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.     

车联网仿真测试评价技术研究综述

智能化和网联化是汽车发展的重要方向,车联网技术作为智能网联汽车体系架构的重要组成部分,近年来成为业界的研究热点之一。车联网的大规模部署及其在智能汽车领域的广泛应用前需要对其性能及功能进行全面、深入的测试评价,然而在真实环境下进行车联网的测试与评估存在成本高、难度大的问题,因此通过仿真手段对其进行评价分析是当前的主流测试手段。本文总结了主流的网络仿真器和交通仿真器,对现有车联网仿真平台进行了分类,研究并对比分析了典型的车联网仿真平台;针对车联网的应用特性,研究并归纳了影响车联网仿真性能的车辆移动模型、信道传播模型及驾驶员行为等;从网络仿真指标、车联网应用相关指标归纳了车联网功能及性能测试的典型评价指标。     

智能缝制设备嵌入实时软件平台的定制与实现*

依据智能缝制设备数字化、网络化的应用特点,首先分析了面向智能缝制设备的嵌入式系统体系结构,重点研究了基于Linux开发面向领域的网络化嵌入实时软件平台的关键技术和方法。目前,基于该方法所实现的嵌入式软件平台已被成功应用于新型的电子花样缝纫机。     

A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems with time windows

This paper presents a hybrid evolutionary algorithm (HEA) to solve heterogeneous fleet vehicle routing problems with time windows. There are two main types of such problems, namely the fleet size and mix vehicle routing problem with time windows (F) and the heterogeneous fixed fleet vehicle routing problem with time windows (H), where the latter, in contrast to the former, assumes a limited availability of vehicles. The main objective is to minimize the fixed vehicle cost and the distribution cost, where the latter can be defined with respect to en-route time (T) or distance (D). The proposed unified algorithm is able to solve the four variants of heterogeneous fleet routing problem, called FT, FD, HT and HD, where the last variant is new. The HEA successfully combines several metaheuristics and offers a number of new advanced efficient procedures tailored to handle the heterogeneous fleet dimension. Extensive computational experiments on benchmark instances have shown that the HEA is highly effective on FT, FD and HT. In particular, out of the 360 instances we obtained 75 new best solutions and matched 102 within reasonable computational times. New benchmark results on HD are also presented.     

固定双桨驱动的无人水面艇自主直线路径跟踪系统

针对直流电机驱动固定双桨的无人水面艇,介绍了一种自主直线路径跟踪系统,该系统由岸基监控系统和艇载控制系统组成,具有自主航行和遥控航行两种工作模式,可在自主航行出现危险时切换到遥控模式,保证航行安全。岸基监控系统通过数传电台与艇载控制系统通信,向艇载控制系统发送控制命令,接收并显示其传回的状态信息;艇载控制系统以工控机为主控单元,进行数据采集与解算,与岸基监控系统通信,并为直线路径跟踪控制算法提供程序接口;GPS双天线高精度测向定位系统为直线路径跟踪控制算法提供位置和航向信息,直线路径跟踪控制算法根据距离偏差和航向偏差计算出左右两侧电机电压,进而控制无人水面艇航行。实验分别采用了PID、模糊控制和模糊PID三种控制方法,系统实际水上实验表明,在风力2~3级,晴到多云天气条件下,无人水面艇对目标路径的最大跟踪误差小于0.6 m。     

分布式网络测量中测量节点的智能选择算法

大规模网络结构复杂,需要有针对性的网络监测方法。测量节点的自动选择必须在测量代价和覆盖范围之间进行权衡。合理地测量节点选择,能在获取全网性能状况的同时,有效减少测量给待测网络带来的带宽占用和软硬件资源消耗的影响。以最小化测量节点数量为目标,选择蚁群算法作为测量节点自动选择的基本算法,并通过对基本算法进行改进和创新,可形成一种针对分布式网络测量的测量节点智能选择算法。     

面向动目标围捕的Multi-UUV编队协调控制研究

针对动目标围捕过程对Multi-UUV编队结构的灵活性要求较高的问题,基于欠驱动UUV的数学模型,在目标轨迹能够准确预测的前提下,改进了协调编队运动控制器。协调编队运动控制器的控制目标是在Multi-UUV编队跟踪并围捕动目标过程中,避免Multi-UUV之间,UUV与障碍物以及UUV与动目标之间的碰撞并以稳定的编队结构围捕动目标。将控制器设计过程分解为运动学控制和动力学控制。在运动学控制部分,实现动目标跟踪、UUV之间避撞及UUV偏航角误差为零的控制目标。在动力学控制部分,应用反步法设计实际的控制输入。仿真案例验证了协调运动控制器在动目标围捕中的有效性。     

基于PC的智能家电无线红外遥控系统

基于PC的智能家电遥控系统是以单片机CC2530为核心驱动、以YS-IR01F为红外学习核心模块的控制系统,该系统包括三部分,分别为用户控制界面、网关模块以及底层红外驱动模块,该系统可以通过对家用红外遥控数据的学习进而实现利用PC用户界面控制包括电视、投影仪等普通家电,初步实现家电智能化。满足一般家庭对智能家居的要求,方便人们的生活。