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Recovery and enhancement of unknown aperiodic binary signal by adaptive aperiodic stochastic resonance

PRAMANA-JOURNAL OF PHYSICS 2021年第1期95卷 1-8页

作者： Wu, Chengyang Wu, Chengjin China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China

In this study, the system with fractional power nonlinearity is introduced into the theory of aperiodic stochastic resonance (ASR). The fractional exponent is a key parameter and its effect on the ASR phenomenon excited by aperiodic binary signal is investigated in this system. Compared to the classical bistable system, the system with fractional power nonlinearity shows better performance. It can adjust not only the noise intensity but also the fractional exponent to enhance weak signal. In the field of signal transmission, pure aperiodic binary signal is usually submerged in the noise and the signal is unknown. Thus, an effective method is proposed based on ASR and moving average. By the method, the unknown aperiodic binary signal can be recovered in the noise background. To improve the efficiency of the signal recovery, the adaptive ASR is realised with the help of adaptive particle swarm optimisation (APSO) algorithm to optimise the parameters. The method may provide some reference to the engineering field.

关键词： Aperiodic stochastic resonance system with fractional power nonlinearity aperiodic binary signal optimisation algorithm 05 45 -a 02 50 -r

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Tiki-taka algorithm: a novel metaheuristic inspired by football playing style

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ENGINEERING COMPUTATIONS 2021年第1期38卷 313-343页

作者： Ab Rashid, Mohd Fadzil Faisae Univ Malaysia Coll Engn Dept Ind Engn Pahang Kuantan Malaysia

Purpose Metaheuristic algorithms have been commonly used as an optimisation tool in various fields. However, optimisation of real-world problems has become increasingly challenging with to increase in system complexity. This situation has become a pull factor to introduce an efficient metaheuristic. This study aims to propose a novel sport-inspired algorithm based on a football playing style called tiki-taka. Design/methodology/approach The tiki-taka football style is characterised by short passing, player positioning and maintaining possession. This style aims to dominate the ball possession and defeat opponents using its tactical superiority. The proposed tiki-taka algorithm (TTA) simulates the short passing and player positioning behaviour for optimisation. The algorithm was tested using 19 benchmark functions and five engineering design problems. The performance of the proposed algorithm was compared with 11 other metaheuristics from sport-based, highly cited and recent algorithms. Findings The results showed that the TTA is extremely competitive, ranking first and second on 84% of benchmark problems. The proposed algorithm performs best in two engineering design problems and ranks second in the three remaining problems. Originality/value The originality of the proposed algorithm is the short passing strategy that exploits a nearby player to move to a better position.

关键词： Metaheuristic Tiki-taka optimisation algorithm Football-inspired

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Multi-objective constraint and hybrid optimisation-based VM migration in a community cloud

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IET COMPUTERS AND DIGITAL TECHNIQUES 2020年第1期14卷 37-45页

作者： Parthiban, Pradeepa Raman, Pushpalakshmi PSNA Coll Engn & Technol Dindigul Tamil Nadu India

The growing demand for the cloud community market towards attracting and sustaining the incoming and the available cloud users is addressed actively to meet the competitive environment. There is a good scope for improving the provider capabilities in the cloud in order to satisfy the users with attractive benefits. The study introduces an effective virtual machine (VM) migration strategy using an optimisation algorithm in such a way to facilitate the user selection of the providers based on their budgetary requirements in running their own platforms. The constraints associated with the selection of the provider include cost, revenue, and resource, which are altogether confined as an elective factor. The optimisation algorithm employed for the VM migration is referred to as Taylor series-based salp swarm algorithm (Taylor-SSA) that is the integration of the Taylor series with SSA. The evaluation of the method is progressed using three setups by varying the number of providers and users. The cost, the revenue, and the resource of the proposed method are analysed and concluded that the proposed method acquired a minimal cost, maximal resource gain and revenue.

关键词： particle swarm optimisation virtual machines cloud computing optimisation multiobjective constraint hybrid optimisation-based VM migration community cloud cloud community market available cloud users good scope provider capabilities attractive benefits effective virtual machine migration strategy optimisation algorithm user selection budgetary requirements Taylor series-based salp swarm algorithm Taylor-SSA

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General generative model-based image compression method using an optimisation encoder

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IET IMAGE PROCESSING 2020年第9期14卷 1750-1758页

作者： Wu, Mengtian He, Zaixing Zhao, Xinyue Zhang, Shuyou Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China

Image compression is an intensively studied subject in computer vision. The deep generative model, especially generative adversarial networks (GANs), is a popular new direction for this subject. In this study, the authors propose a new compression method based on a generative model and focus on its application by GANs. The decoder in the proposed method is modified from the GAN generator model, which can produce visually real-like synthetic images. It is one of the two models in GANs, which is trained through a two-players' contest game. The encoder is an optimisation algorithm called backpropagation-to-the-input, which derives from an image inpainting algorithm based on generative models. In the proposed method, the authors turn the encoding process into an optimisation task to search for optimal encoded representations. Compared with traditional methods, the proposed method can compress images from certain domains into extremely small and shape-fixed encoded space but still retain better visual representations. It is easy and convenient to apply without any retraining or additional modification to the generative models.

