The COVID-19 DNA-RNA Genetic Code Analysis Using Information Theory of Double Stochastic Matrix

We present a COVID-19 DNA-RNA genetic code where A=T=U=31% and C=G=19%, which has been developed from a base matrix CUAG where C, U, A, and G are RNA bases while C, U, A, and T are DNA bases that E. Chargaff found them complementary like A=T=U=30%, and C=G=20% from his experimental results, which implied the structure of DNA double helix and its complementary combination. Unfortunately, they have not been solved mathematically yet. Therefore, in this paper, we present a simple solution by the information theory of a doubly stochastic matrix over the Shannon symmetric channel as well as prove it mathematically. Furthermore, we show that DNA-RNA genetic code is one kind of block circulant Jacket matrix. Moreover, general patterns by block circulant, upper-lower, and left-right scheme are presented, which are applied to the correct communication as well as means the healthy condition because it perfectly consists of 4 bases. Henceforth, we also provide abnormal patterns by block circulant, upper-lower, and left-right scheme, which cover the distorted signal as well as COVID-19

Model Prediction-Based Approach to Fault Tolerant Control with Applications

Abstract— Fault-tolerant control (FTC) is an integral component in industrial processes as it enables the system to continue robust operation under some conditions. In this paper, an FTC scheme is proposed for interconnected systems within an integrated design framework to yield a timely monitoring and detection of fault and reconfiguring the controller according to those faults. The unscented Kalman filter (UKF)-based fault detection and diagnosis system is initially run on the main plant and parameter estimation is being done for the local faults. This critical information\ud is shared through information fusion to the main system where the whole system is being decentralized using the overlapping decomposition technique. Using this parameter estimates of decentralized subsystems, a model predictive control (MPC) adjusts its parameters according to the\ud fault scenarios thereby striving to maintain the stability of the system. Experimental results on interconnected continuous time stirred tank reactors (CSTR) with recycle and quadruple tank system indicate that the proposed method is capable to correctly identify various faults, and then controlling the system under some conditions

Dynamic Context-guided Capsule Network for Multimodal Machine Translation

Multimodal machine translation (MMT), which mainly focuses on enhancing text-only translation with visual features, has attracted considerable attention from both computer vision and natural language processing communities. Most current MMT models resort to attention mechanism, global context modeling or multimodal joint representation learning to utilize visual features. However, the attention mechanism lacks sufficient semantic interactions between modalities while the other two provide fixed visual context, which is unsuitable for modeling the observed variability when generating translation. To address the above issues, in this paper, we propose a novel Dynamic Context-guided Capsule Network (DCCN) for MMT. Specifically, at each timestep of decoding, we first employ the conventional source-target attention to produce a timestep-specific source-side context vector. Next, DCCN takes this vector as input and uses it to guide the iterative extraction of related visual features via a context-guided dynamic routing mechanism. Particularly, we represent the input image with global and regional visual features, we introduce two parallel DCCNs to model multimodal context vectors with visual features at different granularities. Finally, we obtain two multimodal context vectors, which are fused and incorporated into the decoder for the prediction of the target word. Experimental results on the Multi30K dataset of English-to-German and English-to-French translation demonstrate the superiority of DCCN. Our code is available on https://github.com/DeepLearnXMU/MM-DCCN

Robust Multiple Object Tracking Using ReID features and Graph Convolutional Networks

Deep Learning allows for great advancements in computer vision research and development. An area that is garnering attention is single object tracking and multi-object tracking. Object tracking continues to progress vastly in terms of detection and building re-identification features, but more effort needs to be dedicated to data association. In this thesis, the goal is to use a graph neural network to combine the information from both the bounding box interaction as well as the appearance feature information in a single association chain. This work is designed to explore the usage of graph neural networks and their message passing abilities during tracking to come up with stronger data associations. This thesis combines all steps from detection through association using state of the art methods along with novel re-identification applications. The metrics used to determine success are Multi-Object Tracking Accuracy (MOTA), Multi-Object Tracking Precision (MOTP), ID Switching (IDs), Mostly Tracked, and Mostly Lost. Within this work, the combination of multiple appearance feature vectors to create a stronger single feature vector is explored to improve accuracy. Different types of data augmentations such as random erase and random noise are explored and their results are examined for effectiveness during tracking. A unique application of triplet loss is also implemented to improve overall network performance as well. Throughout testing, baseline models have been improved upon and each successive improvement is added to the final model output. Each of the improvements results in the sacrifice of some performance metrics but the overall benefits outweigh the costs. The datasets used during this thesis are the UAVDT Benchmark and the MOT Challenge Dataset. These datasets cover aerial-based vehicle tracking and pedestrian tracking. The UAVDT Benchmark and MOT Challenge dataset feature crowded scenery as well as substantial object overlap. This thesis demonstrates the increased matching capabilities of a graph network when paired with a robust and accurate object detector as well as an improved set of appearance feature vectors

