Lidia Jackowska-Strumiłło

Simulation of gravitational solids flow process and its parameters estimation by the use of Electrical Capacitance Tomography and Artificial Neural Networks

The paper presents a new approach to monitoring changes of characteristic parameters of gravitational solids flow. Electrical Capacitance Tomography (ECT) is applied for non-invasive process monitoring. Artificial Neural Networks (ANN) are used to estimate important flow parameters knowing the measured capacitances. The proposed approach solves the ECT inverse problem in a direct manner and provides a rapid parameterization of the funnel flow. The simulation of the silo discharging process is performed relying on real flow behaviour obtained from the authors’ previous work. The simulated data are used to new approach testing and verification. The obtained results proved that proposed ANN-based method will allow for on-line gravitational solids flow monitoring.

The influence of using fractal analysis in hybrid MLP model for short-term forecast of close prices on Warsaw Stock Exchange

The paper describes a new method of combining Artificial Neural Networks (ANN), technical analysis and fractal analysis for predicting share prices on the Warsaw Stock Exchange. The proposed hybrid model consists of two consecutive modules. In the first step share prices are preprocessed and calculated into moving averages and oscillators. Then, in the next step, they are given to the ANN inputs, which provides the closing values of the asset for the next day. ANN of Multi-Layer Perceptron (MLP) type, and fractal analysis are applied. The hybrid model combining ANN with technical and fractal analysis is compared with hybrid model combining ANN with technical analysis. The obtained results indicate that hybrid model combined with fractal analysis is more accurate and stable in the long run than the hybrid model.

Neural network approach to ECT inverse problem solving for estimation of gravitational solids flow

A new method to solve the inverse problem of electrical capacitance tomography is proposed. Our method is based on artificial neural network to estimate the radius of an object present inside a pipeline. This information is useful to predict the distribution of material inside the pipe. The capacitance data used to train and test the neural network is simulated on Matlab using the electrical capacitance tomography toolkit ECTsim. The provided accuracy is promising and shows efficiency to solve the inverse problem in a simple manner and on reduced computational time about 120 times when compared to the existing Landweber iterative algorithm for tomographic image reconstruction that can be encouraging for dynamic industrial applications.

Prediction of Closing Prices on the Stock Exchange with the Use of Artificial Neural Networks

Abstract Article describes, the use of Artificial Neural Networks (ANN) for predicting values of Stock Exchange shares. Rules of Stock Exchange functioning, principles of technical analysis and the most important stock market indices are described, which support investors, who plan to make transactions. ANN of Multi-Layer Perceptron (MLP) type, and a moving window method are applied. A hybrid method is also proposed, in which time series of CLOSE values as a function of the following trading days are used to stock market indices calculation, such as moving averages and oscillators, which are applied to ANN inputs. Research was conducted for 80 companies, selected from the 1218 companies functioning on Stock Exchange. The achieved maximum error in one day ahead CLOSE value prediction is 1,31%.

Design and implementation of a pervasive occupancy displaying system in an academic environment

This paper describes the design of a system providing room occupancy information in a university campus. The system and its possible influence on the human environment are designed to enhance the work environment through pervasive information delivery. The work is motivated by recent advances in the domain of ubiquitous computing. The authors describe a case study of delivering a solution to university students informing them where they can find free spaces in rooms designated for quiet study. The data is available on the web and through ambient displays. The proof-of-concept study presents the methodology applied, discusses the design issues that were encountered by the project team and reflects on the devices and techniques used in the implementation. An application of standard image processing techniques to provide a contextualized, pervasive user-centered service is also depicted.

Application of the Layered Model Management System in an Interactive Map of the University Campus

Abstract The paper presents a web application for navigation through the university campus, which is based on Global Positioning System GPS and OpenStreetMap. The application has a multilayer structure and multi-labelling support. The proposed solution ensures better management of visual data and more efficient image processing comparing to the other known methods. With the new search system, users can place a lot of information on one layer without losing the legibility of displayed data. All the information that was displayed on the map was grouped and assigned to the appropriate categories.Therefore a map contains a lot of related information that needs to be linked to each other. The system has been divided into modules that ensure the integrity of the displayed things. Presenting so much information at the same time is managed by modules. Their main job is to provide results that is then segregated and grouped. The system presented in this paper was applied for Lodz University of Technology.

