基于AR-EMD方法的扩展非平稳船舶运动极短期预报AR模型

准确的极短期预报技术能够提高对船舶摇荡运动敏感的海洋特种作业安全性和效率。自回归（auto-regressive,AR）预报模型由于其自适应性强、计算效率高而被广泛应用于船舶运动的极短期预报研究。但该模型基于平稳随机假设,因而在非平稳船舶运动的极短期预报中存在困难。针对非平稳船舶运动极短期预报,文章提出一种基于AR-EMD方法的扩展AR模型,称为EMD-AR预报模型。其中,AR-EMD方法是指在经验模态分解（empirical mode decomposition,EMD）的过程中,采用AR预报的方法处理端点效应问题。 EMD-AR预报模型将非平稳信号分解成若干平稳的固有模态函数分量及余项,然后对各个分量分别用AR模型预报,得到最终的预报结果,以此克服非平稳性对AR预报模型的影响。研究基于船舶试验数据将EMD-AR模型与线性AR模型、非线性支持向量机回归（support vector regression,SVR）预报模型进行对比分析,结果表明,AR-EMD方法能够有效处理船舶运动非平稳性对AR预报模型的影响,提高该模型的预报精度,且EMD-AR模型预报性能较线性AR模型和非线性SVR模型更优。     

Analysis of velocity fluctuations and their intermittency properties in the surf zone using empirical mode decomposition

We consider here surf zone turbulence measurements, recorded in the Eastern English Channel using a sonic anemometer. In order to characterize the intermittent properties of their fluctuations at many time scales, we analyze the experimental time series using the Empirical Mode Decomposition (EMD) method. The series is decomposed into a sum of modes, each one narrow-banded, and we show that some modes are associated with the energy containing wave breaking scales, and other modes are associated with small-scale intermittent fluctuations. We use the EMD approach in association with a newly developed method based on Hilbert spectral analysis, representing the probability density function in an amplitude–frequency space. We then characterize the fluctuations in a stochastic framework using a cumulant generating function for all scales, and compare the results obtained from direct and classical structure function analysis, to EMD–Hilbert spectral analysis results, showing that the former method saturates at large scales, whereas the latter method is more precise in its scale approach. These results show the strength of the new EMD–Hilbert spectral analysis method for data presenting a strong forcing such as found in shallow water, wave dominated situations.     

An analysis of trends in passenger and freight transport energy consumption in India

This primary objective of this paper is to examine the causes for the change in energy consumption in the transport sector in India. The pattern of energy consumption and their causes for change are benchmarked against select countries. A mathematical model that decomposes changes in energy consumption to various factors has been used. The changes in the energy consumption are attributed to growth in transport volume, structural change or modal shift, and energy intensity. The analysis is conducted for passenger and freight transport separately. Results indicate that the growth in transport volume has been the main cause for increase in energy consumption for both passenger and freight transport in India despite the decline in energy intensity of various transport modes. Though not surprising for a growing economy like India, this poses a challenge for the future. Currently, India is a low carbon economy.     

Part 2. Beyond single-outcome models: Decompositions of aggregate and disaggregate road safety risk

The second part of the state-of-the-art focuses on the development of the founders' double streams explaining single-outcome indicators (probability of accidents and fatalities, respectively) by fixed form regression, as outlined in the Part 1. Following Page (1997, pp. 67–122, 2001) and others, we use as turning point of the evolution of both aggregate and discrete approaches the DRAG-1 model of 1984, itself based on aggregate data, which introduced four key innovations in principle applicable to both streams.     

计算小波分布判定海洋平台结构损伤方法研究

由于海洋平台结构长期处于恶劣的海洋环境中,并受到各种载荷的交互作用,结构容易产生各种形式的损伤。因此,对海洋平台进行实时监测有着十分重要的现实意义。本文以单筒简易导管架平台为例,主要在结构损伤的判定和定位两方面对海洋平台的实时结构健康监测进行研究,结果表明通过对结构响应信号进行小波分析,小波变换系数和小波包能量分布可以很好地定义损伤识别指标。     

