随机风荷载作用下桅杆结构响应矩计算的离散分析法

整体考虑纤绳的空间振动以及纤绳、杆身的相互影响,推导出桅杆结构在随机风荷载作用下的非线性振动方程;在此基础上采用随机振动的离散分析理论,推导出结构响应均值和方差的递推式。由于结构的非线性特征,矩方程中包含位移的高阶矩,递推方程式不闭合。引入Gauss闭合假定,将影响的高阶矩用值和方差表示,递推方程式成为响应均值和方差的非线性方程,可以迭代求解。该方法计算效率高,收敛快。算例表明,该方法计算结果与Monte Carlo模拟结果符合较好。     

Analyzing the effect of introducing a kurtosis parameter in Gaussian Bayesian networks

Gaussian Bayesian networks are graphical models that represent the dependence structure of a multivariate normal random variable with a directed acyclic graph (DAG). In Gaussian Bayesian networks the output is usually the conditional distribution of some unknown variables of interest given a set of evidential nodes whose values are known. The problem of uncertainty about the assumption of normality is very common in applications. Thus a sensitivity analysis of the non-normality effect in our conclusions could be necessary. The aspect of non-normality to be considered is the tail behavior. In this line, the multivariate exponential power distribution is a family depending on a kurtosis parameter that goes from a leptokurtic to a platykurtic distribution with the normal as a mesokurtic distribution. Therefore a more general model can be considered using the multivariate exponential power distribution to describe the joint distribution of a Bayesian network, with a kurtosis parameter reflecting deviations from the normal distribution. The sensitivity of the conclusions to this perturbation is analyzed using the Kullback-Leibler divergence measure that provides an interesting formula to evaluate the effect.     

A statistical model for the calculation of the frequency of turbulent collisions of particles of arbitrary density

A statistical model is given for determining the kernel of collisions of inertial particles (droplets, bubbles) in the entire range of variation of the density ratio between the dispersed and continuous phases. The model is based on the assumption that the joint probability density function of velocities of continuous medium and particles is a Gaussian distribution. The ranges of variation of bubble sizes are determined, in which the decisive part is played by the “inertial” and “spatial” collision mechanisms.     

Efficient evaluation of the pdf of a random variable through the kernel density maximum entropy approach

In this paper, a new approach for the evaluation of the probability density function (pdf) of a random variable from the knowledge of its lower moments is presented. At first the classical moment problem (MP) is revisited, which gives the conditions such that the assigned sequence of sample moments represent really a sequence of moments of any distribution. Then an alternative approach is presented, termed as the kernel density maximum entropy (MaxEnt) method by the authors, which approximates the target pdf as a convex linear combination of kernel densities, transforming the original MP into a discrete MP, which is solved through a MaxEnt approach. In this way, simply solving a discrete MaxEnt problem, not requiring the evaluation of numerical integrals, an approximating pdf converging toward the MaxEnt pdf is obtained. The method is first demonstrated by approximating some known analytical pdfs (the chi‐square and the Gumbel pdfs) and then it is applied to some experimental engineering problems, namely for modelling the pdf of concrete strength, the circular frequency and the damping ratio of strong ground motions, the extreme wind speed in Messina's Strait region. All the developed numerical applications show the goodness and efficacy of the proposed procedure. Copyright © 2008 John Wiley & Sons, Ltd.     

A static empty equipment allocation model for long-haul networks with constrained crew routes

A realistic and efficient tactical model is developed that is able to optimize equipment and crew movements in long-haul trucking networks so that drivers are able to return home within a reasonable amount of time. A unique feature of the model is that driver, tractor, and trailer routes are simultaneously optimized. An underlying assumption is that routes remain in place for extended periods; though the trailers, tractors, and drivers may flow across these routes at variable rates. We formulate a static linear programming model for this tactical problem, and propose a column generation algorithm. The algorithm is tested with real data from a less-than-truckload carrier and randomly generated test data. Networks with up to 40 nodes (including the 30-node real-life example) are successfully optimized. For our sample problems, we found that static routes produced solutions within an average 0.22% of the optimum under dynamic conditions, provided that variable flows are permitted across the static routes.     

