核算前移在冶炼企业优化成本控制中的应用

针对国际黄金价格的走低,黄金冶炼企业如何在激烈的市场竞争中寻求更大的利润空间,优化成本控制显得尤为重要。以某冶炼厂为例,阐述核算前移在优化企业成本控制中的应用,以达到降本增效的目的,实现企业利润的最大化。     

A new wavelet based fault detection,classification and location in transmission lines

This paper deals with the application of wavelet transforms for the detection, classification and location of faults on transmission lines. A Global Positioning System clock is used to synchronize sampling of voltage and current signals at both the ends of the transmission line. The detail coefficients of current signals of both the ends are utilized to calculate fault indices. These fault indices are compared with threshold values to detect and classify the faults. Artificial Neural Networks are employed to locate the fault, which make use of approximate decompositions of the voltages and currents of local end. The proposed algorithm is tested successfully for different locations and types of faults.     

Controllability and observability of multi-agent systems with heterogeneous and switching topologies

ABSTRACT

This paper focuses on controllability and observability of multi-agent systems with heterogeneous and switching topologies, where the first- and the second-order information interaction topologies are different and switching. First, based on the controllable state set, a controllability criterion is obtained in terms of the controllability matrix corresponding to the switching sequence. Next, by virtue of the subspace sequence, two necessary and sufficient algebraic conditions are established for controllability in terms of the system matrices corresponding to all the possible topologies. Furthermore, controllability is considered from the graphic perspective. It is proved that the system is controllable if the union graph of all the possible topologies is controllable. With respect to observability, two sufficient and necessary conditions are derived by taking advantage of the system matrices and the corresponding invariant subspace, respectively. Finally, some simulation examples are worked out to illustrate the theoretical results.     

大口径光学元件装夹转运结构设计及分析

为了实现大口径光学元件的安全装夹、转运,通过光学元件开槽与不开槽两种装夹方式的分析,得出开槽夹紧转运方式将带来微裂纹、应力集中、成本高等缺陷,提出了利用摩擦力克服光学零件的重力和惯性力的低应力装夹转运方案。通过对光学元件低应力夹紧结构设计,并利用有限元分析方法,得到不开槽装夹方式下,光学元件的最大主应力为1.11 MPa,最大切应力为0.73 MPa,远低于光学元件破坏的强度极限,且受力均匀,无应力集中现象。     

A review on some classes of algebraic systems

ABSTRACT

In this paper, we review some algebraic control system. Precisely, linear and bilinear systems on Euclidean spaces and invariant and linear systems on Lie groups. The fourth classes of systems have a common issue: to any class, there exists an associated subgroup. From this object, we survey the controllability property. Especially, from those coming from our contribution to the theory.     

倾斜摄影测量技术在古建筑测绘中的运用研究——以郴州市汝城县朱氏宗祠建筑测绘为例

为测试倾斜摄影测量技术路线,并通过实际项目的应用,解决传统古建筑建模过程中存在的时间成本高、难以还原复杂场景等问题。按照倾斜摄影技术的技术路线,进行朱氏宗祠建筑测绘试验,并通过基于altizure软件建立三维模型来展现倾斜摄影测量技术流程。验证无人机倾斜摄影应用于古建筑三维建模的可行性和可操作性。     

Alternatives for improving energy security in Cape Verde

The lack of consensus on the meaning of energy security and how it should be measured hinders the evaluation of alternatives for improving the Cape Verdean energy sector. The objective of the paper is to develop an energy security index adjusted to the country's context. The methods used are the multi-criteria analysis and the Delphi survey. Different alternatives to improve energy security are assessed: electrically interconnect Cape Verde's islands, increase the share of renewable energy, and a combination of the previous alternatives. The results of our analysis indicate that increasing renewable energy is the optimal alternative for Cape Verde.     

公路隧道喷射混凝土套拱加固结构力学特性

为研究喷射混凝土套拱加固前后二次衬砌与混凝土套拱的受力状况,依托陕西汉中至留坝段八里关隧道,运用有限元软件建立隧道混凝土结构套拱加固分析模型,并通过现场监测获取二次衬砌与套拱间的接触压力、套拱格栅拱架钢筋应力、套拱混凝土应力,将数值模拟结果与现场测试结果相结合,得出套拱结构的一般受力变化规律。结果表明:套拱加固前衬砌结构的最不利荷载位置位于施工缝附近的拱顶、拱肩与拱脚处; 由于衬砌局部变形与温度应力的影响,套拱混凝土应力变化呈现反复“上升-下降-上升”最后趋于稳定的特点; 套拱的作用是控制二次衬砌的进一步变形,套拱施作后所承受荷载较小,套拱反作用力远小于围岩作用于衬砌的应力,在应力计算中不应将衬砌与套拱作为整体计算; 套拱结构数值模拟所得的结果与现场测试套拱结构内力(轴力、弯矩)的大小及分布相似度高,但在衬砌裂损严重部位,数值模拟所得结果误差较大,应以现场测试结果为准。     

An adaptive local search algorithm for vehicle routing problem with simultaneous and mixed pickups and deliveries

The Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is an extension to the classical Vehicle Routing Problem (VRP), where customers may both receive and send goods simultaneously. The Vehicle Routing Problem with Mixed Pickup and Delivery (VRPMPD) differs from the VRPSPD in that the customers may have either pickup or delivery demand. However, the solution approaches proposed for the VRPSPD can be directly applied to the VRPMPD. In this study, an adaptive local search solution approach is developed for both the VRPSPD and the VRPMPD, which hybridizes a Simulated Annealing inspired algorithm with Variable Neighborhood Descent. The algorithm uses an adaptive threshold function that makes the algorithm self-tuning. The proposed approach is tested on well-known VRPSPD and VRPMPD benchmark instances derived from the literature. The computational results indicate that the proposed algorithm is effective in solving the problems in reasonable computation time.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.