by Jan C. Willems. / Bibliography: p. 11. / "August, 1981." / Supported in part by the U.S. Dept. of Energy under Contract DOE/ET-76-A-012295

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 95-99).

The organization I work in has 13 subsidiary businesses operating in the branded footwear and apparel industry. The industry currently faces significant macroeconomic and industry challenges. One of our biggest challenges is how to avoid excessive and wasteful new product development whilst still building an attractive range of products for the customer. So the focus of my research is on the management control and governance of the New Product Development (NPD) process to solve a pressing business problem. However, there is a gap in the literature. Many authors have claimed that our knowledge of the governance of NPD processes is incomplete and there is a dearth of actual studies in this area. My literature review looked at management control and in particular at the enduring problem of the need to generate control without stifling creativity. The literature led me to focus on the use of feedforward controls to influence NPD management teams to improve portfolio value and strategic alignment whilst simultaneously encouraging NPD experimentation. During this research I developed the concept of Feedforward Anticipatory Control (FAC), which encompasses the combination of feedforward control and double-loop learning. From this start my research question became “How does the use of FAC influence NPD management teams to improve portfolio value and strategic alignment?” From theory and my initial case study research I developed, tested and refined a tool for ascertaining the level of FAC sophistication in use by NPD teams in their development process. The tool was then used in action research interventions to help the teams develop their sophistication in the use of FAC. The tool was found to be useable, useful and have value. The action research case studies were embedded in a case study protocol to ensure the rigour of my research. This involved developing a framework to investigate the consequences of my interventions, in terms of both hard performance metrics and softer team perceptions. The contribution is in the use of management controls in NPD. The findings show that different levels of FAC sophistication can be applied in NPD and that the use of higher levels of FAC influences NPD teams to improve portfolio value and strategic alignment. The contribution to practice is an intervention “toolkit” that can influence NPD teams to develop higher levels of FAC sophistication and generate improvements in NPD portfolio performance.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 69-71). Also available via the Internet.

Thesis (M. S.)--Industrial Design, Georgia Institute of Technology, 2007. / Randy Bernard, Committee Member ; Abir Mullick, Committee Member ; Wayne Chung, Committee Chair.

Active structural acoustic control (ASAC) in conjunction with the adaptive feedforward control has been proved to be an efficient practical approach to reduce structure-borne sound. ASAC works on the principles of reducing the vibration amplitude of the structure (modal reduction), as well as changing the vibration distributions of the structure so that the vibration distributions of each structural modes destructively interfere with one another in their associated radiating acoustic field (modal restructuring). Based on these observations, two different but related design strategies, namely the non-volumetric design and the minimum supersonic wavenumber design, were developed for designing efficient ASAC system. The eigenassignment method for feedforward control system serves as the fundamental design tool for both formulations. In this study, the dynamic characteristics of a multiple-input, multiple-output (MIMO) feedforward controlled system was investigated both analytically and experimentally on a simply supported plate under harmonic excitation. It was demonstrated that, when the control system has equal number of control inputs and error sensor outputs, the feedforward controller can effectively modify the system dynamics (i.e., resonance frequencies and mode shapes). This provides the theoretical basis for the eigenassignment method. For the non-volumetric design, the single-input, single-output (SISO) eigenassignment technique is used to modify the eigenproperties of a planar structure using structure actuators and sensors so that all the controlled modes are non-volumetric (inefficient sound radiators at low frequencies, i.e., k_0a << 1), leading large global sound attenuation in the far field. The effectiveness of this formulation was demonstrated through numerical simulations for the control of radiation from simply supported and clamped-free beams. The experimental validation of the non-volumetric design was also carried out on a simply supported beam using PZT actuators and shaped PVDF film as error sensor. The filtered-x LMS algorithm was used in the experiment. Excellent global sound attenuation was achieved in the low frequencies. The minimum supersonic wavenumber design stems from the fact that only supersonic wavenumber components of the structural velocity spectra radiate to the far field. A SISO eigenassignment technique is used to modify the eigenproperties of a planar structure so that the eigenfunctions of the controlled system have minimum supersonic wavenumber in the frequency range of study. The sound pressure or sound power radiated by the structure is therefore reduced. The design was demonstrated on a simply supported beam to minimize the supersonic wavenumber components contributed by the odd-order modes only. Significant global sound attenuation was achieved in the frequency range of study. The main advantage of the proposed design methods is that they do not depend on the characteristics of the external disturbance, such as the form, location and frequency contents. Also, the error sensor and control input are optimized simultaneously, resulting in better acoustic control performance. The practical implementations of the proposed designs require accurate system modeling, this is the major limitation of the proposed designs. / Ph. D.

