Reduction In Runtime Research Articles

Optimization of best management practices to control runoff water quality in an urban watershed using a novel framework of embedding- response surface model

Best Management Practices (BMPs) are measures implemented to reduce urban runoff volume and pollution load. Determination of a cost-effective selection of BMP combinations is a challenge. In this study, an optimization model was developed to determine the optimal number, location, and type of BMPs with minimum cost and pollution load in the Majidieh catchment in Tehran, Iran. A novel framework was proposed combining the embedding technique with Response Surface Method (RSM) called “Em-RSM” in the form of a simulation–optimization (S/O) model. First, the storm water management model)SWMM(as the simulation model was linearized, and the linear programming results were used as the initial population of the genetic algorithm (GA). Then, the linearized model along with the SWMM model were alternatively used as the fitness function in the GA evolution process to increase the model run speed and results' accuracy. The results showed that the permeable pavement and infiltration trench were more effective than other BMPs because of the physical and local characteristics of the study area. It was demonstrated that the proposed model makes a considerable reduction in the model run time with acceptable accuracy in obtaining the compromise solution of the Pareto front. The proposed framework proved its effectiveness in the solution of GA-based S/O problems. It can also be applied in other case studies or optimization problems by replacing and simplifying the behavior of the simulation model in the optimization procedure.

Speedup of the Metropolis protocol via algorithmic optimization

The Metropolis algorithm is widely used in Monte Carlo (MC) simulations in diverse areas of science and technology, especially for problems formulated in terms of lattice models. A common situation is the necessity to perform long sequential processing, e.g., when looking for equilibrium states of distinct physical systems. Hence, even marginal increases in efficiency of the algorithm individual steps can lead to significant reductions in absolute execution runtimes. Usual speedup procedures include hardware updates, parallelization (when possible) and sampling methods. Here we follow a different direction in trying to decrease the full execution times of MC approaches: algorithmic optimization. We show that the algorithms can be improved by implementing relatively few and simple changes in their organization and structure. First, we discuss some refinements for the pseudo-random number generator, addressing the broadly employed Mersenne-Twister algorithm (MT19937-64). Second, we develop a protocol to precalculate the Boltzmann factor, thereby avoiding the high cost of repeatedly calls to this exponential function (indeed, a very recurring step in the standard Metropolis method). To benchmark our proposals we choose the Ising model since it is one of the best known and more extensively studied problems in statistical physics. We consider the mentioned optimizations and different computational elements, like compilers and Hamiltonian variables ranges, testing the efficiency to obtain the system solutions. Our results suggest that the present set of improvement schemes—namely; decreasing the processing time for both, to generate a random number and to implement the one-flip Metropolis step; systematically enforcing optimization for the maximum quantity of algorithm structures accessing random numbers in a code; and considerably reducing the amount of required computations of the MC probabilistic actualization term—might constitute a relevant addition to the existing collection of expediting techniques in MC computational routines.

SPCM: Efficient semi-possibilistic c-means clustering algorithm

The required division and exponentiation operations needed per iteration for the possibilistic c-means (PCM) clustering algorithm complicate its implementation, especially on homomorphically-encrypted data. This paper presents a novel efficient soft clustering algorithm based on the possibilistic paradigm, termed SPCM. It aims at easing future applications of PCM to encrypted data. It reduces the required exponentiation and division operations at each iteration by restricting the membership values to an ordered set of discrete values in [0,1], resulting in a better performance in terms of runtime and several other performance indices. At each iteration, distances to the new clusters’ centers are determined, then the distances are compared to the initially computed and dynamically updated range of values, that divide the entire range of distances associated with each cluster center into intervals (bins), to assign appropriate soft memberships to objects. The required number of comparisons is O(log the number of discretization levels). Thus, the computation of centers and memberships is greatly simplified during execution. Also, the use of discrete values for memberships allows soft modification (increment or decrement) of the soft memberships of identified outliers and core objects instead of rough modification (setting to zero or one) in related algorithms. Experimental results on synthetic and standard test data sets verified the efficiency and effectiveness of the proposed algorithm. The average percent of the achieved reduction in runtime is 35% and the average percent of the achieved increase in v-measure, adjusted mutual information, and adjusted rand index is 6% on five datasets compared to PCM. The larger the dataset, the higher the reduction in runtime. Also, SPCM achieved a comparable performance with less computational complexity compared to variants of related algorithms.

