On Process Modelling Using Physical Oriented And Phenomena Based Principles

This work presents a modelling framework based on phenomena description of the process. The approach is taken to easy understand and construct process model in heterogeneous possible distributed modelling and simulation environments. A simplified case study of a heat exchanger is considered and Modelica modelling language to check the proposed concept. The partial results are promising and the research effort will be extended in a computer aided modelling environment based on phenomena

Spectral density correction of a signal at frequency variable transformation

The goal of this paper is to determine analytical expression for the spectral density function of a signal, affected by a known frequency transformation, which do not modify the process energy. Such transformations of frequency variable can frequently appear on spectral density function of a signal, due to physical events (e.g. Doppler effect) or mathematical considerations (e.g. changing the coordinate system). In this case, all components of the spectral density function are modified. The formulas are valid for every spectral component and can be used in signal processing, for model simulation or implementation of advanced algorithm. A case study is illustrated on wave spectrum correction

Properties Of Potential Function- Based Clustering Algorithms

The clustering algorithms based on potential functions are capable of clustering a set of data, making no implicit assumptions on the cluster shapes and without knowing in advance the number of clusters. They are similarity-based type clustering algorithms and do not use any prototype vectors of the clusters. In this paper, some properties of these algorithms are studied: points arrangement tendency, constant potential surface, cluster shapes and robustness to noise

Emotion Recognition based on EEG Signals

In this paper we propose some methods for analyzing EEG signals in order to recognize emotions, using the representation of signals on Poincaré plots and the calculation of the fractal dimension. EEG signals were acquired on a single channel, using a laboratory equipment produced by BIOPAC, and the subject was relaxed with his eyes open or in one of the states of joy, anger, and music listening for about 60 seconds. Separate analyzes were also performed for the 4 frequency bands of the EEG signals: alpha, beta, theta and delta waves

Emotion Detection Using EEG Signals

In this paper we propose some new methods for detecting emotions in EEG signals, using both time analysis and frequency analysis of signals. In the last section of the paper we present a method of classifying EEG signals using a feedforward neural network. EEG signals were acquired on a single channel, using a laboratory equipment produced by BIOPAC, and the subject was relaxed with his eyes open or in one of the states of joy, anger, and music listening for about 60 seconds. Separate analyzes were also performed for the 4 frequency bands of the EEG signals: alpha, beta, theta and delta waves