A bio-inspired knowledge system for control tuning (BIO-KSY)

This study presents a novel bio-inspired knowledge system, based in closed loop tuning, for the calculation of the Proportional-Integral-Derivative (PID) controller parameters. The aim is to achieve automatically the best parameters according with the work point and the dynamics of the plant. For it, in our study, several typical expressions and systems have been taken into account to build the model. Each of these expressions is appropriated for a particular system. The novel method is empirically verified with a real dataset obtained by a liquid-level laboratory plant

A bio-inspired knowledge system for control tuning (BIO-KSY)

This study presents a novel bio-inspired knowledge system, based in closed loop tuning, for the calculation of the Proportional-Integral-Derivative (PID) controller parameters. The aim is to achieve automatically the best parameters according with the work point and the dynamics of the plant. For it, in our study, several typical expressions and systems have been taken into account to build the model. Each of these expressions is appropriated for a particular system. The novel method is empirically verified with a real dataset obtained by a liquid-level laboratory plant

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

Machine Learning for Prediction of Trabecular and Cortical Bone Mineral Density

Osteoporosis becomes very common problem for people after a certain age, which results in fragility fractures without any previous symptoms. One of the primary predictors of osteoporosis is bone mineral density (BMD). BMD is the mineral content of bone, at the optimal levels, that makes the bone strong enough to bear the regular load and elastic enough to handle the irregular twisting load. Two of the major parts of the bone that help to acquire such property are trabecular and cortical bone. This thesis focuses on predicting the BMDs of trabecular and cortical bone for men. For this purpose we performed Genome Wide Association Study (GWAS) for quality control and obtained new subsets of 537 and 536 Single Nucleotide Polymorphisms (SNPs) associated with trabecular and cortical BMDs. Various machine learning algorithms were used for the predictive analysis, among which linear regression (LR), support vector machine (SVM) and multi-layer perceptron (MLP) gave much better results with the newly obtained subset of SNPs, compared to the results using the 1103 and 307 SNPs associated with BMD found in the existing literature. LR gave mean squared error (MSE) of 0.000658 and coefficient of determination (r2) of 0.643479, SVM gave MSE of 0.000628 and r2 of 0.65971, and MLP gave MSE 0.000683 and r2 0.62989 for trabecular BMD with 537 SNPs. Similarly, LR, SVM, and MLP gave MSEs of 0.001109, 0.001103, and 0.00112, and r2 of 0.707548, 0.709079 and 0.703947, respectively, for cortical BMD with 536 SNPs. In both cases, SVM gave better results

Doctor of Philosophy

dissertationClosed-loop control of wireless capsule endoscopes is an active area of research because it would drastically improve screening of the gastrointestinal tract. Traditional endoscopic procedures are unable to view the entire gastrointestinal tract and current commercial wireless capsule endoscopes are limited in their effectiveness due to their passive nature. This dissertation advances the field of active capsule endoscopy by developing methods to localize the full six-degree-of-freedom (6-DOF) pose of a screw-type magnetic capsule while it is being propelled through a lumen (such as the small intestines) using an external rotating magnetic dipole. The same external magnetic dipole is utilized for both propulsion and localization. Hardware was designed and constructed to enable testing of the magnetic localization and propulsion methods, including a robotic end-effector used as the external actuator magnet, and a prototype capsule embedded with Hall-effect sensors. Due to the use of a rotating magnetic field for propulsion, at any given time, the capsule can be in one of three regimes: synchronously rotating with the applied field, in "step-out" where it is free to move but the external field is rotating too quickly for the capsule to remain synchronously rotating, or completely stationary. We show that it is only necessary to distinguish whether or not the capsule is synchronously rotating (i.e., a single localization method can be used for a capsule in either the step-out or stationary regimes). Two magnetic localization methods are developed. The first uses nonlinear least squares to estimate the capsule's pose when it has no (or approximately no) net motion (e.g., to find the initial capsule pose or when it is stuck in an intestinal fold). The second method estimates the 6-DOF capsule pose as it synchronously rotates with the applied magnetic field using a square-root variant of the Unscented Kalman filter. A simple process model is adopted that restricts the capsule's movement to translation along and rotation about its principle axis. The capsule is actively propelled forward or backward, but it is not actively steered, rather, steering is provided by the lumen. The propulsion parameters that transform magnetic force and torque to the capsule's spatial velocity and angular velocity are estimated with an additional square-root Unscented Kalman filter to enable the capsule to navigate heterogeneous environments such as the small intestines. An optimized localization-propulsion system is described using the two localization algorithms and prior work in screw-type magnetic capsule propulsion with a single rotating dipole field. The capsule's regime is determined and the corresponding localization method is employed. Based on the capsule's estimated pose and the current estimates of its propulsion parameters, the actuator magnet's pose relative to the capsule is optimized to maximize the capsule's forward propulsion. Using this system, our prototype magnetic capsule successfully completed U-shaped and S-shaped trajectories in fresh bovine intestines with an average forward velocity of 5.5mm/s and 3.5 mm/s, respectively. At this rate it would take approximately 18-30 minutes to traverse the 6 meters of a typical human small intestine

