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Boarder security is usually based on observing and analyzing the movement of MovingPoint Objects (MPOs): vehicle, boats, pedestrian or aircraft for example. This movementanalysis can directly be made by an operator observing the MPOs in real-time, but theprocess is time-consuming and approximate. This is why the states of each MPO (ID, location,speed, direction) are sensed in real-time using Global Navigation Satellite System(GNSS), Automatic Identification System (AIS) and radar sensing, thus creating a streamof MPO states. This research work proposes and carries out (1) a method for detectingfour different moving point patterns based on this input stream (2) a comparison betweenthree possible implementations of the moving point pattern detectors based on three differentData Stream Management Systems (DSMS). Moving point patterns can be dividedin two groups: (1) individual location patterns are based on the analysis of the successivestates of one MPO, (2) set-based relative motion patterns are based on the analysis ofthe relative motion of groups of MPOs within a set. This research focuses on detectingfour moving point patterns: (1) the geofence pattern consists of one MPO enteringor exiting one of the predefined areas called geofences, (2) the track pattern consists ofone MPO following the same direction for a given number of time steps and satisfying agiven spatial constraint, (3) the flock pattern consists of a group of geographically closeMPOs following the same direction, (4) the leadership pattern consists of a track patternwith the corresponding MPO anticipating the direction of geographically close MPOs atthe last time step. The two first patterns are individual location patterns, while the othersare set-based relative motion patterns. This research work proposes a method for detectinggeofence patterns based on the update of a table storing the last sensed state of eachMPO. The approach used for detecting track, flock and leadership patterns is based on theupdate of a REMO matrix (RElative MOtion matrix) where rows correspond to MPOs,columns to time steps and cells record the direction of movement. For the detection offlock patterns a simple but effective probabilistic grid-based approach is proposed in orderto detect clusters of MPOs within the MPOs following the same direction: (1) the Filteringphase partitions the study area into square-shaped cells -according to the dimensionof the spatial constraint- and selects spatially contiguous grid cells called candidate areasthat potentially contain flock patterns (2) for each candidate area, the Refinement phasegenerates disks of the size of the spatial constraint within the selected area until one diskcontains enough MPOs, so that the corresponding MPOs are considered to build a flockpattern. The pattern detectors are implemented on three DSMSs presenting differentcharacteristics: Esri ArcGIS GeoEvent Extension for Server (GeoEvent Ext.), a workflow-based technology that ingests each MPO state separately, Apache Spark Streaming(Spark), a MapReduce-based technology that processes the input stream in batches in ahighly-parallel processing framework and Apache Flink (Flink), a hybrid technology thatingests the states separately but offers several MapReduce semantics. GeoEvent Ext. onlylends itself for a nature implementation of the geofence detector, while the other DSMSsaccommodate the implementation of all detectors. Therefore, the geofence, track, flockand leadership pattern detectors are implemented on Spark and Flink, and empiricallyevaluated in terms of scalability in time/space based on the variation of parameters characterizingthe patterns and/or the input stream. The results of the empirical evaluationshows that the implementation on Flink uses globally less computer resources than theone on Spark. Moreover, the program based on Flink is less sensitive to the variability ofparameters describing either the input stream or the patterns to be detected.

Med flera alternativ att välja mellan för att designa och utveckla webbsidor kan det vara svårt för en nybörjarutvecklare att veta vad man ska välja. Detta examensarbete jämför hur enkelt det är för en nybörjare att lära sig och använda de två Content Management Systemen (CMS) Concrete5 och RefineryCMS för att bygga en webbapplikation. Concrete5 använder PHP och RefineryCMS använder Ruby och lättheten i att lära sig grunderna i dessa programmeringsspråk diskuteras också.För att jämföra dessa två CMS, dokumenterades olika steg. Implementationen utfördes på en MacBook Pro, OS X 10.9.2, sen 2011 modell. Relevanta delar av funktionaliteten jämfördes även med hjälp av Authoring Tool Accessibility Guidelines 2.0.Resultaten visade en signifikant skillnad mellan de två CMS:en, även om båda har sina för- och nackdelar. Sammanfattningsvis ansågs Concrete5 som det CMS:et med bättre aspekter för att bygga en enkel hemsida med de förutsättningar som fanns och tack vare de många funktioner det erbjöd användaren. RefineryCMS kanske kunde ha fungerat bättre om det var under andra förutsättningar, vilket inte kan dras som en slutsats i denna uppsats. / With many options to choose from when designing and developing websites, it can be difficult for a beginner developer to know what to choose. This thesis compares how easy it is for a beginner to learn and use the two Content Management Systems (CMS) Concrete5 and RefineryCMS to build a web application. Concrete5 uses PHP and RefineryCMS uses Ruby and the ease of learning the basics of these programming languages is also discussed.To compare the two CMSs, different steps were documented and compared. The implementation was done on a MacBook Pro, OS X 10.9.2, late 2011 model. Relevant features were also compared according to the Authoring Tool Accessibility Guidelines 2.0.The results showed a significant difference between the two CMSs, even though both had their benefits and drawbacks. In conclusion, Concrete5 was better for building a basic website under the writer’s conditions, thanks to the many features, modules, packages, plugins and templates available, and because it required less time and effort to install. This conclusion is limited to the writer’s conditions and it is possible that RefineryCMS could have worked better than found, if the conditions had been different.

