Improvement In Computational Efficiency Research Articles

Research on Student Classroom Behavior Detection Based on the Real-Time Detection Transformer Algorithm

With the rapid development of artificial intelligence and big data technology, intelligent education systems have become a key research focus in the field of modern educational technology. This study aims to enhance the intelligence level of educational systems by accurately detecting student behavior in the classroom using deep learning techniques. We propose a method for detecting student classroom behavior based on an improved RT DETR (Real-Time Detection Transformer) object detection algorithm. By combining actual classroom observation data with AI-generated data, we create a comprehensive and diverse student behavior dataset (FSCB-dataset). This dataset not only more realistically simulates the classroom environment but also effectively addresses the scarcity of datasets and reduces the cost of dataset construction. The study introduces MobileNetV3 as a lightweight backbone network, reducing the model parameters to one-tenth of the original while maintaining nearly the same accuracy. Additionally, by incorporating learnable position encoding and dynamic upsampling techniques, the model significantly improves its ability to recognize small objects and complex scenes. Test results on the FSCB-dataset show that the improved model achieves significant improvements in real-time performance and computational efficiency. The lightweight network is also easy to deploy on mobile devices, demonstrating its practicality in resource-constrained environments.

Harmonic Mean Optimizer (HMO) for global problems solving

This study introduces the Harmonic Mean Optimizer (HMO), a novel meta-heuristic algorithm designed to address complex global optimization problems. The primary objective of this research is to develop an optimization technique that effectively balances exploration and exploitation without requiring extensive parameter tuning, thereby simplifying the optimization process and enhancing its robustness across various problem domains. The HMO employs a unique dual-fitness index that leverages the harmonic mean to assess both the quality and diversity of candidate solutions dynamically. This approach ensures a balanced search process that avoids premature convergence and improves the ability to find global optima. The methodology involves extensive benchmarking of the HMO against a comprehensive set of test functions, including 23 traditional functions from the Congress on Evolutionary Computation (CEC) 2017 test suite. The performance of the HMO is compared with that of established meta-heuristic algorithms, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to validate its efficacy in terms of convergence speed and solution accuracy. Additionally, the HMO is applied to several real-world engineering problems to demonstrate its practical utility. The results show that the HMO consistently outperforms the benchmark algorithms, achieving faster convergence and higher accuracy in finding optimal solutions across a diverse range of test problems. In practical applications, the HMO effectively optimized complex engineering tasks, achieving significant improvements in both solution quality and computational efficiency. These findings highlight the potential of the HMO as a versatile and powerful tool for global optimization tasks. the HMO offers a significant advancement in optimization methodologies by providing a robust, parameter-free approach that effectively balances exploration and exploitation. This study underscores the HMO’s applicability to a wide range of optimization challenges and sets the foundation for future research and development in enhancing and applying this innovative algorithm to more complex and diverse problem domains.

Active Learning for a Recursive Non-Additive Emulator for Multi-Fidelity Computer Experiments

Computer simulations have become essential for analyzing complex systems, but high-fidelity simulations often come with significant computational costs. To tackle this challenge, multi-fidelity computer experiments have emerged as a promising approach that leverages both low-fidelity and high-fidelity simulations, enhancing both the accuracy and efficiency of the analysis. In this paper, we introduce a new and flexible statistical model, the Recursive Non-Additive (RNA) emulator, that integrates the data from multi-fidelity computer experiments. Unlike conventional multi-fidelity emulation approaches that rely on an additive auto-regressive structure, the proposed RNA emulator recursively captures the relationships between multi-fidelity data using Gaussian process priors without making the additive assumption, allowing the model to accommodate more complex data patterns. Importantly, we derive the posterior predictive mean and variance of the emulator, which can be efficiently computed in a closed-form manner, leading to significant improvements in computational efficiency. Additionally, based on this emulator, we introduce four active learning strategies that optimize the balance between accuracy and simulation costs to guide the selection of the fidelity level and input locations for the next simulation run. We demonstrate the effectiveness of the proposed approach in a suite of synthetic examples and a real-world problem. An R package RNAmf for the proposed methodology is provided on CRAN.

