Computational Efficiency Research Articles

Simulation of the SIR dengue fever nonlinear model: A numerical approach

This paper introduces a new numerical technique that utilizes the Chebyshev collocation method to solve the nonlinear SIR mathematical model for dengue disease dynamics. The main goal of this study is the development of a highly accurate and efficient numerical approach that allows for the simulation of dengue fever infection and transmission mechanisms. The suggested methodology provides a strong and adaptable tool for studying the intricate dynamics of dengue fever, which is essential for improving our comprehension and control of this significant public health problem. The numerical approach is totally programmable and can be customized to incorporate a user-friendly interface for data input, hence enhancing its accessibility and practicality for researchers and public health practitioners. The conducted numerical simulations provide evidence of the accuracy and computational efficiency of the suggested technique. This unique numerical framework is positioned as a standard for managing and regulating a diverse array of biological problems. The creation of this sophisticated numerical tool signifies significant progress in the realm of dengue fever modeling. It offers a versatile and dependable platform to investigate different epidemiological scenarios and assess the possible effects of intervention efforts. The results of this study have significant consequences for enhancing dengue monitoring, safeguarding, and control initiatives in areas where the disease is prevalent.

An efficient exact method with polynomial time-complexity to achieve [formula omitted]-strong barrier coverage in heterogeneous wireless multimedia sensor networks

Barrier coverage in Wireless Sensor Networks (WSNs) plays a pivotal role in surveillance and security applications. It serves as a fundamental mechanism for identifying and detecting potential intruders who endeavor to infiltrate a sensor barrier. Achieving k-strong barrier coverage is a vital indicator of a WSN’s capability to detect unauthorized intrusions. This paper establishes efficient k-strong barrier coverage in hybrid wireless multimedia sensor networks, referred to as MMS-KSB. The primary goal is to identify a minimal number of mobile sensors to obtain k-barrier coverage. Exhibiting the combinatorial structure, previous research on building k-strong barrier has failed to provide a polynomial time solution for the considered problem and resorted to approximation algorithms. We, therefore, introduce a precise algorithm, named ExA-KSB, and provide theoretical analysis to substantiate that our proposed method achieves an exact solution with polynomial time complexity. Furthermore, we conduct comprehensive experiments to evaluate the efficacy of our algorithm by comparing it with existing approaches. Numerical results demonstrate that ExA-KSB surpasses previous algorithms, and offers superior solution quality with competitive computational efficiency.

LightDepth: A resource efficient depth estimation approach for dealing with ground truth sparsity via curriculum learning

Accurate depth estimation from monocular images is critical for various applications such as robotics, augmented reality, and autonomous navigation. However, achieving high accuracy while maintaining computational efficiency is a major challenge, particularly for resource-constrained devices. In this paper, we present LightDepth, an approach that leverages curriculum learning to estimate depth efficiently while taking into account resource constraints. It modifies the ground truth sparse depth maps from the KITTI dataset by resizing them to 31 extents during training to reduce sparsity and control complexity. The resulting model achieves comparable accuracy to state-of-the-art large models while outperforming them in response time by 71%. Our approach outperforms resource-efficient models regarding depth accuracy (measured by RMSE), achieving a 56% improvement. LightDepth is designed to be fast and resource-efficient, making it suitable for deployment in resource-constrained devices. It also balances the trade-off between accuracy and resource efficiency. All codes are available online at https://github.com/fatemehkarimii/lightdepth.

