Acceptable Computational Efficiency Research Articles

Hybrid Simulated Annealing‐Evaporation Rate‐Based Water Cycle Algorithm Application for Medical Image Enhancement

Visualizing medical images is difficult due to artifacts, poor local contrast, low soft tissue contrast, excessive noise levels, and a wide dynamic range. This has created a serious problem for physicians, resulting in unapproachable and inaccurate disease diagnoses. To circumvent this problem, this research proposes a medical image enhancement (MIE) approach based on the hybrid simulated annealing‐evaporation rate‐based water cycle algorithm (SA‐ERWCA). The ERWCA enhances distorted medical images by finding the best optimal solution for the transformation parameters according to the fitness function. The fitness function consists of three objective measurements, namely, entropy, number of edges, and sum of edge intensities. Due to its high potential for finding a globally optimal solution, SA is applied solely to determine the optimized initial population of the ERWCA, thereby enhancing its convergence characteristics. To simply put, the main objective of SA is to avoid premature convergence of the ERWCA. Thus, blending SA and ERWCA produces better‐quality medical images. The performance of the proposed algorithm was compared to histogram equalization (HE), low contrast stretching (LCS), contrast‐limited adaptive histogram equalization (CLAHE), particle swarm optimization (PSO), accelerated PSO (APSO), and the water cycle algorithm (WCA). Along with the objective function fitness, seven full reference (FR) image quality assessment (IQA) metrics were implemented to evaluate image quality and compare performance. The findings of the present study showed that the proposed strategy outperforms all of the compared methods in terms of objective function fitness and perceptual visual IQA metrics such as the Harr wavelet‐based perceptual similarity index (HaarPSI), visual signal‐to‐noise ratio (VSNR), and information content weighted structural similarity index measure (IW‐SSIM). The suggested technique also exhibited better stability and faster convergence time to the optimum solution. Furthermore, the proposed approach outperformed others by avoiding premature convergence to the best solution and providing excellent optimization results with acceptable computational efficiency.

Cooperative Ecological Adaptive Cruise Control for Plug-In Hybrid Electric Vehicle Based on Approximate Dynamic Programming

Eco-driving control generates significant energy-saving potential in car-following scenarios. However, the influence of preceding vehicle may impose unnecessary velocity waves and deteriorate fuel economy. In this research, a learning-based method is exploited to achieve satisfied fuel economy for connected plug-in hybrid electric vehicles (PHEVs) with the advantage of vehicle-to-vehicle communication system. A data-driven energy consumption model is leveraged to generate reinforcement signals for approximate dynamic programming (ADP) with the consideration of nonlinear efficiency characteristics of hybrid powertrain system. An advanced ADP scheme is designed for connected PHEVs driving in car-following scenarios. Additionally, the cooperative information is incorporated to further improve the fuel economy of the vehicle under the premise of driving safety. The proposed method is mode-free and showcases acceptable computational efficiency as well as adaptability. The simulation results demonstrate that the fuel economy during car-following processes is remarkably improved through cooperative driving information, thereby partially paving the theoretical basis for energy-saving transportation.

Improved image-based, full-field structural displacement measurement using template matching and camera calibration methods

Although the use of artificial-markers improves the performance of the image-based procedures, there is a problem with the access to their installation place. Moreover, increasing the accuracy and efficiency of the available methods for structural-health-monitoring (SHM) is an important issue in recent years. Therefore, the purpose of this study is to enhance the image-based, full-field displacement measurement using new template-matching and camera-calibration methods. Furthermore, considering practical methods to increase the number of pixels in the images, the possibility of using free markers and improving the camera-calibration methods are investigated. The suitability of the proposed procedures was experimentally evaluated using single-degree-of-freedom and multi-degrees-of-freedom structural models and a railway bridge as a realistic application of the introduced methods. The proposed techniques are independent of employing artificial-markers or checkerboards with marker-less calibration abilities. Also, the effectiveness of the proposed schemes lies in the acceptable accuracy, computational efficiency, and sufficient applicability for SHM problems.

