Automated cutting tool selection and cutting tool sequence optimisation for rotational parts

The aim of this work is to define computer-aided optimum operation and tool sequences that are to be used in Generative Process Planning System developed for rotational parts. The software developed for this purpose has a modular structure. Cutting tools are selected automatically using the machinability data, workpiece feature information, machine tool data, workholding method and the set-up number. An optimum tool sequence is characterised by a minimum number of tool changes and minimum tool travel time. Tool and operation sequence for minimum tool change are optimised with a developed optimisation method that is based on “Rank Order Clustering”.     

On the separation in 2-period double round robin tournaments with minimum breaks

This paper considers the separation in 2-period double round robin tournaments (2P-DRRTs) with minimum breaks. The separation is a lower bound on the number of slots between the two games with the same opponents. None of known schemes provides 2P-DRRTs with minimum breaks and a positive separation. We first propose a new scheme to generate 2-separation 2P-DRRTs with minimum breaks, based on single round robin tournaments (SRRTs) with minimum breaks which have the last break in the third slot from the end. Our experiment results show that such SRRTs exist for 8-68 teams. Secondly, we consider maximizing the separation in general 2P-DRRTs with minimum breaks by integer programming and constraint programming, respectively. The two approaches of direct formulation and “first-break, then-schedule” decomposition are presented and compared. We obtain the maximum separation for up to 14 teams. Furthermore, we consider the application with place constraints to show the flexibility and efficiency of scheduling 2P-DRRTs with minimum breaks and a positive separation.     

On minimum length scale control in density based topology optimization

The archetypical topology optimization problem concerns designing the layout of material within a given region of space so that some performance measure is extremized. To improve manufacturability and reduce manufacturing costs, restrictions on the possible layouts may be imposed. Among such restrictions, constraining the minimum length scales of different regions of the design has a significant place. Within the density filter based topology optimization framework the most commonly used definition is that a region has a minimum length scale not less than D if any point within that region lies within a sphere with diameter D >?0 that is completely contained in the region. In this paper, we propose a variant of this minimum length scale definition for subsets of a convex (possibly bounded) domain. We show that sets with positive minimum length scale are characterized as being morphologically open. As a corollary, we find that sets where both the interior and the exterior have positive minimum length scales are characterized as being simultaneously morphologically open and (essentially) morphologically closed. For binary designs in the discretized setting, the latter translates to that the opening of the design should equal the closing of the design. To demonstrate the capability of the developed theory, we devise a method that heuristically promotes designs that are binary and have positive minimum length scales (possibly measured in different norms) on both phases for minimum compliance problems. The obtained designs are almost binary and possess minimum length scales on both phases.     

最小描述的多粒度覆盖粗糙集模型

在覆盖广义粗糙集理论中,对最小描述的定义是建立在单一粒度基础上。将最小描述从单一粒度推广到多个粒度,建立了多粒度覆盖粗糙集模型。在此基础上,用最小描述建立了两类不同的上下近似算子,研究其性质,给出了一种基于最小描述下求属性约简的新算法。     

A Comparison of Pixel Complexity in Composition Techniques for Sort-Last Rendering

Sort-last rendering is a method of parallelizing compute-intensive computer graphics; specifically, the primitives that describe a scene are first allocated to a set of renderers, and the rendered images are then composited to give the final image. This paper surveys six such techniques and compares their performance with active pixels, i.e., pixels that are covered by at least one primitive. Active pixels offer a uniform way of accounting for the time, space, and bandwidth costs in sort-last rendering. The comparison highlights the strengths of each technique. For example, tree composition has minimum work, binary-swap (hypercube) has minimum composition latency, direct pixel forwarding (mesh) has minimum bandwidth latency, and snooping (bus) has minimum bandwidth volume; binary-swap's bandwidth and composition latencies decrease, whereas the bandwidth volumes for direct pixel forwarding and snooping are constant, when the number of renderers increase; etc.     

