Computational Performance Research Articles

Coarse graining with control points: a cubic-Bézier based approach to modeling athermal fibrous materials

Fibrous materials continue to be of central importance to modern life, including traditional materials, such as textiles and paper, modern materials, such as polymeric materials and glass fiber, and emerging materials, such as carbon nanotube yarns. Developing constitutive laws for the mechanical behavior of such materials can be challenging owing to the often non-periodic microstructure, resulting in fairly large and computationally intractable representative volume elements. Therefore, it is imperative to design novel coarse-grained models that can bridge the computational gap and capture accurate physics associated with the mechanical behavior of fibrous materials. In this work, cubic-Béziers are used to represent fiber-segments, allowing for up to C 2(curvature)-continuous representation of curved fibers. Equations of motion are derived in terms of the control points, which allow for minimization and time integration strategies with the Lagrangian and Hamiltonian formalism. This coarse-grained model promises faster and more accurate computational performance compared to discrete approaches.

Privacy-preserving Scanpath Comparison for Pervasive Eye Tracking

As eye tracking becomes pervasive with screen-based devices and head-mounted displays, privacy concerns regarding eye-tracking data have escalated. While state-of-the-art approaches for privacy-preserving eye tracking mostly involve differential privacy and empirical data manipulations, previous research has not focused on methods for scanpaths. We introduce a novel privacy-preserving scanpath comparison protocol designed for the widely used Needleman-Wunsch algorithm, a generalized version of the edit distance algorithm. Particularly, by incorporating the Paillier homomorphic encryption scheme, our protocol ensures that no private information is revealed. Furthermore, we introduce a random processing strategy and a multi-layered masking method to obfuscate the values while preserving the original order of encrypted editing operation costs. This minimizes communication overhead, requiring a single communication round for each iteration of the Needleman-Wunsch process. We demonstrate the efficiency and applicability of our protocol on three publicly available datasets with comprehensive computational performance analyses and make our source code publicly accessible.

Design and Implementation of Cost-Effective Assurance Engineering Using Wake-Sleep Algorithm-Based WideResNet with Nonlinear Analysis and Topology Integration

The proliferation of artificial intelligence (AI) in recent years has resulted in an increase in the AI-driven applications across diverse fields. The assurance of the credibility of these AI systems has emerged as a crucial worry. This paper introduces a new model for achieving cost-effective assurance engineering by employing the Wake-Sleep algorithm with the WideResNet architecture. The Wake-Sleep method is utilized in unsupervised learning. This methodology improves the comprehensibility of AI but also establishes a basis for cost-efficient assurance engineering. The Wake-Sleep algorithm has two distinct phases: the Wake Phase, which entails the generative model using actual data, and the Sleep Phase, which enhancement of the encoder through the generated data utilization. The framework is the WideResNet neural network design, which is widely recognized for its effectiveness. The characteristics offer an equilibrium between computational resources and model performance, making it a viable choice for cost-effective solution.

Feature selection for high-dimensional neural network potentials with the adaptive group lasso

Neural network potentials are a powerful tool for atomistic simulations, allowing to accurately reproduce ab initio potential energy surfaces with computational performance approaching classical force fields. A central component of such potentials is the transformation of atomic positions into a set of atomic features in a most efficient and informative way. In this work, a feature selection method is introduced for high dimensional neural network potentials, based on the adaptive group lasso (AGL) approach. It is shown that the use of an embedded method, taking into account the interplay between features and their action in the estimator, is necessary to optimize the number of features. The method’s efficiency is tested on three different monoatomic systems, including Lennard–Jones as a simple test case, Aluminium as a system characterized by predominantly radial interactions, and Boron as representative of a system with strongly directional components in the interactions. The AGL is compared with unsupervised filter methods and found to perform consistently better in reducing the number of features needed to reproduce the reference simulation data at a similar level of accuracy as the starting feature set. In particular, our results show the importance of taking into account model predictions in feature selection for interatomic potentials.

