Heuristic search for scheduling flexible manufacturing systems using lower bound reachability matrix

For scheduling flexible manufacturing systems efficiently, we propose new heuristic functions for A^* algorithm that is based on the T-timed Petri net. In minimizing makespan, the proposed heuristic functions are usually more efficient than the previous functions in the required number of states and computation time. We prove that these heuristic functions are all admissible and one of them is more informed than that using resource cost reachability matrix. We also propose improved versions of these heuristic functions that find a first near-optimal solution faster. In addition, we modify the heuristic function of Yu, Reyes, Cang, and Lloyd (2003b) and propose an admissible version in all states. The experimental results using a random problem generator show that the proposed heuristic functions perform better as we expected.     

Search strategy for scheduling flexible manufacturing systems simultaneously using admissible heuristic functions and nonadmissible heuristic functions

To scheduling flexible manufacturing system (FMS) efficiently, we propose and evaluate an improved search strategy and its application to FMS scheduling in the P-timed Petri net framework. On the execution of Petri net, the proposed method can simultaneously use admissible heuristic functions and nonadmissible heuristic functions for A^* algorithm. We also prove that the resulting combinational heuristic function is still admissible and more informed than any of its constituents. The experimental results of an example FMS and several sets of random generated problems show that the proposed search method performs better as we expected.     

Interpolating G Bézier surfaces over irregular curve networks for ship hull design

We propose a local method of constructing piecewise G¹ Bézier patches to span an irregular curve network, without modifying the given curves at odd- and 4-valent node points. Topologically irregular regions of the network are approximated by implicit surfaces, which are used to generate split curves, which subdivide the regions into triangular and/or rectangular sub-regions. The subdivided regions are then interpolated with Bézier patches. We analyze various singular cases of the G¹ condition that is to be met by the interpolation and propose a new G¹ continuity condition using linear and quartic scalar weight functions. Using this condition, a curve network can be interpolated without modification at 4-valent nodes with two collinear tangent vectors, even in the presence of singularities. We demonstrate our approach in a ship hull.     

Extending ?-diversity to generalize sensitive data

Generalization is an important technique for protecting privacy in data dissemination. In the framework of generalization, ?-diversity is a strong notion of privacy. However, since existing ?-diversity measures are defined in terms of the most specific (rather than general) sensitive attribute (SA) values, algorithms based on these measures can have narrow eligible ranges for data that has a heavily skewed distribution of SA values and produce anonymous data that has a low utility. In this paper, we propose a new ?-diversity measure called the functional (τ, ?)-diversity, which extends ?-diversity by using a simple function to constrain frequencies of base SA values that are induced by general SA values. As a result, algorithms based on (τ, ?)-diversity may generalize SA values, thus are much less constrained by skew SA distributions. We show that (τ, ?)-diversity is more flexible and elaborate than existing ?-diversity measures. We present an efficient heuristic algorithm that uses a novel order of quasi-identifier (QI) values to achieve (τ, ?)-diversity. We compare our algorithm with two state-of-the-art algorithms that are based on existing ?-diversity measures. Our preliminary experimental results indicate that our algorithm not only provides a stronger privacy protection but also results in better utility of anonymous data.     

Development of a configuration space motion planner for robot in dynamic environment

In this paper, the on-line motion planning of articulated robots in dynamic environment is investigated. We propose a practical on-line robot motion planning approach that is based upon pre-computing the global configuration space (C-space) connectivity with respect to all possible obstacle positions. The proposed motion planner consists of an off-line stage and an on-line stage. In the off-line stage, the obstacles in the C-space (C-obstacle) with respect to the obstacle positions in the workspace are computed, which are then stored using a hierarchical data structure with non-uniform 2^m trees. In the on-line stage, the real obstacle cells in the workspace are identified and the corresponding 2^m trees from the pre-computed database are superposed to construct the real-time C-space. The collision-free path is then searched in this C-space by using the A* algorithm under a multi-resolution strategy which has excellent computational efficiency. In this approach, the most time-consuming operation is performed in the off-line stage, while the on-line computing only need to deal with the real-time obstacles occurring in the dynamic environment. The minimized on-line computational cost makes it feasible for real-time on-line motion planning. The validity and efficiency of this approach is demonstrated using manipulator prototypes with 5 and 7 degree-of-freedom.     