Enhancing Computational Efficiency in Porous Media Analysis: Integrating Machine Learning with Monte Carlo Ray Tracing

Abstract Monte Carlo ray tracing (MCRT) has been a widely implemented and reliable computational method for calculating light-matter interaction in porous media, the computational modeling of porous media and performing MCRT becomes significantly expensive when dealing with intricate structures and numerous dependent variables. Hence, Machine Learning (ML) models have been utilized to overcome computational burdens. In this study, we investigate two distinct frameworks for characterizing radiative properties in porous media for pack-free and packing-based methods. We employ two different regression tools for each case, namely Gaussian process regressions for pack-free MCRT and Convolutional Neural Network (CNN) models for pack-based MCRT to predict the radiative properties. Our study highlights the importance of selecting the appropriate regression method based on the physical model, which can lead to significant computational efficiency improvement. Our results show that both models can predict the radiative properties with high accuracy (>90%). Furthermore, we demonstrate that combining MCRT with ML inference not only enhances predictive accuracy but also reduces the computational cost of simulation by more than 96% using the GP model and 99% for the CNN model.

Exploring time-series transformers for spatio-temporal prediction of microstructural evolution of polycrystalline grain

This study introduces a novel microstructure prediction model (polycrystalline grain transformers: PGTs) for metal solidification processes, leveraging the capabilities of transformer networks. The model’s primary purpose is to predict the dynamic evolution of polycrystalline microstructures within additive manufacturing conditions. To facilitate the training of the PGTs model, we generated a comprehensive dataset through a two-dimensional multi-phase field numerical simulation model. The inherent strength of transformer networks lie in its multi-head attention mechanism, which enables the PGTs model to autonomously identify crucial feature information within the input sequence, particularly related to structural evolution. Our research outcomes underscore the robustness of the PGTs model, as it closely aligns with predictions obtained from conventional phase-field calculations. Importantly, this model exhibits a remarkable improvement in computational efficiency, surpassing traditional phase-field methods by several orders of magnitude. This breakthrough opens up new avenues for research, offering an efficient and cost-effective strategy for delving deep into the intricate relationship between solidification processes and material mechanical properties. Notably, we mark this study as the pioneering application of transformer networks in numerical simulations of the metal solidification process, showcasing its immense potential across various domains, including material design.

Efficient performance analysis and rapid evaluation for spot-welded stainless steel vehicles

Abstract The structural performance analysis and solder joint strength evaluation of spot-welded stainless-steel vehicles are associated with many working conditions, significant data, and repeated work. This will help researchers evaluate and improve calculation efficiency quickly. Taking the body structure of a spot-welded stainless-steel vehicle as the research object, this study conducted a finite element simulation analysis of its stiffness, static strength, mode, fatigue strength, and solder joint strength by the EN12663 standard. A rapid evaluation system for the solder joint strength was developed. The results showed that the performance of the vehicle body under various working conditions can meet the design requirements, and the evaluation results of the solder joint strength confirm the practicability and rapidity of the system, which can provide technical support for the subsequent improvement and optimization of vehicle body structures.

Provably secure optimal homomorphic signcryption for satellite-based internet of things

Satellites have long been reliable and widely used mediums for communication. With recent advancements in the Internet of Things (IoT), satellite-based IoT has emerged, enhancing global connectivity by enabling real-time communication between remote sensing devices and central hubs through satellite links. However, secure and authenticated communication between Low Earth Orbit (LEO) satellites and resource-constrained ground devices remains underexplored. This paper introduces a novel homomorphic signcryption scheme designed for satellite IoT environments. Our scheme employs hyperelliptic curve cryptography and homomorphic encryption to facilitate the privacy-preserving transmission of aggregated data from multiple IoT devices to LEO satellites. We benchmark our homomorphic signcryption scheme against several established schemes tailored for different network environments, including for satellite networks, Identity-based Signcryption for Smart Grids, 3-Factor Authentication schemes and heterogeneous Vehicular Ad-hoc Networks (VANETs) encryption schemes. Our scheme demonstrates substantial improvements in computational efficiency, communication overhead, and storage capacity. It reduces computational costs by 90% to 99%, communication overhead by 41% to 92%, and storage demands by 62% to 93% compared to existing methods. These quantitative findings demonstrate the practical viability of our approach for satellite IoT deployments with limited resources. Additionally, the robustness of our approach is confirmed through formal security validation using BAN Logic and Scyther, which verifies its strong security, optimal performance, and privacy preservation capabilities, making it ideally suited for real-world satellite IoT systems.

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations.

Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for TensorNet models, with performance gains ranging from 2× to 10× over previous, nonoptimized, iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net.

