Discrete functions over a continuous domain are approximated by discrete functions with fewer levels. These quantizations are endowed with different types of constraints such as monotonicity and variational constraint...
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Discrete functions over a continuous domain are approximated by discrete functions with fewer levels. These quantizations are endowed with different types of constraints such as monotonicity and variational constraints. Quantization functions exactly or approximately minimize the squared error to a given discrete function. Exact algorithms are derived from dynamic programming with finite horizon. All algorithms have polynomial run time. (C) 2003 Elsevier B.V. All rights reserved.
The optimal path-finding algorithm which is an important module in developing route guidance systems and traffic control systems has to provide correct paths to consider U-turns, P-turns, and no-left-turns in urban tr...
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The optimal path-finding algorithm which is an important module in developing route guidance systems and traffic control systems has to provide correct paths to consider U-turns, P-turns, and no-left-turns in urban transportation networks. Traditional methods which have been used to consider those regulations on urban transportation networks can be categorized into network representation and algorithmic methods like the vine-building algorithm. First, network representation methods use traditional optimal path-finding algorithms with modifications to the network structure: for example, just adding dummy nodes and links to the existing network allows constraint-search in the network. This method which creates large networks is hard to implement and introduces considerable difficulties in network coding. With the increased number of nodes and links, the memory requirement tremendously increases, which causes the processing speed to slow down. For these reasons, the method has not been widely accepted for incorporating turning regulations in optimal path-finding problems in transportation networks. Second, algorithmic methods, as they are mainly based on the vine-building algorithm, have been suggested for determining optimal path for networks with turn penalties and prohibitions. However, the algorithms, although they nicely reflect the characteristics of urban transportation networks, frequently provide infeasible or suboptimal solutions. The algorithm to be suggested in this research is a method which is basically based on Dijkstra's algorithm [1] and the tree-building algorithm used to construct optimal paths. Unlike the traditional node labeling algorithms which label each node with minimum estimated cost, this algorithm labels each link with minimum estimated cost. Comparison with the vine-building algorithm shows that the solution of the link-labeling algorithm is better than that of the vine-building algorithm which very frequently provides suboptimal solutions
An example is presented to show that the worst‐case complexity of Bertsekas' small‐label‐first strategy for the shortest path problem is exponential. It becomes polynomial if, when scanning a node i , its succe...
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In this paper, modified versions of the classical deterministic maximum flow and minimum cost network flow problems are presented in a stochastic queueing environment. In the maximum flow network model, the throughput...
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In this paper, modified versions of the classical deterministic maximum flow and minimum cost network flow problems are presented in a stochastic queueing environment. In the maximum flow network model, the throughput rate in the network is maximized such that for each are of the network the resulting probability of finding congestion along that are in excess of a desirable threshold does not exceed an acceptable value. In the minimum cost network flow model, the minimum cost routing of a flow of given magnitude is determined under the same type of constraints on the arcs. After proper transformations, these models are solved by Ford and Fulkerson's labeling algorithm and out-of-kilter algorithm, respectively.
In this paper, we present a vision system for a depalletizing robot which recognizes carton objects. The algorithm consists of the extraction of object candidates and a labeling process to determine whether or not the...
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In this paper, we present a vision system for a depalletizing robot which recognizes carton objects. The algorithm consists of the extraction of object candidates and a labeling process to determine whether or not they actually exist. We consider this labeling a combinatorial optimization of labels, we propose a new labeling method applying Genetic algorithm (GA). GA is an effective optimization method, but it has been inapplicable to real industrial systems because of its processing time and difficulty of finding the global optimum solution. We have solved these problems by using the following guidelines for designing GA: (1) encoding high-level information to chromosomes, such as the existence of object candidates;(2) proposing effective coding method and genetic operations based on the building block hypothesis;and (3) preparing a support procedure in the vision system for compensating for the mis-recognition caused by the pseudo optimum solution in labeling. Here, the hypothesis says that a better solution can be generated by combining parts of good solutions. In our problem, it is expected that a global desirable image interpretation can be obtained by combining sub-images interpreted consistently. Through real image experiments, we have proven that the reliability of the vision system we have proposed is more than 98% and the recognition speed is 5 seconds/image, which is practical enough for the real-time robot task.
Grotschel and Pulleyblank (1981) describe a simple reduction for the computation of shortest paths with an even or odd number of edges. With this approach, the path problem is reduced to the problem of finding a min-...
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Grotschel and Pulleyblank (1981) describe a simple reduction for the computation of shortest paths with an even or odd number of edges. With this approach, the path problem is reduced to the problem of finding a min-cost perfect matching in a related graph. Using basically the same ideas, it is demonstrated that the shortest odd/even path problem in a graph G is the equivalent to finding a shortest alternating path in a related graph. This problem can be solved by employing a Dijkstra-like labeling technique which has previously been used to solve the min-cost perfect matching problem (Derigs, 1981). Because of the special structure of the underlying graph, the labeling procedure may be significantly simplified, from a computational and logical point of view. The basic theory and transformations are developed, and it is shown how the labeling procedure can be implemented for finding the shortest odd/even path directly on the original graph.
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