<p>The aim of this thesis is to develop the knowledge about nonlinear and/or adaptive solid fuel boiler control at Vattenfall Utveckling AB. The aim is also to make a study of implemented and published control strategies.</p><p>A solid fuel boiler is a large-scale heat (and power) generating plant. The Idbäcken boiler studied in this work, is a one hundred MW furnace mainly fired with wood chips. The control system consists of several linear PID controllers working together, and the furnace is a nonlinear system. That, and the fact that the fuel-flow is not monitored, are the main reasons for the control problems. The system fluctuates periodically and the CO outlets sometimes rise high above the permitted level.</p><p>There is little work done in the area of advanced boiler control, but some interesting approaches are described in scientific articles. MPC (Model Predictive Control), nonlinear system identification using ANN (Artificial Neural Network), fuzzy logic, Hµ loop shaping and MIMO (Multiple Input Multiple Output) PID tuning methods have been tested with good results.</p><p>Both linear and nonlinear system identification is performed in the thesis. The linear models are able to explain about forty percent of the system behavior and the nonlinear models explain about sixty to eighty percent. The main result is that nonlinear models improve the performance and that there are considerable disturbances complicating the identification. Another identification issue was the feedback during the data collection.</p>

<p>Six methods are used for clustering data containing two different objects: sugar-beet plants </p><p>and weed. These objects are described by 19 different features, i.e. shape and color features. </p><p>There is also information about the distance between sugar-beet plants that is used for </p><p>labeling clusters. The methods that are evaluated: k-means, k-medoids, hierarchical clustering, </p><p>competitive learning, self-organizing maps and fuzzy c-means. After using the methods on </p><p>plant data, clusters are formed. The clusters are labeled with three different proposed </p><p>methods: expert, database and context method. Expert method is using a human for giving </p><p>initial cluster centers that are labeled. The database method is using a database as an expert </p><p>that provides initial cluster centers. The context method is using information about the </p><p>environment, which is the distance between sugar-beet plants, for labeling the clusters. </p><p> </p><p>The algorithms that were tested, with the lowest achieved corresponding error, are: k-means </p><p>(3.3%), k-medoids (3.8%), hierarchical clustering (5.3%), competitive learning (6.8%), self- </p><p>organizing maps (4.9%) and fuzzy c-means (7.9%). Three different datasets were used and the </p><p>lowest error on dataset0 is 3.3%, compared to supervised learning methods where it is 3%. </p><p>For dataset1 the error is 18.7% and for dataset2 it is 5.8%. Compared to supervised methods, </p><p>the error on dataset1 is 11% and for dataset2 it is 5.1%. The high error rate on dataset1 is due </p><p>to the samples are not very well separated in different clusters. The features from dataset1 are </p><p>extracted from lower resolution on images than the other datasets, and another difference </p><p>between the datasets are the sugar-beet plants that are in different growth stages. </p><p> </p><p>The performance of the three methods for labeling clusters is: expert method (6.8% as the </p><p>lowest error achieved), database method (3.7%) and context method (6.8%). These results </p><p>show the clustering results by competitive learning where the real error is 6.8%. </p><p> </p><p>Unsupervised-learning methods for clustering can very well be used for plant identification. </p><p>Because the samples are not classified, an automatic labeling technique must be used if plants </p><p>are to be identified. The three proposed techniques can be used for automatic labeling of </p><p>plants.</p>

No description available.

A method of estimating the mass, the location of center of mass, and the moments of inertia of each rigid body link of a robot during general manipulator movement is presented. The algorithm is derived from the Newton-Euler equations, and uses measurements of the joint torques as well as the measurement and calculation of the kinematics of the manipulator while it is moving. The identification equations are linear in the desired unknown parameters, and a modified least squares algorithm is used to obtain estimates of these parameters. Some of the parameters, however, are not identifiable due to restricted motion of proximal links and the lack of full force/torque sensing. The algorithm was implemented on the MIT Serial Link Direct Drive Arm. A good match was obtained between joint torques predicted from the estimated parameters and the joint torques computed from motor currents.

Two different types of switching mechanism are considered in this thesis; one is featured with abrupt/sudden switching while the other one shows gradual changing behavior in its dynamics. It is shown that, through the comparison of the identification results from the proposed method and a benchmark method, the proposed robust identification method can achieve better performance when dealing with the data set mixed with outliers. To model the switched systems exhibiting gradual or smooth transition among different local models, in addition to estimating the local sub-systems parameters, a smooth validity (an exponential function) function is introduced to combine all the local models so that throughout the working range of the gradual switched system, the dynamics of the nonlinear process can be appropriately approximated. Verification results on a simulated numerical example and CSTR process confirm the effectiveness of the proposed Linear Parameter Varying (LPV) identification algorithm. / Process Control

Fractional order calculus (FOC) has wide applications in modeling natural behavior of systems related to different areas of engineering including bioengineering, viscoelasticity, electronics, robotics, control theory and signal processing. This thesis aims at modeling a lossy transmission line using fractional order calculus and identifying its parameters. A lossy transmission line is considered where its behavior is modeled by a fractional order transfer function. A semi-infinite lossy transmission line is presented with its distributed parameters R, L, C and ordinary AC circuit theory is applied to find the partial differential equations. Furthermore, applying boundary conditions and the Laplace transformation a generalized fractional order transfer function of the lossy transmission line is obtained. A finite length lossy transmission line terminated with arbitrary load is also considered and its fractional order transfer function has been derived. Next, the frequency responses of lossy transmission lines from their fractional order transfer functions are also derived. Simulation results are presented to validate the frequency responses. Based on the simulation results it can be concluded that the derived fractional order transmission line model is capable of capturing the phenomenon of a distributed parameter transmission line. The achievement of modeling a highly accurate transmission line requires that a realistic account needs to be taken of its parameters. Therefore, a parameter identification technique to identify the parameters of the fractional order lossy transmission line is introduced. Finally, a few open problems are listed as the future research directions. / Controls

