Kiinteistön palautetiedon analysointi

Diplomityö käsittelee kiinteistön palautetiedon analysointia. Tässä diplomityössä palautetiedolla tarkoitetaan kiinteistön talotekniikkaan liittyviä teknisiä ja inhimillisiä tietoja, joiden perusteella rakennuksen ja sen tekniikan toimivuudesta voidaan tehdä päätelmiä. Tiedot kerätään järjestelmistä mittauspisteiden kautta etävalvomoon, josta niitä voidaan tarkastella ja edelleen analysoida. Valvomosta saatavan tiedon rinnalle pyritään suunnitteluvaiheessa kokoamaan valvottavasta kohteesta laskennallisia energiankulutusmalleja. Myös kiinteistön asukkaat, rakennuksen ominaisuudet ja ympäristö muodostavat osan palautetiedosta. Kiinteistön palautetieto voidaankin jakaa tekniseen ja inhimilliseen tietoon, ja edelleen analysoinnin kannalta staattiseen tai dynaamiseen tietoon. Tässä diplomityössä selvitettiin palautetiedon keruuta etävalvomolla yhdistämällä tähän rakentamisen laadunvalvonnan työkaluja ja sumeaa logiikkaa. Työn käytännön osio koostuu kiinteistön ja siihen yhdistetyn etävalvomon välillä kulkevan tiedon käsittelystä, mittauspisteiden määrittämisestä ja tietojen analysoinnista. Työssä esitellään kolme esimerkkikohdetta Helsingin seudulta. Työssä laadittiin arviointimalli, joka sisältää kaiken kiinteistön palautetietoon liittyvän aineiston ja sen analysointitavat, sekä erityisesti uutena asiana myös sumeaa logiikkaa. Tiedon analysointi etenee oletusarvojen muodostamisesta, simuloinnin laatimisesta ja sen vertaamisesta todellisiin tietoihin ja edelleen näistä tehtäviin päätelmiin. Tavanomaisten teknisten tietojen lisäksi sumean logiikan avulla tuotiin esille poikkeamia selittäviä tekijöitä mm. kiinteistön asukkaiden ominaisuuksista.

Computational visual quality of digitally printed images

The problem of understanding how humans perceive the quality of a reproduced image is of interest to researchers of many fields related to vision science and engineering: optics and material physics, image processing (compression and transfer), printing and media technology, and psychology. A measure for visual quality cannot be defined without ambiguity because it is ultimately the subjective opinion of an “end-user” observing the product. The purpose of this thesis is to devise computational methods to estimate the overall visual quality of prints, i.e. a numerical value that combines all the relevant attributes of the perceived image quality. The problem is limited to consider the perceived quality of printed photographs from the viewpoint of a consumer, and moreover, the study focuses only on digital printing methods, such as inkjet and electrophotography. The main contributions of this thesis are two novel methods to estimate the overall visual quality of prints. In the first method, the quality is computed as a visible difference between the reproduced image and the original digital (reference) image, which is assumed to have an ideal quality. The second method utilises instrumental print quality measures, such as colour densities, measured from printed technical test fields, and connects the instrumental measures to the overall quality via subjective attributes, i.e. attributes that directly contribute to the perceived quality, using a Bayesian network. Both approaches were evaluated and verified with real data, and shown to predict well the subjective evaluation results.

Utilizing online demand generation in an engineering company

Työn tarkoituksena on selvittää miten sähköistä kysynnän herättämistä voidaan hyödyntää Mantsinen Group Ltd Oy:ssä siten, että sillä pystytään tukemaan myyntiä. Lisäksi sähköisen kysynnän herättämisen tehokkuutta tutkitaan, jotta saadaan selville onko se kannattavaa ja kuinka hyvin se sopii yritykselle. Kysynnän herättämisjärjestelmän käyttö on määritelty kirjallisuuteen perustuen ja sen jälkeen järjestelmän käyttö on aloitettu. Sähköisen kysynnän herättämisen tehokkuus mitataan kolmen kuukauden tarkastelujakson todellisella datalla. Sähköisen kysynnän herättämisen sopivuutta arvioidaan perustuen sen kustannustehokkuuteen ja tuloksiin. Työn tulokset osoittavat, että sähköinen kysynnän herättäminen on kannattavaa ja se sopii yritykselle. Sillä voidaan parhaiten tukea myyntiä järjestelmän tuottaessa laadukkaita myyntimahdollisuuksia tasaisena virtana myynnille. Myös aiemmin manuaalisesti tehtyjä työtehtäviä voidaan automatisoida ja näin vähentää myyjien työtaakkaa.