Location strategy for logistics distribution centers utilizing improved whale optimization algorithm

Abstract An enhanced whale optimization algorithm is introduced to address the challenges associated with the logistics distribution center location problem, aiming to overcome the traditional heuristic algorithm’s susceptibility to local optima and enhance the overall efficiency of the logistics system. This improvement involves the integration of stochastic sinusoidal inertia weights and a variational strategy to augment the global search capability and convergence accuracy of the conventional algorithm. The results of experimental simulations indicate that the upgraded whale algorithm exhibits superior computational performance compared to other heuristic algorithms. Furthermore, it demonstrates the ability to effectively compute optimal distribution center locations, thereby enhancing the delivery efficiency of the logistics distribution system.

Protection of Personal Privacy Data with Enhanced Encryption & Revocation (PEER)

Abstract: In light of frequent non-public statistics breaches, safeguarding such facts is now greater crucial than ever. By combining Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with blockchain, stable facts storage and sharing are facilitated. However, in excessive-dimensional characteristic domains, existing schemes stumble upon issues like compromised safety, excessive computational overhead, and steeply-priced attribute revocation. This paper proposes a novel scheme to address those demanding situations. It includes 3 components: Fast High-dimensional Attribute Domain-based totally Message Encryption (HAD-FME) for more desirable protection and reduced computational charges, an Attribute Revocation Mechanism Based on Sentry Mode (SM-ARM) using clever contracts for green revocation, and a Blockchain-based totally Model for Personal Privacy Data Protection (BC-PPDP) integrating HAD-FME with SM-ARM. Security analysis confirms the effectiveness of HAD-FME beneath the DLIN assumption and the pride of forward and backward safety for attribute revocation. Experiments show advanced computational performance for HAD-FME and decrease revocation costs for SM-ARM as compared to present methods, with smart contracts and blockchain proving effective in making sure information safety and privateness.

The Generation of Equal-Intensity and Multi-Focus Optical Vortices by a Composite Spiral Zone Plate

We propose a new vortex lens for producing multiple focused coaxial vortices with approximately equal intensities along the optical axis, termed equal-intensity multi-focus composite spiral zone plates (EMCSZPs). In this typical methodology, two concentric conventional spiral zone plates (SZPs) of different focal lengths were composited together and the alternate transparent and opaque zones were arranged with specific m-bonacci sequence. Based on the Fresnel–Kirchhoff diffraction theory, the focusing properties of the EMCSZPs were calculated in detail and the corresponding demonstration experiment was been carried out to verify our proposal. The investigations indicate that the EMCSZPs indeed exhibit superior performance, which accords well with our physical design. In addition, the topological charges (TCs) of the multi-focus vortices can be flexibly selected and controlled by optimizing the parameters of the zone plates. These findings which were demonstrated by the performed experiment may open new avenues towards improving the performance of biomedical imaging, quantum computation and optical manipulation.

Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ

Patient blood samples are invaluable in clinical omics databases, yet current methodologies often fail to fully uncover the molecular mechanisms driving patient pathology. While genome-scale metabolic models (GEMs) show promise in systems medicine by integrating various omics data, having only exometabolomic data remains a limiting factor. To address this gap, we introduce a comprehensive pipeline integrating GEMs with patient plasma metabolome. This pipeline constructs case-specific GEMs using literature-based and patient-specific metabolomic data. Novel computational methods, including adaptive sampling and an in-house developed algorithm for the rational exploration of the sampled space of solutions, enhance integration accuracy while improving computational performance. Model characterization involves task analysis in combination with clustering methods to identify critical cellular functions. The new pipeline was applied to a cohort of trauma patients to investigate shock-induced endotheliopathy using patient plasma metabolome data. By analyzing endothelial cell metabolism comprehensively, the pipeline identified critical therapeutic targets and biomarkers that can potentially contribute to the development of therapeutic strategies. Our study demonstrates the efficacy of integrating patient plasma metabolome data into computational models to analyze endothelial cell metabolism in disease contexts. This approach offers a deeper understanding of metabolic dysregulations and provides insights into diseases with metabolic components and potential treatments.