Fault detection and fault tolerant control in wind turbines

Renewable energy is an important sustainable energy in the world. Up to now, as an essential part of low emissions energy in a lot of countries, renewable energy has been important to the national energy security, and played a significant role in reducing carbon emissions. It comes from natural resources, such as wind, solar, rain, tides, biomass, and geothermal heat. Among them, wind energy is rapidly emerging as a low carbon, resource efficient, cost effective sustainable technology in the world. Due to the demand of higher power production installations with less environmental impacts, the continuous increase in size of wind turbines and the recently developed offshore (floating) technologies have led to new challenges in the wind turbine systems.Wind turbines (WTs) are complex systems with large flexible structures that work under very turbulent and unpredictable environmental conditions for a variable electrical grid. The maximization of wind energy conversion systems, load reduction strategies, mechanical fatigue minimization problems, costs per kilowatt hour reduction strategies, reliability matters, stability problems, and availability (sustainability) aspects demand the use of advanced (multivariable and multiobjective) cooperative control systems to regulate variables such as pitch, torque, power, rotor speed, power factors of every wind turbine, etc. Meanwhile, with increasing demands for efficiency and product quality and progressing integration of automatic control systems in high-cost and safety-critical processes, the fields of fault detection and isolation (FDI) and fault tolerant control (FTC) play an important role. This thesis covers the theoretical development and also the implementation of different FDI and FTC techniques in WTs. The purpose of wind turbine FDI systems is to detect and locate degradations and failures in the operation of WT components as early as possible, so that maintenance operations can be performed in due time (e.g., during time periods with low wind speed). Therefore, the number of costly corrective maintenance actions can be reduced and consequently the loss of wind power production due to maintenance operations is minimized. The objective of FTC is to design appropriate controllers such that the resulting closed-loop system can tolerate abnormal operations of specific control components and retain overall system stability with acceptable system performance. Different FDI and FTC contributions are presented in this thesis and published in different JCR-indexed journals and international conference proceedings. These contributions embrace a wide range of realistic WTs faults as well as different WTs types (onshore, fixed offshore, and floating). In the first main contribution, the normalized gradient method is used to estimate the pitch actuator parameters to be able to detect faults in it. In this case, an onshore WT is used for the simulations. Second contribution involves not only to detect faults but also to isolate them in the pitch actuator system. To achieve this, a discrete-time domain disturbance compensator with a controller to detect and isolate pitch actuator faults is designed. Third main contribution designs a super-twisting controller by using feedback of the fore-aft and side-to-side acceleration signals of the WT tower to provide fault tolerance capabilities to the WT and improve the overall performance of the system. In this instance, a fixed-jacket offshore WT is used. Throughout the aforementioned research, it was observed that some faults induce to saturation of the control signal leading to system instability. To preclude that problem, the fourth contribution of this thesis designs a dynamic reference trajectory based on hysteresis. Finally, the fifth and last contribution is related to floating-barge WTs and the challenges that this WTs face. The performance of the proposed contributions are tested in simulations with the aero-elastic code FAST.La energía renovable es una energía sustentable importante en el mundo. Hasta ahora, como parte esencial de la energía de bajas emisiones en muchos países, la energía renovable ha sido importante para la seguridad energética nacional, y jugó un papel importante en la reducción de las emisiones de carbono. Proviene de recursos naturales, como el viento, la energía solar, la lluvia, las mareas, la biomasa y el calor geotérmico. Entre ellos, la energía eólica está emergiendo rápidamente como una tecnología sostenible de bajo carbono, eficiente en el uso de los recursos y rentable en el mundo. Debido a la demanda de instalaciones de producción de mayor potencia con menos impactos ambientales, el aumento continuo en el tamaño de las turbinas eólicas y las tecnologías offshore (flotantes) recientemente desarrolladas han llevado a nuevos desafíos en los sistemas de turbinas eólicas. Las turbinas eólicas son sistemas complejos con grandes estructuras flexibles que funcionan en condiciones ambientales muy turbulentas e impredecibles para una red eléctrica variable. La maximización de los sistemas de conversión de energía eólica, los problemas de minimización de la fatiga mecánica, los costos por kilovatios-hora de estrategias de reducción, cuestiones de confiabilidad, problemas de estabilidad y disponibilidad (sostenibilidad) exigen el uso de sistemas avanzados de control cooperativo (multivariable y multiobjetivo) para regular variables tales como paso, par, potencia, velocidad del rotor, factores de potencia de cada aerogenerador, etc. Mientras tanto, con las crecientes demandas de eficiencia y calidad del producto y la progresiva integración de los sistemas de control automático en los procesos de alto costo y de seguridad crítica, los campos de detección y aislamiento de fallos (FDI) y control tolerante a fallos (FTC) juegan un papel importante. Esta tesis cubre el desarrollo teórico y también la implementación de diferentes técnicas de FDI y FTC en turbinas eólicas. El propósito de los sistemas FDI es detectar y ubicar las degradaciones y fallos en la operación de los componentes tan pronto como sea posible, de modo que las operaciones de mantenimiento puedan realizarse a su debido tiempo (por ejemplo, durante periodos con baja velocidad del viento). Por lo tanto, se puede reducir el número de costosas acciones de mantenimiento correctivo y, en consecuencia, se reduce al mínimo la pérdida de producción de energía eólica debido a las operaciones de mantenimiento. El objetivo de la FTC es diseñar controladores apropiados de modo que el sistema de bucle cerrado resultante pueda tolerar operaciones anormales de componentes de control específicos y retener la estabilidad general del sistema con un rendimiento aceptable del sistema. Diferentes contribuciones de FDI y FTC se presentan en esta tesis y se publican en diferentes revistas indexadas a JCR y en congresos internacionales. Estas contribuciones abarcan una amplia gama de fallos WTs realistas, así como diferentes tipos de turbinas (en tierra, en alta mar ancladas al fondo del mar y flotantes). El rendimiento de las contribuciones propuestas se prueba en simulaciones con el código aeroelástico FAST.Postprint (published version