Monte Carlo Hypothesis Testing with The Sharpe Ratio

The purpose of this master thesis was to perform simulations that involve use of random number while testing hypotheses especially on two samples populations being compared weather by their means, variances or Sharpe ratios. Specifically, we simulated some well known distributions by Matlab and check out the accuracy of an hypothesis testing. Furthermore, we went deeper and check what could happen once the bootstrapping method as described by Effrons is applied on the simulated data. In addition to that, one well known RobustSharpe hypothesis testing stated in the paper of Ledoit and Wolf was applied to measure the statistical significance performance between two investment founds basing on testing weather there is a statistically significant difference between their Sharpe Ratios or not. We collected many literatures about our topic and perform by Matlab many simulated random numbers as possible to put out our purpose; As results we come out with a good understanding that testing are not always accurate; for instance while testing weather two normal distributed random vectors come from the same normal distribution. The Jacque-Berra test for normality showed that for the normal random vector r1 and r2, only 94,7% and 95,7% respectively are coming from normal distribution in contrast 5,3% and 4,3% failed to shown the truth already known; but when we introduce the bootstrapping methods by Effrons while estimating pvalues where the hypothesis decision is based, the accuracy of the test was 100% successful. From the above results the reports showed that bootstrapping methods while testing or estimating some statistics should always considered because at most cases the outcome are accurate and errors are minimized in the computation. Also the RobustSharpe test which is known to use one of the bootstrapping methods, studentised one, were applied first on different simulated data including distribution of many kind and different shape secondly, on real data, Hedge and Mutual funds. The test performed quite well to agree with the existence of statistical significance difference between their Sharpe ratios as described in the paper of Ledoit andWolf.

Forecasting of wind speeds and directions with artificial neural networks

In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.

Mathematical modeling and optimal control of malaria

Malaria continues to infect millions and kill hundreds of thousands of people worldwide each year, despite over a century of research and attempts to control and eliminate this infectious disease. Challenges such as the development and spread of drug resistant malaria parasites, insecticide resistance to mosquitoes, climate change, the presence of individuals with subpatent malaria infections which normally are asymptomatic and behavioral plasticity in the mosquito hinder the prospects of malaria control and elimination. In this thesis, mathematical models of malaria transmission and control that address the role of drug resistance, immunity, iron supplementation and anemia, immigration and visitation, and the presence of asymptomatic carriers in malaria transmission are developed. A within-host mathematical model of severe Plasmodium falciparum malaria is also developed. First, a deterministic mathematical model for transmission of antimalarial drug resistance parasites with superinfection is developed and analyzed. The possibility of increase in the risk of superinfection due to iron supplementation and fortification in malaria endemic areas is discussed. The model results calls upon stakeholders to weigh the pros and cons of iron supplementation to individuals living in malaria endemic regions. Second, a deterministic model of transmission of drug resistant malaria parasites, including the inflow of infective immigrants, is presented and analyzed. The optimal control theory is applied to this model to study the impact of various malaria and vector control strategies, such as screening of immigrants, treatment of drug-sensitive infections, treatment of drug-resistant infections, and the use of insecticide-treated bed nets and indoor spraying of mosquitoes. The results of the model emphasize the importance of using a combination of all four controls tools for effective malaria intervention. Next, a two-age-class mathematical model for malaria transmission with asymptomatic carriers is developed and analyzed. In development of this model, four possible control measures are analyzed: the use of long-lasting treated mosquito nets, indoor residual spraying, screening and treatment of symptomatic, and screening and treatment of asymptomatic individuals. The numerical results show that a disease-free equilibrium can be attained if all four control measures are used. A common pitfall for most epidemiological models is the absence of real data; model-based conclusions have to be drawn based on uncertain parameter values. In this thesis, an approach to study the robustness of optimal control solutions under such parameter uncertainty is presented. Numerical analysis of the optimal control problem in the presence of parameter uncertainty demonstrate the robustness of the optimal control approach that: when a comprehensive control strategy is used the main conclusions of the optimal control remain unchanged, even if inevitable variability remains in the control profiles. The results provide a promising framework for the design of cost-effective strategies for disease control with multiple interventions, even under considerable uncertainty of model parameters. Finally, a separate work modeling the within-host Plasmodium falciparum infection in humans is presented. The developed model allows re-infection of already-infected red blood cells. The model hypothesizes that in severe malaria due to parasite quest for survival and rapid multiplication, the Plasmodium falciparum can be absorbed in the already-infected red blood cells which accelerates the rupture rate and consequently cause anemia. Analysis of the model and parameter identifiability using Markov chain Monte Carlo methods is presented.

