汽车发动机节能优化控制仿真与研究

在车辆发动机优化设计的研究中,车辆节能控制的优化能够有效提高汽车发动机燃油燃烧效率,降低发动机的燃油消耗.在汽车行驶过程中,存在着起步突变、急停、忽然转向等特殊情况,利用传统的PID节能控制方法需要大幅度、快速的调整控制参数,以满足意外情况的控制稳定性需求,但改变控制过程中没有考虑对能量的消耗问题,大幅改变参数势必造成能量的无谓损耗.提出一种采用免疫PID的车辆节能控制系统.根据人体免疫应答机制的基本原理,将免疫反馈引入到PID车辆节能控制系统中.采用单层神经网络作为抑制抗体的调节系数,能够实现车辆复杂行驶状态下的非线性过程的拟合.利用神经网络和PID结合的方法建立免疫PID车辆节能控制模型,根据模型的输出实现了车辆节能的精确控制.实验结果表明,利用免疫PID的车辆节能控制系统进行车辆节能控制,能够有效提高控制的精度.     

车辆悬架减震优化设计方法仿真

车辆在不同路况和不同车速等情况下,悬架系统弹性元件受到的冲击大小差异很大,无法形成固定的振动规则,振动剧烈程度与阻尼系数无法关联.传统车辆悬架减振设计方法,采用固定阻尼系数来减少弹性元件的振动,没有考虑不同路况及车速产生的振动剧烈程度不同的问题,减振效果较差.提出一种车辆悬架减震优化有限元设计方法,分析车辆悬架减震器的功能和结构,塑造车辆悬架减震优化模型,采用ANSYS有限元软件中一阶优化方法进行车辆悬架减振优化设计,以悬架减震器阻尼力为优化目标,以减震器阻尼系数为优化设计变量,以静强度和最大减震强度为约束条件,对车辆悬架减震进行有限元优化设计,确保车辆车身振动迅速衰弱,使车身能够很快达到一种稳定状态.实验结果表明,上述方法使车辆减震阻尼力和最大减震强度都优于传统方法,具有较高的稳定性和控制精度.     

基于GPS和加速度感应器的组合车辆定位系统研究

GPS位置信号的获取是城市智能交通系统中的一个基础问题,为解决城市中由于建筑物遮挡或信号反射造成GPS信号误差大或根本无法获取GPS信号的问题,设计一种基于GPS接收设备和采用加速度感应器进行航位推导的组合车辆定位系统。     

零能耗小屋能源管理与智能监控系统的设计与实现

针对2013中国国际太阳能十项全能竞赛比赛规则及零能耗建筑的特点,结合比赛地点山西大同的气候数据,给出了厦门大学参赛作品,零能耗小屋Sunny Inside的能源管理与智能监控系统设计方案.以光伏建筑一体化理论为基础,设计15.25kW光伏并网发电系统,并用相关软件对能耗平衡进行了模拟验证.以三菱PLC为核心,构建了能源管理与智能监控系统,分析了不同条件下空调、相变及遮阳系统的控制策略,并给出具体实现方法.     

A systematic literature review on intelligent automation: Aligning concepts from theory,practice, and future perspectives

With the recent developments in robotic process automation (RPA) and artificial intelligence (AI), academics and industrial practitioners are now pursuing robust and adaptive decision making (DM) in real-life engineering applications and automated business workflows and processes to accommodate context awareness, adaptation to environment and customisation. The emerging research via RPA, AI and soft computing offers sophisticated decision analysis methods, data-driven DM and scenario analysis with regard to the consideration of decision choices and provides benefits in numerous engineering applications. The emerging intelligent automation (IA) – the combination of RPA, AI and soft computing – can further transcend traditional DM to achieve unprecedented levels of operational efficiency, decision quality and system reliability. RPA allows an intelligent agent to eliminate operational errors and mimic manual routine decisions, including rule-based, well-structured and repetitive decisions involving enormous data, in a digital system, while AI has the cognitive capabilities to emulate the actions of human behaviour and process unstructured data via machine learning, natural language processing and image processing. Insights from IA drive new opportunities in providing automated DM processes, fault diagnosis, knowledge elicitation and solutions under complex decision environments with the presence of context-aware data, uncertainty and customer preferences. This sophisticated review attempts to deliver the relevant research directions and applications from the selected literature to the readers and address the key contributions of the selected literature, IA’s benefits, implementation considerations, challenges and potential IA applications to foster the relevant research development in the domain.     

