The aim of strategic group analysis is to find out if clusters of firms that have a similar strategic position exist within an industry or not. In this thesis, by using a conceptual framework that reflects the strategic context, contents and process of construction companies and utilising alternative clustering methods such as traditional cluster analysis, self-organizing maps, and fuzzy C-means technique, a strategic group analysis was conducted for the Turkish construction industry. Results demonstrate that there are three strategic groups among which significant performance differences exist. Self-organising maps provide a visual representation of group composition and help identification of hybrid structures. Fuzzy C-means technique reveals the membership degrees of a firm to each strategic group. It is recommended that real strategic group structure can only be identified by using alternative cluster analysis methods. The positive effect of differentiation strategy on achieving competitive advantage is widely acknowledged in the literature and proved to be valid for the Turkish construction industry as a result of strategic group analysis. In this study, a framework is proposed to model the differentiation process in construction. The relationships between the modes and drivers of differentiation are analyzed by structural equation modeling. The results demonstrate that construction companies can either differentiate on quality or productivity. Project management related factors extensively influence productivity differentiation whereas they influence quality differentiation indirectly. Corporate management related factors only affect quality differentiation. Moreover, resources influence productivity differentiation directly whereas they have an indirect effect on quality differentiation.

Change detection is an ability that allows intelligent agents to react to unexpected situations. This mechanism is fundamental in providing more autonomy to robots. It has been used in many different fields including quality control and network intrusion. In the visual domain, however, most research has been confined to stationary cameras and only recently have researchers started to shift to mobile cameras. \ We propose a general framework for building internal spatial models of the visual experiences. These models are used to retrieve expectations about visual inputs which can be compared to the actual observation in order to identify the presence of changes. Our framework leverages the tolerance to small view changes of optic flow and color histogram representations and a self-organizing map to build a compact memory of camera observations. The effectiveness of the approach is demonstrated in a walking simulation, where spatial information and color histograms are combined to detect changes in a room. The location signal allows the algorithm to query the self-organizing map for the expected color histogram and compare it to the current input. Any deviations can be considered changes and are then localized on the input image. Furthermore, we show how detecting a vehicle entering or leaving the camera's lane can be reduced to a change detection problem. This simplifies the problem by removing the need to track or even know about other vehicles. Matching Pursuit is used to learn a compact dictionary to describe the observed experiences. Using this approach, changes are detected when the learned dictionary is unable to reconstruct the current input. The human experiments presented in this dissertation support the idea that humans build statistical models that evolve with experience. We provide evidence that not only does this experience improve people's behavior in 3D environments, but also enables them to detect chromatic changes. Mobile cameras are now part of our everyday lives, ranging from built-in laptop cameras to cell phone cameras. The vision of this research is to enable these devices with change detection mechanisms to solve a large class of problems. Beyond presenting a foundation that effectively detects changes in environments, we also show that the algorithms employed are computationally inexpensive. The practicality of this approach is demonstrated by a partial implementation of the algorithm on commodity hardware such as Android mobile devices. / text

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19616号 / 医博第4123号 / 新制||医||1015(附属図書館) / 32652 / 京都大学大学院医学研究科医学専攻 / (主査)教授 佐藤 俊哉, 教授 富樫 かおり, 教授 藤渕 航 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Proteins are among the most versatile organic macromolecules essential for living systems and present in almost all biological processes. Cancer is associated with mutations that either enhance or disrupt the conformation of proteins. These mutations have been shown to accumulate in specific regions of a proteins three dimensional structure. In this thesis, the aim is to find connections that secondary structure elements make and explore them using a self-organizing map (SOM). The detection of these connections is done by first mapping the three-dimensional structure onto a novice type of distance matrix that also incorporates chemical information, and then deploying a density-based clustering algorithm. The connections found are mapped onto the SOM and later analyzed in order to see if highly mutated connections are more common among certain SOM-nodes. This was tested with an ANOVA that indicated that there are indeed mutational asymmetries among the nodes. By further analyzing the map it could also be stated that certain nodes were to a large extent activated by connections from genes associated with cancer. / Proteiner tillhör några av de mest mångsidiga organiska makromolekylerna, och är direkt nödvändiga för alla levande system och biologiska processer. Cancer orsakas av mutationer som antingen förstärker eller stör strukturen hos proteinet. Dessa mutationer tenderar att att samlas i specifika områden av proteinets tredimensionella struktur. I den här rapporten är målet att hitta kopplingar som sekundärstrukturselement skapar, och utforska dem med hjälp av en självorganiserande karta. Dessa kopplingar finnes genom att först skapa en tvådimensionell representation av proteinets tredimensionella struktur, och sedan använda en densitetsbaserad klustringsalgoritm. De funna kopplingarna mappas till de olika neuronerna i kartan och analyseras sedan för att se om kopplingar med hög mutationsnivå är mer vanliga hos vissa neuron. För att undersöka detta användes ett ANOVA-test som visade att så var fallet. Genom att ytterligare studera kartan upptäcktes fynd som indikerade att vissa neuron i högre utsträckning var aktiverade av kopplingar som härstammar från gener vi vet är associerade med cancer.

