Computational Time Constraints Research Articles

Horizon picking algorithm using global optimization and coherence measurement in poststacked seismic data

Accurate horizon recognition within poststack seismic sections is important in seismic interpretation. Horizon picking techniques influence diverse seismic structural analyses and inversion methodologies. Despite encountering challenges such as computational demands and time constraints, the past few years have witnessed the development of numerous 2D and 3D methods. With the progress of modern computing, more robust and efficient techniques, harnessing artificial intelligence, have come to the fore. In this context, we develop an innovative cost-effective algorithm grounded in a global optimization framework. This algorithm combines the very fast simulated annealing method with a set of stability parameters and coherence measurements between neighboring seismic traces, which is used as the objective function. The search is executed on continuous and sequential clusters of seismic traces, with a focus on maximizing coherence amidst the presented events. We evaluate the algorithm’s efficacy by applying it to two distinct input data sets — enveloped and nonenveloped seismic traces. The initial application is to the time-migrated Marmousi data set, a synthetic marine data set originated from a complex geologic sedimentary basin situated in Angola, Africa. Subsequently, we apply the algorithm to depth-migrated land data extracted from a past survey conducted in the Tacutu Basin in northern Brazil. Outcomes arising from the application to 2D synthetic and field data sets underscore the method’s viability as a compelling alternative for seismic horizon picking within the time or depth domain.

Evaluating missing data handling methods for developing building energy benchmarking models

This study explored methods for handling missing data in the development of machine learning-based energy benchmarking models, assessing their training time, performance, and variance. Unlike the common assumption of missing completely at random, this study adopted a missing at random (MAR) perspective, which is more appropriate for building data. We compared the inherent missing data handling method of extreme gradient boosting (XGBoost) with the Median, k-nearest neighbors (KNN), and classification and regression trees (CART) methods, alongside Shapley additive explanation (SHAP) method for model interpretability. The findings indicate that, despite its computational demands, the CART method most accurately mirrors the original data distribution, thereby enhancing model performance and stability. The KNN method is effective, while the XGBoost method is viable under computational time constraints. This work highlights the importance of reliable test data for performing accurate evaluations of imputation methods. These results offer guidelines for the selection of imputation methods in model development, contributing to the improved accuracy of energy benchmarking models. The MAR-based approach for missing data analysis holds promise for future research on building energy data, providing crucial insights for accurate energy benchmark model performance assessments.

A matrix-free parallel two-level deflation preconditioner for two-dimensional heterogeneous Helmholtz problems

We propose a matrix-free parallel two-level deflation method combined with the Complex Shifted Laplacian Preconditioner (CSLP) for two-dimensional heterogeneous Helmholtz problems encountered in seismic exploration, antennas, and medical imaging. These problems pose challenges in terms of accuracy and convergence due to scalability issues with numerical solvers. Motivated by the limitations imposed by excessive computational time and memory constraints when employing a sequential solver with constructed matrices, we parallelize the two-level deflation method without constructing any matrices. Our approach utilizes preconditioned Krylov subspace methods and approximates the CSLP preconditioner with a parallel geometric multigrid V-cycle. For the two-level deflation, standard inter-grid deflation vectors and further high-order deflation vectors are considered. As another main contribution, the matrix-free Galerkin coarsening approach and a novel re-discretization scheme as well as high-order finite-difference schemes on the coarse grid are studied to obtain wavenumber-independent convergence. The optimal settings for an efficient coarse-grid problem solver are investigated. Numerical experiments of model problems show that the wavenumber independence has been obtained for medium wavenumbers. The matrix-free parallel framework shows satisfactory weak and strong parallel scalability.