A fast pattern matching algorithm with multi-byte search unit for high-speed network security

A signature-based intrusion detection system identifies intrusions by comparing the data traffic with known signature patterns. In this process, matching of packet strings against signature patterns is the most time-consuming step and dominates the overall system performance. Many signature-based network intrusion detection systems (NIDS), e.g., the Snort, employ one or multiple pattern matching algorithms to detect multiple attack types. So far, many pattern matching algorithms have been proposed. Most of them use single-byte standard unit for search, while a few algorithms such as the Modified Wu-Manber (MWM) algorithm use typically two-byte unit, which guarantees better performance than others even as the number of different signatures increases. Among those algorithms, the MWM algorithm has been known as the fastest pattern matching algorithm when the patterns in a rule set rarely appear in packets. However, the matching time of the MWM algorithm increases as the length of the shortest pattern in a signature group decreases.In this paper, by extending the length of the shortest pattern, we minimize the pattern matching time of the algorithm which uses multi-byte unit. We propose a new pattern matching algorithm called the L⁺¹-MWM algorithm for multi-pattern matching. The proposed algorithm minimizes the performance degradation that is originated from the dependency on the length of the shortest pattern. We show that the L⁺¹-MWM algorithm improves the performance of the MWM algorithm by as much as 20% in average under various lengths of shortest patterns and normal traffic conditions. Moreover, when the length of the shortest pattern in a rule set is less than 5, the L⁺¹-MWM algorithm shows 38.87% enhancement in average. We also conduct experiments on a real campus network and show that 12.48% enhancement is obtained in average. In addition, it is shown that the L⁺¹-MWM algorithm provides a better performance than the MWM algorithm by as much as 25% in average under various numbers of signatures and normal traffic conditions, and 20.12% enhancement in average with real on-line traffic.     

Using compressed index structures for processing moving objects in large spatio-temporal databases

This paper develops a novel, compressed B⁺-tree based indexing scheme that supports the processing of moving objects in one-, two-, and multi- dimensional spaces. The past, current, and anticipated future trajectories of movements are fully indexed and well organized. No parameterized functions and geometric representations are introduced in our data model so that update operations are not required and the maintenance of index structures can be accomplished by basic insertion and deletion operations. The proposed method has two contributions. First, the spatial and temporal attributes of trajectories are accurately preserved and well organized into compact index structures with very efficient memory space utilization and storage requirement. Second, index maintenance overheads are more economical and query performance is more responsive than those of conventional methods. Both analytical and empirical studies show that our proposed indexing scheme outperforms the TPR-tree.     

A*树搜索算法代价与误差关系的研究

本文对Ｐｅａｒｌ提出的Ａ^*算法所使用的可采纳性启发式函数ｈ的准确性和期望代价之间的精确关系的两个定理做了介绍，并提出和证明了为确保这两个定理正确性的附加条件．     

Lattice structure of e-implications on L

In this paper, we consider the set of all e-implications on L^∗ and define two operations on the set of all representable e-implications on L^∗, thus endowing it with monoid structure which is also a distributive complete lattice.     

Bounded size dictionary compression: SC-completeness and NC algorithms

We study the parallel complexity of a bounded size dictionary version (LRU deletion heuristic) of the LZ2 compression algorithm. The unbounded version was shown to be P-complete. When the size of the dictionary is O(log^kn), the problem of computing the LZ2 compression is shown to be hard for the class of problems solvable simultaneously in polynomial time and O(log^kn) space (that is, SC^k). We also introduce a variation of this heuristic that turns out to be an SC^k-complete problem (the original heuristic belongs to SC^k+1). In virtue of these results, we argue that there are no practical parallel algorithms for LZ2 compression with LRU deletion heuristic or any other heuristic deleting dictionary elements in a continuous way. For simpler heuristics (SWAP, RESTART, FREEZE), practical parallel algorithms are given.     

Efficient continuous skyline computation

In a number of emerging streaming applications, the data values that are produced have an associated time interval for which they are valid. A useful computation over such streaming data is to produce a continuous and valid skyline summary. Previous work on skyline algorithms have only focused on evaluating skylines over static data sets, and there are no known algorithms for skyline computation in the continuous setting. In this paper, we introduce the continuous time-interval skyline operator, which continuously computes the current skyline over a data stream. We present a new algorithm called LookOut for evaluating such queries efficiently, and empirically demonstrate the scalability of this algorithm. In addition, we also examine the effect of the underlying spatial index structure when evaluating skylines. Whereas previous work on skyline computations have only considered using the R^∗-tree index structure, we show that for skyline computations using an underlying quadtree has significant performance benefits over an R^∗-tree index.     

A periodic tabular policy for scheduling of a single stage production-inventory system

In this paper, we consider scheduling of a multi-item single stage production-inventory system in the presence of uncertainty regarding demand patterns, production times and switchover times. For a given specification of base-stock levels of individual items and under (S − 1, S) requests for replenishment policy, a mathematical program to minimize long-run average system wide costs is formulated. We derive approximations for the first two moments of demand over lead time using residual service analysis of vacation queue models. Subsequently, we develop an approximate convex program for the original cost model and determine optimal production frequencies for individual types. Based on these relative frequencies, we determine a table size and devise an efficient heuristic to construct a tabular sequence in which individual items appear according to their respective absolute frequencies and items are positioned such that variance of their inter-visit times is minimized. A numerical study that demonstrates effectiveness of the proposed policy against cyclic policies is given.     

