Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels

This work investigates theoretical properties of symmetric and anti-symmetric kernels. First chapters give an overview of the theory of kernels used in supervised machine learning. Central focus is on the regularized least squares algorithm, which is motivated as a problem of function reconstruction through an abstract inverse problem. Brief review of reproducing kernel Hilbert spaces shows how kernels define an implicit hypothesis space with multiple equivalent characterizations and how this space may be modified by incorporating prior knowledge. Mathematical results of the abstract inverse problem, in particular spectral properties, pseudoinverse and regularization are recollected and then specialized to kernels. Symmetric and anti-symmetric kernels are applied in relation learning problems which incorporate prior knowledge that the relation is symmetric or anti-symmetric, respectively. Theoretical properties of these kernels are proved in a draft this thesis is based on and comprehensively referenced here. These proofs show that these kernels can be guaranteed to learn only symmetric or anti-symmetric relations, and they can learn any relations relative to the original kernel modified to learn only symmetric or anti-symmetric parts. Further results prove spectral properties of these kernels, central result being a simple inequality for the the trace of the estimator, also called the effective dimension. This quantity is used in learning bounds to guarantee smaller variance.

Heat-transfer improvements in an axial-flux permanent-magnet synchronous machine

Axial-flux machines tend to have cooling difficulties since it is difficult to arrange continuous heat path between the stator stack and the frame. One important reason for this is that no shrink fitting of the stator is possible in an axial-flux machine. Using of liquid-cooled end shields does not alone solve this issue. Cooling of the rotor and the end windings may also be difficult at least in case of two-stator-single-rotor construction where air circulation in the rotor and in the end-winding areas may be difficult to arrange. If the rotor has significant losses air circulation via the rotor and behind the stator yokes should be arranged which, again, weakens the stator cooling. In this paper we study a novel way of using copper bars as extra heat transfer paths between the stator teeth and liquid cooling pools in the end shields. After this the end windings still suffer of low thermal conductivity and means for improving this by high-heat-conductance material was also studied. The design principle of each cooling system is presented in details. Thermal models based on Computational Fluid Dynamics (CFD) are used to analyse the temperature distribution in the machine. Measurement results are provided from different versions of the machine. The results show that significant improvements in the cooling can be gained by these steps.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

A substantive theory of the realization of stroke patients’ dignity in hospital care

Dignity is seen important in health care context but considered as a controversial and complex concept. In health care context, it is described as being influenced by for example autonomy, respect, communication, privacy and hospital environment. Patient dignity is related to satisfaction with care, reduced stress, better confidence in health services, enhanced patient outcomes and shorter stay in a hospital. Stroke patients may struggle for dignity as being dependent on other people has impact on the patients’ self-image. In all, stroke patients are very specific patient group and considered vulnerable from emotional aspect. Therefore study findings from other patient groups in the area of ethical problems cannot be transferred to the stroke patients. This master’s thesis consists of two parts. The first part is the literature review of patients’ dignity in hospital care. The literature defined dignity and described factors promoting and reducing it. The results were ambiguous and thus a clear understanding was not able to create. That was the basis for the second part of the master’s thesis, the empirical study. This part aimed to develop theoretical construction to explore the realization of stroke patients’ dignity in hospital care. The data of the second part was collected by interviewing 16 stroke patients and analyzed using the constant comparison of Grounded Theory. The result was ‘The Theory of Realization of Stroke Patients’ Dignity in Hospital Care’ which is described not only in this master’s thesis but also as a scientific article. The theory consists of the core category, four generic elements and five specific types on realization. The core category emerged as ‘dignity in a new situation’. After a stroke, dignity is defined in a new way which is influenced by the generic elements: life history, health history, individuality and a stroke. Stroke patient’s dignity is realized through five specific types on realization: person related dignity type, control related dignity type, independence related dignity type, social related dignity type and care related dignity type. The theory points out possible special characteristics of stroke patients’ dignity in control related dignity type and independence related dignity type. Before implementing the theory, the relation between the core category, generic elements and specific types on realization needs to be studied further.

Design and implementation of machine vision system for the mobile assembly robot

The review of intelligent machines shows that the demand for new ways of helping people in perception of the real world is becoming higher and higher every year. This thesis provides information about design and implementation of machine vision for mobile assembly robot. The work has been done as a part of LUT project in Laboratory of Intelligent Machines. The aim of this work is to create a working vision system. The qualitative and quantitative research were done to complete this task. In the first part, the author presents the theoretical background of such things as digital camera work principles, wireless transmission basics, creation of live stream, methods used for pattern recognition. Formulas, dependencies and previous research related to the topic are shown. In the second part, the equipment used for the project is described. There is information about the brands, models, capabilities and also requirements needed for implementation. Although, the author gives a description of LabVIEW software, its add-ons and OpenCV which are used in the project. Furthermore, one can find results in further section of considered thesis. They mainly represented by screenshots from cameras, working station and photos of the system. The key result of this thesis is vision system created for the needs of mobile assembly robot. Therefore, it is possible to see graphically what was done on examples. Future research in this field includes optimization of the pattern recognition algorithm. This will give less response time for recognizing objects. Presented by author system can be used also for further activities which include artificial intelligence usage.