Kalman filter for computational market dynamics

Kalman filter is a recursive mathematical power tool that plays an increasingly vital role in innumerable fields of study. The filter has been put to service in a multitude of studies involving both time series modelling and financial time series modelling. Modelling time series data in Computational Market Dynamics (CMD) can be accomplished using the Jablonska-Capasso-Morale (JCM) model. Maximum likelihood approach has always been utilised to estimate the parameters of the JCM model. The purpose of this study is to discover if the Kalman filter can be effectively utilized in CMD. Ensemble Kalman filter (EnKF), with 50 ensemble members, applied to US sugar prices spanning the period of January, 1960 to February, 2012 was employed for this work. The real data and Kalman filter trajectories showed no significant discrepancies, hence indicating satisfactory performance of the technique. Since only US sugar prices were utilized, it would be interesting to discover the nature of results if other data sets are employed.

Automatic recognition of fish from video sequences

The problem of automatic recognition of the fish from the video sequences is discussed in this Master’s Thesis. This is a very urgent issue for many organizations engaged in fish farming in Finland and Russia because the process of automation control and counting of individual species is turning point in the industry. The difficulties and the specific features of the problem have been identified in order to find a solution and propose some recommendations for the components of the automated fish recognition system. Methods such as background subtraction, Kalman filtering and Viola-Jones method were implemented during this work for detection, tracking and estimation of fish parameters. Both the results of the experiments and the choice of the appropriate methods strongly depend on the quality and the type of a video which is used as an input data. Practical experiments have demonstrated that not all methods can produce good results for real data, whereas on synthetic data they operate satisfactorily.

Nested Rollout Policy Adaptation for optimizing vehicle selection in complex VRPs

The goal of Vehicle Routing Problems (VRP) and their variations is to transport a set of orders with the minimum number of vehicles at least cost. Most approaches are designed to solve specific problem variations independently, whereas in real world applications, different constraints are handled concurrently. This research extends solutions obtained for the traveling salesman problem with time windows to a much wider class of route planning problems in logistics. The work describes a novel approach that:  supports a heterogeneous fleet of vehicles  dynamically reduces the number of vehicles  respects individual capacity restrictions  satisfies pickup and delivery constraints  takes Hamiltonian paths (rather than cycles) The proposed approach uses Monte-Carlo Tree Search and in particular Nested Rollout Policy Adaptation. For the evaluation of the work, real data from the industry was obtained and tested and the results are reported.

Methods for the analysis of two dimensional measurement data of paper web

This work is devoted to the problem of reconstructing the basis weight structure at paper web with black{box techniques. The data that is analyzed comes from a real paper machine and is collected by an o®-line scanner. The principal mathematical tool used in this work is Autoregressive Moving Average (ARMA) modelling. When coupled with the Discrete Fourier Transform (DFT), it gives a very flexible and interesting tool for analyzing properties of the paper web. Both ARMA and DFT are independently used to represent the given signal in a simplified version of our algorithm, but the final goal is to combine the two together. Ljung-Box Q-statistic lack-of-fit test combined with the Root Mean Squared Error coefficient gives a tool to separate significant signals from noise.

