Simple Set Research Articles

Application of stochastic filter to three-phase nonuniform transmission lines

ABSTRACT This paper implements the stochastic filters for state and parameter estimation of nonuniform transmission lines (NTL). In general, transmission line (TL) problem is a continuous time and space problem. By taking the line loading noise into account, the TL equations become a stochastic partial differential equation (PDE) rather than a simple set of coupled finite stochastic differential equations (SDE). By transforming the spatial variables into the Fourier domain, the stochastic PDE can be transformed into an infinite sequence of SDE. After truncation to a finite set of Fourier series coefficients, it becomes a finite set of coupled linear SDE, which is the required domain in which extended Kalman filter (EKF) and unscented Kalman filter (UKF) can be applied. For state space equation of EKF and UKF, the voltage and current of periodic NTL are expanded into an infinite set of spatial harmonics. In this way, the voltage and current measurement of NTL become an eigenvalue problem. The NTL is considered as cascade of small infinite NTL and the four distributed primary parameters of the periodic NTL are expressed using Fourier series expansion. Finally, the Kalman filter (KF)-based state estimation and the EKF- and UKF-based parameter estimation have been compared with recursive least squares (RLS) method. The simulation results present the superiority of the method.

Impacts of thermal radiation with nanoparticle aggregation and variable viscosity on unsteady bidirectional rotating stagnation point flow of nanofluid

Interest in nanofluids with nanoparticle-induced aggregation effects like solar-powered energy and crossflow heat transfer is growing in the modern industrial sector. The main goal of this research is to find out how nonlinear radiation, changes in viscosity, and aggregation affect the flow of ethylene glycol-based nanofluids in three dimensions. It is concerned with stagnation point flows that are not axisymmetric and that take place on stretching sheets. In accordance with the imposed hypotheses, the governing equations will be modelled. With the help of a similarity transformation, a complicated set of nonlinear partial differential equations can be turned into a simpler set of ordinary differential equations. This simplification facilitates the process of solving the equations. The algorithm will then be used to perform a numerical solution of the simplified set of equations. Runge-Kutta (RK-IV) and the shooting method are used to derive the numerical results. Different parameters and their effects are graphically displayed. As the concentration of nanoparticles in the skin increases, it appears that both the rate of heat transmission and the coefficient of friction increase. The heat transfer rate and the skin coefficient of friction inclination both increase as the instability parameter is increased. To the contrary, it was found that delaying boundary layer's separation by increasing stress rate of surrounding fluid was effective. Tables also compare the nanoparticles aggregation effects with and without radiation and show the variation in Nusselt numbers. High levels of agreement were found between the findings of the current study and those of a previous publication for the same instance, lending further support to the findings.

Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques

Abstract. Glacier mass balance is typically estimated using a range of in situ measurements, remote sensing measurements, and physical and temperature index modelling techniques. With improved data collection and access to large datasets, data-driven techniques have recently gained prominence in modelling natural processes. The most common data-driven techniques used today are linear regression models and, to some extent, non-linear machine learning models such as artificial neural networks. However, the entire host of capabilities of machine learning modelling has not been applied to glacier mass balance modelling. This study used monthly meteorological data from ERA5-Land to drive four machine learning models: random forest (ensemble tree type), gradient-boosted regressor (ensemble tree type), support vector machine (kernel type), and artificial neural networks (neural type). We also use ordinary least squares linear regression as a baseline model against which to compare the performance of the machine learning models. Further, we assess the requirement of data for each of the models and the requirement for hyperparameter tuning. Finally, the importance of each meteorological variable in the mass balance estimation for each of the models is estimated using permutation importance. All machine learning models outperform the linear regression model. The neural network model depicted a low bias, suggesting the possibility of enhanced results in the event of biased input data. However, the ensemble tree-based models, random forest and gradient-boosted regressor, outperformed all other models in terms of the evaluation metrics and interpretability of the meteorological variables. The gradient-boosted regression model depicted the best coefficient of determination value of 0.713 and a root mean squared error of 1.071 m w.e. The feature importance values associated with all machine learning models suggested a high importance of meteorological variables associated with ablation. This is in line with predominantly negative mass balance observations. We conclude that machine learning techniques are promising in estimating glacier mass balance and can incorporate information from more significant meteorological variables as opposed to a simplified set of variables used in temperature index models.