Advancing nodal leakage estimation in decentralized water networks: Integrating Bayesian optimization, realistic hydraulic modeling, and data-driven approaches

The expansion of cities escalates the demand on water utilities amid global water scarcity, making leakage management a critical challenge for water sector sustainability. On this subject, the study introduces an advanced practical approach for estimating nodal leakage through the calibration of decentralized networks using available data. The approach includes conducting a pressure step-test for data collection, employing the DBSCAN algorithm for outlier detection, and employing Bayesian optimization for effective calibration. Hydraulic leakage models, particularly the power and modified orifice equations, are innovatively applied to model and identify existing leaks at each network node through deep comparison. The computational analysis demonstrated efficiency and accuracy in overcoming vast computational complexity and time constraints, with the calibration of a real-life network resulting in a cumulative MSE of 0.892 and an average R² and NSE values near 0.98. Additionally, realistic leakage modeling revealed inaccuracies in the acknowledged connection between the modified orifice equation and the leakage power equation. This study also provides key insights for enhancing water loss management and conducting on-site inspections in an environmentally conscious manner, especially crucial for budget-constrained water utilities and authorities experiencing revenue declines.

Robotic Process Automation Efficiency for Mobile App Testing: An Empirical Investigation

In today’s rapidly evolving software environment, the graphical user interface (GUI) plays a crucial role in providing intuitive, user-friendly interaction. However, traditional intrusive GUI testing methods often face challenges such as interrupting user workflows, requiring significant manual effort and insufficient test scenario coverage. Non-intrusive testing methods, such as Robotic Process Automation (RPA), offer a solution to validate GUI functionality without modifying the application’s code or affecting the user experience. RPA systems automate repetitive tasks by simulating user interactions, becoming valuable tools in GUI testing. However, challenges like limited computational resources, time constraints, or restricted exploration capabilities may limit the efficiency of individual RPA agents, thus restricting coverage and effectiveness. To address this issue, this study explores the performance of a single RPA agent versus an RPA cluster under different testing conditions, using three popular testing methods: Monkey, Stoat and Q-testing. Experimental results indicate that an RPA cluster outperforms a single RPA in GUI coverage and error detection, making a significant contribution to the field of non-intrusive GUI exploration testing. The findings of this study provide directions for future research to ensure the delivery of high-quality mobile applications.

Application of a data-driven approach for maximum fatigue damage prediction of an unbonded flexible riser

The fatigue damage prediction is a challenging issue in the design and maintenance phases of unbonded flexible pipes. This arises from the necessity of conducting numerous numerical analyses, encompassing both global performance and local structural assessments, to determine the long-term fatigue damage. To handle the computational-demanding problem for fatigue damage estimation, this work applies an efficient machine learning approach known as the AK-MDAmax approach for maximum fatigue damage estimation of unbonded flexible risers. Kriging surrogate models are employed to predict the short-term distribution of fatigue damage over the sea states. The maximum long-term fatigue damage of the flexible risers is then estimated using the AK-MDAmax approach. The verification process of the AK-MDAmax model unfolds incrementally, testing its feasibility and stability across four different scenarios with varying numbers of considered locations, ranging from 4 to 512. Comparative analyses against numerical results reveal that the AK-MDAmax approach efficiently and accurately estimates maximum long-term fatigue damage across different locations. Remarkably, a 25-fold increase in efficiency is achieved with an error margin of less than 1%. This study demonstrates the substantial potential of the proposed machine learning methodology, offering designers an efficient tool for optimizing flexible risers and mitigating computational costs and time constraints.

A Solvency II Partial Internal Model Considering Reinsurance and Counterparty Default Risk

Estimating the expected capital and its variability is a crucial objective for a non-life insurance company, which enables the firm to develop effective management strategies. Many studies have been devoted to this topic, with simulative approaches being especially employed for solving the complexity of the interacting risks, not manageable through closed-form solutions. In this paper, we present a realistic framework based on Solvency II for the definition of next-year capital of a non-life insurer, including reinsurance treaties and counterparty default risk, in a multi-line of business setting. We determine the mean and variance of the stochastic capital considering both quota share and excess-of-loss reinsurance. We show how these closed-form results enable the analysis of many different real-world strategies, granting the insurer the possibility of choosing the optimal policy without the computational resources and time constraints required by simulative approaches.