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nÃo hà / A Rede Auto-OrganizÃvel de Kohonen (Self-Organizing Map - SOM), por empregar um algoritmo de aprendizado nÃo supervisionado, vem sendo tradicionalmente aplicada na Ãrea de processamento de sinais em tarefas de quantizaÃÃo vetorial, enquanto que redes MLP (Multi-layer Perceptron) e RBF (Radial Basis Function) dominam as aplicaÃÃes que exigem a aproximaÃÃo de mapeamentos entrada-saÃda. Este tipo de aplicaÃÃo à comumente encontrada em tarefas de filtragem adaptativa que podem ser formatadas segundo a Ãtica da modelagem direta e inversa de sistemas, tais como identificaÃÃo equalizaÃÃo de canais de comunicaÃÃo. Nesta dissertaÃÃo, a gama de aplicaÃÃes da rede SOM à estendida atravÃs da proposiÃÃo de filtros adaptativos neurais baseados nesta rede, mostrando que os mesmos sÃo alternativas viÃveis aos filtros nÃo-lineares baseados nas redes MLP e RBF. Isto torna-se possÃvel graÃas ao uso de uma tÃcnica recentemente proposta, Quantized Temporal Associative Memory - VQTAM), que basicamente usa a filosofia de chamada MemÃria Associativa Temporal por QuantizaÃÃo Vetorial (Vector )treinamento da rede SOM para realizar a quantizaÃÃo vetorial simultÃnea dos espaÃos de entrada e de saÃda relativos ao problema de filtragem analisado. A partir da tÃcnica VQTAM, sÃo propostos trÃs arquiteturas de filtros adaptativos baseadas na rede SOM, cujos desempenhos foram avaliados em tarefas de identificaÃÃo e equalizaÃÃo de canais nÃolineares. O canal usado nas simulaÃÃes foi modelado como um processo auto-regressivo de Gauss-Markov de primeira ordem, contaminado com ruÃdo branco gaussiano e dotado de nÃo-linearidade do tipo saturaÃÃo (sigmoidal). Os resultados obtidos mostram que filtros adaptativos baseados na rede SOM tÃm desempenho equivalente ou superior aos tradicionais filtros transversais lineares e aos filtros nÃo-lineares baseados na rede MLP.

<p>This study has been carried out in cooperation with Unilever and partly with the EC founded project, Smartdoc IST-2000-28137. </p><p>In areas of statistics and image processing, both the amount of data and the dimensions are increasing rapidly and an interactive visualization tool that lets the user perform real-time analysis can save valuable time. Real-time cropping and drill-down considerably facilitate the analysis process and yield more accurate decisions. </p><p>In the Smartdoc project, there has been a request for a component used for smart filtering in multidimensional data sets. As the Smartdoc project aims to develop smart, interactive components to be used on low-end systems, the implementation of the self-organizing map algorithm proposes which dimensions to visualize. </p><p>Together with Dr. Robert Treloar at Unilever, the SOM Visualizer - an application for interactive visualization and analysis of multidimensional data - has been developed. The analytical part of the application is based on Kohonen’s self-organizing map algorithm. In cooperation with the Smartdoc project, a component has been developed that is used for smart filtering in multidimensional data sets. Microsoft Visual Basic and components from the graphics library AVS OpenViz are used as development tools.</p>

