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Win32环境下基于VC++6.0串口通信编程方法 总被引:6,自引:0,他引:6
本文介绍了在Witadows32环境下串口通信的实现机制,提出了用VC 6.0实现目前常用串口通信程序的四种方法,同时分析了这四种方法的优缺点。最后结合深海底自动采矿实例,实现水下工控机与光端机之间串口数据通信。 相似文献
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针对目前矿山领域异构数据融合时先验知识获取困难、物联网本体库实时性差、实例对象数据手动标注方式效率较低等问题,提出了一种矿山语义物联网自动语义标注方法。给出了传感数据语义化处理框架:一方面,确定本体的专业领域和范畴,通过重用流注释本体(SAO)构建领域本体,作为驱动语义标注的基础;另一方面,使用机器学习方法对感知数据流进行特征提取与数据分析,从海量数据中挖掘出概念间的关系;通过数据挖掘知识来驱动本体的更新与完善,实现本体的动态更新、拓展与更精确的语义标注,增强机器的理解力。以矿井提升系统主轴故障为例阐述从本体到实例化的语义标注过程:结合领域专家知识及本体重用,采用"七步法"建立矿井提升系统主传动故障本体;为了加强实例数据属性描述的准确性,使用主成分分析法(PCA)与K-means聚类方法对数据集进行降维和分组,提取出数据属性与概念的关系;通过基于语义Web的规则语言(SWRL)标注具体先行条件与后续概念的关系,优化领域本体。实验结果表明:在本体实例化过程中,可利用机器学习技术从传感数据中自动提取概念,实现传感数据的自动语义标注。 相似文献
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深海采矿扬矿系统水下构形与动力学特性数值仿真 总被引:3,自引:0,他引:3
深海多金属结核开采系统的扬矿系统主要由扬矿硬管、扬矿泵、中间仓、扬矿软管等组成的长管线系统构成,其水下运动学和动力学行为,决定了深海采矿的成败.运用非线性有限元方法,建立了基于1000m深海采矿试验扬矿系统的有限元模型,进行了系统水下空间构形的静态和动态数值仿真,计算分析了海洋环境中的波流载荷,软管长度,软管集中吊挂浮力配置,以及采矿船和集矿机的运动状态等因素,对系统空间构形,尤其对软管形态的影响.水下管线的液动力载荷采用Morison公式计算. 相似文献
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海底采矿车多工作于稀软底质,其面临的外部扰动较大,难以快速收敛跟踪误差,精准地跟踪预设轨迹。为此,本文提出了一种海底采矿车的滑模预测控制(sliding model predictive control,SMPC)轨迹跟踪算法。基于海底采矿车的运动学模型,首先设计滑模控制率实现轨迹跟踪误差快速收敛,其次利用少预测时域的线性时变模型预测控制算法(linear time varying model predictive control,LTV-MPC)优化该滑模控制率。而后,通过证明滑模控制率收敛和模型预测控制稳定,保证了闭环控制系统的稳定性。RecurDyn&Simulink联合仿真结果表明,与单一的滑模控制(sliding mode control,SMC)和线性时变模型预测控制算法相比,所提出的SMPC轨迹跟踪算法提高了轨迹跟踪精度,且算法具有较好的实时性。 相似文献
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本文提出了一种新的基于人工智能的感知/计划/动作agent结构实现智能车辆自动驾驶的方案。首先通过描述该结构的原理说明该结构可以解决自动驾驶中存在的一些问题,接着通过建立自动驾驶知识库阐述如何具体实现自动驾驶,最后通过仿真实验验证了该方法能够为智能车辆实现自动或辅助驾驶提供一种非常有效的机制。 相似文献
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自适应巡航控制系统是一种车辆高级辅助驾驶系统,不仅可以减轻驾驶员工作负担,还能增强车辆的行车安全和驾乘舒适性。基于MATLAB和Python混合编程,在网络协议环境下设计智能车巡航控制算法验证平台;该平台能够模拟多种不同典型行车场景,其实时的信息采集与动画演示功能能够直观有效地展现车辆状态。结合所开发的平台软件,设计出增量式模型预测控制器(IMPC)和智能驱动驾驶(IDM)控制器,所提出的IMPC算法不仅能综合考虑网联车巡航多目标的特点,同时还满足巡航系统快速性和准确性的要求。最后结合典型驾驶工况,开展智能车的车辆自适应巡航控制实验。实验结果表明,基于MATLAB和Python混合编程的软件系统能有效模拟各种驾驶情景,并能结合智能车验证该自适应巡航控制算法的结果。 相似文献
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This article focuses on automatic cruise control for electrically driven vehicles. The objective is to track a given vehicle‐velocity profile. For this type of application, the so‐called wheel slip plays a key role, as it is a measure for the force transmitted from the wheel to the road. Conventional wheel‐ slip controllers are usually activated if the absolute value of the slip exceeds pre‐assumed thresholds. Furthermore, it is distinguished between a braking and acceleration maneuver using separately designed and implemented controllers. In contrast, the proposed concept requires neither an activation strategy for the slip controller nor a distinction between braking and acceleration. The cascaded control structure is based upon adaptive‐gains super twisting sliding‐mode algorithm, and the friction force estimator is realized as a second‐order sliding‐mode observer with constant gains. The effectiveness and robustness of the proposed concept are demonstrated in numerical simulations using a complex multibody vehicle model. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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Ameer Hamza Khan Zili Shao Shuai Li Qixin Wang Nan Guan 《IEEE/CAA Journal of Automatica Sinica》2020,7(2):451-460
