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- Kaiserslautern - Fachbereich Mathematik (12)
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The detection and characterisation of undesired lead structures on shaft surfaces is a concern in production and quality control of rotary shaft lip-type sealing systems. The potential lead structures are generally divided into macro and micro lead based on their characteristics and formation. Macro lead measurement methods exist and are widely applied. This work describes a method to characterise micro lead on ground shaft surfaces. Micro lead is known as the deviation of main orientation of the ground micro texture from circumferential direction. Assessing the orientation of microscopic structures with arc minute accuracy with regard to circumferential direction requires exact knowledge of both the shaft’s orientation and the direction of surface texture. The shaft’s circumferential direction is found by calibration. Measuring systems and calibration procedures capable of calibrating shaft axis orientation with high accuracy and low uncertainty are described. The measuring systems employ areal-topographic measuring instruments suited for evaluating texture orientation. A dedicated evaluation scheme for texture orientation is based on the Radon transform of these topographies and parametrised for the application. Combining the calibration of circumferential direction with the evaluation of texture orientation the method enables the measurement of micro lead on ground shaft surfaces.
The present situation of control engineering in the context of automated production can be described as a tension field between its desired outcome and its actual consideration. On the one hand, the share of control engineering compared to the other engineering domains has significantly increased within the last decades due to rising automation degrees of production processes and equipment. On the other hand, the control engineering domain is still underrepresented within the production engineering process. Another limiting factor constitutes a lack of methods and tools to decrease the amount of software engineering efforts and to permit the development of innovative automation applications that ideally support the business requirements.
This thesis addresses this challenging situation by means of the development of a new control engineering methodology. The foundation is built by concepts from computer science to promote structuring and abstraction mechanisms for the software development. In this context, the key sources for this thesis are the paradigm of Service-oriented Architecture and concepts from Model-driven Engineering. To mold these concepts into an integrated engineering procedure, ideas from Systems Engineering are applied. The overall objective is to develop an engineering methodology to improve the efficiency of control engineering by a higher adaptability of control software and decreased programming efforts by reuse.
A Multi-Sensor Intelligent Assistance System for Driver Status Monitoring and Intention Prediction
(2017)
Advanced sensing systems, sophisticated algorithms, and increasing computational resources continuously enhance the advanced driver assistance systems (ADAS). To date, despite that some vehicle based approaches to driver fatigue/drowsiness detection have been realized and deployed, objectively and reliably detecting the fatigue/drowsiness state of driver without compromising driving experience still remains challenging. In general, the choice of input sensorial information is limited in the state-of-the-art work. On the other hand, smart and safe driving, as representative future trends in the automotive industry worldwide, increasingly demands the new dimensional human-vehicle interactions, as well as the associated behavioral and bioinformatical data perception of driver. Thus, the goal of this research work is to investigate the employment of general and custom 3D-CMOS sensing concepts for the driver status monitoring, and to explore the improvement by merging/fusing this information with other salient customized information sources for gaining robustness/reliability. This thesis presents an effective multi-sensor approach with novel features to driver status monitoring and intention prediction aimed at drowsiness detection based on a multi-sensor intelligent assistance system -- DeCaDrive, which is implemented on an integrated soft-computing system with multi-sensing interfaces in a simulated driving environment. Utilizing active illumination, the IR depth camera of the realized system can provide rich facial and body features in 3D in a non-intrusive manner. In addition, steering angle sensor, pulse rate sensor, and embedded impedance spectroscopy sensor are incorporated to aid in the detection/prediction of driver's state and intention. A holistic design methodology for ADAS encompassing both driver- and vehicle-based approaches to driver assistance is discussed in the thesis as well. Multi-sensor data fusion and hierarchical SVM techniques are used in DeCaDrive to facilitate the classification of driver drowsiness levels based on which a warning can be issued in order to prevent possible traffic accidents. The realized DeCaDrive system achieves up to 99.66% classification accuracy on the defined drowsiness levels, and exhibits promising features such as head/eye tracking, blink detection, gaze estimation that can be utilized in human-vehicle interactions. However, the driver's state of "microsleep" can hardly be reflected in the sensor features of the implemented system. General improvements on the sensitivity of sensory components and on the system computation power are required to address this issue. Possible new features and development considerations for DeCaDrive are discussed as well in the thesis aiming to gain market acceptance in the future.
