Search results for "Machine"

An approximate/exact objective based search technique for solving general scheduling problems

2018

Abstract In this paper, we analyze single machine scheduling problems under the following minimization objectives: the maximum completion time (makespan), the total completion time and the maximum lateness, including fundamental practical aspects, which often occur in industrial or manufacturing reality: release dates, due dates, setup times, precedence constraints, deterioration (aging) of machines, as well as maintenance activities. To solve the problems, we propose an efficient representation of a solution and a fast neighborhood search technique, which calculates an approximation of criterion values in a constant time per solution in a neighborhood. On this basis, a novel approximate/ex…

2021

ObjectivesTo assess the ability to predict individual unfavourable future status and development in the 20m shuttle run test (20MSRT) during adolescence with machine learning (random forest (RF) classifier).MethodsData from a 2-year observational study (2013‒2015, 12.4±1.3 years, n=633, 50% girls), with 48 baseline characteristics (questionnaires (demographics, physical, psychological, social and lifestyle factors), objective measurements (anthropometrics, fitness characteristics, physical activity, body composition and academic scores)) were used to predict: (Task 1) unfavourable future 20MSRT status (identification of individuals in the lowest 20MSRT tertile after 2 years), and (Task 2) u…

Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Specie…

2018

Recognition of tree species and geospatial information on tree species composition is essential for forest management. In this study, tree species recognition was examined using hyperspectral imagery from visible to near-infrared (VNIR) and short-wave infrared (SWIR) camera sensors in combination with a 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum with a diverse selection of 26 tree species from 14 genera was used as a test area. Aerial hyperspectral imagery and high spatial resolution photogrammetric color imagery were acquired from the test area using unmanned aerial vehicle (UAV) borne sensors. Hyperspectral imagery was processed to calibrated …

Regularization operators for natural images based on nonlinear perception models.

2006

Image restoration requires some a priori knowledge of the solution. Some of the conventional regularization techniques are based on the estimation of the power spectrum density. Simple statistical models for spectral estimation just take into account second-order relations between the pixels of the image. However, natural images exhibit additional features, such as particular relationships between local Fourier or wavelet transform coefficients. Biological visual systems have evolved to capture these relations. We propose the use of this biological behavior to build regularization operators as an alternative to simple statistical models. The results suggest that if the penalty operator take…

The Algorithm of a Game-Based System in the Relation between an Operator and a Technical Object in Management of E-Commerce Logistics Processes with …

2021

Machine learning (ML) is applied in various logistic processes utilizing innovative techniques (e.g., the use of drones for automated delivery in e-commerce). Early challenges showed the insufficient drones’ steering capacity and cognitive gap related to the lack of theoretical foundation for controlling algorithms. The aim of this paper is to present a game-based algorithm of controlling behaviours in the relation between an operator (OP) and a technical object (TO), based on the assumption that the game is logistics-oriented and the algorithm is to support ML applied in e-commerce optimization management. Algebraic methods, including matrices, Lagrange functions, systems of differential e…

Cloud Infrastructure for Skin Cancer Scalable Detection System

2018

Skin cancer diagnostics is one of the medical areas where early diagnostic allows achieving patients’ high survival rate. Typically, skin cancer diagnostic is performed by dermatologist, since the amount of such specialists is limited, mortality rate is high [1]. By creating the low cost and easy to use diagnostic device, it is possible to bring skin cancer diagnostic to primary care physicians and allow to check much more persons and diagnose skin cancer on the early stages. There are several existing devices, that provide skin cancer diagnostics [2]. Most of them process the skin images locally and have limited diagnostic capabilities; some of them send images to dermatologists for manual…

Non-linear System Identification with Composite Relevance Vector Machines

2007

Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selection. Teoría de la Señal y Comunicaciones

Discrete Time Signal Processing Framework with Support Vector Machines

2007

Digital signal processing (DSP) of time series using SVM has been addressed in the literature with a straightforward application of the SVM kernel regression, but the assumption of independently distributed samples in regression models is not fulfilled by a time-series problem. Therefore, a new branch of SVM algorithms has to be developed for the advantageous application of SVM concepts when we process data with underlying time-series structure. In this chapter, we summarize our past, present, and future proposal for the SVM-DSP frame-work, which consists of several principles for creating linear and nonlinear SVM algorithms devoted to DSP problems. First, the statement of linear signal mod…

On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth

2015

In many applications, data from different sensors are aggregated in order to obtain more reliable information about the process that the sensors are monitoring. However, the quality of the aggregated information is intricately dependent on the reliability of the individual sensors. In fact, unreliable sensors will tend to report erroneous values of the ground truth, and thus degrade the quality of the fused information. Finding strategies to identify unreliable sensors can assist in having a counter-effect on their respective detrimental influences on the fusion process, and this has has been a focal concern in the literature. The purpose of this paper is to propose a solution to an extreme…

A Constrained Optimal Model Predictive Control for Mono Inverter Dual Parallel PMSM Drives

2018

The actual trends in the design of AC drives are directed to the reduction of the total weight, volume and cost. Usually, this implies the necessity to adopt new motor topologies and converter architectures. An important role is played by the mono-inverter dual parallel motor (MIDP), which gives the possibility to reduce the total weight and costs of power converters. This paper proposes a novel model predictive control algorithm in order to improve the transient performances of a MIDP used for an overhead carrier. The effectiveness of the proposal control is verified through some numerical simulations.