Iowa Influenza Surveillance Network: Weekly Activity Report Week 49, December 12, 2015

The Iowa Influenza Surveillance Network (IISN) was established in 2004, though surveillance has been conducted at the Iowa Department of Public Health. Schools and long-term care facilities report data weekly into a Web-based reporting system. Schools report the number of students absent due to illness and the total enrolled. Long-term care facilities report cases of influenza and vaccination status of each case. Both passively report outbreaks of illness, including influenza, to IDPH.

Iowa Influenza Surveillance Network: Weekly Activity Report Week 50, December 19, 2015

The Iowa Influenza Surveillance Network (IISN) was established in 2004, though surveillance has been conducted at the Iowa Department of Public Health. Schools and long-term care facilities report data weekly into a Web-based reporting system. Schools report the number of students absent due to illness and the total enrolled. Long-term care facilities report cases of influenza and vaccination status of each case. Both passively report outbreaks of illness, including influenza, to IDPH.

Iowa Influenza Surveillance Network: Weekly Activity Report Week 51, December 26, 2015

The Iowa Influenza Surveillance Network (IISN) was established in 2004, though surveillance has been conducted at the Iowa Department of Public Health. Schools and long-term care facilities report data weekly into a Web-based reporting system. Schools report the number of students absent due to illness and the total enrolled. Long-term care facilities report cases of influenza and vaccination status of each case. Both passively report outbreaks of illness, including influenza, to IDPH.

Iowa Influenza Surveillance Network: Weekly Activity Report Week 52, January 2, 2016

The Iowa Influenza Surveillance Network (IISN) was established in 2004, though surveillance has been conducted at the Iowa Department of Public Health. Schools and long-term care facilities report data weekly into a Web-based reporting system. Schools report the number of students absent due to illness and the total enrolled. Long-term care facilities report cases of influenza and vaccination status of each case. Both passively report outbreaks of illness, including influenza, to IDPH.

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.

Soft Computing Methods in Metallurgy

In metallurgic plants a high quality metal production is always required. Nowadays soft computing applications are more often used for automation of manufacturing process and quality control instead of mechanical techniques. In this thesis an overview of soft computing methods presents. As an example of soft computing application, an effective model of fuzzy expert system for the automotive quality control of steel degassing process was developed. The purpose of this work is to describe the fuzzy relations as quality hypersurfaces by varying number of linguistic variables and fuzzy sets.

Methods of Design and Testing of Real Time Systems

Teollusuussovelluksissa vaaditaan nykyisin yhä useammin reaaliaikaista tiedon käsittelyä. Luotettavuus on yksi tärkeimmistä reaaliaikaiseen tiedonkäsittelyyn kykenevän järjestelmän ominaisuuksista. Sen saavuttamiseksi on sekä laitteisto, että ohjelmisto testattava. Tämän työn päätavoitteena on laitteiston testaaminen ja laitteiston testattavuus, koska luotettava laitteistoalusta on perusta tulevaisuuden reaaliaikajärjestelmille. Diplomityössä esitetään digitaaliseen signaalinkäsittelyyn soveltuvan prosessorikortin suunnittelu. Prosessorikortti on tarkoitettu sähkökoneiden ennakoivaa kunnonvalvontaa varten. Uusimmat DFT (Desing for Testability) menetelmät esitellään ja niitä sovelletaan prosessorikortin sunnittelussa yhdessä vanhempien menetelmien kanssa. Kokemukset ja huomiot menetelmien soveltuvuudesta raportoidaan työn lopussa. Työn tavoitteena on kehittää osakomponentti web -pohjaiseen valvontajärjestelmään, jota on kehitetty Sähkötekniikan osastolla Lappeenrannan teknillisellä korkeakoululla.

Asiantuntijajärjestelmä kattilakonseptin valintaan

Polttoaine asettaa puitteet kattilasuunnittelulle. Kiertoleijukattilakonseptin valinta kytkeytyy kiinteästi mitoitusarvoihin ja polttoaineen ominaisuuksiin. Asiakkaan vaatimuk-set kattilalle asettavat lähtökohdan kattilasuunnittelulle. Suorituskyky, kustannukset ja luotettavuus ovat asiakaslähtöisiä tekijöitä, joiden painotukset vaikuttavat kattilakonseptin valintaan. Korkeat lämpötilat tulistimien alueella tekevät tulistinjärjestelystä vaikean ja määräävän osan kattilakonseptin valintaa. Konvektiotulistimien altistuminen kuumille savukaasuille tekee niistä herkkiä likaantumiselle ja korroosiolle. Mitoitusarvojen ja tulistimien rakenteen oikeanlaisella valinnalla voidaan näitä polttoaineperäisiä ongelmia ehkäistä. Lisäksi kiertoleijukattiloissa käytetyt tulipesän ulkopuoliset tulistimet soveltuvat konvektiotulistimia korkeammille lämpötiloille huonolaatuisillakin polttoaineilla. Tässä työssä rakennettu asiantuntijajärjestelmä valitsee alustavan kattilakonseptin mitoitusta varten käyttäjän antamien vähäisten lähtötietojen pohjalta.

