Pienloistelamppujen johtuvat RF-häiriöt verrattuna CISPR 15 - standardin raja-arvoihin

Hehkulamput tullaan kieltämään Euroopan Unionin alueella vuoden 2016 syyskuuhun mennessä. Hehkulamput korvataan suurimmaksi osaksi pienloistelampuilla, jotka omaavat hehkulamppuihin verrattuna paremman hyötysuhteen. Pienloistelamput aiheuttavat kuitenkin häiriöitä niitä syöttävään sähköverkkoon. Tässä kandidaatin työssä mitattiin 15 erilaisen pienloistelampun aiheuttamat johtuvat RF-taajuiset häiriöt. Tuloksia verrattiin CISPR 15 – standardissa esitettyihin raja-arvoihin, joita pienloistelamppujen tulisi noudattaa. Mittaukset suoritettiin käyttämällä CISPR 16 –standardin kanssa yhteensopivaa mittavastaanotinta (Rohde & Schwarz ESHS30) sekä myöskin CISPR 16 yhteensopivaa keinoverkkoa (Rohde & Schwarz ESH2Z5). Lamput mitattiin CISPR 15 –standardin mukaisesti, muutamia poikkeuksia lukuun ottamatta. Lamppuja ei mitattu käyttämällä CISPR 15 –standardissa vaadittua lampun pidintä. Eikä lamppuja myöskään esikäytetty ollenkaan. Lukuun ottamatta yhtä pienloistelamppua, kaikki jäivät raja-arvon alapuolelle. Rajan ylittäneen lampun tulos tosin mahtuu mittavastaanottimen mittavirheeseen. Joten kyseessä ei ollut suuren suuri ylitys. Muiden tutkittujen pienloistelamppujen johtuvat RF-häiriöt ovat hyväksyttävällä tasolla ja ne tuskin tulevat aiheuttamaan ongelmia.

Osaamisen johtaminen tietohallintopalveluita tarjoavan organisaation suorituskyvyn kehittämisessä

Tässä diplomityössä tutkitaan osaamisen johtamisen järjestelmää strategisesta näkö-kulmasta tietohallintopalveluita tuottavassa organisaatiossa. Tutkimuksen tarkoitukse-na oli kehittää osaamisen johtamisen kokonaisuutta kohdeorganisaation suorituskyvyn kehittämiseksi. Tutkimuksen lähtökohtana ovat organisaation arvot, missio ja visio sekä näiden kautta rakentuva organisaation osaamisen strategia. Osaamisen strategian näkökulmasta tarkastellaan asiantuntijuutta, osaamisen johtamista ja organisaation ydinosaamisia. Teoreettisesta näkökulmasta tutkitaan myös verkostoitumista ja oppi-misen verkostomallin hyödyntämismahdollisuuksia kohdeorganisaatiossa. Empiirinen aineisto kerättiin laadullisella teemahaastattelulla. Tutkimuksessa saatiin selkeä kuva kohdeorganisaation osaamisen johtamisen kokonai-suudesta. Verkostoituminen ja verkostomainen työskentely ovat kohdeorganisaatiossa erittäin suuressa roolissa, joten oppimisen verkostomallia voitaisiin hyödyntää organi-saatiossa tehokkaasti. Tämä edellyttää kuitenkin verkoston johtamista ja tavoitteellista osallistumista verkostoihin sekä asetettujen tavoitteiden mittaamista.

Post-industrial 2.0 – Social Web as the second coming of the post-industrial society

This study presents a review of theories of the so-called post-industrial society, and proposes that the concept of post-industrial society can be used to understand the recent developments of the World Wide Web, often described as Web 2.0 or social Web. The study combines theories ranging from post-war management science and cultural studies to software development, and tries to build a holistic view of the development of the post-industrial society, and especially the Internet. The discourse on the emergence of a post-industrial society after the World Wars has addressed the ways in which the growing importance of information, and innovations in digital communications technology, are changing our society. It is furthermore deeply connected with the discourse on the postmodern society, which emphasizes cultural fragmentation, intertextuality, and pluralism. The Internet age is characterized by increasing masses of information that are managed through various technologies. While 1990s Internet technologies often used the network as a traditional broadcasting channel with added interactivity, Web 2.0 technologies are specifically designed to utilize the network model by facilitating communication between various services and devices, and analyzing the relationships between users and objects in order to produce intelligent insight. The wide adoption of the Internet, and recently of Internet-enabled mobile devices, is furthermore continuously producing new ways of communicating, consuming, and producing. Applications of the social Web, such as social media or social networking services, are permanently changing our traditional social, cultural, and economic practices. The study first presents an overview of the post-industrial society, the Internet, and the concept of Web 2.0. Then the concept of social Web is described with an analysis of the term social media, the brief histories of the interactive Web and social networking services, and a description of the concept ―long tail‖, used to represent the masses of information available in the Web that do not receive mainstream attention. Finally, methods for retrieving and filtering information, modeling social and cultural relationships, and communicating with customers, are presented.