Robust and Efficient Identification of Hydraulic Flow Units using Differential Evolution Optimization and Two-Stage Clustering Techniques

Summary One essential process in reservoir characterization is the identification of hydraulic flow units (HFUs). It plays a critical role in determining hydrocarbon reserves and improving reservoir productivity. Flow zone indicator (FZI), determined from core data, is widely used to identify HFUs. One of the challenges in the FZI technique is that the number of HFUs is identified using qualitative methods and subjective estimation. This work proposes robust methods to identify the optimal HFUs using differential evolution (DE) and two-stage clustering. The first method tested in this work enumerates through a large number of HFUs scenarios using 10 clustering algorithms and different input parameters (number of clusters, minimum number of samples, etc.). The scenario with the largest average correlation coefficient is selected as optimum. The second method uses the DE algorithm to maximize the average correlation coefficient and hence obtain the optimal HFUs. The third method consists of two stages. The first stage uses the OPTICS clustering algorithm to determine the number of HFUs, while the second stage generates the desired clusters using the Gaussian mixture algorithm. Both iterative evaluation and DE optimization methods achieved the same clustering results. However, DE optimization resulted in 85% reduction in runtime due to the robust search capability of the DE algorithm which leads to the solution more efficiently. Furthermore, another significant reduction in runtime was achieved using the two-stage clustering method which yielded very close results. The proposed methods in this work provide unique and potential opportunity to improve the use of FZI data analysis to identify HFUs. This work uses the power of clustering and stochastic algorithms to support a critical process in reservoir characterization.

The The Efficiency of Hybridised Genetic Algorithm and Ant Colony Optimisation (HGA-ACO) in a Restaurant Recommendation System

A recommendation system (RS) is used to provide recommendations to users by filtering items based on given inputs. Metaheuristic algorithms such as Genetic Algorithm (GA) and Ant Colony Optimisation (ACO) are known to be used in many RS to provide optimal and good recommendations. Both algorithms are designed based on nature-inspired events, where GA is designed based on the natural evolution process while ACO is based on ants’ behaviour in their natural habit. In this paper, both GA and ACO algorithms were implemented in a restaurant RS and evaluated by using the restaurant’s attributes, which was then followed by a list of recommended restaurants as the output. With the highest score of 99.64% of accuracy, GA overtakes ACO in terms of recommendation accuracy while ACO computed 67.12% lesser runtime than GA. Considering the results acquired, a new hybrid framework known as the HGA-ACO algorithm was proposed. The proposed HGA-ACO has a recommendation accuracy of 99.57% and achieved a 31.37% runtime reduction from GA. Thus, the proposed framework was observed to have improved the output accuracy of ACO and improved the processing time in GA, thus, improving the overall efficiency of the RS.

Maritime inventory routing with transshipment: the case of Yamal LNG

The LNG-ADP problem is a tactical planning problem for creating an annual delivery program (ADP) for a liquefied natural gas (LNG) producer. An ADP specifies the departure dates of the LNG carriers as well as the delivery dates at the different customers for a period of 12–18 months. The problem can be formulated as maritime inventory routing problem, as it is an important requirement to plan the deliveries such that inventory levels are kept within minimum and maximum limits at the customers as well as the LNG production facility. Inspired by the case of Yamal LNG, we propose a novel discrete-time formulation for the LNG-ADP problem with transshipment and intermediate storage. Our formulation also allows for waiting at the unloading ports. The problem is solved using a rolling horizon heuristic (RHH) for a case based on the Yamal LNG project. We study the impact of different RHH configurations on run time and solution quality. The results show that using a central period that is shorter than the forecast period provides the best objective function, whereas a central period that is longer than the forecast period improves run time. We also explore the effect of allowing waiting at the unloading ports. Waiting does not necessarily improve the objective function value, despite increasing the solution space. However, we observe a reduction in run time for instances where waiting is allowed.

