Monitoring System Resources During Automated Testing in Symbian Operating System

Tässä diplomityössä esitellään ohjelmistotestauksen ja verifioinnin yleisiä periaatteita sekä käsitellään tarkemmin älypuhelinohjelmistojen verifiointia. Työssä esitellään myös älypuhelimissa käytettävä Symbian-käyttöjärjestelmä. Työn käytännön osuudessa suunniteltiin ja toteutettiin Symbian-käyttöjärjestelmässä toimiva palvelin, joka tarkkailee ja tallentaa järjestelmäresurssien käyttöä. Verifiointi on tärkeä ja kuluja aiheuttava tehtävä älypuhelinohjelmistojen kehityssyklissä. Kuluja voidaan vähentää automatisoimalla osa verifiointiprosessista. Toteutettu palvelin automatisoijärjestelmäresurssien tarkkailun tallentamalla tietoja niistä tiedostoon testien ajon aikana. Kun testit ajetaan uudestaan, uusia tuloksia vertaillaan lähdetallenteeseen. Jos tulokset eivät ole käyttäjän asettamien virherajojen sisällä, siitä ilmoitetaan käyttäjälle. Virherajojen ja lähdetallenteen määrittäminen saattaa osoittautua vaikeaksi. Kuitenkin, jos ne määritetään sopivasti, palvelin tuottaa hyödyllistä tietoa poikkeamista järjestelmäresurssien kulutuksessa testaajille.

Container Handling Equipment Monitoring System, case: Steveco Oy

Container Handling Equipment Monitoring System (CHEMS) is a system developed by Savcor One Oy. CHEMS measures important information for container ports performance and produces performance indicators. The aim of this thesis was to clarify performance measurement contents to Savcor and to develop, as an example, performance measures to Steveco Oy's container operations. The theoretical part of the thesis clarifies performance measurement and which of its components are important to container port. Performance measurement and measures are presented from the operational level's point of view, in which CHEMS is planned to aim. The theory of development process of performance measures is introduced at the end of the theoretical part. To make sure that performance measures are efficiently used, Steveco Oy's performance measures are developed in cooperation with the users. The measurement in operational level is continuous and the results must be reacted asquickly as possible. CHEMS is very suitable to continuous measurement and to produce real time-measures of container operations which are hard to get any otherway.

Deep-hole drilling process, monitoring system and sensors modifications

 In the drilling processes and especially deep-hole drilling process, the monitoring system and having control on mechanical parameters (e.g. Force, Torque,Vibration and Acoustic emission) are essential. The main focus of this thesis work is to study the characteristics of deep-hole drilling process, and optimize the monitoring system for controlling the process. The vibration is considered as a major defect area of the deep-hole drilling process which often leads to breakage of the drill, therefore by vibration analysis and optimizing the workpiecefixture, this area is studied by finite element method and the suggestions are explained. By study on a present monitoring system, and searching on the new sensor products, the modifications and recommendations are suggested for optimize the present monitoring system for excellent performance in deep-hole drilling process research and measurements.

Design of a Condition Monitoring System for a Glass Wool Production Line

Työn tarkoituksena oli suunnitella kunnonvalvontajärjestelmä kahdelle lasivillan tuotantolinjalle. Suunnitteluprosessin lisäksi työssä on esitelty erilaisia kunnonvalvontamenetelmiä. Työn alussa on kerrottu erilaisista kunnonvalvontamenetelmistä, joilla voidaan seurata erilaisten laitteiden ja koneiden toimintakuntoa.Erityisesti työssä on tarkasteltu teollisuudessa yleistyviä kunnonvalvonnan värähtelymittauksia. Työssä suunniteltu kunnonvalvontajärjestelmä perustuu viiteen eri menetelmään, jotka ovat värähtelymittaus, lämpötilanmittaus lämpökameralla, lämpötilanmittaus kannettavalla mittarilla, kuuntelu elektronisella stetoskoopilla ja pyörivien osien kunnontarkkailu stroboskoopilla. Kunnonvalvontajärjestelmän suunnittelu on tehty useassa eri vaiheessa. Ensin työssä on kartoitettu tuotannon kannalta tärkeimmät laitteet ja niiden mahdolliset vikaantumistavat. Seuraavaksi on valittu sopivat kunnonvalvontamenetelmät ja tehty mittaussuunnitelma, jossa on esitetty eri laitteille suoritettavat mittaukset ja mittausten aikavälit.Lopuksi työssä on esitelty muutama esimerkkitapaus kunnonvalvontamenetelmien käytöstä sekä kerrottu mahdollisista tulevaisuuden kehitysmahdollisuuksista.

