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9 results for “Discrete Optimization”
A Linked Application of Discrete Differential Evolution Algorithm Coupled with Simulation- Optimization Model and Comparative Analysis by Genetic Algorithm for Discrete Groundwater Management Problems
<p>Complete dataset of publication name as "The complete publication dataset is "A Discrete Differential Evolution- Linear Programming Algorithm for Groundwater Management Problems." You can find all the written codes in the zip file.</p>
Figure 6. (a1), (a2), (a3), (a4), (a5), (a6), (a7) and (a8) watermarked image is degraded respectively through JPEG2000 compression, JPEG compression, median filtering, adding Salt&Pepper noise, rotating, center cropping, surrounding cropping and scaling. (b1), (b2), (b3), (b4), (b5), (b6), (b7) and (b8) The corresponding extracted watermarks.-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>This paper has described a scheme for digital watermarking of still images based on discrete<br> wavelet transform. In the proposed method, the embedded logo watermark can be extracted without<br> access to the original image. It has been confirmed that the proposed watermarking method is able<br> to extract the embedded logo watermark from the watermarked images that have degraded through<br> compression, filtering, cropping and scaling. Although this algorithm is not robust against rotation,<br> it can completely extract the watermark from watermarked images that lose about 35% of their<br> areas by cropping attack.</p>
Figure 1. (a) Original watermark (b) extracted watermarks after compression(c) merged watermark-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>Therefore, each bit of the logo watermark is stored in one coefficient of a sub-block to keep<br> the capacity of watermarking fixed.<br> When a region of the watermarked image is destroyed; the whole watermark can be<br> extracted using other regions of the watermarked image by merging extracted watermarks. Figure 1<br> shows result of merging logo watermarks that were extracted from a compressed (with JPEG2000<br> algorithm) watermarked image.</p>
Figure 4. (a) The original "Hookah" image (b) Watermarked "Hookah" with Q=35 (c) The original "Baby" image (d) Watermarked "Baby" with Q=35-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>A set of distortions is applied to the watermarked image and the watermark is extracted from<br> the distorted image. We used bit correct rate (BCR) to evaluate our proposed algorithm and it is<br> calculated from the following equation [6].</p>
Figure 2. LL2 sub-band is divided into sub-block-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>In the following experiments, two gray-level images with size of 512 by 512, “Baby” and<br> “Hookah” are the test images. The binary image “IAU” with size of 32 by 32 is used in our<br> simulations as a watermark. Figure 3 shows the watermark. In the experiments Haar wavelet filter<br> was used for discrete wavelet transform. The level of wavelet decomposition (n) and the number of<br> sub-blocks (K) were also assumed to be 2 and 16 respectively.<br> The proposed watermarking algorithm is evaluated from the point view of embedded<br> watermark transparency and robustness; the result of each is shown in next two sections.</p>
Dataset of Paper "Optimization and parallelization of the Discrete Ordinate Method for radiation transport simulation in OpenFOAM: Hierarchical combination of shared and distributed memory approaches"
<p>Dataset of Paper "Optimization and parallelization of the Discrete Ordinate Method for radiation transport simulation in OpenFOAM: Hierarchical combination of shared and distributed memory approaches":</p> <ul> <li>Time profiling of the DOM model stages.</li> <li>Speed-up and parallel fraction of the “global results generation” stage in the DOM model.</li> <li>Comparison of computational time between original and modified DOM model stages.</li> <li>Scalability of the peer-to-peer communication (OpenFOAM) and master-slave communication (ANSYS Fluent) architectures.</li> <li>Results of the benchmarking of the model with ANSYS Fluent in three reactors.</li> <li>Mesh of the jerrycan.</li> <li>Mesh of the anular reactor</li> <li>Mesh of the tubular reactor couple to a compound parabolic collector.</li> </ul>
Results for Several Simple Algorithms on the W-Model for Black-Box Discrete Optimization Benchmarking