An efficient greedy heuristic for the real-time train platforming problem

This paper focuses on a special class of real-time railway traffic management problem: efficiently reallocating trains to platforms at stations in case of slight schedule changes or major disruptions. The need for a real-time response with a high level of quality makes this problem particularly challenging.To address this issue, the authors propose a mesoscopic approach that involves preprocessing the data to determine feasible routes and other disruption parameters. They develop a greedy interchange heuristic to solve the mesoscopic real-time train platforming problem, providing high-quality routing and timing decisions within the computational time constraints of real-time management problems.The performance of the proposed heuristic is analyzed through case studies using both synthetic and realistic scenarios from the Spanish railway traffic system. For large instances of the Atocha-Cercanías station case study, the solutions are generated from 5 to 10 times faster by the heuristic algorithm than by the exact method. The authors conclude that the proposed heuristic is a promising solution for real-time train platforming problems.

A rapid element pressure field simulation method for transcranial phase correction in focused ultrasound therapy

Transcranial focused ultrasound ablation has emerged as a promising technique for treating neurological disorders. The clinical system exclusively employed the ray tracing method to compute phase aberrations induced by the human skull, taking into account computational time constraints. However, this method compromises slightly on accuracy compared to simulation-based methods. This study evaluates a fast simulation method that simulates the time-harmonic pressure field within the region of interest for effective phase correction. Experimental validation was carried out using a 512-element, 670 kHz hemispherical transducer for four ex vivo skulls. The ray tracing method achieved a restoration ratio of 64.81% ± 4.33% of acoustic intensity normalized to hydrophone measurements. In comparison, the rapid simulation method demonstrated improved results with a restoration ratio of 73.10% ± 7.46%, albeit slightly lower than the full-wave simulation which achieved a restoration ratio of 75.87% ± 5.40%. The rapid simulation methods exhibited computational times that were less than five minutes for parallel computation with 8 threads. The incident angle was calculated, and a maximum difference of 6.8 degrees was found when the fixed position of the skull was changed. Meanwhile, the restoration ratio of acoustic intensity was validated to be above 70% for different target positions away from the geometrical focus of the transducer. The favorable balance between time consumption and correction accuracy makes this method valuable for clinical treatment applications.

Rapid prediction of regenerator performance for regenerative cryogenics cryocooler based on convolutional neural network

The regenerator is the core component of the regenerative cryogenic refrigerator, for its structure sizes, operating parameters and phase characteristics at the cold and hot ends co-determine the power and efficiency of the refrigerator, and the design parameters of other coupled components. Efficiently predicting the regenerator performance can reduce the design period of cryogenic refrigerators. Addressing the long computational time constraints in the traditional numerical simulation methods, a novel approach based on a one-dimensional convolutional neural network (1D-CNN) was proposed. Initially, a program capable of multi-threading and automatically running the specialized regenerator calculation software REGEN 3.3 was developed. The performance of the regenerators with various parameter combinations at the cold end temperature of 60–120 K were calculated and 181,440 pieces of data were obtained. Subsequently, the architecture and hyperparameters of the model were determined. The trained model exhibits an average relative error of 3.83% for predicting regenerator power, 0.13% for predicting pressure ratio at the hot end, and 1.55% for predicting the coefficient of performance (COP). The model's generalization ability was confirmed by generating data points beyond the original dataset. Additionally, the model allows for the simultaneous calculation of multiple sets of irregular regenerator parameters, and reduces the calculation time from 2500 min for 1000 pieces using REGEN 3.3 software to just 130 ms, representing a decrease by nearly six orders of magnitude. This approach effectively resolves the long computation time associated with traditional numerical simulation methods, and will present a new solution for the rapid and precise design of regenerators.