Acceleration of image reconstruction in 3D Electrical Capacitance Tomography in heterogeneous, multi-GPU system using sparse matrix computations and Finite Element Method

3D Electrical Capacitance Tomography provides a lot of challenging computational issues that have been reported in the past by many researchers. Image reconstruction using deterministic methods requires execution of many basic operations of linear algebra. Due to significant sizes of matrices used in ECT for image reconstruction and the fact that best image quality is achieved by using algorithms of which significant part is FEM and which are hard to parallelize or distribute. In order to solve these issues a new set of algorithms had to be developed.

Decision system for stock data forecasting based on Hopfield artificial neural network

The paper describes a new method using Hopfield artificial neural network combined with technical analysis fractal analysis and feed-forward artificial neural networks for predicting share prices for a next day on a Stock Exchange. The developed method and networks are implemented in an Expert System, which is proposed as a valuable comprehensive, analytical tool. A new algorithm for artificial neural networks training and testing is also presented. It automatically chooses the best network structure, and the most important input parameters. Słowa kluczowe: Hybrid intelligent system, Hopfield artificial neural network SYSTEM DECYZYJNY DO PRZEWIDYWANIA CEN AKCJI OPARTY NA SZTUCZNEJ SIECI NEURONOWEJ HOPFIELDA Streszczenie. Artykuł opisuje nową metodę zastosowania sztucznej sieci neuronowej Hopfielda połączonej z analizą techniczną, fraktalną oraz jednokierunkowymi sztucznymi sieciami neuronowymi do przewidywania przyszłych cen akcji na Giełdzie Papierów Wartościowych. Opisane nowe metody zostały zaimplementowane w systemie ekspertowym, który jest polecany jako kompleksowe narzędzie do badania aktualnych i przyszłych zachowań rynku. Zaprezentowany został również algorytm nauki testowania sztucznych sieci neuronowych, który na końcu wybiera najlepszą z nich. Słowa kluczowe: Hybrydowy inteligentny system, sztuczna sieć neuronowa Hopfielda

Optimization of Distributed Multi-Node, Multi-GPU, Heterogeneous System for 3D Image Reconstruction in Electrical Capacitance Tomography

Abstract Electrical Capacitance Tomography is a non-invasive imaging technique, which allows visualization of the industrial processes interior and can be applied to many branches of the industry. Image reconstruction process, especially in case of 3D images, is a very time consuming task (when using classic processors and algorithms), which in turn leads to an unacceptable waiting time and currently limits the use of 3D Electrical Capacitance Tomography. Reconstruction using deterministic methods requires execution of many basic operations of linear algebra, such as matrix transposition, multiplication, addition and subtraction. In order to reach real-time reconstruction a 3D ECT computational subsystem must be able to transform capacitance data into images in a fraction of a second. By assuming, that many of the computations can be performed in parallel using modern, fast graphics processor and by altering the algorithms, time to achieve high quality image reconstruction will be shortened significantly. The research conducted while analysing ECT algorithms has also shown that, although dynamic development of GPU computational capabilities and its recent application for image reconstruction in ECT has significantly improved calculations time, in modern systems a single GPU is not enough to perform many tasks. Distributed Multi-GPU solutions can reduce reconstruction time to only a fraction of what was possible on pure CPU systems. Nevertheless performed tests clearly illustrate the need for further optimizations of previously developed algorithms.

Analysis of Application of Distributed Multi-Node, Multi-GPU Heterogeneous System for Acceleration of Image Reconstruction in Electrical Capacitance Tomography

Abstract 3D ECT provides a lot of challenging computational issues that have been reported in the past by many researchers. Image reconstruction using deterministic methods requires execution of many basic operations of linear algebra, such as matrix transposition, multiplication, addition and subtraction. In order to reach real-time reconstruction a 3D ECT computational subsystem has to be able to transform capacitance data into image in fractions of seconds. By assuming, that many of the computations can be performed in parallel using modern, fast graphics processor and by altering the algorithms time to achieve high quality image reconstruction will be shortened significantly. The research conducted while analysing ECT algorithms has also shown that, although dynamic development of GPU computational capabilities and its recent application for image reconstruction in ECT has significantly improved calculations time, in modern systems a single GPU is not enough to perform many tasks. Distributed Multi-GPU solutions can reduce reconstruction time to only a fraction of what was possible on pure CPU systems. Nevertheless performed tests clearly illustrate the need for developing a new distributed platform, which would be able to fully utilize the potential of the hardware. It has to take into account specific nature of computations in Multi-GPU systems.