The FIRST model: Spatiotemporal fusion incorrporting spectral autocorrelation

Over the past decade, spatiotemporal fusion has become an indispensable tool for monitoring land surface dynamics due to its promising ability to produce surface reflectance products with both high spatial and temporal resolutions. However, existing fusion methods usually generate multispectral band products by predicting each spectral band separately, so the useful information of spectral autocorrelation within the spectrum has been ignored and waits to be exploited. To address this issue, we propose a novel spatiotemporal fusion method, the spatiotemporal Fusion Incorrporting Spectral autocorrelaTion (FIRST) model, to fully utilize the multiple spectral bands of surface reflectance products. Compared with other fusion methods, the model has three distinct advantages: (1) it utilizes spectral autocorrelation in a many-to-many regression framework that simultaneously inputs and predicts multispectral bands without the collinearity effect; (2) it maintains high fusion accuracy when the spatiotemporal variation is large with acceptable computational efficiency; and (3) it can produce robust results even with input images contaminated by haze and thin clouds. We tested the FIRST model at several experimental sites and compared it with four typical methods, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF) model, the regression model Fitting, spatial Filtering and residual Compensation (Fit-FC) model and the enhanced STARFM (ESTARFM). The results demonstrate that FIRST yields better overall performance for its simple and effective technical principles. FIRST is thus expected to provide high-quality remotely sensed data with high spatial resolution and frequent observations for various applications.

A new multi-fidelity flow-acoustics simulation framework for wind farm application

To reduce the release of CO2 from traditional energy, wind energy is developed very fast all over the world. This means that more large wind turbines will be installed on land and more environmental impacts will be created from wind turbines. Noise generated from rotating wind turbines in wind farm propagates through a complex flow-field including a varying atmospheric flow and varying wakes created by the wind turbines, which can greatly influence the sound propagation. This paper presents a new multi-fidelity flow-acoustics simulation framework to predict the sound propagation in both near and far fields of wind turbines in wind farm, which has the potential for wind farm application with good accuracy and acceptable computing time (i.e. 8 min with 6 CPUs for the centre frequencies between 20 Hz and 1000 Hz in 1/3 octave bands). The new multi-fidelity flow-acoustics simulation framework (named WindSTAR) consists of a three-dimensional wind farm engineering flow model, an engineering sound source model, and a high-fidelity sound propagation model. To validate the new framework, a comprehensive measurement strategy was designed and performed to measure simultaneously the turbine operations, inflow conditions, wake flow fields and acoustic fields from near to far fields of a 3.9/4.1 MW SGRE wind turbine in Drantum, Denmark. Comparisons between measurements and computations in various flow conditions from stable to unstable atmospheres show good agreements with an averaged absolute difference of 0.8 dBA. Based on its good accuracy and acceptable computational efficiency, the new multi-fidelity framework is able to be used for designing and controlling wind farms.

Deep Learning Neural Network Model for Tunnel Ground Surface Settlement Prediction Based on Sensor Data

Monitoring and prediction of ground settlement during tunnel construction are of great significance to ensure the safe and reliable operation of urban tunnel systems. Data-driven techniques combining artificial intelligence (AI) and sensor networks are popular methods in the field, which have several advantages, including high prediction accuracy, efficiency, and low cost. Deep learning, as one of the advanced techniques in AI, is demanded for the tunnel settlement forecasting problem. However, deep neural networks often require a large amount of training data. Due to the tunnel construction, the available training data samples are limited, and the data are univariate (i.e., containing only the settlement data). In response to the above problems, this research proposes a deep learning model that only requires limited number of training data for short-period prediction of the tunnel surface settlement. In the proposed complete ensemble empirical mode decomposition with adaptive noise long short term memory (CEEMDAN-LSTM model), single-dimensional data is divided into multidimensional data by CEEMDAN through the complete ensemble empirical mode decomposition. Each component is then predicted by a LSTM neural network and superimposed for obtaining the final prediction result. Experimental results show that, compared with existing machine learning techniques and algorithms, this deep learning method has higher prediction accuracy and acceptable computational efficiency. In the case of small samples, this method can significantly improve the accuracy of time series forecasting.

Parallelization and application of SACOS for whole core thermal-hydraulic analysis

SACOS series of subchannel analysis codes have been developed by XJTU-NuTheL for many years and are being used for the thermal-hydraulic safety analysis of various reactor cores. To achieve fine whole core pin-level analysis, the input preprocessing and parallel capabilities of the code have been developed in this study. Preprocessing is suitable for modeling rectangular and hexagonal assemblies with less error-prone input; parallelization is established based on the domain decomposition method with the hybrid of MPI and OpenMP. For domain decomposition, a more flexible method has been proposed which can determine the appropriate task division of the core domain according to the number of processors of the server. By performing the calculation time evaluation for the several PWR assembly problems, the code parallelization has been successfully verified with different number of processors. Subsequent analysis results for rectangular- and hexagonal-assembly core imply that the code can be used to model and perform pin-level core safety analysis with acceptable computational efficiency.