Adaptive FeedforWard algorithms have been successfully applied in the active control of sound and vibrations~ Current approaches, such as the Filtered-X Least Mean Square algorithm, are limited in adaptability and applicability by the computationally intensive system identification process. The Time Average approach is a technique that promises to be a major advance in the active control of sound and vibration. This approach is based on statistical estimates of the error signals, and does not require having previous knowledge of the system. A new minimization algorithm has been developed, which enhances the performance of the Time Average controllers. A new technique to estimate the cost function is also presented. This technique is called the Real Time Cost Function Evaluation method. Based upon these new controller design concepts, single channel and multi-channel controllers were developed and experimentally tested. The experiments included control of a harmonically excited, simply supported beam, and the control of inlet tonal noise radiated from a JT1SD-l turbofan engine. / Master of Science

The present work introduces a synthesis of neocerebellar state estimation and feedforward control with multi-level language processing. The approach combines insights from clinical, imaging, and modelling work on the cerebellum with psycholinguistic and historical linguistic research. It finally provides the first experimental attempts towards the empirical validation of this synthesis, employing transcranial magnetic stimulation. A neuroanatomical locus traditionally seen as limited to lower sensorimotor functions, the cerebellum has, over the last decades, emerged as a widely accepted foundation of feedforward control and state estimation. Its cytoarchitectural homogeneity and diverse connectivity with virtually all parts of the central nervous system strongly support the idea of a uniform, domain-general cerebellar computation. Its reciprocal connectivity with language-related cortical areas suggests that this uniform cerebellar computation is also applied in language processing. Insight into the latter, however, remains an elusive desideratum; instead, research on cerebellar language functions is predominantly involved in the frontal cortical-like deficits (e.g. aphasias) seldom induced by cerebellar impairment. At the same time, reflections on cerebellar computations in language processing remain at most speculative, given the lack of discourse between cerebellar neuroscientists and psycholinguists. On the other hand, the fortunate contingency of the recent accommodation of these computations in psycholinguistic models provides the foundations for satisfying the desideratum above. The thesis thus formulates a neurolinguistic model whereby multi-level, predictive, associative linguistic operations are acquired and performed in neocerebello-cortical circuits, and are adaptively combined with cortico-cortical categorical processes. A broad range of psycholinguistic phenomena, involving, among others, "pragmatic normalization", "verbal/semantic illusions", associative priming, and phoneme restoration, are discussed in the light of recent findings on neocerebellar cognitive functions, and provide a rich research agenda for the experimental validation of the proposal. The hypothesis is then taken further, examining grammaticalization changes in the light of neocerebellar linguistic contributions. Despite a) the broad acceptance of routinization and automatization processes as the domain-general core of grammaticalization, b) the growing psycholinguistic research on routinized processing, and c) the evidence on neural circuits involved in automatization processes (crucially involving the cerebellum), interdisciplinary discourse remains strikingly poor. Based on the above, a synthesis is developed, whereby grammaticalization changes are introduced in routinized dialogical interaction as the result of maximized involvement of associative neocerebello-cortical processes. The thesis then turns to the first steps taken towards the verification of the hypothesis at hand. In view of the large methodological limitations of clinical research on cerebellar cognitive functions, the transcranial magnetic stimulation apparatus is employed instead, producing the very first linguistic experiments involving cerebellar stimulation. Despite the considerable technical difficulties met, neocerebellar loci are shown to be selectively involved in formal- and semantic-associative computations, with far-reaching consequences for neurolinguistic models of sentence processing. In particular, stimulation of the neocerebellar vermis is found to selectively enhance formal-associative priming in native speakers of English, and to disrupt, rather selectively, semantic-categorical priming in native speakers of Modern Greek, as well as to disrupt the practice-induced facilitation in processing repeatedly associated letter strings. Finally, stimulation of the right neocerebellar Crus I is found to enhance, quite selectively, semantic-associative priming in native speakers of English, while stimulation of the right neocerebellar vermis is shown to disrupt semantic priming altogether. The results are finally discussed in the light of a future research agenda overcoming the technical limitations met here.

