P2P应用层数据流量优化

为减少对主干网络带宽的消耗,提出了一套数据流量优化方法。首先通过把Peer间的邻居关系明确划分为物理邻居关系和逻辑邻居关系,并用探路者算法来发现Peer的物理邻居,实现拓扑匹配;然后在数据调度算法中,引入通报/退避机制,把大部分数据传输控制在城域网络内部。通过建立模拟仿真环境进行实验分析,证实了该方法可以减少90%以上的网络数据流量。     

Pricing decisions in a multi-criteria setting for product recovery facilities

Unpredictability in the arrival time and quantity of discarded products at product recovery facilities (PRFs) and varying demand for recovered components contribute to the volatility in their inventory levels. Achieving profit under such capricious inventory levels and stringent environmental legislations remains a challenge to many PRFs. This paper presents a multi-criteria decision model to determine a pricing policy that can simultaneously address two issues: stabilize inventory fluctuations and boost profits. The model considers that PRFs passively accepts discarded products as well as acquires them proactively if necessary. Under a multi-criteria setting, the current work determines prices of reusable and recyclable components to maximize revenue and minimize product recovery costs. A genetic algorithm is employed to solve the multi-criteria decision making problem. Sensitivity analysis is performed to investigate the effect of sorting yield, disassembly yield, and reusable component yield on the profits, prices, inventory levels, and disposal quantities.     

梯度相关的无线传感器网络成簇路由协议

分析了梯度和成簇无线传感器网络路由协议,确定了协议中存在的缺陷,设计了一种基于梯度的高效节能成簇路由协议(GBCRP)。采用基于跳数的梯度建立、能量相关的成簇算法、关键节点转发和功率自适应的簇头路由等技术,解决了无线传感器网络的节点能耗和路由失败而导致的数据传输可靠性问题。仿真实验表明,该协议有效地延长了网络的生命周期。针对不同密度的传感器节点,网络生命周期具有很好的稳定性,同时该协议也具有较好的可靠性保障。     

鱼类吞噬细胞和吞噬作用的研究进展

对鱼类吞噬细胞的分离鉴定、吞噬过程等方面的国内外研究成果进行了综述,并介绍了鱼类细胞吞噬作用的机理以及各种因子对吞噬作用的调节,对今后的研究方向进行了展望.     

实时调度算法研究

对实时调度进行了讨论：研究了单处理器下的经典调度算法：单调速率调度、最早死线调度和最短空闲时间优先调度;分析了多处理器系统中的典型调度算法：对分布式系统中的两种调度算法：广义单调速率调度和分布式风车调度做了简要论述;指出实时调度研究策略方向。     

《诗经》中的鱼文化

《诗经》中出现鱼字和鱼名的地方约有三十余处,涉及的鱼名有鲂、鳢、鲔等十二种;《诗经》中咏鱼诗篇的表现手法有实叙、比拟、象征等多种,也就是古人常说的"赋、比、兴"。并推断出《诗经》时代的捕鱼方法,有竿钓、网捕、笼捕、设池围捕等。文章最后一节,归纳出鱼在《诗经》时代人们生活中具有用于祭礼、烹制佳肴,表示兴盛,作为象征,取以饰物等多方面的作用。     

决策支持系统的自适应建模研究及应用实例分析

在进行决策的过程中 ,建立正确的系统模型是解决问题的关键 .由于多方面的原因 ,有时要针对研究对象选用适用的模型来进行分析是很困难的 .本文根据遗传算法在程序设计中的应用 ,探讨了基于遗传算法的自适应建模方法 ,并对实际应用中自适应建模的算法存在的问题提出了改进方法 .最后应用自适应建模方法对全国的集装箱与外贸数据进行了建模分析 ,应用实例表明自适应建模方法对于解决难以选择合适的模型进行建模分析时是很有效的     

Approximation Algorithms for the Multiple Knapsack Problem with Assignment Restrictions

Motivated by a real world application, we study the multiple knapsack problem with assignment restrictions (MKAR). We are given a set of items, each with a positive real weight, and a set of knapsacks, each with a positive real capacity. In addition, for each item a set of knapsacks that can hold that item is specified. In a feasible assignment of items to knapsacks, each item is assigned to at most one knapsack, assignment restrictions are satisfied, and knapsack capacities are not exceeded. We consider the objectives of maximizing assigned weight and minimizing utilized capacity.We focus on obtaining approximate solutions in polynomial computational time. We show that simple greedy approaches yield 1/3-approximation algorithms for the objective of maximizing assigned weight. We give two different 1/2-approximation algorithms: the first one solves single knapsack problems successively and the second one is based on rounding the LP relaxation solution. For the bicriteria problem of minimizing utilized capacity subject to a minimum requirement on assigned weight, we give an (1/3,2)-approximation algorithm.     

Quantifying the similarity of 2D images using edge pixels: an application to the forensic comparison of footwear impressions

We propose a novel method to quantify the similarity between an impression (Q) from an unknown source and a test impression (K) from a known source. Using the property of geometrical congruence in the impressions, the degree of correspondence is quantified using ideas from graph theory and maximum clique (MC). The algorithm uses the x and y coordinates of the edges in the images as the data. We focus on local areas in Q and the corresponding regions in K and extract features for comparison. Using pairs of images with known origin, we train a random forest to classify pairs into mates and non-mates. We collected impressions from 60 pairs of shoes of the same brand and model, worn over six months. Using a different set of very similar shoes, we evaluated the performance of the algorithm in terms of the accuracy with which it correctly classified images into source classes. Using classification error rates and ROC curves, we compare the proposed method to other algorithms in the literature and show that for these data, our method shows good classification performance relative to other methods. The algorithm can be implemented with the R package shoeprintr.     

Comparison of a genetic algorithm and simulated annealing in an application to statistical image reconstruction

Genetic algorithms (GAs) are adaptive search techniques designed to find near-optimal solutions of large scale optimization problems with multiple local maxima. Standard versions of the GA are defined for objective functions which depend on a vector of binary variables. The problem of finding the maximum a posteriori (MAP) estimate of a binary image in Bayesian image analysis appears to be well suited to a GA as images have a natural binary representation and the posterior image probability is a multi-modal objective function. We use the numerical optimization problem posed in MAP image estimation as a test-bed on which to compare GAs with simulated annealing (SA), another all-purpose global optimization method. Our conclusions are that the GAs we have applied perform poorly, even after adaptation to this problem. This is somewhat unexpected, given the widespread claims of GAs' effectiveness, but it is in keeping with work by Jennison and Sheehan (1995) which suggests that GAs are not adept at handling problems involving a great many variables of roughly equal influence.We reach more positive conclusions concerning the use of the GA's crossover operation in recombining near-optimal solutions obtained by other methods. We propose a hybrid algorithm in which crossover is used to combine subsections of image reconstructions obtained using SA and we show that this algorithm is more effective and efficient than SA or a GA individually.