Energetic, economic and environmental (3E) multi-objective optimization of the back-end separation of ethylene plant based on adaptive surrogate model

Ethylene separation is an important part in olefin production process, but it brings about high energy consumption and emissions. Therefore, the energetic, economic and environmental (3E) multi-objective optimization of ethylene separation process is of great significance for the sustainable development of olefin industry. Regarding the high computation cost of conventional optimization approach based on process simulation model, a novel optimization framework named multi-objective adaptive surrogate model assisted optimization (MOASO) is introduced. In this framework, adaptive sampling based on sparsity and IGGD improvement is proposed, aiming at promoting the accuracy of surrogate model successively as optimization proceeds. In addition, a modified non-dominant sorting genetic algorithm-II (NSGAII) embedding a density-based local search operator is developed for evolutionary optimization. The MOASO is applied to typical test functions and a practical ethylene separation process. The results show that the proposed framework has highly acceptable optimization performance and computational efficiency, reducing 72% of the calculation burden. Compared with the actual operating condition, the energy consumption and CO2 emission can be reduced by 3.5 × 104 GJ/year and 3.81 × 105 kg/year while increasing the annual gross profit by 4.36 × 105 $/year under a typical optimized condition. In general, the proposed framework can be used as an effective tool for multi-objective optimization, from which insights for clean production and sustainable development in complex large-scale chemical processes could be gained.

Detecting of Overshooting Cloud Tops via Himawari-8 Imagery Using Dual Channel Multiscale Deep Network

The occurrence of overshooting cloud tops can cause extremely severe weather such as strong winds and heavy rainfalls. The traditional overshooting cloud top (OT) detection methods not only need to choose a reasonable threshold, it is also very hard to make full advantage of the multispectral information of cloud images. These make small-scale OT detection very difficult with poor accuracy of OT boundary determination. In order to utilize the multispectral information of Himawari-8 satellite cloud images, in this article, we propose a method for detecting OT based on the dual channel multiscale deep network (DCMSDN). The brightness temperature of infrared window and the difference of brightness temperature between the infrared window and water vapor window are used as dual channel inputs, respectively. Then, DCMSDN introduces a multiscale prediction module to improve the accuracy of small target detection, which makes the network more suitable for the detection of the OT with small spatial scale. Experimental results indicate that the proposed method provides competitive performance with acceptable computational efficiency. Specifically, for the quantitative indicators of OTs detection, our approach achieves the accuracy of 89.36%, the precision of 95.63%, the recall of 88.90%, and the F1-measure of 91.61% for the test cloud images, which outperforms that of comparative methods.

A Monte Carlo simulation-based approach to realistic modelling of additively manufactured lattice structures

Lattice structures fabricated via Additive Manufacturing (AM) offer improved performance over traditional manufacturing methods, however, predicting their mechanical behaviour both accurately and with acceptable computational efficiency remains a challenge. AM associated defects combined with multiple high aspect-ratio strut elements require fine 3D finite-element (FE) meshes; resulting in high computational complexity that limits the number of lattice unit cells that can be practically simulated. Alternatively, Euler-Bernoulli or Timoshenko beam elements can be specified to reduce computational complexity. However, these beam elements are typically based on idealised representations that exclude AM associated defects. This research proposes a novel method which combines data driven AM defect modelling, Markov Chains and Monte Carlo (MCS) simulation techniques to predict the stiffness of an AM lattice structure. Furthermore, this method accommodates stochastic distributions of AM associated defects within computationally effective beam models; thereby enabling the simulation of large-scale lattice structures at a relatively low computational cost. The proposed method is aimed at reliability analysis or a probabilistic approach to structural analysis of AM lattice structures. The combination of generating AM strut digital realisations and MCS, resulted in a variety of possible strut deformation shapes and effective diameters under axial compression. The propagation of effective diameter variability to the lattice-scale level displayed the possible variation in the mechanical response of AM lattice structure. Simulations are validated and insight into how a lattice structures unit cell topology affects simulation accuracy is discussed.

A discrete kinetic scheme to model anisotropic liquid–solid phase transitions

An extended Boltzmann equation is constructed to model phase transitions with anisotropic effects. It is directly transformed into a discrete kinetic scheme in both time and phase space. The new scheme can be used to derive anisotropic lattice Boltzmann-phase field equation and convective diffusion equations. A lattice Boltzmann algorithm with the anisotropic streaming-relaxation operator is proposed, and numerical simulations of liquid–solid phase transition are performed as examples. The results agree well with previous numerical data and analytical solutions. It demonstrates that the scheme has acceptable numerical accuracy and computational efficiency, and the scheme can be used to study anisotropic liquid–solid phase transitions. This work provides an alternative numerical approach to model and simulate phase transitions with relatively fast computational efficiency.

