Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow tasks.In cloud data centers,fog computing takes more time to run workflow applications.Therefore,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing environments.Effective task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog nodes.This process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource bottlenecks.In this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local exploitation.This balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization algorithms.The FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response time.In relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

Light-Activated Virtual Sensor Array with Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease

Early non-invasive diagnosis of coronary heart disease(CHD)is critical.However,it is challenging to achieve accurate CHD diagnosis via detecting breath.In this work,heterostructured complexes of black phosphorus(BP)and two-dimensional carbide and nitride(MXene)with high gas sensitivity and photo responsiveness were formulated using a self-assembly strategy.A light-activated virtual sensor array(LAVSA)based on BP/Ti_(3)C_(2)Tx was prepared under photomodulation and further assembled into an instant gas sensing platform(IGSP).In addition,a machine learning(ML)algorithm was introduced to help the IGSP detect and recognize the signals of breath samples to diagnose CHD.Due to the synergistic effect of BP and Ti_(3)C_(2)Tx as well as photo excitation,the synthesized heterostructured complexes exhibited higher performance than pristine Ti_(3)C_(2)Tx,with a response value 26%higher than that of pristine Ti_(3)C_(2)Tx.In addition,with the help of a pattern recognition algorithm,LAVSA successfully detected and identified 15 odor molecules affiliated with alcohols,ketones,aldehydes,esters,and acids.Meanwhile,with the assistance of ML,the IGSP achieved 69.2%accuracy in detecting the breath odor of 45 volunteers from healthy people and CHD patients.In conclusion,an immediate,low-cost,and accurate prototype was designed and fabricated for the noninvasive diagnosis of CHD,which provided a generalized solution for diagnosing other diseases and other more complex application scenarios.

Personalized assessment and training of neurosurgical skills in virtual reality:An interpretable machine learning approach

Background Virtual reality technology has been widely used in surgical simulators,providing new opportunities for assessing and training surgical skills.Machine learning algorithms are commonly used to analyze and evaluate the performance of participants.However,their interpretability limits the personalization of the training for individual participants.Methods Seventy-nine participants were recruited and divided into three groups based on their skill level in intracranial tumor resection.Data on the use of surgical tools were collected using a surgical simulator.Feature selection was performed using the Minimum Redundancy Maximum Relevance and SVM-RFE algorithms to obtain the final metrics for training the machine learning model.Five machine learning algorithms were trained to predict the skill level,and the support vector machine performed the best,with an accuracy of 92.41%and Area Under Curve value of 0.98253.The machine learning model was interpreted using Shapley values to identify the important factors contributing to the skill level of each participant.Results This study demonstrates the effectiveness of machine learning in differentiating the evaluation and training of virtual reality neurosurgical performances.The use of Shapley values enables targeted training by identifying deficiencies in individual skills.Conclusions This study provides insights into the use of machine learning for personalized training in virtual reality neurosurgery.The interpretability of the machine learning models enables the development of individualized training programs.In addition,this study highlighted the potential of explanatory models in training external skills.

Human-Machine Symbiosis:Philosophical Reflection on Virtual Human

Virtual human is the simulation of human under the synthesis of virtual reality,artificial intelligence,and other technologies.Modern virtual human technology simulates both the external characteristics and the internal emotions and personality of humans.The relationship between virtual human and human is a concrete expression of the modern symbiotic relationship between human and machine.This human-machine symbiosis can either be a fusion of the virtual human and the human or it can cause a split in the human itself.

A Service Level Agreement Aware Online Algorithm for Virtual Machine Migration

The demand for cloud computing has increased manifold in the recent past.More specifically,on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing needs.The cloud service provider fulfills different user requirements using virtualization-where a single physical machine can host multiple VirtualMachines.Each virtualmachine potentially represents a different user environment such as operating system,programming environment,and applications.However,these cloud services use a large amount of electrical energy and produce greenhouse gases.To reduce the electricity cost and greenhouse gases,energy efficient algorithms must be designed.One specific area where energy efficient algorithms are required is virtual machine consolidation.With virtualmachine consolidation,the objective is to utilize the minimumpossible number of hosts to accommodate the required virtual machines,keeping in mind the service level agreement requirements.This research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized host.The online algorithm is analyzed using a competitive analysis approach.In addition,an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark algorithms.Our proposed online algorithm consumed 25%less energy and performed 43%fewer migrations than the benchmark algorithms.