EDCNNS: Federated learning enabled evolutionary deep convolutional neural network for Alzheimer disease detection

Alzheimer’s is a dangerous disease prevalent in human societies, and unfortunately, its incidence is increasing daily. The number of patients is on the rise, while the availability of physical doctors has become limited and their schedules are packed. Consequently, the adoption of digital healthcare systems for Alzheimer’s disease (AD) has become more common, aiming to alleviate the burden on both AD patients and doctors. AD digital healthcare is a highly complex domain that incorporates various technologies, including fog computing, cloud computing, and deep learning algorithms. However, the implementation of these fog, cloud, and deep learning technologies has encountered challenges related to high computational time during AD detection processes. To address these challenges, this paper focuses on the convex optimization problem, which aims to optimize computation time and accuracy constraints in digital healthcare applications for AD. Convex optimization necessitates the use of an evolutionary algorithm that can enhance different AD constraints in distinct phases. The paper introduces a novel scheme called Evolutionary Deep Convolutional Neural Network Scheme (EDCNNS), designed to minimize computation time and achieve the highest prediction accuracy criteria for AD. EDCNNS comprises several phases, including feature extraction, selection, execution, and scheduling on the fog cloud nodes. The simulation results demonstrate that EDCNNS optimized security by 38%, reduced the deadline failure ratio by 29%, and improved selection accuracy by 50% across different Alzheimer’s classes compared to existing studies.

Analysis and Prediction of Image Quality Degradation Caused by Diffraction of Infrared Optical System Turning Marks

This paper addresses the issue of reduced image quality due to annular turning marks formed by single-point diamond turning (SPDT) during the processing of metal-based mirrors and infrared lenses. An ideal single-point diamond turning marks diffraction action model to quantitatively analyze the impact of turning marks diffraction on imaging quality degradation is proposed. Based on this model, a fast estimation algorithm for the optical modulation transfer function of the system under turning marks diffraction (TMTF) is proposed. The results show that the TMTF algorithm achieves high computational accuracy, with a relative error of only 3% in diffraction efficiency, while being hundreds of times faster than rigorous coupled wave analysis (RCWA). This method is significant for reducing manufacturing costs and improving production efficiency, as it avoids the problem of being unable to compute large-size optical systems due to computational resource and time constraints.

Subselection of seasonal ensemble precipitation predictions for East Africa

AbstractThe Greater Horn of Africa (GHA) is highly vulnerable to climate and weather hazards such as drought, heat waves, and floods. There is a need for accurate seasonal forecasts to prepare for risks (such as crop failure and reduced grazing opportunities) and take advantage of favorable conditions (rains arrive on time and where they are needed) when they arise. As such, information at finer spatial scales than current state‐of‐the‐art global prediction models can provide is needed. Dynamical downscaling is one method employed to obtain information at finer scales. However, providers of seasonal forecasts over the GHA are hampered by limited computational resources and time constraints that restrict the number of global model ensemble members that can be downscaled. Some ensemble subselection criteria must be employed. Currently, providers take an uninformed (or random) approach. Specifically, forecasters simply take the first ensemble member of the global model seasonal forecast ensemble. However, recent work, focused on decadal prediction, has shown that subselecting global model ensemble members in an informed way, that is, according to their ability to reproduce key features of the climate system, results in improved predictions. This emerges from the fact that the climate system is likely more predictable than our models would have us believe. Seeing an opportunity for improvement, we apply the same thinking to the seasonal context and assess several procedures for subselecting ensemble members from seasonal predictions with exchangeable members. Such informed subselections have the potential to take advantage of information in an ensemble of global simulations that might be missed by random selection. Three subselection methods are investigated, with a focus on seasonal predictions for rainfall over GHA. We demonstrate that informed subselection leads to systematically higher skill than random subselection. We find that (1) for small subsample sizes, such as would be chosen for dynamical downscaling and/or downstream impact modeling, informed subselection nearly always outperforms random subselection, (2) subselecting based on well‐known teleconnections benefits those seasons in which such pathways are active, such as OND and JJAS, and (3) ‐means subselection outperforms random selection for small ensemble sizes throughout all seasons, including the notoriously difficult to predict MAM season. These techniques require only input that is available at the time of the forecast release and are easy to apply operationally.

