Integrated Design of Aluminum-Containing High-entropy Refractory B2 Alloys with Synergy of High Strength and Ductility

Refractory high-entropy alloys, RHEAs, are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations to effectively navigate phase-selection and mechanical-properties predictions, developing aluminum-containing RHEAs in single-phase ordered B2 alloys demonstrating both high strength and ductility. These aluminum-containing RHEAs achieve remarkable mechanical properties, including compressive yield strengths up to 1.6 GPa, fracture strains exceeding 50 percent, and significant high-temperature strength retention. They also demonstrate a tensile yield strength of 1.1 GPa with a tension ductility of 6.3 percent. Besides, we identify a valence-electron-count domain for alloy brittleness with the explanation from density-functional theory and provide crucial insights into elements' influence on atomic ordering and mechanical performance. The work sets forth a strategic blueprint for high-throughput alloy design and reveals fundamental principles that govern the mechanical properties of advanced structural alloys

Feature Selection Using Genetic Algorithms and Genetic Programming

Rodrigues, N. M., Batista, J. E., La Cava, W., Vanneschi, L., & Silva, S. (2024). Exploring SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. SN Computer Science, 5(1), 1-17. [91]. https://doi.org/10.1007/s42979-023-02106-3 --- Open access funding provided by FCT|FCCN (b-on). This work was partially supported by the FCT, Portugal, through funding of the LASIGE Research Unit (UIDB/00408/2020 and UIDP/00408/2020); MAR2020 program via project MarCODE (MAR$$-$$01.03.01-FEAMP-0047); project AICE (DSAIPA/DS/0113/2019). Nuno Rodrigues and João Batista were supported by PhD Grants 2021/05322/BD and SFRH/BD/143972/2019, respectively; William La Cava was supported by the National Library Of Medicine of the National Institutes of Health under Award Number R00LM012926We present SLUG, a recent method that uses genetic algorithms as a wrapper for genetic programming and performs feature selection while inducing models. SLUG was shown to be successful on different types of classification tasks, achieving state-of-the-art results on the synthetic datasets produced by GAMETES, a tool for embedding epistatic gene–gene interactions into noisy datasets. SLUG has also been studied and modified to demonstrate that its two elements, wrapper and learner, are the right combination that grants it success. We report these results and test SLUG on an additional six GAMETES datasets of increased difficulty, for a total of four regular and 16 epistatic datasets. Despite its slowness, SLUG achieves the best results and solves all but the most difficult classification tasks. We perform further explorations of its inner dynamics and discover how to improve the feature selection by enriching the communication between wrapper and learner, thus taking the first step toward a new and more powerful SLUG.publishersversionpublishe

Method for solving nonlinearity in recognising tropical wood species

Classifying tropical wood species pose a considerable economic challenge and failure to classify the wood species accurately can have significant effects on timber industries. Hence, an automatic tropical wood species recognition system was developed at Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia. The system classifies wood species based on texture analysis whereby wood surface images are captured and wood features are extracted from these images which will be used for classification. Previous research on tropical wood species recognition systems considered methods for wood species classification based on linear features. Since wood species are known to exhibit nonlinear features, a Kernel-Genetic Algorithm (Kernel-GA) is proposed in this thesis to perform nonlinear feature selection. This method combines the Kernel Discriminant Analysis (KDA) technique with Genetic Algorithm (GA) to generate nonlinear wood features and also reduce dimension of the wood database. The proposed system achieved classification accuracy of 98.69%, showing marked improvement to the work done previously. Besides, a fuzzy logic-based pre-classifier is also proposed in this thesis to mimic human interpretation on wood pores which have been proven to aid the data acquisition bottleneck and serve as a clustering mechanism for large database simplifying the classification. The fuzzy logic-based pre-classifier managed to reduce the processing time for training and testing by more than 75% and 26% respectively. Finally, the fuzzy pre-classifier is combined with the Kernal-GA algorithm to improve the performance of the tropical wood species recognition system. The experimental results show that the combination of fuzzy preclassifier and nonlinear feature selection improves the performance of the tropical wood species recognition system in terms of memory space, processing time and classification accuracy

