High CPU Time Research Articles

Regularization techniques and inverse approaches in 3D Traction Force Microscopy

The conception of inverse methods in the context of Traction Force Microscopy (TFM) is influenced by multiple factors, such as nonlinear effects, dimensionality (2D/3D), and regularization/constraints, amongst others. Solving the inverse problem often requires the inversion of a matrix, and the presence of noise in the measured displacements can lead to unrealistic reconstructed tractions. To address this issue, Tikhonov regularization is commonly used, penalizing high norm values of computed tractions. The aim of this study is to compare the performance of different inverse methodologies (including some original variations) in 3D TFM, considering constraint imposition and regularization along the formulation. The different methodologies are numerically elaborated within a novel combined Newton–Raphson/Finite Element Method scheme that provides converged solutions in few iterations. The impact of constraint imposition and regularization in traction reconstruction is evaluated in terms of accuracy, efficiency (CPU time), and inherent characteristics of the methodology. Results show that, applying regularization and constraints (based on the fulfillment of fundamental principles) provides the best traction reconstruction, while simultaneously ensuring an optimum estimate of the maximum traction, at the cost of high CPU time and low efficiency. Moreover, regularization-based methods introduce the challenge of calibrating the regularization parameter, usually done under subjective criteria. On the other hand, non-regularized but constrained methods may represent a nice compromise between accuracy and efficiency, while avoiding pre-processing and calibration of such regularization parameter. It is emphasized the importance of considering not only traction reconstruction quality but also the efficiency and complexity of implementation (intrusiveness) when selecting an appropriate inverse method for TFM analysis.

Biased versus unbiased numerical methods for stochastic simulations

Approximate numerical methods are one of the most used strategies to extract information from many-interacting-agents systems. In particular, numerical approximations are of extended use to deal with epidemic, ecological and biological models, since unbiased methods like the Gillespie algorithm can become unpractical due to high CPU time usage required. However, the use of approximations has been debated and there is no clear consensus about whether unbiased methods or biased approach is the best option. In this work, we derive scaling relations for the errors in approximations based on binomial extractions. This finding allows us to build rules to compute the optimal values of both the discretization time and number of realizations needed to compute averages with the biased method with a target precision and minimum CPU-time usage. Furthermore, we also present another rule to discern whether the unbiased method or biased approach is more efficient. Ultimately, we will show that the choice of the method should depend on the desired precision for the estimation of averages.

Fuzzy-based video compression using bilinear fuzzy relation equations

We present a novel color video compression method using the greatest solution of a system of bilinear fuzzy relation equations to assess the similarity between frames. The frames in each band are treated separately and each frame is classified as an Intra frame or a Predictive frame. A frame is labelled as Predictive frame, and compressed more than an Intra-frame, if the similarity value with the previous Intra frame is higher than a selected threshold; A pre-processing activity is performed to select the optimal threshold value of the similarity between frames. The proposed method allows to supply a high quality of the reconstructed frames and has the advantage of not requiring high CPU time and memory storage for its execution; it was tested on color videos of the Fast-Moving Objects dataset; the results show that it produces better performances than the Lukasiewicz similarity-based video compression method and comparable with those achieved by MPEG-4 and the deep learning video compression method DVC_pro. The results show that the quality of the reconstructed frames obtained with BFRE is comparable with that of DVC Pro, but has a lower computational complexity, providing better performances in terms of video encoding speed.

Physics-informed machine learning for reservoir management of enhanced geothermal systems