Electroweak input schemes and universal corrections in SMEFT

The choice of an electroweak (EW) input scheme is an important component of perturbative calculations in Standard Model Effective Field Theory (SMEFT). In this paper we perform a systematic study of three different EW input schemes in SMEFT, in particular those using the parameter sets {MW, MZ, GF}, {MW, MZ, α}, or {α, MZ, GF}. We discuss general features and calculate decay rates of Z and W bosons to leptons and Higgs decays to bottom quarks in these three schemes up to next-to-leading order (NLO) in dimension-six SMEFT. We explore the sensitivity to Wilson coefficients and perturbative convergence in the different schemes, and show that while the latter point is more involved than in the Standard Model, the dominant scheme-dependent NLO corrections are universal and can be taken into account by a simple set of substitutions on the leading-order results. Residual NLO corrections are then of similar size between the different input schemes, and performing calculations in multiple schemes can give a useful handle on theory uncertainties in SMEFT predictions and fits to data.

Exploration Policies for On-the-Fly Controller Synthesis: A Reinforcement Learning Approach

Controller synthesis is in essence a case of model-based planning for non-deterministic environments in which plans (actually “strategies”) are meant to preserve system goals indefinitely. In the case of supervisory control environments are specified as the parallel composition of state machines and valid strategies are required to be “non-blocking” (i.e., always enabling the environment to reach certain marked states) in addition to safe (i.e., keep the system within a safe zone). Recently, On-the-fly Directed Controller Synthesis techniques were proposed to avoid the exploration of the entire -and exponentially large- environment space, at the cost of non-maximal permissiveness, to either find a strategy or conclude that there is none. The incremental exploration of the plant is currently guided by a domain-independent human-designed heuristic. In this work, we propose a new method for obtaining heuristics based on Reinforcement Learning (RL). The synthesis algorithm is thus framed as an RL task with an unbounded action space and a modified version of DQN is used. With a simple and general set of features that abstracts both states and actions, we show that it is possible to learn heuristics on small versions of a problem that generalize to the larger instances, effectively doing zero-shot policy transfer. Our agents learn from scratch in a highly partially observable RL task and outperform the existing heuristic overall, in instances unseen during training.

External Behavior of a Logic Program and Verification of Refactoring

AbstractRefactoring is modifying a program without changing its external behavior. In this paper, we make the concept of external behavior precise for a simple answer set programming language. Then we describe a proof assistant for the task of verifying that refactoring a program in that language is performed correctly.

Numerical Simulation of Deformation in Hot Runner Manifold.

This study simulated the deformation of a hot runner manifold and nozzle assembly during operation, aiming to address potential leaks and premature failure. Both thermal and mechanical models were used simultaneously to accurately capture system behavior. A simplified set of boundary conditions was proposed for efficient problem-solving. Analysis of the simulation results revealed that thermal deformation posed a risk of catastrophic failures and leaks. Deformation from melt pressure was relatively small compared to thermal loading, not exceeding 12%. The study provided design recommendations based on the simulation findings, guiding the development of hot runner systems for improved reliability.