Intelligent non-invasive elderly fall monitoring by designing software defined radio frequency sensing system

The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population, especially in relation to falls. While falls can lead to significant cognitive impairments, timely intervention can mitigate their adverse effects. In this context, the need for non-invasive, efficient monitoring systems becomes paramount. Although wearable sensors have gained traction for monitoring health activities, they may cause discomfort during prolonged use, especially for the elderly. To address this issue, we present an intelligent, non-invasive Software-Defined Radio Frequency (SDRF) sensing system, tailored red for monitoring elderly people's falls during routine activities. Harnessing the power of deep learning and machine learning, our system processes the Wireless Channel State Information (WCSI) generated during regular and fall activities. By employing sophisticated signal processing techniques, the system captures unique patterns that distinguish falls from normal activities. In addition, we use statistical features to streamline data processing, thereby optimizing the computational efficiency of the system. Our experiments, conducted for a typical home environment while using treadmill, demonstrate the robustness of the system. The results show high classification accuracies of 92.5%, 95.1%, and 99.8% for three Artificial Intelligence (AI) algorithms. Notably, the SDRF-based approach offers flexibility, cost-effectiveness, and adaptability through software modifications, circumventing the need for hardware overhaul. This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring, providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.

Self-Organizing Hierarchical Incremental Learning Framework and Universal Approximation Analysis Based on Stochastic Configuration Mechanism

Conventional machine learning algorithms face significant limitations when dealing with high-dimensional data. Besides, deep learning models often require substantial computational resources and have a high processing time despite their excellent performance. Hence, this paper proposes an expanded stochastic configuration network and a self-organizing hierarchical incremental learning (SHIL) framework to overcome these challenges. Specifically, this study introduces a novel supervised hierarchical clustering tree based on the minimum redundancy maximum correlation algorithm, which mines internal data structures to construct diverse hierarchies. Subsequently, by exploiting the parent-child node relationships in the tree structure, SHIL defines the maximum number of nodes as the switching condition between levels uses the supervisory mechanism as the parameter selection criterion, and adopts the tolerance error as the termination criterion for the training. Furthermore, the universal approximation property of the SHIL framework is provided. The proposed SHIL framework is validated on several benchmark datasets, image datasets, and industrial robot cases, with the corresponding experimental results demonstrating that SHIL significantly improves computational efficiency and ensures high accuracy.

Efficient computation of horizontal stabilizer incidence angle for low pitching moments based on actuator model

Efficient computation of horizontal stabilizer incidence angle for low pitching moments based on actuator model

Comparative Analysis of Manifold Learning-Based Dimension Reduction Methods: A Mathematical Perspective

Manifold learning-based approaches have emerged as prominent techniques for dimensionality reduction. Among these methods, t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) stand out as two of the most widely used and effective approaches. While both methods share similar underlying procedures, empirical observations indicate two distinctive properties: global data structure preservation and computational efficiency. However, the underlying mathematical principles behind these distinctions remain elusive. To address this gap, this study presents a comparative analysis of the subprocesses involved in these methods, aiming to elucidate the mathematical mechanisms underlying the observed distinctions. By meticulously examining the equation formulations, the mathematical mechanisms contributing to global data structure preservation and computational efficiency are elucidated. To validate the theoretical analysis, data are collected through a laboratory experiment, and an open-source dataset is utilized for validation across different datasets. The consistent alignment of results obtained from both balanced and unbalanced datasets robustly confirms the study’s findings. The insights gained from this study provide a deeper understanding of the mathematical underpinnings of t-SNE and UMAP, enabling more informed and effective use of these dimensionality reduction techniques in various applications, such as anomaly detection, natural language processing, and bioinformatics.

A Practical Approach to Intrusion Detection in IoV Networks Using LCCDE Ensemble Framework

Abstract: With the rapid development of the Internet of Things (IoT) and the Internet of Vehicles (IoV) technologies, modern vehicles are increasingly adopting network-controlled functionalities, exposing them to a variety of cyber threats. To address these security challenges, this paper implements and evaluates a novel ensemble Intrusion Detection System (IDS) framework, named Leader Class and Confidence Decision Ensemble (LCCDE). The LCCDE framework integrates three advanced Machine Learning (ML) algorithms—XGBoost, LightGBM, and CatBoost—to detect various types of cyber-attacks on both intra-vehicle and external vehicular networks.This study demonstrates the effectiveness of the LCCDE framework using two public IoV security datasets: the Car-Hacking dataset and the CICIDS2017 dataset. By identifying the best-performing ML model for each class of attacks and leveraging prediction confidence values, LCCDE achieves high detection accuracy and robustness against diverse attack types. The experimental results show that the proposed framework outperforms traditional IDS approaches in terms of detection accuracy, computational efficiency, and adaptability to different types of cyber-attacks. This paper provides practical insights into the implementation and deployment of IDS in IoV systems, highlighting the potential of ensemble learning methods to enhance vehicular network security.