The accurate and reliable estimation of the State-of-Charge (SOC) and State-of-Health (SOH) of Li-ion batteries is paramount to the safe and reliable operation of any electrified vehicle. Not only is accuracy and reliability necessary, but these estimation techniques must also be practical and intelligent since their use in real world applications can include noisy input signals, varying ambient conditions and incomplete or partial sequences of measured battery data. To that end, a novel framework, utilizing deep learning techniques, is considered whereby battery modelling and state estimation are performed in a single unified step. For SOC estimation, two different deep learning techniques are used with experimental data. These include a Recurrent Neural Network with Long Short-Term Memory (LSTM-RNN) and a Deep Feedforward Neural Network (DNN); each one possessing its own set of advantages. The LSTM-RNN achieves a Mean Absolute Error (MAE) of 0.57% over a fixed ambient temperature and a MAE of 1.61% over a dataset with ambient temperatures increasing from 10°C to 25°C. The DNN algorithm, on the other hand, achieves a MAE of 1.10% over a 25°C dataset while, at -20°C, a MAE of 2.17% is obtained. A Convolutional Neural Network (CNN), which has the advantage of shared weights, is used with randomized battery usage data to map raw battery measurements directly to an estimated SOH value. Using this strategy, average errors of below 1% are obtained when using fixed reference charge profiles. To further increase the practicality of this algorithm, the CNN is trained and validated over partial reference charge curves. SOH is estimated with a partial reference profile with the SOC ranging from 60% to 95% and achieves a MAE of 0.81%. A smaller SOC range is then used where the partial charge profile spans a SOC of 85% to 95% and a MAE of 1.60% is obtained. Finally, a fused convolutional recurrent neural network (CNN-RNN) is used to perform combined SOC and SOH estimation over constant charge profiles. This is performed by feeding the estimated SOH from the CNN into a LSTM-RNN, which, in turn, estimates SOC with a MAE of less than 0.5% over the lifetime of the battery. / Thesis / Doctor of Philosophy (PhD)

This dissertation introduces a hierarchical optimization framework for power converter-based energy management systems, with a primary focus on weight minimization. Emphasizing modularity and scalability, the research systematically tackles the challenges in optimizing these systems, addressing complex design variables, couplings, and the integration of heterogeneous models. The study begins with a comparative evaluation of various metaheuristic optimization methods applied to power inductors and converters, including genetic algorithm, particle swarm optimization, and simulated annealing. This is complemented by a global sensitivity analysis using the Morris method to understand the impact of different design variables on the design objectives and constraints in power electronics. Additionally, a thorough evaluation of different modeling methods for key components is conducted, leading to the validation of selected analytical models at the component level through extensive experiments. Further, the research progresses to studies at the converter level, focusing on a weight-optimized design for the thermal management systems for silicon carbide (SiC) MOSFET-based modular converters and the development of a hierarchical digital control system. This stage includes a thorough assessment of the accuracy of small-signal models for modular converters. At this point, the research methodically examines various design constraints, notably thermal considerations and transient responses. This examination is critical in understanding and addressing the specific challenges associated with converter-level design and the implications on system performance. The dissertation then presents a systematic approach where design variables and constraints are intricately managed across different hierarchies. This strategy facilitates the decoupling of subsystem designs within the same hierarchy, simplifying future enhancements to the optimization process. For example, component databases can be expanded effortlessly, and diverse topologies for converters and subsystems can be incorporated without the need to reconfigure the optimization framework. Another notable aspect of this research is the exploration of the scalability of the optimization architecture, demonstrated through design examples. This scalability is pivotal to the framework's effectiveness, enabling it to adapt and evolve alongside technological advancements and changing design requirements. Furthermore, this dissertation delves into the data transmission architecture within the hierarchical optimization framework. This architecture is not only critical for identifying optimal performance measures, but also for conveying detailed design information across all hierarchy levels, from individual components to entire systems. The interrelation between design specifications, constraints, and performance measures is illustrated through practical design examples, showcasing the framework's comprehensive approach. In summary, this dissertation contributes a novel, modular, and scalable hierarchical optimization architecture for the design of power converter-based energy management systems. It offers a comprehensive approach to managing complex design variables and constraints, paving the way for more efficient, adaptable, and cost-effective power system designs. / Doctor of Philosophy / This dissertation introduces an innovative approach to designing energy control systems, inspired by the creativity and adaptability of a Lego game. Central to this concept is a layered design methodology. The journey begins with power components, the fundamental 'Lego bricks'. Each piece is meticulously optimized for compactness, forming the robust foundation of the system. Like connecting individual Lego bricks into a module, these power components come together to form standardized power converters. These converters offer flexibility and scalability, similar to how numerous structures can be built from the same set of Lego pieces. The final layer involves assembling these power converters in order to construct comprehensive energy control systems. This mirrors the process of using Lego subassemblies to build larger, more intricate structures. At this system-level design, the standardized converters are integrated to optimize overall system performance. Key to this dissertation's methodology is an emphasis on modularity and scalability. It enables the creation of diverse energy control systems of varying sizes and functionalities from these fundamental units. The research delves into the intricacies of design variables and constraints, ensuring that each 'Lego piece' contributes optimally to the bigger picture. This includes exploring the scalability of the architecture, allowing it to evolve with technological advancements and design requirements, as well as examining data transmission within the system to ensure efficient data communication across all levels. In essence, this dissertation is about recognizing the potential in the smallest components and understanding their role in the grand scheme of the system. It is akin to playing a masterful game of Lego, where building something greater from small, well-designed parts leads to more efficient, adaptable, and cost-effective energy control system designs. This approach is particularly relevant for applications in transportation systems and renewable energy in remote locations, showcasing the universal applicability of this 'Lego game' to energy management.