On Modelling, System Identification and Control of Servo-Systems with a Flexible Load

The present study was done with two different servo-systems. In the first system, a servo-hydraulic system was identified and then controlled by a fuzzy gainscheduling controller. The second servo-system, an electro-magnetic linear motor in suppressing the mechanical vibration and position tracking of a reference model are studied by using a neural network and an adaptive backstepping controller respectively. Followings are some descriptions of research methods. Electro Hydraulic Servo Systems (EHSS) are commonly used in industry. These kinds of systems are nonlinearin nature and their dynamic equations have several unknown parameters.System identification is a prerequisite to analysis of a dynamic system. One of the most promising novel evolutionary algorithms is the Differential Evolution (DE) for solving global optimization problems. In the study, the DE algorithm is proposed for handling nonlinear constraint functionswith boundary limits of variables to find the best parameters of a servo-hydraulic system with flexible load. The DE guarantees fast speed convergence and accurate solutions regardless the initial conditions of parameters. The control of hydraulic servo-systems has been the focus ofintense research over the past decades. These kinds of systems are nonlinear in nature and generally difficult to control. Since changing system parameters using the same gains will cause overshoot or even loss of system stability. The highly non-linear behaviour of these devices makes them ideal subjects for applying different types of sophisticated controllers. The study is concerned with a second order model reference to positioning control of a flexible load servo-hydraulic system using fuzzy gainscheduling. In the present research, to compensate the lack of dampingin a hydraulic system, an acceleration feedback was used. To compare the results, a pcontroller with feed-forward acceleration and different gains in extension and retraction is used. The design procedure for the controller and experimental results are discussed. The results suggest that using the fuzzy gain-scheduling controller decrease the error of position reference tracking. The second part of research was done on a PermanentMagnet Linear Synchronous Motor (PMLSM). In this study, a recurrent neural network compensator for suppressing mechanical vibration in PMLSM with a flexible load is studied. The linear motor is controlled by a conventional PI velocity controller, and the vibration of the flexible mechanism is suppressed by using a hybrid recurrent neural network. The differential evolution strategy and Kalman filter method are used to avoid the local minimum problem, and estimate the states of system respectively. The proposed control method is firstly designed by using non-linear simulation model built in Matlab Simulink and then implemented in practical test rig. The proposed method works satisfactorily and suppresses the vibration successfully. In the last part of research, a nonlinear load control method is developed and implemented for a PMLSM with a flexible load. The purpose of the controller is to track a flexible load to the desired position reference as fast as possible and without awkward oscillation. The control method is based on an adaptive backstepping algorithm whose stability is ensured by the Lyapunov stability theorem. The states of the system needed in the controller are estimated by using the Kalman filter. The proposed controller is implemented and tested in a linear motor test drive and responses are presented.

Environmental Monitoring using Image Analysis

Tärkeä tehtävä ympäristön tarkkailussa on arvioida ympäristön nykyinen tila ja ihmisen siihen aiheuttamat muutokset sekä analysoida ja etsiä näiden yhtenäiset suhteet. Ympäristön muuttumista voidaan hallita keräämällä ja analysoimalla tietoa. Tässä diplomityössä on tutkittu vesikasvillisuudessa hai vainuja muutoksia käyttäen etäältä hankittua mittausdataa ja kuvan analysointimenetelmiä. Ympäristön tarkkailuun on käytetty Suomen suurimmasta järvestä Saimaasta vuosina 1996 ja 1999 otettuja ilmakuvia. Ensimmäinen kuva-analyysin vaihe on geometrinen korjaus, jonka tarkoituksena on kohdistaa ja suhteuttaa otetut kuvat samaan koordinaattijärjestelmään. Toinen vaihe on kohdistaa vastaavat paikalliset alueet ja tunnistaa kasvillisuuden muuttuminen. Kasvillisuuden tunnistamiseen on käytetty erilaisia lähestymistapoja sisältäen valvottuja ja valvomattomia tunnistustapoja. Tutkimuksessa käytettiin aitoa, kohinoista mittausdataa, minkä perusteella tehdyt kokeet antoivat hyviä tuloksia tutkimuksen onnistumisesta.

