Impact of regulatory rules on economic performance of PV power plants

The rapid growth of Renewable Energy Sources (RES) power plants connected to the grid has introduced new problems related to the safe and reliable operation of the electricity network from transmission to distribution sectors. New regulatory rules can promote RES producers to make a commitment on the energy amount that is likely to be supplied to the network. The present paper concerns the analysis of the energy production of a PV power plant from the economic point of view, with reference to the presence of regulatory rules. Costs of penalty and value of energy are compared, in order to evaluate the economic efficiency of the plant. Use of auxiliary energy storage devices is investigated, with the aim to determine the relevant dimensions that increase the economic efficiency of the PV plant. A software instrument that implements these algorithms is described and applied to a case study.     

基于改进蚁群算法的三维路径规划

针对移动机器人在海水环境中的三维路径规划问题容易陷入局部最优和收敛慢等瑕疵，根据三维环境全局信息来改进蚁群算法以提高实时性和收敛速度。改进蚁群算法的启发函数，采用局部信息和全局信息结合动态地改进信息素更新方式，以及根据三维空间中路径的平坦程度和光滑度二阶微分分别增加了一阶微分和二阶微分来再次修改信息素更新规则。仿真对比实验结果显示改进后的蚁群算法克服了收敛速度慢、容易陷入局部最优的缺点。     

Stochastic collocation-based nonlinear analysis of concrete bridges with uncertain parameters

This paper focuses on the stochastic response of concrete bridges considering uncertainty in bearing and abutment stiffness. A multi-span simply supported bridge with concrete girders is selected. A 3D-dimensional model is prepared, and nonlinear response history analyses are performed. For the numerical dynamic simulation, the non-sampling stochastic method based on generalized polynomial chaos (gPC) expansion is utilised. The uncertain parameters include the vertical and shear stiffness of bearings and the lateral stiffness of abutments are presented by the truncated gPC expansions. Furthermore, the system response such as base shear, acceleration, velocity and displacement in different columns is presented by gPC expansion with unknown deterministic coefficients. The stochastic Galerkin projection is employed to calculate a set of deterministic equations. A non-intrusive solution, as a set of collocation points, determines the unknown gPC coefficients of the system response and the results are compared with Monte Carlo simulations. The key advantage of spectral discretization is the combination of the mentioned method with the spatial discretization, e.g. finite element model. This study also emphasises the accuracy in results and time efficiency of the proposed non-sampling method for uncertainty quantification of stochastic systems comparing to sampling procedure (e.g. Monte Carlo simulation).     

Linear uncertain extensions of the minimum cost consensus model based on uncertain distance and consensus utility

Uncertainty theory adopts the belief degree and uncertainty distribution to ensure good alignment with a decision-maker’s uncertain preferences, making the final decisions obtained from the consensus-reaching process closer to the actual decision-making scenarios. Under the constraints of the uncertain distance measure and consensus utility, this article explores the minimum-cost consensus model under various linear uncertainty distribution-based preferences. First, the uncertain distance is used to measure the deviation between individual opinions and the consensus through uncertainty distributions. A nonlinear analytical formula is derived to avoid the computational complexity of integral and piecewise function operations, thus reducing the calculation cost of the uncertain distance measure. The consensus utility function defined in this article characterizes the adjustment value and degree of aggregation of individual opinions. Three new consensus models are constructed based on the consensus utility and linear uncertainty distribution. The results show that, in complex group decision-making contexts, the uncertain consensus models are more flexible than traditional minimum-cost consensus models: compared with the high volatility of the adjusted opinions in traditional deterministic consensus models with crisp number-based preferences, the variation trends of both individual adjusted opinions and the collective opinion with a linear uncertainty distribution are much smoother and the fitting range is closer to reality. The introduction of the consensus utility not only reflects the relative changes of individual opinions, but also accounts for individual psychological changes during the opinion-adjustment process. Most importantly, it reduces the cost per unit of consensus utility, facilitates the determination of the optimal threshold for the consensus utility, and improves the efficiency of resource allocation.     