Ligand Based Virtual Screening of Molecular Compounds in Drug Discovery Using GCAN Fingerprint and Ensemble Machine Learning Algorithm

The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.

Homogeneous Batch Memory Deduplication Using Clustering of Virtual Machines

Virtualization is the backbone of cloud computing,which is a developing and widely used paradigm.Byfinding and merging identical memory pages,memory deduplication improves memory efficiency in virtualized systems.Kernel Same Page Merging(KSM)is a Linux service for memory pages sharing in virtualized environments.Memory deduplication is vulnerable to a memory disclosure attack,which uses covert channel establishment to reveal the contents of other colocated virtual machines.To avoid a memory disclosure attack,sharing of identical pages within a single user’s virtual machine is permitted,but sharing of contents between different users is forbidden.In our proposed approach,virtual machines with similar operating systems of active domains in a node are recognised and organised into a homogenous batch,with memory deduplication performed inside that batch,to improve the memory pages sharing efficiency.When compared to memory deduplication applied to the entire host,implementation details demonstrate a significant increase in the number of pages shared when memory deduplication applied batch-wise and CPU(Central processing unit)consumption also increased.

人机一体、虚拟影像与伦理预警——人工智能时代电影工业美学“接着讲”之一

以人工智能为引擎的高新科技加速度式发展,给电影工业,电影人的思维、想象力、主体性与工作理性、生存方式等都带来巨大冲击,也对电影理论建设提出了新挑战。电影工业美学立足于电影产业发展、工业化升级和新生代导演的创作,涉及原理论、方法论、创作论、本体论、受众论、传播论等维度,系新媒介语境下建构的电影阐释学术话语和知识体系。当下的电影工业美学需要在人工智能背景下“接着讲”:在主体维度,“人机一体”成为导演身体的延伸、器官的再造,带来了主体性危机;在影像维度,大量智能化生成的虚拟影像颠覆了影像本体论与真假标准;在伦理维度,必须关注日益加剧的内部/外部电影伦理问题,遵循“以人为本”原则。

五轴联动机床虚拟加工系统构建及仿真验证

五轴联动机床具有3个直线轴和2个回转轴,能在一次装夹中完成复杂曲面及异形几何特征类零件加工。实际加工过程中,刀具与工件相对运动复杂,常遇到刀具干涉。基于此,以五轴联动机床为研究对象,进行机床结构的运动链分析和零部件建模、装配,进而阐述了虚拟加工系统构建方法。最后以风扇叶轮加工为例,在已构建的系统上进行仿真验证。结果表明,该系统可以模拟双转台型五轴联动机床实际切削运动过程,可以发现加工过程中可能存在的干涉、碰撞等,对实际生产起到一定的指导作用。该研究不仅可以为类似五轴数控机床虚拟加工系统构建提供借鉴,而且对提高加工仿真技术及五轴机床应用水平也具有重要的价值。

基于Gazebo虚拟世界的草莓采摘机器人仿真研究

【目的】提高草莓采摘的自动化水平,解放劳动力并降低采摘成本。【方法】研究小组设计了一种基于曲柄连杆机构的三爪同步夹持式采摘机器人,分析了其主要部件Kinect深度相机、六轴机械臂以及三爪夹持式采摘机械手的结构设计,并在Gezebo虚拟世界中构建了采摘机器人的模型,生成了草莓采摘机器人的末端运动轨迹,完成了该草莓采摘机器人的前期开发试验和算法验证。【结果】该草莓采摘机器人可精准地夹持草莓,减小了采摘过程中对草莓表皮的损伤,同时机械臂末端运动半径最大可达770 mm,完全满足小型辅助采摘设备的基本需求。【结论】Gazebo能真实地还原草莓采摘的自然环境,充分验证了该草莓采摘机器人的可行性,同时还可以在虚拟环境中模拟不同的任务场景,可为六轴机械臂或其他多轴运动机械的运行轨迹设计提供准确的实验数据,并为机器人的实际应用提供指导。