Evolutionary digital twin: A new approach for intelligent industrial product development

To fulfill increasingly difficult and demanding tasks in the ever-changing complex world, intelligent industrial products are to be developed with higher flexibility and adaptability. Digital twin (DT) brings about a possible means, due to its ability to provide candidate behavior adjustments based on received “feedbacks” from its physical part. However, such candidate adjustments are deterministic, and thus lack of flexibility and adaptability. To address such problem, in this paper an extended concept – evolutionary digital twin (EDT) and an EDT-based new mode for intelligent industrial product development has been proposed. With our proposed EDT, a more precise approximated model of the physical world could be established through supervised learning, based on which the collaborative exploration for optimal policies via parallel simulation in multiple cyberspaces could be performed through reinforcement learning. Hence, more flexibility and adaptability could be brought to industrial products through machine learning (such as supervised learning and reinforcement learning) based self-evolution. As a primary verification of the effectiveness of our proposed approach, a case study has been carried out. The experimental results have well confirmed the effectiveness of our EDT based development mode.     

A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways

Real-time highway traffic monitoring systems play a vital role in road traffic management, planning, and preventing frequent traffic jams, traffic rule violations, and fatal road accidents. These systems rely entirely on online traffic flow info estimated from time-dependent vehicle trajectories. Vehicle trajectories are extracted from vehicle detection and tracking data obtained by processing road-side camera images. General-purpose object detectors including Yolo, SSD, EfficientNet have been utilized extensively for real-time object detection task, but, in principle, Yolo is preferred because it provides a high frame per second (FPS) performance and robust object localization functionality. However, this algorithm’s average vehicle classification accuracy is below 57%, which is insufficient for traffic flow monitoring. This study proposes improving the vehicle classification accuracy of Yolo, and developing a novel bounding box (Bbox)-based vehicle tracking algorithm. For this purpose, a new vehicle dataset is prepared by annotating 7216 images with 123831 object patterns collected from highway videos. Nine machine learning-based classifiers and a CNN-based classifier were selected. Next, the classifiers were trained via the dataset. One out of ten classifiers with the highest accuracy was selected to combine to Yolo. This way, the classification accuracy of the Yolo-based vehicle detector was increased from 57% to 95.45%. Vehicle detector 1 (Yolo) and vehicle detector 2 (Yolo + best classifier), and the Kalman filter-based tracking as vehicle tracker 1 and the Bbox-based tracking as vehicle tracker 2 were applied to the categorical/total vehicle counting tasks on 4 highway videos. The vehicle counting results show that the vehicle counting accuracy of the developed approach (vehicle detector 2 + vehicle tracker 2) was improved by 13.25% and this method performed better than the other 3 vehicle counting systems implemented in this study.     

中继协同无人机辅助的认知无线电网络安全通信

针对中继协同无人机（UAV）辅助的无线通信网络,提出一种基于认知无线电网络（CRN）的物理层安全通信方案。利用二次发射机协同解码转发中继向目的接收机发送机密消息,将UAV用作移动干扰器发送干扰噪声,以降低窃听者的解码能力。在不影响主用户通信的前提下,通过联合优化UAV的飞行轨迹和发射功率提高系统的平均保密率,采用基于连续凸逼近的算法求解近似凸规划的保密率最大化问题。仿真结果表明,相较于优化功率和优化轨迹这两种传统方案,该方案能够进一步提高CRN系统通信的安全性。     

适用于飞行自组网的闲置时隙预约TDMA协议

在飞行自组网中,固定时隙分配时分多址接入（TDMA）协议存在闲置时隙无法成功使用的问题。通过对TDMA协议引入闲置时隙预约机制,提出一种支持业务优先级传输机制的闲置时隙预约TDMA协议。采用短帧长的方式满足协同与控制业务的低时延传输需求,并利用闲置时隙预约机制允许节点使用闲置时隙传输感知业务,从而满足感知业务的高吞吐量传输性能要求。仿真结果表明,与CF-MAC和CTMAC协议相比,该协议能够在降低传输时延的同时,有效提高信道利用率和网络吞吐量。     

无人机航拍图像中裸露地表的识别

电力输电线路下方或附近的无覆盖物的裸露地表,是引起输电线路事故的主要隐患之一;从无人机电力巡检航拍图像中识别裸露地表可以预防类似事故的发生;由于Mask RCNN识别无人机电力巡检航拍图像中裸露地表的精度较低,提出一种图像特征融合的方法,即人工提取HOG和LBP两种不同的图像特征,经过不同权重的融合共同表征图像中裸露地表区域的特征,再对SVM进行训练并用于识别;实验结果表明,采用该方法识别率可以达到80％以上,识别时间少于60 ms;HOG和LBP两种特征在进行融合时,当两种特征的数量级相当时,得到的识别率最高;可见,该方法在具有较高识别率的同时,具有比较好的实时性,适合于无人机机载平台对航拍图像的初筛,且训练时间较少,权重参数规模小,为无人机航拍图像中目标物的识别提供一种新思路.