Success factors of customer and supplier master data system

Especially in global enterprises, key data is fragmented in multiple Enterprise Resource Planning (ERP) systems. Thus the data is inconsistent, fragmented and redundant across the various systems. Master Data Management (MDM) is a concept, which creates cross-references between customers, suppliers and business units, and enables corporate hierarchies and structures. The overall goal for MDM is the ability to create an enterprise-wide consistent data model, which enables analyzing and reporting customer and supplier data. The goal of the study was defining the properties and success factors of a master data system. The theoretical background was based on literature and the case consisted of enterprise specific needs and demands. The theoretical part presents the concept, background, and principles of MDM and then the phases of system planning and implementation project. Case consists of background, definition of as is situation, definition of project, evaluation criterions and concludes the key results of the thesis. In the end chapter Conclusions combines common principles with the results of the case. The case part ended up dividing important factors of the system in success factors, technical requirements and business benefits. To clarify the project and find funding for the project, business benefits have to be defined and the realization has to be monitored. The thesis found out six success factors for the MDM system: Well defined business case, data management and monitoring, data models and structures defined and maintained, customer and supplier data governance, delivery and quality, commitment, and continuous communication with business. Technical requirements emerged several times during the thesis and therefore those can’t be ignored in the project. Conclusions chapter goes through these factors on a general level. The success factors and technical requirements are related to the essentials of MDM: Governance, Action and Quality. This chapter could be used as guidance in a master data management project.

Inversion and assimilation methods with applications in geophysical remote sensing

Stratospheric ozone can be measured accurately using a limb scatter remote sensing technique at the UV-visible spectral region of solar light. The advantages of this technique includes a good vertical resolution and a good daytime coverage of the measurements. In addition to ozone, UV-visible limb scatter measurements contain information about NO2, NO3, OClO, BrO and aerosols. There are currently several satellite instruments continuously scanning the atmosphere and measuring the UVvisible region of the spectrum, e.g., the Optical Spectrograph and Infrared Imager System (OSIRIS) launched on the Odin satellite in February 2001, and the Scanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY (SCIAMACHY) launched on Envisat in March 2002. Envisat also carries the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument, which also measures limb-scattered sunlight under bright limb occultation conditions. These conditions occur during daytime occultation measurements. The global coverage of the satellite measurements is far better than any other ozone measurement technique, but still the measurements are sparse in the spatial domain. Measurements are also repeated relatively rarely over a certain area, and the composition of the Earth’s atmosphere changes dynamically. Assimilation methods are therefore needed in order to combine the information of the measurements with the atmospheric model. In recent years, the focus of assimilation algorithm research has turned towards filtering methods. The traditional Extended Kalman filter (EKF) method takes into account not only the uncertainty of the measurements, but also the uncertainty of the evolution model of the system. However, the computational cost of full blown EKF increases rapidly as the number of the model parameters increases. Therefore the EKF method cannot be applied directly to the stratospheric ozone assimilation problem. The work in this thesis is devoted to the development of inversion methods for satellite instruments and the development of assimilation methods used with atmospheric models.