The cytosolic Fe65 adaptor protein family, consisting of Fe65, Fe65L1 and Fe65L2 is involved in many intracellular signaling pathways linking via its three interaction domains a continuously growing list of proteins by facilitating functional interactions. One of the most important binding partners of Fe65 family proteins is the amyloid precursor protein (APP), which plays an important role in Alzheimer Disease.
To gain deeper insights in the function of the ubiquitously expressed Fe65 and the brain enriched Fe65L1, the goal of my study was I) to analyze their putative synaptic function in vivo, II) to examine structural analysis focusing on a putative dimeric complex of Fe65, III) to consider the involvement of Fe65 in mediating LRP1 and APP intracellular trafficking in murine hippocampal neurons. By utilizing several behavioral analyses of Fe65 KO, Fe65L1 KO and Fe65/Fe65L1 DKO mice I could demonstrate that the Fe65 protein family is essential for learning and memory as well as grip strength and locomotor activity. Furthermore, immunohistological as well as protein biochemical analysis revealed that the Fe65 protein family is important for neuromuscular junction formation in the peripheral nervous system, which involves binding of APP and acting downstream of the APP signaling pathway. Via Co-immunoprecipitation analysis I could verify that Fe65 is capable to form dimers ex vivo, which exclusively occur in the cytosol and upon APP expression are shifted to membrane compartments forming trimeric complexes. The influence of the loss of Fe65 and/or Fe65L1 on APP and/or LRP1 transport characteristics in axons could not be verified, possibly conditioned by the compensatory effect of Fe65L2. However, I could demonstrate that LRP1 affects the APP transport independently of Fe65 by shifting APP into slower types of vesicles leading to changed processing and endocytosis of APP.
The outcome of my thesis advanced our understanding of the Fe65 protein family, especially its interplay with APP physiological function in synapse formation and synaptic plasticity.
The development of autonomous mobile robots is a major topic of current research. As those robots must be able to react to changing environments and avoid collisions also with moving obstacles, the fulfilment of safety requirements is an important aspect. Behaviour-based systems (BBS) have proven to meet several of the properties required for these kindsof robots, such as reactivity, extensibility and re-usability of individual components. BBS consist of a number of behavioural components that individually realise simple tasks. Their interconnection allows to achieve complex robot behaviour, which implies that correct
connections are crucial. The resulting networks can get very large making them difficult to verify. This dissertation presents a novel concept for the analysis and verification of complex autonomous robot systems controlled by behaviour-based software architectures with special focus on the integration of environmental aspects into the processes.
Several analysis techniques have been investigated and adapted to the special requirements of BBS. These include a structural analysis, which is used to find constraint violations and faults in the network layout. Fault tree analysis is applied to identify root causes of hazards and the relationship of system events. For this, a technique to map the behaviour-based control network to the structure of a fault tree has been developed. Testing and data analysis are used for the detection of failures and their root causes. Here, a new concept that identifies patterns in data recorded during test runs has been introduced.
All of these methods cannot guarantee failure-free and safe robot behaviour and can never prove the absence of failures. Therefore, model checking as formal verification technique that proves a property to be correct for the given system, has been chosen to complement the set of analysis techniques. A novel concept for the integration of environmental influences into the model checking process is proposed. Environmental situations and the sensor processing chain are represented as synchronised automata similar to the modelling of the behavioural network. Tools supporting the whole verification process including the creation of formal queries in its environment have been developed.
During the verification of large behavioural networks, the scalability of the model checking approach appears as a big problem. Several approaches that deal with this problem have been investigated and the selection of slicing and abstraction methods has been justified. A concept for the application of these methods is provided, that reduces the behavioural network to the relevant parts before the actual verification process.
