DoF robot demonstrating the capability of avoiding concave obstacles, while maintaining local attractor stability in both quasi-static and highly dynamic cluttered environments.

Accelerating value function approximations for dynamic dial-a-ride problems via dimensionality reductions

As the success of ride-sharing mobility service providers shows, customer demand for shared mobility services is increasing. The availability of mobile devices enables the constant accessibility of mobility apps and the immediate placement of transport requests. To provide such a dynamic dial-a-ride service, an effective control of the fleet is necessary. One promising solution approach is the value function approximation (VFA), which on the one hand convinces through good performance, but on the other hand also stands out through fast response times for a request. Training a VFA can be a challenging task since, among other things, the dimensionality of the state space plays a decisive role. If many variables to describe a state are used, a high amount of information can produce good performance after completion of the learning process. If the state space is too high-dimensional, there is also a risk that the method will not be able to find a reasonable solution. In contrast, if the number of variables is reduced, the learning speed can be accelerated, but the eventual performance may suffer from the associated loss of information. Furthermore, not all variables are equally relevant, as they contain different amounts of information. This paper presents a hybrid strategy, temporarily lowering the dimensionality of the problem using dimension reduction methods and subsequently increasing it by mapping the lower-dimensional state representations back onto a high-dimensional state space in order to exploit the advantages of both space dimensionalities. VFA in itself results in competitive performance for the dynamic dial-a-ride problem with shared rides. The proposed hybrid state representation can outperform the reference state representations by 3%, which corresponds to a meaningful acceleration in VFA learning speed.

NSimplex Zen : A Novel Dimensionality Reduction for Euclidean and Hilbert Spaces

Dimensionality reduction techniques map values from a high dimensional space to one with a lower dimension. The result is a space which requires less physical memory and has a faster distance calculation. These techniques are widely used where required properties of the reduced-dimension space give an acceptable accuracy with respect to the original space. Many such transforms have been described. They have been classified in two main groups: linear and topological . Linear methods such as Principal Component Analysis (PCA) and Random Projection (RP) define matrix-based transforms into a lower dimension of Euclidean space. Topological methods such as Multidimensional Scaling (MDS) attempt to preserve higher-level aspects such as the nearest-neighbour relation, and some may be applied to non-Euclidean spaces. Here, we introduce nSimplex Zen , a novel topological method of reducing dimensionality. Like MDS, it relies only upon pairwise distances measured in the original space. The use of distances, rather than coordinates, allows the technique to be applied to both Euclidean and other Hilbert spaces, including those governed by Cosine, Jensen–Shannon and Quadratic Form distances. We show that in almost all cases, due to geometric properties of high-dimensional spaces, our new technique gives better properties than others, especially with reduction to very low dimensions.

On the autonomous validation and comparison of particle models for a Newtonian laminar flow mixing model using PEPT

A methodology for the validation of a Computational Fluid Dynamics (CFD) mixing model using Positron Emission Particle Tracking (PEPT) was applied to a horizontal laminar stirred tank containing Newtonian glycerol. Particle trajectories recorded using PEPT were compared to three one-way particle-coupled CFD models (CFD-DEM, and CFD-Lagrangian, and CFD-Massless) through time-averaging and binning into a grid of 3-D voxels. A cell-by-cell comparison of particle velocities between the computational models and PEPT highlighted the accuracy of the CFD-DEM and CFD-Massless models over the Lagrangian model. Furthermore, using PEPT as a validation dataset the Pearson r-squared was used as a cost function to guide an autonomous calibration of a 5-dimensional DEM parameter space. A novel statistic, the simulation quality coefficient Qsim, was applied to assess accuracy between trajectory datasets and revealed the extent to which a model can be considered calibrated. Considering the balance between computational cost and precision, the CFD-massless model was the most effective particle model for this validation workflow. Further investigations will apply this methodology to calibrate non-Newtonian laminar CFD models.

