In this paper, we combine inertial sensing and sensor network technology to create a pedestrian dead reckoning system. The core of the system is a lightweight sensor-and-wireless-embedded device ...called NavMote that is carried by a pedestrian. The NavMote gathers information about pedestrian motion from an integrated magnetic compass and accelerometers. When the NavMote comes within range of a sensor network (composed of NetMotes), it downloads the compressed data to the network. The network relays the data via a RelayMote to an information center where the data are processed into an estimate of the pedestrian trajectory based on a dead reckoning algorithm. System details including the NavMote hardware/software, sensor network middleware services, and the dead reckoning algorithm are provided. In particular, simple but effective step detection and step length estimation methods are implemented in order to reduce computation, memory, and communication requirements on the Motes. Static and dynamic calibrations of the compass data are crucial to compensate the heading errors. The dead reckoning performance is further enhanced by wireless telemetry and map matching. Extensive testing results show that satisfactory tracking performance with relatively long operational time is achieved. The paper also serves as a brief survey on pedestrian navigation systems, sensors, and techniques.
Permanent magnet synchronous motor and power electronics-based three-phase inverter are the major components in the modern industrial electric drive system, such as electrical actuators in an ...all-electric subsea Christmas tree. Inverters are the weakest components in the drive system, and power switches are the most vulnerable components in inverters. Fault detection and diagnosis of inverters are extremely necessary for improving drive system reliability. Motivated by solving the uncertainty problem in fault diagnosis of inverters, which is caused by various reasons, such as bias and noise of sensors, this paper proposes a Bayesian network-based data-driven fault diagnosis methodology of three-phase inverters. Two output line-to-line voltages for different fault modes are measured, the signal features are extracted using fast Fourier transform, the dimensions of samples are reduced using principal component analysis, and the faults are detected and diagnosed using Bayesian networks. Simulated and experimental data are used to train the fault diagnosis model, as well as validate the proposed fault diagnosis methodology.
Evolutionary Structural Optimization (ESO) and its later version bi-directional ESO (BESO) have gained widespread popularity among researchers in structural optimization and practitioners in ...engineering and architecture. However, there have also been many critical comments on various aspects of ESO/BESO. To address those criticisms, we have carried out extensive work to improve the original ESO/BESO algorithms in recent years. This paper summarizes latest developments in BESO for stiffness optimization problems and compares BESO with other well-established optimization methods. Through a series of numerical examples, this paper provides answers to those critical comments and shows the validity and effectiveness of the evolutionary structural optimization method.
Bayesian Networks in Fault Diagnosis Cai, Baoping; Huang, Lei; Xie, Min
IEEE transactions on industrial informatics,
10/2017, Letnik:
13, Številka:
5
Journal Article
Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various ...uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.
If there are datasets, too large to fit into a single computer or too expensive for a computationally intensive data analysis, what should we do? We propose a split-and-conquer approach and ...illustrate it using several computationally intensive penalized regression methods, along with a theoretical support. We show that the split-and-conquer approach can substantially reduce computing time and computer memory requirements. The proposed methodology is illustrated numerically using both simulation and data examples.
Despite declines in heart failure morbidity and mortality with current therapies, rehospitalization rates remain distressingly high, substantially affecting individuals, society, and the economy. As ...a result, the need for new therapeutic advances and novel medical devices is urgent. Disease-related left ventricular remodeling is a complex process involving cardiac myocyte growth and death, vascular rarefaction, fibrosis, inflammation, and electrophysiological remodeling. Because these events are highly interrelated, targeting a single molecule or process may not be sufficient. Here, we review molecular and cellular mechanisms governing pathological ventricular remodeling.
Metal‐halide perovskites (MHPs) are regarded as ideal photovoltaic materials because of their variable crystal material composition and superb optoelectronic performance. However, this compositional ...variability results in a complicated crystallization process during MHP film fabrication, leading to reduced MHP film crystallinity and decreased performance of devices containing such films. The crystallization kinetics of MHPs have therefore been extensively explored in efforts to determine the effect of crystallization properties on MHP film properties and figure out the corresponding modulating strategies. Here, the first comprehensive review of reported studies on the crystallization properties of 3D MHPs is presented. The experimental and theoretical research on 3D MHP crystallization kinetics is systematically surveyed, and the methods that are used for modulating MHP crystallization are summarized, namely, solution engineering, compositional engineering, interfacial engineering, and additive passivation. Meanwhile, the prospects and current challenges in revealing perovskite crystallization kinetics are suggested.
The vast literature on experimental and theoretical research on the nucleation and growth of metal‐halide perovskite films, as well as the relationships between film properties and crystallization modulating methods including solution engineering, composition engineering, interface engineering, and additive passivation are systematically reviewed. This work consolidates the research on metal‐halide perovskite crystallization kinetics, and highlights new and promising areas of study.