Lambert-Free Solution of Multiple-Gravity-Assist Optimization Problem

A new method to find optimized multiple-gravity-assist (MGA) interplanetary trajectories that does not require the solution of Lambert’s problem is presented. The method, applicable to MGA sequences with ballistic transfer arcs and flybys, exploits Godal’s hyperbolic locus of incoming and outgoing velocities connecting the different trajectory legs combined with the v∞ sphere flyby constraint. The intersection between the two loci, when it exists, can be used to find feasible candidate solutions for each interplanetary trajectory segment and can be obtained analytically after solving a quartic equation. A time-of-flight compatibility condition can then be introduced that replaces the classical C3-matching constraint for locating properly phased transfer arcs, whereby opening the door to a very significant improvement in computational efficiency. The implementation of the method and its applicability to interplanetary trajectory optimization and moon tours are discussed in detail using representative test cases.

A New Cyclic Cohesive Zone Model for Fatigue Damage Analysis of Welded Vessel

A new cyclic cohesive zone fatigue damage model is proposed to address the fatigue problem spanning high and low cycle stages. The new damage model is integrated with the damage extrapolation technique to improve calculation efficiency. The model's effectiveness in regulating the low-cycle fatigue evolution rate, overall fatigue damage evolution rate, and stress level at the fatigue turning point is assessed through the comparison of the S-N curves. The fatigue damage model's high precision is proved based on the minor deviation of stress at the turning point of the S-N curve from the actual scenario. Finally, the fatigue damage evolution is simulated considering the effects of pre-load pressure and welding residual stress. It is observed that laser welding induces a significant residual tensile stress, accelerating fatigue damage evolution, while compressive loading impedes fatigue damage progression.

Delay-dependent stability analysis for load frequency control systems with time-varying delays

This paper introduces a delay-dependent criterion that addresses both computational accuracy and efficiency in dealing with delay-dependent stability challenges within multi-area load frequency control (LFC) systems. Firstly, the traditional power system LFC model is decomposed into delay-related and delay-independent parts using a model reconstruction technique. Subsequently, an augmented Lyapunov–Krasovskii functional (LKF) is formulated, focusing on the delay-related part of the model. The double integral component within the derivative of the LKF is constrained using the parameter-dependent slack matrices approach, resulting in a less conservative stability criterion. Finally, the proposed result is validated through case studies. Simulation results demonstrate that the proposed stability criterion significantly improves computational accuracy while maintaining better validity compared to existing approaches. Moreover, compared to the traditional LFC model, the reconstructed model offers substantial computational efficiency improvements, albeit with a slight decrease in computational precision.

GeoTaichi: A Taichi-powered high-performance numerical simulator for multiscale geophysical problems

This study introduces GeoTaichi, an open-source high-performance numerical simulator designed for addressing multiscale geophysical problems. By leveraging the power of the Taichi parallel language, GeoTaichi maximizes the utilization of modern computer resources on multicore CPU and GPU architectures. It offers robust and reliable modules for the discrete element method (DEM), material point method (MPM), and coupled material point-discrete element method (MPDEM). These modules enable efficient solving of large-scale problems while being implemented in pure Python. The design philosophy of GeoTaichi focuses on creating a framework that is readable, extensible, and user-friendly. This paper highlights the coupling procedure of MPDEM, the code structures, and the most important features of GeoTaichi. Rigorous benchmark tests have been conducted to verify the validity and robustness of GeoTaichi. Additionally, the performance of GeoTaichi is compared with similar software tools in the field, underscoring a notable improvement in both computational efficiency and memory savings when compared to existing alternatives. Program summaryProgram title: GeoTaichiCPC Library link to program files:https://doi.org/10.17632/858bmcf7j6.1Developer's repository link:https://github.com/Yihao-Shi/GeoTaichiLicensing provisions: GNU General Public License v3.0Programming language: PythonNature of problem: The simulations of large-deformation geophysical flows and their interaction with structures play a crucial role in the field of geophysics. To address the complexities of these nonlinear problems, the discrete element method (DEM), material point method (MPM), and their coupling (MPDEM) have proven to be highly suitable numerical schemes. However, these schemes impose substantial computational demands, necessitating the development of an efficient framework that can harness modern computer resources on multicore CPU and GPU architectures.Solution method: The open-source code GeoTaichi implements the DEM, MPM, and coupled MPDEM, encompassing a range of constitutive models and contact laws for different geologic materials. The clump particle model is also introduced in DEM to solve granular mechanics involving complex-shaped particles. One significant advantage of GeoTaichi is its utilization of the Taichi parallel language, which is designed to be user-friendly and easily extensible for customized applications.