Tunnel Surface Settlement Forecasting with Ensemble Learning

Ground surface settlement forecasting in the process of tunnel construction is one of the most important techniques towards sustainable city development and preventing serious damages, such as landscape collapse. It is evident that modern artificial intelligence (AI) models, such as artificial neural network, extreme learning machine, and support vector regression, are capable of providing reliable forecasting results for tunnel surface settlement. However, two limitations exist for the current forecasting techniques. First, the data provided by the construction company are usually univariate (i.e., containing only the settlement data). Second, the demand of tunnel surface settlement is immediate after the construction process begins. The number of training data samples is limited. Targeting at the above two limitations, in this study, a novel ensemble machine learning model is proposed to forecast tunnel surface settlement using univariate short period of real-world tunnel settlement data. The proposed Adaboost.RT framework fully utilizes existing data points with three base machine learning models and iteratively updates hyperparameters using current surface point locations. Experimental results show that compared with existing machine learning techniques and algorithms, the proposed ensemble learning method provides a higher prediction accuracy with acceptable computational efficiency.

Generating efficient basis sets for unbounded domains

The treatment of the problems involving unbounded domains (UDs) with vanishing boundary conditions is always challenging. For spectral methods, in particular, very limited basis sets are commonly used for such domains, in which the ranges of the decay rates with acceptable computational efficiency, are very small. Furthermore, maintaining high level of analyticity becomes burdensome. Developing efficient mapped basis tailored for such problem is one of the main strategies to overcome these difficulties. In this work, we present a technique to generate efficient basis sets for UDs. This approach allows using basis sets defined for bounded domains (BDs) for problems in UDs, and hence, providing more freedom to choose from a variety of basis sets. To ensure computational efficiency, the designed transformations cover a wide range of decay rates and allow solving integrals analytically. The method is applied to solve many differential equations encountered frequently in many physics related problems. The results illustrate the efficiency of the developed technique and mapped basis sets.

Lightweight and Secure Three-Factor Authentication Scheme for Remote Patient Monitoring Using On-Body Wireless Networks

On-body wireless networks (oBWNs) play a crucial role in improving the ubiquitous healthcare services. Using oBWNs, the vital physiological information of the patient can be gathered from the wearable sensor nodes and accessed by the authorized user like the health professional or the doctor. Since the open nature of wireless communication and the sensitivity of physiological information, secure communication has always been the vital issue in oBWNs-based systems. In recent years, several authentication schemes have been proposed for remote patient monitoring. However, most of these schemes are so susceptible to security threats and not suitable for practical use. Specifically, all these schemes using lightweight cryptographic primitives fail to provide forward secrecy and suffer from the desynchronization attack. To overcome the historical security problems, in this paper, we present a lightweight and secure three-factor authentication scheme for remote patient monitoring using oBWNs. The proposed scheme adopts one-time hash chain technique to ensure forward secrecy, and the pseudonym identity method is employed to provide user anonymity and resist against desynchronization attack. The formal and informal security analyses demonstrate that the proposed scheme not only overcomes the security weaknesses in previous schemes but also provides more excellent security and functional features. The comparisons with six state-of-the-art schemes indicate that the proposed scheme is practical with acceptable computational and communication efficiency.

A Monte Carlo Emissivity Model for Wind-Roughened Sea Surface.

Sea surface emissivity (SSE) is a key variable in the estimation of sea surface temperature and the sea surface radiation budget. A physical base SSE model with adequate accuracy and acceptable computational efficiency is highly desired. This paper develops a Monte Carlo ray-tracing model to compute the SSE of a wind-roughened sea surface. The adoption of a two-dimensional continuous surface model and averaging the two polarization components in advance before ray-tracing gives the model acceptable computational efficiency. The developed model can output the contributions of direct emission and the reflected component to the effective emissivity. The contribution of the reflected component to the effective emissivity reaches 0.035 at an 80° emission angle for a wind speed larger than 10 m/s. The emissivity spectra and channel emissivities collected from two field campaigns and one set of outdoor measurements are used to validate the developed model. Statistical results indicate that the absolute value of bias or difference is less than 0.5% when the view angle is less than 65°, which means the retrieval accuracy of sea surface temperature (SST) is guaranteed from the view of SSE. When the view angle increases, the accuracy of the developed model degraded, especially at the view angle of 85°. Without considering this view angle, the absolute value of bias or difference is less than 0.016, and the root mean square difference (RMSD) is less than 0.018.

Subtlety of TiO2 phase stability: Reliability of the density functional theory predictions and persistence of the self-interaction error.