An improved approach to find membership functions and multiple minimum supports in fuzzy data mining

Fuzzy mining approaches have recently been discussed for deriving fuzzy knowledge. Since items may have their own characteristics, different minimum supports and membership functions may be specified for different items. In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting minimum supports and membership functions for items from quantitative transactions. In that paper, minimum supports and membership functions of all items are encoded in a chromosome such that it may be not easy to converge. In this paper, an enhanced approach is proposed, which processes the items in a divide-and-conquer strategy. The approach is called divide-and-conquer genetic-fuzzy mining algorithm for items with Multiple Minimum Supports (DGFMMS), and is designed for finding minimum supports, membership functions, and fuzzy association rules. Possible solutions are evaluated by their requirement satisfaction divided by their suitability of derived membership functions. The proposed GA framework maintains multiple populations, each for one item’s minimum support and membership functions. The final best minimum supports and membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experimental results also show the effectiveness of the proposed approach.     

An O(log n) algorithm for parallel update of minimum spanning trees

Parallel algorithms are presented for updating a minimum spanning tree when the cost of an edge changes or when a new node is inserted in the underlying graph. Our model of computation is a parallel random access machine which allows simultaneous reads but prohibits simultaneous writes into the same memory location. The algorithms described in this paper for updating a minimum spanning tree require O(log n) time and O(n²) processors. These algorithms are efficient when compared to previously known algorithms for initial construction of a minimum spanning tree that require O(log²n) time and use O(n²) processors. The two main contributions of this paper are: (i) usage of an inverted tree for updating minimum spanning trees, and (ii) discovery of an interesting property of minimum spanning trees that we exploit to develop a novel algorithm for vertex insertion update.     

Bounds on the minimum code distance for nonbinary codes based on bipartite graphs

The minimum distance of codes on bipartite graphs (BG codes) over GF(q) is studied. A new upper bound on the minimum distance of BG codes is derived. The bound is shown to lie below the Gilbert-Varshamov bound when q ≤ 32. Since the codes based on bipartite expander graphs (BEG codes) are a special case of BG codes and the resulting bound is valid for any BG code, it is also valid for BEG codes. Thus, nonbinary (q ≤ 32) BG codes are worse than the best known linear codes. This is the key result of the work. We also obtain a lower bound on the minimum distance of BG codes with a Reed-Solomon constituent code and a lower bound on the minimum distance of low-density parity-check (LDPC) codes with a Reed-Solomon constituent code. The bound for LDPC codes is very close to the Gilbert-Varshamov bound and lies above the upper bound for BG codes.     

基于随机匹配的复杂网络最小驱动点集分析

控制复杂网络在很多领域都有着重要的应用价值. 将控制复杂网络所需的最少节点集合称为最小驱动点集. 针对网络的最小驱动点集并不唯一, 提出一种随机匹配方法来获取网络中不同的最小驱动点集, 并分析最小驱动点 集集合的平均度分布以及节点在最小驱动点集集合中的出现频率. 研究发现, 多数网络的最小驱动点集分布紧密, 其节点构成与网络度分布有关; 同时, 网络中节点的控制重要性与其入度密切相关. 所得到的相关结论对于复杂网络的控制具有重要的研究意义.

     

A Cut-Based Algorithm for the Nonlinear Dual of the Minimum Cost Network Flow Problem

We consider a convex, or nonlinear, separable minimization problem with constraints that are dual to the minimum cost network flow problem. We show how to reduce this problem to a polynomial number of minimum s,t-cut problems. The solution of the reduced problem utilizes the technique for solving integer programs on monotone inequalities in three variables, and a so-called proximity-scaling technique that reduces a convex problem to its linear objective counterpart. The problem is solved in this case in a logarithmic number of calls, O(log U), to a minimum cut procedure, where U is the range of the variables. For a convex problem on n variables the minimum cut is solved on a graph with O(n²) nodes. Among the consequences of this result is a new cut-based scaling algorithm for the minimum cost network flow problem. When the objective function is an arbitrary nonlinear function we demonstrate that this constrained problem is solved in pseudopolynomial time by applying a minimum cut procedure to a graph on O(nU) nodes.     