An Efficient Multi-Class Privacy-Preserving-Based Encryption Framework for Large Distributed Databases

This paper introduces a novel hybrid filter-based ensemble multi-class classification model for distributed privacy-preserving applications. The conventional privacy-preserving multi-class learning models have limited capacity to enhance the true positive rate, mainly due to computational time and memory constraints, as well as the static nature of metrics for parameter optimization and multi-class perturbation processes. In this research, we develop the proposed model on large medical and market databases with the aim of enhancing multi-party data confidentiality through a security framework during the privacy-preserving process. Moreover, we also introduce a secure multi-party data perturbation process to improve computational efficiency and privacy-preserving performance. Experimental results were evaluated on different real-time privacy-preserving datasets, such as medical and market datasets, using different statistical metrics. The evaluation results demonstrate that the proposed multi-party-based multi-class privacy-preserving model performs statistically better than conventional approaches.

Identification of the effective thermal conductivity law in a nuclear fuel rod for the evaluation of neutronic feedbacks

One of the features of nuclear reactor simulation codes is the determination of the temperature distribution inside the uranium fuel rod. When modeling neutronic feedbacks in a nuclear reactor code, the methodology currently used to perform this kind of calculation is based on a simplified model which represents the fuel rod as an infinite cylinder with a one dimensional radial mesh. This choice is due to computation time constraints. Among the data needed by the model is the law giving the thermal conductivity as a function of the temperature. According to standard methodology, an effective thermal conductivity is used and the coefficients of the law are determined by best-fit of results obtained using detailed correlations. An alternative method is presented here to determine this simplified law by solving an inverse problem of heat conduction, formulated in the framework of the nodal expansion method. The results obtained using the simplified law are compared to the ones obtained with two detailed correlations.

Real‐time estimation of the electron temperature profile in DIII‐Dby leveragingneural‐networksurrogate models

AbstractControl of both the magnitude and the shape of tokamak profiles will be necessary to achieve stable, high‐performance plasmas. In order to reject disturbances in real time, feedback‐control algorithms rely on accurate real‐time knowledge of the plasma state. When diagnostics alone are insufficient, because they are limited in number or their measurements are too noisy, observers can be used to combine diagnostic data with a response model to provide a better estimation of different plasma properties. An observer has been developed to estimate the electron temperature profile in real time using both diagnostic data from the Thomson scattering system and a model based on the electron heat transport equation describing the evolution of the electron temperature profile. Neural network surrogate models are leveraged to help improve the overall model prediction while staying within computation time constraints for real‐time use. The observer algorithm is shown in offline tests to produce smooth profiles that are consistent with both the diagnostic data and the electron heat transport equation. When implemented into the real‐time plasma control system, this observer will provide valuable information on the electron temperature profile to many potential feedback‐control applications.

Autonomous Satellite Rendezvous and Proximity Operations with Time-Constrained Sub-Optimal Model Predictive Control

This paper presents a time-constrained model predictive control strategy for the 6 degree-of-freedom (6DOF) autonomous rendezvous and docking problem between a controllable “deputy” spacecraft and an uncontrollable “chief” spacecraft. The control strategy accounts for computational time constraints due to limited onboard processing speed. The translational dynamics model is derived from the Clohessy-Wiltshire equations and the angular dynamics are modeled on gas jet actuation about the deputy's center of mass. Simulation results are shown to achieve the docking configuration under computational time constraints by limiting the number of allowed algorithm iterations when computing each input. Specifically, we show that upwards of 90% of computations can be eliminated from a model predictive control implementation without significantly harming control performance.