Liikkuvan kenttätyön hallinta etätunnistusta ja paikannusta hyödyntävällä mobiilijärjestelmällä

Liikkuvan kenttätyön työntekijät suorittavat työpäivän aikana työtä useassa eri työkohteessa. Työkohteilla suoritettujen työtehtävien kirjaus tapahtuu usein jälkikäteen ja muistinvaraisesti. Diplomityön tavoitteena on esittää ratkaisu liikkuvan kenttätyön työkirjausten automatisointiin. Tavoitteen mukaisen etätunnistusta ja paikannusta hyödyntävän mobiilijärjestelmän avulla työntekijöiden käsinkirjoitetut ja mahdollisesti puutteelliset työkirjaukset korvataan automaattisilla ja täsmällisillä kirjauksilla. Järjestelmän tavoitteena on vähentää työntekijöiden työkuormaa. Työssä arvioidaan etätunnistuksen hyödyntämistä mobiilisovelluksen käyttöä helpottavana tekniikkana. Parhaimmillaan etätunnistus poistaa kokonaan mobiililaitteen näppäilytarpeen, mutta aiheuttaa osaltaan lisää työtä järjestelmän ylläpitoon. Työn tuloksissa esitetään myös satelliittipaikannuksen todettu tarkkuus ja soveltuvuus liikkuvan kenttätyön tarpeisiin taajama- ja kaupunkialueella. Varsinkin kaupunkialueella satelliittiperustaisen paikkatiedon tehokas hyödyntäminen edellyttää suodatusta ja älykkyyttä paikkatiedon prosessointiin.

Satelliittipaikannukseen perustuvan reaaliaikaisen jäljitysohjelmiston toteutus

Satelliittipaikannuksen hyödyntäminen eri sovellusaloilla ja siviilikäytössä on kasvanut merkittävästi 2000-luvulla Yhdysvaltojen puolustusministeriön lopetettua GPS-järjestelmän tarkoituksenmukaisen häirinnän. Langattomien datayhteyksien yleistyminen ja nopeuksien kasvaminen on avannut paikkatiedon käyttämiseksi ja hyödyntämiseksi reaaliaikaisesti uusia mahdollisuuksia. Kustannusten kasvaessa on tehokkaasta liikennöinnistä tullut tänä päivänä erittäin tärkeä osa yritysten päivittäisiä toimintoja. Ajoneuvojen hallinta on yksi tapa, jolla pyritään tehostamaan logistisia toimintoja ja vähentämään siitä aiheutuvia kustannuksia. Seuraamalla reaaliaikaisesti ajoneuvojen liikennöintiä voidaan pyrkiä saavuttamaan säästöjä optimoimalla aikatauluja ja reittejä sekä uudelleenohjaamalla ajoneuvoja sijaintien mukaan vähentäen näin kuljettua matkaa ja aikaa. Tässä diplomityössä tavoitteena on tutkia kuinka satelliittipaikannusta, paikkatietoa ja langattomia datayhteyksiä hyödyntämällä voidaan toteuttaa reaaliaikainen jäljitysohjelmisto. Työssä esitellään aluksi paikannustekniikat ja niiden toiminta. Lisäksi tutkitaan kuinka tiedonsiirto voidaan järjestelmässä toteuttaa sekä tarkastellaan järjestelmän kehityksessä huomioitavia tietoturvanäkökohtia. Tutkimuksen pohjalta suunniteltiin ja toteutettiin reaaliaikainen jäljitysohjelmisto kotipalveluyrityksen ajoneuvojen paikannustarpeisiin. Järjestelmän avulla voidaan valvoa ja jäljittää ajoneuvojen sijainteja kartalla reaaliaikaisesti sekä paikantaa tiettyä kohdetta lähimpänä olevat ajoneuvot. Tämä mahdollistaa hälytyksen sattuessa lähimpänä olevan työntekijän lähettämisen asiakaskohteeseen mahdollisimman nopeasti. Järjestelmän avulla käyttäjät voivat lisäksi seurata ajamiaan matkoja ja pitää automaattista ajopäiväkirjaa. Lopuksi työssä arvioidaan toteutetun järjestelmän toimintaa testauksessa saatujen mittaustulosten perusteella.

