Analytical models of avian flight have previously been used to predict mechanical and metabolic power consumption during cruise. These models are limited, in that they neglect details of wing kinematics, and model power by assuming a fixed or rotary wing (actuator disk) weight support mechanism. Theoretical methods that incorporate wing kinematics potentially offer more accurate predictions of power consumption by calculating instantaneous aerodynamic loads on the wing. However, the success of these models inherently depends on the availability and accuracy of experimental kinematic data. The predictive simulation approach offers an alternative strategy, whereby kinematics are neither neglected nor measured experimentally, but calculated as part of the solution procedure. This thesis describes the development of a predictive tool for simulating avian wingbeat kinematics and wakes. The tool is designed in a modular format, in order to be extensible for future research in the biomechanics community. The primary simulation module is an inverse dynamic avian wing model that predicts aerodynamic forces and mechanical power consumption for given wing kinematics. The model is constructed from previous experimental studies of avian wing biomechanics. Wing motion is defined through joint kinematic time histories, and aerodynamic forces are predicted using blade element momentum theory. Mechanical power consumption at the shoulder joint is derived from both aerodynamic and inertial torque components associated with the shoulder joint rotation rate. An optimisation module is developed to determine wing kinematics that generate aerodynamic loads for propulsion and weight support in given flight conditions, while minimising mechanical power consumption. For minimum power cruise, optimisation reveals numerous local minima solutions that exhibit large variations in wing kinematics. Validation of the model against wind tunnel data shows that optimised solutions capture qualitative trends in wing kinematics with varying cruise speed. Sensitivity analyses show that the model outputs are most affected by the defined maximum lift coefficient and wing length, whereby perturbations in these parameters lead to significant changes in the predicted amount of upstroke wing retraction. Optimised solutions for allometrically scaled bird models show only small differences in predicted advance ratio, which is consistent with field study observations. Accelerating and climbing flight solutions also show similar qualitative trends in wing kinematics to experimental measurements, including a reduction in stroke plane inclination for increasing acceleration or climb angle. The model predicts that both climb angle and climb speed should be greater for birds with more available instantaneous mechanical power. Simulations of the wake using a discrete vortex model capture fundamental features of the wake geometry that have been observed experimentally. Reconstruction of the velocity field shows that this method overpredicts induced velocity in retracting-wing wakes, and should therefore only be applied to extended-wing phases of an avian wingbeat.

Online crowdfunding, an emerging business model, has been thriving for the last decade. It enables small firms and individuals to conduct financial transactions that would previously been impossible. Along with unprecedented opportunities, two fundamental issues still hinder crowdfunding ability to fulfill its potentials: the information asymmetry and the understanding of the impact of crowdfunding. Both are actually exacerbated by the "virtual" nature of these marketplaces. The success of this new market therefore critically depends on both improving existing mechanisms or designing new ones to mitigate the issue of unobservable fundraiser quality, which can lead to adverse selection and market collapse; and better understanding the impact of crowdfunding, and particularly its offline impact, which will allow the effective allocation of scarce resources. My dissertation includes three essays around these topics, using data from debt-, reward- and donation-based crowdfunding contexts, respectively. My first two essays focus on two popular but understudied components in crowdfunding campaigns, texts and videos, and aim at predicting fundraiser quality by quantifying texts and videos. In particular, the first essay focuses on developing scalable approaches to extracting linguistic features from texts provided by borrowers when they request funds; and on using those features to explain and predict the repayment probability of the problematic loans. The second essay focuses on videos in reward crowdfunding, and preliminary results show excellent predictive performance and strong associations between multi-dimensional video information and crowdfunding campaign success and quality. The last essay investigates the impact of educational crowdfunding on school performance, using data from a crowdfunding platform for educational purposes. The results show that educational crowdfunding plays a role far beyond simply a financial source. Overall, my dissertation identifies the non-financial impact of crowdfunding as well as potential opportunities for efficiency improvement in the crowdfunding market, which have thus far not been documented in the literature.