With the energy demand arising globally, geothermal recovery by Enhanced Geothermal Systems (EGS) becomes a promising option to bring sustainable energy supply along with mitigating CO2 emission. However, reservoir management of EGS primarily relies on reservoir simulation, which is quite expensive due to the reservoir heterogeneity, the interaction of matrix and fractures, and the intrinsic multi-physics coupled nature. Therefore, a robust optimization framework is critical for the management of EGS. We develop a general Physics-Informed Machine Learning (PIML) framework for reservoir management with multiple optimization options. A robust forward surrogate model fl is developed based on a convolutional neural network, and it successfully learns the nonlinear relationship between input reservoir model parameters (e.g., fracture permeability field) and interested state variables (e.g., temperature field and produced fluid temperature). fl is trained using simulation data from the EGS coupled thermal-hydro simulation model by sampling reservoir model parameters. As fl is accurate, efficient and fully differentiable, EGS thermal efficiency can be optimized following two schemes: (1) training a control network fc to map reservoir geological parameters to reservoir decision parameters by coupling it with fl; (2) directly optimizing the reservoir decision parameters based on coupling the existing optimizers with fl. We evaluate the impact of reservoir model parameters on the thermal recovery based on simulation datasets through sensitivity analyses, and demonstrated that injection mass rate dominates thermal recovery. Further, the forward model fl performs accurate and stable predictions of evolving temperature fields (relative error 1.27 ± 0.89%) in EGS and the time series of produced fluid temperature (relative error 0.26 ± 0.46%), and its speedup to the counterpart high-fidelity simulator is 4564 times. When optimizing with fc, we achieve thermal recovery with a reasonable accuracy but significantly low CPU time during inference, 0.11 seconds/task. When optimizing with Adam optimizer, we achieve the objective perfectly with relatively high CPU time, 4.58 seconds/task. This is because the former optimization scheme requires a training stage for fc but its inference is non-iterative, while the latter scheme requires an iterative inference without training. We also investigate the option to use fc inference as an initial guess for Adam optimizer, which decreases Adam’s CPU time but achieves excellent convergence in the objective function. This is the highest recommended option among the three evaluated. The efficiency, scalability and accuracy observed in our reservoir management framework makes it highly applicable to near real-time reservoir management in EGS as well as other similar system management processes.

Research on linguistic multi-attribute decision making method for normal cloud similarity

Aiming at the uncertainty linguistic transformation problem in multi-attribute decision making, a decision-making method based on normal cloud similarity was proposed. Firstly, starting from the normal cloud characteristic curves, a normal cloud similarity measurement method based on Wasserstein distance is proposed by combing with the normal cloud entropy-containing expectation curve, which is using the Wasserstein distance to characterize the similarity characteristics of probability distribution. The properties of the proposed similarity measure are discussed in the paper. Secondly, the performance of the proposed method is compared and analyzed with the existed methods by numerical simulation experiment and time series data classification experiment. The experimental results show that the proposed method has good similarity discrimination ability, high classification accuracy and low CPU time cost. Finally, the method was successfully applied into linguistic multi-attribute decision making, and TOPSIS thought is used to compare and rank the schemes, so as to realize the final decision.

Non-Iterative Calculation of Quasi-Dynamic Energy Flow in the Heat and Electricity Integrated Energy Systems

Quasi-dynamic energy flow calculation is an indispensable tool for the heat and electricity integrated energy system (HE-IES) analysis. One solves the nonlinear partial differential algebraic equations to obtain thermal, hydraulic and electric variations. However, mainstream iteration solvers face the challenges of inefficiency and bad robustness. For one thing, the frequent update and factorization of Jacobian matrices utilize high CPU time. For another, the per-step iteration numbers grow exponentially as the system loading level creeps up. This paper presents a novel non-iterative algorithm for the quasi-dynamic energy flow calculation. The kernel of the proposed algorithm is to transform these nonlinear equations into linear recursive ones, by solving which, we obtain explicit closed-form solutions of unknown variables. In each step, the proposed algorithm requires only one matrix factorization and fixed times of arithmetic operations regardless of the loading levels, so that it achieves small and consistent per-step time costs. A semi-discrete scheme is used in PDE solution to avoid dissipative and dispersive errors that are often overlooked in previous literature. To ensure convergence, we also propose to control the temporal step sizes adaptively by estimating the simulation errors. Case studies showed that the proposed method manifested efficient and robust time performance compared with the iterative algorithms, and meanwhile preserved high accuracy.