A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems

Job shop scheduling problem (JSSP) is a typical NP-hard problem. In order to improve the solving efficiency for JSSP, a hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search is proposed in this paper, which combines the merits of Estimation of distribution algorithm and Differential evolution (DE). Meanwhile, to strengthen the searching ability of the proposed algorithm, a chaotic strategy is introduced to update the parameters of DE. Two mutation operators are adopted. A neighbourhood search (NS) algorithm based on blocks on critical path is used to further improve the solution quality. Finally, the parametric sensitivity of the proposed algorithm has been analysed based on the Taguchi method of design of experiment. The proposed algorithm was tested through a set of typical benchmark problems of JSSP. The results demonstrated the effectiveness of the proposed algorithm for solving JSSP.     

An Application-Oriented Buffer Management Strategy in Opportunistic Networks

In Opportunistic networks (ONs), buffer management is critical to improve the message exchanging efficiency due to the limited storage space and transmission bandwidth at the wireless edge. Current solutions make message scheduling and drop policy based on assumptions that messages can always been forwarded in a single contact, and all node pairs have the same contact rates. However, such ideal assumptions are invalid for realistic mobility traces of hand-held. Recent studies show that the single contact duration is limited and the mobility of nodes is heterogeneous in reality. In this paper, a buffer management strategy based on contact duration and heterogeneous mobility is proposed to improve the efficiency of buffer policy in the practical applications. We mainly focus on the minimization of the total expected delivery delay for all messages in ONs with resource constraints. Using the global network information including existing copies of message in the network, the distribution of pair-wise inter-contact time and contact duration between nodes, we develop a function to compute per-message utility which reflects the contribution of single message to the total expected delivery delay. Messages are scheduled or dropped according to their utilities. Simulation results show that our proposed strategy not only achieves lower delivery delay than mainstream strategies, but also keeps a high delivery ratio and a low network overhead.     

An extended continuous estimation of distribution algorithm for solving the permutation flow-shop scheduling problem

This article proposes an extended continuous estimation of distribution algorithm (ECEDA) to solve the permutation flow-shop scheduling problem (PFSP). In ECEDA, to make a continuous estimation of distribution algorithm (EDA) suitable for the PFSP, the largest order value rule is applied to convert continuous vectors to discrete job permutations. A probabilistic model based on a mixed Gaussian and Cauchy distribution is built to maintain the exploration ability of the EDA. Two effective local search methods, i.e. revolver-based variable neighbourhood search and Hénon chaotic-based local search, are designed and incorporated into the EDA to enhance the local exploitation. The parameters of the proposed ECEDA are calibrated by means of a design of experiments approach. Simulation results and comparisons based on some benchmark instances show the efficiency of the proposed algorithm for solving the PFSP.     

EA-RDSP: Energy Aware Rapidly Deployable Wireless Ad hoc System for Post Disaster Management

In post disaster scenarios such as war zones floods and earthquakes, the cellular communication infrastructure can be lost or severely damaged. In such emergency situations, remaining in contact with other rescue response teams in order to provide inputs for both headquarters and disaster survivors becomes very necessary. Therefore, in this research work, a design, implementation and evaluation of energy aware rapidly deployable system named EA-RDSP is proposed. The proposed research work assists the early rescue workers and victims to transmit their location information towards the remotely located servers. In EA-RDSP, two algorithms are proposed i.e., Hop count Assignment (HCA) algorithm and Maximum Neighbor Selection (MNS) algorithm. The EA-RDSP contains three types of nodes; the client node sends information about casualty in the disaster area to the server, the relay nodes transmit this information from client node to server nodes via multi-hop transmission, the server node receives messages sent by client node to alert rescue teams. The EAM-RDSP contains three types of nodes; the client node sends information about casualty in the disaster area to the server, the relay nodes transmit this information from client node to server nodes via multi-hop transmission, the server node receives messages sent by client node to alert rescue teams. The proposed EA-RDSP scheme is simulated using NS-2 simulator and its performance is compared with existing scheme in terms of end-to-end delay, message delivery ratio, network overhead and energy consumption.     

