Search results for "Data pre-processing"

2020

Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution to the field of gait analysis, e.g., in increasing the classification performance. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their im…

Online detection of rem sleep based on the comprehensive evaluation of short adjacent eeg segments by artificial neural networks

1997

Abstract 1. 1. For scientific and clinical requirements the present objective is a robust automatic online algorithm to detect rapid eye movement (REM) steep from single channel sleep EEG data without using EMG or EOG information. 2. 2. For data preprocessing 20 seconds time periods of the continuous EEG activity are digitally filtered in 7 frequency bands. Then the RMS values of these filtered signals are calculated along segments of 2.5 seconds. The resulting matrix of RMS values is representing information on the power of the signal localized in time and frequency and serves as input to an artificial neural network. A pooled set of EEG data together with the corresponding manual evaluati…

Executable Data Quality Models

2017

The paper discusses an external solution for data quality management in information systems. In contradiction to traditional data quality assurance methods, the proposed approach provides the usage of a domain specific language (DSL) for description data quality models. Data quality models consists of graphical diagrams, which elements contain requirements for data object's values and procedures for data object's analysis. The DSL interpreter makes the data quality model executable therefore ensuring measurement and improving of data quality. The described approach can be applied: (1) to check the completeness, accuracy and consistency of accumulated data; (2) to support data migration in c…

Local dimensionality reduction and supervised learning within natural clusters for biomedical data analysis

2006

Inductive learning systems were successfully applied in a number of medical domains. Nevertheless, the effective use of these systems often requires data preprocessing before applying a learning algorithm. This is especially important for multidimensional heterogeneous data presented by a large number of features of different types. Dimensionality reduction (DR) is one commonly applied approach. The goal of this paper is to study the impact of natural clustering--clustering according to expert domain knowledge--on DR for supervised learning (SL) in the area of antibiotic resistance. We compare several data-mining strategies that apply DR by means of feature extraction or feature selection w…

Research on Application of Data Mining Methods to Diagnosing Gastric Cancer

2012

Constantly evolving technologies bring new possibilities for supporting decision making in different areas - finance, marketing, production, social area, healthcare and others. Decision support systems are widely used in medicine in developed countries and show positive results. This research reveals several possibilities of application of data mining methods to diagnosing gastric cancer, which is the fourth leading cancer type in incidence after the breast, lung and colorectal cancers. A simple decision support system model was introduced and tested using gastric cancer inquiry form statistical data. The obtained results reveal both the benefits and potential of application of DSS aimed to…

Influence of raw data analysis for the use of neural networks for win farms productivity prediction

2011

In the last decade wind energy had a strong growth because of cost effectiveness of the technology and the high remunerative of investments.

Nonlinear Dynamics Techniques for the Detection of the Brain Areas Using MER Signals

2008

A methodology for identifying brain areas from the brain MER signals (microelectrode recordings) is presented, which is based on a nonlinear feature set. We propose nonlinear dynamics measures such as correlation dimension, Hurst exponent and the largest Lyapunov exponent to characterize the dynamic structure. The MER records belong to the Polytechnical University of Valencia, 24 records for each zone (black substance, thalamus, subthalamus nucleus and uncertain area). The detection of each area using characteristics derived from complexity analysis was obtained through a classifier (support vector machine). The joint information between areas is remarkable and the best accuracy result was …

2014

This paper considers the parameter estimation for linear time-invariant (LTI) systems in an input-output setting with output error (OE) time-delay model structure. The problem of missing data is commonly experienced in industry due to irregular sampling, sensor failure, data deletion in data preprocessing, network transmission fault, and so forth; to deal with the identification of LTI systems with time-delay in incomplete-data problem, the generalized expectation-maximization (GEM) algorithm is adopted to estimate the model parameters and the time-delay simultaneously. Numerical examples are provided to demonstrate the effectiveness of the proposed method.

Using NASA'S Long Term Data Record version 3 for the monitoring of land surface vegetation

2011

Numerous datasets have been made available for the observation of our planet from space. The aim of this work is the observation of changes in vegetation, through the use of a recent remote sensing dataset, NASA's Long Term Data Record (LTDR). Several authors have pointed out that vegetation monitoring benefits of the simultaneous use of Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST). Therefore, this work presents the procedure developed to monitor vegetation with the LTDR dataset, using both NDVI and LST parameters. This procedure includes data preprocessing (estimation of NDVI and LST, orbital drift correction, atmospherically contaminated data reconstruc…

Pre-production validation of the ATLAS level-1 calorimeter trigger system

2006

The Level-1 Calorimeter Trigger is a major part of the first stage of event selection for the ATLAS experiment at the LHC. It is a digital, pipelined system with several stages of processing, largely based on FPGAs, which perform programmable algorithms in parallel with a fixed latency to process about 300 Gbyte/s of input data. The real-time output consists of counts of different types of trigger objects and energy sums. Prototypes of all module types have been undergoing intensive testing before final production during 2005. Verification of their correct operation has been performed stand-alone and in the ATLAS test-beam at CERN. Results from these investigations will be presented, along …