Level‐set‐based method for designing novel brushed synchronous machines

AbstractNowadays, research on electromagnetic devices increasingly focuses on multiphysics three‐dimensional complex systems. However, this kind of simulations require huge computational resources and consequently very high CPU time. Ristagno et al. proposed a level‐set based method allowing meshing step savings in any iterative processes (as movement modeling or optimization processes). The level‐set method lies in implicit description of moving fronts as described in the study by Osher et al. (1988). Inspired by Räisänen et al., all physical parameters (material properties, supply or armature movement) are implemented by projection of mathematical functions. In this paper, the authors proposes to study and design a new kind of mechanical commutator for DC machines thanks to this coupled method. Indeed, there is a renewed interest in DC machines because they can be suitable for small mobilities. To be noted, small mobility concerns all kind of vehicles (2 wheels–4 wheels–autonomous vehicles such as drones) that have speeds below 50 km/h. However, specifications for traction applications are difficult to achieve for such a rustic machine and requires some modifications. That is why, inspired by usual synchronous machine, the commutator has been redesigned to obtain expected theoretically signal waveform. Furthermore, numerical coupled simulations have been used to design both machine and supply simultaneously. This work has led to the manufacture of a proof of concept and preliminary results are very promising.

Area and power optimization approach for mixed polarity Reed–Muller logic circuits based on multi-strategy bacterial foraging algorithm

Area and power optimization have become the primary constraints in chip design. Existing area and power optimization approaches have poor optimization performance and high CPU time in logic circuits with multiple inputs. To solve this problems, we propose a multi-strategy bacterial foraging algorithm (MBFA) for multi-objective optimization, which includes nondominated add procedure, dual-population cooperative procedure, high-quality replication procedure, distributed migration procedure and the termination criterion. In addition, considering the characteristics of mixed polarity Reed–Muller (MPRM) logic circuits such as high dimension and large solution space domain, we propose an area and power optimization approach (APOA) based on XNOR/OR, which uses MBFA to search MPRM circuits with Pareto optimal solutions. Experimental results show that the APOA has better performance in optimizing MPRM circuits area and power.

RLPlace: Using Reinforcement Learning and Smart Perturbations to Optimize FPGA Placement

Simulated annealing (SA) is one of the most common FPGA placement techniques, and is used both as a standalone algorithm and to improve an initial analytical placement. While SA-based placers can achieve high-quality results, they suffer from long runtimes. In this article, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RLPlace</i> , a novel SA-based FPGA placer that utilizes both reinforcement learning (RL) and targeted perturbations (directed moves). The proposed moves target both wirelength and timing optimization and explore the solution space more efficiently than traditional random moves while preventing oscillation in the Quality of Results (QoR). RL techniques are used to dynamically select the most effective move types as optimization progresses. The experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RLPlace</i> outperforms the widely used VTR 8 placer across all runtime/quality tradeoff points, achieving better QoR placement solutions in less runtime. On average, across the Titan23 suite of large FPGA benchmarks, RLPlace can reduce CPU time by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.5\times $ </tex-math></inline-formula> with result quality comparable to VTR 8, or improve wirelength by 8% (at a high CPU time budget) −26% (at a low CPU time budget) versus VTR 8.0 given the same CPU time.

Optimum Design of Multi-Section Asymmetrical Transdirectional Couplers with Port Impedance Matching up to S Band Frequency

The method of least squares is developed for the optimum design of multi-section asymmetrical transdirectional (ATRD) couplers with arbitrary port impedance matching. Firstly, transmission matrix of a section of coupler is obtained, then the transmission matrix of whole multi-section coupler is determined, from which the scattering matrix of device is obtained. An error function is then constructed by scattering parameters. According to design specifications, error function depends on physical parameters of coupler (such as widths, spacings and lengths of transmission line sections and capacitor values), of which minimum point gives optimum values of it’s dimensions and capacitors. Optimum design is carried out for a specified frequency response and bandwidth. Error minimization is implemented by the combination of genetic algorithm (GA) and conjugate gradient method (CGM). The design is verified by full-wave simulation software, fabrication and measurement. Several 3 dB, 6 dB and 10 dB couplers are designed with different bandwidths and port impedances. Compared to the proposed design method, design of asymmetrical multi-section couplers by full-wave simulation softwares is by trial and error and at best random and almost impossible because of extremely high CPU time due to the large number of parameters of coupler geometrical structure.