TiO2 is an important material with broad applications that can exist in different phases with dramatically different properties. Theoretical prediction of their polymorph energetics is therefore critical for the material design and for identifying thermodynamically accessible structures. Determining TiO2 relative phase stabilities remains challenging for first-principles methods, and density functional theory is the only approach available for studying phase stabilities at finite temperatures with acceptable computational efficiency. Here, we show that density functional theory using the recently developed efficient strongly constrained and appropriately normed (SCAN) [Sun et al., Phys. Rev. Lett. 115, 036402 (2015)] exchange-correlation functional for the first time predicts the phase stability in qualitative agreement with the experimental results at realistic conditions. Further analysis shows that the self-interaction error intrinsic in the density functional persists in the stability prediction. By correcting the self-interaction error through an empirical approach, SCAN predicts the relative stability as well as defect properties in excellent agreement with the experimental results.

Soft computing for solar radiation potential assessment in Algeria

ABSTRACT Precise estimation of solar radiation is a highly required parameter for the design and assessment of solar energy applications. Over the past years, many machine learning techniques have been proposed in order to improve the forecasting performance using different input attributes. The aim of this study is the forecasting of one day ahead of horizontal global solar radiation using a set of meteorological and geographical inputs. In this respect, the Gaussian process regression methodology (GPR) and least-square support vector machine (LS-SVM) with different kernels are evaluated in order to select the most appropriate forecasting model. In order to assess the proposed models, the southern Algerian city, Ghardaia regions, was selected for this study. A historical data of five years (2013–2017) of meteorological data collected at Renewable Energies (URAER) in Ghardaia city are used. The achieved results demonstrate that all the proposed models give approximately similar results in terms of statistical indicators. In term of processing time, all the models showed acceptable computational efficiency with less computational costs of the GPR model among all machine learning models.

Air‐to‐Air Path Loss Prediction Based on Machine Learning Methods in Urban Environments

Recently, unmanned aerial vehicle (UAV) plays an important role in many applications because of its high flexibility and low cost. To realize reliable UAV communications, a fundamental work is to investigate the propagation characteristics of the channels. In this paper, we propose path loss models for the UAV air‐to‐air (AA) scenario based on machine learning. A ray‐tracing software is employed to generate samples for multiple routes in a typical urban environment, and different altitudes of Tx and Rx UAVs are taken into consideration. Two machine‐learning algorithms, Random Forest and KNN, are exploited to build prediction models on the basis of the training data. The prediction performance of trained models is assessed on the test set according to the metrics including the mean absolute error (MAE) and root mean square error (RMSE). Meanwhile, two empirical models are presented for comparison. It is shown that the machine‐learning‐based models are able to provide high prediction accuracy and acceptable computational efficiency in the AA scenario. Moreover, Random Forest outperforms other models and has the smallest prediction errors. Further investigation is made to evaluate the impacts of five different parameters on the path loss. It is demonstrated that the path visibility is crucial for the path loss.

Illumination robust face recognition using random projection and sparse representation

Under uneven illumination, the performances degrade significantly for some existing face recognition methods. It is a challenge for face recognition methods to work effectively under different illumination conditions. In this paper, an illumination robust face recognition method, based on random projection and sparse representation, is proposed. In the proposed method, face images are preliminary illumination normalized by gamma correction and difference of Gaussian filtering, and then several projection spaces are obtained by iterative random projection, followed by constructing an initial sample space using Fisher discrimination analysis. This scheme enriches the discrimination abilities of sample features and achieves the security and completeness for biometric template. Test samples are sparsely decomposed into each subspace, and based on statistical average residual, a modified sparse representation method is proposed to realize face recognition with higher stability and illumination robustness. Experimental results indicate that the proposed method provides competitive performance with acceptable computational efficiency. Specifically, for the five subsets of Yale B database, our approach achieves 99.74% average recognition rate, which performs higher accuracy than that of comparative methods.

Hierarchical K-means Method for Clustering Large-Scale Advanced Metering Infrastructure Data

Clustering of the load patterns from distribution network customers is of vital importance for several applications. However, the growing number of advanced metering infrastructures (AMI) and a variety of customer behaviors make the clustering task quite challenging due to the increasing amount of load data. K-means is a widely used clustering algorithm in processing a large dataset with acceptable computational efficiency, but it suffers from local optimal solutions. To address this issue, this paper presents a hierarchical K-means (H-K-means) method for better clustering performance for big data problems. The proposed method is applied to a large-scale AMI dataset and its effectiveness is evaluated by benchmarking with several existing clustering methods in terms of five common adequacy indices, outliers detection, and computation time.