Browsing by Author "Guo, Jia"

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    Convergence of Kernel Methods for Modeling and Estimation of Dynamical Systems
    Guo, Jia (Virginia Tech, 2021-01-14)
    As data-driven modeling becomes more prevalent for representing the uncertain dynamical systems, concerns also arise regarding the reliability of these methods. Recent developments in approximation theory provide a new perspective to studying these problems. This dissertation analyzes the convergence of two kernel-based, data-driven modeling methods, the reproducing kernel Hilbert space (RKHS) embedding method and the empirical-analytical Lagrangian (EAL) model. RKHS embedding is a non-parametric extension of the classical adaptive estimation method that embeds the uncertain function in an RKHS, an infinite-dimensional function space. As a result the original uncertain system of ordinary differential equations are understood as components of a distributed parameter system. Similarly to the classical approach for adaptive estimation, a novel definition of persistent excitation (PE) is introduced, which is proven to guarantee the pointwise convergence of the estimate of function over the PE domain. The finite-dimensional approximation of the RKHS embedding method is based on approximant spaces that consist of kernel basis functions centered at samples in the state space. This dissertation shows that explicit rate of convergence of the RKHS embedding method can be derived by choosing specific types of native spaces. In particular, when the RKHS is continuously embedded in a Sobolev space, the approximation error is proven to decrease at a rate determined by the fill distance of the samples in the PE domain. This dissertation initially studies scalar-valued RKHS, and subsequently the RKHS embedding method is extended for the estimation of vector-valued uncertain functions. Like the scalar-valued case, the formulation of vector-valued RKHS embedding is proven to be well-posed. The notion of partial PE is also generalized, and it is shown that the rate of convergence derived for the scalar-valued approximation still holds true for certain separable operator-valued kernels. The second part of this dissertation studies the EAL modeling method, which is a hybrid mechanical model for Lagrangian systems with uncertain holonomic constraints. For the singular perturbed form of the system, the kernel method is applied to approximate a penalty potential that is introduced to approximately enforce constraints. In this dissertation, the accuracy confidence function is introduced to characterize the constraint violation of an approximate trajectory. We prove that the confidence function can be decomposed into a term representing the bias and another term representing the variation. Numerical simulations are conducted to examine the factors that affect the error, including the spectral filtering, the number of samples, and the accumulation of integration error.
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    Motion Prediction of Human Wearing Powered Exoskeleton
    Jin, Xin; Guo, Jia; Li, Zhong; Wang, Ruihao (Hindawi, 2020-12-22)
    With the development of powered exoskeleton in recent years, one important limitation is the capability of collaborating with human. Human-machine interaction requires the exoskeleton to accurately predict the human motion of the upcoming movement. Many recent works implement neural network algorithms such as recurrent neural networks (RNN) in motion prediction. However, they are still insufficient in efficiency and accuracy. In this paper, a Gaussian process latent variable model (GPLVM) is employed to transform the high-dimensional data into low-dimensional data. Combining with the nonlinear autoregressive (NAR) neural network, the GPLVM-NAR method is proposed to predict human motions. Experiments with volunteers wearing powered exoskeleton performing different types of motion are conducted. Results validate that the proposed method can forecast the future human motion with relative error of 2%∼5% and average calculation time of 120 s∼155 s, depending on the type of different motions.
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    Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes
    Guo, Jia; Khan, Jahangir; Pradhan, Sumit; Shahi, Dipendra; Khan, Naeem; Avci, Muhsin; McBreen, Jordan; Harrison, Stephen; Brown-Guedira, Gina L.; Murphy, Joseph Paul; Johnson, Jerry W.; Mergoum, Mohamed; Mason, Richard Esten; Ibrahim, Amir M. H.; Sutton, Russell L.; Griffey, Carl A.; Babar, Md Ali (MDPI, 2020-10-28)
    The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.
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    Trust-based Service Management of Internet of Things Systems and Its Applications
    Guo, Jia (Virginia Tech, 2018-04-18)
    A future Internet of Things (IoT) system will consist of a huge quantity of heterogeneous IoT devices, each capable of providing services upon request. It is of utmost importance for an IoT device to know if another IoT service is trustworthy when requesting it to provide a service. In this dissertation research, we develop trust-based service management techniques applicable to distributed, centralized, and hybrid IoT environments. For distributed IoT systems, we develop a trust protocol called Adaptive IoT Trust. The novelty lies in the use of distributed collaborating filtering to select trust feedback from owners of IoT nodes sharing similar social interests. We develop a novel adaptive filtering technique to adjust trust protocol parameters dynamically to minimize trust estimation bias and maximize application performance. Our adaptive IoT trust protocol is scalable to large IoT systems in terms of storage and computational costs. We perform a comparative analysis of our adaptive IoT trust protocol against contemporary IoT trust protocols to demonstrate the effectiveness of our adaptive IoT trust protocol. For centralized or hybrid cloud-based IoT systems, we propose the notion of Trust as a Service (TaaS), allowing an IoT device to query the service trustworthiness of another IoT device and also report its service experiences to the cloud. TaaS preserves the notion that trust is subjective despite the fact that trust computation is performed by the cloud. We use social similarity for filtering recommendations and dynamic weighted sum to combine self-observations and recommendations to minimize trust bias and convergence time against opportunistic service and false recommendation attacks. For large-scale IoT cloud systems, we develop a scalable trust management protocol called IoT-TaaS to realize TaaS. For hybrid IoT systems, we develop a new 3-layer hierarchical cloud structure for integrated mobility, service, and trust management. This architecture supports scalability, reconfigurability, fault tolerance, and resiliency against cloud node failure and network disconnection. We develop a trust protocol called IoT-HiTrust leveraging this 3-layer hierarchical structure to realize TaaS. We validate our trust-based IoT service management techniques developed with real-world IoT applications, including smart city air pollution detection, augmented map travel assistance, and travel planning, and demonstrate that our trust-based IoT service management techniques outperform contemporary non-trusted and trust-based IoT service management solutions.