In this paper we will briefly revisit the optical vibration measurement system (OVMS) at the Large Binocular Telescope (LBT) and how these values are used for disturbance compensation and particularly for the LBT Interferometer (LBTI) and the LBT Interferometric Camera for Near-Infrared and Visible Adaptive Interferometry for Astronomy (LINC-NIRVANA). We present the now centralized software architecture, called OVMS+, on which our approach is based and illustrate several challenges faced during the implementation phase. Finally, we will present measurement results from LBTI proving the effectiveness of the approach and the ability to compensate for a large fraction of the telescope induced vibrations.

by Hau-san Wong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves [178-183]). / Chapter 1 --- INTRODUCTION / Chapter 1.1 --- Learning versus Explicit Programming --- p.1-1 / Chapter 1.2 --- Artificial Neural Networks --- p.1-2 / Chapter 1.3 --- Learning in ANN --- p.1-3 / Chapter 1.4 --- Problems of Learning in BP Networks --- p.1-5 / Chapter 1.5 --- Dynamic Node Architecture for BP Networks --- p.1-7 / Chapter 1.6 --- Incremental Learning --- p.1-10 / Chapter 1.7 --- Research Objective and Thesis Organization --- p.1-11 / Chapter 2 --- THE FEEDFORWARD MULTILAYER NEURAL NETWORK / Chapter 2.1 --- The Perceptron --- p.2-1 / Chapter 2.2 --- The Generalization of the Perceptron --- p.2-4 / Chapter 2.3 --- The Multilayer Feedforward Network --- p.2-5 / Chapter 3 --- SOLUTIONS TO THE BP LEARNING PROBLEM / Chapter 3.1 --- Introduction --- p.3-1 / Chapter 3.2 --- Attempts in the Establishment of a Viable Hidden Representation Model --- p.3-5 / Chapter 3.3 --- Dynamic Node Creation Algorithms --- p.3-9 / Chapter 3.4 --- Concluding Remarks --- p.3-15 / Chapter 4 --- THE GROWTH ALGORITHM FOR NEURAL NETWORKS / Chapter 4.1 --- Introduction --- p.4-2 / Chapter 4.2 --- The Radial Basis Function --- p.4-6 / Chapter 4.3 --- The Additional Input Node and the Modified Nonlinearity --- p.4-9 / Chapter 4.4 --- The Initialization of the New Hidden Node --- p.4-11 / Chapter 4.5 --- Initialization of the First Node --- p.4-15 / Chapter 4.6 --- Practical Considerations for the Growth Algorithm --- p.4-18 / Chapter 4.7 --- The Convergence Proof for the Growth Algorithm --- p.4-20 / Chapter 4.8 --- The Flow of the Growth Algorithm --- p.4-21 / Chapter 4.9 --- Experimental Results and Performance Analysis --- p.4-21 / Chapter 4.10 --- Concluding Remarks --- p.4-33 / Chapter 5 --- KNOWLEDGE REPRESENTATION IN NEURAL NETWORKS / Chapter 5.1 --- An Alternative Perspective to Knowledge Representation in Neural Network: The Temporal Vector (T-Vector) Approach --- p.5-1 / Chapter 5.2 --- Prior Research Works in the T-Vector Approach --- p.5-2 / Chapter 5.3 --- Formulation of the T-Vector Approach --- p.5-3 / Chapter 5.4 --- Relation of the Hidden T-Vectors to the Output T-Vectors --- p.5-6 / Chapter 5.5 --- Relation of