Probabilistic characterization and synthesis of complex driven systems

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000.Includes bibliographical references (leaves 194-204).Real-world systems that have characteristic input-output patterns but don't provide access to their internal states are as numerous as they are difficult to model. This dissertation introduces a modeling language for estimating and emulating the behavior of such systems given time series data. As a benchmark test, a digital violin is designed from observing the performance of an instrument. Cluster-weighted modeling (CWM), a mixture density estimator around local models, is presented as a framework for function approximation and for the prediction and characterization of nonlinear time series. The general model architecture and estimation algorithm are presented and extended to system characterization tools such as estimator uncertainty, predictor uncertainty and the correlation dimension of the data set. Furthermore a real-time implementation, a Hidden-Markov architecture, and function approximation under constraints are derived within the framework. CWM is then applied in the context of different problems and data sets, leading to architectures such as cluster-weighted classification, cluster-weighted estimation, and cluster-weighted sampling. Each application relies on a specific data representation, specific pre and post-processing algorithms, and a specific hybrid of CWM. The third part of this thesis introduces data-driven modeling of acoustic instruments, a novel technique for audio synthesis. CWM is applied along with new sensor technology and various audio representations to estimate models of violin-family instruments. The approach is demonstrated by synthesizing highly accurate violin sounds given off-line input data as well as cello sounds given real-time input data from a cello player.by Bernd Schoner.Ph.D

Dependence-driven techniques in system design

Burstiness in workloads is often found in multi-tier architectures, storage systems, and communication networks. This feature is extremely important in system design because it can significantly degrade system performance and availability. This dissertation focuses on how to use knowledge of burstiness to develop new techniques and tools for performance prediction, scheduling, and resource allocation under bursty workload conditions.;For multi-tier enterprise systems, burstiness in the service times is catastrophic for performance. Via detailed experimentation, we identify the cause of performance degradation on the persistent bottleneck switch among various servers. This results in an unstable behavior that cannot be captured by existing capacity planning models. In this dissertation, beyond identifying the cause and effects of bottleneck switch in multi-tier systems, we also propose modifications to the classic TPC-W benchmark to emulate bursty arrivals in multi-tier systems.;This dissertation also demonstrates how burstiness can be used to improve system performance. Two dependence-driven scheduling policies, SWAP and ALoC, are developed. These general scheduling policies counteract burstiness in workloads and maintain high availability by delaying selected requests that contribute to burstiness. Extensive experiments show that both SWAP and ALoC achieve good estimates of service times based on the knowledge of burstiness in the service process. as a result, SWAP successfully approximates the shortest job first (SJF) scheduling without requiring a priori information of job service times. ALoC adaptively controls system load by infinitely delaying only a small fraction of the incoming requests.;The knowledge of burstiness can also be used to forecast the length of idle intervals in storage systems. In practice, background activities are scheduled during system idle times. The scheduling of background jobs is crucial in terms of the performance degradation of foreground jobs and the utilization of idle times. In this dissertation, new background scheduling schemes are designed to determine when and for how long idle times can be used for serving background jobs, without violating predefined performance targets of foreground jobs. Extensive trace-driven simulation results illustrate that the proposed schemes are effective and robust in a wide range of system conditions. Furthermore, if there is burstiness within idle times, then maintenance features like disk scrubbing and intra-disk data redundancy can be successfully scheduled as background activities during idle times

Optimal Control Strategies for Constrained Relative Orbits

The US Air Force\u27s ability to protect space assets is enhanced by a proficiency in satellite proximity operations and Space Situational Awareness (SSA). In pursuit of that proficiency, this research develops a key capability of interest to mission planners; the ability of a deputy satellite to hover within a defined volume fixed in the vicinity of a chief satellite for an extended period of time. This research finds optimal trajectories, produced with discrete-thrusts, that minimize fuel spent per unit time and stay within the user-defined volume, thus providing a practical hover capability in the vicinity of the chief. The work assumes the Clohessy-Wiltshire closeness assumption between the deputy and chief is valid, however, elliptical chief orbits are allowed. Using the new methodology developed in this work, feasible closed and non-closed relative orbits are found and evaluated based on a fuel criterion and compared to an easily calculated continuous-thrust baseline. It is shown that in certain scenarios the discrete-thrust solution provides the lowest overall fuel cost. These scenarios are generally constrained to a smaller total time-of-flight. A simple check is proposed that enables the mission planner to make the correct strategy choice

