基于随机机会约束规划的班轮联盟 舱位租赁最优分配

面对持续低迷的航运市场和激烈的市场竞争,各大集装箱班轮公司通过组建联盟 来共享舱位资源,降低其系统成本,增加市场竞争力.舱位租赁是联盟合作的最基础形式,研究 该模式下的舱位资源最优分配具有很大实际意义.本文在考虑航运市场不确定需求的基础上, 研究了班轮联盟舱位租赁最优分配问题,构建了1 个班轮联盟舱位租赁最优分配模型,并运用 随机机会约束规划的方法,将其应用到班轮联盟舱位租赁分配决策中.结果表明,本文提出的 方法和模型能够有效地对班轮联盟系统舱位进行协同配置,研究结果与实际情况相符,检验 了方法和模型的有效性与实用性.     

高速公路瓶颈区域可变限速控制方法

高速公路交通流处于高峰时,公路主线路段可能出现拥挤的瓶颈路段,导致车辆运行时间增加,路段运行效率降低等问题.本文从高速公路瓶颈区域路段交通流运行的时空特征出发,对现有的Papageorgiou模型进行扩展并考虑速度控制因素,使其适用于可变限速控制环境下的真实交通流运行状态,提出了适用于高速公路瓶颈区域的可变限速控制条件的改进模型,以控制周期内总通行量最大和车辆总行程时间最小为目标,建立高速公路主线可变限速控制优化模型.仿真结果表明,相对于固定限速控制,本文提出的可变限速控制方法可降低总行程时间7.45%,提高平均速度8.78%,表明该可变限速控制模型能在一定程度上缓解高速公路瓶颈区域的拥堵问题.     

基于Adaptive Lasso与RF的航班运行风险预测改进研究

为了解决航班运行风险高维数组运算过于复杂的问题,同时为防止模型过度拟合影响预测精度,基于中国民航局发布的风险评估体系,以某航450组真实航班数据为标准样本,首先使用自适应套索算法（Adaptive Lasso）进行降维,从63项风险自变量中筛选出15项独立变量;然后,使用随机森林算法(Random Forest,RF)进行防过拟合处理,结果显示当使用重要度排序前12项变量拟合时,结果误差达到最小值,即得到最终预测指标;最后,构建Adaptive Lasso和RF的二阶段混合模型,同时选取主成分分析(Principal Component Analysis,PCA)、径向基函数(Radial Basis Function,RBF)网络、支持向量机(Support Vector Machine,SVM)3种对比方法,使用十折交叉验证精度.结果表明：Adaptive Lasso方法在筛选掉48项指标后,结果精度未见下降;经RF处理后4种方法评估精度均大于未处理前;Adaptive Lasso-RF混合模型的预测准确率和稳定性均优于PCA、RBF神经网络和SVM等方法.综上说明混合模型实现了有效降维和防过拟合,可大幅提升预测精度,用于解决航班风险预测问题可行并有效.     

一种改进的民用航空网络空间脆弱性模型

脆弱性是表征1个系统面临灾害时行为表现的重要概念.要尽可能真实地反映民用航空网络面临空间灾害时的脆弱性水平,必须充分合理地考虑灾害影响在民用航空网络中的扩散效应,即灾害间接影响.在前期有关网络系统空间脆弱性研究的基础上,详细地分析了灾害影响在民用航空网络中扩散的特点,确定了计算灾害间接影响的3个关键因素,即机场航班连接关系、机场间距离和机场失效门限,提出了改进的民用航空网络空间脆弱性模型.之后,以中国民用航空网络系统为例开展了案例研究,验证了所改进模型的有效性.     

车联网环境下公交路径交通状态估计方法研究

传统感应线圈的交通状态估计方法已无法满足准确性和实时性的状态估计需要,为此提出了基于联网公交车辆实时速度的交通状态估计模型。所提模型借助实时信息采集系统的高效性和准确性的优势,对道路交通运行状态进行估计,同时利用卡尔曼滤波算法对交通状态变量进行更新。基于历史观测数据对更新后的交通状态变量进行修正,进而得到交通状态的估计值。通过采集数据并进行大量的实验,研究结果表明：基于联网公交实时速度的状态估计模型,在各种交通环境条件和占有率下,估计值误差指数（变异系数） 均小于15%,最大仅为13.15%;状态估计修正模型与状态估计模型相比,估计值误差指数下降了2%,总体误差优化性能提升了11.87%。在确保实时性和高效性的同时,基于联网公交车辆实时速度的交通状态估计模型解决了传统道路交通状态估计方法准确性低的问题。     