Economic model predictive control for building HVAC system: A comparative analysis of model-based and data-driven approaches using the BOPTEST Framework

Model Predictive Control (MPC) has demonstrated its capability to significantly enhance energy efficiency while ensuring indoor comfort. This study focuses on the development and fair comparison of two Economic Model Predictive Control(EMPC) frameworks with the goal of optimizing demand response for utility cost reduction and ensuring thermal comfort in residential buildings. The first framework, known as RC-MPC, utilizes a reduced-order thermal Resistance–Capacitance network model to represent the thermal dynamics of the building. It formulates the optimization problem as a Linear Programming (LP) task, which is solved using CPLEX. The second framework, known as ANN-MPC, employs an Artificial Neural Network model capture the thermal behavior of the building, It addresses a non-convex optimization problem through projected stochastic gradient descent. To evaluate the performance of these frameworks, experiments are conducted using a single-zone dwelling within the BOPTEST framework. The results demonstrate that both approaches effectively reduce operational costs and enhance comfort, achieving comparable performance and outperforming the baseline Rule-Based Control (RBC) method. The discomfort reductions range from 30% to 95%, and energy expense savings range from 17% to 34%. Furthermore, the study examines the impact of various MPC hyperparameters. The findings highlight that a proper control horizon and formulating the objective function accurately are crucial for achieving optimal control performance. Additionally, tightening the comfort range is found to be important in managing the comfort violations, and the choice of solver can influence both the control performance and computational efficiency.

GRDATFusion: A gradient residual dense and attention transformer infrared and visible image fusion network for smart city security systems in cloud and fog computing

AbstractThe infrared and visible fusion technology holds a pivotal position in smart city for cloud and fog computing, particularly in security system. By fusing infrared and visible image information, this technology enhances target identification, tracking and monitoring precision, bolstering overall system security. However, existing deep learning‐based methods rely heavily on convolutional operations, which excel at extracting local features but have limited receptive fields, hampering global information capture. To overcome this difficulty, we introduce GRDATFusion, a novel end‐to‐end network comprising three key modules: transformer, gradient residual dense and attention residual. The gradient residual dense module extracts local complementary features, leveraging a dense‐shaped network to retain potentially lost information. The attention residual module focuses on crucial input image details, while the transformer module captures global information and models long‐range dependencies. Experiments on public datasets show that GRDATFusion outperforms state‐of‐the‐art algorithms in qualitative and quantitative assessments. Ablation studies validate our approach's advantages, and efficiency comparisons demonstrate its computational efficiency. Therefore, our method makes the security systems in smart city with shorter delay and satisfies the real‐time requirement.

Decentralized energy management regime for prosumer community Microgrids using flexible tube model predictive control

Achieving reliable, cost-effective, and low-carbon energy management for community microgrids (CMs) under suitable computational complexity is challenging due to uncertainties in renewable generation and load demand. To address this challenge, this paper proposes a flexible tube model predictive control-based energy management regime (EMR) for prosumer CMs, enabling robustness against system uncertainties in the energy management strategy with fewer sacrifices in economic performance and computational efficiency. The proposed EMR adopts a multistage receding horizon optimization structure, which allows the prosumers to dynamically adjust their energy scheduling according to the updated energy price and forecast information. Further, a decentralized algorithm with a conditional communication strategy is developed, highlighting less communication burden and private information preservation. Case studies on a typical prosumer CM demonstrate the effectiveness of the proposed method in confronting high system uncertainties. Notably, the proposed method ensures that CMs operate with high reliability (having a maximum energy supply shortage probability of just 0.42 %), in a low-carbon and economical manner (the local accommodation rate of renewable energy generation with low costs reaches 98.83 %), even in scenarios where disturbances occur simultaneously on both the energy supply and demand sides.

