The current procedures for achieving industrial process surveillance, waste reduction, and prognosis of critical process states are still insufficient in some parts of the manufacturing industry. Increasing competitive pressure, falling margins, increasing cost, just-in-time production, environmental protection requirements, and guidelines concerning energy savings pose new challenges to manufacturing companies, from the semiconductor to the pharmaceutical industry.
New, more intelligent technologies adapted to the current technical standards provide companies with improved options to tackle these situations. Here, knowledge-based approaches open up pathways that have not yet been exploited to their full extent. The Knowledge-Discovery-Process for knowledge generation describes such a concept. Based on an understanding of the problems arising during production, it derives conclusions from real data, processes these data, transfers them into evaluated models and, by this open-loop approach, reiteratively reflects the results in order to resolve the production problems. Here, the generation of data through control units, their transfer via field bus for storage in database systems, their formatting, and the immediate querying of these data, their analysis and their subsequent presentation with its ensuing benefits play a decisive role.
The aims of this work result from the lack of systematic approaches to the above-mentioned issues, such as process visualization, the generation of recommendations, the prediction of unknown sensor und production states, and statements on energy cost.
Both science and commerce offer mature statistical tools for data preprocessing, analysis and modeling, and for the final reporting step. Since their creation, the insurance business, the world of banking, market analysis, and marketing have been the application fields of these software types; they are now expanding to the production environment.
Appropriate modeling can be achieved via specific machine learning procedures, which have been established in various industrial areas, e.g., in process surveillance by optical control systems. Here, State-of-the-art classification methods are used, with multiple applications comprising sensor technology, process areas, and production site data. Manufacturing companies now intend to establish a more holistic surveillance of process data, such as, e.g., sensor failures or process deviations, to identify dependencies. The causes of quality problems must be recognized and selected in real time from about 500 attributes of a highly complex production machine. Based on these identified causes, recommendations for improvement must then be generated for the operator at the machine, in order to enable timely measures to avoid these quality deviations.
Unfortunately, the ability to meet the required increases in efficiency – with simultaneous consumption and waste minimization – still depends on data that are, for the most part, not available. There is an overrepresentation of positive examples whereas the number of definite negative examples is too low.
The acquired information can be influenced by sensor drift effects and the occurrence of quality degradation may not be adequately recognized. Sensorless diagnostic procedures with dual use of actuators can be of help here.
Moreover, in the course of a process, critical states with sometimes unexplained behavior can occur. Also in these cases, deviations could be reduced by early countermeasures.
The generation of data models using appropriate statistical methods is of advantage here.
Conventional classification methods sometimes reach their limits. Supervised learning methods are mostly used in areas of high information density with sufficient data available for the classes under examination. However, there is a growing trend (e.g., spam filtering) to apply supervised learning methods to underrepresented classes, the datasets of which are, at best, outliers or not at all existent.
The application field of One-Class Classification (OCC) deals with this issue. Standard classification procedures (e.g., k-nearest-neighbor classifier, support vector machines) can be modified in adjustment to such problems. Thereby, a control system is able to classify statements on changing process states or sensor deviations. The above-described knowledge discovery process was employed in a case study from the polymer film industry, at the Mondi Gronau GmbH, taken as an example, and accomplished by a real-data survey at the production site and subsequent data preprocessing, modeling, evaluation, and deployment as a system for the generation of recommendations. To this end, questions regarding the following topics had to be clarified: data sources, datasets and their formatting, transfer pathways, storage media, query sequences, the employed methods of classification, their adjustment to the problems at hand, evaluation of the results, construction of a dynamic cycle, and the final implementation in the production process, along with its surplus value for the company.
Pivotal options for optimization with respect to ecological and economical aspects can be found here. Capacity for improvement is given in the reduction of energy consumption, CO\(_2\) emissions, and waste at all machines. At this one site, savings of several million euros per month can be achieved.