Graph Neural Network for domain segmentation to predict regions of non-ideal mixing in two-dimensional baffle flow systems

This paper presents a novel approach to address computational challenges in predicting flow features by employing a Graph Neural Network (GNN), which is proficient in predicting flow domain values. Traditional Computational Fluid Dynamics (CFD) simulations, although effective, often require substantial computational resources and time, limiting their applicability in time-sensitive scenarios and optimization studies necessitating extensive case studies. The main objective was to evaluate the feasibility of employing node classification on a graph generated from a 2D baffle flow system to segment the domain based on relative fluid age. A second objective was to compare the computational time required for CFD simulations with the inference time of the network to quantify the efficiency gains achieved by utilizing the network. Results demonstrate the potential of utilizing graph convolutional networks for domain segmentation to predict regions of holdup and bypass. The GNN achieved 97% and 92% accuracy in predicting recirculation regions in single and double baffle cases, particularly excelling in high Reynolds number scenarios. Importantly, the proposed GNN-based approach reduces computation time by over 2100%, showcasing significant efficiency gains. Results here highlight the promise of employing graph convolutional networks for flow feature prediction, offering substantial computational efficiency improvements over traditional CFD simulations.

Cryptanalysis with improvement on lattice‐based authenticated key exchange protocol for mobile satellite communication networks

SummaryThe mobile satellite communication (MSC) system represents a crucial means of communication, facilitating connections between mobile users and network control centres through satellites. Given the wireless nature of satellite communication, ensuring communication security is paramount for maintaining accountability. Key exchange and authentication (KEA) mechanisms are commonly employed to secure data transmitted over insecure or open channels. Developing a lattice‐based KEA protocol for MSC systems is imperative to achieve both quantum security and efficient communication. In this direction, Yadav et al. devised ring learning with an error‐based KEA technique to ensure a quantum‐safe environment. Their scheme is quantum‐safe and satisfies desirable security attributes but has low efficiency in computation. Moreover, we have identified that their scheme is susceptible to denial of service attack and session key established at user and NCC ends is different. To overcome the flaws of Yadav et al., we propose a lattice‐based KEA protocol for the MSC system to improve computation efficiency, and get resistance against key mismatch attack. The security analysis of the proposed scheme is presented in the random oracle model. Comparative study is also presented to observe advantages in computation efficiency.

Reliability analysis of complex systems using subset simulations with Hamiltonian Neural Networks

We present a new Subset Simulation approach using Hamiltonian neural network-based Monte Carlo sampling for reliability analysis. The proposed strategy combines the superior sampling of the Hamiltonian Monte Carlo method with computationally efficient gradient evaluations using Hamiltonian neural networks. This combination is especially advantageous because the neural network architecture conserves the Hamiltonian, which defines the acceptance criteria of the Hamiltonian Monte Carlo sampler. Hence, this strategy achieves high acceptance rates at low computational cost. Our approach estimates small failure probabilities using Subset Simulations. However, in low-probability sample regions, the gradient evaluation is particularly challenging. The remarkable accuracy of the proposed strategy is demonstrated on different reliability problems, and its efficiency is compared to the traditional Hamiltonian Monte Carlo method. We note that this approach can reach its limitations for gradient estimations in low-probability regions of complex and high-dimensional distributions. Thus, we propose techniques to improve gradient prediction in these particular situations and enable accurate estimations of the probability of failure. The highlight of this study is the reliability analysis of a system whose parameter distributions must be inferred with Bayesian inference problems. In such a case, the Hamiltonian Monte Carlo method requires a full model evaluation for each gradient evaluation and, therefore, comes at a very high cost. However, using Hamiltonian neural networks in this framework replaces the expensive model evaluation, resulting in tremendous improvements in computational efficiency.

Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications

Abstract. The wflow_sbm hydrological model, recently released by Deltares, as part of the Wflow.jl (v0.7.3) modelling framework, is being used to better understand and potentially address multiple operational and water resource planning challenges from a catchment scale to national scale to continental and global scale. Wflow.jl is a free and open-source distributed hydrological modelling framework written in the Julia programming language. The development of wflow_sbm, the model structure, equations and functionalities are described in detail, including example applications of wflow_sbm. The wflow_sbm model aims to strike a balance between low-resolution, low-complexity and high-resolution, high-complexity hydrological models. Most wflow_sbm parameters are based on physical characteristics or processes, and at the same time wflow_sbm has a runtime performance well suited for large-scale high-resolution model applications. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets and through the use of point-scale (pedo)transfer functions and suitable upscaling rules and generally result in a satisfactory (0.4 ≥ Kling–Gupta efficiency (KGE) < 0.7) to good (KGE ≥ 0.7) performance for discharge a priori (without further tuning). Wflow_sbm includes relevant hydrological processes such as glacier and snow processes, evapotranspiration processes, unsaturated zone dynamics, (shallow) groundwater, and surface flow routing including lakes and reservoirs. Further planned developments include improvements on the computational efficiency and flexibility of the routing scheme, implementation of a water demand and allocation module for water resource modelling, the addition of a deep groundwater concept, and computational efficiency improvements through for example distributed computing and graphics processing unit (GPU) acceleration.