Clustering assisted artificial neural network for handling noisy big data: An application for estimation of parameter in combined mode conduction and radiation heat transfer

AbstractA pattern net assisted mapping artificial neural network (PAMANN) model for estimation of parameters in problem with large data (1300 × 121 matrix size) is reported. A pattern net‐based multilayer perceptron neural network (MLPNN) model for clustering the data, followed by mapping MLPNN model for mapping the target with the input, is developed as PAMANN model. A heat transfer problem with combined mode conduction and radiation in porous medium is solved numerically, and is called direct model. In the inverse model, a PAMANN model is developed by using data generated through the direct model. The PAMANN model is able to estimate two parameters (extinction coefficient β and convective coupling P2) after taking temperature profile as input. The model is tested for different number of neurons in hidden layer, and different levels of noise in input data. Twelve different algorithms are explored in training of mapping MLPNN, and compared for performance. Levenberg–Marquardt algorithm is found to estimate the parameters with high accuracy, but took high CPU time. Bayesian regularization is found to consume very high CPU time with moderate accuracy in estimation of parameters. Variations in hidden layer neuron number and noise in input data, were done to analyze the performance of mapping MLPNN with different training algorithms. Algorithms O‐Step Secant, conjugate gradient with Polak‐Ribiére updates, and conjugate gradient with Fletcher‐Reeves updates are able to handle all variations of noise and number of neurons in hidden layer, with good accuracy of estimation and low CPU time consumption. Under high computational resource LM algorithm can be used for all cases. Up to 0.99132 value of regression coefficient is obtained in mapping MLPNN model with 15 hidden neurons, indicating the high accuracy of the model. With the help of PAMANN model, highly accurate (absolute error 1.78%) estimation of parameters is obtained. The model can handle upto 1% noise in input data, while giving accurate results.

A clustering approach for EOS lumping — Using evolutionary-based metaheuristic optimization algorithms

It is always desirable to keep the number of components in compositional simulation low due to the high CPU time and storage space requirement; on the other hand, the model's accuracy in describing the phase behavior decreases with reducing the number of components. As a result, an optimal lumping approach is needed to group the components to attain high accuracy in the least amount of time. This paper's main objective is to find the best lumping scheme to maintain the split model's accuracy and reach an optimal lumped model in the least amount of time. In accordance with this purpose, five different evolutionary-based metaheuristic optimization algorithms known as genetic algorithm (GA), differential evolution (DE), harmony search (HS), imperialist competitive algorithm (ICA), and shuffled-frog leaping algorithm (SFLA) are incorporated into the objective function developed to cluster heavy fractions using the k-means algorithm. To find the optimal algorithm, the best cost of the objective function and the number of function evaluations (NFE) have been compared, and the final results were substantiated using flash calculation outputs, such as density, viscosity, phase diagram, and Wilson plot. The results indicate that the proposed algorithms are capable of finding the optimal lumped model with high accuracy for all reservoir samples tested in this study. This study also marked ICA as the best evolutionary-based optimization algorithm that can be used in clustering problems.

Developing a scenario-based video game generation framework for computer and virtual reality environments: a comparative usability study

