钢管材质计算机在线分检系统及其核心算法

介绍了钢管材质计算机在线分检系统的总体结构, 提出了一种基于经验值的统计递推分检算法. 当同规格不同批的钢管检测数据严重离散, 且在检测前不用样管先调试的情况下, 这种方法很好地解决了生产线上钢管材质的检测问题. 文中给出了现场测试的结果.     

面向颗粒状农产品的分选方法

为提高颗粒状农产品分选精度,提出了一种基于现场可编程门阵列(FPGA)的k最近邻(k-NN)方法.该方法分两步:第一步对基于FPGA的彩色线阵CCD成像系统得到的图像在PC上进行保存,并对得到的图像进行特征提取,然后用k-NN方法对提取的特征进行特征筛选得到最优特征集.第二步将训练好的最优特征集放在FPGA的ROM上,FPGA对线阵CCD得到的图像数据实时提取特征与ROM上最优特征集做距离计算实现k-NN分选算法.对花生和开心果两种颗粒状农产品用该方法进行实验,以RGB颜色空间为主要特征,结果表明:在选择合理特征个数和k值情况下对花生和开心果的分选正确率都达到了95％以上.     

基于双ibutterfly网络的插入排序算法

为实现密码算法硬件实现过程中序列插入排序的高效性,对序列排序特点和当前最为有效的GRP插入排序算法进行分析,基于ibutterfly网络的插入排序实现效率的评估策略,针对GRP算法存在的潜在缺陷,给出GRP算法的改进算法及其硬件实现。利用Matlab对改进算法的实现效率进行验证,基于Design Complier综合工具对其硬件电路进行性能评估,评估结果表明,在硬件面积增加8?2％的基础上,该方案能够有效提升GRP算法的灵活高效性,验证了改进方案的合理性。     

多阶段多产品调度问题的链式智能体遗传算法

将遗传算法的编码方式与智能体系统的演化结构相结合,提出一种求解多阶段多产品调度问题的链式智能体遗传算法.算法采用基于订单序列的编码方式,采用一种新的后向指派规则实现编码和可行调度间的一一对应.通过各智能体与其邻域环境的竞争与合作以及自身的自学习操作实现种群的演化过程.对多阶段多产品调度问题的仿真结果表明:链式智能体遗传...     

多阶段煤炭供应链网络设计及其遗传算法

煤炭供应链网络设计旨在为大型煤炭集团选择合理的设施网络布局与最佳运量,以便提高效率并降低成本。考虑配煤加工与流量平衡等特有约束,建立了煤炭供应链网络混合整数规划模型,其优化目标是最小化固定设施成本、运输总成本与采购成本。考虑到模型求解的复杂度,设计了一种遗传算法,结合优先权与整数规则对染色体进行了编码与解码。实验算例表明所建立的模型能够真实地模拟煤炭供应链网络中设施布局与最佳运量的决策环境,其算法能够在允许的运算时间内获得稳定的满意解,随着算例规模的增大,其计算时间与优化结果均优于LINGO软件。     

A VLSI algorithm for sorting variable-length character strings

This paper proposes a sorting hardware module that can directly cope with variable length character strings. It gives a pipelined heap sort algorithm for a set of variable-length character strings, and a VLSI architecture that implements this algorithm. The hardware consists of a specially designed single chip module and an external memory bank. This special chip module is called a V-Sort Engine Core. The number of words in the external memory bank should be larger than the total length of strings to be sorted. A hardware module that can sort no more than 2 ^L strings uses a V-Sort Engine core consisting ofL levels. Thei-th level of a V-Sort Engine Core has a logic cell and a memory bank with 2 ⁱ words. Each word consists of three fields and a mark bit, i. e., level number, character, and path number. A triple (j, c, i) consisting of these field values denotes thej+1st characterc of thei-th input string. Concurrent execution of the external memory bank and all the level logic cells of the V-Sort Engine Core allows the hardware module to receive a sequence of strings sequentially character by character, and to begin the sequential output of the sort result immediately after receiving the last input character. It requires no extra time other than those required for sequential data transfer to and from itself.     

一种改进的非支配排序多目标遗传算法

多目标进化算法的研究目标主要是使算法快速收敛,并且广泛而均匀分布于问题的非劣最优域。在NSGA-II算法的基础上,提出了一种新的构造种群的策略——按照聚集距离选取部分非支配个体,并选取部分较好的支配个体形成下一代种群。该策略与原算法相结合后的算法（NSGA-II+IMP）与原NSGA-II进行比较,结果表明新算法较好地改善了分布性和收敛性。     

A Parallel sorting algorithm for tightly coupled multiprocessors

In this paper we present a new parallel sorting algorithm suitable for implementation on a tightly coupled multiprocessor. The algorithm utilizes P processors to sort a set of N data items subdivided into M subsets. The performance of the algorithm is investigated, and the results of experiments carried out on the Balance 8000 multiprocessor are presented.     