基于VDM与APSO优化极限学习机的船舶运动姿态预报

为提高船舶在海上运动的耐波性与适航性,并为解决具有非线性、随机性和非平稳性特点的船舶运动姿态难以准确预测的问题,提出运用一种基于变分模态分解和自适应粒子群算法优化极限学习机的组合预测模型。该算法首先利用变分模态分解将船舶运动姿态序列分解为一系列限带内本征模态函数,并且变分模态分解可以避免经验模态分解技术所产生的模态混叠和端点效应,可以降低序列的非平稳性对预测精度的影响;然后对各模态分量分别建立极限学习机预测模型,并用改进的粒子群算法对极限学习机的初始权值和阈值进行优化;最后将各模态分量预测结果进行叠加,得到最终的船舶运动姿态预测值。通过模拟试验测试并与其他传统的预测方法进行比较,结果表明所建立的组合预测模型具有更高的预测精度。     

等离子体净化有机臭味气体研究

研究清除空气中的甲硫醇,在环保领域有积极的意义。本文采用等离子体作为氧化活化手段开展了等离子体对空气中甲硫醇的净化研究。考察了甲硫醇浓度、湿度、空速变化对等离子体净化甲硫醇脱除效果的影响。实验结果表明：等离子体法能够高效脱除空气中的甲硫醇。水不参加等离子体氧化反应,空气中甲硫醇浓度的增加和空气气速的增加会降低净化的效率。     

Design of ANFIS networks using hybrid genetic and SVD methods for modeling and prediction of rubber engine mount stiffness

Genetic Algorithm (GA) and Singular Value Decomposition (SVD) are deployed for optimal design of both the Gaussian membership functions of antecedents and the vector of linear coefficients of consequents, respectively, of ANFIS networks. These networks are used for stiffness modelling and prediction of rubber engine mounts. The aim of such modelling is to show how the stiffness of an engine mount changes with variations in geometric parameters. It is demonstrated that SVD can be optimally used to find the vector of linear coefficients of conclusion parts using ANFIS (Adaptive Neuro-Fuzzy Inference Systems) models. In addition, the Gaussian membership functions in premise parts can be determined using a GA. In this study, the stiffness training data of 36 different bush type engine mounts were obtained using the finite element analysis (FEA).     

Decomposition of general facility disruption correlations via augmentation of virtual supporting stations

Infrastructure facilities may be subject to probabilistic disruptions that compromise individual facility functionality as well as overall system performance. Disruptions of distributed facilities often exhibit complex spatial correlations, and thus it is difficult to describe them with succinct mathematical models. This paper proposes a new methodological framework for analyzing and modeling facility disruptions with general correlations. This framework first proposes pairwise transformations that unify three probabilistic representations (i.e., based on conditional, marginal, and scenario probabilities) of generally correlated disruption profile among multiple distributed facilities. Then facilities with any of these disruption profile representations can be augmented into an equivalent network structure consisting of additional supporting stations that experience only independent failures. This decomposition scheme largely reduces the complexity associated with system evaluation and optimization. We prove analytical properties of the transformations and the decomposition scheme, and illustrate the proposed methodological framework using a set of numerical case studies and sensitivity analyses. Managerial insights are also drawn.     

A decomposition-based iterative optimization algorithm for traveling salesman problem with drone

This study investigates a new delivery problem that has emerged after the attempts of several e-commerce and logistics firms to deploy drones in their operations to increase efficiency and reduce delivery times. In this problem, a delivery truck that carries a drone on its roof serves customers in coordination with a drone. The drone is considered to complement the truck due to its cost-efficiency and ability to access difficult terrains and to travel without exposure to congestion. This study presents an iterative algorithm that is based on a decomposition approach to minimize delivery completion time. In the first stage of the proposed methodology, the truck route and the customers assigned to the drone are determined. In the second stage, a mixed-integer linear programming model is solved to optimize the drone route by fixing the routing and the assignment decisions that are made in the first stage. Beginning with the shortest truck route, the assignment and the routing decisions are iteratively improved. The solution times of our algorithm are compared with the solution times of the state-of-the-art formulations that are solved by CPLEX. The results demonstrate that our algorithm yields shorter solution times for the instances that we generated with the specified parameters. An optimization-based heuristic algorithm, which obtains solutions for medium-sized instances, is developed by reducing the feasible search area.