On state and parameter estimation in chaotic systems

State-of-the-art predictions of atmospheric states rely on large-scale numerical models of chaotic systems. This dissertation studies numerical methods for state and parameter estimation in such systems. The motivation comes from weather and climate models and a methodological perspective is adopted. The dissertation comprises three sections: state estimation, parameter estimation and chemical data assimilation with real atmospheric satellite data. In the state estimation part of this dissertation, a new filtering technique based on a combination of ensemble and variational Kalman filtering approaches, is presented, experimented and discussed. This new filter is developed for large-scale Kalman filtering applications. In the parameter estimation part, three different techniques for parameter estimation in chaotic systems are considered. The methods are studied using the parameterized Lorenz 95 system, which is a benchmark model for data assimilation. In addition, a dilemma related to the uniqueness of weather and climate model closure parameters is discussed. In the data-oriented part of this dissertation, data from the Global Ozone Monitoring by Occultation of Stars (GOMOS) satellite instrument are considered and an alternative algorithm to retrieve atmospheric parameters from the measurements is presented. The validation study presents first global comparisons between two unique satellite-borne datasets of vertical profiles of nitrogen trioxide (NO3), retrieved using GOMOS and Stratospheric Aerosol and Gas Experiment III (SAGE III) satellite instruments. The GOMOS NO3 observations are also considered in a chemical state estimation study in order to retrieve stratospheric temperature profiles. The main result of this dissertation is the consideration of likelihood calculations via Kalman filtering outputs. The concept has previously been used together with stochastic differential equations and in time series analysis. In this work, the concept is applied to chaotic dynamical systems and used together with Markov chain Monte Carlo (MCMC) methods for statistical analysis. In particular, this methodology is advocated for use in numerical weather prediction (NWP) and climate model applications. In addition, the concept is shown to be useful in estimating the filter-specific parameters related, e.g., to model error covariance matrix parameters.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Sensor communication in Smart cities and regions: An efficient IoT-based remote health monitoring system

Recent advances in Information and Communication Technology (ICT), especially those related to the Internet of Things (IoT), are facilitating smart regions. Among many services that a smart region can offer, remote health monitoring is a typical application of IoT paradigm. It offers the ability to continuously monitor and collect health-related data from a person, and transmit the data to a remote entity (for example, a healthcare service provider) for further processing and knowledge extraction. An IoT-based remote health monitoring system can be beneficial in rural areas belonging to the smart region where people have limited access to regular healthcare services. The same system can be beneficial in urban areas where hospitals can be overcrowded and where it may take substantial time to avail healthcare. However, this system may generate a large amount of data. In order to realize an efficient IoT-based remote health monitoring system, it is imperative to study the network communication needs of such a system; in particular the bandwidth requirements and the volume of generated data. The thesis studies a commercial product for remote health monitoring in Skellefteå, Sweden. Based on the results obtained via the commercial product, the thesis identified the key network-related requirements of a typical remote health monitoring system in terms of real-time event update, bandwidth requirements and data generation. Furthermore, the thesis has proposed an architecture called IReHMo - an IoT-based remote health monitoring architecture. This architecture allows users to incorporate several types of IoT devices to extend the sensing capabilities of the system. Using IReHMo, several IoT communication protocols such as HTTP, MQTT and CoAP has been evaluated and compared against each other. Results showed that CoAP is the most efficient protocol to transmit small size healthcare data to the remote servers. The combination of IReHMo and CoAP significantly reduced the required bandwidth as well as the volume of generated data (up to 56 percent) compared to the commercial product. Finally, the thesis conducted a scalability analysis, to determine the feasibility of deploying the combination of IReHMo and CoAP in large numbers in regions in north Sweden.

Value perceptions of measurement and monitoring system customers

The value that the customer perceives from a supplier’s offering, impacts customer’s decision making and willingness to pay at the time of the purchase, and the overall satisfaction. Thus, for a business supplier, it is critical to understand their customers’ value perceptions. The objective of this thesis is to understand what measurement and monitoring system customers value, by examining their key purchasing criteria and perceived benefits. Theoretical part of this study consists on reviewing relevant literature on organizational buying behavior and customer perceived value. This study employs a qualitative interview research method. The empirical part of this research consisted of conducting 20 in-depth interviews with life science customers in USA and in Europe. Quality and technical features are the most important purchasing criteria, while product-related benefits seem to be the most important perceived benefits. At the marketing of the system, the emphasis should be at which regulations the system complies with, references of supplier’s prior experience, the reliability and usability of the system, and total costs. The benefits that should be emphasized are the better control of customer’s process, and the proof of customer’s product quality