The marketing communications industry in flux: Large B2C advertisers’ viewpoint

The recent digitization, fragmentation of the media landscape and consumers’ changing media behavior are all changes that have had drastic effects on creating marketing communications. In order to create effective marketing communications large advertisers are now co-operating with a variety of marketing communications companies. The purpose of the study is to understand how advertisers perceive these different companies and more importantly how do advertisers expect their roles to change in the future as the media landscape continues to evolve. Especially the changing roles of advertising agencies and media agencies are examined as they are at the moment the most relevant partners of the advertisers. However, the research is conducted from a network perspective rather than focusing on single actors of the marketing communications industry network. The research was conducted using a qualitative theme interview method. The empirical data was gathered by interviewing representatives from nine of the 50 largest Finnish advertisers measured by media spending. Thus, the research was conducted solely from large B2C advertisers’ perspective while the views of their other relevant actors of the network were left unexplored. The interviewees were chosen with a focus on variety of points of view. The analytical framework that was used to analyze the gathered data was built the IMP group’s industrial network model that consists of actors, their resources and activities. As technology driven media landscape fragmentation and consumers’ changing media behavior continue to increase the complexity of creating marketing communications, advertisers are going to need to rely on a growing number of partnerships as they see that the current actors of the network will not be able to widen their expertise to answer to these new needs. The advertisers expect to form new partnerships with actors that are more specialized and able to react and produce activities more quickly than at the moment. Thus, new smaller and more agile actors with looser structures are going to appear to fill these new needs. Therefore, the need of co-operation between the actors is going to become more important. These changes pose the biggest threat for traditional advertising agencies as they were seen as being most unable to cope with the ongoing change. Media agencies are in a more favorable position for remaining relevant for the advertisers as they will be able to justify their activities and provided value by leveraging their data handling abilities. In general the advertisers expect to be working with a limited number of close actors and in addition having a network of smaller actors, which are used on a more ad hoc basis.

Aurinkosähköjärjestelmien vieminen Afrikan kehittyviin maihin

Aurinkosähköjärjestelmien käyttäminen sähkön tuotannossa on kasvanut viime vuosina teknologian hinnan laskun ja tiukentuvien ympäristömääräyksien seurauksena. Monet aurinkosähköön keskittyvät yritykset etsivät uusia markkinoita Afrikan kehittyvistä maista. Aurinkosähkön avulla voidaan vastata kehittyvien maiden kasvavaan sähköntarpeeseen ja samalla vähentää maiden hiilidioksidipäästöjä sekä nostaa maiden kehittyvää elintasoa. Tässä työssä tutkitaan aurinkosähköjärjestelmien viemistä kahteen Afrikan kehittyvään maahan Tansaniaan ja Etelä-Afrikkaan. Työn tavoitteena on kohdemaiden liiketoimintaympäristöjen tutkiminen ja markkinapotentiaalin selvittäminen suomalaisen sähkö- ja automaatiotekniikkaan keskittyvän pk-yrityksen näkökulmasta. Työssä selvitetään Tansanian ja Etelä-Afrikan liiketoimintaympäristöjen ja aurinkosähkömarkkinoiden erityispiirteet. Tuloksissa käsitellään myös sopivinta kansainvälistymistapaa tutkitulle pk-yritykselle sekä haasteita, joita yritys kohtaa Afrikan markkinoilla. Kansainvälistymismalleista tutkimuksessa käsitellään Uppsala-mallia ja verkostomallia.

Non-Linear Unsteady Aerodynamic Response Approximation Using Multi-Layer Functionals

Non-linear functional representation of the aerodynamic response provides a convenient mathematical model for motion-induced unsteady transonic aerodynamic loads response, that accounts for both complex non-linearities and time-history effects. A recent development, based on functional approximation theory, has established a novel functional form; namely, the multi-layer functional. For a large class of non-linear dynamic systems, such multi-layer functional representations can be realised via finite impulse response (FIR) neural networks. Identification of an appropriate FIR neural network model is facilitated by means of a supervised training process in which a limited sample of system input-output data sets is presented to the temporal neural network. The present work describes a procedure for the systematic identification of parameterised neural network models of motion-induced unsteady transonic aerodynamic loads response. The training process is based on a conventional genetic algorithm to optimise the network architecture, combined with a simplified random search algorithm to update weight and bias values. Application of the scheme to representative transonic aerodynamic loads response data for a bidimensional airfoil executing finite-amplitude motion in transonic flow is used to demonstrate the feasibility of the approach. The approach is shown to furnish a satisfactory generalisation property to different motion histories over a range of Mach numbers in the transonic regime.

