沥青混凝土路面施工机群智能调度与监控系统

根据沥青混凝土路面施工工艺及机械运行规律，分析了机群智能调度与监控系统的功能需求，提出了基于无线局域网的机群智能控制系统结构方案，对机群智能调度系统中的优化调度技术进行了探讨。     

基于支持向量机的水下机器人操纵运动参数辨识

根据平面运动机构试验所得到的水动力系数确定水下运载器的运动方程,应用最小二乘支持向量机对水下运载器的试验数据进行分析,辨识得到了水动力系数,并将辨识得到的参数同平面运动机构试验所确定的参数相比,验证了最小二乘支持向量机进行水下运载器操纵运动参数辨识的有效性。根据所辨识的水动力系数进行了模型验证,证明所辨识得到的水动力参数是可靠的。该方法对水下运载器的操纵和控制的研究有重要意义。     

Daily activity pattern recognition by using support vector machines with multiple classes

The focus of this paper is to learn the daily activity engagement patterns of travelers using Support Vector Machines (SVMs), a modeling approach that is widely used in Artificial intelligence and Machine Learning. It is postulated that an individual’s choice of activities depends not only on socio-demographic characteristics but also on previous activities of individual on the same day. In the paper, Markov Chain models are used to study the sequential choice of activities. The dependencies among activity type, activity sequence and socio-demographic data are captured by employing hidden Markov models. In order to learn model parameters, we use sequential multinomial logit models (MNL) and multiclass Support Vector Machines (K-SVM) with two different dependency structures. In the first dependency structure, it is assumed that type of activity at time ‘t’ depends on the last previous activity and socio-demographic data, whereas in the second structure we assume that activity selection at time ‘t’ depends on all of the individual’s previous activity types on the same day and socio-demographic characteristics. The models are applied to data drawn from a set of California households and a comparison of the accuracy of estimation of activity types and their sequence in the agenda, indicates the superiority of K-SVM models over MNL. Additionally, we show that accuracy in estimating activity patterns increases using different sets of explanatory variables or tuning parameters of the kernel function in K-SVM.     

一种基于贝叶斯特征的视频图像人脸检测

提出了一种基于仿射运动模型和贝叶斯理论的视频图像人脸检测方法.建立仿射运动模型进行运动估计,提取运动对象区域;对训练图像提取人脸与非人脸的统计特征,利用贝叶斯准则建立概率模型;根据贝叶斯分类器和支持向量机分类器将图像特征分为人脸类与非人脸类,从而检测出视频运动图像中的人脸区域.     

一种多学科设计优化近似模型构建方法

通过采用单个参数控制样本的误差界限、采用Laplace损失函数和改变置信区间项,给出了一种基于单参数的Lagrangian支持向量回归算法。以实际数值函数为例,通过采用三组不同样本集进行拟合训练,构建了基于支持向量机的近似模型;以石油平台支援船总阻力估算为例,通过与模型试验及其他典型方法的对比,检验了算法用于近似模型构建的准确性和适用性。研究结果表明,采用支持向量机方法构建近似模型在小样本条件下比神经网络等传统方法具有更好的泛化性和推广能力,能够有效提高计算精度与优化效率,在复杂系统多学科优化设计中具有很大的应用价值。     

基于影响因素的SVR备件需求预测研究

文章介绍了支持向量机学习算法,说明其特点,并引出基于影响因素的支持向量回归的备件需求预测方法,用某型备件的历史需求数据例证此法的可行性与精确度。     

Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport study

Ground-based aircraft trajectory prediction is a major concern in air traffic control and management. A safe and efficient prediction is a prerequisite to the implementation of new automated tools.In current operations, trajectory prediction is computed using a physical model. It models the forces acting on the aircraft to predict the successive points of the future trajectory. Using such a model requires knowledge of the aircraft state (mass) and aircraft intent (thrust law, speed intent). Most of this information is not available to ground-based systems.This paper focuses on the climb phase. We improve the trajectory prediction accuracy by predicting some of the unknown point-mass model parameters. These unknown parameters are the mass and the speed intent. This study relies on ADS-B data coming from The OpenSky Network. It contains the climbing segments of the year 2017 detected by this sensor network. The 11 most frequent aircraft types are studied. The obtained data set contains millions of climbing segments from all over the world. The climbing segments are not filtered according to their altitude. Predictive models returning the missing parameters are learned from this data set, using a Machine Learning method. The trained models are tested on the two last months of the year and compared with a baseline method (BADA used with the mean parameters computed on the first ten months). Compared with this baseline, the Machine Learning approach reduce the RMSE on the altitude by 48% on average on a 10 min horizon prediction. The RMSE on the speed is reduced by 25% on average. The trajectory prediction is also improved for small climbing segments. Using only information available before the considered aircraft take-off, the Machine Learning method can predict the unknown parameters, reducing the RMSE on the altitude by 25% on average.The data set and the Machine Learning code are publicly available.     

基于LS-SVM船舶动力定位有限时间控制器设计

为了解决船舶模型存在任意不确定性的船舶动力定位问题,基于有限时间Lyapunov理论提出了一种非奇异快速终端滑模控制策略,提高了系统收敛速度及抗干扰能力。针对模型不确定性问题,利用最小二乘支持向量机(LS-SVM)的非线性函数逼近技术进行补偿控制,引入“最小参数”技术,将在线学习参数减少到一个,解决了“维数灾难”问题。最后的仿真对比结果验证了提出的控制策略的优越性。     

Machine learning applications in activity-travel behaviour research: a review

ABSTRACT

This paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.     

基于改进的LS-SVM的潜水器拘束模试验分析方法

本文以最小二乘支持向量机为基础,将增量法和滑动窗口法相结合,建立适用于分析处理大样本数据的方法。采用该方法对某潜水器拘束模斜航试验和纯横荡试验进行了分析,获取了可以反映潜水器横向力、转首力矩的数学模型中的水动力系数,并利用构建的数学模型,预报多种工况下的潜水器所受到的水动力。通过比较预报结果和试验结果,证明了该方法的有效性。