多自由度结构动力可靠度分析的小波方法

该文发展了基于小波分析的局部平稳法在多自由度结构动力可靠度中的应用。首先,基于广义谐和小波和随机过程的局部平稳小波模型,发展了线性多自由度结构系统在各时间-频率子域上激励功率谱与响应功率谱之间的关系,并计算得到了在一般随机动力激励下结构随机动力响应功率谱密度和各阶谱矩。随后,根据随机动力激励和响应的高斯假定及超越过程的Markov假定,得到了线性多自由度结构在均匀/非均匀调制随机激励下层间位移的动力可靠度指标。结构动力可靠度的Monte Carlo模拟显示了所提方法的可靠性与计算高效性。     

自由空间光通信的最大后验概率检测

为了减小大气闪烁对自由空间光通信的通信性能的影响,基于光信号的对数振幅序列服从联合高斯分布特性,提出了一种最大后验概率算法,给出了随时间变化的判决阈值.模拟结果表明,此算法可以有效减小大气闪烁对通信性能的影响,降低其误码率.     

Volumetric strain measurement of polymeric materials subjected to uniaxial tension

A novel method for measuring and calculating volumetric strain in circular cylindrical uniaxial tension samples made from polymeric materials is proposed. It is shown that special considerations must be taken when calculating volumetric strain when a sample is in a postnecking state. Solely based on surface data, the key feature of the proposed correction is that it allows for an inhomogeneous distribution of longitudinal strain through the diameter of the sample, where a more traditional approach would be to assume a homogeneous distribution. These two approaches are evaluated by applying them to data from a close‐to‐incompressible steel sample. Whereas the proposed method indicates only a small positive increase in volume, the assumption of a homogeneous distribution results in substantial negative volumetric strains. Applying the two methods to tension samples made from HDPE and PVC, where plastic dilatation is nonlinear, again shows an initial negative volumetric strain for HDPE with the assumption of a homogeneous longitudinal strain. The proposed method predicts close‐to‐zero early‐stage volumetric strain for the same test. The differences are more subtle for samples of PVC. Micrographs obtained with scanning electron microscope show that the dilatation of PVC is related to voiding of the material around filler particles, while the underlying mechanism for HDPE is less clear. The results indicate that earlier reports of negative volumetric strain in polymers subjected to uniaxial tension might be artefacts of the implicit assumption made when calculating the volumetric strain.     

Explicit solutions in the stochastic dynamics of structural systems

The paper presents a procedure to derive in explicit form the stationary response of a linear structure subjected to Gaussian white noise stochastic excitation. Namely, the analytical relationship between the second order statistical moments of the response and the structural parameters (element stiffness and modal damping ratio) is furnished. The method is based on the solution of complex eigenvalue problems, one for each variable structural parameter, possessing a number of eigenvalues different from zero much smaller than the problem dimension. If a single structural quantity is treated as a parameter then the exact explicit solution is found. When more parameters are present, the explicit solution is approximate and the introduction of cross terms is suggested to obtain more accurate predictions.The aforementioned explicit solution is exploited herein in the field of uncertain structures. The structural parameters are modeled as random variables and a Monte Carlo procedure is adopted to get the conditional, given the structural parameters, probability density function of the second order moments of the response. The efficiency and the accuracy of the proposed procedure are evidenced by numerical applications.     

Algorithm based on rigorous coupled-wave analysis for diffractive optical element design

Diffractive optical element design is an important problem for many applications and is usually achieved by the Gerchberg-Saxton or the Yang-Gu algorithm. These algorithms are formulated on the basis of monochromatic wave propagation and the far-field assumption, because the Fourier transform is used to model the wave propagation. We propose an iterative algorithm (based on rigorous coupled-wave analysis) for the design of a diffractive optical element. Since rigorous coupled-wave analysis (instead of Fourier transformation) is used to calculate the light-field distribution behind the optical element, the diffractive optical element can thus be better designed. Simulation results are provided to verify the proposed algorithm for designing a converging lens. Compared with the well-known Gerchberg-Saxton and Yang-Gu algorithms, our method provides 7.8% and 10.8%, respectively, improvement in converging the light amplitude when a microlens is desired. In addition, the proposed algorithm provides a solution that is very close to the solution obtained by the simulated annealing method (within 1.89% error).     