In frequentisi inference, we commonly use a single point (point estimator) or an interval (confidence interval/"interval estimator") to estimate a parameter of interest. A very simple question is: ...Can we also use a distribution function ("distribution estimator") to estimate a parameter of interest in frequentisi inference in the style of a Bayesian posterior? The answer is affirmative, and confidence distribution is a natural choice of such a "distribution estimator". The concept of a confidence distribution has a long history, and its interpretation has long been fused with fiducial inference. Historically, it has been misconstrued as a fiducial concept, and has not been fully developed in the frequentist framework. In recent years, confidence distribution has attracted a surge of renewed attention, and several developments have highlighted its promising potential as an effective inferential tool. This article reviews recent developments of confidence distributions, along with a modern definition and interpretation of the concept. It includes distributional inference based on confidence distributions and its extensions, optimality issues and their applications. Based on the new developments, the concept of a confidence distribution subsumes and unifies a wide range of examples, from regular parametric (fiducial distribution) examples to bootstrap distributions, significance (p-value) functions, normalized likelihood functions, and, in some cases, Bayesian priors and posteriors. The discussion is entirely within the school of frequentist inference, with emphasis on applications providing useful statistical inference tools for problems where frequentist methods with good properties were previously unavailable or could not be easily obtained. Although it also draws attention to some of the differences and similarities among frequentist, fiducial and Bayesian approaches, the review is not intended to re-open the philosophical debate that has lasted more than two hundred years. On the contrary, it is hoped that the article will help bridge the gaps between these different statistical procedures. II est courant, en inference fréquentielle, d'utiliser un point unique (une estimation ponctuelle) ou un intervalle (intervalle de confiance) dans le but d'estimer un paramètre d'intérêt. Une question très simple se pose: peut-on également utiliser, dans le même but, et dans la même optique fréquentielle, à la façon dont les Bayésiens utilisent une loi a posteriori, une distribution de probabilité? La réponse est affirmative, et les distributions de confiance apparaissent comme un choix naturel dans ce contexte. Le concept de distribution de confiance a une longue histoire, longtemps associée, à tort, aux théories d'inférence fiducielle, ce qui a compromis son développement dans l'optique fréquentielle. Les distributions de confiance ont récemment attiré un regain d'intérêt, et plusieurs résultats ont mis en évidence leur potentiel considérable en tant qu'outil inférentiel. Cet article présente une définition moderne du concept, et examine les ses évolutions récentes. Il aborde les méthodes d'inférence, les problèmes d'optimalité, et les applications. A la lumière de ces nouveaux développements, le concept de distribution de confiance englobe et unifie un large éventail de cas particuliers, depuis les exemples paramétriques réguliers (distributions fiducielles), les lois de rééchantillonnage, les p-valeurs et les fonctions de vraisemblance normalisées jusqu'aux a priori et posteriori bayésiens. La discussion est entièrement menée d'un point de vue frequentici, et met l'accent sur les applications dans lesquelles les solutions fréquentielles sont inexistantes ou d'une application difficile. Bien que nous attirions également l'attention sur les similitudes et les différences que présentent les approches fréquentielle, fiducielle, et Bayésienne, notre intention n'est pas de rouvrir un débat philosophique qui dure depuis près de deux cents ans. Nous espérons bien au contraire contribuer à combler le fossé qui existe entre les différents points de vue.
Asthma-associated risk for COVID-19 development Skevaki, Chrysanthi; Karsonova, Antonina; Karaulov, Alexander ...
Journal of allergy and clinical immunology,
12/2020, Letnik:
146, Številka:
6
Journal Article
Recenzirano
Odprti dostop
The newly described severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for a pandemic (coronavirus disease 2019 COVID-19). It is now well established that certain ...comorbidities define high-risk patients. They include hypertension, diabetes, and coronary artery disease. In contrast, the context with bronchial asthma is controversial and shows marked regional differences. Because asthma is the most prevalent chronic inflammatory lung disease worldwide and SARS-CoV-2 primarily affects the upper and lower airways leading to marked inflammation, the question arises about the possible clinical and pathophysiological association between asthma and SARS-CoV-2/COVID-19. Here, we analyze the global epidemiology of asthma among patients with COVID-19 and propose the concept that patients suffering from different asthma endotypes (type 2 asthma vs non–type 2 asthma) present with a different risk profile in terms of SARS-CoV-2 infection, development of COVID-19, and progression to severe COVID-19 outcomes. This concept may have important implications for future COVID-19 diagnostics and immune-based therapy developments.
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