One major difficulty so far has been hardly accessible process data which, distributed on various data sources and unconnected, in some areas led to an increased analysis effort and a lack of holistic real-time quality surveillance. Monitoring of specifications and the thus obtained support for the operator at the installation resulted in a clear disadvantage with regard to cost minimization.
The data of the case study, captured according to their purposes and in coordination with process experts, amounted to 21,900 process datasets from cast film extrusion during 2 years’ time, including sensor data from dosing facilities and 300 site-specific energy datasets from the years 2002–2014.
In the following, the investigation sequence is displayed:
1. In the first step, industrial approaches according to Industrie 4.0 and related to Big Data were investigated. The applied statistical software suites and their functions were compared with a focus on real-time data acquisition from database systems, different data formats, their sensor locations at the machines, and the data processing part. The linkage of datasets from various data sources for, e.g., labeling and downstream exploration according to the knowledge discovery process is of high importance for polymer manufacturing applications.
2. In the second step, the aims were defined according to the industrial requirements, i.e. the critical production problem called “cut-off” as the main selection, and with regard to their investigation with machine learning methods. Therefore, a system architecture corresponding to the polymer industry was developed, containing the following processing steps: data acquisition, monitoring \& recommendation, and self-configuration.
3. The novel sensor datasets, with 160–2,500 real and synthetic attributes, were acquired within 1-min intervals via PLC and field bus from an Oracle database. The 160 features were reduced to 6 dimensions with feature reduction methods. Due to underrepresentation of the critical class, the learning approaches had to be modified and optimized for one-class classification, which achieved 99% accuracy after training, testing and evaluation with real datasets.
4. In the next step, the 6-dimensional dataset was scaled into lower 1-, 2-, or 3-dimensional space with classical and non-classical mapping approaches for downstream visualization. The mapped view was separated into zones of normal and abnormal process conditions by threshold setting.
5. Afterwards, the boundary zone was investigated and an approach for trajectory extraction consisting of condition points in sequence was developed, to optimize the prediction behavior of the model. The extracted trajectories were trained, tested and evaluated by State-of-the-art classification methods, achieving a 99% recognition ratio.
6. In the last step, the best methods and processing parts were converted into a specifically developed domain-specific graphical user interface for real-time visualization of process condition changes. The requirements of such an interface were discussed with the operators with regard to intuitive handling, interactive visualization and recommendations (as e.g., messaging and traffic lights), and implemented.
The software prototype was tested at a laboratory machine. Correct recognition of abnormal process problems was achieved at a 90\% ratio. The software was afterwards transferred to a group of on-line production machines.
As demonstrated, the monthly amount of waste arising at machine M150 could be decreased from 20.96% to 12.44% during the application time. The frequency of occurrence of the specific problem was reduced by 30% related to monthly savings of 50,000 EUR.
In the approach pertaining to the energy prognosis of load profiles, monthly energy data from 2002 to 2014 (about 36 trajectories with three to eight real parameters each) were used as the basis, analyzed and modeled systematically. The prognosis quality increased with approaching target date. Thereby, the site-specific load profile for 2014 could be predicted with an accuracy of 99%.
The achievement of sustained cost reductions of several 100,000 euros, combined with additional savings of EUR 2.8 million, could be demonstrated.
The process improvements achieved while pursuing scientific targets could be successfully and permanently integrated at the case study plant. The increase in methodical and experimental knowledge was reflected by first economical results and could be verified numerically. The expectations of the company were more than fulfilled and further developments based on the new findings were initiated. Among the new finding are the transfer of the scientific findings onto more machines and even the initiation of further studies expanding into the diagnostics area.
Considering the size of the enterprise, future enhanced success should also be possible for other locations. In the course of the grid charge exemption according to EEG, the energy savings at further German locations can amount to 4–11% on a monetary basis and at least 5% based on energy. Up to 10% of materials and cost can be saved with regard to waste reduction related to specific problems. According to projections, material savings of 5–10 t per month and time savings of up to 50 person-hours are achievable. Important synergy effects can be created by the knowledge transfer.