This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots.Fabricated using soft materials,soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction.However,due to structural flexibility and compliance,mathematical models for these soft robots are nonlinear with an infinite degree of freedom(DOF).Therefore,accurate position(or orientation)control and optimization of their dynamic response remains a challenging task.Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID.However,to the best of our knowledge,there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response,i.e.,reduce overshoot and settling time.In this paper,we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results.Additionally,most of the existing work on modelfree control in pneumatic soft-robotic literature use manually tuned parameters,which is a time-consuming,labor-intensive task.We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically.We presented results for both manual tuning and automatic tuning using the Ziegler-Nichols method and proposed algorithm,respectively.We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy.The experiment results show that for soft robots,the PID-controller essentially reduces to the PI controller.This behavior was observed in both manual and automatic tuning experiments;we also discussed a rationale for removing the derivative term. 相似文献
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利用超声图像对甲状腺结节进行检测在医学诊断中具有至关重要的作用。针对传统机器学习方法处理过程中存在噪声复杂、特征提取困难等问题,提出一种基于中值滤波和深度学习残差网络的甲状腺超声图像结节检测方法。采用统计阈值中值滤波方法,提高结节边缘特征,实现超声图像自动增强;构建CNN6-Residual模型提取和筛选结节特征,使用跨层连接和残差学习降低网络训练难度。实验结果表明,该方法检测准确率达到97.03%,具有较高的临床应用价值。 相似文献
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王文蓉 《自动化与仪器仪表》2011,(3):95-96,99
台达B系列变频器采用无传感器向量控制方式,具有自动转矩补偿,转差补偿功能,通过用台达B系列变频器对蒸呢机电气控制系统的改造实现了生产效率和产品质量上的双提高。 相似文献
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目的 海冰分类是海冰监测的主要任务之一。目前基于合成孔径雷达SAR影像的海冰分类方法分为两类:一类是基于海冰物理特性与SAR成像特征等进行分类,这需要一定的专业背景;另一类基于传统的图像特征分类,需要人为设计特征,受限于先验知识。近年来深度学习在图像分类和目标识别方面取得了巨大的成功,为了提高海冰分类精度及海冰分类速度,本文尝试将卷积神经网络(CNN)和深度置信网络(DBN)用于海冰的冰水分类,评估不同类型深度学习模型在SAR影像海冰分类方面的性能及其影响因素。方法 首先根据加拿大海冰服务局(CIS)的冰蛋图构建海冰的冰水数据集;然后设计卷积神经网络和深度置信网络的网络架构;最后评估两种模型在不同训练样本尺寸、不同数据集大小和网络层数、不同冰水比例的测试影像以及不同中值滤波窗口的分类性能。结果 两种模型的总体分类准确率达到93%以上,Kappa系数0.8以上,根据分类结果得到的海冰区域密集度与CIS的冰蛋图海冰密集度数据一致。海冰的训练样本尺寸对分类结果影响显著,而训练集大小以及网络层数的影响较小。在本文的实验条件下,CNN和DBN网络的最佳分类样本尺寸分别是16×16像素和32×32像素。结论 利用CNN和DBN模型对SAR影像海冰冰水分类,并进行性能分析。发现深度学习模型用于SAR影像海冰分类具有潜力,与现有的海冰解译图的制作流程和信息量相比,基于深度学习模型的SAR影像海冰分类可以提供更加详细的海冰地理分布信息,并且减小时间和资源成本。 相似文献
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目的 决策系统是无人驾驶技术的核心研究之一。已有决策系统存在逻辑不合理、计算效率低、应用场景局限等问题,因此提出一种动态环境下无人驾驶路径决策仿真。方法 首先,基于规则模型构建适于无人驾驶决策系统的交通有限状态机;其次,针对交通动态特征,提出基于统计模型的动态目标路径算法计算状态迁移风险;最后,将交通状态机和动态目标路径算法有机结合,设计出一种基于有限状态机的无人驾驶动态目标路径模型,适用于交叉口冲突避免和三车道换道行为。将全速度差连续跟驰模型运用到换道规则中,并基于冲突时间提出动态临界跟车距离。结果 为验证模型的有效性和高效性,对交通环境进行虚拟现实建模,模拟交叉口通行和三车道换道行为,分析文中模型对车流量和换道率的影响。实验结果显示,在交叉口通行时,自主车辆不仅可以检测冲突还可以根据风险评估结果执行安全合理的决策。三车道换道时,自主车辆既可以实现紧急让道,也可以通过执行换道达成自身驾驶期望。通过将实测数据和其他两种方法对比,当车流密度在0.20.5时,本文模型的平均速度最高分别提高32 km/h和22 km/h。当车流密度不超过0.65时,本文模型的换道成功率最高分别提升37%和25%。结论 实验结果说明本文方法不仅可以在动态城区环境下提高决策安全性和正确性,还可以提高车流量饱和度,缓解交通堵塞。 相似文献