Self-attention mechanism to enhance the generalizability of data-driven time-series prediction: A case study of intra-hour power forecasting of urban distributed photovoltaic systems

The emergence of small-scale urban distributed solar generation (DSG) has urged the exploration of site-adaptive forecasting models designed to accurately predict future power outputs for unseen DSGs. In such scenarios, with numerous DSGs spread across utility-scale cities and a lack of historical data, it is not economically viable to use conventional approaches that develop individual models for each DSG. Therefore, this work aims to tackle this real-world challenge by adapting the state-of-the-art, attention-based temporal fusion transformer (TFT) model to 188 real-world operational DSG data, thereby validating the generalizability of self-attention mechanism for multi-step time series forecasting. When adapted to unseen DSGs without training data, the experiment results demonstrate that the proposed solar TFT (STFT) improves by 11.07%, 17.58%, and 22.76% over the persistence model at the 10-, 20-, and 30-minute forecasts, respectively. Even when compared to representative deep-learning models, such as a long short-term memory model specialized in time series forecasting, STFT has demonstrated improved forecast accuracy, achieving 3.34%, 4.18%, and 5.85% enhancements at the 10-, 20-, and 30-minute forecast horizons, respectively. However, the model architecture of STFT is more complex, and the computational cost associated with it is relatively higher compared to other deep learning models. This trade-off between accuracy and computational efficiency should be considered in practical applications. The forecast performance is analyzed in three typical weather conditions, namely, clear, partly cloudy, and overcast. STFT demonstrates advantages in high variability periods, especially during weather transition periods, where reference models experience lagged predictions yielding relatively large errors.

Research on Multi-Source Data Fusion and Satellite Selection Algorithm Optimization in Tightly Coupled GNSS/INS Navigation Systems

With the increase in the number of Global Navigation Satellite System (GNSS) satellites and their operating frequencies, richer observation data are provided for the tightly coupled Global Navigation Satellite System/Inertial Navigation System (GNSS/INS). In this paper, we propose an efficient and robust combined navigation scheme to address the key issues of system accuracy, robustness, and computational efficiency. The tightly combined system fuses multi-source data such as the pseudo-range, the pseudo-range rate, and dual-antenna observations from the GNSS and the horizontal attitude angle from the vertical gyro (VG) in order to realize robust navigation in a sparse satellite observation environment. In addition, to cope with the high computational load faced by the system when the satellite observation conditions are good, we propose a weighted quasi-optimal satellite selection algorithm that reduces the computational burden of the navigation system by screening the observable satellites while ensuring the accuracy of the observation data. Finally, we comprehensively evaluate the proposed system through simulation experiments. The results show that, compared with the loosely coupled navigation system, our system has a significant improvement in state estimation accuracy and still provides reliable attitude estimation in regions with poor satellite observation conditions. In addition, in comparison experiments with the optimal satellite selection algorithm, our proposed satellite selection algorithm demonstrates greater advantages in terms of computational efficiency and engineering practicability.

A fast Euler–Maruyama scheme and its strong convergence for multi-term Caputo tempered fractional stochastic differential equations

In this paper, we consider a kind of multi-term Caputo tempered fractional stochastic differential equations and prove the existence and uniqueness of the true solution. Then we derive an Euler–Maruyama (EM) scheme to solve the considered equations. In view of the huge computational cost caused by the EM scheme to achieve reasonable accuracy, a fast EM scheme is proposed based on the sum-of-exponentials approximation to improve its computational efficiency. Moreover, the strong convergence of our two numerical schemes are proved. Finally, several numerical examples are carried out to support our theoretical results and demonstrate the superior computational efficiency of the fast EM scheme.