Census operations are very important events in the history of a nation. These operations cover every bit of land and property of the country and its citizens. Census data is also known as demographic data providing valuable information to various users, particularly planners to know the trends in the key areas. Since 2006, Turkey aims to produce this census data not as &ldquo / de-facto&rdquo / (static) but as &ldquo / de-jure&rdquo / (real-time) by the new Address Based Register Information System (ABPRS). Besides, by this new register based census, personal information is matched with their address information and censuses gained a spatial dimension. Data obtained from this kind of a system can be a great input for the creation of &ldquo / small statistical areas (SSAs)&rdquo / which can compose of street blocks or any other small geographical unit to which social data can be referenced and to establish a complete census geography for Turkey. Because, statistics on large administrative units are only necessary for policy design only at an extremely abstracted level of analysis which is far from &quot / real&quot / problems as experienced by individuals. In this thesis, it is aimed to employ some spatial clustering and districting methodologies to automatically produce SSAs which are basically built upon the ABPRS data that is geo-referenced with the aid of geographical information systems (GIS) and thus help improving the census geography concept which is limited with only higher level administrative boundaries in Turkey. In order to have a clear idea of what strategy to choose for its realization, small area identification criteria and methodologies are searched by looking into the United Nations&rsquo / recommendations and by taking some national and international applications into consideration. In addition, spatial clustering methods are examined for obtaining SSAs which fulfills these criteria in an automated fashion. Simulated annealing on k-means clustering, only k-means clustering and simulated annealing on k-means clustering of Self-Organizing Map (SOM) unified distances are deemed as suitable methods. Then these methods are implemented on parcel and block datasets having either raw data or socio-economic status (SES) indices in nine neighborhoods of Ke&ccedil / i&ouml / ren whose graphical and non-graphical raw data are manipulated, geo-referenced and combined in common basemaps. Consequently, simulated annealing refinement on k-means clustering of SOM u-distances is selected as the optimum method for constructing SSAs for all datasets after making a comparative quality assessment study which allows us to see how much each method obeyed the basic criteria of small area identification while creating SSA layers.

Often there is a need to establish and understand the structure of multidimensional data: their clusters, outliers, similarity and dissimilarity. One of solution ways is a dimensionality reduction and visualization of the data. If a huge datasets is analyzed, it is purposeful to reduce the number of the data items before visualization. The area of research is reduction of the number of the data analyzed and mapping the data in a plane. In the dissertation, vector quantization methods, based on artificial neural networks, and visualization methods, based on a dimensionality reduction, have been investigated. The consecutive and integrated combinations of neural gas and multidimensional scaling have been proposed here as an alternative to combinations of self-organizing maps and multidimensional scaling. The visualization quality is estimated by König’s topology preservation measure, Spearman’s rho and MDS error. The measures allow us to evaluate the similarity preservation quantitatively after a transformation of multidimensional data into a lower dimension space. The ways of selecting the initial values of two-dimensional vectors in the consecutive combination and the first training block of the integrated combination have been proposed and the ways of assigning the initial values of two-dimensional vectors in all the training blocks, except the first one, of the integrated combination have been developed. The dependence of the quantization error on the values of training... [to full text] / Dažnai iškyla būtinybė nustatyti ir giliau pažinti daugiamačių duomenų struktūrą: susidariusius klasterius, itin išsiskiriančius objektus, objektų tarpusavio panašumą ir skirtingumą. Vienas iš sprendimų būdų – duomenų dimensijos mažinimas ir jų vizualizavimas. Kai analizuojamos didelės duomenų aibės, tikslinga prieš vizualizavimą sumažinti ne tik dimensiją, bet ir duomenų skaičių. Šio darbo tyrimų sritis yra daugiamačių duomenų skaičiaus mažinimas ir duomenų atvaizdavimas plokštumoje. Disertacijoje nagrinėjami dirbtiniais neuroniniais tinklais grindžiami vektorių kvantavimo ir dimensijos mažinimu pagrįsti vizualizavimo metodai. Kaip alternatyva saviorganizuojančių neuroninių tinklų ir daugiamačių skalių junginiams, darbe pasiūlyti nuoseklus neuroninių dujų ir daugiamačių skalių junginys bei integruotas, atsižvelgiantis į neuroninių dujų metodo mokymosi eigą ir leidžiantis gauti tikslesnę daugiamačių vektorių projekciją plokštumoje. Junginiais gautų vaizdų kokybės vertinimui pasirinkti Konigo matas, Spirmano koeficientas bei MDS paklaida. Šie matai leidžia kiekybiškai įvertinti panašumų išlaikymą po daugiamačių duomenų transformavimo į mažesnės dimensijos erdvę. Taip pat pasiūlyti dvimačių vektorių pradinių koordinačių parinkimo būdai nuosekliame junginyje ir integruoto junginio pirmame mokymo bloke bei koordinačių reikšmių priskyrimo būdai integruoto junginio kituose mokymo blokuose. Eksperimentiškai nustatyta kvantavimo paklaidos priklausomybė nuo neuroninių dujų tinklo... [toliau žr. visą tekstą]

