Review on application progress of federated learning model and security hazard protection

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy.As data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data security.Data is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our reality.In the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied level.The paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.

Ensuring User Privacy and Model Security via Machine Unlearning: A Review

As an emerging discipline,machine learning has been widely used in artificial intelligence,education,meteorology and other fields.In the training of machine learning models,trainers need to use a large amount of practical data,which inevitably involves user privacy.Besides,by polluting the training data,a malicious adversary can poison the model,thus compromising model security.The data provider hopes that the model trainer can prove to them the confidentiality of the model.Trainer will be required to withdraw data when the trust collapses.In the meantime,trainers hope to forget the injected data to regain security when finding crafted poisoned data after the model training.Therefore,we focus on forgetting systems,the process of which we call machine unlearning,capable of forgetting specific data entirely and efficiently.In this paper,we present the first comprehensive survey of this realm.We summarize and categorize existing machine unlearning methods based on their characteristics and analyze the relation between machine unlearning and relevant fields(e.g.,inference attacks and data poisoning attacks).Finally,we briefly conclude the existing research directions.

DDoS Detection in SDN using Machine Learning Techniques

Software-defined network(SDN)becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure.The SDN controller is considered as the operating system of the SDN based network infrastructure,and it is responsible for executing the different network applications and maintaining the network services and functionalities.Despite all its tremendous capabilities,the SDN face many security issues due to the complexity of the SDN architecture.Distributed denial of services(DDoS)is a common attack on SDN due to its centralized architecture,especially at the control layer of the SDN that has a network-wide impact.Machine learning is now widely used for fast detection of these attacks.In this paper,some important feature selection methods for machine learning on DDoS detection are evaluated.The selection of optimal features reflects the classification accuracy of the machine learning techniques and the performance of the SDN controller.A comparative analysis of feature selection and machine learning classifiers is also derived to detect SDN attacks.The experimental results show that the Random forest(RF)classifier trains the more accurate model with 99.97%accuracy using features subset by the Recursive feature elimination(RFE)method.

Green Project System Design of Machine Process Factory

As the specialty of the product and the dim conscio us ness of environmental protection, the status of dirty, chaos and difference is l ong-term existed in the machine process factory. It seriously affects workers’ work and living environment, and restricts the total level of the environment p rotection in our country. The project is the fatal scientific research task of H enan province in 2001. As the members’ endeavor of task group, we have finished the total plan of green project system and some other key equipment to the mach ine process factory, such as the design of conveyer of chip, hydraulic former of chip, rough conveyer and dirt collector. And the green project system is made i nto model that the manufacturer can select. This item is a fire-new work. We ho pe that the expert of machine, environment protection and government official ca n put forward some advices by lodging this article. We contribute for our countr y’ environment protection and make it attain a new level.

Fault diagnosis and protection of motors in coal shearer

Symmetrical components method is employed in analysis of the characteristic motor faults.Motor protection method is put forward based on detecting positive sequence,negative sequence and zero sequence current.And problems of lack of motor overload capacity in existing mining motor protection system,impact of dynamic current on stage and definite-time delay operation and inaccuracy of criterion phase failure protection are analyzed.The unbalanced faults protection and inverse-time overload protection,which can make protection time change with the current movement,are proposed.The above problems can be solved,and the reliability and intelligent of coal shearer are improved.

Research and improvement of leakage protection system in traction motor

Because the leakage protection circuit in traction motor of coal mining machine is impacted by frequency converter devices,malfunctions appear frequently.This paper makes an in-depth analysis,proposes a solution of using the subtraction circuit to offset interference signals and conducts a simulation analysis.The above scheme,which is simple and easily realized,can improve the reliability of leakage protection device in traction motor.

Real-time prediction of projectile penetration to laminates by training machine learning models with finite element solver as the trainer

Studies on ballistic penetration to laminates is complicated,but important for design effective protection of structures.Experimental means of study is expensive and can often be dangerous.Numerical simulation has been an excellent supplement,but the computation is time-consuming.Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model.A large number of finite element models were developed;the residual velocities of projectiles from finite element simulations were used as the target data and processed to produce sufficient number of training samples.Study focused on steel 4340tpolyurea laminates with various configurations.Four different 3D shapes of the projectiles were modeled and used in the training.The trained neural network and decision tree model was tested using independently generated test samples using finite element models.The predicted projectile velocity values using the trained machine learning models are then compared with the finite element simulation to verify the effectiveness of the models.Additionally,both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples.Performance of both the models was evaluated and compared.Models trained with Finite element simulation data samples were found capable to give more accurate predication,compared to the models trained with experimental data,because finite element modeling can generate much larger training set,and thus finite element solvers can serve as an excellent teacher.This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.

