Lomborg, Vanhanen ja Intelligent Design - kommentti Matti Kamppiselle

Kommentti Matti Kamppisen kirjoitukseen TT -lehdessä 1/2005

Report of the Workshop on Intelligent Construction for Earthworks: April 14-16, 2009

The objectives of this workshop were to update the strategies identified during the 2008 workshop; provide a collaborative exchange of ideas and experiences; share research results; increase participants' knowledge; develop research, education, and implementation initiatives for intelligent compaction (IC) and automated machine guidance (AMG) technologies; and develop strategies to move forward. The 2 1/2 day workshop was organized as follows: Day 1: Review of 2008 workshop proceedings, technical presentations on IC and AMG technologies, and participating state department of transportation (DOT) briefings. Day 2: Industry/equipment manufacturer presentations and breakout interactive sessions on three topic areas. Day 3: Breakout session summary reporting and panel discussion involving state DOT, contractor, and industry representatives. The results of the breakout sessions on day 2 were analyzed to identify the priorities for advancement in each of the three topic areas. Key issues for each topic were prioritized by reviewing the recorder's notes in detail, finding common topics among sessions, and summarizing the participant votes.

Report of the 4th Workshop for Technology Transfer for Intelligent Compaction Consortium, TPF-5(233), 2015

This document summarizes the discussion and findings of the 4th workshop held on October 27–28, 2015 in Frankfort, Kentucky as part of the Technology Transfer Intelligent Compaction Consortium (TTICC) Transportation Pooled Fund (TPF-5(233)) study. The TTICC project is led by the Iowa Department of Transportation (DOT) and partnered by the following state DOTs: California, Georgia, Iowa, Kentucky, Missouri, Ohio, Pennsylvania, Virginia, and Wisconsin. The workshop was hosted by the Kentucky Transportation Cabinet and was organized by the Center for Earthworks Engineering Research (CEER) at Iowa State University of Science and Technology. The objective of the workshop was to generate a focused discussion to identify the research, education, and implementation goals necessary for advancing intelligent compaction for earthworks and asphalt. The workshop consisted of a review of the TTICC goals, state DOT briefings on intelligent compaction implementation activities in their state, voting and brainstorming sessions on intelligent compaction road map research and implementation needs, and identification of action items for TTICC, industry, and Federal Highway Administration (FHWA) on each of the road map elements to help accelerate implementation of the technology. Twenty-three attendees representing the state DOTs participating in this pooled fund study, the FHWA, Iowa State University, University of Kentucky, and industry participated in this workshop.

Utilisation of Statistics in Intelligent Web Applications

Web-portaalien aiheenmukaista luokittelua voidaan hyödyntää tunnistamaan käyttäjän kiinnostuksen kohteet keräämällä tilastotietoa hänen selaustottumuksistaan eri kategorioissa. Tämä diplomityö käsittelee web-sovelluksien osa-alueita, joissa kerättyä tilastotietoa voidaan hyödyntää personalisoinnissa. Yleisperiaatteet sisällön personalisoinnista, Internet-mainostamisesta ja tiedonhausta selitetään matemaattisia malleja käyttäen. Lisäksi työssä kuvaillaan yleisluontoiset ominaisuudet web-portaaleista sekä tilastotiedon keräämiseen liittyvät seikat.

Development of Paperboard Forming Tools with a Focus on Intelligent Manufacturing

Tämä diplomityö käsittelee kartonginmuovaukseen käytettävien puristintyökalujen kehittämistä. Työntavoitteina oli kehittää työkalutekniikan suunnittelua ja valmistusta edullisemmaksi, nopeammaksi ja työkaluja toiminnoiltaan tehokkaammiksi. Työn tuli sisältää myös ohjeet työkalujen suunnittelemiseksi ja valmistamiseksi jatkoa ajatellen. Työn aikana selvitettiin mahdollisia työkalurakennevaihtoehtoja, valmistusmateriaaleja sekä niiden käsittelymenetelmiä ja lastuamista sekä sen tarjoamia mahdollisuuksia valmistusmenetelmänä. Työkalupari suunniteltiin modulaariseksi siten, että uusia työkaluja varten vain osa komponenteista täytyy valmistaa uudelleen, samalla työkalun osien lukumäärää pienennettiin merkittävästi. Valmistusmateriaaliksi valittiin hyvin lastuttava työkaluteräs ja sen koneistaminen tapahtui vaakakaraisessa koneistuskeskuksessa. Työn loppuvaiheessa työkalukokonaisuudelle tehtiin kustannuslaskelma jaoteltuna eri työvaiheille sekä komponenteittain. Työkalu asennettiin puristimeen ja sille suoritettiin käyttötestaus. Työn aikana karttuneen kokemuksen sekä koekäytön perusteella tehtiin jatkokehitysehdotuksia.

Artificially Intelligent and Adaptive Methods for Prediction and Analysis of Superheater Fireside Corrosion in Fluidized Bed Boilers

Superheater corrosion causes vast annual losses for the power companies. With a reliable corrosion prediction method, the plants can be designed accordingly, and knowledge of fuel selection and determination of process conditions may be utilized to minimize superheater corrosion. Growing interest to use recycled fuels creates additional demands for the prediction of corrosion potential. Models depending on corrosion theories will fail, if relations between the inputs and the output are poorly known. A prediction model based on fuzzy logic and an artificial neural network is able to improve its performance as the amount of data increases. The corrosion rate of a superheater material can most reliably be detected with a test done in a test combustor or in a commercial boiler. The steel samples can be located in a special, temperature-controlled probe, and exposed to the corrosive environment for a desired time. These tests give information about the average corrosion potential in that environment. Samples may also be cut from superheaters during shutdowns. The analysis ofsamples taken from probes or superheaters after exposure to corrosive environment is a demanding task: if the corrosive contaminants can be reliably analyzed, the corrosion chemistry can be determined, and an estimate of the material lifetime can be given. In cases where the reason for corrosion is not clear, the determination of the corrosion chemistry and the lifetime estimation is more demanding. In order to provide a laboratory tool for the analysis and prediction, a newapproach was chosen. During this study, the following tools were generated: · Amodel for the prediction of superheater fireside corrosion, based on fuzzy logic and an artificial neural network, build upon a corrosion database developed offuel and bed material analyses, and measured corrosion data. The developed model predicts superheater corrosion with high accuracy at the early stages of a project. · An adaptive corrosion analysis tool based on image analysis, constructedas an expert system. This system utilizes implementation of user-defined algorithms, which allows the development of an artificially intelligent system for thetask. According to the results of the analyses, several new rules were developed for the determination of the degree and type of corrosion. By combining these two tools, a user-friendly expert system for the prediction and analyses of superheater fireside corrosion was developed. This tool may also be used for the minimization of corrosion risks by the design of fluidized bed boilers.

Smart List : Llistat intel·ligent de compres

Treball de final de carrera que consisteix en una aplicació per a mòbils que connecta el servei REST amb un servidor.

Intelligent Internet Data Searching

In this thesis author approaches the problem of automated text classification, which is one of basic tasks for building Intelligent Internet Search Agent. The work discusses various approaches to solving sub-problems of automated text classification, such as feature extraction and machine learning on text sources. Author also describes her own multiword approach to feature extraction and pres-ents the results of testing this approach using linear discriminant analysis based classifier, and classifier combining unsupervised learning for etalon extraction with supervised learning using common backpropagation algorithm for multilevel perceptron.