基于改进关联规则算法的警情数据分析

针对传统Apriori算法挖掘警情数据的缺点，提出一种改进的Apriori算法。该算法首先在关联规则发现阶段引入权值参数，设计支持度阈值函数，以挖掘不常发生的重大案情发生规律；然后提出一种压缩矩阵优化算法，将数据压缩存储在只有0或1的矩阵中，并用2个数组来记录矩阵中每一行及每一列1的总数，可多次压缩矩阵，提升挖掘效率；最后将改进的算法用于实际的警情数据挖掘分析，给出关联规则挖掘结果。实验表明，改进算法不仅执行效率较传统算法有所提升，且针对警情数据的挖掘结果准确性也有所提高。     

Rule induction for hierarchical attributes using a rough set for the selection of a green fleet

Rough set theory (RST) has been the subject of much study and numerous applications in many areas. However, most previous studies on rough sets have focused on finding rules where the decision attribute has a flat, rather than hierarchical structure. In practical applications, attributes are often organized hierarchically to represent general/specific meanings. This paper (1) determines the optimal decision attribute in a hierarchical level-search procedure, level by level, (2) merges the two stages, generating reducts and inducting decision rules, into a one-shot solution that reduces the need for memory space and the computational complexity and (3) uses a revised strength index to identify meaningful reducts and to improve their accuracy. The selection of a green fleet is used to validate the superiority of the proposed approach and its potential benefits to a decision-making process for transportation industry.     

用语图分析揭示语言系统中的隐性规律 ——赢家通吃和赢多输少算法

该文用“图”这一数学工具,通过定量分析来揭示语言系统中的隐性规律,设计了“赢家通吃”和“赢多输少”两种生成算法,将理想算法“步步竞争、择优而行”的博弈论思路贯彻到非理想状态。两种新算法都较前人有更好的概括能力。赢多输少算法更兼顾了充分概括和适度概括均衡。生成语图后,该设计着重准确率的最小简图和着重覆盖率的最大简图归纳算法,挖掘控制的主流规则、分析语言系统的语言学规律。在最小简图基础上提出控制度公式以评价语言系统。
     

Engineering knowledge formalization and proposition for informatics development towards a CAD-integrated DfX system for product design

Target design methodologies (DfX) were developed to cope with specific engineering design issues such as cost-effectiveness, manufacturability, assemblability, maintainability, among others. However, DfX methodologies are undergoing the lack of real integration with 3D CAD systems. Their principles are currently applied downstream of the 3D modelling by following the well-known rules available from the literature and engineers’ know-how (tacit internal knowledge).This paper provides a method to formalize complex DfX engineering knowledge into explicit knowledge that can be reused for Advanced Engineering Informatics to aid designers and engineers in developing mechanical products. This research work wants to define a general method (ontology) able to couple DfX design guidelines (engineering knowledge) with geometrical product features of a product 3D model (engineering parametric data). A common layer for all DfX methods (horizontal) and dedicated layers for each DfX method (vertical) allow creating the suitable ontology for the systematic collection of the DfX rules considering each target. Moreover, the proposed framework is the first step for developing (future work) a software tool to assist engineers and designers during product development (3D CAD modelling).A design for assembly (DfA) case study shows how to collect assembly rules in the given framework. It demonstrates the applicability of the CAD-integrated DfX system in the mechanical design of a jig-crane. Several benefits are recognized: (i) systematic collection of DfA rules for informatics development, (ii) identification of assembly issues in the product development process, and (iii) reduction of effort and time during the design review.     

模糊控制在高频感应加热中的应用

用模糊控制的思想设计出一种模糊控制器，并用高频加热装置进行高温疲劳试验，控制结果表明这种控制器能使系统具有满意的性能指标。     

受到外部扰动的空间机械臂基于模糊递归神经网络的控制策略

针对载体位置不受控、姿态受控的空间机械臂运动控制问题,对具有不确定系统参数的空间机械臂在受到外部扰动工况下动力学建模以及控制算法进行了研究。运用拉格朗日第二类动力学方程及被控系统动量守恒关系,对该工况下空间机械臂运动特性进行了分析,建立了系统在受到外部扰动时的动力学模型,并对动力学特性进行了归纳,提出了一种基于模糊递归神经网络的控制策略。根据李雅普诺夫第二类方法,证明了整个闭环控制系统的运动渐进稳定性。使用计算机数值仿真实验与计算力矩法进行对比,阐述了整个控制系统的有效性。研究结果表明,在存在外部扰动及系统参数不确定时,所设计的控制方案相比于传统计算力矩控制方法具有更好的轨迹跟踪效果和更高的可靠性。