我国保护性耕作的发展与政策建议

保护性耕作是一项可持续农业技术,以少免耕和作物残茬覆盖地表为主要特点,为我国农业生产带来了显著的经济、环境、社会效益;在我国农业生产转向大规模、高效率、智能化发展的背景下,推动保护性耕作的深化发展,有利于耕地保护、藏粮于地、藏粮于技等国家战略实施。本文从技术与机具研发、示范推广与能力建设、东北黑土区应用三方面,全面回顾了我国保护性耕作的发展历程;系统梳理了我国保护性耕作技术与机具的发展现状,涵盖代表性技术模式,地表秸秆及作物残茬处理、浅层土壤耕作、免少耕播种等关键机具;深入分析了适宜的模式与机具、法律和政策的支持、规模化种植大户的带动等影响我国保护性耕作发展的主要因素。研究建议,筹建国家级科技创新平台、整县推进高标准示范工程,加强政策引导、高质量实施国家级行动计划,持续开展保护性耕作技术宣传,以促进我国保护性耕作技术及应用的稳健发展。

双目视觉三维重建下的植保机作业优化研究

以喷雾植保机自动化作业路径规划为研究对象,利用双目视觉摄像机对作业环境区域内的障碍物进行检测,并基于三维重建技术构建障碍物空间位置坐标,根据检测到的障碍物位置信息对喷雾植保机的避障路径进行规划,以保证喷雾植保机作业过程可实时避障。试验结果表明:喷雾植保机能够按照预先设定的作业路径行进,遇到障碍物时能够及时进行障碍检测,并根据规划路径进行避障,保证了喷雾作业的有效实施。

自动巡航草方格固沙机结构设计分析及试验

针对荒漠化治理对草方格固沙机的需求,提出了一种集底盘、开沟机构、草箱机构、插草机构、覆压沙机构、播种及洒水机构、升降机构与一体的多功能草方格固沙机。分析了“T+一”式草方格种植模式的优势,提出了自动巡航草方格固沙机整体结构方案,并对开沟机构、草箱机构、插草机构、覆压沙机构、播种及洒水机构、升降机构等关键组件的功能和结构进行了详细分析。应用三维建模软件建立虚拟机构模型,对推草和插草机构进行了运动仿真分析,并对整机进行了作业性能试验。结果表明:各机构构件间无干涉,构件运动仿真轨迹符合功能要求,杆件尺寸参数设计合理,机器作业效率高,可有效保证草方格铺设的准确性和有效性,满足机械化固沙的要求,具有良好的通用性和适应性。

不均衡小样本下多特征优化选择的生命体触电故障识别方法

针对现有的剩余电流保护装置无法有效识别触电事故的问题,该文提出了一种不均衡小样本下多特征优化选择的生命体触电故障识别方法。首先通过变分自编码器(VAE)对实验收集到的生命体触电小样本数据进行增殖以实现正负样本均衡;然后在时域上提取能够反映波形动态变化特性的23个特征量,并利用高斯核Fisher判别分析(GKFDA)与最大信息系数(MIC)法从中选择最优表达特征组;最后,提出基于遗忘因子的在线顺序极限学习机(FOS-ELM)算法实现生命体触电行为的鉴别。实验结果表明,所提方法利用不均衡小样本触电数据集就可以训练出一个优秀的分类模型,诊断准确率可达98.75%,诊断时间仅为1.33 ms。其优良的性能结合在线增量式学习分类器设计,使得模型具备新知识学习能力,具有极好的工程应用前景。

一种基于AdaBoost模型的区块链异常交易检测方案

针对区块链系统加密货币交易记录中存在的盗币异常行为,文章基于Ada Boost模型提出一种具有隐私保护功能的异常交易检测方案。该方案采用加法同态加密和矩阵混淆技术,在有效识别并预测异常交易的同时,保证交易数据的隐私性。此外,在云外包环境中设计实现方案的底层协议,并证明了方案的正确性和隐私保护性质。与同类协议相比,该方案在保证隐私性的同时,具有较高的检测准确率和召回率,平均每条记录的检测时间为毫秒级,适用于真实加密货币交易的检测场景。