Approximate Sampling for Doubly-intractable Distributions and Modeling Choice Interdependence in a Social Network.

With the advent and continuous growth of social media such as Facebook and Twitter, innovative advertising strategies have been invented to capitalize on the social networks embedding on these websites. Users’ behavior thus becomes more visible to their friends, which may facilitate social influences. The need to examine and justify the necessity for new marketing tools calls for statistical models that are capable of measuring and quantifying the effect of social network in this process. Random field models offer a class of statistical models to realize this objective. However, the applicability of many models, such as Markov random fields, is hampered by the existence of intractable normalizing constants. In this thesis, we propose an efficient Markov chain Monte Carlo (MCMC) algorithm to tackle this problem, which allows researchers to fit realistic models to interdependent choice data in a Bayesian framework. The theoretical and empirical studies show that our algorithm is asymptotically consistent with good mixing properties, and particularly efficient on large data sets. In addition, we propose a Metropolis-Hastings algorithm to efficiently simulate social networks from exponential random graph models, which are special cases of random field models. To better understand how consumers make choices in a network, we conducted a novel field experiment that mimics interactive advertising on Facebook. A Markov random field, estimated by the above MCMC algorithm, and a discrete-time Markov chain are applied to model two different types of data. We are able to build a theoretical connection between the two models. We propose model specifications that can accommodate multiple sources of dependence and asymmetric social interactions. Our findings suggest that consumers rely on choices of others both at the micro (friends) and macro (a reference group) levels in making their own decisions. Finally, we study the problem of estimating ratio of normalizing constants, which has a wide range of applications, including the calculation of Bayes factor, a key quantity in Bayesian inference. We propose a flexible implementation of the path sampling identity (Gelman and Meng 1998), which generates a consistent estimator. The preliminary simulation study indicates a good potential of the method.Ph.D.StatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86260/1/jingjw_1.pd