A framework for feature selection in high-dimensional domains

The introduction of DNA microarray technology has lead to enormous impact in cancer research, allowing researchers to analyze expression of thousands of genes in concert and relate gene expression patterns to clinical phenotypes. At the same time, machine learning methods have become one of the dominant approaches in an effort to identify cancer gene signatures, which could increase the accuracy of cancer diagnosis and prognosis. The central challenges is to identify the group of features (i.e. the biomarker) which take part in the same biological process or are regulated by the same mechanism, while minimizing the biomarker size, as it is known that few gene expression signatures are most accurate for phenotype discrimination. To account for these competing concerns, previous studies have proposed different methods for selecting a single subset of features that can be used as an accurate biomarker, capable of differentiating cancer from normal tissues, predicting outcome, detecting recurrence, and monitoring response to cancer treatment. The aim of this thesis is to propose a novel approach that pursues the concept of finding many potential predictive biomarkers. It is motivated from the biological assumption that, given the large numbers of different relationships which are possible between genes, it is highly possible to combine genes in many ways to produce signatures with similar predictive power. An intriguing advantage of our approach is that it increases the statistical power to capture more reliable and consistent biomarkers while a single predictor may not necessarily provide important clues as to biological differences of interest. Specifically, this thesis presents a framework for feature selection that is based upon a genetic algorithm, a well known approach recently proposed for feature selection. To mitigate the high computationally cost usually required by this algorithm, the framework structures the feature selection process into a multi-step approach which combines different categories of data mining methods. Starting from a ranking process performed at the first step, the following steps detail a wrapper approach where a genetic algorithm is coupled with a classifier to explore different feature subspaces looking for optimal biomarkers. The thesis presents in detail the framework and its validation on popular datasets which are usually considered as benchmark by the research community. The competitive classification power of the framework has been carefully evaluated and empirically confirms the benefits of its adoption. As well, experimental results obtained by the proposed framework are comparable to those obtained by analogous literature proposals. Finally, the thesis contributes with additional experiments which confirm the framework applicability to the categorization of the subject matter of documents

Optimal design and control of stationary electrochemical double-layer capacitors for light railways

The optimisation algorithm has been further investigated to understand the influence of the weight coefficients that affect the solution of all the optimisation problems and it is very often overlooked in the traditional approach. In fact, the choice of weight coefficients leading to the optimum among different optimal solutions also presents a challenge and this specific problem does not give any a priori indications. This challenge has been tackled using both genetic algorithms and particle swarm optimisations, which are the best methods when there are multiple local optima and the number of parameters is large. The results show that, when the optimal set of coefficients are used and the optimal positions and capacitances of EDLCs are selected, the energy savings can be up to 42%. The second problem of the control of the storage has been tackled with a linear state of charge control based on a piece-wise linear characteristic between the current and the voltage deviation from the nominal voltage of the supply at the point of connection of the storage. The simulations show that, regardless of the initial state of charge, the control maintain the state of charge of EDLCs within the prescribed range with no need of using the on-board braking resistor and, hence, dissipating braking energy. The robustness of the control algorithm has been verified by changing the characteristics of the train loading and friction force, with an energy saving between 26 - 27%

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

On Knowledge Discovery Experimented with Otoneurological Data

Diagnosis of otoneurological diseases can be challenging due to similar kind of and overlapping symptoms that can also vary over time. Thus, systems to support and aid diagnosis of vertiginous patients are considered beneficial. This study continues refinement of an otoneurological decision support system ONE and its knowledge base. The aim of the study is to improve the classification accuracy of nine otoneurological diseases in real world situations by applying machine learning methods to knowledge discovery in the otoneurological domain. The phases of the dissertation is divided into three parts: fitness value formation for attribute values, attribute weighting and classification task redefinition. The first phase concentrates on the knowledge update of the ONE with the domain experts and on the knowledge discovery method that forms the fitness values for the values of the attributes. The knowledge base of the ONE needed update due to changes made to data collection questionnaire. The effect of machine learnt fitness values on classification are examined and classification results are compared to the knowledge set by the experts and their combinations. Classification performance of nearest pattern method of the ONE is compared to k-nearest neighbour method (k-NN) and Naïve Bayes (NB). The second phase concentrates on the attribute weighting. Scatter method and instance-based learning algorithms IB4 and IB1w are applied in the attribute weighting. These machine learnt attribute weights in addition to the weights defined by the domain experts and equal weighting are tested with the classification method of the ONE and attribute weighted k-NN with One-vs-All classifiers (wk-NN OVA). Genetic algorithm (GA) approach is examined in the attribute weighting. The machine learnt weight sets are utilized as a starting point with the GA. Populations (the weight sets) are evaluated with the classification method of the ONE, the wk-NN OVA and attribute weighted k-NN using neighbour’s class-based attribute weighting (cwk-NN). In the third phase, the effect of the classification task redefinition is examined. The multi-class classification task is separated into several binary classification tasks. The binary classification is studied without attribute weighting with the k-NN and support vector machines (SVM)