Particle generation to minimize the computing time of the discrete element method for particle packing simulation

There are computation time constraints caused by the number and size of particles in the powder packing simulation using DEM. In this paper, newly suggested packing model transforms a general packing sequence —particle generation, stack, and compression-into particle generation and packing by growing particles. To verify the new packing model, it was compared using three contact models widely used in DEM, in terms of radial distribution function, porosity, and coordination number. As a result, contact between particles showed a similar trend, and the pore distribution was also similar. Using the new packing model can reduce simulation time by 400 % compared to the normal packing model without any other coarse graining methods. This model has only been applied to particle packing simulations in this paper, but it can be expanded to other simulations with complex domain based on DEM.

Continuous Piecewise Linear Approximation of Plant-Based Hydro Production Function for Generation Scheduling Problems

An essential challenge in generation scheduling (GS) problems of hydrothermal power systems is the inclusion of adequate modeling of the hydroelectric production function (HPF). The HPF is a nonlinear and nonconvex function that depends on the head and turbined outflow. Although the hydropower plants have multiple generating units (GUs), due to a series of complexities, the most attractive modeling practice is to represent one HPF per plant, i.e., a single function is built for representing the plant generation instead of the generation of each GU. Furthermore, due to the computation time constraints and representation of nonlinearities, the HPF must be given by a piecewise linear (PWL) model. This paper presented some continuous PWL models to include the HPF per plant in GS problems of hydrothermal systems. Depending on the type of application, the framework allows a choice between the concave PWL for HPF modeled with one or two variables and the nonconvex (more accurate) PWL for HPF dependent only on the turbined outflow. Basically, in both PWL models, offline, mixed-integer linear (or quadratic) programming techniques are used with an optimized pre-selection of the original HPF dataset obtained through the Ramer-Douglas-Peucker algorithm. As a highlight, the framework allows the control of the number of hyperplanes and, consequently, the number of variables and constraints of the PWL model. To this end, we offer two possibilities: (i) minimizing the error for a fixed number of hyperplanes, or (ii) minimizing the number of hyperplanes for a given error. We assessed the performance of the proposed framework using data from two large hydropower plants of the Brazilian system. The first has 3568 MW distributed in 50 Bulb-type GUs and operates as a run-of-river hydro plant. In turn, the second, which can vary the reservoir volume by up to 1000 hm3, possesses 1140 MW distributed in three Francis-type units. The results showed a variation from 0.040% to 1.583% in terms of mean absolute error and 0.306% to 6.356% regarding the maximum absolute error even with few approximations.

Articulating Spatial Statistics and Spatial Optimization Relationships: Expanding the Relevance of Statistics

Both historically and in terms of practiced academic organization, the anticipation should be that a flourishing synergistic interface exists between statistics and operations research in general, and between spatial statistics/econometrics and spatial optimization in particular. Unfortunately, for the most part, this expectation is false. The purpose of this paper is to address this existential missing link by focusing on the beneficial contributions of spatial statistics to spatial optimization, via spatial autocorrelation (i.e., dis/similar attribute values tend to cluster together on a map), in order to encourage considerably more future collaboration and interaction between contributors to their two parent bodies of knowledge. The key basic statistical concept in this pursuit is the median in its bivariate form, with special reference to the global and to sets of regional spatial medians. One-dimensional examples illustrate situations that the narrative then extends to two-dimensional illustrations, which, in turn, connects these treatments to the spatial statistics centrography theme. Because of computational time constraints (reported results include some for timing experiments), the summarized analysis restricts attention to problems involving one global and two or three regional spatial medians. The fundamental and foundational spatial, statistical, conceptual tool employed here is spatial autocorrelation: geographically informed sampling designs—which acknowledge a non-random mixture of geographic demand weight values that manifests itself as local, homogeneous, spatial clusters of these values—can help spatial optimization techniques determine the spatial optima, at least for location-allocation problems. A valuable discovery by this study is that existing but ignored spatial autocorrelation latent in georeferenced demand point weights undermines spatial optimization algorithms. All in all, this paper should help initiate a dissipation of the existing isolation between statistics and operations research, hopefully inspiring substantially more collaborative work by their professionals in the future.