Estimation of the Total Heat Exchange Factor for the Reheating Furnace Based on the First-Optimize-Then-Discretize Approach and an Improved Hybrid Conjugate Gradient Algorithm

The total heat exchange factor is one of the most important thermal physical parameters in the heat transfer model for a reheating furnace machine. In this paper, a novel general strategy, which is combined with the first-optimize-then-discretize (FOTD) approach and an improved hybrid conjugate gradient (IHCG) algorithm, is proposed to identify the total heat exchange factor by solving a nonlinear inverse heat conduction problem (IHCP). Firstly, a nonlinear IHCP with the Dirichlet-type boundary condition Tm(t)=T(0,t) is built to determine the unknown total heat exchange factor w(t). Secondly, the analysis of the Fréchet gradient of the cost functional is given and the gradient is proved as Lipschitz continuous by the FOTD approach. Thirdly, based on the gradient information by FOTD, a new IHCG algorithm, whose global convergence is proved by us, is proposed for fast solving of the optimization problem. Finally, simulation experiments are given to verify the effectiveness of the proposed strategy. Compared with the first-discretize-then-optimize (FDTO) approach, the FOTD approach can reduce running time and iteration number. Compared with other CG algorithms, the proposed IHCG algorithm has better convergence performance. The experimental data by the thermocouples experiments from a reheating furnace are also given to identify the total heat exchange factor.

Optimal operation scheduling of household energy hub: A multi-objective optimization model considering integrated demand response

This paper proposes the multi-energy co-optimization of a household energy hub (EH) from the perspective of the supply side. In this paper, firstly, a household load optimization model is established with the minimization problem of the volatility of each load after participating in the integrated demand response. Secondly, to make this study more relevant to reality, an EH multi-objective optimization scheduling model is established in three dimensions: operation cost, carbon emission, and energy efficiency. Finally, a new α-constrained dominated differential evolution algorithm is proposed to obtain the Pareto optimal solution set for EH multi-objective optimization. Then, a compromise optimal day-ahead scheduling scheme is obtained using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. The algorithm used in this paper has better convergence and distributivity than the traditional non-dominant classification genetic algorithm II (NSGA-II) method with constrained internal penalty functions. The results show that users’ cost has dropped by an average of 30%, and energy efficiency has improved by 4.8%. The reduction in run time also allows for handling more significant amounts of load data.

Quality Optimization of Adaptive Applications via Deep Reinforcement Learning in Energy Harvesting Edge Devices

Applications with adaptability are widely available on the edge devices with energy harvesting capabilities. For their runtime quality optimization, however, current approaches cannot tackle the variations of quality modeling and harvested energy simultaneously. Therefore, in this article, we are the first to propose a deep reinforcement learning (DRL)-based dynamic voltage frequency scaling (DVFS) method that optimizes the application execution quality of energy harvesting edge devices to mitigate the variations. First, we propose a baseline DRL formulation that novelly migrates the objective of quality maximization into a reward function and constructs a DRL quality agent. Second, we devise a long short-term memory (LSTM)-based selector that performs DRL quality agent selection based on the energy harvesting history. Third, we further propose two optimization methods to alleviate the nonnegligible overhead of DRL computations: 1) an improved thinking-while-moving concurrent DRL scheme to compromise the “state drifting” issue during the DRL decision process and 2) a variable interstate duration decision scheme that compromises the DVFS overhead incurred in each action taken. The experiments take an adaptive stereo matching application as a case study. The results show that the proposed DRL-based DVFS method on average achieves 17.9% runtime reduction and 22.05% quality improvement compared to state-of-the-art solutions.