Serious games—games that have additional purposes rather than only entertainment—aim to educate people, solve, and plan several real-life tasks and circumstances in an interactive, efficient, and user-friendly way. Emergency training and planning provide structured curricula, rule-based action items, and interdisciplinary collaborative entities to imitate and teach real-life tasks. This rule-based structure enables the curricula to be transferred into other systematic learning platforms. Although emergency training includes these highly structured and repetitive action responses, a general framework to map the training scenarios’ actions, roles, and collaborative structures to serious games’ game mechanics and game dialogues, is still not available. To address this issue, in this study, a scenario-based game generator, which maps domain-oriented tasks to game rules and game mechanics, was developed. Also, two serious games (i.e., Hospital game and BioGarden game) addressing the training mechanisms of Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNe) domain, were developed by both the game developers and the scenario-based game generator for comparative analysis. Finally, the outcomes of these games were mapped to the virtual reality environment to provide a thorough training program. To test the usability, immersion, presence, and technology acceptance aspects of the proposed game generator’s outcomes, 15 game developer participants tested a complete set of games and answered the questionnaires of the corresponding phenomenon. The results show that although the game generator has higher CPU time and memory usage, it highly outperforms the game development pipeline performance of the game developers and provides usable and immersive games. Thus, this study provides a promising game generator which bridges the CBRNe practitioners and game developers to transform real-life training scenarios into video games efficiently and quickly.

Efficient multiple constraint acquisition

Constraint acquisition systems such as QuAcq and MultiAcq can assist non-expert users to model their problems as constraint networks by classifying (partial) examples as positive or negative. For each negative example, the former focuses on one constraint of the target network, while the latter can learn a maximum number of constraints. Two bottlenecks of the acquisition process where both these algorithms encounter problems are the large number of queries required to reach convergence, and the high cpu times needed to generate queries, especially near convergence. In this paper we propose algorithmic and heuristic methods to deal with both these issues. We first describe an algorithm, called MQuAcq, that blends the main idea of MultiAcq into QuAcq resulting in a method that learns as many constraints as MultiAcq does after a negative example, but with a lower complexity. A detailed theoretical analysis of the proposed algorithm is also presented. %We also present a technique that boosts the performance of constraint acquisition by reducing the number of queries significantly. Then we turn our attention to query generation which is a significant but rather overlooked part of the acquisition process. We describe %in detail how query generation in a typical constraint acquisition system operates, and we propose heuristics for improving its efficiency. Experiments from various domains demonstrate that our resulting algorithm that integrates all the new techniques does not only generate considerably fewer queries than QuAcq and MultiAcq, but it is also by far faster than both of them, in average query generation time as well as in total run time, and also largely alleviates the premature convergence problem.

Next-day operating room scheduling with uncertain surgery durations: Exact analysis and heuristics

Operating rooms are units of particular interest in hospitals as they constitute more than 40% of total expenses and revenues. Managing operating rooms is challenging due to conflicting priorities and preferences of various stakeholders and the inherent uncertainty of surgery durations. In this study, we consider the next-day scheduling problem of a hospital operating room. Given the list and the sequence of non-identical surgeries to be performed in the next day, one needs to determine the scheduled durations of surgeries where the actual duration of each surgery is uncertain. Our objective is to minimize the weighted sum of expected patient waiting times, room idle time and overtime. First, we provide a reformulation of the objective function in terms of auxiliary functions with a recursive pattern that enables exact analysis of the optimal surgery durations at the expense of high CPU time. Next, we develop and analyze simple-to-use and close-to-optimal scheduling heuristics motivated by practice, for the OR managers to deploy in the field. Our proposed hybrid heuristic attains 1.22% average performance gap and worst average optimality gap of 2.77%. Our solution is easy to implement as it does not require any advanced optimization tool, which is the reality of many operating room environments.

GTS – Garfield-based Triple-GEM Simulator

Triple-GEM detectors are gaseous devices used in high energy physics to measure the path of the particles which cross them. The characterisation of triple GEM detectors and the estimation of the performance for real data experiments require a complete comprehension of the mechanisms which transform the passage of one particle in the detector into electric signals, and dedicated MonteCarlo simulations are needed. In this work we will describe GTS (Garfield-based Triple-gem Simulator), a MonteCarlo code which has been developed to simulate the detector response to the passage of particles inside triple GEMs. The software takes into account the processes from the primary ionization up to the signal formation, e.g. avalanche multiplication and the effects of the diffusion in the gas volume. It uses a parametrization of the variables of interest meant to reproduce the same detector response (i.e. the charge distribution at the anode) as provided by Garfield++, a well known software that already performs this kind of simulation more detailed and therefore with a high CPU time consumption. In addition to the detector response, the simulation of the APV-25 electronics is implemented and the output is used to reconstruct the particle position with the Charge Centroid (CC) and the microTime Projection Chamber (µTPC) methods. A comparison of the simulated performance and the one collected in testbeams is used to tune the parameters used in GTS. Results in different conditions of incident angle will be shown.

