基子逆向物流管理的产品回收网络规划模型

逆向物流在社会利益和经济利益两方面都具有一定的重要性，均是企业降低成本，提高竞争力，实现可持续发展的措施之一。因此，有必要在对逆向物流配送及产品回收网络进行分析的基础上，结合国内物流业的发展现状，运用混合整数规划方法，构建逆向物流的回收网络规划数学模型，并采用遗传算法进行求解。     

应用于火灾探测的气味分析技术及其算法

提出了一种新的应用于火灾探测的算法,通过与BP神经网络算法的分析比较,认为RBF神经网络算法比BP神经网络算法在数据处理方面更加迅速和准确,因此RBF神经网络算法在火灾的实时探测方面具有更好的发展潜力,而基于RBF神经网络的气味分析技术在火灾探测方面表现出广阔的应用前景.     

Classification decision tree algorithm assisting in diagnosing solitary pulmonary nodule by SPECT/CT fusion imaging

Objective To develop a classification tree algorithm to improve diagnostic performances of 99mTc-MIBI SPECT/CT fusion imaging in differentiating solitary pulmonary nodules (SPNs). Methods Forty-four SPNs, including 30 malignant cases and 14 benign ones that were eventually pathologically identified, were included in this prospective study. All patients received 99Tcm-MIBI SPECT/CT scanning at an early stage and a delayed stage before operation. Thirty predictor variables, including 11 clinical variables, 4 variables of emission and 15 variables of transmission information from SPECT/CT scanning, were analyzed independently by the classification tree algorithm and radiological residents. Diagnostic rules were demonstrated in tree-topology, and diagnostic performances were compared with Area under Curve (AUC) of Receiver Operating Characteristic Curve (ROC). Results A classification decision tree with lowest relative cost of 0.340 was developed for 99Tcm-MIBI SPECT/CT scanning in which the value of Target/Normal region of 99Tcm-MIBI uptake in the delayed stage and in the early stage, age, cough and specula sign were five most important contributors. The sensitivity and specificity were 93.33% and 78. 57e, respectively, a little higher than those of the expert. The sensitivity and specificity by residents of Grade one were 76.67% and 28.57%, respectively, and AUC of CART and expert was 0.886±0.055 and 0.829±0.062, respectively, and the corresponding AUC of residents was 0.566±0.092. Comparisons of AUCs suggest that performance of CART was similar to that of expert (P=0.204), but greater than that of residents (P<0.001). Conclusion Our data mining technique using classification decision tree has a much higher accuracy than residents. It suggests that the application of this algorithm will significantly improve the diagnostic performance of residents.     

汽车付加轴断裂的失效分析

付加轴在汽车运行过程中发生断裂,断裂发生在付加轴与弹簧支架的焊接部位.焊缝结合区的金相组织为淬火马氏体,局部焊接区域内存在未熔合和夹渣缺陷,这些是导致付加轴断裂的根本原因.     

An innovative localisation algorithm for railway vehicles

In modern railway automatic train protection and automatic train control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. The aim of this work has been developing an innovative localisation algorithm for railway vehicles able to enhance the performances, in terms of speed and position estimation accuracy, of the classical odometry algorithms, such as the Italian Sistema Controllo Marcia Treno (SCMT). The proposed strategy consists of a sensor fusion between the information coming from a tachometer and an Inertial Measurements Unit (IMU). The sensor outputs have been simulated through a 3D multibody model of a railway vehicle. The work has provided the development of a custom IMU, designed by ECM S.p.a, in order to meet their industrial and business requirements. The industrial requirements have to be compliant with the European Train Control System (ETCS) standards: the European Rail Traffic Management System (ERTMS), a project developed by the European Union to improve the interoperability among different countries, in particular as regards the train control and command systems, fixes some standard values for the odometric (ODO) performance, in terms of speed and travelled distance estimation. The reliability of the ODO estimation has to be taken into account basing on the allowed speed profiles. The results of the currently used ODO algorithms can be improved, especially in case of degraded adhesion conditions; it has been verified in the simulation environment that the results of the proposed localisation algorithm are always compliant with the ERTMS requirements. The estimation strategy has good performance also under degraded adhesion conditions and could be put on board of high-speed railway vehicles; it represents an accurate and reliable solution. The IMU board is tested via a dedicated Hardware in the Loop (HIL) test rig: it includes an industrial robot able to replicate the motion of the railway vehicle. Through the generated experimental outputs the performances of the innovative localisation algorithm have been evaluated: the HIL test rig permitted to test the proposed algorithm, avoiding expensive (in terms of time and cost) on-track tests, obtaining encouraging results. In fact, the preliminary results show a significant improvement of the position and speed estimation performances compared to those obtained with SCMT algorithms, currently in use on the Italian railway network.     

基于深度可分离卷积和多级特征金字塔网络的行人检测

为提高行人检测的准确率,提出一种基于卷积神经网络的行人检测方法。该方法以YOLOv3-tiny算法为基础,在骨干网络部分,用深度可分离卷积的网络结构代替原卷积网络结构,加深网络深度。在检测部分,提出一种改进的多级特征金字塔网络,该网络由8个结构相同的使用深度可分离卷积的特征金字塔组成,特征金字塔之间串联连接,将不同金字塔得到的相同尺寸的特征进行融合,利用融合后的特征金字塔进行检测。在Caltech Pedestrian数据集上进行测试。结果表明:该方法的漏检率为57.83%,比梯度方向直方图(HOG)方法低32.53%,比基于深度学习的方法SA Fast-RCNN和MS-CNN分别低4.67%、3.21%;运行速度为34 ms/帧。因而,该方法满足了实时性要求。     

基于FMEA技术的客滚船电力系统安全评估

为了降低由于故障状态分析偏差引起的电力系统安全评估误差,提出基于FMEA技术的客滚船电力系统安全评估.利用FMEA技术计算故障前后的各个设备内电能信号的传输状态,确定目标区域,再根据继电器与断路器对目标区域的状态进行针对性计算,降低由于海量计算带来的统计偏差,以此为基础通过作用强度以及衰弱系数对故障是否存在安全隐患进行...     

遗传算法在JTIDS相对导航及源选择中的应用

根据联合战术信息分发系统（JTIDS）相对导航定位原理及其误差特性,多源定位可以有效地提高系统定位精度,而导航源快速优化选择将影响到定位实时性。通过将遗传算法应用到多源定位及导航源快速选择中,以减少导航源的优化选择时间,满足舰艇实时定位需求。     

多传感器数据融合并行处理方法研究

首先介绍了多传感器数据融合并行技术发展现状,分析了影响多传感器数据融合处理并行化的主要难点,在此基础上提出了一种基于数据空间独立划分的并行数据融合理论框架,并基于该框架给出了一种数据空间的近邻聚类非规则区域＂软＂划分方法,将融合计算自适应地分解成多个独立子任务,均衡地分发到各计算节点自治计算。该方法能较好克服并行数据融合中数据耦合、共享同步等技术问题,提升数据和计算空间并行度,具有良好的并行扩展性。试验结果表明了所提出方法的有效性。     

基于信息融合的涡轮发动机故障诊断方法研究

对近年来信息融合方法发展进行总结,着重介绍贝叶斯信息融合法、神经网络信息融合法、基于特征的信息融合法和D-S证据理论信息融合法,分析各种方法的原理和特点。以CFM56发动机故障诊断为例,用BP神经网络的输出结果为输入,构建D-S证据融合的识别框架,进行故障诊断。结果表明,采用D-S证据理论的方法,缩短了故障诊断的时间并提高了故障诊断的精确度。