基于输入均压与虚拟直流电机相结合的直流电能路由器控制策略

针对中压直流配电网接入下的直流电能路由器在新能源出力等工况下,使用传统控制策略对直流母线的控制效果一般、电压易越限等问题,基于模块化的输入串联输出并联(input-series output-parallel,ISOP)型拓扑结构,提出一种基于输入均压与虚拟直流电机相结合的直流电能路由器控制策略。首先,研究输入均压控制过程中模块间的功率均衡控制特性并与输出均流控制进行比较;接着,将虚拟直流电机控制应用到控制算法中,使变流器模拟出直流电机的惯性特性;然后,对虚拟直流电机建立小信号数学模型,分析其工作机理以及参数对系统的影响;最后,在MATLAB/Simulink中搭建仿真模型进行验证。结果表明:所提控制策略能够在实现直流电能路由器各模块间功率均衡的同时,具有类似直流电机的惯量特性与阻尼特性,可显著提高直流配电网直流母线电压稳定性。

基于LabVIEW虚拟仪器和机器视觉测量普朗克常量和光波波长

设计了基于LabVIEW虚拟仪器和机器视觉的普朗克常量和光波波长测量综合实验装置.该装置以发光二极管为测量对象,综合运用了基于机器视觉技术的NI Vision Builder AI,ImageJ以及科学数据绘图软件Origin测得普朗克常量,其大小与普朗克常量的公认值接近.另外,该装置为学生提供了测量普朗克常量新的思路和方法,可作为对传统光电效应法测量普朗克常量实验的补充,有利于学生创新能力的培养和提高.

包覆面料裁片切割机横梁进给系统建模及动态特性分析

包覆面料裁片切割机是用于制作软体家具的主要设备之一,其横梁进给系统存在复杂的机电耦合作用。为在设计阶段研究其横梁进给系统切割定位工况下的动态特性,优化横梁进给伺服驱动控制系统参数,建立了横梁进给系统的机电-刚柔混合模型。拟定横梁进给系统的响应效率、定位精度和刀尖运动稳定性作为动态特性评价指标,探究了横梁进给伺服驱动控制系统参数对横梁进给系统动态特性的影响。结果表明:建立的横梁进给系统机电-刚柔混合模型,为横梁进给系统动态特性分析奠定了基础。增大位置环比例系数可提高横梁进给系统的响应效率和定位精度,增大速度环比例系数可有效提高横梁进给系统的刀尖运动稳定性。研究结果可为提高软体家具生产效率和制造质量提供技术保障。

基于贪婪算法的云计算数据块节能存储仿真

针对云数据储存能量消耗大的问题,提出基于贪婪算法的云计算数据块节能存储方法。建立具有用户访问层、核心服务层和服务管理层的云计算架构,了解数据块产生过程和储存环境;将物理机利用率、能源消耗量和主机储存能力作为节能储存的约束条件,将待储存的数据块封装为虚拟机,利用贪婪算法描述虚拟机部署问题,构建贪婪算法下虚拟机分配环境;计算单个物理机和整个数据中心的数据块储存能力和资源请求能力,综合考虑虚拟机分配的相关向量,运算数据储存时的能量消耗;以总体能量最小为目标函数,将虚拟机分为主模块与备用模块,通过设置虚拟机状态转换规则来减少储存开销,实现节能储存。实验结果表明,上述方法在数据储存过程中能够有效减少服务器开启数量,节省储存功率,达到节能目的。