A Model-Based Approach Digital Pre-Distortion Method for Current-Steering Digital-to-Analog Converters

This paper presents a novel static digital pre-distortion (DPD) method for a current-steering digital-to-analog converter (CS-DAC). The proposed method utilizes the knowledge of the current cell array architecture to calculate the static mismatch currents of the cells. The mismatch values are stored in memory and added to the original input code to generate the new pre-distorted input word. The converter corrects the static error with its own current cells without incorporating an additional calibration DAC (CALDAC) or programmable current sources. This results in a reduction in area, power and simulation run times because of the simpler circuit design. An Overflow-Cell-Selection (OCS) is introduced as a novel solution to further enhance the static linearity of the converter. It also can be implemented as a software solution for already existing DAC designs which do not have an integrated DPD and lab/measurement equipment (e.g., arbitrary wave generator (AWG)). This poses as a strong differentiation factor compared to other state-of-the-art static DPD methods. The evaluation of the proposed DPD is done via simulations in MATLAB and on- chip measurements with a 14-bit CS-DAC in 16 nm. Single tone measurements show a performance gain of the total harmonic distortion (THD) of 12 dB.

A Two-Level Approximate Logic Synthesis Combining Cube Insertion and Removal

Approximate computing is an attractive paradigm for reducing the design complexity of error-resilient systems, therefore improving performance and saving power consumption. In this work, we propose a new two-level approximate logic synthesis method based on cube insertion and removal procedures. Experimental results have shown significant literal count and runtime reduction compared to the state-of-the-art approach. The method scalability is illustrated for a high error threshold over large benchmark circuits. The obtained solutions have presented a literal number reduction up to 38%, 56% and 93% with respect to an error rate of 1%, 3% and 5%, respectively.

Sensitivity-Analysis-Driven Surrogate Model for Molten Salt Reactors Control

The numerical analysis for the controllability assessment of a new design nuclear reactor is typically carried out by means of complex multiphysics codes, solving high fidelity partial differential equations governing the system neutronics as well as the fluid dynamics. Multiphysics codes deliver very accurate solutions at the expense of high computational times, which could be of several hours depending on the specific case study. In this work, to efficiently reduce runtimes, a sensitivity analysis (SA) is carried out to identify the most important input parameters affecting the solution of a multiphysics model developed for the controllability assessment of molten salt reactors (MSRs). The numerical modeling of these innovative systems is fundamental to allow for a safer and more sustainable power production (e.g., due to the lower radiotoxicity of the actinide inventory in MSRs and to the possibility of operation at atmospheric pressure). In this paper, four global sensitivity measures are calculated first, including the Pearson correlation coefficient, δ, Kolmogorov–Smirnov and Kuiper indices, whose results are aggregated by an ensemble strategy and confirmed by the CUmulative SUm of NOrmalized Reordered Output (CUSUNORO) plot. The results of the SA point out that the fuel density is the most important parameter yielding the largest variations in the system reactivity, fundamental for guaranteeing the MSR controllability. In light of this result, a simplified, surrogate model is then developed, which uses density as the only input parameter to determine reactivity, guaranteeing runtime reductions from several hours to a few seconds and, at the same time, a comparable level of accuracy of the multiphysics model. This result demonstrates the capability of global sensitivity analysis approaches to effectively identify the most relevant parameters in MSR systems, supporting the development of simplified, control-oriented models for these innovative reactors.

Predicting gas-liquid mass transfer rates in reactors using a bubble parcel model

A parcel-based approach for predicting mass transfer in gassed bioreactors is presented. Predictions from this parcel model, which handles bubble break-up and coalescence implicitly, are compared directly to measured data and predictions from full-fidelity discrete bubble models. Guidance for determining when this coarse-graining approach is acceptable, as a function of the vessel operating conditions, is presented. When the approach is appropriate, the parcel-based model can present order-of-magnitude reductions in computational runtime with minimal loss in physical fidelity.

Neural Optimizer for Inverse Design of Complex‐Modulated Hologram Implemented by Plasmonic Metasurfaces

Inverse design of a metasurface involves searching parameters in a high‐dimensional space, which needs huge computational power. To ease the computational burden, neural network, a well‐researched computer science stream, has demonstrated its potential usage in the inverse design of a photonic device. Many studies primarily focused on the nanostructure's configuration. However, the near field of a metasurface requires further optimization to achieve a desired holographic image at relieved computational power. Here, a convolutional neural network is developed to optimize the complex field of a computer‐generated hologram, which can be fabricated into a metasurface to generate the desired holographic image upon lensless image projection. The neural optimizer can accelerate the design speed 400 times faster than theoretical computation and reduce the time complexity from O(n 4) to O(n 2). The neural optimizer has been compared against three other methods, e.g., gradient‐based optimization, global genetic algorithm, and coupled‐mode theory, to demonstrate a lowered error rate from more than 10% to 1.38% for the benchmark testing and a reduced running time from hours to near 1 s. The neural optimizer is envisioned to play a key role in lensless image projection and real‐time metasurface pattern design.