Design and Application of Automatic English Translation Grammar Error Detection System based on BERT Machine Vision

Given the traditional handwritten English fonts, the accuracy of grammar error detection is unsatisfactory. This attribute leads to poor grammar error correction. Based on the optimized BERT machine vision model, an automatic English translation grammar error detection system is proposed in this paper. First, the basic architecture of the Transformer model and BERT model is considered, and a mixed attention module is discussed into the Transformer model to capture the features of the target in space and channel dimensions and realize the modeling of the context dependence of the target features. The feature maps are sampled by multiple parallel only if then cavity convolutions with different void rates to obtain multi-scale features and enhance local feature representation. Then, the input words of the BERT model are weighted by TFIDF to improve the feature extraction ability of the BERT model and construct the TF-BERT model. A database query rewriting model based on BERT and Transformer is proposed. The construction details of the model are described from the aspects of encoding processing, table embedding, and decoding processing respectively. Based on the principles of English translation, we extract grammatical features and build a grammar error detection method. TF-BERT model is selected as the basic framework. Combined with the hybrid attention mechanism, an automatic error correction model of English grammar is constructed. Finally, it is found that the loss value of the traditional system is as high as 0.7411, and the accuracy rate is 75%, while the loss value of the English grammar error detection system proposed in this paper is 0.2639, and the accuracy rate is 100%, which is 25% higher than that of the traditional system, and the performance is remarkable.

A new approach to the evaluation and solution of the relativistic kinetic dispersion relation and verification with continuum kinetic simulation

The present work describes a new approach to evaluation and root finding for the kinetic dispersion relation of Langmuir waves, which is central to the analytical understanding of collisionless damping in plasmas. The plasma dispersion function is solved to machine precision using direct integration in the complex plane in combination with an analytic evaluation of the residue to account for the deformation along the Landau contour. To efficiently attain machine precision, the contour is displaced in the complex plane prior to integration, and numerical subtleties related to the placement of the contour are discussed. The approach is generic in that it applies to arbitrary distribution functions, with the present manuscript focused on relativistic cases. Detailed verification of results via direct kinetic simulation in a variety of configuration space dimensions is also presented. Finally, the technique is applied to the challenging case of highly relativistic (i.e. extremely hot) plasmas. Here we show both qualitative agreement with prior work, as well as the disappearance of the Landau root which would have significant implication for real-life observation or experiment.

A bicoherence approach to analyze multi-dimensional cross-frequency coupling in EEG/MEG data

We introduce a blockwise generalisation of the Antisymmetric Cross-Bicoherence (ACB), a statistical method based on bispectral analysis. The Multi-dimensional ACB (MACB) is an approach that aims at detecting quadratic lagged phase-interactions between vector time series in the frequency domain. Such a coupling can be empirically observed in functional neuroimaging data, e.g., in electro/magnetoencephalographic signals. MACB is invariant under orthogonal trasformations of the data, which makes it independent, e.g., on the choice of the physical coordinate system in the neuro-electromagnetic inverse procedure. In extensive synthetic experiments, we prove that MACB performance is significantly better than that obtained by ACB. Specifically, the shorter the data length, or the higher the dimension of the single data space, the larger the difference between the two methods.

Reduced-order modeling of conjugate heat transfer in lithium-ion batteries

Reduced-order modeling (ROM) techniques involving physics are gaining importance due to their ability to generate physically meaningful results swiftly and accurately. They are finding applications in design and optimization and in digital twins where real-time results are required. The proper orthogonal decomposition (POD)-Galerkin ROM based on finite volume discretization methodology is utilized here for solving a high-dimensional system in a reduced dimensional space in which the reduced basis is spanned by the POD modes. The reduced-order equations are obtained by projecting the discretized form of the full-order model (FOM) equations onto the reduced basis which eliminates the need for boundary control techniques. The proposed methodology is demonstrated for transient conjugate heat transfer (CHT) in an electric vehicle battery. The POD modes are computed using simulation data for three Reynolds numbers and the ROM is evaluated for transient analysis of temperature from initial state to steady state for seven Reynolds numbers. The ROM predictions are fairly accurate both in the interpolated and the extrapolated regions with significant computational speed-up. Five modes are sufficient for constructing the fluid and battery temperature ROM solution. The ROM computational speed-up obtained with 5 modes is approximately 4–7.4 times compared to the FOM. The proposed methodology is generic and can be readily applied for any engineering system involving CHT.

