algorithm

摘要： Task offloading is an important concept for edge computing and the Internet of Things(IoT)because computationintensive tasksmust beoffloaded tomore resource-powerful remote devices.Taskoffloading has several advantages,including increased battery life,lower latency,and better application performance.A task offloading method determines whether sections of the full application should be run locally or offloaded for execution remotely.The offloading choice problem is influenced by several factors,including application properties,network conditions,hardware features,and mobility,influencing the offloading system’s operational environment.This study provides a thorough examination of current task offloading and resource allocation in edge computing,covering offloading strategies,algorithms,and factors that influence offloading.Full offloading and partial offloading strategies are the two types of offloading strategies.The algorithms for task offloading and resource allocation are then categorized into two parts:machine learning algorithms and non-machine learning algorithms.We examine and elaborate on algorithms like Supervised Learning,Unsupervised Learning,and Reinforcement Learning(RL)under machine learning.Under the non-machine learning algorithm,we elaborate on algorithms like non(convex)optimization,Lyapunov optimization,Game theory,Heuristic Algorithm,Dynamic Voltage Scaling,Gibbs Sampling,and Generalized Benders Decomposition(GBD).Finally,we highlight and discuss some research challenges and issues in edge computing.

摘要： This article shows genomic alignment methods using the classic“Needleman”and“Smith-Waterman”algorithms,the latter they were optimized by the ABC(artificial bee colony)algorithm.In the genomic alignment,a goal state is not presented,the experiments that are carried out show alternative alignments by ABC were proposed.Different types of alignments could exist within the classical algorithm,based on a horizontal,vertical,diagonal and inverse search mechanism on a match value table.Our ABC-Smith Waterman algorithm was generated from the genomic sequences written in rows and columns for the search for similarities that will provide values that ABC uses to process and provide more results of alignments that can be used by scientists for their experiments and research.

摘要： Process optimization in equation-oriented(EO)modeling environments favors the gradient-based optimization algorithms by their abilities to provide accurate Jacobian matrices via automatic or symbolic differentiation.However,computational inefficiencies including that in initial-point-finding for Newton type methods have significantly limited its application.Recently,progress has been made in using a pseudo-transient(PT)modeling method to address these difficulties,providing a fresh way forward in EO-based optimization.Nevertheless,research in this area remains open,and challenges need to be addressed.Therefore,understanding the state-of-the-art research on the PT method,its principle,and the strategies in composing effective methodologies using the PT modeling method is necessary for further developing EO-based methods for process optimization.For this purpose,the basic concepts for the PT modeling and the optimization framework based on the PT model are reviewed in this paper.Several typical applications,e.g.,complex distillation processes,cryogenic processes,and optimizations under uncertainty,are presented as well.Finally,we identify several main challenges and give prospects for the development of the PT based optimization methods.

摘要： Partial discharge(PD)signals are an important index to evaluate the operation state of intelligent substations.The correct distinction of PD pulse and interference pulse has become a challenging task.Because of the noise and the low signal-to-noise ratio,the stable signals become non-stationary.The selection of a wavelet basis,the selection rule of thresholdλand the design of the threshold function are the key factors affecting the final denoising effect.Therefore,an enhanced ant colony optimisition wavelet(ACOW)algorithm was applied to find the global optimal threshold through the continuous derivative threshold function and the ant colony optimisation(ACO)algo-rithm.At the same time the efficiency of adaptive search calculation,was also significantly improved.The method of the ACOW algorithm was compared with the soft wavelet method,gradient-based wavelet method and the genetic optimisation wavelet(GOW)method.Using these four methods to denoise four typical signals,different mean square errors(MSE),magnitude errors(ME)and time costs were obtained.Interestingly,the results show that the ACOW method can achieve the minimum MSE and has less time cost.It generates significantly smaller waveform distortion than the other three threshold estimation methods.In addition,the high efficiency and good quality of the output signals are beneficial to the diagnosis of local discharge signals in intelligent substations.

摘要： Soil swelling-related disaster is considered as one of the most devastating geo-hazards in modern history.Hence,proper determination of a soil’s ability to expand is very vital for achieving a secure and safe ground for infrastructures.Accordingly,this study has provided a novel and intelligent approach that enables an improved estimation of swelling by using kernelised machines(Bayesian linear regression(BLR)&bayes point machine(BPM)support vector machine(SVM)and deep-support vector machine(D-SVM));(multiple linear regressor(REG),logistic regressor(LR)and artificial neural network(ANN)),tree-based algorithms such as decision forest(RDF)&boosted trees(BDT).Also,and for the first time,meta-heuristic classifiers incorporating the techniques of voting(VE)and stacking(SE)were utilised.Different independent scenarios of explanatory features’combination that influence soil behaviour in swelling were investigated.Preliminary results indicated BLR as possessing the highest amount of deviation from the predictor variable(the actual swell-strain).REG and BLR performed slightly better than ANN while the meta-heuristic learners(VE and SE)produced the best overall performance(greatest R2 value of 0.94 and RMSE of 0.06%exhibited by VE).CEC,plasticity index and moisture content were the features considered to have the highest level of importance.Kernelized binary classifiers(SVM,D-SVM and BPM)gave better accuracy(average accuracy and recall rate of 0.93 and 0.60)compared to ANN,LR and RDF.Sensitivity-driven diagnostic test indicated that the meta-heuristic models’best performance occurred when ML training was conducted using k-fold validation technique.Finally,it is recommended that the concepts developed herein be deployed during the preliminary phases of a geotechnical or geological site characterisation by using the best performing meta-heuristic models via their background coding resource.