The advances in sensor technology have introduced smart electronic products with
high integration of multi-sensor elements, sensor electronics and sophisticated signal
processing algorithms, resulting in intelligent sensor systems with a significant level
of complexity. This complexity leads to higher vulnerability in performing their
respective functions in a dynamic environment. The system dependability can be
improved via the implementation of self-x features in reconfigurable systems. The
reconfiguration capability requires capable switching elements, typically in the form
of a CMOS switch or miniaturized electromagnetic relay. The emerging DC-MEMS
switch has the potential to complement the CMOS switch in System-in-Package as
well as integrated circuits solutions. The aim of this thesis is to study the feasibility
of using DC-MEMS switches to enable the self-x functionality at system level.
The self-x implementation is also extended to the component level, in which the
ISE-DC-MEMS switch is equipped with self-monitoring and self-repairing features.
The MEMS electrical behavioural model generated by the design tool is inadequate,
so additional electrical models have been proposed, simulated and validated. The
simplification of the mechanical MEMS model has produced inaccurate simulation
results that lead to the occurrence of stiction in the actual device. A stiction conformity
test has been proposed, implemented, and successfully validated to compensate
the inaccurate mechanical model. Four different system simulations of representative
applications were carried out using the improved behavioural MEMS model, to
show the aptness and the performances of the ISE-DC-MEMS switch in sensitive
reconfiguration tasks in the application and to compare it with transmission gates.
The current design of the ISE-DC-MEMS switch needs further optimization in terms
of size, driving voltage, and the robustness of the design to guarantee high output
yield in order to match the performance of commercial DC MEMS switches.
This paper describes an audio interface written in VHDL, that connects the ADAU1761 audio codec on the Zedboard to the Zynq PL. Audio signals can be received in stereo from the line in jack and/or transmitted to the headphone out jack.
In embedded systems, there is a trend of integrating several different functionalities on a common platform. This has been enabled by increasing processing power and the arise of integrated system-on-chips.
The composition of safety-critical and non-safety-critical applications results in mixed-criticality systems. Certification Authorities (CAs) demand the certification of safety-critical applications with strong confidence in the execution time bounds. As a consequence, CAs use conservative assumptions in the worst-case execution time (WCET) analysis which result in more pessimistic WCETs than the ones used by designers. The existence of certified safety-critical and non-safety-critical applications can be represented by dual-criticality systems, i.e., systems with two criticality levels.
In this thesis, we focus on the scheduling of mixed-criticality systems which are subject to certification. Scheduling policies cognizant of the mixed-criticality nature of the systems and the certification requirements are needed for efficient and effective scheduling. Furthermore, we aim at reducing the certification costs to allow faster modification and upgrading, and less error-prone certification. Besides certification aspects, requirements of different operational modes result in challenging problems for the scheduling process. Despite the mentioned problems, schedulers require a low runtime overhead for an efficient execution at runtime.
The presented solutions are centered around time-triggered systems which feature a low runtime overhead. We present a transformation to include event-triggered activities, represented by sporadic tasks, already into the offline scheduling process. Further, this transformation can also be applied on periodic tasks to shorten the length of schedule tables which reduces certification costs. These results can be used in our method to construct schedule tables which creates two schedule tables to fulfill the requirements of dual-criticality systems using mode changes at runtime. Finally, we present a scheduler based on the slot-shifting algorithm for mixed-criticality systems. In a first version, the method schedules dual-criticality jobs without the need for mode changes. An already certified schedule table can be used and at runtime, the scheduler reacts to the actual behavior of the jobs and thus, makes effective use of the available resources. Next, we extend this method to schedule mixed-criticality job sets with different operational modes. As a result, we can schedule jobs with varying parameters in different modes.
The heterogeneity of today's access possibilities to wireless networks imposes challenges for efficient mobility support and resource management across different Radio Access Technologies (RATs). The current situation is characterized by the coexistence of various wireless communication systems, such as GSM, HSPA, LTE, WiMAX, and WLAN. These RATs greatly differ with respect to coverage, spectrum, data rates, Quality of Service (QoS), and mobility support.