Defining a systems framework for characterizing physical work demands with wearable sensors.

Measuring the physical demands of work is important in understanding the relationship between exposure to these job demands and their impact on the safety, health, and well-being of working people. However, work is changing and our knowledge of job demands should also evolve in anticipation of these changes. New opportunities exist for noninvasive long-term measures of physical demands through wearable motion sensors, including inertial measurement units, heart rate monitors, and muscle activity monitors. Inertial measurement units combine accelerometers, gyroscopes, and magnetometers to provide continuous measurement of a segment's motion and the ability to estimate orientation in 3-dimensional space. There is a need for a system-thinking perspective on how and when to apply these wearable sensors within the context of research and practice surrounding the measurement of physical job demands. In this paper, a framework is presented for measuring the physical work demands that can guide designers, researchers, and users to integrate and implement these advanced sensor technologies in a way that is relevant to the decision-making needs for physical demand assessment. We (i) present a literature review of the way physical demands are currently being measured, (ii) present a framework that extends the International Classification of Functioning to guide how technology can measure the facets of work, (iii) provide a background on wearable motion sensing, and (iv) define 3 categories of decision-making that influence the questions that we can ask and measures that are needed. By forming questions within these categories at each level of the framework, this approach encourages thinking about the systems-level problems inherent in the workplace and how they manifest at different scales. Applying this framework provides a systems approach to guide study designs and methodological approaches to study how work is changing and how it impacts worker safety, health, and well-being.

On the power of bounded asynchrony: convergence by autonomous robots with limited visibility

AbstractA distributed algorithm $${\mathcal {A}}$$ A solves the Point Convergence task if an arbitrarily large collection of entities, starting in an arbitrary configuration, move under the control of $${\mathcal {A}}$$ A to eventually form and thereafter maintain configurations in which the separation between all entities is arbitrarily small. This fundamental task in the standard $$\mathcal {OBLOT}$$ OBLOT model of autonomous mobile entities has been previously studied in a variety of settings, including full visibility, exact measurements (including distances and angles), and synchronous activation of entities. Our study concerns the minimal assumptions under which entities, moving asynchronously with limited and unknown visibility range and subject to limited imprecision in measurements, can be guaranteed to converge in this way. We present an algorithm operating under these constraints that solves Point Convergence, for entities moving in two or three dimensional space, with any bounded degree of asynchrony. We also prove that under similar realistic constraints, but unbounded asynchrony, Point Convergence in the plane is not possible in general, contingent on the natural assumption that algorithms maintain the (visible) connectivity among entities present in the initial configuration. This variant, that we call Cohesive Convergence, serves to distinguish the power of bounded and unbounded asynchrony in the control of autonomous mobile entities, settling a long-standing question whether in the Euclidean plane synchronously scheduled entities are more powerful than asynchronously scheduled entities.

Distance Functions in Some Class of Infinite Dimensional Vector Spaces

Distance Functions in Some Class of Infinite Dimensional Vector Spaces

Evaluating the Predictive Modeling Performance of Kernel Trick SVM, Market Basket Analysis and Naive Bayes in Terms of Efficiency

Among many corresponding matters in predictive modeling, the efficiency and effectiveness of the several approaches are the most significant. This study delves into a comprehensive comparative analysis of three distinct methodologies: Finally, Kernel Trick Support Vector Machines (SVM), market basket analysis (MBA), and naive Bayes classifiers invoked. The research we aim at clears the advantages and benefits of these approaches in terms of providing the correct information, their accuracy, the complexity of their computation, and how much they are applicable in different domains. Kernel function SVMs that are acknowledged for their ability to tackle the problems of non-linear data transfer to a higher dimensional space, the essence of which is what to expect from them in complex classification are probed. The feature of their machine-based learning relied on making exact confusing decision boundaries detailed, with an analysis of different kernel functions that more the functionality. The performance of the Market Basket Analysis, a sophisticated tool that exposes the relationship between the provided data in transactions, helped me to discover a way of forecasting customer behavior. The technique enables paints suitable recommendation systems and leaders to make strategic business decisions using the purchasing habits it uncovers. The research owes its effectiveness to processing large volumes of data, looking for meaningful patterns, and issuing beneficial recommendations. Along with that, an attempt to understand a Bayes classifier of naive kind will be made, which belongs to a class of probabilistic models that are used largely because of their simplicity and efficiency. The author outlines the advantages and drawbacks of its assumption in terms of the attribute independence concept when putting it to use in different classifiers. The research scrutinizes their effectiveness in text categorization and image recognition as well as their ability to adapt to different tasks. In this way, the investigation aims to find out how to make the application more appropriate for various uses. The study contributes value to the competencies of readers who will be well informed about the accuracy, efficiency, and the type of data, domain, or problem for which a model is suitable for the decision on a particular model choice.