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Many valued algebraic structures as measures of comparison

This thesis studies the properties and usability of operators called t-norms, t-conorms, uninorms, as well as many valued implications and equivalences. Into these operators, weights and a generalized mean are embedded for aggregation, and they are used for comparison tasks and for this reason they are referred to as comparison measures. The thesis illustrates how these operators can be weighted with a differential evolution and aggregated with a generalized mean, and the kinds of measures of comparison that can be achieved from this procedure. New operators suitable for comparison measures are suggested. These operators are combination measures based on the use of t-norms and t-conorms, the generalized 3_-uninorm and pseudo equivalence measures based on S-type implications. The empirical part of this thesis demonstrates how these new comparison measures work in the field of classification, for example, in the classification of medical data. The second application area is from the field of sports medicine and it represents an expert system for defining an athlete's aerobic and anaerobic thresholds. The core of this thesis offers definitions for comparison measures and illustrates that there is no actual difference in the results achieved in comparison tasks, by the use of comparison measures based on distance, versus comparison measures based on many valued logical structures. The approach has been highly practical in this thesis and all usage of the measures has been validated mainly by practical testing. In general, many different types of operators suitable for comparison tasks have been presented in fuzzy logic literature and there has been little or no experimental work with these operators.

Preventive diagnosis of dairy cow lameness

This research aimed to develop a Fuzzy inference based on expert system to help preventing lameness in dairy cattle. Hoof length, nutritional parameters and floor material properties (roughness) were used to build the Fuzzy inference system. The expert system architecture was defined using Unified Modelling Language (UML). Data were collected in a commercial dairy herd using two different subgroups (H1 and H2), in order to validate the Fuzzy inference functions. The numbers of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) responses were used to build the classifier system up, after an established gold standard comparison. A Lesion Incidence Possibility (LIP) developed function indicates the chances of a cow becoming lame. The obtained lameness percentage in H1 and H2 was 8.40% and 1.77%, respectively. The system estimated a Lesion Incidence Possibility (LIP) of 5.00% and 2.00% in H1 and H2, respectively. The system simulation presented 3.40% difference from real cattle lameness data for H1, while for H2, it was 0.23%; indicating the system efficiency in decision-making.

Fraud detection in the banking sector : a multi-agent approach

Fraud is an increasing phenomenon as shown in many surveys carried out by leading international consulting companies in the last years. Despite the evolution of electronic payments and hacking techniques there is still a strong human component in fraud schemes. Conflict of interest in particular is the main contributing factor to the success of internal fraud. In such cases anomaly detection tools are not always the best instruments, since the fraud schemes are based on faking documents in a context dominated by lack of controls, and the perpetrators are those ones who should control possible irregularities. In the banking sector audit team experts can count only on their experience, whistle blowing and the reports sent by their inspectors. The Fraud Interactive Decision Expert System (FIDES), which is the core of this research, is a multi-agent system built to support auditors in evaluating suspicious behaviours and to speed up the evaluation process in order to detect or prevent fraud schemes. The system combines Think-map, Delphi method and Attack trees and it has been built around audit team experts and their needs. The output of FIDES is an attack tree, a tree-based diagram to ”systematically categorize the different ways in which a system can be attacked”. Once the attack tree is built, auditors can choose the path they perceive as more suitable and decide whether or not to start the investigation. The system is meant for use in the future to retrieve old cases in order to match them with new ones and find similarities. The retrieving features of the system will be useful to simplify the risk management phase, since similar countermeasures adopted for past cases might be useful for present ones. Even though FIDES has been built with the banking sector in mind, it can be applied in all those organisations, like insurance companies or public organizations, where anti-fraud activity is based on a central anti-fraud unit and a reporting system.