关键词： backpropagation image restoration encoding optimisation image coding data compression computer vision general generative model-based image compression method optimisation encoder intensively studied subject deep generative model generative adversarial networks GANs GAN generator model synthetic images optimisation algorithm image inpainting algorithm generative models encoding process optimisation task optimal encoded representations extremely small shape-fixed encoded space

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Optimised joint resource allocation for NOMA MIMO-based wireless powered sensor networks

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2021年第1期35卷 1-9页

作者： Wang, Qiang Liu, Hai-Lin Yulin Normal Univ Sch Phys & Telecommun Engn Yulin Guangxi Peoples R China Guangdong Univ Technol Sch Appl Math Guangzhou Guangdong Peoples R China

Wireless powered sensor network has captured a lot of attentions due to its higher lifetime. In this paper, we investigate the resource allocation in the non-orthogonal multiple access (NOMA) multiple-input multiple-output (MIMO)-based wireless powered sensor networks in terms of the wireless energy transfer time allocated and power allocation, and the joint resource allocation is formulated as a nonconvex optimisation problem. To reduce the complexity, the original optimisation problem is decomposed into the time allocation subproblem and power allocation subproblem. Specifically, the particle swarm optimisation (PSO) is adopted to solve the time allocation subproblem. TheDC(difference of two convex functions) programming method is adopted to solve the power allocation subproblem, and the objective function is approximated as a convex function by the first order Taylor expansion. Simulation results illustrate that the proposed joint resource allocation scheme can significantly improve the system throughput.

关键词： WSN wireless sensor networks resource allocation NOMA non-orthogonal multiple access optimisation algorithm

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Comparative Study of Hybrid Artificial Intelligence Approaches for Predicting Peak Shear Strength Along Soil-Geocomposite Drainage Layer Interfaces

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INTERNATIONAL JOURNAL OF GEOSYNTHETICS AND GROUND ENGINEERING 2021年第3期7卷 1-19页

作者： Chao, Zhiming Fowmes, Gary Dassanayake, S. M. Univ Warwick Sch Engn Coventry CV4 7AL W Midlands England Monash Univ Malaysia Sch Engn Jalan Lagoon Selatan Bandar Sunway Selangor Darul Malaysia

Peak shear strength of soil-Geocomposite Drain Layer (GDL) interfaces is an important parameter in the designing and operating related engineering structures. In this paper, a database compiled from 316 large direct shear tests on soil-GDL interfaces has been established. Based on this database, five different machine learning models: Back Propagation Artificial Neural Network (BPANN) and Support Vector Machine (SVM), with hyperparameters optimised by Particle Swarm optimisation algorithm (PSO) and Genetic algorithm (GA), respectively, and Extreme Learning Machine (ELM) optimised by Exhaustive Method, were adopt to assess the peak shear strength of soil-GDL interfaces. Then, a comprehensive investigation and comparison of the predictive performance for the models was conducted. Also, based on the selected optimal machine learning model, sensitivity analysis was conducted, and an empirical equation developed based on it. The research indicated that GA and PSO could significantly increase forecasting precision in a small number of iterations. The BPANN model optimised by PSO has the highest forecasting precision based on the statistics criteria: Root-Mean-Square Error, Correlation Coefficient, Coefficient of Determination, Wilmot's Index of Agreement, and Mean Absolute Percentage Error. The normal stress has the biggest impact on the peak shear strength, followed by drainage core type, moisture saturation of the soil layer, shearing surface, soil type, consolidation condition, geotextile specification, soil density and drainage core thickness, and the ranking is affected partly by the data distribution of input parameters in the database based on mechanism analysis. An empirical equation developed from the optimal model was proposed to estimate the peak shear strength, which provides convenience for geotechnical engineering personnel with limited knowledge of machine learning technique.

关键词： Geosynthetics Geocomposite drainage layer Soil-Interface shear strength Machine learning algorithm optimisation algorithm

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Using artificial intelligence methods to predict the compressive strength of concrete containing sugarcane bagasse ash

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CONSTRUCTION AND BUILDING MATERIALS 2023年 409卷

作者： Pazouki, Gholamreza Tao, Zhong Saeed, Nariman Kang, Won-Hee Western Sydney Univ Ctr Infrastruct Engn Sch Engn Design & Built Environm Penrith NSW Australia Charles Sturt Univ Fac Business Justice & Behav Sci Sch Comp Math & Engn Bathurst Australia

Sugarcane bagasse ash is an agricultural and industrial waste material produced in millions of tonnes annually. While traditionally used as a fertilizer or buried underground, incorporating this material into concrete production not only reduces cement usage and addresses environmental concerns but also enhances the mechanical properties of concrete. This research aims to investigate the predictive capabilities of various Artificial Intelligence (AI) models, including radial basis function neural network (RBFNN), artificial neural network (ANN), and support vector regression (SVR), for estimating the compressive strength of concrete containing sugarcane bagasse ash (SCBA). A dataset comprising 1819 data points from previous studies was collected, consisting of three main groups: 340 data of concrete with SCBA, 459 data of concrete without SCMs, and 1020 concrete data incorporating other supplementary cementitious materials (SCMs). The dataset was utilised in three different ways to evaluate the influence of data on model performance. Firstly, models were trained solely on SCBA data (Group A);subsequently, SCBA data was combined with concrete data without any SCMs (Group B);finally, the entire dataset was utilised (Group C). Statistical analysis revealed that the SVR model trained on SCBA data and the RBFNN model trained on group B data demonstrated the best results and accuracy. However, further refinement of the concrete without SCMs data in group B led to improved model performance, surpassing even that of the SVR model trained on group A data.

关键词： Sugarcane bagasse ash Compressive strength Radial basis function neural network Support vector regression optimisation algorithm

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Land Surface Model Calibration Using Satellite Remote Sensing Data

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SENSORS 2023年第4期23卷 1848-1848页

作者： Khaki, Mehdi Univ Newcastle Sch Engn Callaghan NSW 2308 Australia

Satellite remote sensing provides a unique opportunity for calibrating land surface models due to their direct measurements of various hydrological variables as well as extensive spatial and temporal coverage. This study aims to apply terrestrial water storage (TWS) estimated from the gravity recovery and climate experiment (GRACE) mission as well as soil moisture products from advanced microwave scanning radiometer-earth observing system (AMSR-E) to calibrate a land surface model using multi-objective evolutionary algorithms. For this purpose, the non-dominated sorting genetic algorithm (NSGA) is used to improve the model's parameters. The calibration is carried out for the period of two years 2003 and 2010 (calibration period) in Australia, and the impact is further monitored over 2011 (forecasting period). A new combined objective function based on the observations' uncertainty is developed to efficiently improve the model parameters for a consistent and reliable forecasting skill. According to the evaluation of the results against independent measurements, it is found that the calibrated model parameters lead to better model simulations both in the calibration and forecasting period.

关键词： model calibration satellite remote sensing optimisation algorithm terrestrial water storage soil moisture

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An efficient and robust algorithm for estimating parameters in a horizontal subsurface flow of a constructed wetland treating greywater

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INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY 2021年第4期11卷 422-438页

作者： Mouaouia, Ihsane Charkaoui, Abderrahim Alaa, Nour Eddine Cadi Ayyad Univ Fac Sci & Technol Lab LAMAI Marrakech Morocco

We establish a new method for estimating parameters in reaction-adsorption-diffusion systems modelling the greywater treatment through a horizontal subsurface flow of a constructed wetland. Two cost functions are presented to estimate the permeability coefficients, the initial concentration and the degradation of the phosphor in the considered model. A Lagrangian method is considered to compute the derivative of the cost functions with respect to the state variable. An iterative algorithm is illustrated with the reaction-adsorption-diffusion systems using optimisation techniques and the adjoint state model. We were able to estimate our parameters and our initial data. In addition, the performance of our algorithm is evaluated by perturbing the data with various levels of additive noise.

关键词： constructed wetland mathematical modelling advection-dispersion-reaction equation inverse problem Lagrangian method optimisation algorithm

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Resilience-based post-earthquake recovery strategies for substation systems

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2023年 96卷

作者： Liu, Xiao Xie, Qiang Tongji Univ Coll Civil Engn Shanghai 200092 Peoples R China

Electrical substation systems require significant manpower and material resources for repair after an earthquake;thus, repair efficiency directly affects economic loss. To quantify the functional recovery process of substation systems, this study proposes a seismic resilience assessment frame- work based on the functional characteristics of substation systems. Reliability models of the equipment and systems were constructed to characterise the structural connection features and power transmission paths of substation systems, and the system functional state matrix was ob- tained through a seismic simulation. Moreover, factors influencing functional recovery and repair path control parameters were proposed to determine efficient post -earthquake recovery strate- gies. The Monte Carlo method was used to construct a post -earthquake recovery strategy analysis framework for a substation system. By analysing a typical 220 kV substation using static -based repair strategies, we obtained the functional recovery process and seismic resilience level. Fur- thermore, based on the actual functional requirements, a dynamic recovery strategy analysis method was proposed based on an improved genetic algorithm. Notably, optimal recovery strate- gies for different earthquake intensities and recovery priorities were obtained through iterative reproduction analysis.

关键词： Substation system Functional state model Seismic resilience framework Post-earthquake recovery strategy optimisation algorithm

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