Gradient-Variation Bound for Online Convex Optimization with Constraints

We study online convex optimization with constraints consisting of multiple functional constraints and a relatively simple constraint set, such as a Euclidean ball. As enforcing the constraints at each time step through projections is computationally challenging in general, we allow decisions to violate the functional constraints but aim to achieve a low regret and cumulative violation of the constraints over a horizon of T time steps. First-order methods achieve an O(sqrt{T}) regret and an O(1) constraint violation, which is the best-known bound under the Slater's condition, but do not take into account the structural information of the problem. Furthermore, the existing algorithms and analysis are limited to Euclidean space. In this paper, we provide an instance-dependent bound for online convex optimization with complex constraints obtained by a novel online primal-dual mirror-prox algorithm. Our instance-dependent regret is quantified by the total gradient variation V_*(T) in the sequence of loss functions. The proposed algorithm works in general normed spaces and simultaneously achieves an O(sqrt{V_*(T)}) regret and an O(1) constraint violation, which is never worse than the best-known (O(sqrt{T}), O(1)) result and improves over previous works that applied mirror-prox-type algorithms for this problem achieving O(T^{2/3}) regret and constraint violation. Finally, our algorithm is computationally efficient, as it only performs mirror descent steps in each iteration instead of solving a general Lagrangian minimization problem.

3D Cartographic Generalization of LiDAR Point Clouds Based on the Principle of Self-Similarity of a Deterministic Fractal Structure

The rendering of virtual three-dimensional (3D) structures represented by Point Cloud (PC) allows the representation of internal and/or external environments to buildings. However, the compilation of 3D geometric models is influenced by the intrinsic characteristics of PCs, which can be mitigated by the application of an PC simplification operator. According to the mathematical norms of fractal geometry, it was assumed that a PC is characterized by self-similarity. Two experimental datasets acquired with an SLT in static mode indoors were used. Four tasks were accomplished: sampling and structuring of a PC to solve the problem of random distribution, from an octree structure; estimation of the curvature of the points and the roughness of a neighbourhood for the extraction of edge points by the analysis of self-similarity and application of the Statistical Outliers Remove (SOR) algorithm, for the elimination of outliers points; uniform voxelization, to simplify the intermediate points; application of the Iterative Closest Point (ICP) algorithm to register the sets generated in the same local coordinate system. The use of voxelization was satisfactory, but once the voxel size is manually defined, the PC can be oversimplified and lose essential characteristics. This can be minimized by the primary analysis of the edge points, generating a set that is uniform, less noisy, and self- similar to the original set. To achieve a minimum density of points to model an environment three-dimensionally, one must analyse the geometric self- similarity characteristics of the PC to produce a simplified set self-similar to the original, considering the premises of fractal geometry. It is recommended to create an automatic simplification process to minimize the subjectivity coming from the analyst.

Guidelines for ex vivo mechanical testing of tendon.

Tendons are critical for the biomechanical function of joints. Tendons connect muscles to bones and allow for the transmission of muscle forces to facilitate joint motion. Therefore, characterizing the tensile mechanical properties of tendons is important for the assessment of functional tendon health and efficacy of treatments for acute and chronic injuries. In this guidelines paper, we review methodological considerations, testing protocols, and key outcome measures for mechanical testing of tendons. The goal of the paper is to present a simple set of guidelines to the nonexpert seeking to perform tendon mechanical tests. The suggested approaches provide rigorous and consistent methodologies for standardized biomechanical characterization of tendon and reporting requirements across laboratories.

Online Broadband Impedance Identification for Lithium-Ion Batteries Based on a Nonlinear Equivalent Circuit Model

Models play a crucial role in explaining internal processes, estimating states, and managing lithium-ion batteries. Electrochemical models can effectively illustrate the battery’s mechanism; however, their complexity renders them unsuitable for onboard use in electric vehicles. On the other hand, equivalent circuit models (ECMs) utilize a simple set of circuit elements to simulate voltage–current characteristics. This approach is less complex and easier to implement. However, most ECMs do not currently account for the nonlinear impact of operating conditions on battery impedance, making it difficult to obtain accurate wideband impedance characteristics of the battery when used in online applications. This article delves into the intrinsic mechanism of batteries and discusses the influence of nonstationary conditions on impedance. An ECM designed for non-steady state conditions is presented. Online adaptive adjustment of model parameters is achieved using the forgetting factor recursive least squares (FFRLS) algorithm and varied parameters approach (VPA) algorithm. Experimental results demonstrate the impressive performance of the model and parameter identification method, enabling the accurate acquisition of online impedance.