Dažnai iškyla būtinybė nustatyti ir giliau pažinti daugiamačių duomenų struktūrą: susidariusius klasterius, itin išsiskiriančius objektus, objektų tarpusavio panašumą ir skirtingumą. Vienas iš sprendimų būdų – duomenų dimensijos mažinimas ir jų vizualizavimas. Kai analizuojamos didelės duomenų aibės, tikslinga prieš vizualizavimą sumažinti ne tik dimensiją, bet ir duomenų skaičių. Šio darbo tyrimų sritis yra daugiamačių duomenų skaičiaus mažinimas ir duomenų atvaizdavimas plokštumoje. Disertacijoje nagrinėjami dirbtiniais neuroniniais tinklais grindžiami vektorių kvantavimo ir dimensijos mažinimu pagrįsti vizualizavimo metodai. Kaip alternatyva saviorganizuojančių neuroninių tinklų ir daugiamačių skalių junginiams, darbe pasiūlyti nuoseklus neuroninių dujų ir daugiamačių skalių junginys bei integruotas, atsižvelgiantis į neuroninių dujų metodo mokymosi eigą ir leidžiantis gauti tikslesnę daugiamačių vektorių projekciją plokštumoje. Junginiais gautų vaizdų kokybės vertinimui pasirinkti Konigo matas, Spirmano koeficientas bei MDS paklaida. Šie matai leidžia kiekybiškai įvertinti panašumų išlaikymą po daugiamačių duomenų transformavimo į mažesnės dimensijos erdvę. Taip pat pasiūlyti dvimačių vektorių pradinių koordinačių parinkimo būdai nuosekliame junginyje ir integruoto junginio pirmame mokymo bloke bei koordinačių reikšmių priskyrimo būdai integruoto junginio kituose mokymo blokuose. Eksperimentiškai nustatyta kvantavimo paklaidos priklausomybė nuo neuroninių dujų tinklo... [toliau žr. visą tekstą] / Often there is a need to establish and understand the structure of multidimensional data: their clusters, outliers, similarity and dissimilarity. One of solution ways is a dimensionality reduction and visualization of the data. If a huge datasets is analyzed, it is purposeful to reduce the number of the data items before visualization. The area of research is reduction of the number of the data analyzed and mapping the data in a plane. In the dissertation, vector quantization methods, based on artificial neural networks, and visualization methods, based on a dimensionality reduction, have been investigated. The consecutive and integrated combinations of neural gas and multidimensional scaling have been proposed here as an alternative to combinations of self-organizing maps and multidimensional scaling. The visualization quality is estimated by König’s topology preservation measure, Spearman’s rho and MDS error. The measures allow us to evaluate the similarity preservation quantitatively after a transformation of multidimensional data into a lower dimension space. The ways of selecting the initial values of two-dimensional vectors in the consecutive combination and the first training block of the integrated combination have been proposed and the ways of assigning the initial values of two-dimensional vectors in all the training blocks, except the first one, of the integrated combination have been developed. The dependence of the quantization error on the values of training... [to full text]