Investigation of a new method for direct chemistry integration in Conditional Source-term Estimation

This paper examines a new Conditional Source-term Estimation (CSE) implementation in which an integral inversion is performed for species mass fractions without any chemistry tabulation. To tackle the numerical stiffness due to chemistry, a constant-pressure chemical reactor is considered in conditional space. The objective of this work is to investigate this new method in CSE by comparing its numerical performance (accuracy of predictions and computational efficiency) with previous CSE implementations using tabulated chemistry. For the first time, the constraints for mass and mixture fraction conservation in conditional space are included in the inversion process. The investigation is performed in a Reynolds Averaged Navier-Stokes (RANS) framework using a well-documented turbulent flame with detailed measurements in conditional and physical spaces for many reacting scalars. A chemical mechanism including 16 species and 35 reactions is incorporated. CSE predictions of conditional and Favre averaged temperature and different species mass fractions are analysed. CSE with full chemistry performs as well as CSE with tabulated chemistry, if not slightly better. Numerical errors are discussed. The new CSE implementation is made possible by the proposed numerical treatment of stiffness due to chemistry and first-order Tikhonov regularisation technique with additional physical constraints. The new approach is found to be more CPU intensive than CSE with a pre-tabulation technique, but as a preliminary analysis, the computational cost of CSE with direct integration of a chemical mechanism appears to be much smaller than the run time associated with CMC for a similar set-up. Further investigation is required to refine this initial conclusion. These results open new opportunities towards CSE simulations that involve fuel blends and more complex operating conditions for example with spray and multi-stream inlets with non-uniform compositions for which accurate chemistry tabulations are difficult to generate.

A fast adaptive spatio-temporal fusion method to enhanced Fit-FC.

Space-time fusion is an economical and efficient way to solve "space-time contradiction". Among all kinds of space-time fusion methods, Fit-FC space-time fusion method based on weight Function is widely used. However, this method is based on the linear model to depict the phase change, but the phase change in the real scene is complicated, and the linear model is difficult to accurately capture the phase change, resulting in the spectral distortion of the fusion image. In addition, pixel-by-pixel scanning with moving Windows leads to inefficiency issues, limiting its use in large-scale and long-term tasks. To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. Secondly, an adaptive window selection Function is established to overcome the problem of manually setting parameters on different data sets, improve the convenience of the algorithm and robustness of the application on different data sets, and make the algorithm simpler and more efficient. Finally, the improved AL-FF algorithm is compared with other algorithms to verify the performance improvement. Compared with the current advanced Spatio-Temporal fusion methods, AL-FF algorithm has stronger detail capture ability and can generate more accurate fusion results. In addition, the computational efficiency is significantly improved, and the efficiency is increased by more than 20 times compared with the current mainstream method.

A methodology to correlate river inflows in state sampling Monte Carlo simulation

This paper proposes a methodology to sample inflow states of run-of-the-river hydropower plants that are spatially correlated, preserving the statistical dependence among them, in Non-Sequential Monte Carlo Simulation (MCS). The technique used to correlate the inflows of hydro plants in the Brazilian synthetic series generation model, which is based on the correlation matrix and Cholesky Decomposition, is adapted to be used in the sampling stage. Nataf transformation is used for addressing non-normality of the inflow data. The solution method is such that no assumption about the data probability distribution is necessary. Although the methodology is applied to inflows in this paper, it can be applied to any other quantity, such as winds. A case study is proposed to evaluate the correlation of the sampled inflows and the impact on the reliability indices. Besides, it is investigated the effect when inflows states are clustered to reduce the number of states in the system for better computational efficiency. Case study shows that the reliability indices obtained with the proposed methodology are within the confidence interval of the indices calculated by the Sequential MCS with an execution time as fast as the Non-Sequential one, while the non-consideration of correlation causes very significant errors. Simulation with 50 clusters produces equally good results with a reduction of 75 % of the execution time. Sensitivity analysis showed that halving the number of clusters maintains the accuracy of the results, while 10 clusters results in overestimating system reliability. Different degrees of correlation have been simulated, showing that the higher the correlation among the hydro plants, the less reliable the system becomes.