In real systems, mobility-related events, such as Handover (HO) procedures, directly affect resource efficiency and End-To-End (E2E) performance, in particular with respect to signaling efforts and users' QoS. In order to lay a basis for realistic multi-radio network evaluation, a novel evaluation methodology is introduced in this thesis.
A central hypothesis of this thesis is that the consideration and exploitation of additional information characterizing user, network, and environment context, is beneficial for enhancing Heterogeneous Access Management (HAM) and Self-Optimizing Networks (SONs). Further, Mobile Network Operator (MNO) revenues are maximized by tightly integrating bandwidth adaptation and admission control mechanisms as well as simultaneously accounting for user profiles and service characteristics. In addition, mobility robustness is optimized by enabling network nodes to tune HO parameters according to locally observed conditions.
For establishing all these facets of context awareness, various schemes and algorithms are developed and evaluated in this thesis. System-level simulation results demonstrate the potential of context information exploitation for enhancing resource utilization, mobility support, self-tuning network operations, and users' E2E performance.
In essence, the conducted research activities and presented results motivate and substantiate the consideration of context awareness as key enabler for cognitive and autonomous network management. Further, the performed investigations and aspects evaluated in the scope of this thesis are highly relevant for future 5G wireless systems and current discussions in the 5G infrastructure Public Private Partnership (PPP).
This work establishes the novel category of coordinated Wireless Backhaul Networks (WBNs) for energy-autarkic point-to-point radio backhauling. The networking concept is based on three major building blocks: cost-efficient radio transceiver hardware, a self-organizing network operations framework, and power supply from renewable energy sources. The aim of this novel backhauling approach is to combine carrier-grade network performance with reduced maintenance effort as well as independent and self-sufficient power supply. In order to facilitate the success prospects of this concept, the thesis comprises the following major contributions: Formal, multi-domain system model and evaluation methodology
First, adapted from the theory of cyber-physical systems, the author devises a multi-domain evaluation methodology and a system-level simulation framework for energy-autarkic coordinated WBNs, including a novel balanced scorecard concept. Second, the thesis specifically addresses the topic of Topology Control (TC) in point-to-point radio networks and how it can be exploited for network management purposes. Given a set of network nodes equipped with multiple radio transceivers and known locations, TC continuously optimizes the setup and configuration of radio links between network nodes, thus supporting initial network deployment, network operation, as well as topology re-configuration. In particular, the author shows that TC in WBNs belongs to the class of NP-hard quadratic assignment problems and that it has significant impact in operational practice, e.g., on routing efficiency, network redundancy levels, service reliability, and energy consumption. Two novel algorithms focusing on maximizing edge connectivity of network graphs are developed.
Finally, this work carries out an analytical benchmarking and a numerical performance analysis of the introduced concepts and algorithms. The author analytically derives minimum performance levels of the the developed TC algorithms. For the analyzed scenarios of remote Alpine communities and rural Tanzania, the evaluation shows that the algorithms improve energy efficiency and more evenly balance energy consumption across backhaul nodes, thus significantly increasing the number of available backhaul nodes compared to state-of-the-art TC algorithms.
Real-time systems are systems that have to react correctly to stimuli from the environment within given timing constraints.
Today, real-time systems are employed everywhere in industry, not only in safety-critical systems but also in, e.g., communication, entertainment, and multimedia systems.
With the advent of multicore platforms, new challenges on the efficient exploitation of real-time systems have arisen:
First, there is the need for effective scheduling algorithms that feature low overheads to improve the use of the computational resources of real-time systems.
The goal of these algorithms is to ensure timely execution of tasks, i.e., to provide runtime guarantees.
Additionally, many systems require their scheduling algorithm to flexibly react to unforeseen events.
Second, the inherent parallelism of multicore systems leads to contention for shared hardware resources and complicates system analysis.
