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In this thesis, the recognition problem and the properties of eigenvalues and eigenvectors of matrices which are strictly sign-regular of a given order, i.e., matrices whose minors of a given order have the same strict sign, are considered. The results are extended to matrices which are sign-regular of a given order, i.e., matrices whose minors of a given order have the same sign or are allowed to vanish. As a generalization, a new type of matrices called oscillatory of a specific order, are introduced. Furthermore, the properties for this type are investigated. Also, same applications to dynamic systems are given.
Path planning and collision avoidance for safe autonomous vessel navigation in dynamic environments
(2017)
The main goal of this work was to experimentally characterize the hot air-drying process of agricultural products (Potato, Carrot, Tomato) and verify it with numerical solutions at single layer and industrial scale dryer using Comsol Multiphysics® 5.3.
Input parameters at single layer dryer effects on quality attributes were examined. Two strategies of drying were applied on batch dryer to examine the input effects on quality attributes. Constant input parameters strategy was designed by using central composite design formulation and optimized by Response Surface Methodology (RSM). The second strategy was applied for further optimization of the selected region by using square wave profile of the air temperature and relative humidity. Similarly, numerical method for single layer dryer, unsteady-state partial differential equations have been solved by means of the Finite Elements Method coupled to the Arbitrary Lagrangian-Eulerian (ALE). Also, for batch dryer, the mechanistic mathematical models of coupled heat and mass transfer were developed and solved as solid porous moist material.
With this work, the process of convective drying of agricultural products could be optimized. Furthermore, important knowledge about the basic mechanisms of the drying process was found and implemented in the numerical models.
The influence of sleep on human life, including physiological, psychological, and mental aspects, is remarkable. Therefore, it is essential to apply appropriate therapy in the case of sleep disorders. For this, however, the irregularities must first be recognised, preferably conveniently for the person concerned. This dissertation, structured as a composition of research articles, presents the development of mathematically based algorithmic principles for a sleep analysis system. The particular focus is on the classification of sleep stages with a minimal set of physiological parameters. In addition, the aspects of using the sleep analysis system as part of the more complex healthcare systems are explored. Design of hardware for non-obtrusive measurement of relevant physiological parameters and the use of such systems to detect other sleep disorders, such as sleep apnoea, are also referred to. Multinomial logistic regression was selected as the basis for development resulting from the investigations carried out. By following a methodical procedure, the number of physiological parameters necessary for the classification of sleep stages was successively reduced to two: Respiratory and Movement signals. These signals might be measured in a contactless way. A prototype implementation of the developed algorithms was performed to validate the proposed method, and the evaluation of 19324 sleep epochs was carried out. The results, with the achieved accuracy of 73% in the classification of Wake/NREM/REM stages and Cohen's kappa of 0.44, outperform the state of the art and demonstrate the appropriateness of the selected approach. In the future, this method could enable convenient, cost-effective, and accurate sleep analysis, leading to the detection of sleep disorders at an early stage so that therapy can be initiated as soon as possible, thus improving the general population's health status and quality of life.
Nowadays, there is a continuous need for many corporations to renew their business portfolio strategically in anticipation of changes in the business environment (e.g., technological change). The ongoing booming of founding international start-ups suggests that small entrepreneurial teams are an effective means to develop new businesses. Corporations should be able to benefit from this form of self-organized innovation when entering novel business domains for strategic renewal. However, corporations that establish small entrepreneurial teams (corporate ventures) are facing two obstacles. First, corporate ventures often fail for reasons that are not well explored. Second, it remains unclear how the partial successes may be improved to large successes. Although the key success factors remain ambiguous, there is little hope that corporate ventures will be successful without effective management. Since an empirical model for corporate venture management does not exists so far, the thesis formulates and answers the following problem statement: How can corporate management effectively manage corporate ventures? Building on qualitative and quantitative research methodologies, a model for effective corporate venture management is developed and tested statistically in the German IT consulting industry. The research results reveal some of the essential management principles through which corporate management can increase corporate venture success systematically.
Simon Grimm examines new multi-microphone signal processing strategies that aim to achieve noise reduction and dereverberation. Therefore, narrow-band signal enhancement approaches are combined with broad-band processing in terms of directivity based beamforming. Previously introduced formulations of the multichannel Wiener filter rely on the second order statistics of the speech and noise signals. The author analyses how additional knowledge about the location of a speaker as well as the microphone arrangement can be used to achieve further noise reduction and dereverberation.
This thesis considers bounding functions for multivariate polynomials and rational functions over boxes and simplices. It also considers the synthesis of polynomial Lyapunov functions for obtaining the stability of control systems. Bounding the range of functions is an important issue in many areas of mathematics and its applications like global optimization, computer aided geometric design, robust control etc.
Particularly for manufactured products subject to aesthetic evaluation, the industrial manufacturing process must be monitored, and visual defects detected. For this purpose, more and more computer vision-integrated inspection systems are being used. In optical inspection based on cameras or range scanners, only a few examples are typically known before novel examples are inspected. Consequently, no large data set of non-defective and defective examples could be used to train a classifier, and methods that work with limited or weak supervision must be applied. For such scenarios, I propose new data-efficient machine learning approaches based on one-class learning that reduce the need for supervision in industrial computer vision tasks. The developed novelty detection model automatically extracts features from the input images and is trained only on available non-defective reference data. On top of the feature extractor, a one-class classifier based on recent developments in deep learning is placed. I evaluate the novelty detector in an industrial inspection scenario and state-of-the-art benchmarks from the machine learning community. In the second part of this work, the model gets improved by using a small number of novel defective examples, and hence, another source of supervision gets incorporated. The targeted real-world inspection unit is based on a camera array and a flashing light illumination, allowing inline capturing of multichannel images at a high rate. Optionally, the integration of range data, such as laser or Lidar signals, is possible by using the developed targetless data fusion method.