All techniques have been applied to the behaviour-based control system of the autonomous outdoor robot RAVON. Its complex network with more than 400 components allows for demonstrating the soundness of the presented concepts. The set of different techniques provides a fundamental basis for a comprehensive analysis and verification of BBS acting in changing environments.
This thesis is concerned with different null-models that are used in network analysis. Whenever it is of interest whether a real-world graph is exceptional regarding a particular measure, graphs from a null-model can be used to compare the real-world graph to. By analyzing an appropriate null-model, a researcher may find whether the results of the measure on the real-world graph is exceptional or not.
Deciding which null-model to use is hard and sometimes the difference between the null-models is not even considered. In this thesis, there are several results presented: First, based on simple global measures, undirected graphs are analyzed. The results for these measures indicates that it is not important which null-model is used, thus, the fastest algorithm of a null-model may be used. Next, local measures are investigated. The fastest algorithm proves to be the most complicated to analyze. The model includes multigraphs which do not meet the conditions of all the measures, thus, the measures themselves have to be altered to take care of multigraphs as well. After careful consideration, the conditions are met and the analysis shows, that the fastest is not always the best.
The same applies for directed graphs, as is shown in the last part. There, another more complex measure on graphs is introduced. I continue testing the applicability of several null-models; in the end, a set of equations proves to be fast and good enough as long as conditions regarding the degree sequence are met.
The proliferation of sensors in everyday devices – especially in smartphones – has led to crowd sensing becoming an important technique in many urban applications ranging from noise pollution mapping or road condition monitoring to tracking the spreading of diseases. However, in order to establish integrated crowd sensing environments on a large scale, some open issues need to be tackled first. On a high level, this thesis concentrates on dealing with two of those key issues: (1) efficiently collecting and processing large amounts of sensor data from smartphones in a scalable manner and (2) extracting abstract data models from those collected data sets thereby enabling the development of complex smart city services based on the extracted knowledge.
Going more into detail, the first main contribution of this thesis is the development of methods and architectures to facilitate simple and efficient deployments, scalability and adaptability of crowd sensing applications in a broad range of scenarios while at the same time enabling the integration of incentivation mechanisms for the participating general public. During an evaluation within a complex, large-scale environment it is shown that real-world deployments of the proposed data recording architecture are in fact feasible. The second major contribution of this thesis is the development of a novel methodology for using the recorded data to extract abstract data models which are representing the inherent core characteristics of the source data correctly. Finally – and in order to bring together the results of the thesis – it is demonstrated how the proposed architecture and the modeling method can be used to implement a complex smart city service by employing a data driven development approach.
In change-point analysis the point of interest is to decide if the observations follow one model
or if there is at least one time-point, where the model has changed. This results in two sub-
fields, the testing of a change and the estimation of the time of change. This thesis considers
both parts but with the restriction of testing and estimating for at most one change-point.
A well known example is based on independent observations having one change in the mean.
Based on the likelihood ratio test a test statistic with an asymptotic Gumbel distribution was
derived for this model. As it is a well-known fact that the corresponding convergence rate is
very slow, modifications of the test using a weight function were considered. Those tests have
a better performance. We focus on this class of test statistics.
The first part gives a detailed introduction to the techniques for analysing test statistics and
estimators. Therefore we consider the multivariate mean change model and focus on the effects
of the weight function. In the case of change-point estimators we can distinguish between
the assumption of a fixed size of change (fixed alternative) and the assumption that the size
of the change is converging to 0 (local alternative). Especially, the fixed case in rarely analysed
in the literature. We show how to come from the proof for the fixed alternative to the
proof of the local alternative. Finally, we give a simulation study for heavy tailed multivariate
observations.
