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This paper ends with demonstrating the effectiveness of the proposed method with benchmark industrial data.
We apply our method to trimmed and untrimmed NURBS structures and illustrate a good performance of the method with benchmark test models and a shell model from engineering practice.
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We tested the method with a benchmark intrusion dataset to verify its performance and effectiveness.
Finally, we compare the performance of the proposed cost aggregation method with two benchmark methods on two datasets in Section 3.5.
In this section, we compare, using the two datasets described in Section 3.1, the proposed cost aggregation method with two benchmark methods: Min et al.'s method [14], and the census transform [39, 40].
We compare the two best results from our ensemble method with the benchmark methods (we first report the 54 topic experimental results, and followed by the 133 topics experiment).
At last we compare the results from our GA-based method with the benchmark products that are individually designed to the optimal performance.
While presenting the formalisms and algorithm, we will analyze the correctness and complexity of the algorithm, and demonstrate the proposed method with two benchmark CSPs, i.e., n-queen problems and coloring problems.
In the performance index 3, the significance of the overlap of the list of PCTFPs from a method with the benchmark set of 27 known cooperative TF pairs is used to evaluate the performance of a method.
We tested our method with several benchmarks and the results show that we receive good performance of the design, with area and timing estimated efficiently.
Finally, the application of the method is exemplified with benchmark cases involving live oil or wet gas.
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