With numerous distinct advantages, quadrotors have found a wide range of applications, such as structural inspection, traffic control, search and rescue, agricultural surveillance, etc. To better serve applications in cluttered environment, quadrotors are further equipped with vision sensors to enhance their state sensing and environment perception capabilities. Moreover, visual information can also be used to guide the motion control of the quadrotor. This is referred to as visual servoing of quadrotor. In this thesis, we identify the challenging problems arising in the area of visual servoing of the quadrotor and propose effective control strategies to address these issues. The control objective considered in this thesis is to regulate the relative pose of the quadrotor to a ground target using a limited number of sensors, e.g., a monocular camera and an inertia measurement unit. The camera is attached underneath the center of the quadrotor and facing down. The ground target is a planar object consisting of multiple points. The image features are selected as image moments defined in a ``virtual image plane". These image features offer an image kinematics that is independent of the tilt motion of the quadrotor. This independence enables the separation of the high level visual servoing controller design from the low level attitude tracking control. A high-gain observer-based model predictive control (MPC) scheme is proposed in this thesis to address the image-based visual servoing of the quadrotor. The high-gain observer is designed to estimate the linear velocity of the quadrotor which is part of the system states. Due to a limited number of sensors on board, the linear velocity information is not directly measurable. The high-gain observer provides the estimates of the linear velocity and delivers them to the model predictive controller. On the other hand, the model predictive controller generates the desired thrust force and yaw rate to regulate the pose of the quadrotor relative to the ground target. By using the MPC controller, the tilt motion of the quadrotor can be effectively bounded so that the scene of the ground target is well maintained in the field of view of the camera. This requirement is referred to as visibility constraint. The satisfaction of visibility constraint is a prerequisite of visual servoing of the quadrotor. Simulation and experimental studies are performed to verify the effectiveness of the proposed control strategies. Moreover, image processing algorithms are developed to extract the image features from the captured images, as required by the experimental implementation. / Graduate / 2020-12-11

abstract: The current study assessed whether the interrater reliability and predictive validity of fidelity ratings differed significantly across the modalities of audio and video recordings. As empirically supported programs are moving to scale, attention to fidelity, the extent to which a program is delivered as intended, is essential because high fidelity is needed for positive program effects. Consequently, an important issue for prevention science is the development of feasible and acceptable methods for assessing fidelity. Currently, fidelity monitoring is rarely practiced, as the typical way of measuring fidelity, which uses video of sessions, is expensive, time-consuming, and intrusive. Audio recording has multiple advantages over video recording: 1) it is less intrusive; 2) equipment is less expensive; 3) recording procedures are simpler; 4) files are smaller so it takes less time to upload data and storage is less expensive; 5) recordings contain less identifying information; and 6) both clients and providers may be more willing to have sensitive interactions recorded with audio only. For these reasons, the use of audio recording may facilitate the monitoring of fidelity and increase the acceptability of both the intervention and implementation models, which may serve to broaden the scope of the families reached and improve the quality of the services provided. The current study compared the reliability and validity of fidelity ratings across audio and video rating modalities using 77 feedback sessions drawn from a larger randomized controlled trial of the Family Check-Up (FCU). Coders rated fidelity and caregiver in-session engagement at the age 2 feedback session. The composite fidelity and caregiver engagement scores were tested using path analysis to examine whether they predicted parenting behavior at age 3. Twenty percent of the sessions were double coded to assess interrater reliability. The interrater reliability and predictive validity of fidelity scores and caregiver engagement did not significantly differ across rating modality. However, caution must be used in interpreting these results because the interrater reliabilities in both conditions were low. Possible explanations for the low reliability, limitations of the current study, and directions for future research are discussed. / Dissertation/Thesis / Doctoral Dissertation Psychology 2019

Stochastic model predictive control (MPC) is a fascinating field for research and of increasing practical importance since optimal control techniques have been intensively investigated in modern control system design. With the development of computer technologies and communication networks, networked control systems (NCSs) or cyber-physical systems (CPSs) have become an interest of research due to the comprehensive integration of physical systems, such as sensors, actuators and plants, with intricate cyber components, possessing information communication and computation. In CPSs, advantages of low installation cost, high reliability, flexible modularity, improved efficiency, and greater autonomy can be obtained by the tight coordination of physical and cyber components. Several sectors, including robotics, transportation, health care, smart buildings, and smart grid, have witnessed the successful application of CPSs design. The integration of extensive cyber capability and physical plants with ubiquitous uncertainties also introduces concerns over communication efficiency, robustness and stability of the CPSs. Thus, to achieve satisfactory performance metrics of efficiency, robustness and stability, a detailed investigation into control synthesis of CPSs under the stochastic model predictive control framework is of importance. The stochastic model predictive control synthesis plays a vital role in CPSs design since the multivariable stochastic system subject to probabilistic constraints can be controlled in an optimized way. On the other hand, aperiodically sampled, or event-based, model predictive control has also been applied to CPSs extensively to improve communication efficiency. In this thesis, the control synthesis and analysis of aperiodically sampled stochastic model predictive control for CPSs is considered. Chapter 1 provides an introductory literature review of the current development of stochastic MPC, distributed stochastic MPC and event-based MPC. Chapter 2 presents a stochastic self-triggered model predictive control scheme for linear systems with additive uncertainty and with the states and inputs being subject to chance constraints. In the proposed control scheme, the succeeding sampling time instant and current control inputs are computed online by solving a formulated optimization problem. Chapter 3 discusses a stochastic self-triggered model predictive control algorithm with an adaptive prediction horizon. The communication cost is explicitly considered by adding a damping factor in the cost function. Sufficient conditions are provided to guarantee closed-loop chance constraints satisfactions. Furthermore, the recursive feasibility of the algorithm is analyzed, and the closed-loop system is shown to be stable. Chapter 4 proposes a distributed self-triggered stochastic MPC control scheme for CPSs under coupled chance constraints and additive disturbances. Based on the assumptions on stochastic disturbances, both local and coupled probabilistic constraints are transformed into the deterministic form using the tube-based method, and improved terminal constraints are constructed to guarantee the recursive feasibility of the control scheme. Theoretical analysis has shown that the overall closed-loop CPSs are quadratically stable. Numerical examples illustrate the efficacy of the proposed control method in terms of data transmission reductions. Chapter 5 concludes the thesis and suggests some promising directions for future research. / Graduate / 2022-01-15