A Privacy Protection Scheme of Certificateless Aggregate Ring Signcryption Based on SM2 Algorithm in Smart Grid

With the rapid increase in smart grid users and the increasing cost of user data transmission, proposing an encryption method that does not increase the construction cost while increasing the user ceiling has become the focus of many scholars. At the same time, the increase in users will also lead to more security problems, and it is also necessary to solve the privacy protection for users during information transmission. In order to solve the above problems, this paper proposes an aggregated ring encryption scheme based on the SM2 algorithm with special features, referred to as SM2-CLARSC, based on the certificateless ring signcryption mechanism and combining with the aggregate signcryption. SM2-CLARSC is designed to satisfy the basic needs of the smart grid, and it can be resistant to replay attacks, forward security and backward security, etc. It has better security and higher efficiency than existing solutions. Comparing SM2-CLARSC with existing typical solutions through simulation, the result proves that this solution has more comprehensive functions, higher security, and significant computational efficiency improvement.

Large language models for biomedicine: foundations, opportunities, challenges, and best practices.

Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of LLMs in downstream NLP applications effectively. This can be accomplished through a variety of approaches, including augmented prompting, instruction prompt tuning, and reinforcement learning from human feedback (RLHF). Our focus is on making LLMs accessible to the broader biomedical informatics community, including clinicians and researchers who may be unfamiliar with NLP. Additionally, NLP practitioners may gain insight from the described best practices. We focus on 3 broad categories of NLP tasks, namely natural language understanding, natural language inferencing, and natural language generation. We review the emerging trends in prompt tuning, instruction fine-tuning, and evaluation metrics used for LLMs while drawing attention to several issues that impact biomedical NLP applications, including falsehoods in generated text (confabulation/hallucinations), toxicity, and dataset contamination leading to overfitting. We also review potential approaches to address some of these current challenges in LLMs, such as chain of thought prompting, and the phenomena of emergent capabilities observed in LLMs that can be leveraged to address complex NLP challenge in biomedical applications.

An efficiency-improved GPU algorithm for the 2 + 2 + 1 method in nonlinear beamforming

Abstract Nonlinear beamforming (NLBF) has emerged as a highly effective technology for enhancing seismic data quality. The crux of NLBF's success lies in its ability to robustly estimate local traveltime operators directly from input data, a process that entails solving millions or even billions of nonlinear optimization problems per input gather. Among the solvers utilized for estimating these operators is the 2 + 2 + 1 method, for which we have previously introduced algorithmic implementations on both the CPU and GPU platforms. In this paper, we present an efficiency-improved GPU algorithm for the 2 + 2 + 1 method, particularly beneficial when dealing with small data apertures in NLBF. Our enhanced GPU algorithm brings significant improvements in computation efficiency through several strategic measures, which include leveraging Horner's method to minimize the mathematical overhead of traveltime calculation, implementing a GPU-friendly data reduction algorithm to exploit GPU computational power, and optimizing shared GPU memory usage as the primary workspace whenever feasible. To demonstrate the tangible efficiency enhancement achieved by our new GPU algorithm, via two illustrative examples, we compare its performance with that of our previous implementation.

Multiphysics simulation of two-phase viscous fluid flow steered by electric field for jetting of microdroplets

In this research, we delve into the intricacies of viscous fluid flow with electric field coupling by employing the Finite Element Method (FEM) in tandem with the level set method. We generate a weak form for satisfying governing equations for electric field and fluid velocity while two phases are tracked by the level set function. The primary focus of this study is the complex interactions between free-falling jet and electric field, and the behavior of droplet encompassing deformation, fission, and fusion under the influence of electric field. The main contribution of this paper is given a new implement by using the P1/P1 scheme to directly solve the weak forms of coupled governing equations, which significantly improves calculation efficiency compared to the P2/P1 scheme, and we open source the code. This implement is verified by comparing with the experimental results of oil droplets deforming under an electric field. Computations are performed by FEniCS open-source packages. The phenomena documented underscore the multifaceted relationship between electrodynamic forces and fluid mechanics, accentuated distinctly under non-uniform electric field conditions.