Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods

Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and shape(k),is crucial in describing the actual wind speed data and evaluating the wind energy potential.Therefore,this study compares the most common conventional numerical(CN)estimation methods and the recent intelligent optimization algorithms(IOA)to show how precise estimation of c and k affects the wind energy resource assessments.In addition,this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia,namely Aljouf,Rafha,Tabuk,Turaif,and Yanbo.Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data.Also,with six wind turbine technologies rating between 1 and 3MW,the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy($/kWh)compared to the assessments by IOAs.The energy cost analyses show that Turaif is the windiest site,with an electricity cost of$0.016906/kWh.The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding$0.02739/kWh.Finally,the outcomes of this study exhibit the potential of wind energy in Saudi Arabia,and its environmental goals can be acquired by harvesting wind energy.

Research on the Application of Intelligent Optimization Algorithm in Mechanical Design

Intelligent optimization algorithm belongs to a kind of emerging technology,show good characteristics,such as high performance,applicability,its algorithm includes many contents,including genetic,particle swarm and artificial neural network algorithm,compared with the traditional optimization way,these algorithms can be applied to a variety of situations,meet the demand of solution,in the mechanical design industry has wide application prospects.This paper analyzes the application of the algorithm in mechanical design and the comparison of the results to verify the significance of the intelligent optimization algorithm in mechanical design.

Intelligent algorithms: new avenues for designing nanophotonic devices [Invited]

The research on nanophotonic devices has made great progress during the past decades. It is the unremitting pursuit of researchers that realize various device functions to meet practical applications. However, most of the traditional methods rely on human experience and physical inspiration for structural design and parameter optimization, which usually require a lot of resources, and the performance of the designed device is limited. Intelligent algorithms, which are composed of rich optimized algorithms, show a vigorous development trend in the field of nanophotonic devices in recent years. The design of nanophotonic devices by intelligent algorithms can break the restrictions of traditional methods and predict novel configurations, which is universal and efficient for different materials, different structures, different modes, different wavelengths, etc. In this review, intelligent algorithms for designing nanophotonic devices are introduced from their concepts to their applications, including deep learning methods, the gradient-based inverse design method, swarm intelligence algorithms, individual inspired algorithms, and some other algorithms. The design principle based on intelligent algorithms and the design of typical new nanophotonic devices are reviewed. Intelligent algorithms can play an important role in designing complex functions and improving the performances of nanophotonic devices, which provide new avenues for the realization of photonic chips.

Smart epidermal electrophysiological electrodes:Materials,structures,and algorithms

Epidermal electrophysiological monitoring has garnered significant attention for its potential in medical diagnosis and healthcare,particularly in continuous signal recording.However,simultaneously satisfying skin compliance,mechanical properties,environmental adaptation,and biocompatibility to avoid signal attenuation and motion artifacts is challenging,and accurate physiological feature extraction necessitates effective signal-processing algorithms.This review presents the latest advancements in smart electrodes for epidermal electrophysiological monitoring,focusing on materials,structures,and algorithms.First,smart materials incorporating self-adhesion,self-healing,and self-sensing functions offer promising solutions for long-term monitoring.Second,smart meso-structures,together with micro/nanostructures endowed the electrodes with self-adaption and multifunctionality.Third,intelligent algorithms give smart electrodes a“soul,”facilitating faster and more-accurate identification of required information via automatic processing of collected electrical signals.Finally,the existing challenges and future opportunities for developing smart electrodes are discussed.Recognized as a crucial direction for next-generation epidermal electrodes,intelligence holds the potential for extensive,effective,and transformative applications in the future.

Performance evaluation for intelligent optimization algorithms in self-potential data inversion

The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and efficiently: simulated annealing, genetic, particle swarm optimization, and ant colony optimization. Using both noise-free and noise-added synthetic data, it is demonstrated that all four intelligent algorithms can perform self-potential data inversion effectively. During the numerical experiments, the model distribution in search space, the relative errors of model parameters, and the elapsed time are recorded to evaluate the performance of the inversion. The results indicate that all the intelligent algorithms have good precision and tolerance to noise. Particle swarm optimization has the fastest convergence during iteration because of its good balanced searching capability between global and local minimisation.

Research on tasks schedule and data transmission of video sensor networks based on intelligent agents and intelligent algorithms

As a novel application technology,wireless video sensor networks become the current research focus,especially on target tracking and surveillance scenario.Based on multiple agents＇ technique,this article introduces a series of intelligent algorithms such as simulated annealing algorithm（SA）,genetic algorithm（GA）,and ant colony optimization algorithm（ACO） or their mixed algorithms,to resolve the optimization of tasks schedule and data transmission.This article analyzes the performance of abovementioned algorithms and verifies their feasibility associated with agents.The simulations demonstrates that the mixed algorithms based on SA and GA obtain the optimal solution to tasks schedule,and those combined with SA-ACO show advantages on multimedia sensor networks routing optimization.