Global Solution and Stability of a Haptotaxis Mathematical Model for Complex MAP

A critical function of polymeric matrices in biological systems is to exert selective control over the transport of thousands of nanoparticulate species. Utilizing “third-party” molecular anchors to crosslink nanoparticulates to the matrix is an effective strategy, and a trapped nanoparticulate formed a desired complex MAP that is necessary to keep the nanoparticulate immobilized at any given time. In this paper, the global solution and stability of a parabolic–ordinary-parabolic haptotaxis system to complex MAP are studied. First, the existence of a local classical solution to system () has been observed using fixed point argument and parabolic Schauder estimates. Furthermore, some a priori estimates that can raise the regularity estimate of the solution for the relatively complicated first equation of system () from Lρ to L2ρ (ρ≥1) are given; then, the local classic solution can thus extend to the global classic solution when the space dimension N≤3. Lastly, by using various analytical methods, a threshold value ξ00(ξ00<0) is found, such that positive constant steady state (u*,v*,w*) becomes unstable when ξ<ξ00. Our results show that the haptotaxis plays a crucial role in determining the stability to the model (), that is, it can have a destabilizing effect.

A novel feature selection method based on quantum support vector machine

Feature selection is critical in machine learning to reduce dimensionality and improve model accuracy and efficiency. The exponential growth in feature space dimensionality for modern datasets directly results in ambiguous samples and redundant features, which can severely degrade classification accuracy. Quantum machine learning offers potential advantages for addressing this challenge. In this paper, we propose a novel method, quantum support vector machine feature selection (QSVMF), integrating quantum support vector machines with multi-objective genetic algorithm. QSVMF optimizes multiple simultaneous objectives: maximizing classification accuracy, minimizing selected features and quantum circuit costs, and reducing feature covariance. We apply QSVMF for feature selection on a breast cancer dataset, comparing the performance of QSVMF against classical approaches with the selected features. Experimental results show that QSVMF achieves superior performance. Furthermore, the Pareto front solutions of QSVMF enable analysis of accuracy versus feature set size trade-offs, identifying extremely sparse yet accurate feature subsets. We contextualize the biological relevance of the selected features in terms of known breast cancer biomarkers. This work highlights the potential of quantum-based feature selection to enhance machine learning efficiency and performance on complex real-world data.

Tidal turbine hydrofoil design and optimization based on deep learning

The optimal design of hydrofoils is critical to improve the hydrodynamic performance of the tidal turbine. However, the global optimization of hydrofoils is limited by the high dimensionality of the design space, which requires extensive computational fluid dynamics simulations. This paper proposes an interactive framework for hydrofoil design and optimization based on deep learning. Generative adversarial networks are used to parameterize the hydrofoil design, which automatically learns representations from existing hydrofoils and controls new hydrofoil generation using fewer variables to reduce optimization dimensions. Moreover, the surrogate model based on convolutional neural networks is constructed, which realizes the mapping of hydrofoil design and operating parameters to hydrodynamic performance parameters. The framework can generate a large number of smooth and realistic hydrofoils with three design variables and quickly predict the performance, enabling effective optimization design of hydrofoils. The results show that the optimized hydrofoil shapes have larger lift-to-drag ratios than those of the common hydrofoils. Furthermore, the optimized hydrofoil is applied to the design of 3D horizontal axis tidal turbine blades. The simulation results show that the framework is effective and stable, which can facilitate the design of tidal turbine rotors and provide hydrofoils with higher power coefficients.