The main part of this thesis focuses on two points. First, analysing test statistics and, secondly,
analysing the corresponding change-point estimators. In both cases, we first consider a
change in the mean for independent observations but relaxing the moment condition. Based on
a robust estimator for the mean, we derive a new type of change-point test having a randomized
weight function. Secondly, we analyse non-linear autoregressive models with unknown
regression function. Based on neural networks, test statistics and estimators are derived for
correctly specified as well as for misspecified situations. This part extends the literature as
we analyse test statistics and estimators not only based on the sample residuals. In both
sections, the section on tests and the one on the change-point estimator, we end with giving
regularity conditions on the model as well as the parameter estimator.
Finally, a simulation study for the case of the neural network based test and estimator is
given. We discuss the behaviour under correct and mis-specification and apply the neural
network based test and estimator on two data sets.
For many years, most distributed real-time systems employed data communication systems specially tailored to address the specific requirements of individual domains: for instance, Controlled Area Network (CAN) and Flexray in the automotive domain, ARINC 429 [FW10] and TTP [Kop95] in the aerospace domain. Some of these solutions were expensive, and eventually not well understood.
Mostly driven by the ever decreasing costs, the application of such distributed real-time system have drastically increased in the last years in different domains. Consequently, cross-domain communication systems are advantageous. Not only the number of distributed real-time systems have been increasing but also the number of nodes per system, have drastically increased, which in turn increases their network bandwidth requirements. Further, the system architectures have been changing, allowing for applications to spread computations among different computer nodes. For example, modern avionics systems moved from federated to integrated modular architecture, also increasing the network bandwidth requirements.
Ethernet (IEEE 802.3) [iee12] is a well established network standard. Further, it is fast, easy to install, and the interface ICs are cheap [Dec05]. However, Ethernet does not offer any temporal guarantee. Research groups from academia and industry have presented a number of protocols merging the benefits of Ethernet and the temporal guarantees required by distributed real-time systems. Two of these protocols are: Avionics Full-Duplex Switched Ethernet (AFDX) [AFD09] and Time-Triggered Ethernet (TTEthernet) [tim16]. In this dissertation, we propose solutions for two problems faced during the design of AFDX and TTEthernet networks: avoiding data loss due to buffer overflow in AFDX networks with multiple priority traffic, and scheduling of TTEthernet networks.
AFDX guarantees bandwidth separation and bounded transmission latency for each communication channel. Communication channels in AFDX networks are not synchronized, and therefore frames might compete for the same output port, requiring buffering to avoid data loss. To avoid buffer overflow and the resulting data loss, the network designer must reserve a safe, but not too pessimistic amount of memory of each buffer. The current AFDX standard allows for the classification of the network traffic with two priorities. Nevertheless, some commercial solutions provide multiple priorities, increasing the complexity of the buffer backlog analysis. The state-of-the-art AFDX buffer backlog analysis does not provide a method to compute deterministic upper bounds
iiifor buffer backlog of AFDX networks with multiple priority traffic. Therefore, in this dissertation we propose a method to address this open problem. Our method is based on the analysis of the largest busy period encountered by frames stored in a buffer. We identify the ingress (and respective egress) order of frames in the largest busy period that leads to the largest buffer backlog, and then compute the respective buffer backlog upper bound. We present experiments to measure the computational costs of our method.
In TTEthernet, nodes are synchronized, allowing for message transmission at well defined points in time, computed off-line and stored in a conflict-free scheduling table. The computation of such scheduling tables is a NP-complete problem [Kor92], which should be solved in reasonable time for industrial size networks. We propose an approach to efficiently compute a schedule for the TT communication channels in TTEthernet networks, in which we model the scheduling problem as a search tree. As the scheduler traverses the search tree, it schedules the communication channels on a physical link. We presented two approaches to traverse the search tree while progressively creating the vertices of the search tree. A valid schedule is found once the scheduler reaches a valid leaf. If on the contrary, it reaches an invalid leaf, the scheduler backtracks searching for a path to a valid leaf. We present a set of experiments to demonstrate the impact of the input parameters on the time taken to compute a feasible schedule or to deem the set of virtual links infeasible.