Water distribution system utilities must be able to maintain a system’s assets (i.e., pumps, tanks, water mains, etc.) in good working condition in order to provide adequate water quantity and quality to its customers. Various asset management approaches are employed by utilities in order to make optimal decisions regarding the renewal of system components. Part of a good asset management approach is performing a comprehensive risk analysis which consists of considering all potential ways in which the system may fail, the likelihood failure of for each scenario, and the consequences of said failure. This study investigates a water distribution system’s risk of failure due to both acute transient events (e.g., pump trip) and standard pressure fluctuations due to daily system operations. Such an analysis may be useful in optimal decision making such as asset monitoring, scheduling of condition assessments or system renewal projects, policy implementation, and investment priorities in order to keep the utility’s total costs at a minimum. It may also be useful as a precautionary measure to help prevent catastrophic failures such as large main blowouts for which the utility would incur substantial costs, both direct and indirect. As part of this thesis, a database of water distribution system models is used to analyze the effects of an acute transient event for different system configurations. The database was created at the University of Kentucky and has been made available to the research community to test newly developed algorithms for various studies including optimal system operations and optimal system design.

In recent years, water utilities have placed a greater emphasis on the reliability and resilience of their water distribution networks. This focus has increased due to the continuing aging of such infrastructure and the potential threat of natural or man-made disruptions. As a result, water utilities continue to look for ways to evaluate the resiliency of their systems with a goal of identifying critical elements that need to be reinforced or replaced. The simulation of pipe breaks in water reliability studies is traditionally modeled as the loss of a single pipe element. This assumes that each pipe has an isolation valve on both ends of the pipe that can be readily located and operated under emergency conditions. This is seldom the case. The proposed methodology takes into account that multiple pipes may be impacted during a single failure as a result of the necessity to close multiple isolation valves in order to isolate the “segment” of pipes necessary to contain the leak. This document presents a simple graphical metric for use in evaluating the performance of a system in response to a pipe failure. The metrics are applied to three different water distribution systems in an attempt to illustrate the fact that different pipe segments may impact system performance in different ways. This information is critical for use by system managers in deciding which segments to prioritize for upgrades or replacement.

Water distribution systems are vulnerable to intentional, along with accidental, contamination of the water supply. Contamination warning systems (CWS) are strategies to lessen the effects of contamination by delivering early indication of an event. Online quality monitoring, a network of sensors that can assess water quality and alert an operator of contamination, is a critical component of CWS, but utilities are faced with the decision of what locations are optimal for deployment of sensors. A sensor placement algorithm was developed and implemented in a commercial network distribution model (i.e. KYPIPE) to aid small utilities in sensor placement. The developed sensor placement tool was then validated using 12 small distribution system models and multiple contamination scenarios for the placement of one and two sensors. This thesis also addresses the issue that many sensor placement algorithms require calibrated hydraulic/water quality models, but small utilities do not always possess the financial resources or expertise to build calibrated models. Because of such limitations, a simple procedure is proposed to recommend optimal placement of a sensor without the need for a model or complicated algorithm. The procedure uses simple information about the geometry of the system and does not require explicit information about flow dynamics.

The final thesis deals with the and design of the information system in the form of a conceptual design in the company Imperial invest funds s.r.o., the aim of the thesis is the efficiency of the work in the company, reducing the load of human resources, automating the processes and increasing the integrity of the IS in the functioning of the company. The main objective is to analyze existing workflows and utilization of existing information systems, and to eliminate these drawbacks or replace them with a new solution on the basis of a bottleneck analysis.

This study conducted an experiment that compares the multi-model NoSQL DBMS ArangoDB with other NoSQL DBMS, in terms of the average response time of queries. The DBMS compared in this experiment are the following: Redis, MongoDB, Couchbase, and OrientDB. The hypothesis that is answered in this study is the following: “There is a significant difference between ArangoDB, OrientDB, Couchbase, Redis, MongoDB in terms of the average response time of queries”. This is examined by comparing the average response time of 1 000, 100 000, and 1 000 000 queries between these database systems. The results show that ArangoDB performs worse compared to the other DBMS. Examples of future work include using additional DBMS in the same experiment and replacing ArangoDB with another multi-model DBMS to decide whether such a DBMS, in general, performs worse than single-model DBMS.