Paperiteollisuusyrityksen hankintaprosessien parantaminen - Case M-real

Tämä tutkimus käsittelee teollisuusyrityksen hankintaprosessien parantamista. Tavoitteena oli selvittää, millaisilla toimenpiteillä raakaaineiden ja MRO (maintenance, repair and operating)-tuotteiden hankintaprosesseista saadaan toimivia ja tehokkaita. Tutkielmassa tarkastellaan hankintaprosessiin vaikuttavia asioita, erityisesti keskittämistä sekä informaatioteknologiaa, ja tuodaan esille erilaisia hankintaprosessin parantamiseen tähtääviä lähestymistapoja. Tutkimus toteutettiin laadullisena tapaustutkimuksena. Empiirinen aineisto kerättiin kohdeyritys M-realilla tehtyjen haastattelujen sekä havainnoinnin avulla. Tutkimuksessa havaittiin, että raaka-aineiden hankintaprosessia voidaan tehostaa lisäämällä automatiikkaa sekä parantamalla yhteistyötä hankintaprosessiin osallistuvien tahojen kesken. MRO-tuotteiden hankintaa voidaan tehostaa monilla operationaalisilla toimenpiteillä, kuten yksinkertaistamalla hankintaehdotuksen tekemistä. Yhtenä keskeisimpänä tutkimustuloksena on se, että toiminnanohjausjärjestelmä on merkittävimpiä hankintaprosessin parantamista rajoittavia tekijöitä. Merkittävänä tutkimustuloksena voidaan pitää myös sitä, että hankintaprosessin osien keskittämisen myötä kosketus paikallisiin asioihin vähenee, mutta tämä ei ole kuitenkaan ylivoimainen este hankintaprosessin tehokkaalle hoitamiselle. Tutkimuksessa havaittiin, että informaatioteknologian kehittymisen myötä hankintaprosessi on nopeutunut.

Cost Optimisation of Mobile Advertising Client Data Transfer

Data traffic caused by mobile advertising client software when it is communicating with the network server can be a pain point for many application developers who are considering advertising-funded application distribution, since the cost of the data transfer might scare their users away from using the applications. For the thesis project, a simulation environment was built to mimic the real client-server solution for measuring the data transfer over varying types of connections with different usage scenarios. For optimising data transfer, a few general-purpose compressors and XML-specific compressors were tried for compressing the XML data, and a few protocol optimisations were implemented. For optimising the cost, cache usage was improved and pre-loading was enhanced to use free connections to load the data. The data traffic structure and the various optimisations were analysed, and it was found that the cache usage and pre-loading should be enhanced and that the protocol should be changed, with report aggregation and compression using WBXML or gzip.

Real-Time Simulation of Mobile and Industrial Machines Using the Multibody Simulation Approach

This thesis introduces a real-time simulation environment based on the multibody simulation approach. The environment consists of components that are used in conventional product development, including computer aided drawing, visualization, dynamic simulation and finite element software architecture, data transfer and haptics. These components are combined to perform as a coupled system on one platform. The environment is used to simulate mobile and industrial machines at different stages of a product life time. Consequently, the demands of the simulated scenarios vary. In this thesis, a real-time simulation environment based on the multibody approach is used to study a reel mechanism of a paper machine and a gantry crane. These case systems are used to demonstrate the usability of the real-time simulation environment for fault detection purposes and in the context of a training simulator. In order to describe the dynamical performance of a mobile or industrial machine, the nonlinear equations of motion must be defined. In this thesis, the dynamical behaviour of machines is modelled using the multibody simulation approach. A multibody system may consist of rigid and flexible bodies which are joined using kinematic joint constraints while force components are used to describe the actuators. The strength of multibody dynamics relies upon its ability to describe nonlinearities arising from wearing of the components, friction, large rotations or contact forces in a systematic manner. For this reason, the interfaces between subsystems such as mechanics, hydraulics and control systems of the mechatronic machine can be defined and analyzed in a straightforward manner.

Analysis of Satellite Data for Forest Segmentation

In this work we study the classification of forest types using mathematics based image analysis on satellite data. We are interested in improving classification of forest segments when a combination of information from two or more different satellites is used. The experimental part is based on real satellite data originating from Canada. This thesis gives summary of the mathematics basics of the image analysis and supervised learning , methods that are used in the classification algorithm. Three data sets and four feature sets were investigated in this thesis. The considered feature sets were 1) histograms (quantiles) 2) variance 3) skewness and 4) kurtosis. Good overall performances were achieved when a combination of ASTERBAND and RADARSAT2 data sets was used.

Novel Regression Methods For Spectral Data

Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) are some of the mathematical pre- liminaries that are discussed prior to explaining PLS and PCR models. Both PLS and PCR are applied to real spectral data and their di erences and similarities are discussed in this thesis. The challenge lies in establishing the optimum number of components to be included in either of the models but this has been overcome by using various diagnostic tools suggested in this thesis. Correspondence analysis (CA) and PLS were applied to ecological data. The idea of CA was to correlate the macrophytes species and lakes. The di erences between PLS model for ecological data and PLS for spectral data are noted and explained in this thesis. i