<p>The W-Model follows a layered approach, where each layer can either be omitted or introduce a different characteristic feature such as neutrality via redundancy, ruggedness and deceptiveness, epistasis, and multi-objectivity, in a tunable way. The model problem is defined over bit string representations, which allows for extracting some of its layers and stacking them on top of existing problems that use this representation, such as OneMax, the Maximum Satisfisiability or the Set Covering tasks, and the NK landscape. The ruggedness and deceptiveness layer can be stacked on top of any problem with integer-valued objectives.</p> <p>Here we provide some experimental results with several simple algorithms on the W-Model along with the algorithm and experiment executor implementation. The most recent version of the code can be found at <a href="http://www.github.com/thomasWeise/BBDOB_W_Model">http://www.github.com/thomasWeise/BBDOB_W_Model</a>, while the program used in the experiment are attached to this dataset. The implemented algorithms are</p> <ul> <li>1-flip hill climber with restarts,</li> <li>2-flip hill climber with restarts,</li> <li>μ+λ Evolutionary Algorithms (for different values of μ, λ, and different crossover rates),</li> <li>μ+λ Evolutionary Algorithms with Frequency Fitness Assignment (FFA) (for different values of μ, λ, and different crossover rates),</li> <li>exhaustive enumerate (EE), and</li> <li>random sampling (RS)</li> </ul> <p>These algorithms are applied to a range of different parametric setups of the W-Model for single-objective optimization and fixed-length bit string representations.</p> <p>The experiments are executed on a HP Z640 Work Station with 32 GB DDR4-2400 RAM and Intel Xeon E5-2609v4 CPU under Ubuntu Server Linux 16.04, Kernel 4.4.0-116-generic, and with Java 1.8.0_151. Each run was granted at most 1048576 function evaluations (FEs). The total amount of data collected is about 45 GB, with tar.xz compression down to about 1.2 GB. The text files containing the algorithm traces are fairly self-describing.</p> <p>Publications that describe the W-Model:</p> <ul> <li>Thomas Weise and Zijun Wu. Difficult Features of Combinatorial Optimization Problems and the Tunable W-Model Benchmark Problem for Simulating them. In Black Box Discrete Optimization Benchmarking (<a href="http://iao.hfuu.edu.cn/bbdob-gecco18">BB-DOB</a>) Workshop of <em>Companion Material Proceedings of the Genetic and Evolutionary Computation Conference (<a href="http://gecco-2018.sigevo.org/">GECCO 2018</a>)</em>, July 15th-19th 2018, Kyoto, Japan, ISBN: 978-1-4503-5764-7. ACM. doi:<a href="http://dx.doi.org/10.1145/3205651.3208240">10.1145/3205651.3208240</a> / <a href="http://github.com/thomasWeise/BBDOB_W_Model">source codes</a> [This paper contains some errata for the one below]</li> <li>Thomas Weise, Stefan Niemczyk, Hendrik Skubch, Roland Reichle, and Kurt Geihs. A Tunable Model for Multi-Objective, Epistatic, Rugged, and Neutral Fitness Landscapes. In Maarten Keijzer, Giuliano Antoniol, Clare Bates Congdon, Kalyanmoy Deb, Benjamin Doerr, Nikolaus Hansen, John H. Holmes, Gregory S. Hornby, Daniel Howard, James Kennedy, Sanjeev P. Kumar, Fernando G. Lobo, Julian Francis Miller, Jason H. Moore, Frank Neumann, Martin Pelikan, Jordan B. Pollack, Kumara Sastry, Kenneth Owen Stanley, Adrian Stoica, El-Ghazali, and Ingo Wegener, editors, <em>Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation Conference (GECCO'08)</em>, pages 795-802, July 12-16, 2008, Renaissance Atlanta Hotel Downtown: Atlanta, GA, USA. ISBN: 978-1-60558-130-9, New York, NY, USA: ACM Press. doi:<a href="http://dx.doi.org/10.1145/1389095.1389252">10.1145/1389095.1389252</a> / <a href="http://iao.hfuu.edu.cn/images/publications/WNSRG2008ATMFMOERANFL.pdf">pdf</a> [This paper has some deficits fixed in the paper above]</li> </ul>
Data from: Dynamics of pre-choice and post-choice behaviour: rats approximate optimal strategy in a discrete-trial decision task
We simulate two types of environments to investigate how closely rats approximate optimal foraging. Rats initiated a trial where they chose between two spouts for sucrose, which was delivered at distinct probabilities. The discrete trial procedure used allowed us to observe the relationship between choice proportions, response latencies and obtained rewards. Our results show that rats approximate the optimal strategy across a range of environments that differ in the average probability of reward as well as the dynamics of the depletion-renewal cycle. We found that the constituent components of a single choice differentially reflect environmental contingencies. Post-choice behaviour, measured as the duration of time rats spent licking at the spouts on unrewarded trials, was the most sensitive index of environmental variables, adjusting most rapidly to changes in the environment. These findings have implications for the role of confidence in choice outcomes for guiding future choices.
Data from: Dynamics of pre-choice and post-choice behaviour: rats approximate optimal strategy in a discrete-trial decision task
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