Intelligent component selection

Component-based software engineering (CBSE) provides solutions to the development of complex and evolving systems. As these systems are created and maintained, the task of selecting components is repeated. The context-driven component evaluation (CdCE) project is developing strategies and techniques for automating a repeatable process for assessing software components. This paper describes our work using artificial intelligence (AI) techniques to classify components based on an ideal component specification. Using AI we are able to represent dependencies between attributes, overcoming some of the limitations of existing aggregation-based approaches to component selection

Spectral high resolution feature selection for retrieval of combustion temperature profiles

Proceeding of: 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 (Burgos, Spain, September 20-23, 2006)The use of high spectral resolution measurements to obtain a retrieval of certain physical properties related with the radiative transfer of energy leads a priori to a better accuracy. But this improvement in accuracy is not easy to achieve due to the great amount of data which makes difficult any treatment over it and it's redundancies. To solve this problem, a pick selection based on principal component analysis has been adopted in order to make the mandatory feature selection over the different channels. In this paper, the capability to retrieve the temperature profile in a combustion environment using neural networks jointly with this spectral high resolution feature selection method is studied.Publicad

Encyclopedia of software components

Intelligent browsing through a collection of reusable software components is facilitated with a computer having a video monitor and a user input interface such as a keyboard or a mouse for transmitting user selections, by presenting a picture of encyclopedia volumes with respective visible labels referring to types of software, in accordance with a metaphor in which each volume includes a page having a list of general topics under the software type of the volume and pages having lists of software components for each one of the generic topics, altering the picture to open one of the volumes in response to an initial user selection specifying the one volume to display on the monitor a picture of the page thereof having the list of general topics and altering the picture to display the page thereof having a list of software components under one of the general topics in response to a next user selection specifying the one general topic, and then presenting a picture of a set of different informative plates depicting different types of information about one of the software components in response to a further user selection specifying the one component

Neural networks and spectra feature selection for retrival of hot gases temperature profiles

Proceeding of: International Conference on Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, Vienna, Austria 28-30 Nov. 2005Neural networks appear to be a promising tool to solve the so-called inverse problems focused to obtain a retrieval of certain physical properties related to the radiative transference of energy. In this paper the capability of neural networks to retrieve the temperature profile in a combustion environment is proposed. Temperature profile retrieval will be obtained from the measurement of the spectral distribution of energy radiated by the hot gases (combustion products) at wavelengths corresponding to the infrared region. High spectral resolution is usually needed to gain a certain accuracy in the retrieval process. However, this great amount of information makes mandatory a reduction of the dimensionality of the problem. In this sense a careful selection of wavelengths in the spectrum must be performed. With this purpose principal component analysis technique is used to automatically determine those wavelengths in the spectrum that carry relevant information on temperature distribution. A multilayer perceptron will be trained with the different energies associated to the selected wavelengths. The results presented show that multilayer perceptron combined with principal component analysis is a suitable alternative in this field.Publicad

Empirical Research on Machine Learning Models and Feature Selection for Traffic Congestion Prediction in Smart Cities

The development of smart cities has occurred over the past ten years. One primary goal of “smart city” initiatives is to lessen vehicle congestion. Several innovative technologies, including vehicular communications, navigation, and traffic control, have been created by Vehicle Networking System to address this problem. The traffic data gathered by smart devices aids in the forecasting of traffic in smart cities. This project created an Intelligent Traffic Congestion Management System (ITCMS) that uses machine learning techniques and traffic data from Kaggle to decrease the amount of time spent stuck in traffic. This study aims to assess feature selection methods and machine learning models for traffic forecasting in smart cities. The feature dimension is reduced using feature selection techniques, such information gain, correlation attribute, and principal component analysis. The recommended model successfully predicted traffic flow, assisting in the alleviation of congestion. The principal component analysis with random forest model outperforms the other machine learning models and has a 95% accuracy rate

Aurinkopaneeleiden liittäminen energianhallintajärjestelmään

Tämän opinnäytetyön toimeksiantaja on Varkaudessa sijaitseva Proxion Solutions Oy. Yrityksen pääasiallinen tuote on energianhallintajärjestelmä, jolla taataan telekommunikaatiolaitteiden sähkönsaanti maissa joissa sähköverkon sähkönlaatu on heikkoa ja katkonaista. Työn tarkoituksena on korvata nykyisen energianhallintajärjestelmän lataukseen vaadittu sähköverkon kolmivaihevirtaliitäntä täysin aurinkosähköllä käytettäväksi toteutukseksi. Opinnäytetyö käsittelee aurinkopaneeleiden liittämistä yrityksen tuottamaan energianhallintajärjestelmään sekä sen vaatimia komponentteja ja niiden perusteltuja valintoja. Työssä käsitellään aiheeseen liittyviä standardeja ja käytäntöjä, joiden avulla saatuja tuloksia perustellaan. Työssä käydään myös läpi valittujen komponenttien toimintaperiaatteita. Opinnäytetyö keskittyy pääosin komponenttivalintaan, maadoittamiseen, johdinmitoitukseen ja asennuskokonaisuuden standardien mukaiseen toteuttamiseen. Työssä saatuja tuloksia voidaan hyödyntää Proxion Solutionsin tuotekehityksessä.This bachelor’s thesis was assigned by Proxion Solutions Oy, located in Varkaus, Finland. The company’s main product is an intelligent back-up power solution, which assures electricity to telecommunication devices in countries with low and discontinuous grid quality. The purpose of this thesis was to replace present three-phase current connection of the grid with photovoltaic solution for charging an intelligent back-up power solution. The thesis discusses how to connect solar panels to a back-up power solution and what electrical components it requires. Each component selection and result is validated by standards and regulations. Issue related standards and regulations used in validating results were thoroughly studied for the thesis. The operational principle of the selected components is also discussed. The thesis focuses mainly on component selection, grounding, conductor sizing and executing overall installation according to standards. The results of this thesis can be used in Proxion Solution’s research and development work

Distribution of Road Hazard Warning Messages to Distant Vehicles in Intelligent Transport Systems

© 2018 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] The efficient distribution of intelligent transport system (ITS) messages is fundamental for the deployment and acceptance of ITS applications by mobile network operators and the automotive industry. In particular, the distribution of road hazard warning (RHW) messages to distant vehicles requires special mechanisms. In this case, the combination of direct communication between vehicles and the wide area coverage provided by cellular networks might be crucial not only for reducing the data transmission costs but also for improving the timeliness of ITS information. Moreover, the application of clustering and cluster head selection mechanisms among vehicles can increase the efficiency of hybrid vehicular and cellular communication networks. This paper introduces a novel cluster head selection technique for the distribution of RHW messages, and proposes an implementation of another legacy technique that was originally intended for mobile ad-hoc networks (MANETs). This paper evaluates the performance of these techniques by the means of computer simulations in two scenarios with distinct congestion and propagation conditions. The simulation results show the potential benefit of hybrid networks compared with pure cellular transmissions, especially, if the novel cluster head selection technique is used.Calabuig Soler, D.; Martín-Sacristán, D.; Monserrat Del Río, JF.; Botsov, M.; Gozálvez Serrano, D. (2018). Distribution of Road Hazard Warning Messages to Distant Vehicles in Intelligent Transport Systems. IEEE Transactions on Intelligent Transportation Systems. 19(4):1152-1165. https://doi.org/10.1109/TITS.2017.2718103S1152116519