Modelling and reviewing the reliability and multi-objective optimization of wind-turbine system and photovoltaic panel with intelligent algorithms

One of the options for non-dependence on fossil fuels is the use of renewable energy,which has not grown significantly due to the variable nature of this type of energy.The combined use of wind and solar energy as energy sources can be a good solution to the problem of variable energy output.Therefore,the purpose of this research is to model a combination of the wind-turbine system and photovoltaic cell,which is needed to investigate their ability to supply electrical energy.To determine this important power production,real data of solar-radiation intensity and wind are used and,in modelling photovoltaic cells,the effects of ambient temperature are also considered.In order to generalize the studied system in all dimensions,different scenarios have been considered.According to the amount of electrical power generated,during the evaluation of these scenarios,two economic parameters,namely the selected scenario of a wind/solar system with diesel-generator support,was determined.

Thermal Properties Reconstruction and Temperature Fields in Asphalt Pavements: Inverse Problem and Optimisation Algorithms

A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt pavement.The calculation of each layer only needs four iterations to achieve convergence.Furthermore,in order to improve the calculation accuracy a swarm intelligence optimization algorithm is also exploited to inversely analyze the laws by which the thermal physical parameters of the asphalt pavement materials change with temperature.Using the basic cuckoo and the gray wolf algorithms,an adaptive hybrid optimization algorithm is obtained and used to determine the relationship between the thermal diffusivity of two types of asphalt pavement materials and the temperature.As shown by the results,the prediction accuracy achievable with this approach is higher than that of the linear model.

Product quality prediction based on RBF optimized by firefly algorithm

With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

An intelligent task offloading algorithm(iTOA)for UAV edge computing network

Unmanned Aerial Vehicle(UAV)has emerged as a promising technology for the support of human activities,such as target tracking,disaster rescue,and surveillance.However,these tasks require a large computation load of image or video processing,which imposes enormous pressure on the UAV computation platform.To solve this issue,in this work,we propose an intelligent Task Offloading Algorithm(iTOA)for UAV edge computing network.Compared with existing methods,iTOA is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search(MCTS),the core algorithm of Alpha Go.MCTS will simulate the offloading decision trajectories to acquire the best decision by maximizing the reward,such as lowest latency or power consumption.To accelerate the search convergence of MCTS,we also proposed a splitting Deep Neural Network(sDNN)to supply the prior probability for MCTS.The sDNN is trained by a self-supervised learning manager.Here,the training data set is obtained from iTOA itself as its own teacher.Compared with game theory and greedy search-based methods,the proposed iTOA improves service latency performance by 33%and 60%,respectively.

Comparison of differential evolution, particle swarm optimization,quantum-behaved particle swarm optimization, and quantum evolutionary algorithm for preparation of quantum states

Four intelligent optimization algorithms are compared by searching for control pulses to achieve the preparation of target quantum states for closed and open quantum systems, which include differential evolution(DE), particle swarm optimization(PSO), quantum-behaved particle swarm optimization(QPSO), and quantum evolutionary algorithm(QEA).We compare their control performance and point out their differences. By sampling and learning for uncertain quantum systems, the robustness of control pulses found by these four algorithms is also demonstrated and compared. The resulting research shows that the QPSO nearly outperforms the other three algorithms for all the performance criteria considered.This conclusion provides an important reference for solving complex quantum control problems by optimization algorithms and makes the QPSO be a powerful optimization tool.

VGWO: Variant Grey Wolf Optimizer with High Accuracy and Low Time Complexity

The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf hunting.Along with its advantages of simple principle and few parameters setting,GWO bears drawbacks such as low solution accuracy and slow convergence speed.A few recent advanced GWOs are proposed to try to overcome these disadvantages.However,they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence.To solve the abovementioned issues,a high-accuracy variable grey wolf optimizer(VGWO)with low time complexity is proposed in this study.VGWO first uses the symmetrical wolf strategy to generate an initial population of individuals to lay the foundation for the global seek of the algorithm,and then inspired by the simulated annealing algorithm and the differential evolution algorithm,a mutation operation for generating a new mutant individual is performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing global search and fast convergence capability of VGWO.A series of experiments are conducted on 19 benchmark functions from CEC2014 and CEC2020 and three real-world engineering cases.For 19 benchmark functions,VGWO’s optimization results place first in 80%of comparisons to the state-of-art GWOs and the CEC2020 competition winner.A further evaluation based on the Friedman test,VGWO also outperforms all other algorithms statistically in terms of robustness with a better average ranking value.

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

An Adaptive Fruit Fly Optimization Algorithm for Optimization Problems

In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance.