the Hidden T-Vectors to the Input T-Vectors --- p.5-10 / Chapter 5.6 --- An Inspiration for a New Training Algorithm from the Current Model --- p.5-12 / Chapter 6 --- THE DETERMINISTIC TRAINING ALGORITHM FOR NEURAL NETWORKS / Chapter 6.1 --- Introduction --- p.6-1 / Chapter 6.2 --- The Linear Independency Requirement for the Hidden T-Vectors --- p.6-3 / Chapter 6.3 --- Inspiration of the Current Work from the Barmann T-Vector Model --- p.6-5 / Chapter 6.4 --- General Framework of Dynamic Node Creation Algorithm --- p.6-10 / Chapter 6.5 --- The Deterministic Initialization Scheme for the New Hidden Nodes / Chapter 6.5.1 --- Introduction --- p.6-12 / Chapter 6.5.2 --- Determination of the Target T-Vector / Chapter 6.5.2.1 --- Introduction --- p.6-15 / Chapter 6.5.2.2 --- Modelling of the Target Vector βQhQ --- p.6-16 / Chapter 6.5.2.3 --- Near-Linearity Condition for the Sigmoid Function --- p.6-18 / Chapter 6.5.3 --- Preparation for the BP Fine-Tuning Process --- p.6-24 / Chapter 6.5.4 --- Determination of the Target Hidden T-Vector --- p.6-28 / Chapter 6.5.5 --- Determination of the Hidden Weights --- p.6-29 / Chapter 6.5.6 --- Determination of the Output Weights --- p.6-30 / Chapter 6.6 --- Linear Independency Assurance for the New Hidden T-Vector --- p.6-30 / Chapter 6.7 --- Extension to the Multi-Output Case --- p.6-32 / Chapter 6.8 --- Convergence Proof for the Deterministic Algorithm --- p.6-35 / Chapter 6.9 --- The Flow of the Deterministic Dynamic Node Creation Algorithm --- p.6-36 / Chapter 6.10 --- Experimental Results and Performance Analysis --- p.6-36 / Chapter 6.11 --- Concluding Remarks --- p.6-50 / Chapter 7 --- THE GENERALIZATION MEASURE MONITORING SCHEME / Chapter 7.1 --- The Problem of Generalization for Neural Networks --- p.7-1 / Chapter 7.2 --- Prior Attempts in Solving the Generalization Problem --- p.7-2 / Chapter 7.3 --- The Generalization Measure --- p.7-4 / Chapter 7.4 --- The Adoption of the Generalization Measure to the Deterministic Algorithm --- p.7-5 / Chapter 7.5 --- Monitoring of the Generalization Measure --- p.7-6 / Chapter 7.6 --- Correspondence between the Generalization Measure and the Generalization Capability of the Network --- p.7-8 / Chapter 7.7 --- Experimental Results and Performance Analysis --- p.7-12 / Chapter 7.8 --- Concluding Remarks --- p.7-16 / Chapter 8 --- THE ESTIMATION OF THE INITIAL HIDDEN LAYER SIZE / Chapter 8.1 --- The Need for an Initial Hidden Layer Size Estimation --- p.8-1 / Chapter 8.2 --- The Initial Hidden Layer Estimation Scheme --- p.8-2 / Chapter 8.3 --- The Extension of the Estimation Procedure to the Multi-Output Network --- p.8-6 / Chapter 8.4 --- Experimental Results and Performance Analysis --- p.8-6 / Chapter 8.5 --- Concluding Remarks --- p.8-16 / Chapter 9 --- CONCLUSION / Chapter 9.1 --- Contributions --- p.9-1 / Chapter 9.2 --- Suggestions for Further Research --- p.9-3 / REFERENCES --- p.R-1 / APPENDIX --- p.A-1