More than 10 billion tons of construction and demolition waste (CW) are generated globally each year, exerting a significant impact on the environment. In the CW recycling process, the government and the carrier are t...
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Dual degeneracy, i.e., the presence of multiple optimal bases to a linear programming (LP) problem, heavily affects the solution process of mixed integer programming (MIP) solvers. Different optimal bases lead to diff...
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Dual degeneracy, i.e., the presence of multiple optimal bases to a linear programming (LP) problem, heavily affects the solution process of mixed integer programming (MIP) solvers. Different optimal bases lead to different cuts being generated, different branching decisions being taken and different solutions being found by primal heuristics. Nevertheless, only a few methods have been published that either avoid or exploit dual degeneracy. The aim of the present paper is to conduct a thorough computational study on the presence of dual degeneracy for the instances of well-known public MIP instance collections. How many instances are affected by dual degeneracy? How degenerate are the affected models? How does branching affect degeneracy: Does it increase or decrease by fixing variables? Can we identify different types of degenerate MIPs? As a tool to answer these questions, we introduce a new measure for dual degeneracy: the variable-constraint ratio of the optimal face. It provides an estimate for the likelihood that a basic variable can be pivoted out of the basis. Furthermore, we study how the so-called cloud intervals-the projections of the optimal face of the LP relaxations onto the individual variables-evolve during tree search and the implications for reducing the set of branching candidates.
In recent years, due to the strong need to control blood pressure in patients, especially patients with heart problems, several control structures have been designed and implemented to control their blood pressure. Th...
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ISBN:
(数字)9798350377170
ISBN:
(纸本)9798350377187
In recent years, due to the strong need to control blood pressure in patients, especially patients with heart problems, several control structures have been designed and implemented to control their blood pressure. The reason for this is the instability of blood pressure in this group of patients, and nurses and doctors need to use methods to prevent this instability, which can be a fatal factor for patients. To achieve this goal, a fractional-order PID controller whose coefficients are calculated using a hybrid-evolutionary algorithm called BH-PSO has been used in this paper. This control combination can excessively improve the system’s output response. This control structure can significantly reduce the error rate and settling time. Also, for the objective function, three functions integral absolute error (IAE), integral squared error (ISE), and integral time absolute error (ITAE) have been used to compare and reduce the system’s error rate.
In the task of reference-based image inpainting, an additional reference image is provided to restore a damaged target image to its original state. The advancement of diffusion models, particularly Stable Diffusion, a...
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ISBN:
(数字)9798331510831
ISBN:
(纸本)9798331510848
In the task of reference-based image inpainting, an additional reference image is provided to restore a damaged target image to its original state. The advancement of diffusion models, particularly Stable Diffusion, allows for simple formulations in this task. However, existing diffusion-based methods often lack explicit constraints on the correlation between the reference and damaged images, resulting in lower faithfulness to the reference images in the inpainting results. In this work, we propose CorrFill, a training-free module designed to enhance the awareness of geometric correlations between the reference and target images. This enhancement is achieved by guiding the inpainting process with correspondence constraints estimated during inpainting, utilizing attention masking in self-attention layers and an objective function to update the input tensor according to the constraints. Experimental results demonstrate that CorrFill significantly enhances the performance of multiple baseline diffusion-based methods, including state-of-the-art approaches, by emphasizing faithfulness to the reference images.
This study introduces a novel framework for topology optimization in structural design by integrating global and local search algorithms. Specifically, a genetic algorithm (GA) is employed as the global search method,...
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ISBN:
(数字)9798331508272
ISBN:
(纸本)9798331508289
This study introduces a novel framework for topology optimization in structural design by integrating global and local search algorithms. Specifically, a genetic algorithm (GA) is employed as the global search method, leveraging its strengths in handling diverse objective functions while preserving interpretability throughout the optimization process. As a specific example of the framework, a system integrating GA as the global search algorithm and Bidirectional Evolutionary Structural Optimization (BESO) as the local search algorithm is introduced. GA is employed for its global exploration capability, enabling exploration on a diverse set of solutions for a wide range of optimization problems. BESO is applied as a local search method to refine solutions, enhancing the optimization results by precisely adjusting the structural design during the searching process. The effectiveness of this approach is demonstrated through two numerical examples focusing on their own primary objectives: one maximizing the structural stiffness, and the other maximizing the displacement. The results show that the combination of GA and BESO effectively meets the set design goals, highlighting the potential for significant structural design improvements through their synergistic effect, confirming the benefits of combining GA's ability to conduct global exploration with BESO's capacity to fine-tune solutions through local search. This study demonstrates the effectiveness of integrating GA and BESO in structural topology optimization, providing a powerful tool for advancing generative structural design.
Spiking Neural Networks (SNNs) have emerged as a compelling alternative to traditional Artificial Neural Networks (ANNs) due to their energy efficiency and biological plausibility. However, current SNN models often re...