Modelling Cutting States in Rough Turning of 34CrNiMo6 Steel

Rough turning is an important form of manufacturing cylinder-symmetric parts. Thus far, increasing the level of automation in rough turning has included process monitoring methods or adaptive turning control methods that aim to keep the process conditions constant. However, in order to improve process safety, quality and efficiency, an adaptive turning control should be transformed into an intelligent machining system optimizing cutting values to match process conditions or to actively seek to improve process conditions. In this study, primary and secondary chatter and chip formation are studied to understand how to measure the effect of these phenomena to the process conditions and how to avoid undesired cutting conditions. The concept of cutting state is used to address the combination of these phenomena and the current use of the power capacity of the lathe. The measures to the phenomena are not developed based on physical measures, but instead, the severity of the measures is modelled against expert opinion. Based on the concept of cutting state, an expert system style fuzzy control system capable of optimizing the cutting process was created. Important aspects of the system include the capability to adapt to several cutting phenomena appearing at once, even if the said phenomena would potentially require conflicting control action.

Développement d’un système d’administration de l’anesthésie en boucle fermée

En salle d’opération, les tâches de l’anesthésiste sont nombreuses. Alors que l’utilisation de nouveaux outils technologiques l’informe plus fidèlement sur ce qui se passe pour son patient, ces outils font que ses tâches deviennent plus exigeantes. En vue de diminuer cette charge de travail, nous avons considérer l’administration automatique d’agents anesthésiques en se servant de contrôle en boucle fermée. À cette fin, nous avons développé un système d’administration d’un agent anesthésique (le propofol) visant à maintenir à un niveau optimal la perte de conscience du patient pendant toute la durée d’une chirurgie. Le système comprend un ordinateur, un moniteur d’anesthésie et une pompe de perfusion. L’ordinateur est doté d’un algorithme de contrôle qui, à partir d’un indice (Bispectral IndexTM ou BIS) fournit par le moniteur d’anesthésie détermine le taux d’infusion de l’agent anesthésiant. Au départ, l’anesthésiste choisit une valeur cible pour la variable de contrôle BIS et l’algorithme, basé sur système expert, calcule les doses de perfusion de propofol de sorte que la valeur mesurée de BIS se rapproche le plus possible de la valeur cible établie. Comme interface-utilisateur pour un nouveau moniteur d’anesthésie, quatre sortes d’affichage ont été considérés: purement numérique, purement graphique, un mélange entre graphique et numérique et un affichage graphique intégré (soit bidimensionnel). À partir de 20 scenarios différents où des paramètres normaux et anormaux en anesthésie étaient présentés à des anesthésistes et des résidents, l’étude des temps de réaction, de l’exactitude des réponses et de la convivialité (évaluée par le NASA-TLX) a montré qu’un affichage qui combine des éléments graphiques et numériques était le meilleur choix comme interface du système. Une étude clinique a été réalisée pour comparer le comportement du système d’administration de propofol en boucle fermée comparativement à une anesthésie contrôlée de façon manuelle et conventionnelle où le BIS était aussi utilisé. Suite à l’approbation du comité d’éthique et le consentement de personnes ayant à subir des chirurgies générales et orthopédiques, 40 patients ont été distribués également et aléatoirement soit dans le Groupe contrôle, soit dans le Groupe boucle fermée. Après l’induction manuelle de propofol (1.5 mg/kg), le contrôle en boucle fermée a été déclenché pour maintenir l’anesthésie à une cible de BIS fixée à 45. Dans l’autre groupe, le propofol a été administré à l’aide d’une pompe de perfusion et l’anesthésiste avait aussi à garder manuellement l’indice BIS le plus proche possible de 45. En fonction du BIS mesuré, la performance du contrôle exercé a été définie comme excellente pendant les moments où la valeur du BIS mesurée se situait à ±10% de la valeur cible, bonne si comprise de ±10% à ±20%, faible si comprise de ±20% à ±30% ou inadéquate lorsque >±30%. Dans le Groupe boucle fermée, le système a montré un contrôle excellent durant 55% du temps total de l’intervention, un bon contrôle durant 29% du temps et faible que pendant 9% du temps. Le temps depuis l’arrêt de la perfusion jusqu’à l’extubation est de 9 ± 3.7 min. Dans le Groupe contrôle, un contrôle excellent, bon, et faible a été enregistré durant 33%, 33% et 15% du temps respectivement et les doses ont été changées manuellement par l’anesthésiste en moyenne 9.5±4 fois par h. L’extubation a été accomplie après 11.9 ± 3.3 min de l’arrêt de la perfusion. Dans le Groupe boucle fermée, un contrôle excellent a été obtenu plus longtemps au cours des interventions (P<0.0001) et un contrôle inadéquat moins longtemps (P=0.001) que dans le Groupe contrôle. Le système en boucle fermée d’administration de propofol permet donc de maintenir plus facilement l’anesthésie au voisinage d’une cible choisie que l’administration manuelle.