Fast emulation of two-point angular statistics for photometric galaxy surveys

ABSTRACT We develop a set of machine-learning-based cosmological emulators, to obtain fast model predictions for the C(ℓ) angular power spectrum coefficients, characterizing tomographic observations of galaxy clustering and weak gravitational lensing from multiband photometric surveys (and their cross-correlation). A set of neural networks are trained to map cosmological parameters into the coefficients, achieving, with respect to standard Boltzmann solvers, a speed-up of $\mathcal {O}(10^3)$ in computing the required statistics for a given set of cosmological parameters, with an accuracy better than 0.175 per cent (<0.1 per cent for the weak lensing case). This corresponds to $\lesssim 2~{{\ \rm per\ cent}}$ of the statistical error bars expected from a typical Stage IV photometric surveys. Such overall improvement in speed and accuracy is obtained through (i) a specific pre-processing optimization, ahead of the training phase, and (ii) an effective neural network architecture. Compared to previous implementations in the literature, we achieve an improvement of a factor of 5 in terms of accuracy, while training a considerably lower amount of neural networks. This results in a cheaper training procedure and a higher computational performance. Finally, we show that our emulators can recover unbiased posteriors when analysing synthetic Stage-IV galaxy survey data sets.

Solar radiation analysis: computational performance analysis for the foldable axis of a responsive external skin on the south façade

Solar radiation analysis: computational performance analysis for the foldable axis of a responsive external skin on the south façade

Simulation-Driven Universal Surrogates of Coupled Mechanical Systems: Real-Time Simulation of a Forestry Crane

Abstract Preparing simulation-driven surrogates for a coupled mechanical system can be challenging because the associated mechanical and actuator dynamics demand high-fidelity numerical solutions. Proposed here is a universal hydraulic surrogate (UHS), which can provide solutions to high-fidelity mechanical systems with a universal actuator in a surrogate-assisted monolithic approach. The UHS acts as an alternative to the standard lumped fluid theory by eliminating the hydraulic pressures differential equations. A surrogate-assisted universal actuator uses an approximated model to define hydraulic force in high-fidelity mechanical systems. The approximated force model was developed through training against the dynamics of a one-dimensional (1D) hydraulic cylinder and spring-damper. A covariance matrix adaption evolutionary strategy (CMA-ES) was used as an optimization algorithm to minimize differences between the standard dynamics and UHS approaches at the position and velocity levels. The robustness of resulting UHS was validated to predict the behaviors of the simple four-bar mechanism and the forestry crane. The focus was on numerical accuracy and computational efficiency. The maximum percent normalized root mean square error (PN-RMSE) between the states of the approximated force model and lumped fluid theory were approximately 2.04% and 6.95%, respectively. The proposed method was approximately 52 times faster than the standard lumped fluid theory method. By providing accurate predictions outside the training data, the simulation-driven UHS promises better computational performance leading to real-time simulation solutions for the coupled mechanical systems. The UHS can be applied in simulation, optimization, control, state and parameter estimation, and Artificial Intelligence (AI) implementations for coupled mechanical systems.

Lightweight Privacy Preserving Scheme for IoT based Smart Home

Background: The Internet of Things (IoT) is the interconnection of physical devices, controllers, sensors and actuators that monitor and share data to another end. In a smart home network, users can remotely access and control home appliances/devices via wireless channels. Due to the increasing demand for smart IoT devices, secure communication also becomes the biggest challenge. Hence, a lightweight authentication scheme is required to secure these devices and maintain user privacy. The protocol proposed is secure against different kinds of attacks and as well as is efficient. Methods: The proposed protocol offers mutual authentication using shared session key establishment. The shared session key is established between the smart device and the home gateway, ensuring that the communication between the smart devices, home gateway, and the user is secure and no third party can access the information shared. Results: Informal and formal analysis of the proposed scheme is done using the AVISPA tool. Finally, the results of the proposed scheme also compare with existing security schemes in terms of computation and communication performance cost. The results show that the proposed scheme is more efficient and robust against different types of attacks than the existing protocols. Conclusion: In the upcoming years, there will be a dedicated network system built inside the home so that the user can have access to the home from anywhere. The proposed scheme offers secure communication between the user, the smart home, and different smart devices. The proposed protocol makes sure that security and privacy are maintained since the smart devices lack computation power which makes them vulnerable to different attacks.