At any time, multiple applications run with varying resource requirements and compete for the scarce resources of the system.
As a result, there is a need for an adaptive resource management.
Achieving and implementing an effective and efficient resource management is a challenging task.
The main goal of resource management is to guarantee a minimum resource availability to real-time applications.
A further goal is to fulfill global optimization objectives, e.g., maximization of the global system performance, or the user perceived quality of service.
In this thesis, we derive methods based on the slot shifting algorithm.
Slot shifting provides flexible scheduling of time-constrained applications and can react to unforeseen events in time-triggered systems.
For this reason, we aim at designing slot shifting based algorithms targeted for multicore systems to tackle the aforementioned challenges.
The main contribution of this thesis is to present two global slot shifting algorithms targeted for multicore systems.
Additionally, we extend slot shifting algorithms to improve their runtime behavior, or to handle non-preemptive firm aperiodic tasks.
In a variety of experiments, the effectiveness and efficiency of the algorithms are evaluated and confirmed.
Finally, the thesis presents an implementation of a slot-shifting-based logic into a resource management framework for multicore systems.
Thus, the thesis closes the circle and successfully bridges the gap between real-time scheduling theory and real-world implementations.
We prove applicability of the slot shifting algorithm to effectively and efficiently perform adaptive resource management on multicore systems.
Specification of asynchronous circuit behaviour becomes more complex as the
complexity of today’s System-On-a-Chip (SOC) design increases. This also causes
the Signal Transition Graphs (STGs) – interpreted Petri nets for the specification
of asynchronous circuit behaviour – to become bigger and more complex, which
makes it more difficult, sometimes even impossible, to synthesize an asynchronous
circuit from an STG with a tool like petrify [CKK+96] or CASCADE [BEW00].
It has, therefore, been suggested to decompose the STG as a first step; this
leads to a modular implementation [KWVB03] [KVWB05], which can reduce syn-
thesis effort by possibly avoiding state explosion or by allowing the use of library
elements. A decomposition approach for STGs was presented in [VW02] [KKT93]
[Chu87a]. The decomposition algorithm by Vogler and Wollowski [VW02] is based
on that of Chu [Chu87a] but is much more generally applicable than the one in
[KKT93] [Chu87a], and its correctness has been proved formally in [VW02].
This dissertation begins with Petri net background described in chapter 2.
It starts with a class of Petri nets called a place/transition (P/T) nets. Then
STGs, the subclass of P/T nets, is viewed. Background in net decomposition
is presented in chapter 3. It begins with the structural decomposition of P/T
nets for analysis purposes – liveness and boundedness of the net. Then STG
decomposition for synthesis from [VW02] is described.
The decomposition method from [VW02] still could be improved to deal with
STGs from real applications and to give better decomposition results. Some
improvements for [VW02] to improve decomposition result and increase algorithm
efficiency are discussed in chapter 4. These improvement ideas are suggested in
[KVWB04] and some of them are have been proved formally in [VK04].
The decomposition method from [VW02] is based on net reduction to find
an output block component. A large amount of work has to be done to reduce
an initial specification until the final component is found. This reduction is not
always possible, which causes input initially classified as irrelevant to become
relevant input for the component. But under certain conditions (e.g. if structural
auto-conflicts turn out to be non-dynamic) some of them could be reclassified as
irrelevant. If this is not done, the specifications become unnecessarily large, which
intern leads to unnecessarily large implemented circuits. Instead of reduction, a
new approach, presented in chapter 5, decomposes the original net into structural
components first. An initial output block component is found by composing the
structural components. Then, a final output block component is obtained by net
As we cope with the structure of a net most of the time, it would be useful
to have a structural abstraction of the net. A structural abstraction algorithm
[Kan03] is presented in chapter 6. It can improve the performance in finding an
output block component in most of the cases [War05] [Taw04]. Also, the structure
net is in most cases smaller than the net itself. This increases the efficiency of the
decomposition algorithm because it allows the transitions contained in a node of
the structure graph to be contracted at the same time if the structure graph is
used as internal representation of the net.
