Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Review of investment strategy, portfolio management, and scorecard based portfolio selection tool for direct property investments in Finland

The lack of research of private real estate is a well-known problem. Earlier studies have mostly concentrated on the USA or the UK. Therefore, this master thesis offers more information about the performance and risk associated with private real estate investments in Nordic countries, but especially in Finland. The structure of this master thesis is divided into two independent sections based on the research questions. In first section, database analysis is performed to assess risk-return ratio of direct real estate investment for Nordic countries. Risk-return ratios are also assessed for different property sectors and economic regions. Finally, review of diversification strategies based on property sectors and economic regions is performed. However, standard deviation itself is not usually sufficient method to evaluate riskiness of private real estate. There is demand for more explicit assessment of property risk. One solution is property risk scoring. In second section risk scorecard based tool is built to make different real estate comparable in terms of risk. In order to do this, nine real estate professionals were interviewed to enhance the structure of theory-based risk scorecard and to assess weights for different risk factors.

Remote Observation of Radioactive Permit-Required Confined Spaces – Case: Posiva Oy’s Fuel Handling Cell

Posiva Oy’s final disposal facility’s encapsulation plant will start to operate in the 2020s. Once the operation starts, the facility is designed to run more than a hundred years. The encapsulation plant will be first of its kind in the world, being part of the solution to solve a global issue of final disposal of nuclear waste. In the encapsulation plant’s fuel handling cell the spent nuclear fuel will be processed to be deposited into the Finnish bedrock, into ONKALO. In the fuel handling cell, the environment is highly radioactive forming a permit-required enclosed space. Remote observation is needed in order to monitor the fuel handling process. The purpose of this thesis is to map (Part I) and compare (Part II) remote observation methods to observe Posiva Oy’s fuel handling cell’s process, and provide a possible theoretical solution for this case. Secondary purpose for this thesis is to provide resources for other remote observation cases, as well as to inform about possible future technology to enable readiness in the design of the encapsulation plant. The approach was to theoretically analyze the mapped remote observation methods. Firstly, the methods were filtered by three environmental challenges. These are the high levels of radiation, the permit-required confined space and the hundred year timespan. Secondly, the most promising methods were selected by the experts designing the facility. Thirdly, a customized feasibility analysis was created and performed on the selected methods to rank the methods with scores. The results are the mapped methods and the feasibility analysis scores. The three highest scoring methods were radiation tolerant camera, fiberscope and audio feed. A combination of these three methods was given as a possible theoretical solution for this case. As this case is first in the world, remote observation methods for it had not been thoroughly researched. The findings in this thesis will act as initial data for the design of the fuel handling cell’s remote observation systems and can potentially effect on the overall design of the facility by providing unique and case specific information. In addition, this thesis could provide resources for other remote observation cases.