K: A heuristic search algorithm for finding the k shortest paths

We present a directed search algorithm, called K^?, for finding the k shortest paths between a designated pair of vertices in a given directed weighted graph. K^? has two advantages compared to current k-shortest-paths algorithms. First, K^? operates on-the-fly, which means that it does not require the graph to be explicitly available and stored in main memory. Portions of the graph will be generated as needed. Second, K^? can be guided using heuristic functions. We prove the correctness of K^? and determine its asymptotic worst-case complexity when using a consistent heuristic to be the same as the state of the art, , with respect to both runtime and space, where n is the number of vertices and m is the number of edges of the graph. We present an experimental evaluation of K^? by applying it to route planning problems as well as counterexample generation for stochastic model checking. The experimental results illustrate that due to the use of heuristic, on-the-fly search K^? can use less time and memory compared to the most efficient k-shortest-paths algorithms known so far.     

Concordance and consensus

This paper deals with the measurement of concordance and the construction of consensus in preference data, either in the form of preference rankings or in the form of response distributions with Likert-items. We propose a set of axioms of concordance in preference orderings and a new class of concordance measures. The measures outperform classic measures like Kendall’s τ and W and Spearman’s ρ in sensitivity and apply to large sets of orderings instead of just to pairs of orderings. For sets of N orderings of n items, we present very efficient and flexible algorithms that have a time complexity of only O(Nn²). Remarkably, the algorithms also allow for fast calculation of all longest common subsequences of the full set of orderings. We experimentally demonstrate the performance of the algorithms. A new and simple measure for assessing concordance on Likert-items is proposed.     

Designing robustly stabilising controllers for LTI spatially distributed systems using coprime factor synthesis

This paper considers the design of feedback controllers for linear, time-invariant, spatially distributed systems in an approach which generalises the H^∞-framework and in particular the H^∞ loop-shaping method. To this end, we introduce a class of spatially distributed system models called finite dimensional, distributed, linear, time-invariant systems. Sensors and actuators are considered to be part of the controller, rather than part of the plant, and thus the controller we wish to design is itself a spatially distributed system. Optimising over placements and shapes of the sensor and actuator spatial distribution functions is an integrated part of the controller design procedure. As an illustrative design example, we present the feedback stabilisation of an electrostatically destabilised, electrically conducting membrane.     

Branch-and-bound method for minimizing the weighted completion time scheduling problem on a single machine with release dates

In this paper, we consider a single-machine scheduling problem with release dates. The aim is to minimize the total weighted completion time. This problem is known to be strongly NP-hard. We propose two new lower bounds that can be, respectively, computed in O(n²) and in O(nlogn) time where n is the number of jobs. We prove a sufficient and necessary condition for local optimality, which can also be considered as a priority rule. We present an efficient heuristic using such a condition. We also propose some dominance properties. A branch-and-bound algorithm incorporating the heuristic, the lower bounds and the dominance properties is proposed and tested on a large set of instances.     

Best position algorithms for efficient top-k query processing

The general problem of answering top-k queries can be modeled using lists of data items sorted by their local scores. The main algorithm proposed so far for answering top-k queries over sorted lists is the Threshold Algorithm (TA). However, TA may still incur a lot of useless accesses to the lists. In this paper, we propose two algorithms that are much more efficient than TA. First, we propose the best position algorithm (BPA). For any database instance (i.e. set of sorted lists), we prove that BPA stops as early as TA, and that its execution cost is never higher than TA. We show that there are databases over which BPA executes top-k queries O(m) times faster than that of TA, where m is the number of lists. We also show that the execution cost of our algorithm can be (m−1) times lower than that of TA. Second, we propose the BPA2 algorithm, which is much more efficient than BPA. We show that the number of accesses to the lists done by BPA2 can be about (m−1) times lower than that of BPA. We evaluated the performance of our algorithms through extensive experimental tests. The results show that over our test databases, BPA and BPA2 achieve significant performance gains in comparison with TA.     

分数阶微分的CIE L*a*b*颜色空间边缘检测

由于分数阶微积分在细节加强处理方面具有良好的性能,其在边缘检测中的应用正在逐渐增多.目前用分数阶微积分进行边缘检测的研究中大多在RGB空间中进行,但由于RGB颜色空间中亮度信息和色度信息不能相互分离,在其中进行边缘提取不能综合得到亮度边缘和色度边缘,所以分数阶微积分在其他亮度信息和色度信息分开的颜色空间中的应用研究具有一定的理论意义和实际价值.为了综合利用图像的颜色信息,在CIE L*a*b*颜色空间中利用分数阶微分进行边缘检测.相比于直接在RGB颜色空间中得到的边缘,本文方法提取到的图像边缘更符合人眼的视觉感知,边缘的连续性以及抗干扰性也更好.通过与其他经典算法的对比分析表明,本文方法存在明显的优势.