Design and Synthesis of AMBA APB Protocol for SoC

The AMBA (Advanced Microcontroller Bus Architecture) and APB (Advanced Peripheral Bus) protocol is a widely used bus protocol designed by ARM for integrating peripheral devices into a system-on- chip (SoC) design. It provides a low-power, low-complexity interface that allows efficient communication between the master and slave devices within the SoC. The APB protocol offers a straightforward and flexible architecture, making it suitable for a wide range of SoC applications. It supports a single-master, multiple-slave bus structure, where an APB master can communicate with multiple APB slave devices. This architecture enables seamless integration of various peripheral devices, such as timers, UARTs, I/O controllers, and more, into the SoC. One of the key advantages of the APB protocol is its power efficiency. It achieves this by utilizing a simplified set of signals and reduced complexity compared to other bus protocols. The APB bus operates based on a common clock signal, ensuring proper timing and synchronization of data transfers while minimizing power consumption.

Mathematical Modeling on Magnetohydrodynamics Upper Convected Maxwell Fluid Flow Past a Flat Plate Using Spectral Relaxation Approach

The focus of this treatise is on the scrutiny of MHD heat and mass transfer motion of upper convection Maxwell (UCM) fluid in a thermally radiated flat plate. The models of the flow equations were considered when there is a temperature difference: the Cattaneo-Christov model and the Soret-Dufour mechanisms. A magnetic field of firmness was inflicted in opposition to the flow. The flow inspection is controlled by partial differential equations (PDEs). Suitable similarity variables were utilised on the PDEs to obtain a set of nonlinear ordinary differential equations (ODEs). The simplified set of ODEs shall be answered by exploiting the spectral relaxation method (SRM). The SRM is a numerical technique that solves differential equations by utilising the repetition of a sequence of operations that is iterated iteratively by first decoupling the coupled systems of equations. The magnetism was found to decline the velocity and hydrodynamic boundary layer due to the Lorentz force. The Deborah number was found to enhance the velocity contour. The Eckert number was discovered to improve the temperature profile due to the production of heat energy within the boundary layer. An increase in Prandtl number was found to enhance the hydrodynamic and thermal boundary layer thickness. The local skin friction and Nusselt number were found to be elevated by the increase in the Dufour parameter.

Application of artificial neural networks for predicting the bearing capacity of the tip of a pile embedded in a rock mass

Addressing the problem of the bearing capacity of the tip of a pile in rock needs to consider a full-nonlinear behavior of the rock, as the Hoek and Brown failure criterion, and including the various geometric parameters defining the system geometry. It usually needs the use of complex numerical models. They require a high level of training and expertise and are commonly out of the reach of engineers on the day-to-day calculation procedures. Technical codes usually recommend simpler formulae based on one or two rock parameters, their accuracy relying on particular and local empirical testing. This research develops an artificial neural network (ANN) that solves the previous difficulties. The ANN is based on a group of 1440 calculations using the novel numerical procedure Discontinuity Layout Optimization (DLO). This numerical method can efficiently reproduce the non-linear behavior of the rock (including rock type, uniaxial compressive strength, and geological strength index) for every configuration of the model (foundation width, pile embedment, and height of the overlying soil). Once the ANN is trained and optimized, it can easily predict any result within its range of applicability. Furthermore, it can be reduced to a simple set of recursive equations to be implemented in a spreadsheet. The proposed ANN has only one hidden layer besides the input and output layers, with (6)-(8)-(1) neurons, respectively. It gives highly accurate results with minimum cost and can be a useful tool for engineers and scientists in the foundation field.