Systematic sampling is a minimum support design

In order to select a sample in a finite population of N units with given inclusion probabilities, it is possible to define a sampling design on at most N samples that have a positive probability of being selected. Designs defined on minimal sets of samples are called minimum support designs. It is shown that, for any vector of inclusion probabilities, systematic sampling always provides a minimum support design. This property makes it possible to extensively compute the sampling design and the joint inclusion probabilities. Random systematic sampling can be viewed as the random choice of a minimum support design. However, even if the population is randomly sorted, a simple example shows that some joint inclusion probabilities can be equal to zero. Another way of randomly selecting a minimum support design is proposed, in such a way that all the samples have a positive probability of being selected, and all the joint inclusion probabilities are positive.     

Necessary and sufficient conditions for uniqueness of the minimum in Prediction Error Identification

The contribution of this paper is to establish computable necessary and sufficient conditions on the model structure and on the experiment under which the Prediction Error Identification (PEI) criterion has a unique global minimum. We consider a broad class of rational model structures whose numerator and denominator are affine in the unknown parameter vector; this class encompasses all classical model structures used in system identification. The main results in this paper rely on the standard assumption that the system is in the model set, while some intermediate results are valid even when this assumption does not hold (in particular Theorem 4.2 and Lemma 6.1). This is achieved by first establishing necessary and sufficient conditions on the model structure and on the experiment under which a global minimum is isolated; these conditions must hold locally, at the global minimum. A second contribution is to show that these conditions are equivalent to the nonsingularity of the information matrix at that minimum. For open loop identification and, with some additional constraints also for closed loop identification, the nonsingularity of the information matrix is then also equivalent to the uniqueness of the global minimum.     

Constructing a minimum height elimination tree of a tree in linear time

Given a graph, finding an optimal vertex ranking and constructing a minimum height elimination tree are two related problems. However, an optimal vertex ranking does not by itself provide enough information to construct an elimination tree of minimum height. On the other hand, an optimal vertex ranking can readily be found directly from an elimination tree of minimum height. On n-vertex trees, the optimal vertex ranking problem already has a linear-time algorithm in the literature. However, there is no linear-time algorithm for the problem of finding a minimum height elimination tree. A naive algorithm for this problem requires O(nlogn) time. In this paper, we propose a linear-time algorithm for constructing a minimum height elimination tree of a tree.     

Parameter Estimation for Optimal Object Recognition: Theory and Application

Object recognition systems involve parameters such as thresholds, bounds and weights. These parameters have to be tuned before the system can perform successfully. A common practice is to choose such parameters manually on an ad hoc basis, which is a disadvantage. This paper presents a novel theory of parameter estimation for optimization-based object recognition where the optimal solution is defined as the global minimum of an energy function. The theory is based on supervised learning from examples. Correctness and instability are established as criteria for evaluating the estimated parameters. A correct estimate enables the labeling implied in each exemplary configuration to be encoded in a unique global energy minimum. The instability is the ease with which the minimum is replaced by a non-exemplary configuration after a perturbation. The optimal estimate minimizes the instability. Algorithms are presented for computing correct and minimal-instability estimates. The theory is applied to the parameter estimation for MRF-based recognition and promising results are obtained.     

TrCBC: Another look at CBC-MAC

CBC-MAC is the first block-cipher-based MAC algorithm. Despite of its advantages, e.g. minimum key size and minimum number of block-cipher invocations, it is commonly known that CBC-MAC cannot deal with arbitrary-length messages safely. Several variants of CBC-MAC have been proposed to fix this flaw; however, all of them do this at a cost of increasing either key size or number of block-cipher invocations, or even both. In this paper, we solve this problem by applying two different truncation methods to CBC-MAC. The proposed TrCBC is provably secure for arbitrary-length messages, still achieving minimum key size and minimum number of block-cipher invocations. At an expense, TrCBC can only produce short tags and has a relatively larger provable security bound. So, TrCBC is suitable for environments where (1) resources (memory, power, etc.) are limited; (2) high speed is required; (3) low security level is enough; (4) short messages are the majority; (5) short tags are required.     