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This Ph.D. thesis tackles the issue of sill and video object categorization. The objective is to associate semantic labels to 2D objects present in natural images/videos. The principle of the proposed approach consists of exploiting categorized 3D model repositories in order to identify unknown 2D objects based on 2D/3D matching techniques. We propose here an object recognition framework, designed to work for real time applications. The similarity between classified 3D models and unknown 2D content is evaluated with the help of the 2D/3D description. A voting procedure is further employed in order to determine the most probable categories of the 2D object. A representative viewing angle selection strategy and a new contour based descriptor (so-called AH), are proposed. The experimental evaluation proved that, by employing the intelligent selection of views, the number of projections can be decreased significantly (up to 5 times) while obtaining similar performance. The results have also shown the superiority of AH with respect to other state of the art descriptors. An objective evaluation of the intra and inter class variability of the 3D model repositories involved in this work is also proposed, together with a comparative study of the retained indexing approaches . An interactive, scribble-based segmentation approach is also introduced. The proposed method is specifically designed to overcome compression artefacts such as those introduced by JPEG compression. We finally present an indexing/retrieval/classification Web platform, so-called Diana, which integrates the various methodologies employed in this thesis

This dissertation is studying e-learning – electronic form of education. At once it is analysing in details the advantages and disadvantages of this new form of education approach and is describing possibilities of some chosen e-learning systems and their basic structures. In the second part of this dissertation are said basic principles and possibilities of programming language Java in which language is designed it's own concept of system, that should make possible for users to manage and control the e-learning. In this part is described progress of communication between server and client, method of sending off the SQL (query) and receiving result too. In last part is viewed resulting application as a whole, is explained basic controlling, possibilities of administration and methods of entering data. Application itself is tested on local testing server.

Cette thèse porte sur la catégorisation d'objets vidéo. L'objectif est d'associer des étiquettes sémantiques à des objets 2D présents dans les images/vidéos. L'approche proposée consiste à exploiter des bases d'objets 3D classifiés afin d'identifier des objets 2D inconnus. Nous proposons un schéma de reconnaissance d'objet, conçu pour fonctionner pour des applications en temps réel. La similitude entre des modèles 3D et des contenus 2D inconnu est évaluée à l'aide de la description 2D/3D. Une procédure de vote est ensuite utilisée afin de déterminer les catégories les plus probables de l'objet 2D. Nous proposons aussi une stratégie pour la sélection des vues les plus représentatives d'un objet 3D et un nouveau descripteur de contour (nommé AH). L'évaluation expérimentale a montré que, en employant la sélection intelligente de vues, le nombre de projections peut être diminué de manière significative (jusqu'à 5 fois) tout en obtenant des performances similaires. Les résultats ont également montré la supériorité de l'AH par rapport aux autres descripteurs adoptés. Une évaluation objective de la variabilité intra et inter classe des bases de données 3D impliqués dans ce travail est également proposé, ainsi qu'une étude comparative des approches d'indexations retenues. Une approche de segmentation interactive est également introduite. La méthode proposée est spécifiquement conçu pour surmonter les artefacts de compression tels que ceux mis en place par la compression JPEG. Enfin, nous présentons une plate-forme Web pour l'indexation/la recherche/la classification, qui intègre les différentes méthodologies utilisées dans cette thèse / This Ph.D. thesis tackles the issue of sill and video object categorization. The objective is to associate semantic labels to 2D objects present in natural images/videos. The principle of the proposed approach consists of exploiting categorized 3D model repositories in order to identify unknown 2D objects based on 2D/3D matching techniques. We propose here an object recognition framework, designed to work for real time applications. The similarity between classified 3D models and unknown 2D content is evaluated with the help of the 2D/3D description. A voting procedure is further employed in order to determine the most probable categories of the 2D object. A representative viewing angle selection strategy and a new contour based descriptor (so-called AH), are proposed. The experimental evaluation proved that, by employing the intelligent selection of views, the number of projections can be decreased significantly (up to 5 times) while obtaining similar performance. The results have also shown the superiority of AH with respect to other state of the art descriptors. An objective evaluation of the intra and inter class variability of the 3D model repositories involved in this work is also proposed, together with a comparative study of the retained indexing approaches . An interactive, scribble-based segmentation approach is also introduced. The proposed method is specifically designed to overcome compression artefacts such as those introduced by JPEG compression. We finally present an indexing/retrieval/classification Web platform, so-called Diana, which integrates the various methodologies employed in this thesis