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Pritesh Mistry Daniel Neagu Paul R. Trundle Jonathan D. Vessey 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2016,20(8):2967-2979
Drug vehicles are chemical carriers that provide beneficial aid to the drugs they bear. Taking advantage of their favourable properties can potentially allow the safer use of drugs that are considered highly toxic. A means for vehicle selection without experimental trial would therefore be of benefit in saving time and money for the industry. Although machine learning is increasingly used in predictive toxicology, to our knowledge there is no reported work in using machine learning techniques to model drug-vehicle relationships for vehicle selection to minimise toxicity. In this paper we demonstrate the use of data mining and machine learning techniques to process, extract and build models based on classifiers (decision trees and random forests) that allow us to predict which vehicle would be most suited to reduce a drug’s toxicity. Using data acquired from the National Institute of Health’s (NIH) Developmental Therapeutics Program (DTP) we propose a methodology using an area under a curve (AUC) approach that allows us to distinguish which vehicle provides the best toxicity profile for a drug and build classification models based on this knowledge. Our results show that we can achieve prediction accuracies of 80 % using random forest models whilst the decision tree models produce accuracies in the 70 % region. We consider our methodology widely applicable within the scientific domain and beyond for comprehensively building classification models for the comparison of functional relationships between two variables. 相似文献
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Yassine Kebbati Naima Ait-Oufroukh Dalil Ichalal Vincent Vigneron 《Asian journal of control》2023,25(4):2539-2563
Autonomous driving has the ability to reshape mobility and transportation by reducing road accidents, traffic jams, and air pollution. This can yield energy efficiency, convenience, and more productivity as significant driving time will be gained and used in other activities instead. Autonomous vehicles are complex systems consisting of several modules that perform perception, decision-making, planning, and control. Control is essential for achieving automatic driving; it is basically divided into longitudinal control that handles speed tracking and lateral control which ensures accurate steering. The latter is primordial in path tracking applications and recent research has witnessed a huge leap in this field. The aim of this paper is to provide a technical survey of the latest research on the lateral control of autonomous vehicles as well as to highlight technical challenges and limits for further developments. 相似文献
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针对车辆高速过弯时发生的侧滑问题,将预测控制运用于汽车ESP控制系统中,以2自由度车辆模型为预测内部模型,以车辆直接横摆力矩为输出作用于车轮来控制整车的行驶状态。结合Matlab/Simulink建立的七自由度整车模型以及轮胎模型对所设计的ESP控制器进行分析调整。实验结果表明,预测控制器能很好地控制汽车的横摆角速度和限制质心侧偏角,提高了汽车的稳定性和安全性。 相似文献