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

A proposal for the management of data driven services in smart manufacturing scenarios

205 p.This research work focuses on Industrial Big Data Services (IBDS) Providers, a specialization of ITServices Providers. IBDS Providers constitute a fundamental agent in Smart Manufacturing scenarios,given the wide spectrum of complex technological challenges involved in the adoption of the requireddata-related IT by manufacturers aiming at shifting their businesses towards Smart Manufacturing. Theoverarching goal of this research work is to provide contributions that (a) help the business sector ofIBDS Providers to manage their collaboration projects with manufacturing partners in order to deploy therequired data-driven services in Smart Manufacturing scenarios, and (b) adapt and extend existingconceptual, methodological, and technological proposals in order to include those practical elements thatfacilitate their use in business contexts. The main contributions of this dissertation focus on three specificchallenges related to the early stages of the data lifecycle, i.e. those stages that ensure the availability ofnew data to exploit, coming from monitored manufacturing facilities: (1) Devising a more efficient datastorage strategy that reduces the costs of the cloud infrastructure required by an IBDS Provider tocentralize and accumulate the massive-scale amounts of data from the supervised manufacturingfacilities; (2) Designing the required architecture for the data capturing and integration infrastructure thatsustains an IBDS Provider's platform; (3) The collaborative design process with partnering manufacturersof the required data-driven services for a specific manufacturing sector

Black band for Brown students: a culturally relevant pedagogy?

Multicultural education researchers have long argued the advantages of culturally based pedagogical strategies for the education of students with non-dominant cultural backgrounds. Gloria Ladson-Billings theoretical framework — culturally relevant pedagogy (CRP) — is one strategy that, though including acknowledgement of common characteristics of teachers implementing this critical pedagogy, is identified primarily by its results in students who display the three central tenets of CRP — academic achievement, maintained or enhanced cultural competence, and an understanding and critique of the existing social order. Seemingly in contrast to culturally based pedagogical strategies, I have observed Black band teachers who have engaged Hispanic students with pedagogy patterned after HBCU show-style marching bands. The purpose of this study was to investigate a Black band director’s use of show-style band pedagogy to engage Hispanic students as a possible example of CRP. The research questions centered around the three central tenets of CRP: 1. To what extent does the teacher consider students’ culture in the pedagogy in terms of students’ a. academic achievement; b. cultural competence, including i. navigation of and identification with their own culture, and ii. access of another culture; and c. sociopolitical consciousness? 2. What reactions do students have towards show-style pedagogy? 3. What perceptions do students have about the impact of show-style pedagogy on their: a. academic achievement; b. cultural competence, including i. navigation of and identification with their own culture, and ii. access of another culture; and c. sociopolitical consciousness? 4. What nexus exists between the teachers’ pedagogical intent in using show-style pedagogy and students’ a. academic achievement; b. cultural competence, including i. navigation of and identification with their own culture, and ii. access of another culture; and c. sociopolitical consciousness? This was an ethnographic case study executed at an urban high school in Texas with an African American band director and predominantly Hispanic band students. The director was interviewed; band classes, rehearsals, and performances were observed; and student informants along with representative caretakers of those informants were interviewed. Findings included evidence of the three tenets of Ladson-Billings’s culturally relevant pedagogy, though not always along the traditional cultural delineators of race, nationality, or ethnicity. Findings also included band as a culture as a salient theme; another was critique of the status of show-style band in the related milieus of music education and adjudicated scholastic performances The participating band director was found to have implemented some strategies in alignment with CRP independent of any consideration for the students’ Hispanic background. That finding aligned with Ladson-Billings’s own critique that many practices associated with CRP can be conceived of as universal pedagogical goals. The researcher concluded that the implementation of show-style band pedagogy was culturally relevant for the students in the study

