A Review of the Application of Meta-Heuristic Algorithms to 2D Strip Packing Problems

This paper is a review of the approachesdeveloped to solve 2D packing problems withmeta-heuristic algorithms. As packing tasks arecombinatorial problems with very large searchspaces, the recent literature encourages theuse of meta-heuristic search methods, inparticular genetic algorithms. The objective ofthis paper is to present and categorise thesolution approaches in the literature for 2Dregular and irregular strip packing problems.The focus is hereby on the analysis of themethods involving genetic algorithms. Anoverview of the methods applying othermeta-heuristic algorithms including simulatedannealing, tabu search, and artificial neuralnetworks is also given.     

Compositional pattern producing networks: A novel abstraction of development

Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings, i.e. encodings that map the genotype to the phenotype through a process of growth from a small starting point to a mature form. A major challenge in in this effort is to find the right level of abstraction of biological development to capture its essential properties without introducing unnecessary inefficiencies. In this paper, a novel abstraction of natural development, called Compositional Pattern Producing Networks (CPPNs), is proposed. Unlike currently accepted abstractions such as iterative rewrite systems and cellular growth simulations, CPPNs map to the phenotype without local interaction, that is, each individual component of the phenotype is determined independently of every other component. Results produced with CPPNs through interactive evolution of two-dimensional images show that such an encoding can nevertheless produce structural motifs often attributed to more conventional developmental abstractions, suggesting that local interaction may not be essential to the desirable properties of natural encoding in the way that is usually assumed.

无线传感器网络中基于树的动态多路径容错路由协议

多路径路由对无线传感器网络的负载均衡和容错能力等方面都有一定改善作用。在研究一些多路径路由算法的基础上,对分层的无线传感器网络路由进行探索,提出了一种基于树的动态多路径路由的生成方法,快速获得当前节点的主路径与备用路径。仿真结果表明,该方法可有效地均衡网络负载、减少时延、提高网络的可靠性。     

Modeling deterministic echo state network with loop reservoir

Echo state network (ESN),which efficiently models nonlinear dynamic systems,has been proposed as a special form of recurrent neural network.However,most of the proposed ESNs consist of complex reservoi...     

Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area

This study aims to provide a rapid screening tool for assessment of sustainable flood retention basins (SFRBs) to predict corresponding dam failure risks. A rapid expert-based assessment method for dam failure of SFRB supported by an artificial neural network (ANN) model has been presented. Flood storage was assessed for 110 SFRB and the corresponding Dam Failure Risk was evaluated for all dams across the wider Greater Manchester study area. The results show that Dam Failure Risk can be estimated by using the variables Dam Height, Dam Length, Maximum Flood Water Volume, Flood Water Surface Area, Mean Annual Rainfall (based on Met Office data), Altitude, Catchment Size, Urban Catchment Proportion, Forest Catchment Proportion and Managed Maximum Flood Water Volume. A cross-validation R² value of 0.70 for the ANN model signifies that the tool is likely to predict variables well for new data sets. Traditionally, dams are considered safe because they have been built according to high technical standards. However, many dams that were constructed decades ago do not meet the current state-of-the-art dam design guidelines. Spatial distribution maps show that dam failure risks of SFRB located near cities are higher than those situated in rural locations. The proposed tool could be used as an early warning system in times of heavy rainfall.     

Predicting the Outcome of Construction Litigation Using Boosted Decision Trees

Construction litigation has become commonplace in numerous construction projects, particularly in large contracts. Miscommunication, inadequate plans and specifications, rigid contracts, changes in site conditions, nonpayment, catch up profits, limitations on manpower, tools, and equipment, improper supervision, notice requirements, constructive changes not recognized as such by owner, delays, and acceleration measures provoke claims and often result in disputes. A boosted decision tree system was used to predict the outcome of construction litigation. The study was conducted by using the same 114 Illinois court cases that were used in earlier prediction studies conducted with artificial neural networks in 1998 and case-based reasoning in 1999, augmented by an additional 18 cases that were filed in 1990–2000. All cases were extracted from the Westlaw on-line service. The best prediction result obtained with boosted decision trees was 90%. The boosted decision tree model appears to be a promising tool to help create a dispute-free construction industry.     

VPN构建的一种设计思想

Internet的发展对安全问题的要求越来越高，网络安全日益受到重视。文章以虚拟专用网为主要研究对象，结合软件工程的一些开发思想，对虚拟专用网的构建做出了理论上的设计。文章分析了虚拟专用网目前所涉及的主要几大模型，具体构建了一种可行的VPN设计方案，同时结合软件工程，对VPN构建给出自行设计的一种模块结构图，并对各个模块结构给出了具体解释，今后的工作在于将模块具体细化运用到实践中。     

基于神经元网络的燃油锅炉烟气氧含量的软

针对目前各种氧含量检测仪表在运行过程中的可靠性差、使用寿命短及价格昂贵等诸多不足，通过探寻锅炉运行中的其他参数与烟气氧含量的映射关系，建立ＢＰ神经元网络，提出了燃油锅炉烟气氧含量的软测量方法。结果表明，其精度完全达到了设计要求。     

神经网络控制器用于永磁电机的伺服驱动

提出用一种遗传算法（ＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍ，简称ＧＡ）训练的多层神经网络结构（ＮＮ），并把它用于交流直线永磁同步电动机（简称ＬＰＭＳＭ）的辩识与控制，使其运行在伺服驱动环境．ＮＮ可随时辩识被控系统的动态待性，而不需要ＬＰＭＳＭ的精确的数学模型．仿真结果令人满意     

一类时滞神经网络系统的局部稳定性与分枝

给出一类时滞神经网络系统的零解的局部稳定判定准则，并给出该系统出现Ｈｏｐｆ分枝的参数条件