Analytical algorithm to retrieve size distribution of an ensemble of randomly oriented arbitrarily shaped particles from spectral absorption measurements

A new parameterized anomalous diffraction theory (PADT) is proposed, in which the effects of reflection and refraction are included to calculate the orientation-averaged absorption efficiency of arbitrarily shaped particles accurately. Using the PADT, integral transform technique, data mapping approach, and Gaver–Stehfest inversion method, the discretely real summation formula for retrieval of the particle size distribution (PSD) based on absorption data is developed. The spectral representations of moments of PSD are also presented. The retrieval experiments show that the truncation number has a significant impact on the accuracy of PSD inversion, and the relative error reaches the its minimum at the critical truncation number. Compared with the double precision algorithm, the extended precision algorithm can produce more accurate and reliable results at the cost of high CPU time. In addition, the relative error increases with the increase of noise level, but it will be significantly reduced when a smoothing technique is applied.

Energy efficient heuristic application mapping for 2-D mesh-based network-on-chip

Application mapping in 2-D mesh-based Network-on-Chip (NoC) architecture is an optimization problem in which each application task (e.g., processor or memory units) should be mapped one-to-one onto a network element (switch or router) to optimize performance requirements (e.g., communication energy or communication latency) under certain platform constraints (e.g., bandwidth and/or latency). Network-on-Chip is a scheme that establishes links between limited application-specific components within Multi-Processor System-on-Chip (MPSoC), but it has a vital role to ensure the maximum data transfer rate and reduce total number of physical interconnections. Most of the works on heuristic application mapping for mesh-based NoC design aim to minimize both total communication energy and run-time, however they experience the following issues: (i) relatively high CPU time due to linear search for the task and tile mapping combinations, (ii) consumption of relatively high communication energy due to random tile selection when two or more tiles are equivalent in terms of average weighted distance by their adjacent mapped tasks, and (iii) even after constructive application mapping, some of the tasks consume higher communication energy due to their inappropriate placements. In this paper we present a low time-complexity heuristic mapping algorithm of weighted application graph under permissible bandwidth constraint to minimize communication energy of 2-D mesh-based NoC architecture. The experimental results of multimedia benchmarks, as well as randomly generated samples show the low communication energy as well as time-complexity under bandwidth constraints in comparison to the recent heuristic application mapping approaches. In our approach, the communication energy is also close to the optimal solution obtained by Integer Linear Programming (ILP).

Diffusion approximation in a radiative transfer model for astrophysical flows

In this work, we present the diffusion approximation model for radiative transfer when we deal with optically thick astrophysical flows. Since the initial model is high CPU time demanding when dealing with its numerical approximation, solving this simpler system can provide a low cost strategy for the simulation of radiative media. We then use a finite-volume algorithm coupled with an implicit scheme for radiative contributions to solve this simplified system. Numerical experiments in the one-dimensional and two dimensional cases are presented to validate our numerical strategy and to prove the relevance of this asymptotic model.

Read margin analysis of crossbar arrays using the cell-variability-aware simulation method

This paper proposes a new concept of read margin analysis of crossbar arrays using cell-variability-aware simulation. The size of the crossbar array should be considered to predict the read margin characteristic of the crossbar array because the read margin depends on the number of word lines and bit lines. However, an excessively high-CPU time is required to simulate large arrays using a commercial circuit simulator. A variability-aware MATLAB simulator that considers independent variability sources is developed to analyze the characteristics of the read margin according to the array size. The developed MATLAB simulator provides an effective method for reducing the simulation time while maintaining the accuracy of the read margin estimation in the crossbar array. The simulation is also highly efficient in analyzing the characteristic of the crossbar memory array considering the statistical variations in the cell characteristics.