Robust power system stabiliser design under multi-operating conditions using differential evolution

A power system stabiliser (PSS) design method, which aims at enhancing the damping of multiple electromechanical modes in a multi-machine system over a large and pre-specified set of operating conditions, is introduced. With the assumption of normal distribution, the statistical nature of the eigenvalues corresponding to different operating conditions is described by their expectations and variances. A probabilistic eigenvalue-based optimisation problem used for determining PSS parameters is then formulated. Differential evolution (DE) is applied for solving this highly nonlinear optimisation problem. Different strategies for control parameter settings of DE have been studied to verify the robustness of DE in PSS optimisation problems. The performance of the proposed PSS, with a conventional lead/lag structure, has been demonstrated based on two test systems by probabilistic eigenvalue analysis and nonlinear simulation.     

Radial basis function neural networks solution for stationary probability density function of nonlinear stochastic systems

A radial basis function neural networks (RBF-NN) solution of the reduced Fokker–Planck-Kolmogorov (FPK) equation is proposed in this paper. The activation functions consist of normalized Gaussian probability density functions (PDFs). The use of normalized Gaussian PDFs leads to a simple constraint on the coefficients for normalization of the RBF-NN solution, which as a constraint is imposed with the help of the method of Lagrange multiplier. The relationship between the proposed RBF-NN PDF solution and the generalized cell mapping with short-time Gaussian approximation is discussed, which provides a justification for Gaussian PDFs with varying means and variances in the state space. The optimal number of neurons or activation functions, which leads to the smallest error, is investigated. Four examples are presented to show the effectiveness of the proposed solution method. The results indicate that the proposed solution method is a very efficient and accurate way to compute the stationary PDF of nonlinear stochastic systems. It is also found that the distribution of the optimal coefficients as a function of the mean of Gaussian activation functions is similar to the steady-state PDF solution. Finally, we should point out that an important advantage of the RBF-NN method over methods such as finite element and finite difference is its ability to obtain solutions of the FPK equation for multi-degree-of-freedom stochastic systems.     

Probabilistic model of multiple light scattering based on rigorous computation of the first and the second moments of photon coordinates

We consider a concise method based on recurrent relations that permit rigorous computing of the first and the second moments of the components of the vector locating a randomly walking photon in an infinite homogeneous light-scattering medium. On assumption that the components obey a three-dimensional Gaussian distribution a probability density for the photon locations at the Nth scattering event can readily be written down and the light-intensity distribution in the medium may be calculated. The results from theoretical analyses are compared with the solution of a light-diffusion equation and with results of Monte Carlo simulations and show a better fit with simulated data than the diffusion approximation.     

Prophet_TD Routing Algorithm Based on Historical Throughput and Encounter Duration

Opportunistic networks are self-organizing networks that do not require a complete path between the source node and the destination node as it uses encounter opportunities brought by nodes movement to achieve network communication. Opportunistic networks routing algorithms are numerous and can be roughly divided into four categories based on different forwarding strategies. The Prophet routing algorithm is an important routing algorithm in opportunistic networks. It forwards messages based on the encounter probability between nodes, and has good innovation significance and optimization potential. However, the Prophet routing algorithm does not consider the impact of the historical throughput of the node on message transmission, nor does it consider the impact of the encounter duration between nodes on message transmission. Therefore, to improve the transmission efficiency of opportunistic networks, this paper based on the Prophet routing algorithm, fuses the impact of the historical throughput of the node and the encounter duration between nodes on message transmission at the same time, and proposes the Prophet_TD routing algorithm based on the historical throughput and the encounter duration. This paper uses the Opportunistic Networks Environment v1.6.0 (the ONE v1.6.0) as the simulation platform, controls the change of running time and the number of nodes respectively, conducts simulation experiments on the Prophet_TD routing algorithm. The simulation results show that compared to the traditional Prophet routing algorithm, on the whole, the Prophet_TD routing algorithm has a higher message delivery rate and a lower network overhead rate, and its average latency is also lower when node density is large.