基于HMM-SVR的船舶动力设备故障模式识别与状态预测研究

船舶动力设备因故障监测信号样本少、变化缓慢且数据特征呈非线性,使得设备故障模式的准确识别和状态预测比较难。鉴于此,文章研究了基于隐马尔科夫模型的故障模式识别方法,利用该模型将微弱变化的信号特征转换为变化较大的对数似然概率对故障模式实现有效识别。在此基础上进一步提出基于HMM-SVR的设备状态预测模型,将遗传算法用于支持向量回归模型参数寻优,并结合隐马尔科夫模型,实现对设备状态的预测。对船用柴油机进行仿真,结果表明上述模型具有较高的识别率,能准确预测船舶动力设备的当前状态。     

Transit network design with allocation of green vehicles: A genetic algorithm approach

The use of fossil fuels in transportation generates harmful emissions that accounts for nearly half of the total pollutants in urban areas. Dealing with this issue, local authorities are dedicating specific efforts to seize the opportunity offered by new fuels and technological innovations in achieving a cleaner urban mobility. In fact, authorities are improving environmental performances of their public transport fleet by procuring cleaner vehicles, usually called low and zero emission vehicles (LEV and ZEV, respectively). Nevertheless there seems to be a lack of methodologies for supporting stakeholders in decisions related to the introduction of green vehicles, whose allocation should be performed since the network design process in order to optimize their available green capacity.In this paper, the problem of clean vehicle allocation in an existing public fleet is faced by introducing a method for solving the transit network design problem in a multimodal, demand elastic urban context dealing with the impacts deriving from transportation emissions.The solving procedure consists of a set of heuristics which includes a routine for route generation and a genetic algorithm for finding a sub-optimal set of routes with the associated frequencies.     

大跨度预应力混凝土斜拉桥基于定期检测的损伤识别方法

为了在定期检测信息的基础上实现大跨度预应力混凝土斜拉桥的健康状态评估,提出采用无线多点自动综合测试系统监测结构应力,利用环境随机振动法测试全桥索力,并结合桥梁几何测试信息,获得桥梁状态的综合检测方法。针对招宝山大桥,建立最优化遗传静力反分析模型,采用基于遗传算法的大型复杂结构损伤识别程序对模拟的损伤工况进行分析,有效识别出了斜拉桥主梁的损伤位置。并且由于遗传优化算法对参数的类型和数量没有限制,对斜拉桥进行包含不同损伤类型的参数化建模,可以进行结构多类型损伤的识别,因此,可推广至其他复杂桥梁的损伤识别,为同类工程所借鉴。     

钢筋混凝土梁遗传算法优化设计

遗传算法是建立在自然遗传学机理基础上的参数搜索方法。通过介绍遗传算法在钢筋混凝土梁结构参数求解中的应用,利用遗传算法对钢筋混凝土梁进行优化,建立钢筋混凝土矩形截面梁的满足约束、造价最低优化模型及相应的适应度函数,采用二进制编码,通过执行遗传算法的选择、交叉和变异算子,实现对结构的优化设计,并分析了算例,成功求解了钢筋混凝土梁结构非线性优化设计模型。实例结果表明:该法可以节省时间,优化结果准确并可以降低造价。     

基于模糊隶属度曲面的遗传算法图像分割

介绍了遗传算法的发展历史和图像分割的应用现状，分析了基于基本的遗传算法的图像分割方法，并将遗传算法与模糊集理论相结合用于医学图像分割，提出了基于模糊隶属度的遗传算法的医学图像分割方法。并对不同方法，如分割效果、计算代价进行了分析比较。     

Global cutting-path optimization considering the minimum heat effect with microgenetic algorithms

This article considers microgenetic algorithms (GAs), which explore in a small population with a few genetic operators, for cutting-path optimization problems. The major difference between GAs and simple genetic algorithms (SGAs) is how to make a reproductive plan for an improved searching technique because of population choice. It is shown that GAs implementation reaches the near-optimal region much earlier than the SGAs approach, and the GAs give a better solution than simulated annealing (SA). The main objective was to determine what temperature distribution can be obtained from the solution of a travelling distributed heat source. The solution of the travelling heat source on nested raw plate provides information about the vertices of each nested part of the raw plate. From the fact that the initial temperature at a piercing point strongly depends on the heat flow which stems from the previous cutting contour, the temperature of all piercing points must be lower than the critical temperature after each cutting of the components of a part. The critical temperature is identified as the mechanical melting temperature of steels. A heuristic back-tracking method is introduced to find the near-optimum cutting path considering the minimum heat effect on deformation. The heuristic back-tracking method is incorporated with the GAs.     

基于GA的汽车故障特征选择

特征选择是模式识别中的一个关键问题。在很多实际问题中,很难找到那些最重要的特征,或受条件限制不能对它们进行测量,从而使特征选择成为模式识别系统最困难的任务之一。遗传算法作为一种智能化的优化方法,具有适合特征选择的优势。对遗传算法做了简约介绍,通过相应的程序并结合汽车故障实例进行了分析。     

Application of genetic algorithms for joint optimization of signal setting parameters and dynamic traffic assignment for the real network data

This paper presents the joint optimization of signal setting parameters and dynamic user equilibrium (DUE) traffic assignment for the congested urban road network. The simulation-based approach is employed to obtain the DUE condition for the case of multiple-origin multiple-destination traffic flows. The dynamic traffic assignment simulation program (DTASP), developed in C language is used to assign the traffic dynamically on the road network, whereas method of successive averages (MSA) is modified and used to arrive at the DUE condition. The artificial intelligence technique of genetic algorithms (GAs) is applied to obtain the optimal signal setting parameters and path flow distribution factor for DUE condition. The methodology developed in such a way that joint optimization of signal setting parameters with DUE is obtained. The proposed method is applied to the real network data of Fort Area of Mumbai city comprising of 17 nodes and 56 unidirectional links with 72 Origin–Destination pairs, where all the 17 nodes are signalized intersections. The traffic flow condition for the optimized signal setting parameters is considerably improved compared to the existing signal settings. The results prove that the GA is an effective technique to solve the joint optimization problem for the real network data.     

A design methodology for mechatronic vehicles: application of multidisciplinary optimization,multibody dynamics and genetic algorithms

A design methodology for mechatronic vehicles is presented. With multidisciplinary optimization (MDO) methods, strongly coupled mechanical, control and other subsystems are integrated as a synergistic vehicle system. With genetic algorithms (GAs) at the system level, the mechanical, control and other relevant parameters can be optimized simultaneously. To demonstrate the feasibility and efficacy of the proposed design methodology for mechatronic vehicles, it is used to resolve the conflicting requirements for ride comfort, suspension working spaces and unsprung mass dynamic loads in the optimization of half-vehicle models with active suspensions. Both deterministic and random road excitations, both rigid and flexible vehicle bodies and both perfect measurement of full state variables and estimated limited state variables are considered. Numerical results show that the optimized vehicle systems based on the methodology have better overall performance than those using the linear quadratic Gaussian (LQG) controller. It is shown that the methodology is suitable for complex design optimization problems where: (1) there is interaction between different disciplines or subsystems; (2) there are multiple design criteria; (3) there are multiple local optima; (4) there is no need for sensitivity analysis for the optimizer at the system level; and (5) there are multiple design variables.     

轮乘制下的铁路乘务排班系统

针对铁路乘务制度由包乘制改为轮乘制,建立基于回溯算法的乘务排班系统,完成乘务计划快速自动编排,降低乘务成本。