Search results for "machine"

Some aspects regarding the thermic behaviour of a basalt parts in machine building industry

2019

This work aims to present some aspects regarding the opportunity of using basalt in manufacturing the subassemblies of machine tools. Melted and recrystallized basalt has demonstrated promising behavior with regards of its use in manufacturing, as reported in the literature. The researches presented in this work were focused on analyzing the behavior of basalt parts for machine tools submitted to thermic stress. The tests were made using grey cast iron and steel parts as reference for a comparative study. Testing methodology, specific measuring apparatus used, experimental test data records, data processing and resulting conclusions regarding the possibility of using basalt in machine tools…

Analysis of the eigenfrequencies of the basalt machine parts by the FEM

2021

The aim of this paper was to carry out a theoretical (numerical) scientific research on the behavior of cast and recrystallized basalt, in order to use it as a material in the manufacture of structural elements of machine tools. A unitary methodology has been established for studying the eigenfrequency of structural elements for machine tools by the finite element method. Two basalt structural elements (beam type and plate type) were modeled, which were also made physically, and with the help of the COSMOS / Mark finite element program, the eigenfrequencies of the models was studied.

Forecasting basketball players' performance using sparse functional data*

2019

Statistics and analytic methods are becoming increasingly important in basketball. In particular, predicting players’ performance using past observations is a considerable challenge. The purpose of this study is to forecast the future behavior of basketball players. The available data are sparse functional data, which are very common in sports. So far, however, no forecasting method designed for sparse functional data has been used in sports. A methodology based on two methods to handle sparse and irregular data, together with the analogous method and functional archetypoid analysis is proposed. Results in comparison with traditional methods show that our approach is competitive and additio…

Beverage Vending Machine

2015

A Collaborative Filtering Approach for Drug Repurposing

2022

A recommendation system is proposed based on the construction of Knowledge Graphs, where physical interaction between proteins and associations between drugs and targets are taken into account. The system suggests new targets for a given drug depending on how proteins are linked each other in the graph. The framework adopted for the implementation of the proposed approach is Apache Spark, useful for loading, managing and manipulating data by means of appropriate Resilient Distributed Datasets (RDD). Moreover, the Alternating Least Square (ALS) machine learning algorithm, a Matrix Factorization algorithm for distributed and parallel computing, is applied. Preliminary obtained results seem to…

The Datafication of Hate: Expectations and Challenges in Automated Hate Speech Monitoring.

2020

Laaksonen, S-M.; Haapoja, J.; Kinnunen, T., Nelimarkka, M. & Pöyhtäri, R. (2020, accepted). . Frontiers in Big Data: Data Mining and Management / Critical Data and Algorithm Studies. doi:10.3389/fdata.2020.00003 Hate speech has been identified as a pressing problem in society and several automated approaches have been designed to detect and prevent it. This paper reports and reflects upon an action research setting consisting of multi-organizational collaboration conducted during Finnish municipal elections in 2017, wherein a technical infrastructure was designed to automatically monitor candidates' social media updates for hate speech. The setting allowed us to engage in a 2-fold investiga…

Deep learning and process understanding for data-driven Earth system science

2017

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybri…

Cluster-based active learning for compact image classification

2010

In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer…

MicroRNA Interaction Networks

2021

La tesi di Giorgio Bertolazzi è incentrata sullo sviluppo di nuovi algoritmi per la predizione dei legami miRNA-mRNA. In particolare, un algoritmo di machine-learning viene proposto per l'upgrade del web tool ComiR; la versione originale di ComiR considerava soltanto i siti di legame dei miRNA collocati nella regione 3'UTR dell'RNA messaggero. La nuova versione di ComiR include nella ricerca dei legami la regione codificante dell'RNA messaggero. Bertolazzi’s thesis focuses on developing and applying computational methods to predict microRNA binding sites located on messenger RNA molecules. MicroRNAs (miRNAs) regulate gene expression by binding target messenger RNA molecules (mRNAs). Therefo…

Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits.

2008

Abstract Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from a theoretical perspective. It is argued that a nonparametric treatment may be needed for capturing the multiple and complex interactions potentially arising in whole-genome models, i.e., those based on thousands of single-nucleotide polymorphism (SNP) markers. After a review of reproducing kernel Hilbert spaces regression, it is shown that the statistical specification admits a standard mixed-effects linear model representation, with smoothing parameters treated as variance components.…