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11	Supporting flexible workflow processes with a progression model Stavness, Nicole Ann 02 March 2005 (has links) <body>Users require flexibility when interacting with information systems to contend with changing business processes and to support diverse workflow. Model-based user interface design can accommodate flexible business processes by integrating workflow modelling with other modelling approaches. We present a workflow model, the progression model, to help in developing systems that support flexible business processes. <p> The progression model tracks a users interaction with an application as a set of data elements we refer to as a workflow transaction. The steps a user takes to create a workflow transaction and the state of the workflow transaction at each step is made explicit. By making the workflow status and workflow transaction state explicit, the user can change the order of the steps in a process, manage multiple workflow transactions, keep track of data as it is accumulated, and so on. The intent is to provide the user with a mechanism to deal with partial information, interrupted and concurrent workflow transaction entry, and the processing of multiple workflow transactions. <p> This thesis describes the progression model, an XML-compliant notation to specify the progression model, and a prototype system. </body> Process Model Information System Model-based User Interface Design Use Case Task Model Workflow Model Software Engineering User Interface Description Language Interactive System Data Model Workflow XML Business Process
12	Modulating Depth Map Features to Estimate 3D Human Pose via Multi-Task Variational Autoencoders / Modulerande djupkartfunktioner för att uppskatta människans ställning i 3D med multi-task-variationsautoenkoder Moerman, Kobe January 2023 (has links) Human pose estimation (HPE) constitutes a fundamental problem within the domain of computer vision, finding applications in diverse fields like motion analysis and human-computer interaction. This paper introduces innovative methodologies aimed at enhancing the accuracy and robustness of 3D joint estimation. Through the integration of Variational Autoencoders (VAEs), pertinent information is extracted from depth maps, even in the presence of inevitable image-capturing inconsistencies. This concept is enhanced through the introduction of noise to the body or specific regions surrounding key joints. The deliberate introduction of noise to these areas enables the VAE to acquire a robust representation that captures authentic pose-related patterns. Moreover, the introduction of a localised mask as a constraint in the loss function ensures the model predominantly relies on pose-related cues while disregarding potential confounding factors that may hinder the compact representation of accurate human pose information. Delving into the latent space modulation further, a novel model architecture is devised, joining a VAE and fully connected network into a multi-task joint training objective. In this framework, the VAE and regressor harmoniously influence the latent representations for accurate joint detection and localisation. By combining the multi-task model with the loss function constraint, this study attains results that compete with state-of-the-art techniques. These findings underscore the significance of leveraging latent space modulation and customised loss functions to address challenging human poses. Additionally, these novel methodologies pave the way for future explorations and provide prospects for advancing HPE. Subsequent research endeavours may optimising these techniques, evaluating their performance across diverse datasets, and exploring potential extensions to unravel further insights and advancements in the field. / Human pose estimation (HPE) är ett grundläggande problem inom datorseende och används inom områden som rörelseanalys och människa-datorinteraktion. I detta arbete introduceras innovativa metoder som syftar till att förbättra noggrannheten och robustheten i 3D-leduppskattning. Genom att integrera variationsautokodare (eng. variational autoencoder, VAE) extraheras relevant information från djupkartor, trots närvaro av inkonsekventa avvikelser i bilden. Dessa avvikelser förstärks genom att applicera brus på kroppen eller på specifika regioner som omger viktiga leder. Det avsiktliga införandet av brus i dessa områden gör det möjligt för VAE att lära sig en robust representation som fångar autentiska poseringsrelaterade mönster. Dessutom införs en lokaliserad mask som en begränsning i förlustfunktionen, vilket säkerställer att modellen främst förlitar sig på poseringsrelaterade signaler samtidigt som potentiella störande faktorer som hindrar den kompakta representationen av korrekt mänsklig poseringsinformation bortses ifrån. Genom att fördjupa sig ytterligare i den latenta rumsmoduleringen har en ny modellarkitektur tagits fram som förenar en VAE och ett fullständigt anslutet nätverk i en fleruppgiftsmodell. I detta ramverk påverkar VAE och det fullständigt ansluta nätverket de latenta representationerna på ett harmoniskt sätt för att uppnå korrekt leddetektering och lokalisering. Genom att kombinera fleruppgiftsmodellen med förlustfunktionsbegränsningen uppnår denna studie resultat som konkurrerar med toppmoderna tekniker. Dessa resultat understryker betydelsen av att utnyttja latent rymdmodulering och anpassade förlustfunktioner för att hantera utmanande mänskliga poser. Dessutom banar dessa nya metoder väg för framtida utveckling inom uppskattning av HPE. Efterföljande forskningsinsatser kan optimera dessa tekniker, utvärdera deras prestanda över olika datamängder och utforska potentiella tillägg för att avslöja ytterligare insikter och framsteg inom området. 3D pose estimation Joint landmarks Variational autoencoder Multi-task model Loss discrimination Latent-space modulation Depth map 3D-positionsuppskattning Gemensamma landmärken Variationell autoencoder Multitask-modell Förlustdiskriminering Latent-space-modulering Djupkarta Computer Sciences Datavetenskap (datalogi) Computer Engineering Datorteknik Computer and Information Sciences Data- och informationsvetenskap
13	Mechanismy plánování RT úloh při nedostatku výpočetních a energetických zdrojů / Mechanisms for Scheduling RT Tasks during Lack of Computational and Energy Sources Pokorný, Martin January 2012 (has links) This term project deals with the problem of scheduling real-time tasks in overload conditions and techniques for lowering power consumption. Each of these parts features mechanisms and reasons for their using. There are also described specific algorithms, that are implemented, in operating system uC/OS-II, and compared in next phase of master's thesis.

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