A novel unconstrained methodology for economic analysis of the level of repair with a case study of a multi-indenture and multi-echelon repair network

The multi-indenture and multi-echelon economic analysis of repair levels is commonly modelled as a constrained 0–1 programming problem. However, with the increase in the scale of the decision variables, the proportion of feasible solutions in the solution space sharply decreases, posing a challenge for traditional solution methods. In this paper, the transformation of the initial problem into an unconstrained integer programming problem is demonstrated. Specifically, the total number of feasible decisions can be solved analytically through a decision flow method, and a bijective relationship between decision sequence numbers and feasible decisions is established using a mixed-radix system, converting the optimization variables to feasible decision sequence numbers; in this way, the initial problem becomes unconstrained. The advantages of this approach include the following: (1) All the solutions in the transformed solution space are feasible, which significantly improves the efficiency of the solution and reduces the dimensionality of the decision variables; and (2) the proposed model is highly flexible in terms of considering additional variables, such as decision time, transportation modes, and repair locations. Furthermore, the sequence numbers of each feasible solution are transformed into binary sequences, and the binary particle swarm optimizer (BPSO) algorithm can be employed to solve the model. The proposed methodology is applied to the case of a three-indenture and three-echelon repair network considering multiple fault modes, and the results are compared with those of previous studies. The results validate the rationality and substantial advantages of the proposed methodology for economic analysis in terms of computational speed and convergence performance.

Maximum span determination and optimal sizing of cable for improved performance of droop-controlled DC microgrid

DC microgrids are seen as smart solutions to interface DC loads and distributed energy resources (DERs). However, in DC microgrids, the placement of sources and loads as well as the size of cable impact the system’s span, regulation of voltage, and losses. This paper proposes novel algorithms to determine the maximum span and optimal size of cable for the improved performance of a droop-controlled DC microgrid. The proposed algorithms utilize an improved power flow analysis (IPFA) method based on Newton-Raphson technique to determine the maximum span and the optimal size of cable, enhancing the voltage regulation and reducing the cost. Additionally, the impacts of power rating, droop constants, and size of cable on the DC microgrid’s maximum span are investigated and reported. Owing to the intricate nature of the problem concerning the ideal size of cable for the droop-controlled DC microgrid, a heuristic optimization approach is employed. Further to enhance the rate of convergence and computational performance, an improved particle swarm optimization (IPSO) is also proposed. The constraint of keeping the bus voltage variations below the allowable voltage regulation limits is applied to the objective function of the optimization problem. In the case of a droop-controlled, DC microgrid having a specified configuration, the suggested algorithm can determine the ideal size of cable, guaranteeing both the least cost and enhanced voltage regulation. Comprehensive numerical and modelling results have been presented for a droop-controlled DC microgrid with different loads and DERs to verify the effectiveness of the proposed methods. Furthermore, the analysis reveals that configuration III of the DC microgrid with an optimal cable size of 19 mm2 lowers the absolute voltage regulation to as low as 1.06 V. The results of the detailed analysis validate the enhanced performance of the proposed algorithms and are highly useful to both system designers and consumers of the DC microgrids, eventually paving the way for the widespread use of DC microgrids in the future.