Research on Spatially Adaptive High-Order Total Variation Model for Weak Fluorescence Image Restoration

Photoacoustic imaging acquires the absorption contrast of biological tissue with ultrasound resolution. It has been broadly investigated in biomedicine for animal and clinical studies. Recently, a micro-electro-mechanical system (MEMS) scanner has been utilized in photoacoustic imaging systems to enhance their performance and extend the realm of applications. The review provides a recap of recent developments in photoacoustic imaging using MEMS scanner, from instrumentation to applications. The topics include the design of MEMS scanner, its use in photoacoustic microscopy, miniature imaging probes, development of dual-modality systems, as well as cutting-edge bio-imaging studies.     

Full Duplex Communications for Next Generation Cellular Networks: The Capacity Gain

Full duplex communication highly improves spectrum efficiency of a wireless communication link. However, when it is applied to a cellular network, the capacity gain from this technology remains unknown. The reason is that full duplex communication changes the aggregate interference experienced by each communication link in cellular networks. In this paper, the capacity gain from full duplex communication is studied for cellular networks of 4G and beyond, where the same frequency channel is adopted in each cell. A two-layer Poisson point process (PPP) is adopted to model the network topology, and stochastic geometry is employed to derive the coverage probability and the average capacity of typical link in a cellular network. On the basis of these derived parameters, the capacity gain from full duplex communication is determined. Numerical results reveal that without mutual interference cancellation (MIC), the capacity gain is small under various power levels; with perfect MIC at base stations, the capacity gain can exceed 60%; with imperfect MIC at base stations, the capacity gain decreases quickly even with a slight drop of MIC performance.     

Optimal Threshold Policies for Robust Data Center Control

With the simultaneous rise of energy costs and demand for cloud computing, efficient control of data centers becomes crucial. In the data center control problem, one needs to plan at every time step how many servers to switch on or off in order to meet stochastic job arrivals while trying to minimize electricity consumption. This problem becomes particularly challenging when servers can be of various types and jobs from different classes can only be served by certain types of server, as it is often the case in real data centers. We model this problem as a robust Markov decision process (i.e., the transition function is not assumed to be known precisely). We give sufficient conditions (which seem to be reasonable and satisfied in practice) guaranteeing that an optimal threshold policy exists. This property can then be exploited in the design of an efficient solving method, which we provide. Finally, we present some experimental results demonstrating the practicability of our approach and compare with a previous related approach based on model predictive control.     

Estimation of Load-Induced Damage and Repair Cost in Post-Tensioned Concrete Rocking Walls

Post-tensioned concrete rocking walls might be used to avoid severe seismic damage at the base of structural walls, decrease residual drift, and lessen post-earthquake repair costs. The prediction of load-induced damage to the rocking wall resulting from seismic loading can provide an extremely valuable tool to evaluate the status and safety of structural concrete walls following earthquakes. In this study, the behavior and the damage state of monolithic, self-centering, rocking walls, as a new type of concrete rocking wall, are investigated. The nonlinear mechanical behavior of the wall is first modeled numerically, and subsequently the mechanical parameters from the numerical simulation are used to generate the local damage index. The results from the damage index model are compared with the full-scale test results, confirming the viability of the numerically based damage index method for estimating the seismically induced damage in concrete walls. Moreover, the estimated damage can be utilized as a qualitative and quantitative scale to assess the status of the wall following seismic loading events. Finally, an equation is proposed to estimate the repair cost based on the predicted damage state for the studied structural system.     

Decomposing and Cluster Refinement Design Method for Application-Specific Network-on-Chips

Along with higher and higher integration of intellectual properties (IPs) on a single chip, traditional bus-based system-on-chips (SoC) meets several design difficulties (such as low scalability, high power consumption, packet latency and clock tree problem). As a promising solution, network-on-chips (NoC) has been proposed and widely studied. In this work, a novel algorithm for NoC topology synthesis, which is decomposing and cluster refinement (DCR) algorithm, has been proposed to minimize the total power consumption of application-specific NoC. This algorithm is composed of two stages: decomposing with cluster generation, and cluster refinement. For partitioning and cluster generation, an initial low-power solution for NoC topology is generated. For cluster refinement, the clustering is optimized by performing floorplan to further reduce power consumption. Meanwhile, a good tradeoff between power consumption and CPU time can be achieved. Experimental results show that the proposed method outperforms the existing work.