Stable Profiles in Simulation-Based Games via Reinforcement Learning and Statistics

In environments governed by the behavior of strategically interacting agents, game theory provides a way to predict outcomes in counterfactual scenarios, such as new market mechanisms or cybersecurity systems. Simulation-based games allow analysts to reason about settings that are too complex to model analytically with sufficient fidelity. But prior techniques for studying agent behavior in simulation-based games lack theoretical guarantees about the strategic stability of these behaviors. In this dissertation, I propose a way to measure the likelihood an agent could find a beneficial strategy deviation from a proposed behavior, using a limited number of samples from a distribution over strategies, including a theoretically proven bound. This method employs a provably conservative confidence interval estimator, along with a multiple test correction, to provide its guarantee. I show that the method can reliably find provably stable strategy profiles in an auction game, and in a cybersecurity game from prior literature. I also present a method for evaluating the stability of strategy profiles learned over a restricted set of strategies, where a strategy profile is an assignment of a strategy to each agent in a game. This method uses reinforcement learning to challenge the learned behavior as a test of its soundness. This study finds that a widely-used trading agent model, the zero-intelligence trader, can be reasonably strategically stable in continuous double auction games, but only if the strategies have their parameters calibrated for the particular game instance. In addition, I present new applications of empirical game-theoretic analysis (EGTA) to a cybersecurity setting, involving defense against attacker intrusion into a computer system. This work uses iterated deep reinforcement learning to generate more strategically stable attacker and defender strategies, relative to those found in prior work. It also offers empirical insights into how iterated deep reinforcement learning approaches strategic equilibrium, over dozens of rounds.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149991/1/masondw_1.pd

Conflicting Objectives in Decisions

This book deals with quantitative approaches in making decisions when conflicting objectives are present. This problem is central to many applications of decision analysis, policy analysis, operational research, etc. in a wide range of fields, for example, business, economics, engineering, psychology, and planning. The book surveys different approaches to the same problem area and each approach is discussed in considerable detail so that the coverage of the book is both broad and deep. The problem of conflicting objectives is of paramount importance, both in planned and market economies, and this book represents a cross-cultural mixture of approaches from many countries to the same class of problem

System Identification of Bipedal Locomotion in Robots and Humans

The ability to perform a healthy walking gait can be altered in numerous cases due to gait disorder related pathologies. The latter could lead to partial or complete mobility loss, which affects the patients’ quality of life. Wearable exoskeletons and active prosthetics have been considered as a key component to remedy this mobility loss. The control of such devices knows numerous challenges that are yet to be addressed. As opposed to fixed trajectories control, real-time adaptive reference generation control is likely to provide the wearer with more intent control over the powered device. We propose a novel gait pattern generator for the control of such devices, taking advantage of the inter-joint coordination in the human gait. Our proposed method puts the user in the control loop as it maps the motion of healthy limbs to that of the affected one. To design such control strategy, it is critical to understand the dynamics behind bipedal walking. We begin by studying the simple compass gait walker. We examine the well-known Virtual Constraints method of controlling bipedal robots in the image of the compass gait. In addition, we provide both the mechanical and control design of an affordable research platform for bipedal dynamic walking. We then extend the concept of virtual constraints to human locomotion, where we investigate the accuracy of predicting lower limb joints angular position and velocity from the motion of the other limbs. Data from nine healthy subjects performing specific locomotion tasks were collected and are made available online. A successful prediction of the hip, knee, and ankle joints was achieved in different scenarios. It was also found that the motion of the cane alone has sufficient information to help predict good trajectories for the lower limb in stairs ascent. Better estimates were obtained using additional information from arm joints. We also explored the prediction of knee and ankle trajectories from the motion of the hip joints