Нечеткологическая модель многостадийной химико-энерготехнологической системы переработки мелкодисперсного рудного сырья

The results of the study, the purpose of which was to build a software model of a multi-stage integrated system for processing finely dispersed ore raw materials, are presented. The role of such raw materials can be processed waste at mining and processing plants of apatite-nepheline and other types of ores, which accumulate in large volumes in tailing dumps. They create a significant environmental threat in the territories adjacent to the plants due to weathering, dust formation, penetration into the soil and aquifers of chemical compounds and substances hazardous to human health. Therefore, the improvement of existing production processes, the development of new technological systems for mining and processing plants, including the application of the principles of the circular economy, waste recycling, justifies the relevance of the chosen research area. The proposed program model is based on the use of trainable trees of systems (blocks) of fuzzy inference of the first and second types. This approach made it possible to avoid unnecessary complication of the bases of fuzzy inference rules when using only one fuzzy block when building a multi-parameter model of the entire multi-stage complex system. The use of several fuzzy inference blocks that describe the behavior of individual units of the system and their configuration in accordance with the physical structure of the system allows the use of relatively simple sets of rules for individual blocks. The joint selection of their parameters when training a tree of fuzzy blocks makes it possible to achieve high accuracy of the solutions obtained. The novelty of the research results is the proposed software fuzzy model of an integrated system for processing finely dispersed ore raw materials. The results of a simulation experiment conducted in the MatLab environment using a synthetic data set generated in Simulink are presented. The results showed that the trained fuzzy model provides good fidelity of the parameters and variables from the test part of the synthetic set.

A simpler definition of MAFLD precisely predicts incident metabolic diseases: a 7-year cohort study.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a novel definition proposed in 2020 with a relatively complex set of criteria. Thus, simplified criteria that are more applicable are required. This study aimed to develop a simplified set of criteria for identifying MAFLD and predicting MAFLD-related metabolic diseases. We developed a simplified set of metabolic syndrome-based criteria for MAFLD, and compared the performance of the simplified criteria with that of the original criteria in predicting MAFLD-related metabolic diseases in a 7-year follow-up. In the 7-year cohort, a total of 13,786 participants, including 3372 (24.5%) with fatty liver, were enrolled at baseline. Of the 3372 participants with fatty liver, 3199 (94.7%) met the MAFLD-original criteria, 2733 (81.0%) met the simplified criteria, and 164 (4.9%) were metabolic healthy and met neither of the criteria. During 13,612 person-years of follow-up, 431 (16.0%) fatty liver individuals newly developed T2DM, with an incidence rate of 31.7 per 1000 person-years. Participants who met the simplified criteria had a higher risk of incident T2DM than those who met the original criteria. Similar results were observed for incident hypertension, and incident carotid atherosclerotic plaque. The MAFLD-simplified criteria are an optimized risk stratification tool for predicting metabolic diseases in fatty liver individuals.

Jeffery’s Orbits and Microswimmers in Flows: A Theoretical Review

In this review, we provide a theoretical introduction to Jeffery’s equations for the orientation dynamics of an axisymmetric object in a flow at low Reynolds number, and review recent theoretical extensions and applications to the motions of self-propelled particles, so-called microswimmers, in external flows. Bacteria colonize human organs and medical devices even with flowing fluid, microalgae occasionally cause huge harmful toxic blooms in lakes and oceans, and recent artificial microrobots can migrate in flows generated in well-designed microfluidic chambers. The Jeffery equations, a simple set of ordinary differential equation, provide a useful building block in modeling, analyzing, and understanding these microswimmer dynamics in a flow current, in particular when incorporating the impact of the swimmer shape since the equations contain a shape parameter as a single scalar, known as the Bretherton parameter. The particle orientation forms a closed orbit when situated in a simple shear, and this non-uniform periodic rotational motion, referred to as Jeffery’s orbits, is due to a constant of motion in the non-linear equation. After providing a theoretical introduction to microswimmer hydrodynamics and a derivation of the Jeffery equations, we discuss possible extensions to more general shapes, including those with rapid deformation. In the latter part of this review, simple mathematical models of microswimmers in different types of flow fields are described, with a focus on constants of motion and their relation to periodic motions in phase space, together with a breakdown of the degenerate orbits, to discuss the stable, unstable, and chaotic dynamics of the system. The discussion in this paper will provide a comprehensive theoretical foundation for Jeffery’s orbits and will be useful to understand the motions of microswimmers under various flows and to analyze and design computer and laboratory experiments, as well as in the active matter and artificial smart swimmer studies.