Semiconductor manufacturers must ensure that chips conform to their specifications before they are shipped to customers. This is achieved by testing various parameters of a chip to determine whether it is defective or not. Separating defective chips from fault-free ones is relatively straightforward for functional or other Boolean tests that produce a go/no-go type of result. However, making this distinction is extremely challenging for parametric tests. Owing to continuous distributions of parameters, any pass/fail threshold results in yield loss and/or test escapes. The continuous advances in process technology, increased process variations and inaccurate fault models all make this even worse. The pass/fail thresholds for such tests are usually set using prior experience or by a combination of visual inspection and engineering judgment. Many chips have parameters that exceed certain thresholds but pass Boolean tests. Owing to the imperfect nature of tests, to determine whether these chips (called "outliers") are indeed defective is nontrivial. To avoid wasted investment in packaging or further testing it is important to screen defective chips early in a test flow. Moreover, if seemingly strange behavior of outlier chips can be explained with the help of certain process parameters or by correlating additional test data, such chips can be retained in the test flow before they are proved to be fatally flawed. In this research, we investigate several methods to identify true outliers (defective chips, or chips that lead to functional failure) from apparent outliers (seemingly defective, but fault-free chips). The outlier identification methods in this research primarily rely on wafer-level spatial correlation, but also use additional test parameters. These methods are evaluated and validated using industrial test data. The potential of these methods to reduce burn-in is discussed.

In this thesis, a full review on what are the minimum requirements needed to perform an Authentication System is explained. While building the system we have in consideration the users of it, the security needed for each of the resources that must be accessed by the users and what methods can be applied to access to these resources. In basics, an Authentication System is built when we need to keep track to who is entering on an organization, the bigger the organization is and the more information must be keep  safe the more complex the system will be. Although there are other methods, I tried to keep it easy and understandable for all the possible readers. With this, the reader will understand the basics that he need to keep in mind when implementing such a system like this. The organization in mind for the system is a University that consist between twenty two thousand (22.000) and twenty five thousand (25.000) users.

Radio Frequency IDentification (RFID) is a wireless communications technology which allows companies to secure their assets and increase the portability of information. This research was motivated by the increased commercial use of RFID technology. Existing security protocols with high levels of security have high computation requirements, and less intensive protocols can allow a tag to be tracked. The techniques proposed in this thesis result in the increase of ciphertexts available without a significant increase in processing power or storage requirements. The addition of random inputs to the generation of ciphertexts will increase the number of possible results without requiring a more advanced encryption algorithm or an increased number of stored encryption keys. Four methods of altering the plaintext/ciphertext pair (random block, set pattern, random pattern, and indexed placement) are analyzed to determine the effectiveness of each method. The number of ciphertexts generated, generation time, and generation errors were recorded to determine which of the four proposed methods would be the most beneficial in a RFID system. The comparison of these method characteristics determined that the set pattern placement method provided the best solution. The thesis also discusses how RFID transmissions appear to attackers and explains how the random inputs reduce effectiveness of current system attacks. In addition to improving the anonymity of RFID tag transmissions, the concept of authenticating random inputs is also introduced in this thesis. These methods help prevent an adversary from easily associating a tag with its transmissions, thus increasing the security of the RFID system.

Over the last century, images of faces have become integral components of many institutional identification systems. A driver’s licence, a passport and often even a health care card, all usually feature prominently images representing the face of their bearer as part of the mechanism for linking real-world bodies to institutional records. Increasingly the production, distribution and inspection of these documents is becoming computer-mediated. As photo ID documents become ‘enhanced’ by computerization, the design challenges and compromises become increasingly coded in the hierarchy of gazes aimed at individual faces and their technologically mediated surrogates. In Western visual culture, representations of faces have been incorporated into identity documents since the 15th century when Renaissance portraits were first used to visually and legally establish the social and institutional positions of particular individuals. However, it was not until the 20th century that official identity documents and infrastructures began to include photographic representations of individual faces. This work explores photo ID documents within the context of “the face,”—a theoretical model for understanding relationships of power coded using representations of particular human faces as tokens of identity. “The face” is a product of mythology for linking ideas of stable identity with images of particular human beings. This thesis extends the panoptic model of the body and contributes to the understanding of changes posed by computerization to the norms of constructing institutional identity and interaction based on surrogates of faces. The exploration is guided by four key research questions: What is “the face”? How does it work? What are its origins (or mythologies)? And how is “the face” being transformed through digitization? To address these questions this thesis weaves ideas from theorists including Foucault, Deleuze and Lyon to explore the rise of “the face” as a strategy for governing, sorting, and classifying members of constituent populations. The work re-examines the techno-political value of captured faces as identity data and by tracing the cultural and techno-political genealogies tying faces to ideas of stable institutional identities this thesis demonstrates face-based identity practices are being improvised and reconfigured by computerization and why these practices are significant for understanding the changing norms of interaction between individuals and institutions.