安钢4000 m^(3)级高炉炉前设备环保设施概述及改造

当今社会中,环保已经成为越来越多人关注的问题。高炉出铁场,尤其是铁口附近、渣铁沟周边的炉前区域,是整个高炉车间操作人员最多、工作条件最差、自动化程度最低的地方,也是高炉工序除尘的重点和难点。通过对炉前环保设施简单描述,分析铁口区域烟尘产生原因及分布情况,发现其中存在的不足,通过部分改造使炉前环境达到更高的环保标准。

植保无人机实时变量喷洒控制系统设计与试验

针对目前植保无人机在手动作业时,需要人为控制农药流量大小和植保机飞行速度,无法实现农药流量和飞行速度的实时匹配,造成农药喷洒不均匀的问题,开发了一种实时变量农药喷洒的控制系统。系统以STM32F103ZET6单片机为核心,使用GPS北斗定位模块进行速度和位置信息采集,基于PWM技术控制离心喷头的转速和水泵的药液流量,通过流量计反馈实时流量,形成闭环系统,并使用PID算法对整个系统进行精准控制。工作时,基于USART HMI串口屏设计的上位机远程实时地控制离心喷头转速和农药流量,同时在上位机的液晶屏幕上显示飞行参数等信息。试验结果表明:植保无人机实时变量喷洒控制系统可行,系统开机初始化时间为3.5s;在地面站离主设备1m~1km之间,系统响应时间为10~45ms之间,且农药流量控制系统的实际流量和理论流量之间的平均误差为2.2%。

基于区块链的联邦学习:模型、方法与应用

近年来,人类社会快速步入大数据时代,数据安全与隐私保护已成为发展大数据生态及相关数字经济的关键问题.联邦学习(Federated learning)作为分布式机器学习的一种新范式,致力于在保护数据隐私的同时从分布式本地数据集中训练全局模型,因而获得了广泛和深入的研究.然而,联邦学习体系面临的中心化架构、激励机制设计和系统安全等技术挑战仍有待进一步研究,而区块链被认为是应对这些挑战的有效解决方案,并已成功应用于联邦学习的许多研究和实践场景.在系统性地梳理现阶段区块链与联邦学习集成研究成果的基础上,提出基于区块链的联邦学习(Blockchain-enabled federated learning,BeFL)概念模型,阐述其中的若干关键技术、研究问题与当前研究进展,探讨该领域的应用场景以及有待进一步研究的关键问题,并讨论未来发展的潜在方向,致力于为构建去中心化和安全可信的数据生态基础设施、促进数字经济与相关产业的发展提供有益的参考与借鉴.

基于物联网的病虫害绿色防控系统研发与应用

为减少农药施用量、发展病虫害绿色防控技术,保障农产品质量安全,基于物联网技术,结合臭氧灭菌防病、光源诱导除虫、作物健康识别和病虫害辨识预警等技术,研发了设施蔬菜病虫害绿色防控系统,主要由多功能植保机和信息管理系统组成。结果表明,绿色防控系统下草莓白粉病发病率较常规施药法降低27.7%,草莓产量提高19.8%,具有显著的草莓白粉病防控效果和增产潜力。同时,系统的预警服务、多方式消杀、远程控制和智能托管等功能有效缓解了我国植保技术装备落后、过度依赖农药使用、劳动力结构性短缺和维保运营困难等问题。系统的应用在一定程度上实现了设施蔬菜病虫害防治的绿色化、专业化和智能化。

基于ADAMS的植保机操纵稳定性分析与研究

为改善自走式植保机的操纵稳定性,基于动力学软件ADAMS/Car模块建立植保机多体仿真模型,对植保机在不同速度行驶工况下的转向盘角阶跃输入性能及横摆角速度响应等评价指标进行了仿真。对仿真结果进行分析和实车试验验证,结果表明:利用ADAMS/Car模块能恰当地分析和评价自走式植保机的操纵稳定性能,当车速为10m/s时,自走式植保机的操纵稳定性较好,可为动力分配控制系统优化奠定基础。

酸洗板表面黑印的产生机制

近期SPHC钢酸洗板表面出现黑印,影响其使用。为弄清酸洗板出现黑印的原因,检查了酸洗、热轧、炼钢等工序的工艺参数,并进行了轧制油使用试验、调整钢坯运输方式和对钢坯进行机械清理试验等。结果表明:酸洗板黑印的产生与酸洗和热轧工艺、轧制油使用、铸机及钢坯吊装方法均无关系,而与连铸结晶器保护渣品种有关,是加入少量Na2O的液态保护渣粘附于钢坯表面从而局部增碳所致。通过更换保护渣和优化保护渣成分,解决了酸洗SPHC钢板的黑印问题。