A Geostatistical Approach toward Shear Wave Velocity Modeling and Uncertainty Quantification in Seismic Hazard

The ground motion parameters such as amplitude, frequency content and the duration can be affected by the local site condition and may result in amplification or de-amplification to the original bedrock motion. Shear wave velocity is an important site parameter to describe the site condition and is widely used in estimating site response, classifying sites in recent building codes and loss estimation. This dissertation is aimed at: modeling the spatial variation of shear wave velocity using geostatistical tools; improve the random field framework for estimating soil properties to account for multiple sources of data; develop finite element model to qualify the uncertainty propagation in dynamic site response; introduce the response surface concept into seismic hazard analysis and quantifying the uncertainty propagation in dynamic site response caused by the variation of shear wave velocity and design parameters. To model the spatial variation of shear wave velocity, a multiscale random field-based framework is presented and applied to map Vs30 - the time-averaged shear wave velocity in the top 30 meters of subsurface material - over extended areas. In this framework, the random field concept is employed to model the horizontal variation of shear wave velocity. Suzhou Site is selected as the research area and its measured shear wave velocity data is combined with the U.S. Geological Survey (USGS) slope-based Vs30 map for mapping the Vs30 around whole research area. Moreover, a different method of integrating multiple sources of data is used and tested based on a synthetic digital field. To quantify the uncertainty propagation in dynamic site response caused by the variation of shear wave velocity, the finite element method (FEM) is developed and combined with random field realizations of shear wave velocity profiles. A viscoelastic constitutive model is implemented in the FEM model to account for the non-linear hysteresis response of subsurface materials under cyclic loadings. The analyzed site responses, as well as the input parameters generated with Monte Carlo simulations (MCS), are then used to study the peak acceleration at site surface subjected to a given input seismic wave. Finally, the response surface method and the first order second-moment method (FOSM) are integrated into dynamic site response analysis to characterize the variation of site performance caused by spatial variation of shear wave velocity. Through illustrative examples, the effectiveness, advantage, practicability, and significance of improved random field framework and developed uncertainty propagation evaluation methodology are demonstrated

TT-adic exponential sums of polynomials in one variable

The $T$-adic exponential sum of a polynomial in one variable is studied. An explicit arithmetic polygon in terms of the highest two exponents of the polynomial is proved to be a lower bound of the Newton polygon of the $C$-function of the T-adic exponential sum. This bound gives lower bounds for the Newton polygon of the $L$-function of exponential sums of $p$-power order

INTERVENTION EFFECT OF SENSORY INTEGRATION TRAINING ON THE BEHAVIORS AND QUALITY OF LIFE OF CHILDREN WITH AUTISM

Background: Autism is a widespread developmental disorder that occurs mostly among children. Children with autism are prone to problematic behaviors due to their deficiencies in language communication and social development. Thus, children with a high degree of autism suffer lower life satisfaction. Moreover, sensory integration dysfunction is closely related to autism. Therefore, the effect of Sensory Integration Training (SIT) on the behaviors and quality of life of children with autism was explored in this study. Subjects and methods: From September 2017 to December 2018, 108 patients from Fuzhou Fourth Hospital and Xiangtan Fifth Hospital were included in the intervention group (group A) and the control group (group B), with 54 members in each group. The 54 members in group B, with an average age of 5.18±2.94, received routine treatment. In addition to the same routine treatment, the members in group B also received sensory integration training and physical exercise intervention, which lasted for three months. The Childhood Autism Rating Scale (CARS) and Autism Behavior Checklist (ABC) were used before and after the intervention experiment to evaluate the curative effect. Results: After the treatment, statistically significant differences were observed in the CARS and ABC scores (P<0.05); the total effective rate was 86.11% in group A and 64.10% in group B. The difference in the CARS score was statistically significant (P<0.05), whereas the difference in the ABC score was also statistically significant (P<0.05). In general, the difference in CARS is statistically significant. Specifically, group A is better than group B, t=3.492, df=73, and bilateral P=0.001<0.01. Conclusions: SIT intervention had a certain effect on autism and is of great value for the future development of SIT courses or intervention programs for children with autism

Visual-Inertial State Estimation With Information Deficiency

State estimation is an essential part of intelligent navigation and mapping systems where tracking the location of a smartphone, car, robot, or a human-worn device is required. For autonomous systems such as micro aerial vehicles and self-driving cars, it is a prerequisite for control and motion planning. For AR/VR applications, it is the first step to image rendering. Visual-inertial odometry (VIO) is the de-facto standard algorithm for embedded platforms because it lends itself to lightweight sensors and processors, and maturity in research and industrial development. Various approaches have been proposed to achieve accurate real-time tracking, and numerous open-source software and datasets are available. However, errors and outliers are common due to the complexity of visual measurement processes and environmental changes, and in practice, estimation drift is inevitable. In this thesis, we introduce the concept of information deficiency in state estimation and how to utilize this concept to develop and improve VIO systems. We look into the information deficiencies in visual-inertial state estimation, which are often present and ignored, causing system failures and drift. In particular, we investigate three critical cases of information deficiency in visual-inertial odometry: low texture environment with limited computation, monocular visual odometry, and inertial odometry. We consider these systems under three specific application settings: a lightweight quadrotor platform in autonomous flight, driving scenarios, and AR/VR headset for pedestrians. We address the challenges in each application setting and explore how the tight fusion of deep learning and model-based VIO can improve the state-of-the-art system performance and compensate for the lack of information in real-time. We identify deep learning as a key technology in tackling the information deficiencies in state estimation. We argue that developing hybrid frameworks that leverage its advantage and enable supervision for performance guarantee provides the most accurate and robust solution to state estimation

Online Identification of Turbogenerator\u27s Dynamics using a Neuro-Identifier

The increasing complexity of modern power systems highlights the need for effective system identification techniques for the successful control of power system. This paper proposes a robust continually online trained neuroidentifier to predict the outputs of turbogenerator - terminal voltage and speed deviation. The inputs to the neuro-identifier are the changes of the plant\u27s outputs and plant\u27s inputs. It overcomes the drawback of calculating deviation signals from reference signals for different operating points in previous work. Simulation results show that the neuro-identifier can provide accurate identification under different operating conditions. Furthermore, the neuro-identifier can learn the dynamics of the system in a short period of time, which makes it suitable for use with an online adaptive controller for the control of turbogenerators

A Unified Framework for Testing High Dimensional Parameters: A Data-Adaptive Approach

High dimensional hypothesis test deals with models in which the number of parameters is significantly larger than the sample size. Existing literature develops a variety of individual tests. Some of them are sensitive to the dense and small disturbance, and others are sensitive to the sparse and large disturbance. Hence, the powers of these tests depend on the assumption of the alternative scenario. This paper provides a unified framework for developing new tests which are adaptive to a large variety of alternative scenarios in high dimensions. In particular, our framework includes arbitrary hypotheses which can be tested using high dimensional $U$-statistic based vectors. Under this framework, we first develop a broad family of tests based on a novel variant of the $L_p$-norm with $p\in \{1,\dots,\infty\}$. We then combine these tests to construct a data-adaptive test that is simultaneously powerful under various alternative scenarios. To obtain the asymptotic distributions of these tests, we utilize the multiplier bootstrap for $U$-statistics. In addition, we consider the computational aspect of the bootstrap method and propose a novel low-cost scheme. We prove the optimality of the proposed tests. Thorough numerical results on simulated and real datasets are provided to support our theory