On polling directions for randomized direct-search approaches: application to beam angle optimization in intensity-modulated proton therapy

AbstractDeterministic direct-search methods have been successfully used to address real-world challenging optimization problems, including the beam angle optimization (BAO) problem in radiation therapy treatment planning. BAO is a highly non-convex optimization problem typically treated as the optimization of an expensive multi-modal black-box function which results in a computationally time consuming procedure. For the recently available modalities of radiation therapy with protons (instead of photons) further efficiency in terms of computational time is required despite the success of the different strategies developed to accelerate BAO approaches. Introducing randomization into otherwise deterministic direct-search approaches has been shown to lead to excellent computational performance, particularly when considering a reduced number (as low as two) of random poll directions at each iteration. In this study several randomized direct-search strategies are tested considering different sets of polling directions. Results obtained using a prostate and a head-and-neck cancer cases confirmed the high-quality results obtained by deterministic direct-search methods. Randomized strategies using a reduced number of polling directions showed difficulties for the higher dimensional search space (head-and-neck) and, despite the excellent mean results for the prostate cancer case, outliers were observed, a result that is often ignored in the literature. While, for general global optimization problems, mean results (or obtaining the global optimum once) might be enough for assessing the performance of the randomized method, in real-world problems one should not disregard the worst-case scenario and beware of the possibility of poor results since, many times, it is only possible to run the optimization problem once. This is even more important in healthcare applications where the mean patient does not exist and the best treatment possible must be assured for every patient.

Fast-QSGS: A GPU accelerated program for structure generation of granular disordered media

We present Fast-QSGS, a GPU-accelerated program for granular disordered media generation. Based on vectorization, Fast-QSGS is accelerated by modern GPU thanks to the NumPy-compatible API provided by CuPy. We also introduce a variable growth probability function and seed spacing control to improve the speed and accuracy of the original QSGS method. Computational performance benchmarks are conducted on both consumer-grade and professional-grade GPUs. Generation of disordered media of size 4003 can be completed in 30 s on A100 and 110 s on RTX4060, achieving a speedup of over 400 compared with the serial version. Physical benchmarks on the reconstruction of Fontainebleau sandstone and hydrated cement are conducted. Our results demonstrate that the permeability of the reconstructed Fontainebleau sandstone falls within the range of experimental values. Additionally, the average relative error of the volume fraction of the unhydrated cement and capillary porosity of hydrated cement is 1.9 % and 3.4 % compared with Powers’ law, respectively. Program SummaryProgram Title: Fast-QSGSCPC Library link to program files:https://doi.org/10.17632/gswbd7tf3g.1Licensing provisions: MIT licenseProgramming language: PythonNature of Problem: Fast generation/reconstruction of disordered granular media.Solution method: The QSGS method is vectorized and implemented in Python. Via CuPy, the current implementation of QSGS can be easily accelerated by modern GPUs.

CAD and constructive solid geometry modeling of the Molten Salt Reactor Experiment with OpenMC

In this study, we present a detailed comparison of two independently developed models of the Molten Salt Reactor Experiment (MSRE) for Monte Carlo particle transport simulations: the constructive solid geometry (CSG) model that was developed in support of the MSRE benchmark in the International Handbook of Evaluated Reactor Physics Benchmark Experiments, and a CAD model that was developed by Copenhagen Atomics. The original Serpent reference CSG model was first converted to OpenMC’s input format so that it could be systematically compared to the CAD model, which was already available as an OpenMC model, using the same Monte Carlo code. Results from simulations using the Serpent and OpenMC CSG models showed that keff agreed within 10 pcm while the flux distribution in space and energy generally agreed within 0.1%. Larger differences were observed between the OpenMC CAD and CSG models; notably, the keff computed for the CAD model was 1.00872, which is more than 1% lower than the value for the CSG model and much closer to experiment. Several areas of the reactor that were modeled differently in the CSG and CAD models were discussed and, in several cases, their impact on keff was quantified. Lastly, we compared the computational performance and memory usage between the CAD and CSG models. Simulation of the CSG model was found to be 1.4–2.3× faster than simulation of the CAD model based on the Embree ray tracer while using 4× less memory, highlighting the need for continued improvements in the CAD-based particle transport ecosystem. Finally, major performance degradation was observed for CAD-based simulations when using the MOAB ray tracer.

