基于白鲨算法的七自由度机械臂动力学参数辨识

本文提出了基于白鲨算法(WSO)的关节机器人动力学参数辨识方法。首先,利用SYMORO+软件建立了七自由度机械臂的动力学模型并将其转换为可辨识模型。其次,利用关节机器人的性质提取摩擦力矩进行摩擦模型辨识。然后,设计激励轨迹并让机器人跟踪该轨迹,用辨识摩擦模型计算关节力矩信号中的摩擦项并补偿。进而,采用最小二乘法(LS)估计器确定白鲨算法的搜索空间,并由白鲨算法迭代获得最优动力学参数。最后,在Franka协作臂进行了实验验证,结果表明所提方法相较于传统算法在规避局部最优解方面具有更好的表现,且得到的参数更准确。

海洋石油固定平台应急电源设计

随着海洋石油开采的不断深入,海洋石油固定平台作为重要的生产设施,对安全性和可靠性的需求日益增加。应急电源作为固定平台的重要组成部分,在应对突发事件和断电情况时提供电力支持方面具有至关重要的作用。因此,针对海洋石油固定平台应急电源的设计问题进行深入研究和分析。通过介绍海洋石油固定平台的特点,分析现有海洋石油固定平台应急电源的设计方案及其优缺点,结合实际需求提出一种基于多目标白鲨优化(Multi-Objective White Shark Optimizer,MOWSO)算法的柴油发电机组与储能系统相结合的方案。经过实验模拟和数据分析,该方案具备较高的可靠性和稳定性,能够满足海洋石油固定平台关键设备的电力需求。

OVME结合SMHD的风电机组变桨轴承损伤识别

针对风电机组变桨轴承的损伤识别问题,提出一种优化变分模态提取结合稀疏最大谐波噪声比解卷积的新颖损伤识别方法,旨在从复合信号中提取特定信号分量。首先,以能量特征指标为适应度函数,利用白鲨优化算法对变分模态提取算法的最优影响参数组合进行搜索,确定变分模态提取的平衡因子和中心频率的最优值;其次,利用变分模态提取从振动信号中提取特定信号分量,并对提取的信号分量进行稀疏最大谐波噪声比解卷积处理,提高信号的信噪比,得到解卷积信号;最后,对解卷积信号进行包络谱分析,从中提取轴承损伤特征频率。结果表明:该方法能准确识别风电机组变桨轴承的损伤特征,具有一定的实际工程参考价值。

基于白鲨优化极限学习机的三维力传感器非线性解耦

针对三维力传感器维间耦合干扰问题,以基于光纤布拉格光栅(Fiber Bragg Grating,FBG)的一体式三维力传感器为研究对象,提出了一种基于白鲨优化算法的优化极限学习机(White Shark Optimizer-Extreme Learning Machine,WSO-ELM)的非线性解耦算法。首先,设计了基于FBG的一体式三维力传感器,阐明该传感器波长漂移量与三维力的映射关系;然后,搭建静态标定实验系统,分析三维力耦合特征,并建立WSO-ELM算法三维力传感器解耦模型,利用白鲨优化算法(White Shark Optimizer,WSO)稳定、高效特点优化模型,寻找ELM神经网络隐含层神经元数与解耦时间的最佳参数组合,开展基于WSO-ELM的三维力传感器非线性解耦研究;最后,该传感器解耦后最大平均I类误差达到0.51%,最大平均II类误差达到0.65%,实现了基于WSO-ELM的三维力非线性解耦。为验证解耦效果,将WSO-ELM算法与极限学习机神经网络模型、反向传播神经网络、最小二乘法解耦效果进行对比实验。实验结果表明:WSO-ELM算法具有较好的解耦效果,能有效构建三维力维间耦合关系,同时降低传感器耦合干扰,提高传感器的测量精度,具有良好的非线性解耦能力。