Stress-constrained topology optimization of lattice-like structures using component-wise reduced order models

Lattice-like structures can provide a combination of high stiffness with light weight that is useful in many applications, but a resolved finite element mesh of such structures results in a computationally expensive discretization. This computational expense may be particularly burdensome in many-query applications, such as optimization. We develop a stress-constrained topology optimization method for lattice-like structures that uses component-wise reduced order models as a cheap surrogate, providing accurate computation of stress fields while greatly reducing run time relative to a full order model. We demonstrate the ability of our method to produce large reductions in mass while respecting a constraint on the maximum stress in a pair of test problems. The ROM methodology provides a speedup of about 150x in forward solves compared to full order static condensation and provides a relative error of less than 5% in the relaxed stress.

Energy Efficiency Maximization in Large-Scale Cell-Free Massive MIMO: A Projected Gradient Approach

This paper considers the fundamental power allocation problem in cell-free massive mutiple-input and multiple-output (MIMO) systems which aims at maximizing the total energy efficiency (EE) under a sum power constraint at each access point (AP) and a quality-of-service (QoS) constraint at each user. Existing solutions for this optimization problem are based on solving a sequence of second-order cone programs (SOCPs), whose computational complexity scales dramatically with the network size. Therefore, they are not implementable for practical large-scale cell-free massive MIMO systems. To tackle this issue, we propose an iterative power control algorithm based on the frame work of an accelerated projected gradient (APG) method. In particular, each iteration of the proposed method is done by simple closed-form expressions, where a penalty method is applied to bring constraints into the objective in the form of penalty functions. Finally, the convergence of the proposed algorithm is analytically proved and numerically compared to the known solution based on SOCP. Simulations results demonstrate that our proposed power control algorithm can achieve the same EE as the existing SOCPs-based method, but more importantly, its run time is much lower (one to two orders of magnitude reduction in run time, compared to the SOCPs-based approaches).

A fast, single column high-performance anion exchange chromatography with pulsed amperometric detection method for the determination of saccharides in atmospheric aerosol samples.

Saccharides, especially anhydro sugars present in atmospheric aerosols, can be used as tracers to track sources of atmospheric aerosols. High-performance anion-exchange chromatography with pulsed amperometric detection is a commonly used technique for determining these saccharides, but the reported methods suffer from three drawbacks. First, to achieve separation of the complete set of atmospheric saccharides, run times are very long, typically longer than 60 minutes. Second, some methods require two columns to achieve the desired separation. Finally, in an era when electrolytic eluent preparation allows for excellent precision and accuracy, these methods require manually prepared eluents, which can lead to separation inconsistency for closely eluting analytes. These drawbacks make existing methods difficult to automate. To address this issue, we developed a fast method that uses only a single column for separation and electrolytically generated eluent that resolves 12 key atmospheric aerosol saccharides in 20 minutes. The resolved saccharides include anhydro sugars (levoglucosan, galactosan, and mannosan), sugar alcohols (erythritol, xylitol, and mannitol), and mono-/disaccharides (arabinose, galactose, glucose, mannose, fructose, and sucrose). To our knowledge, this report is the first instance of achieving such a significant reduction in run time with good resolution for this set of saccharides.