A platoon formation algorithm for intersections with blue phase control in mixed traffic

Increasing attention is being paid to intersection signal control with cooperative platoons. Assuming platoons being formed, such platoons cannot only improve the intersection capacity but also minimize the number of control units, especially when dedicated connected and automated vehicle (CAV) lanes are considered. However, the platoon formation process is often neglected, especially for lane-changing and overtaking maneuvers in mixed traffic. This may jeopardize the potential of signal control with platoons. This article proposes a platoon formation algorithm that computes the optimal lane, platoon sequence, and speed profiles of CAVs under the requirement of the central traffic controller. The algorithm is designed for mixed traffic conditions and hence the performance of human-driven vehicles is also considered. A mixed integer linear program model is formulated to minimize the deviation from the desired platoon configuration and the disturbance to overall traffic under any arbitrary initial condition. Numerical experiments are designed to test the effectiveness and the computational performance of the proposed algorithm. Results show that CAVs with signal control can form platoons with rational motion. Besides, the platoon penetration significantly affects platooning feasibility, while the platoon length does not. This suggests that CAVs can form long platoons at intersections to improve traffic throughput.

Comprehensive Performance Evaluation between Visual SLAM and LiDAR SLAM for Mobile Robots: Theories and Experiments

SLAM (Simultaneous Localization and Mapping), primarily relying on camera or LiDAR (Light Detection and Ranging) sensors, plays a crucial role in robotics for localization and environmental reconstruction. This paper assesses the performance of two leading methods, namely ORB-SLAM3 and SC-LeGO-LOAM, focusing on localization and mapping in both indoor and outdoor environments. The evaluation employs artificial and cost-effective datasets incorporating data from a 3D LiDAR and an RGB-D (color and depth) camera. A practical approach is introduced for calculating ground-truth trajectories and during benchmarking, reconstruction maps based on ground truth are established. To assess the performance, ATE and RPE are utilized to evaluate the accuracy of localization; standard deviation is employed to compare the stability during the localization process for different methods. While both algorithms exhibit satisfactory positioning accuracy, their performance is suboptimal in scenarios with inadequate textures. Furthermore, 3D reconstruction maps established by the two approaches are also provided for direct observation of their differences and the limitations encountered during map construction. Moreover, the research includes a comprehensive comparison of computational performance metrics, encompassing Central Processing Unit (CPU) utilization, memory usage, and an in-depth analysis. This evaluation revealed that Visual SLAM requires more CPU resources than LiDAR SLAM, primarily due to additional data storage requirements, emphasizing the impact of environmental factors on resource requirements. In conclusion, LiDAR SLAM is more suitable for the outdoors due to its comprehensive nature, while Visual SLAM excels indoors, compensating for sparse aspects in LiDAR SLAM. To facilitate further research, a technical guide was also provided for the researchers in related fields.

VoxIsle: A new method in InterDosi code for improving internal dosimetry simulation performances in voxelized phantom

In order to assess the internal dose due to exposure to radiation, the ICRP recommends the use of a reference voxelized phantom. Although voxelized geometry is a very promising geometric representation of a complex body, it showed a limitation in dose calculation for large-voxel array phantoms that requires more memory and CPU time. We have developed a new voxel compression method called VoxIsle that merges adjacent voxels that belong to the same organ/tissue. The VoxIsle was based on the LRAH (Largest Rectangular Area Histogram) algorithm. To evaluate the performances of this method, compressed and uncompressed geometries of some voxel-based phantoms were used to calculate dosimetric coefficients of gamma, electron and alpha particles. Results showed that for a given particle, there is a smooth difference between coefficients calculated using the two types of geometry. Additionally, depending on the characteristics of the voxelized phantom, the memory and CPU time required to construct geometry and material of a phantom, and its file size for a compressed geometry appear to be reduced up to 77%, 73% and 97%, respectively. Regarding computational performance, with the exception of alpha particles for which the results showed a steady increase in CPU time, the simulation with compressed geometry was up to 59.3% faster than the uncompressed geometry. The new compression method can considerably improve the simulation performances of InterDosi code that can perform internal dosimetry studies on voxelized phantoms.