The minimum gain lemma

This paper focuses on the newly developed notion of minimum gain and the corresponding Large Gain Theorem. The Large Gain Theorem is an input–output stability result particularly well suited to unstable plants connected in feedback with stable or unstable controllers. This paper aims to facilitate the practical application of these results. An altered definition of minimum gain broadens the applicability of the Large Gain Theorem, and the novel Minimum Gain Lemma provides LMI conditions that imply and are often equivalent to a minimum gain for LTI systems. Numerical examples are provided to clarify the differences between the existing and proposed definitions of minimum gain, highlight the utility of the newly established Minimum Gain Lemma, and demonstrate how the paper's contributions may be employed in practice. Copyright © 2014 John Wiley & Sons, Ltd.     

On a circle-cover minimization problem

Given a set of n circular-arcs A₁, A₂,...,A_n, we consider the problem of finding a minimum number of circular-arcs whose union covers the circle. Specifically, if the endpoints of these n arcs are given, we show that O(n log n) is both sufficient and necessary for solving the minimum cover problem. Furthermore, with O(n log n) preprocessing time we find the minimum cover in O(n) time.     

模糊环境下的最小权顶点覆盖问题*

最小权顶点覆盖问题在实际决策中应用广泛,但顶点上的权值在实际应用中通常代表费用、成本等,在很多情况下是不确定的。关注了最小权顶点覆盖问题中的模糊不确定性,对模糊环境下的最小权顶点覆盖问题进行了研究。引入了可信性理论以描述模糊不确定性,并根据不同的决策准则建立了求解模糊环境下最小权顶点覆盖问题的三个决策模型,结合模糊模拟和遗传算法设计了一种求解所建立模型的混合智能算法,并给出了数值实验。数值实验的结果验证了所提出的决策模型与算法的有效性。     

Derivation of optimal input parameters for minimizing execution time of matrix-based computations on a GPU

As GPUs are continually being utilized as coprocessors, the demand for optimally utilizing them for various computations continues to grow. The goal of this work is to derive input parameters which yield the minimum execution time for matrix-based computations executing on a GPU. Input parameters are defined as the dimensions of the grid and blocks assigned for execution on the GPU. Since input parameters inadequately represent the executional behavior of the GPU, execution metrics are formulated as functions of the input parameters to represent the behavior. The execution metrics are architecture independent and are utilized to derive optimal input parameters, which are input parameters that yield the minimum execution time. Optimal input parameters are derived for the following matrix-based computations: matrix–vector multiplication (Mv), matrix–matrix multiplication (MM), and convolution. The derivation allows for selection of optimal input parameters without executing code. Results, for all matrix-based computations and sizes tested, show that utilizing the derived optimal input parameters often yields the minimum execution time, and, at worst, execution time within 13.6% of the minimum.     

Minimum Dominating Sets of Intervals on Lines

We study the problem of computing minimum dominating sets of n intervals on lines in three cases: (1) the lines intersect at a single point, (2) all lines except one are parallel, and (3) one line with t weighted points on it and the minimum dominating set must maximize the sum of the weights of the points covered. We propose polynomial-time algorithms for the first two problems, which are special cases of the minimum dominating set problem for path graphs which is known to be NP-hard. The third problem requires identifying the structure of minimum dominating sets of intervals on a line so as to be able to select one that maximizes the weight sum of the weighted points covered. Assuming that presorting has been performed, the first problem has an O(n) -time solution, while the second and the third problems are solved by dynamic programming algorithms, requiring O(n log n) and O(n + t) time, respectively. Received April 13, 1995; revised July 27, 1996.