A Robust and Tunable Mitotic Oscillator in Artificial Cells

This dissertation aims to develop a droplet-based artificial cell system using cell-free extracts of Xenopus laevis eggs and understand mitotic oscillations with the proposed system. Single-cell analysis is pivotal to deciphering complex phenomena such as cellular heterogeneity, bistable switches, and oscillations, where a population ensemble cannot represent the individual behaviors. Despite having unique advantages of manipulation and characterization of biochemical networks, bulk cell-free systems lack the essential single-cell information to understand out-of-steady-state dynamics including cell cycles. In this dissertation, we present a novel artificial single-cell system for the study of mitotic dynamics by encapsulating Xenopus egg extracts in water-in-oil micro-emulsions. The artificial cells are different from real cells, i.e., their surface is formed by surfactant oil instead of the cell membrane. These “cells”, adjustable in sizes and periods, encapsulate cycling cytoplasmic extracts that can sustain mitotic oscillations for over 30 cycles. The artificial cells function in forms from the simplest cytoplasmic-only oscillators to the more complicated ones involving demembranated sperm chromatin that can reconstitute downstream mitotic events. The dynamic activities of cell cycle clock can be detected by fluorescent reporters such as cyclin B1-YFP and securin-mCherry. This innate flexibility makes it key to studying cell cycle clock tunability and stochasticity. Our experimental results indicate that the mitotic oscillators generated by our system are effectively tunable in frequency with cyclin B1 mRNAs and the dynamic behavior of single droplet oscillators is size-dependent. We also establish a stochastic model that highlights energy supply as an essential regulator of cell cycles. Moreover, the model explains experimental observations including the increase of baseline and amplitude of cyclin B1 time course. This dissertation study demonstrates a simple, powerful, and likely generalizable strategy of integrating single-cell approaches into conventional in vitro systems to study complex clock functions.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144115/1/yeguan_1.pd

Optically Induced Nanostructures

Nanostructuring of materials is a task at the heart of many modern disciplines in mechanical engineering, as well as optics, electronics, and the life sciences. This book includes an introduction to the relevant nonlinear optical processes associated with very short laser pulses for the generation of structures far below the classical optical diffraction limit of about 200 nanometers as well as coverage of state-of-the-art technical and biomedical applications. These applications include silicon and glass wafer processing, production of nanowires, laser transfection and cell reprogramming, optical cleaning, surface treatments of implants, nanowires, 3D nanoprinting, STED lithography, friction modification, and integrated optics. The book highlights also the use of modern femtosecond laser microscopes and nanoscopes as novel nanoprocessing tools

Albuquerque Morning Journal, 07-03-1908

https://digitalrepository.unm.edu/abq_mj_news/4339/thumbnail.jp

NASA Space Engineering Research Center Symposium on VLSI Design

The NASA Space Engineering Research Center (SERC) is proud to offer, at its second symposium on VLSI design, presentations by an outstanding set of individuals from national laboratories and the electronics industry. These featured speakers share insights into next generation advances that will serve as a basis for future VLSI design. Questions of reliability in the space environment along with new directions in CAD and design are addressed by the featured speakers

Control of wave energy converters using machine learning strategies

Wave energy converters are devices that are designed to extract power from ocean waves. Existing wave energy converter technologies are not financially viable yet. Control systems have been identified as one of the areas that can contribute the most towards the increase in energy absorption and reduction of loads acting on the structure, whilst incurring only minimal extra hardware costs. In this thesis, control schemes are developed for wave energy converters, with the focus on single isolated devices. Numerical models of increasing complexity are developed for the simulation of a point absorber, which is a type of wave energy converter with small dimensions with respect to the dominating wave length. After investigating state-of-the-art control schemes, the existing control strategies reported in the literature have been found to rely on the model of the system dynamics to determine the optimal control action. This is despite the fact that modelling errors can negatively affect the performance of the device, particularly in highly energetic waves when non-linear effects become more significant. Furthermore, the controller should be adaptive so that changes in the system dynamics, e.g. due to marine growth or non-critical subsystem failure, are accounted for. Hence, machine learning approaches have been investigated as an alternative, with a focus on neural networks and reinforcement learning for control applications. A time-averaged approach will be employed for the development of the control schemes to enable a practical implementation on WECs based on the standard in the industry at the moment. Neural networks are applied to the active control of a point absorber. They are used mainly for system identification, where the mean power is related to the current sea state and parameters of the power take-off unit. The developed control scheme presents a similar performance to optimal active control for the analysed simulations, which rely on linear hydrodynamics. Reinforcement learning is then applied to the passive and active control of a wave energy converter for the first time. The successful development of different control schemes is described in detail, focusing on the encountered challenges in the selection of states, actions and reward function. The performance of reinforcement learning is assessed against state-of-the-art control strategies. Reinforcement learning is shown to learn the optimal behaviour in a reasonable time frame, whilst recognizing each sea state without reliance on any models of the system dynamics. Additionally, the strategy is able to deal with model non-linearities. Furthermore, it is shown that the control scheme is able to adapt to changes in the device dynamics, as for instance due to marine growth