基于Unity3D的机床加工实训仿真系统

针对目前高校机械专业学生在机床加工实训当中普遍存在设备操作危险、实训加工设备不足且实训效果不佳的情况,本研究以工业机器人转运及机床加工阶梯轴过程为研究对象,借助Unity3D开发平台设计出虚拟现实机床加工实训仿真系统。运用SolidWorks与3DsMax软件完成了三维建模,运用Photoshop软件对模型进行贴图和UI界面设计。使用Unity Animation组件完成阶梯轴生产加工过程动画,利用Unity3D配合C#语言实现了场景漫游、视角定位切换、虚拟手抓取等功能,最终完成并发布了机床生产加工仿真实训系统。该系统展示了阶梯轴在机床上的加工过程,实现了加工过程的可视化及进程控制,同时具备人机交互功能,选择使用虚拟手替代工业机器人完成上料,控制视角转移观察加工过程,能够极大的提高学生的实训兴趣,提高实训效率,改变传统生产加工实训方式。

基于虚拟机自省的Linux恶意软件检测方案

随着物联网和云计算技术的快速发展,Linux恶意软件的数量和种类急剧增加,因此如何有效检测Linux恶意软件成为安全领域的重要研究方向之一。为了解决这一问题,文章提出一种基于虚拟机自省的Linux恶意软件检测方案。该方案利用虚拟机自省技术在沙箱外部安全获取内部运行状态,在实现全方位监控的同时,规避了恶意软件的反动态分析问题。与其他沙箱监控方案相比,文章所提方案增加了恶意软件在沙箱中的恶意行为表现的数量。针对特征之间的时序性,采用时序处理模型对沙箱获取的特征信息进行建模和训练,旨在判断Linux应用是否属于恶意软件。文章使用了3种神经网络,包括循环神经网络、长短期记忆网络和门控循环单元网络。实验结果表明,长短期记忆网络在该应用场景下检测效果更好,准确率达98.02%,同时具有较高的召回率,将虚拟机自省技术与神经网络模型结合应用于恶意软件检测,既能在虚拟机外部监控虚拟机内部,又考虑特征之间的时序性。

基于分组遗传算法的数据中心虚拟机节能映射

近年来,随着人们对云计算业务需求持续增长,数据中心能耗日益增加,由此不仅增加了运营成本,巨大的碳排放对生态环境也产生严重的影响,数据中心节能已成为当前亟须解决的重要难题。对云数据中心的虚拟机放置(Virtual Machine Placement,VMP)进行优化能有效地提高资源利用率,同时,VMP也是减少数据中心能耗的重要技术之一;针对数据中心的能耗感知VMP问题,提出一种基于分组遗传算法的节能算法EEGGA(Energy-Efficient Grouping Genetic Algorithm),算法将节能VMP问题视为装箱问题(Bin Packing Problem,BPP),并应用基于分组编码的遗传算法对其进行求解,通过减少活动物理主机的数量(装箱数量)以实现降低数据中心能耗的目标;在算法迭代过程的交叉和变异等阶段,设计了多种启发优化策略提升子代染色体的适应度,从而提高算法的节能性能和加快迭代收敛的速度;通过仿真实验,在收敛速度和求解性能等方面将提出的算法与传统的节能遗传算法进行对比,实验结果表明:提出的算法能够有效地减少数据中心的能耗,在节能性能和求解收敛速度方面均优于其他算法。

磁感定义与复磁路相量分析

该文以变压器为例,分别建立并联和串联两种形式的等效正弦交变复磁路模型,相应地给出两种磁感的不同定义和物理意义,并比较各自的优劣势。首先,以相量图的形式分析不同负载性质等效复磁路中各物理量的相位关系,并在磁感定义的基础上完善磁路与电路的类比关系;其次,推导任意负载下等效电路有功和无功功率与类比复磁路中等效电路中虚拟有功和无功功率的数值关系;然后,以同步发电机为例,利用基于磁感的等效复磁路分析交流电机中定转子磁场的相互作用,分别基于等效复磁路和有限元方法分析一个变压器算例,验证理论分析的正确性;最后,给出研究结论,并据此客观地分析基于磁感的复磁路的作用。