Chapter 7 discusses the application of STG decomposition in asynchronous
circuit design. Application to speed independent circuits is discussed first. Af-
ter that 3D circuits synthesized from extended burst mode (XBM) specifications
are discussed. An algorithm for translating STG specifications to XBM specifi-
cations was first suggested by [BEW99]. This algorithm first derives the state
machine from the STG specification, then translates the state machine to XBM
specification. An XBM specification, though it is a state machine, allows some
concurrency. These concurrencies can be translated directly, without deriving
all of the possible states. An algorithm which directly translates STG to XBM
specifications, is presented in chapter 7.3.1. Finally DESI, a tool to decompose
STGs and its decomposition results are presented.
The objective of this thesis consists in developing systematic event-triggered control designs for specified event generators, which is an important alternative to the traditional periodic sampling control. Sporadic sampling inherently arising in event-triggered control is determined by the event-triggering conditions. This feature invokes the desire of
finding new control theory as the traditional sampled-data theory in computer control.
Developing controller coupling with the applied event-triggering condition to maximize the control performance is the essence for event-triggered control design. In the design the stability of the control system needs to be ensured with the first priority. Concerning variant control aims they should be clearly incorporated in the design procedures. Considering applications in embedded control systems efficient implementation requires a low complexity of embedded software architectures. The thesis targets at offering such a design to further complete the theory of event-triggered control designs.
In this thesis we studied and investigated a very common but a long existing noise problem and we provided a solution to this problem. The task is to deal with different types of noise that occur simultaneously and which we call hybrid. Although there are individual solutions for specific types one cannot simply combine them because each solution affects the whole speech. We developed an automatic speech recognition system DANSR ( Dynamic Automatic Noisy Speech Recognition System) for hybrid noisy environmental noise. For this we had to study all of speech starting from the production of sounds until their recognition. Central elements are the feature vectors on which pay much attention. As an additional effect we worked on the production of quantities for psychoacoustic speech elements.
The thesis has four parts:
1) The first part we give an introduction. The chapter 2 and 3 give an overview over speech generation and recognition when machines are used. Also noise is considered.
2) In the second part we describe our general system for speech recognition in a noisy environment. This is contained in the chapters 4-10. In chapter 4 we deal with data preparation. Chapter 5 is concerned with very strong noise and its modeling using Poisson distribution. In the chapters 5-8 we deal with parameter based modeling. Chapter 7 is concerned with autoregressive methods in relation to the vocal tract. In the chapters 8 and 9 we discuss linear prediction and its parameters. Chapter 9 is also concerned with quadratic errors, the decomposition into sub-bands and the use of Kalman filters for non-stationary colored noise in chapter 10. There one finds classical approaches as long we have used and modified them. This includes covariance mehods, the method of Burg and others.
3) The third part deals firstly with psychoacoustic questions. We look at quantitative magnitudes that describe them. This has serious consequences for the perception models. For hearing we use different scales and filters. In the center of the chapters 12 and 13 one finds the features and their extraction. The fearures are the only elements that contain information for further use. We consider here Cepstrum features and Mel frequency cepstral coefficients(MFCC), shift invariant local trigonometric transformed (SILTT), linear predictive coefficients (LPC), linear predictive cepstral coefficients (LPCC), perceptual linear predictive (PLP) cepstral coefficients. In chapter 13 we present our extraction methods in DANSR and how they use window techniques And discrete cosine transform (DCT-IV) as well as their inverses.
4) The fourth part considers classification and the ultimate speech recognition. Here we use the hidden Markov model (HMM) for describing the speech process and the Gaussian mixture model (GMM) for the acoustic modelling. For the recognition we use forward algorithm, the Viterbi search and the Baum-Welch algorithm. We also draw the connection to dynamic time warping (DTW). In the rest we show experimental results and conclusions.