利用相对坐标轻松完成网格袋布局

网格袋布局管理器是java布局中最有用、最灵活、最复杂、最难掌握的一种布局管理器,实际上,如果我们明白了在布局时它利用了单元格相对坐标定位这一点,以及各个约束条件在布局中的作用,网格袋布局也会变得很轻松。     

A heuristic for the label printing problem

Label printing finds many applications in industry. However, this task is still labor intensive in many printing factories. Since each template can only accommodate a fixed number of labels, an important task is to work out the compositions of templates by allocating suitable labels to each template in order to fulfill the order requirements effectively. The template design could be rather arbitrary, which usually ends up with a lot of excessive printed labels. Enhancing the template design will significantly improve the efficiency of the printing process, and, at the same time, reduce the waste of resources. This motivates the study of more automatic design methods. In this paper, the problem is first formulated as a nonlinear integer programming problem. The main variables in the formulation are the compositions and the printing frequencies of templates. For practical purpose, each type of label is confined to one template only which allows automated packing and handling. The structure of the problems is carefully analyzed and a new algorithm is proposed. Numerical results show that the proposed method is a simple but effective way of generating good template designs.     

Interweaving genetic programming and genetic algorithm for structural and parametric optimization in adaptive platform product customization

An adaptive product platform offers high customizability for generating feasible product variants for customer requirements. Customization takes place not only to product platform structure but also to its relevant parameters. Structural and parametric optimization processes are interwoven with each other to achieve the total optimality. This paper presents an evolutionary method dealing with interwoven structural and parametric optimization of adaptive platform product customization. The method combines genetic programming and genetic algorithm for handling structural and parametric optimization, respectively. Efficient genetic representation and operation schemes are carefully adapted. While designing these schemes, features specific to structural and parameter customization are considered for the simplification of platform product management. The experimental results show that the performance of the proposed algorithm outperforms that of the tandem evolutionary algorithm in which a genetic algorithm for parametric optimization is totally nested in a genetic programming for structural optimization.     

A dynamic linear programming approach to market allocation of renewable resources in the italian energy system: The case of solar thermal and biogas technologies

In Italy solar thermal energy and energy from biogas are two possible means of reducing dependence on energy imports. Using a multiperiod LP model (MARKAL) the authors assessed the likely potential of both technologies under various circumstances. The study covered the period 1980–2005, in five segments of five years. It focused only on the subsystem of the energy end-uses which can be substituted for by solar thermal and biogas technologies. The overall non-renewable sources which can be saved in 20 years by these technologies total 450 PJ (1 PJ = 10^{1 5} J) if the fuel prices rise at 0 per cent average annual, 1450 PJ if the fuel prices rise at 4.2 per cent average annual, 1860 PJ if the fuel prices rise at 7.2 per cent average annual and 3780 PJ if the fuel prices rise at 15 per cent average annual. However the most competitive technologies appear to be solar water heaters used mainly in the private and commercial sectors and biogas systems used mainly in the agricultural sector. The study was carried out by APRE under ENEA (formerly CNEN) contract and was intended to serve as an analytical basis for establishing an overall development and demonstration strategy for end-use renewable technologies in Italy.     

Development of an undergraduate course – Internet-based instrumentation and control

The objective, strategy, and implementation details of a new undergraduate course, Internet-based Instrumentation and Control, are presented. The course has a companion laboratory that is supported by the National Science Foundation and industry. The combination is offered to senior-level undergraduate engineering students interested in sensing, instrumentation, control, and web programming that want to learn more about the integration of these technologies for solving real-world engineering problems. The course will also be offered to gifted high school seniors with similar interests and can serve as a vehicle to attract them to engineering disciplines. Preliminary assessment of the first offering of the course is encouraging and has shown that the course has achieved success in helping students understand concepts and master basic technologies for developing Internet-based automatic systems.     

A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.     

Kanga(ROO): Handling the micro-DST of the BaBar experiment with ROOT

A system based on ROOT for handling the micro-DST of the BaBar experiment is described. The purpose of the Kanga system is to have micro-DST data available in a format well suited for data distribution within a world-wide collaboration with many small sites. The design requirements, implementation and experience in practice after three years of data taking by the BaBar experiment are presented.     

Data mining of resilience indicators

In recent years, the Asia-Pacific region has experienced several financial setbacks, including speculative attacks in 1998 and the SARS outbreak in 2003. Financial stresses of this nature are unanticipated, and not all of the dangers can be predicted by the examination of market information and macroeconomic indicators. The Early Warning System (EWS) that has been adopted by the International Monetary Fund may not be able to predict future financial crises for all possible scenarios, because shocks come in many different forms. To supplement the EWS, this paper proposes a data mining framework to measure the resilience of an economy. The resilience framework does not predict a crisis, but rather assesses the current state of health of an economy and its ability to withstand a financial shock should one occur. The framework is based on a feedback system consisting of two stages. The first stage assigns a resilience score to each economy based on a fuzzy logic scoring scheme that is built on the ambiguous reasoning of experts. The second stage uses the classification tree approach to estimate thresholds for each economic indicator, and examines the quality of the fuzzy score. The result from the second stage is then passed back to the first stage as feedback. The final result is obtained when the feedback system reaches its equilibrium state. The proposed resilience framework is applied to the external-sector and the public-sector economies of several countries to illustrate its applicability.     

裂缝性潜山储层地震描述技术

裂缝是潜山油气藏的重要储集层 ,潜山的油气产出与裂缝的发育情况密切相关 ,因此 ,对裂缝发育控制因素和裂缝发育规律进行了分析和探讨。尽管单条裂缝在地震资料上难以识别 ,但是裂缝带在地震资料上有其特征。描述了裂缝储层的地球物理特征 ,并利用测井约束反演技术、吸收系数技术、地震相分类和相干分析技术对潜山储层进行预测。通过在车古 2 0 1潜山的应用实例分析 ,说明以上 4种储层描述技术比较有效     

基于模糊信息处理的中板轧机微机在线监测系统

中板轧机是中板厂最重要的生产设备，对其工况进行在线监测意义重大，各种传感器将轧机的工况信息传送给微机系统，微机系统将这些信息进行模糊处理，发出相关信息，指导系统安全，高效运行。