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ISBN:
(数字)9798331510831
ISBN:
(纸本)9798331510848
Spiking Neural Networks (SNNs) have emerged as a compelling alternative to traditional Artificial Neural Networks (ANNs) due to their energy efficiency and biological plausibility. However, current SNN models often rely on ANN architectures that may not fully exploit the unique properties of SNNs. Neural Architecture Search (NAS) approaches have been shown to automate the identification of suitable architectures for various applications. Nevertheless, very few works have been presented on NAS for SNNs and particularly for identifying architectures that achieve high accuracy while capitalizing on the energy efficiency property of SNNs. In this paper, we present a Multi-Objective Neural Architecture Search for Efficient SNNs (MONAS-ESNN) approach that utilizes a training-free NAS to discover SNN architectures that optimize both accuracy and energy efficiency. The proposed MONAS-ESNN uses the NSGA-II evolutionary algorithm to optimize both objective functions while leveraging the unique temporal dynamics of SNNs. We also introduce a new Adjusted Sparsity-Aware Hamming Distance (ASAHD) that enhances the evaluation of potential architectures by representing diverse spike activation patterns for different types of spiking neurons. Experimental results on CIFAR-10, CIFAR-100, and Tiny-ImageNet-200 datasets demonstrate that MONAS-ESNN identifies SNN architectures that have higher accuracy and are more efficient, as measured by the number of generated spikes, than existing methods. Therefore, the proposed MONAS-ESNN can automate the search and discovery of SNN architectures with higher accuracy, fewer generated spikes, and faster convergence paving the way for more energy-efficient neural networks.
High penetrations of the intermittent distributed energy resources in the distribution systems such as rooftop and community solar systems can lead to voltage control and flicker issues. In this study, an efficient va...
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ISBN:
(数字)9798331541125
ISBN:
(纸本)9798331541132
High penetrations of the intermittent distributed energy resources in the distribution systems such as rooftop and community solar systems can lead to voltage control and flicker issues. In this study, an efficient vault-based battery deployment is investigated to mitigate the adverse effects of grid-connected solar systems on voltage rise and flicker with minimum cost. The fast fluctuations in solar power can be remedied by addition of aggregated battery storage systems into the distribution system. A linear programming (LP) optimization problem is used that enables the utility to determine the optimum battery size for both energy and power ratings. Technical constraints such as ramp rate control and charge/discharge cycles factors affecting battery degradation are also taken into consideration. Furthermore, an optimum battery dispatch algorithm is developed through the LP optimization problem. Analysis shows that high power ratings is needed to compensate for rapid fluctuations in the solar profile, whereas the required energy capacity is much less, and using a fast-response storage device along with the battery can significantly reduce the battery size and reduce battery costs.
We consider a probabilistic model for large-scale task allocation problems for multi-agent systems, aiming to determine an optimal deployment strategy that minimizes the overall transport cost. Specifically, we assign...
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Parameter extraction (PE) is a key subproblem of space mapping (SM) design optimization. It consists of a local alignment between the coarse and fine models at each SM iteration. In this work, cognition-driven PE is p...
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ISBN:
(数字)9798331540401
ISBN:
(纸本)9798331540418
Parameter extraction (PE) is a key subproblem of space mapping (SM) design optimization. It consists of a local alignment between the coarse and fine models at each SM iteration. In this work, cognition-driven PE is proposed for SM. In contrast to classical PE, where the full fine model responses are used as targets, the proposed cognitive PE focuses on key features of the fine model response selected from an engineering perspective. It is demonstrated that the proposed cognitive PE approach: 1) yields more accurate extracted parameters regardless of the type of PE objective function employed; and 2) achieves a more meaningful matching to the fine model target response and with less variability. To proof this with independence of the optimization method employed for PE, plots of the PE objective functions are presented over large regions of the coarse model design space. Two synthetic examples are used to support these findings.
This research focuses on the site selection problem of medical waste recovery points, considering its unique characteristics, and constructs an objective function incorporating multiple elements such as recovery point...
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ISBN:
(数字)9798350389579
ISBN:
(纸本)9798350389586
This research focuses on the site selection problem of medical waste recovery points, considering its unique characteristics, and constructs an objective function incorporating multiple elements such as recovery point coverage radius, medical waste capacity constraints, and transportation distances. To efficiently address this complex site selection problem, we employed a genetic algorithm-based site selection model, combined with improved solution strategies to solve the model, significantly enhancing the algorithm's efficiency and quality. In practical implementation, we integrated the powerful mathematical simulation programming tool, Matlab R2021a, with our genetic algorithm model to solve specific case studies. The experimental results validated the effectiveness and practicality of the genetic algorithm model in addressing medical waste recovery point site selection problems, providing a robust theoretical foundation for related decision-makers. This study not only showcases the significant role of computer science in solving complex problems but also offers new insights and methodologies for algorithm optimization and application. It underscores the importance of computational approaches in contributing to better decision-making, particularly in intricate logistics network optimization challenges.
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