Intelligent mapping of stresses in a hydraulic crane

Continuous loading and unloading can cause breakdown of cranes. In seeking solution to this problem, the use of an intelligent control system for improving the fatigue life of cranes in the control of mechatronics has been under study since 1994. This research focuses on the use of neural networks as possibilities of developing algorithm to map stresses on a crane. The intelligent algorithm was designed to be a part of the system of a crane, the design process started with solid works, ANSYS and co-simulation using MSc Adams software which was incorporated in MATLAB-Simulink and finally MATLAB neural network (NN) for the optimization process. The flexibility of the boom accounted for the accuracy of the maximum stress results in the ADAMS model. The flexibility created in ANSYS produced more accurate results compared to the flexibility model in ADAMS/View using discrete link. The compatibility between.ADAMS and ANSYS softwares was paramount in the efficiency and the accuracy of the results. Von Mises stresses analysis was more suitable for this thesis work because the hydraulic boom was made from construction steel FE-510 of steel grade S355 with yield strength of 355MPa. Von Mises theory was good for further analysis due to ductility of the material and the repeated tensile and shear loading. Neural network predictions for the maximum stresses were then compared with the co-simulation results for accuracy, and the comparison showed that the results obtained from neural network model were sufficiently accurate in predicting the maximum stresses on the boom than co-simulation.

Julkishallinnon verkostojohtaminen - Case: Helsingin kaupungin ympäristöjohtaminen

Tämän pro gradu -tutkielman tavoitteena oli tutkia millainen on hyvä ympäristö-johtamisen verkostomalli kunnalle. Lisäksi työssä tarkasteltiin mitä hyötyjä haittoja on kunnan verkostomaisesti organisoidussa ympäristöjohtamisessa verrattuna ei-verkostomaiseen toteutukseen. Työssä selvitettiin myös millaiset verkostojohtamisen mallit ovat tehokkaita ja mitkä ovat verkoston osapuolten odotukset kunnan ympäristöjohtamisen verkostolle. Tutkielman lopputuloksena oli ympäristöjohtamisen verkostomalli kunnalle. Tutkielman teoreettisen viitekehyksen muodostivat julkishallinnon verkoston hallintamalli, verkostojohtaminen, ympäristöjohtaminen ja tehokkuus. Nämä kolme käsitettä muodostivat työn verkostomallin. Tutkimusmenetelmänä työssä käytettiin laadullista tapaustutkimusta, ja tutkimuksen kohteena oli kuntaorganisaation ympäristöjohtamisen verkosto, joka käsitti ympäristöjohtamisen asiantuntijatyöryhmän. Tutkimusaineisto kerättiin teemahaastatteluin ja tutkimuksessa haastateltiin kolmetoista asiantuntijatyöryhmän jäsentä yksilö-, pari- ja ryhmähaastatteluin.

Method for automatic detection of wheezing in lung sounds

The present report describes the development of a technique for automatic wheezing recognition in digitally recorded lung sounds. This method is based on the extraction and processing of spectral information from the respiratory cycle and the use of these data for user feedback and automatic recognition. The respiratory cycle is first pre-processed, in order to normalize its spectral information, and its spectrogram is then computed. After this procedure, the spectrogram image is processed by a two-dimensional convolution filter and a half-threshold in order to increase the contrast and isolate its highest amplitude components, respectively. Thus, in order to generate more compressed data to automatic recognition, the spectral projection from the processed spectrogram is computed and stored as an array. The higher magnitude values of the array and its respective spectral values are then located and used as inputs to a multi-layer perceptron artificial neural network, which results an automatic indication about the presence of wheezes. For validation of the methodology, lung sounds recorded from three different repositories were used. The results show that the proposed technique achieves 84.82% accuracy in the detection of wheezing for an isolated respiratory cycle and 92.86% accuracy for the detection of wheezes when detection is carried out using groups of respiratory cycles obtained from the same person. Also, the system presents the original recorded sound and the post-processed spectrogram image for the user to draw his own conclusions from the data.