A load-balanced parallel sorting algorithm for shared-nothing architectures

With the popularity of parallel database machines based on the shared-nothing architecture, it has become important to find external sorting algorithms which lead to a load-balanced computation, i.e., balanced execution, communication and output. If during the course of the sorting algorithm each processor is equally loaded, parallelism is fully exploited. Similarly, balanced communication will not congest the network traffic. Since sorting can be used to support a number of other relational operations (joins, duplicate elimination, building indexes etc.) data skew produced by sorting can further lead to execution skew at later stages of these operations. In this paper we present a load-balanced parallel sorting algorithm for shared-nothing architectures. It is a multiple-input multiple-output algorithm with four stages, based on a generalization of Batcher's odd-even merge. At each stage then keys are evenly distributed among thep processors (i.e., there is no final sequential merge phase) and the distribution of keys between stages ensures against network congestion. There is no assumption made on the key distribution and the algorithm performs equally well in the presence of duplicate keys. Hence our approach always guarantees its performance, as long asn is greater thanp ³, which is the case of interest for sorting large relations. In addition, processors can be added incrementally. Recommended by: Patrick Valduriez     

非支配排序均匀遗传算法*

根据第二代非支配排序遗传算法（NSGA Ⅱ）的不足之处,提出了一种新的多目标遗传算法——非支配排序均匀遗传算法（NSUGA）。新算法采用了多父本多点交叉方式,同时将均匀设计的思想用于算法的交叉操作;新算法还对拥挤距离的计算过程和算法的终止条件进行了改进。通过两个多目标优化测试函数的仿真计算对比,显示NSUGA算法在求解精度、计算效率和避免算法陷于局部最优解方面均优于NSGA II算法。     

Generalized algorithm for parallel sorting on product networks

We generalize the well-known odd-even merge sorting algorithm, originally due to Batcher (1968), and show how this generalized algorithm can be applied to sorting on product networks. If G is an arbitrary factor graph with N nodes, its r-dimensional product contains N^r nodes. Our algorithm sorts N^r keys stored in the r-dimensional product of G in O(r^rF(N)) time, where F(N) depends on G. We show that, for any factor graph G, F(N) is, at most, O(N), establishing an upper bound of O(r² N) for the time complexity of sorting N^r keys on any product network. For product networks with bounded r(e.g. for grids), this leads to the asymptotic complexity of O(N) to sort N^r keys, which is optimal for several instances of product networks. There are factor graphs for which F(N)=O(log² N), which leads to the asymptotic running time of O(log² N) to sort N^r keys. For networks with bounded N (e.g. in the hypercube N=2, fixed), the asymptotic complexity becomes O(r²). We show how to apply the algorithm to several cases of well-known product networks, as well as others introduced recently. We compare the performance of our algorithm to well-known algorithms developed specifically for these networks, as well as others. The result of these comparisons led us to conjecture that the proposed algorithm is probably the best deterministic algorithm that can be found in terms of the low asymptotic complexity with a small constant     

Neurally-inspired stochastic algorithm for determining multi-stage multi-attribute sampling inspection plans

In manufacturing industries, sampling inspection is a common practice for quality assurance and cost reduction. The basic decisions in sampling inspection are how many manufactured items to be sampled from each lot and how many identified defective items in the sample to accept or reject each lot. Because of the combinatorial nature of alternative solutions on the sample sizes and acceptance criteria, the problem of determining an optimal sampling plan is NP-complete. In this paper, a neurally-inspired approach to generating acceptance sampling inspection plans is proposed. A Bayesian cost model of multi-stage-multi-attribute sampling inspections for quality assurance in serial production systems is formulated. This model can accommodate various dispositions of rejected lott such as scraping and screening. The model also can reflect the relationships between stages and among attributes. To determine the sampling plans based on the formulated model, a neurally-inspired stochastic algorithm is developed. This algorithm simulates the state transition of a primal-dual stochastic neural network to generate the sampling plans. The simulated primal network is responsible for generation of new states whereas the dual network is for recording the generated solutions. Starting with an arbitrary feasible solution, this algorithm is able to converge to a near optimal or an optimal sampling plan with a sequence of monotonically improved solutions. The operating characteristics and performance of the algorithm are demonstratedvia numerical examples.     

自适应的非支配排序遗传算法

针对带精英策略的非支配排序遗传算法不能根据环境变化自适应地动态调整运行参数,难以实现对解空间的高效搜索,提出一种自适应的非支配排序遗传算法.所提出算法根据运行阶段、运行代数和当前临时种群非支配个体数动态调整进化个体的运行参数,通过提高进化算子的自适应能力使算法具有自适应性.经实验对比,所提出算法在收敛性、多样性两方面确有提升,可以有效提高原算法的搜索能力.     

A flexible vision-based algorithm for a book sorting system

A flexible vision-based algorithm for a book sorting system is presented. The algorithm is based on a discrimination model that is adaptively generated for the current object classes by learning. The algorithm consists of an image normalization process, a feature element extraction process, a learning process, and a recognition process. The image normalization process extracts the contour of the object in an image, and geometrically normalizes the image. The feature extraction process converts the normalized image to the pyramidal representation, and the feature element is extracted from each resolution level. The learning process generates a discrimination model, which represents the differences between classes, based on hierarchical clustering. In the recognition process, the input images are hierarchically discriminated under the control of the decision tree. To evaluate the algorithm, a simulation system was implemented on a general-purpose computer and an image processor was developed     

A more efficient algorithm for perfect sorting by reversals

We describe a new algorithm for the problem of perfect sorting a signed permutation by reversals. The worst-case time complexity of this algorithm is parameterized by the maximum prime degree d of the strong interval tree, i.e., f(d).nO(1). This improves the best known algorithm which complexity was based on a parameter always larger than or equal to d.     

区间多目标优化非支配排序云模型算法

针对区间多目标优化问题,利用云模型对NSGA-II算法进行改进,提出一种非支配排序云模型算法（NSCMA）。首先,从初始云团中随机选取一个云滴作为父代,通过正态云算子生成子代云滴,用来替代传统NSGA-II遗传操作中的交叉和变异;其次,用约束条件对生成的云滴进行筛选处理,避免不可行解进入下一步算法;最后,运用区间占优关系对满足约束条件的解进行占优排序,对无法比较的同序值解计算拥挤距离。仿真结果验证了所设计算法的有效性。