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18867号 / 医博第3978号 / 新制||医||1008(附属図書館) / 31818 / 京都大学大学院医学研究科医学専攻 / (主査)教授 鈴木 実, 教授 黒田 知宏, 教授 富樫 かおり / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

This thesis deals with the description and innovation in an industrial company, which is focused on the diagnostics of production machines using mainly big data collection. The theoretical part of the thesis describes industry 4.0, the demands of big data on storage and also the basics of change management. The analytical part includes a description of the company, an analysis of the current state and a proposal for change. The proposed changes are the transition from a relational database to a non relational one and a new web application for data visualization. The last part of the thesis describes the implementation of these changes and their evaluation.

Cyber-physical systems (CPS), integrating advanced computation, communication, and control technologies with the physical process, are widely applied in industry applications such as smart production and manufacturing systems, robotic and automotive control systems, and smart grids. Due to possible exposure to unreliable networks and complex physical environments, CPSs may simultaneously face multiple cyber and physical issues including cyber threats (e.g., malicious cyber attacks) and resource constraints (e.g., limited networking resources and physical constraints). As one of the essential topics in designing efficient CPSs, the controller design for CPSs, aiming to achieve secure and resource-aware control objectives under such cyber and physical issues, is very significant yet challenging. Emphasizing optimality and system constraint handling, model predictive control (MPC) is one of the most widely used control paradigms, notably famous for its successful applications in chemical process industry. However, the conventional MPC methods are not specifically tailored to tackle cyber threats and resource constraints, thus the corresponding theory and tools to design the secure and resource-aware controller are lacking and need to be developed. This dissertation focuses on developing MPC-based methodologies to address the i) secure control problem and ii) resource-aware control problem for CPSs subject to cyber threats and resource constraints. In the resource-aware control problem of CPSs, the nonlinear system with additive disturbance is considered. By using an integral-type event-triggered mechanism and an improved robustness constraint, we propose an integral-type event-triggered MPC so that smaller sampling frequency and robustness to the additive disturbance can be obtained. The sufficient conditions for guaranteeing the recursive feasibility and the closed-loop stability are established. For the secure control problem of CPSs, two aspects are considered. Firstly, to achieve the secure control objective, we design a secure dual-mode MPC framework, including a modified initial feasible set and a new positively invariant set, for constrained linear systems subject to Denial-of-Service (DoS) attacks. The exponential stability of the closed-loop system is guaranteed under several conditions. Secondly, to deal with cyber threats and take advantage of the cloud-edge computing technology, we propose a model predictive control as a secure service (MPCaaSS) framework, consisting of a double-layer controller architecture and a secure data transmission protocol, for constrained linear systems in the presence of both cyber threats and external disturbances. The rigorous recursive feasibility and robust stability conditions are established. To simultaneously address the secure and resource-aware control problems, an event-triggered robust nonlinear MPC framework is proposed, where a new robustness constraint is introduced to deal with additive disturbances, and a packet transmission strategy is designed to tackle DoS attacks. Then, an event-triggered mechanism, which accommodates DoS attacks occurring in the communication network, is proposed to reduce the communication cost for resource-constrained CPSs. The recursive feasibility and the closed-loop stability in the sense of input-to-state practical stable (ISpS) are guaranteed under the established sufficient conditions. / Graduate

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This thesis investigates autonomous landing of a micro air vehicle (MAV) on a nonstationary ground platform. Unmanned aerial vehicles (UAVs) and micro air vehicles (MAVs) are becoming every day more ubiquitous. Nonetheless, many applications still require specialized human pilots or supervisors. Current research is focusing on augmenting the scope of tasks that these vehicles are able to accomplish autonomously. Precise autonomous landing on moving platforms is essential for self-deployment and recovery of MAVs, but it remains a challenging task for both autonomous and piloted vehicles. Model Predictive Control (MPC) is a widely used and effective scheme to control constrained systems. One of its variants, output-feedback tube-based MPC, ensures robust stability for systems with bounded disturbances under system state reconstruction. This thesis proposes a MAV control strategy based on this variant of MPC to perform rapid and precise autonomous landing on moving targets whose nominal (uncommitted) trajectory and velocity are slowly varying. The proposed approach is demonstrated on an experimental setup.