Underwater Remote Welding Technology for Offshore Structures

The construction of offshore structures, equipment and devices requires a high level of mechanical reliability in terms of strength, toughness and ductility. One major site for mechanical failure, the weld joint region, needs particularly careful examination, and weld joint quality has become a major focus of research in recent times. Underwater welding carried out offshore faces specific challenges affecting the mechanical reliability of constructions completed underwater. The focus of this thesis is on improvement of weld quality of underwater welding using control theory. This research work identifies ways of optimizing the welding process parameters of flux cored arc welding (FCAW) during underwater welding so as to achieve desired weld bead geometry when welding in a water environment. The weld bead geometry has no known linear relationship with the welding process parameters, which makes it difficult to determine a satisfactory weld quality. However, good weld bead geometry is achievable by controlling the welding process parameters. The doctoral dissertation comprises two sections. The first part introduces the topic of the research, discusses the mechanisms of underwater welding and examines the effect of the water environment on the weld quality of wet welding. The second part comprises four research papers examining different aspects of underwater wet welding and its control and optimization. Issues considered include the effects of welding process parameters on weld bead geometry, optimization of FCAW process parameters, and design of a control system for the purpose of achieving a desired bead geometry that can ensure a high level of mechanical reliability in welded joints of offshore structures. Artificial neural network systems and a fuzzy logic controller, which are incorporated in the control system design, and a hybrid of fuzzy and PID controllers are the major control dynamics used. This study contributes to knowledge of possible solutions for achieving similar high weld quality in underwater wet welding as found with welding in air. The study shows that carefully selected steels with very low carbon equivalent and proper control of the welding process parameters are essential in achieving good weld quality. The study provides a platform for further research in underwater welding. It promotes increased awareness of the need to improve the quality of underwater welding for offshore industries and thus minimize the risk of structural defects resulting from poor weld quality.

Dynamic capabilities as enablers of value co-creation between an MBA school and its students

This study presents an understanding of how a U.S. based, international MBA school has been able to achieve competitive advantage within a relatively short period of time. A framework is built to comprehend how the dynamic capability and value co-creation theories are connected and to understand how the dynamic capabilities have enabled value co-creation to happen between the school and its students, leading to such competitive advantage for the school. The data collection method followed a qualitative single-case study with a process perspective. Seven semi-structured interviews were made in September and October of 2015; one current employee of the MBA school was interviewed, with the other six being graduates and/or former employees of the MBA school. In addition, the researcher has worked as a recruiter at the MBA school, enabling to build bridges and a coherent whole of the empirical findings. Data analysis was conducted by first identifying themes from interviews, after which a narrative was written and a causal network model was built. Thus, a combination of thematic analysis, narrative and grounded theory were used as data analysis methods. This study finds that value co-creation is enabled by the dynamic capabilities of the MBA school; also capabilities would not be dynamic if value co-creation did not take place. Thus, this study presents that even though the two theories represent different level analyses, they are intertwined and together they can help to explain competitive advantage. The MBA case school’s dynamic capabilities are identified to be the sales & marketing capabilities and international market creation capabilities, thus the study finds that the MBA school does not only co-create value with existing students (customers) in the school setting, but instead, most of the value co-creation happens between the school and the student cohorts (network) already in the recruiting phase. Therefore, as a theoretical implication, the network should be considered as part of the context. The main value created seem to lie in the MBA case school’s international setting & networks. MBA schools around the world can learn from this study; schools should try to find their own niche and specialize, based on their own values and capabilities. With a differentiating focus and a unique and practical content, the schools can and should be well-marketed and proactively sold in order to receive more student applications and enhance competitive advantage. Even though an MBA school can effectively be treated as a business, as the study shows, the main emphasis should still be on providing quality education. Good content with efficient marketing can be the winning combination for an MBA school.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