The Grid environments are inherently unstable. Resources join and leave the environment without any prior notification. Application fault detection, checkpointing and restart is of foremost importance in the Grid environments. The need for fault tolerance is especially acute for large parallel applications since the failure rate grows with the number of processors and the duration of the computation. A Grid job management system hides the heterogeneity of the Grid and the complexity of the Grid protocols from the user. The user submits a job to the Grid job management system and it finds the appropriate resource, submits the job and transfers the output files to the user upon job completion. However, current Grid job management systems do not detect application failures. The goal of this research is to develop a Grid job management system that can efficiently detect application failures. Failed jobs are restarted either on the same resource or the job is migrated to another resource and restarted. The research also aims to identify the role of local resource managers in the fault detection and migration of Grid applications. / Master of Science

Yes / To assess developments over time in the capture, curation and use of quality and safety information in managing hospital services. Setting: Four acute National Health Service hospitals in England. Participants: 111.5 hours of observation of hospital board and directorate meetings, and 72 hours of ward observations. 86 interviews with board level and middle managers and with ward managers and staff. Results: There were substantial improvements in the quantity and quality of data produced for boards and middle managers between 2013 and 2016, starting from a low base. All four hospitals deployed data warehouses, repositories where datasets from otherwise disparate departmental systems could be managed. Three of them deployed real-time ward management systems, which were used extensively by nurses and other staff. Conclusions: The findings, particularly relating to the deployment of real-time ward management systems, are a corrective to the many negative accounts of information technology implementations. The hospital information infrastructures were elements in a wider move, away from a reliance on individual professionals exercising judgements and towards team-based and data-driven approaches to the active management of risks. They were not, though, using their fine-grained data to develop ultrasafe working practices. / NIHR Health Service and Delivery Research (HS&DR) programme, project 13/07/68.

No / Prior research has shown that individual differences in users' cognitive style and gender can have a significant effect on their usage and perceived usefulness of management information systems. We argue that these differences may also extend to computer-mediated knowledge management systems (KMS), although previous research has not tested this empirically. Where employees are expected to use KMS for acquiring and sharing knowledge, we posit that some will gain more benefit than others, due to their innate personal characteristics, specifically gender and cognitive style. Based on a sample of 212 software developers in one large IS organization, we re-open these dormant debates about the effects of cognitive style and gender on technology usage. The paper contains four main findings. First, we present support for the proposition that cognitive style has an impact on KMS usage, although not for all components of the system. Second, that gender significantly affects KMS usage, with males being more likely to use such systems than females. Third, we find a small interaction effect between cognitive style and gender, but only for the use of data mining. Finally, the data suggest that there is a strong association between KMS usage levels and perceived usefulness. We conclude that if organizations do not recognize the inherent diversity of the workforce, and accommodate gender and cognitive style differences into their knowledge management strategies, they may be likely to propagate an intrinsic disadvantage, to the detriment of females and intuitive thinkers.

No / This paper presents a discussion based on a literature review and a case study on the suitability of using an intranet as a platform to implement Knowledge Management System (KMS). A description of Knowledge Management (KM) and the current research carried out in this area, with examples of web-based KMS systems currently implemented in organisations, are presented. Further, this paper then describes how knowledge mapping of an organisation's intranet as a form of a KMS can be used to promote the re-utilisation of knowledge, which will contribute to the competitiveness of the organisation. A case study that illustrates and presents evidence of the need and suitability of such a system is provided. The paper ends with a proposal for future research to be carried out in this area.