Review on Application of Artificial Intelligence in Civil Engineering

In last few years,big data and deep learning technologies have been successfully applied in various fields of civil engineering with the great progress of machine learning techniques.However,until now,there has been no comprehensive review on its applications in civil engineering.To fill this gap,this paper reviews the application and development of artificial intelligence in civil engineering in recent years,including intelligent algorithms,big data and deep learning.Through the work of this paper,the research direction and difficulties of artificial intelligence in civil engineering for the past few years can be known.It is shown that the studies of artificial intelligence in civil engineering mainly focus on structural maintenance and management,and the design optimization.

Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation

Background: Acute kidney injury(AKI) is a common complication after liver transplantation(LT) and is an indicator of poor prognosis. The establishment of a more accurate preoperative prediction model of AKI could help to improve the prognosis of LT. Machine learning algorithms provide a potentially effective approach. Methods: A total of 493 patients with donation after cardiac death LT(DCDLT) were enrolled. AKI was defined according to the clinical practice guidelines of kidney disease: improving global outcomes(KDIGO). The clinical data of patients with AKI(AKI group) and without AKI(non-AKI group) were compared. With logistic regression analysis as a conventional model, four predictive machine learning models were developed using the following algorithms: random forest, support vector machine, classical decision tree, and conditional inference tree. The predictive power of these models was then evaluated using the area under the receiver operating characteristic curve(AUC). Results: The incidence of AKI was 35.7%(176/493) during the follow-up period. Compared with the nonAKI group, the AKI group showed a remarkably lower survival rate( P<0.001). The random forest model demonstrated the highest prediction accuracy of 0.79 with AUC of 0.850 [95% confidence interval(CI): 0.794–0.905], which was significantly higher than the AUCs of the other machine learning algorithms and logistic regression models( P<0.001). Conclusions: The random forest model based on machine learning algorithms for predicting AKI occurring after DCDLT demonstrated stronger predictive power than other models in our study. This suggests that machine learning methods may provide feasible tools for forecasting AKI after DCDLT.

Blackboard Mechanism Based Ant Colony Theory for Dynamic Deployment of Mobile Sensor Networks

A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard mechanism is introduced into the system for making pheromone and completing the algorithm. Every node, which can be looked as an ant, makes one information zone in its memory for communicating with other nodes and leaves pheromone, which is created by ant itself in naalre. Then ant colony theory is used to find the optimization scheme for path planning and deployment of mobile Wireless Sensor Network （WSN）. We test the algorithm in a dynamic and unconfigurable environment. The results indicate that the algorithm can reduce the power consumption by 13% averagely, enhance the efficiency of path planning and deployment of mobile WSN by 15% averagely.

Accurate Torque Modeling With PSO-Based Recursive Robust LSSVR for a Segmented-Rotor Switched Reluctance Motor

In order to improve the reliability in torque calculation of SRM,an accurate nonlinear torque model regresses by recursive robust least squares support vector regression(RR-LSSVR)is proposed in this paper.The model is in terms of a segmented-rotor switched reluctance motor(SSRM).The characteristics of the SSRM is introduced to show its nonlinear characteristics both in magnetic and torque.Then,its mathematic model is established,and an accurate inductance measurement method and a torque calculation method are presented.After this,the principle of the RR-LSSVR and why it can adjust weights according to errors are described.The model used the RR-LSSVR algorithm shows an outstanding capability in accuracy and quickness compared with other algorithms.Finally,to further validate the accuracy of the proposed model in practical application,simulation and experiment are designed based on a 16/10 SSRM.

A Reliability Evaluation Method for Intermittent Jointed Rock Slope Based on Evolutionary Support Vector Machine

The randomness of rock joint development is an important factor in the uncertainty of geotechnical engineering stability.In this study,a method is proposed to evaluate the reliability of intermittent jointed rock slope.The least squares support vector machine(LSSVM)evolved by a bacterial foraging optimization algorithm(BFOA)is used to establish a response surface model to express the mapping relationship between the intermittent joint parameters and the slope safety factor.The training samples are obtained from the numerical calculation based on the joint finite element method during this process.Considering the randomness of the intermittent joint parameters in the actual project,each parameter is evaluated at different locations on the site,and its distribution characteristics are counted.According to these statistical results,a large number of parameter combinations are obtained through Monte Carlo sampling.The trained machine learning mapping model is used to obtain the slope safety factor corresponding to each group,and these results are then used to obtain the slope reliability.When the research results were applied to slope disaster treatment along the Yalu River in China’s Jilin Province,it was found that the joint length and joint inclination angle both play key roles in rock slope stability,which should receive more attention in the slope treatment.In summary,this study establishes a method for evaluating the reliability of intermittent jointed rock slope based on an evolutionary SVM model,and its feasibility is verified by engineering application.