Observational learning of motor skills : Looking for optimal models

L’observation d’un modèle pratiquant une habileté motrice promeut l’apprentissage de l’habileté en question. Toutefois, peu de chercheurs se sont attardés à étudier les caractéristiques d’un bon modèle et à mettre en évidence les conditions d’observation pouvant optimiser l’apprentissage. Dans les trois études composant cette thèse, nous avons examiné les effets du niveau d’habileté du modèle, de la latéralité du modèle, du point de vue auquel l’observateur est placé, et du mode de présentation de l’information sur l’apprentissage d’une tâche de timing séquentielle composée de quatre segments. Dans la première expérience de la première étude, les participants observaient soit un novice, soit un expert, soit un novice et un expert. Les résultats des tests de rétention et de transfert ont révélé que l’observation d’un novice était moins bénéfique pour l’apprentissage que le fait d’observer un expert ou une combinaison des deux (condition mixte). Par ailleurs, il semblerait que l’observation combinée de modèles novice et expert induise un mouvement plus stable et une meilleure généralisation du timing relatif imposé comparativement aux deux autres conditions. Dans la seconde expérience, nous voulions déterminer si un certain type de performance chez un novice (très variable, avec ou sans amélioration de la performance) dans l’observation d’une condition mixte amenait un meilleur apprentissage de la tâche. Aucune différence significative n’a été observée entre les différents types de modèle novices employés dans l’observation de la condition mixte. Ces résultats suggèrent qu’une observation mixte fournit une représentation précise de ce qu’il faut faire (modèle expert) et que l’apprentissage est d’autant plus amélioré lorsque l’apprenant peut contraster cela avec la performance de modèles ayant moins de succès. Dans notre seconde étude, des participants droitiers devaient observer un modèle à la première ou à la troisième personne. L’observation d’un modèle utilisant la même main préférentielle que soi induit un meilleur apprentissage de la tâche que l’observation d’un modèle dont la dominance latérale est opposée à la sienne, et ce, quel que soit l’angle d’observation. Ce résultat suggère que le réseau d’observation de l’action (AON) est plus sensible à la latéralité du modèle qu’à l’angle de vue de l’observateur. Ainsi, le réseau d’observation de l’action semble lié à des régions sensorimotrices du cerveau qui simulent la programmation motrice comme si le mouvement observé était réalisé par sa propre main dominante. Pour finir, dans la troisième étude, nous nous sommes intéressés à déterminer si le mode de présentation (en direct ou en vidéo) influait sur l’apprentissage par observation et si cet effet est modulé par le point de vue de l’observateur (première ou troisième personne). Pour cela, les participants observaient soit un modèle en direct soit une présentation vidéo du modèle et ceci avec une vue soit à la première soit à la troisième personne. Nos résultats ont révélé que l’observation ne diffère pas significativement selon le type de présentation utilisée ou le point de vue auquel l’observateur est placé. Ces résultats sont contraires aux prédictions découlant des études d’imagerie cérébrale ayant montré une activation plus importante du cortex sensorimoteur lors d’une observation en direct comparée à une observation vidéo et de la première personne comparée à la troisième personne. Dans l’ensemble, nos résultats indiquent que le niveau d’habileté du modèle et sa latéralité sont des déterminants importants de l’apprentissage par observation alors que le point de vue de l’observateur et le moyen de présentation n’ont pas d’effets significatifs sur l’apprentissage d’une tâche motrice. De plus, nos résultats suggèrent que la plus grande activation du réseau d’observation de l’action révélée par les études en imagerie mentale durant l’observation d’une action n’induit pas nécessairement un meilleur apprentissage de la tâche.

Development of a Biometric Personal Authentication System Based on Fingerprint and Speech

Biometrics deals with the physiological and behavioral characteristics of an individual to establish identity. Fingerprint based authentication is the most advanced biometric authentication technology. The minutiae based fingerprint identification method offer reasonable identification rate. The feature minutiae map consists of about 70-100 minutia points and matching accuracy is dropping down while the size of database is growing up. Hence it is inevitable to make the size of the fingerprint feature code to be as smaller as possible so that identification may be much easier. In this research, a novel global singularity based fingerprint representation is proposed. Fingerprint baseline, which is the line between distal and intermediate phalangeal joint line in the fingerprint, is taken as the reference line. A polygon is formed with the singularities and the fingerprint baseline. The feature vectors are the polygonal angle, sides, area, type and the ridge counts in between the singularities. 100% recognition rate is achieved in this method. The method is compared with the conventional minutiae based recognition method in terms of computation time, receiver operator characteristics (ROC) and the feature vector length. Speech is a behavioural biometric modality and can be used for identification of a speaker. In this work, MFCC of text dependant speeches are computed and clustered using k-means algorithm. A backpropagation based Artificial Neural Network is trained to identify the clustered speech code. The performance of the neural network classifier is compared with the VQ based Euclidean minimum classifier. Biometric systems that use a single modality are usually affected by problems like noisy sensor data, non-universality and/or lack of distinctiveness of the biometric trait, unacceptable error rates, and spoof attacks. Multifinger feature level fusion based fingerprint recognition is developed and the performances are measured in terms of the ROC curve. Score level fusion of fingerprint and speech based recognition system is done and 100% accuracy is achieved for a considerable range of matching threshold

Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned