560 research outputs found

    Prediction of high-performance concrete compressive strength through a comparison of machine learning techniques

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceHigh-performance concrete (HPC) is a highly complex composite material whose characteristics are extremely difficult to model. One of those characteristics is the concrete compressive strength, a nonlinear function of the same ingredients that compose HPC: cement, fly ash, blast furnace slag, water, superplasticizer, age, and coarse and fine aggregates. Research has shown time and time again that concrete strength is not determined just by the water-to-cement ratio, which was for years the go to metric. In addition, traditional methods that attempt to model HPC, such as regression analysis, do not provide sufficient prediction power due to nonlinear proprieties of the mixture. Therefore, this study attempts to optimize the prediction and modeling of the compressive strength of HPC by analyzing seven different machine learning (ML) algorithms: three regularization algorithms (Lasso, Ridge and Elastic Net), three ensemble algorithms (Random Forest, Gradient Boost and AdaBoost), and Artificial Neural Networks. All techniques were built and tested with a dataset composed of data from 17 different concrete strength test laboratories, under the same experimental conditions, which enabled a fair comparison amongst them and between different previous studies in the field. Feature importance analysis and outlier analysis were also performed, and all models were subject to a Wilcoxon Signed-Ranks Test to ensure statistically significant results. The final results show that the more complex ML algorithms provided greater accuracy than the regularization techniques, with Gradient Boost being the superior model amongst them, providing more accurate predictions than the sate-of-the-art. Better results were achieved using all variables and without removing outlier observations

    Optimization for Energy Management in the Community Microgrids

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    This thesis focuses on improving the energy management strategies for Community Microgrids (CMGs), which are expected to play a crucial role in the future smart grid. CMGs bring many benefits, including increased use of renewable energy, improved reliability, resiliency, and energy efficiency. An Energy Management System (EMS) is a key tool that helps in monitoring, controlling, and optimizing the operations of the CMG in a cost-effective manner. The EMS can include various functionalities like day-ahead generation scheduling, real-time scheduling, uncertainty management, and demand response programs. Generation scheduling in a microgrid is a challenging optimization problem, especially due to the intermittent nature of renewable energy. The power balance constraint, which is the balance between energy demand and generation, is difficult to satisfy due to prediction errors in energy demand and generation. Real-time scheduling, which is based on a shorter prediction horizon, reduces these errors, but the impact of uncertainties cannot be completely eliminated. In regards to demand response programs, it is challenging to design an effective model that motivates customers to voluntarily participate while benefiting the system operator. Mathematical optimization techniques have been widely used to solve power system problems, but their application is limited by the need for specific mathematical properties. Metaheuristic techniques, particularly Evolutionary Algorithms (EAs), have gained popularity for their ability to solve complex and non-linear problems. However, the traditional form of EAs may require significant computational effort for complex energy management problems in the CMG. This thesis aims to enhance the existing methods of EMS in CMGs. Improved techniques are developed for day-ahead generation scheduling, multi-stage real-time scheduling, and demand response implementation. For generation scheduling, the performance of conventional EAs is improved through an efficient heuristic. A new multi-stage scheduling framework is proposed to minimize the impact of uncertainties in real-time operations. In regards to demand response, a memetic algorithm is proposed to solve an incentive-based scheme from the perspective of an aggregator, and a price-based demand response driven by dynamic price optimization is proposed to enhance the electric vehicle hosting capacity. The proposed methods are validated through extensive numerical experiments and comparison with state-of-the-art approaches. The results confirm the effectiveness of the proposed methods in improving energy management in CMGs

    On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem

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    We contribute to the efficient approximation of the Pareto-set for the classical NP\mathcal{NP}-hard multi-objective minimum spanning tree problem (moMST) adopting evolutionary computation. More precisely, by building upon preliminary work, we analyse the neighborhood structure of Pareto-optimal spanning trees and design several highly biased sub-graph-based mutation operators founded on the gained insights. In a nutshell, these operators replace (un)connected sub-trees of candidate solutions with locally optimal sub-trees. The latter (biased) step is realized by applying Kruskal's single-objective MST algorithm to a weighted sum scalarization of a sub-graph. We prove runtime complexity results for the introduced operators and investigate the desirable Pareto-beneficial property. This property states that mutants cannot be dominated by their parent. Moreover, we perform an extensive experimental benchmark study to showcase the operator's practical suitability. Our results confirm that the sub-graph based operators beat baseline algorithms from the literature even with severely restricted computational budget in terms of function evaluations on four different classes of complete graphs with different shapes of the Pareto-front

    Structural optimization in steel structures, algorithms and applications

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

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    Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

    bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease

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    IntroductionAtopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians’ subjective judgment, which may be missed or misdiagnosed sometimes.MethodsThis paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population’s diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO.ResultsTo prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets.DiscussionThe 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD

    Energy-aware coordination of machine scheduling and support device recharging in production systems

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    Electricity generation from renewable energy sources is crucial for achieving climate targets, including greenhouse gas neutrality. Germany has made significant progress in increasing renewable energy generation. However, feed-in management actions have led to losses of renewable electricity in the past years, primarily from wind energy. These actions aim to maintain grid stability but result in excess renewable energy that goes unused. The lost electricity could have powered a multitude of households and saved CO2 emissions. Moreover, feed-in management actions incurred compensation claims of around 807 million Euros in 2021. Wind-abundant regions like Schleswig-Holstein are particularly affected by these actions, resulting in substantial losses of renewable electricity production. Expanding the power grid infrastructure is a costly and time-consuming solution to avoid feed-in management actions. An alternative approach is to increase local electricity consumption during peak renewable generation periods, which can help balance electricity supply and demand and reduce feed-in management actions. The dissertation focuses on energy-aware manufacturing decision-making, exploring ways to counteract feed-in management actions by increasing local industrial consumption during renewable generation peaks. The research proposes to guide production management decisions, synchronizing a company's energy consumption profile with renewable energy availability for more environmentally friendly production and improved grid stability

    2023-2024 Lindenwood University Undergraduate Course Catalog

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    Lindenwood University Undergraduate Course Catalog.https://digitalcommons.lindenwood.edu/catalogs/1209/thumbnail.jp
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