Application and Optimization of Decision Tree ID3 Algorithm in Innovation and Entrepreneurship Analysis Model

In recent years, with the rise of innovation and entrepreneurship, the decision tree ID3 algorithm is more and more widely used in innovation and entrepreneurship analysis models. The decision tree ID3 algorithm is an effective data mining technology, which can mine useful information from a large number of data and provide a scientific basis for innovation and entrepreneurship. The traditional ID3 algorithm has obvious convergence characteristics, which limits its application in innovation and entrepreneurship analysis models. Therefore, it is important to optimize the ID3 algorithm to improve its computational efficiency and accuracy. From the perspective of the innovation and entrepreneurship analysis model, the application of the decision tree ID3 algorithm in innovation and entrepreneurship is studied. The basic principle, advantages, and disadvantages of the ID3 algorithm are analyzed, and a method to optimize the ID3 algorithm is proposed. Through the experimental verification, the application effect of the optimized ID3 algorithm in the analysis model of innovation and entrepreneurship is proved. When the number of records is 5200, the error rate of the optimization algorithm is 70%, while the error probability of the classical algorithm is 95%. When the number of records is 7200, the error rate of the optimized calculation method is 130%, while the error rate of the classic ID3 calculation method is 170%. As the number of records increases, the error rates of both optimization algorithms and classical algorithms will increase. As the number of records increases, the error rate of the optimized ID3 algorithm increases less than that of the classical algorithm. The optimized ID3 algorithm can significantly improve computational efficiency and accuracy and provide an effective data mining technology for innovation and entrepreneurship analysis models.

Continuum and Discrete Analytical Methods for Vibration and Buckling Eigenvalues Shape Sensitivities

AbstractGradient-based optimization techniques need precise and efficient sensitivities. Numerical sensitivity methods such as finite differences are easy to implement but imprecise and computationally inefficient. In contrast, analytical sensitivity methods, such as the discrete and continuum ones are highly accurate and efficient. Continuum Sensitivity Analysis (CSA) is an analytical method used to calculate derivatives of shape or value parameters. While CSA has been successfully applied in static analysis and dynamic problems in the time domain, this work presents an extension of the approach to eigenvalue sensitivities for the first time. However, CSA revealed limitations, prompting the exploration of an alternative approach based on discrete analytical differentiation. This method is employed for the first time in shape sensitivities. The derivatives of the stiffness and mass matrices required by the method are calculated analytically, resulting in high accuracy and computational efficiency. In addition, an element agnostic approach has been developed leveraging primary analysis matrices to calculate their derivative. This characteristic, along with the nonintrusivity, makes the method employable with standard commercial software. Both approaches have been applied and validated in a wide range of scenarios, involving vibration and buckling problems.

Simulating plate and shell structures with anisotropic resolution using adaptive smoothed particle hydrodynamics

When simulating plate and shell structures characterized by large aspect ratios, reduced-dimensional models are frequently employed due to their notable reduction in computational overhead in contrast to traditional isotropic full-dimensional models. However, in scenarios involving variations in the thickness direction, where adequate resolution in this dimension is required, reduced-dimensional models exhibit limitations. To capture variations in the thickness direction while simultaneously mitigating computational costs, an anisotropic full-dimensional model, integrated with an adaptive smoothed particle hydrodynamics method (ASPH), is developed for simulating behaviors of plate and shell structures in this study. The correction matrix, which is applied to ensure the first-order consistency, is modified accordingly by incorporating the nonisotropic kernel into it within the total Lagrangian framework of ASPH. A series of numerical examples, along with a specific application concerning the deformation of a porous film due to nonuniform internal fluid pressure in the thickness direction, are conducted to assess the computational accuracy and efficiency of the proposed ASPH method. Comparative analyses of our results against reference data and traditional isotropic SPH solutions demonstrate close agreements, affirming the suitability of the present ASPH method across various scenarios.