一种基于灰狼策略的大白鲨混合优化算法

大白鲨优化算法可以有效解决一些连续搜索空间的优化问题,但存在易于陷入局部最优解和收敛速度慢等问题。提出的混合灰狼策略的大白鲨优化算法,通过结合灰狼优化算法中头狼的围剿策略,改进大白鲨优化算法中白鲨的围剿方案,可以获得更佳的局部开发能力。在大白鲨优化算法中的声音定位部分,通过引入变异的方式增加随机性,以解决局部最优问题。通过对算法进行Benchmark测试,并与粒子群、灰狼优化、白鲨优化算法进行比较,实验结果表明该算法在局部开发能力、收敛速度等方面有较好提升。

基于深度学习神经网络超参数优化的入库径流预测方法研究——以云南省暮底河水库为例

准确的入库日径流预测在水库优化调度中发挥着重要作用.为提高日径流预测精度,提出了基于小波包变换(WPT)并结合了白鲨优化(WSO)算法的门限循环控制单元(GRU)、长短期记忆神经网络(LSTM)、卷积神经网络(CNN)日径流时间序列预测模型,以云南省暮底河水库2018—2020年入库日径流时间序列预测为例对各模型进行检验.首先利用WPT将日径流时序数据分解为若干子序列分量;其次引入WSO对GRU、LSTM、CNN超参数进行调优,建立WPT-WSO-GRU、WPT-WSO-LSTM、WPT-WSO-CNN模型;最后利用所建立的模型对各子序列分量进行预测及加和重构,并构建WPT-GRU、WPT-LSTM、WPT-CNN及基于BP神经网络的WPT-WSO-BP、WPT-BP作对比分析模型.结果表明:WPT-WSO-GRU、WPT-WSO-LSTM、WPT-WSO-CNN模型对实例日径流预测的平均绝对百分比误差EMAP分别为3.67%、5.52%、8.98%,平均绝对误差EMA分别为0.120、0.155、0.329 m^(3)/s,确定性系数DC分别为0.996 2、0.995 7、0.974 0 s,预报合格率RQ分别为98.1%、96.4%、89.6%,预测效果优于对应未经WSO调优的WPT-GRU、WPT-LSTM、WPT-CNN模型及WPT-WSO-BP、WPT-BP模型,其中WPT-WSO-GRU模型具有更高的预测精度和更好的泛化能力,WPT-WSO-LSTM模型次之.WSO能有效调优GRU、LSTM、CNN超参数,提高GRU、LSTM、CNN预测性能.WPT-WSO-GRU、WPT-WSO-LSTM模型在入库日径流时间序列预测研究中具有较好的应用前景.

Securing Cloud Computing from Flash Crowd Attack Using Ensemble Intrusion Detection System

Flash Crowd attacks are a form of Distributed Denial of Service(DDoS)attack that is becoming increasingly difficult to detect due to its ability to imitate normal user behavior in Cloud Computing(CC).Botnets are often used by attackers to perform a wide range of DDoS attacks.With advancements in technology,bots are now able to simulate DDoS attacks as flash crowd events,making them difficult to detect.When it comes to application layer DDoS attacks,the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate issue.This is mainly because it can imitate typical user behavior,leading to a substantial influx of requests that can overwhelm the server by consuming either its network bandwidth or resources.Therefore,identifying these types of attacks on web servers has become crucial,particularly in the CC.In this article,an efficient intrusion detection method is proposed based on White Shark Optimizer and ensemble classifier(Convolutional Neural Network(CNN)and LighGBM).Experiments were conducted using a CICIDS 2017 dataset to evaluate the performance of the proposed method in real-life situations.The proposed IDS achieved superior results,with 95.84%accuracy,96.15%precision,95.54%recall,and 95.84%F1 measure.Flash crowd attacks are challenging to detect,but the proposed IDS has proven its effectiveness in identifying such attacks in CC and holds potential for future improvement.

Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

Online reviews regarding purchasing services or products offered are the main source of users’opinions.To gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or services.This practice is called review spamming.During the last few years,various techniques have been recommended to solve the problem of spam reviews.Previous spam detection study focuses on English reviews,with a lesser interest in other languages.Spam review detection in Arabic online sources is an innovative topic despite the vast amount of data produced.Thus,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion Text.The presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and truthful.Initially,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible format.Next,unigram and bigram feature extractors are utilized.The SGRU model is employed in this study to identify and classify Arabic spam reviews.Since the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU model.The experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC dataset.An extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.