The interpretability of the activity signal detection model for wood-boring pests Semanotus bifasciatus in the larval stage.

The acoustic detection model of activity signals based on deep learning could detect wood-boring pests accurately and reliably. However, the black-box characteristics of the deep learning model have limited the credibility of the results and hindered its application. Aiming to address the reliability and interpretability of the model, this paper designed an active interpretable model called Dynamic Acoustic Larvae Prototype Network (DalPNet), which used the prototype to assist model decisions and achieve more flexible model explanation through dynamic feature patch computation. In the experiments, the average recognition accuracy of the DalPNet on the simple test set and anti-noise test set for Semanotus bifasciatus larval activity signals reached 99.3% and 98.5%, respectively. The quantitative evaluation of interpretability was measured by the relative area under the curve (RAUC) and the cumulative slope (CS) of the accuracy change curve in this paper. In the experiments, the RAUC and the CS of DalPNet were 0.2923 and -2.0105, respectively. Additionally, according to the visualization results, the explanation results of DalPNet were more accurate in locating the bite pulses of the larvae and could better focus on multiple bite pulses in one signal, which showed better performance compared to the baseline model. The experimental results demonstrated that the proposed DalPNet had better explanation while ensuring recognition accuracy. In view of that, it could improve the trust of forestry custodians in the activity signals detection model and aid in the practical application of the model in the forestry field. © 2023 Society of Chemical Industry.

Training energy-based single-layer Hopfield and oscillatory networks with unsupervised and supervised algorithms for image classification

This paper investigates how to solve image classification with Hopfield neural networks (HNNs) and oscillatory neural networks (ONNs). This is a first attempt to apply ONNs for image classification. State-of-the-art image classification networks are multi-layer models trained with supervised gradient back-propagation, which provide high-fidelity results but require high energy consumption and computational resources to be implemented. On the contrary, HNN and ONN networks are single-layer, requiring less computational resources, however, they necessitate some adaptation as they are not directly applicable for image classification. ONN is a novel brain-inspired computing paradigm that performs low-power computation and is attractive for edge artificial intelligence applications, such as image classification. In this paper, we perform image classification with HNN and ONN by exploiting their auto-associative memory (AAM) properties. We evaluate precision of HNN and ONN trained with state-of-the-art unsupervised learning algorithms. Additionally, we adapt the supervised equilibrium propagation (EP) algorithm to single-layer AAM architectures, proposing the AAM-EP. We test and validate HNN and ONN classification on images of handwritten digits using a simplified MNIST set. We find that using unsupervised learning, HNN reaches 65.2%, and ONN 59.1% precision. Moreover, we show that AAM-EP can increase HNN and ONN precision up to 67.04% for HNN and 62.6% for ONN. While intrinsically HNN and ONN are not meant for classification tasks, to the best of our knowledge, these are the best-reported precisions of HNN and ONN performing classification of images of handwritten digits.

The readiness of water molecules to split into hydrogen + oxygen: a proposed new aspect of water splitting.

The potential of the anode, at which the evolution of oxygen begins, is a key parameter that describes how well water is split in water electrolyzers. Research efforts related to electrocatalytically initiated water splitting that aimed at reducing the oxygen evolution reaction (OER) overpotential to date focused on the optimization of materials used to produce the electrodes. Descriptors for the readiness of the H2 O molecule itself to break down into its components have not been considered in water electrolysis experiments so far. In a simple set of experiments, we found that adding dioxane to aqueous solutions leads to a substantial blue shift of the frequency of the O-H stretch vibration which is a sign for an increased strength of the O-H bond (intramolecular bonding). This phenomenon coincides with a significant increase of the OER onset potential as derived from cyclic voltammetry experiments. Thus, the O-H stretch frequency can be an ideal indicator for the readiness of water molecules to be split in its cleavage products. To our knowledge this is the first example of a study into the relationship between structural features of water as derived from FTIR spectroscopic studies, and key results derived from water electrolysis experiments. This article is protected by copyright. All rights reserved.