Consensus clustering for Bayesian mixture models

BackgroundCluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the output from multiple runs of a non-deterministic clustering algorithm. Here we consider the application of consensus clustering to a broad class of heuristic clustering algorithms that can be derived from Bayesian mixture models (and extensions thereof) by adopting an early stopping criterion when performing sampling-based inference for these models. While the resulting approach is non-Bayesian, it inherits the usual benefits of consensus clustering, particularly in terms of computational scalability and providing assessments of clustering stability/robustness.ResultsIn simulation studies, we show that our approach can successfully uncover the target clustering structure, while also exploring different plausible clusterings of the data. We show that, when a parallel computation environment is available, our approach offers significant reductions in runtime compared to performing sampling-based Bayesian inference for the underlying model, while retaining many of the practical benefits of the Bayesian approach, such as exploring different numbers of clusters. We propose a heuristic to decide upon ensemble size and the early stopping criterion, and then apply consensus clustering to a clustering algorithm derived from a Bayesian integrative clustering method. We use the resulting approach to perform an integrative analysis of three ’omics datasets for budding yeast and find clusters of co-expressed genes with shared regulatory proteins. We validate these clusters using data external to the analysis.ConclustionsOur approach can be used as a wrapper for essentially any existing sampling-based Bayesian clustering implementation, and enables meaningful clustering analyses to be performed using such implementations, even when computational Bayesian inference is not feasible, e.g. due to poor exploration of the target density (often as a result of increasing numbers of features) or a limited computational budget that does not along sufficient samples to drawn from a single chain. This enables researchers to straightforwardly extend the applicability of existing software to much larger datasets, including implementations of sophisticated models such as those that jointly model multiple datasets.

Training Mode of Innovative Accounting Talents in Colleges Using Artificial Intelligence

With the rise of big data revolution, a new generation of artificial intelligence (AI) is gaining momentum and the AI era will come strongly. However, the accounting profession is also showing a booming trend in colleges and universities, which, as the main base of higher education talent training, also undertake a considerable part of the task of training accounting talents. As traditional accounting is gradually replaced, finance professionals are being forced to gradually change to management talents. In the information era, how to realize the transformation of talent cultivation mode of financial management professionals, how to use modern technology to realize the deep integration of AI and education teaching, and how to realize the innovation of intelligent classroom teaching mode are the challenges facing the innovation of applied talent cultivation mode in colleges and universities. The article combines the changes brought by AI to the accounting industry, analyzes the current situation of accounting talent cultivation mode in colleges and universities, sorts out the challenges brought by AI to college accounting talent cultivation, and proposes that the college accounting talent cultivation mode should be further improved in terms of talent cultivation structure, cultivation plan and goal, and cultivation ideas and methods. The experimental results show that the average application time of AI basic scheduling algorithm is 1.47 h shorter than that without AI basic scheduling algorithm, which can not only increase the total number of tasks in a planned and controlled way but also ensure the effective reduction of the whole task running time in the platform. Therefore, the AI-based innovative accounting talents cultivation model in colleges and universities, exploring an excellent new model for cultivating innovative talents in accounting professions, enables the innovative accounting talents cultivated in schools to meet the needs of society and enhance their irreplaceability.

Reduced Operator Inference for Nonlinear Partial Differential Equations

We present a new scientific machine learning method that learns from data a computationally inexpensive surrogate model for predicting the evolution of a system governed by a time-dependent nonlinear partial differential equation (PDE), an enabling technology for many computational algorithms used in engineering settings. Our formulation generalizes to the function space PDE setting the Operator Inference method previously developed in [B. Peherstorfer and K. Willcox, Data-driven operator inference for non-intrusive projection-based model reduction, Computer Methods in Applied Mechanics and Engineering, 306 (2016)] for systems governed by ordinary differential equations. The method brings together two main elements. First, ideas from projection-based model reduction are used to explicitly parametrize the learned model by low-dimensional polynomial operators which reflect the known form of the governing PDE. Second, supervised machine learning tools are used to infer from data the reduced operators of this physics-informed parametrization. For systems whose governing PDEs contain more general (non-polynomial) nonlinearities, the learned model performance can be improved through the use of lifting variable transformations, which expose polynomial structure in the PDE. The proposed method is demonstrated on two examples: a heat equation model problem that demonstrates the benefits of the function space formulation in terms of consistency with the underlying continuous truth, and a three-dimensional combustion simulation with over 18 million degrees of freedom, for which the learned reduced models achieve accurate predictions with a dimension reduction of five orders of magnitude and model runtime reduction of up to nine orders of magnitude.