摘要： In this work, we propose an approach for the separation of coumarins from thin-layer morphological segmentation based on the acquisition of multicomponent images integrating different types of coumarins. The first step is to make a segmentation by region, by thresholding, by contour, etc. of each component of the digital image. Then, we proceeded to the calculations of parameters of the regions such as the color standard deviation, the color entropy, the average color of the pixels, the eccentricity from an algorithm on the matlab software. The mean color values atR = 91.20 in red, atB = 213.21 in blue showed the presence of samidin in the extract. The color entropy values HG = 5.25 in green and HB = 4.04 in blue also show the presence of visnadine in the leaves of Desmodium adscendens. These values are used to consolidate the database of separation and discrimination of the types of coumarins. The relevance of our coumarin separation or coumarin recognition method has been highlighted compared to other methods, such as the one based on the calculation of frontal ratios which cannot discriminate between two coumarins having the same frontal ratio. The robustness of our method is proven with respect to the separation and identification of some coumarins, in particular samidin and anglicine.

摘要： Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization problems.For optimization problems,metaheuristic algorithm is one of the methods to find its optimal solution or approximate solution under limited conditions.Most of the existing metaheuristic algorithms are designed for serial systems.Meanwhile,existing algorithms still have a lot of room for improvement in convergence speed,robustness,and performance.To address these issues,this paper proposes an easily parallelizable metaheuristic optimization algorithm called team competition and cooperation optimization(TCCO)inspired by the process of human team cooperation and competition.The proposed algorithm attempts to mathematically model human team cooperation and competition to promote the optimization process and find an approximate solution as close as possible to the optimal solution under limited conditions.In order to evaluate the performance of the proposed algorithm,this paper compares the solution accuracy and convergence speed of the TCCO algorithm with the Grasshopper Optimization Algorithm(GOA),Seagull Optimization Algorithm(SOA),Whale Optimization Algorithm(WOA)and Sparrow Search Algorithm(SSA).Experiment results of 30 test functions commonly used in the optimization field indicate that,compared with these current advanced metaheuristic algorithms,TCCO has strong competitiveness in both solution accuracy and convergence speed.

摘要： Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational cost.As with other types of algorithms,in metaheuristic algorithms,one of the methods used to improve performance and achieve results closer to the target result is the hybridization of algorithms.In this study,a hybrid algorithm(HSSJAYA)consisting of salp swarm algorithm(SSA)and jaya algorithm(JAYA)is designed.The speed of achieving the global optimum of SSA,its simplicity,easy hybridization and JAYA’s success in achieving the best solution have given us the idea of creating a powerful hybrid algorithm from these two algorithms.The hybrid algorithm is based on SSA’s leader and follower salp system and JAYA’s best and worst solution part.HSSJAYA works according to the best and worst food source positions.In this way,it is thought that the leader-follower salps will find the best solution to reach the food source.The hybrid algorithm has been tested in 14 unimodal and 21 multimodal benchmark functions.The results were compared with SSA,JAYA,cuckoo search algorithm(CS),firefly algorithm(FFA)and genetic algorithm(GA).As a result,a hybrid algorithm that provided results closer to the desired fitness value in benchmark functions was obtained.In addition,these results were statistically compared using wilcoxon rank sum test with other algorithms.According to the statistical results obtained from the results of the benchmark functions,it was determined that HSSJAYA creates a statistically significant difference in most of the problems compared to other algorithms.

摘要： There are many optimization problems in different branches of science that should be solved using an appropriate methodology.Populationbased optimization algorithms are one of the most efficient approaches to solve this type of problems.In this paper,a new optimization algorithm called All Members-Based Optimizer(AMBO)is introduced to solve various optimization problems.The main idea in designing the proposedAMBOalgorithm is to use more information from the population members of the algorithm instead of just a few specific members(such as best member and worst member)to update the population matrix.Therefore,in AMBO,any member of the population can play a role in updating the population matrix.The theory of AMBO is described and then mathematically modeled for implementation on optimization problems.The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions,which belong to three different categories:unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions.In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO,eight other well-known algorithms have been also implemented.The optimization results demonstrate the ability of AMBO to solve various optimization problems.Also,comparison and analysis of the results show that AMBO is superior andmore competitive than the other mentioned algorithms in providing suitable solution.

摘要： Purpose: Historically the classification of Thoracic Outlet Syndrome (TOS) has been based on symptoms rather than the underlying pathology. Therefore, TOS has been classified into Neurogenic (NTOS), Venous (VTOS or Paget Schroetter Syndrome) and Arterial (ATOS) subgroups. This classification has resulted in confusion among medical practitioners, difficulty in making the diagnosis, and the poor results with surgical intervention. Methods: The published papers from PubMed on the newer understanding of the pathogenesis and the surgical treatment of TOS were reviewed. Results: More recently TOS has been classified based on the underlying pathologic entity. Based on this classification, patients who are suspected of having TOS should be classified as having 1. Cervical Rib Disease (CRD), or 2. TOS as the result of “Subclavian Vein Compression Syndrome”. This classification has resulted in more accurate diagnosis, better patient selection for surgery, and excellent surgical results. This paper outlines the algorithm for making the appropriate diagnosis in patients who present with neurovascular symptoms of the upper extremity and the selection of the appropriate patients for surgery. Conclusion: Based on the algorithm for surgical decision making, patients with Cervical Rib Disease should undergo cervical exploration and resection of the pathologic entity which results in compression of the brachial plexus or the subclavian artery in the neck. Patients with Thoracic outlet Syndrome who are found to have extrinsic compression of the subclavian vein by a pathologic tubercle at the sternocostal joint on Multiphasic MRA should undergo robotic first rib resection.