Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifiability of Additive, Time-Varying Actuator and Sensor Faults by State Augmentation

Recent work has provided a set of necessary and sucient conditions for identifiability of additive step faults (e.g., lock-in-place actuator faults, constant bias in the sensors) using state augmentation. This paper extends these results to an important class of faults which may affect linear, time-invariant systems. In particular, the faults under consideration are those which vary with time and affect the system dynamics additively. Such faults may manifest themselves in aircraft as, for example, control surface oscillations, control surface runaway, and sensor drift. The set of necessary and sucient conditions presented in this paper are general, and apply when a class of time-varying faults affects arbitrary combinations of actuators and sensors. The results in the main theorems are illustrated by two case studies, which provide some insight into how the conditions may be used to check the theoretical identifiability of fault configurations of interest for a given system. It is shown that while state augmentation can be used to identify certain fault configurations, other fault configurations are theoretically impossible to identify using state augmentation, giving practitioners valuable insight into such situations. That is, the limitations of state augmentation for a given system and configuration of faults are made explicit. Another limitation of model-based methods is that there can be large numbers of fault configurations, thus making identification of all possible configurations impractical. However, the theoretical identifiability of known, credible fault configurations can be tested using the theorems presented in this paper, which can then assist the efforts of fault identification practitioners.
Closed loop control of the sawtooth instability in nuclear fusion

In nuclear fusion the sawtooth instability is an important plasma phenomenon, having both positive and negative effects on the tokamak plasma. Control of its period is essential in future nuclear fusion reactors. This paper presents a control oriented model of the sawtooth instability, with current drive as input and sawtooth period as output, and validates it with a more extended model and experimental data. This model is numerically implemented and combined with PI-controllers in a feedback loop. Simulations show that closed loop control of the system to a desired sawtooth period is indeed possible, even when time delay is added to the feedback loop.
Texture Analysis for Recognition of Satellite Cloud Images Using Orthogonal Polynomial Model

: The main aim of this work is to classify the satellite cloud images with high accuracy and good recognition rate using texture as a feature. This work is based on Orthogonal Polynomial Transformation (OPT) to extort the low level feature like texture. Texture is a central feature especially for classifying homogeneous images. For achieving high accuracy the multi resolution technique is used with rotation invariant characteristics. Multiresolution is achieved in some existing methods with high complexity rate. In the proposed work complexity level is reduced by means of reordering coefficients in the form of sub bands. Then, the global feature is constructed and the optimized result is achieved for classifying the cloud.
Economic management, income distribution, and poverty in Jamaica

Introduction The Decades of the 1950s and 1960s: An Overview The 1970s: A Period of Decline The Early 1980s: A Brief Recovery 1981-83 1984-86: Vigorous Adjustment and Stagflation Income Distribution during the Years of High Growth: 1958-72 Income Distribution during the Years of Decline: 1972-85 The Impact of Government Expenditure in the 1980s Food Consumption and the Impact of Poverty on Children Bibliography Index
Ontology-Based Recommender for Distributed Machine Learning Environment

Domain experts in different areas have a large number of options for approaching their specific data analysis problem. In exploration of large data sets on HPC systems, choosing which method to use, or how to tune the parameters of an algorithm to achieve good results are challenging tasks for data analysts themselves. In this paper, we propose a recommendation module for a distributed machine learning environment aiming at helping the end-users to obtain optimized results for their data analysis / machine learning problem.
Inference and Explanation in Counterfactual Reasoning

This article reports results from two studies of how people answer counterfactual questions about simple machines. Participants learned about devices that have a specific configuration of components, and they answered questions of the form "If component X had not operated [failed], would component Y have operated? " The data from these studies indicate that participants were sensitive to the way in which the antecedent state is described-whether component X "had not operated" or "had failed. " Answers also depended on whether the device is deterministic or probabilistic--whether X's causal parents "always" or only "usually" cause X to operate. Participants' explanations of their answers often invoked non-operation of causally prior components or unreliability of prior connections. They less often mentioned independence from these causal elements.
A geometrically simple benchmark problem for negative index metamaterial homogenization

The proposed model problem is geometrically very simple, with 2D circular inclusions, but still feature-rich enough to be representative for a class of negative-index metamaterial slabs with resonant magnetic and non-resonant electric response. We discuss the usefulness of this model problem for benchmarking purposes and show some numerical results compared with the quasistatic prediction.
Dielectric and viscoelastic properties of KNbO3 doped BaTiO3

Fine grain (1–2 μm) 2%, 3% KNbO3-BaTiO3 ceramics have been synthesized via the solid state reaction method. Fine and coarse grain ceramics sintered at different temperatures exhibit peaks in dielectric constant and internal friction in the vicinity of transition temperatures. Doping lowered the Curie point and raised the temperatures for the structural transformations between orthorhombic and rhombohedral symmetry and broadened the response near the transformations. Dielectric and viscoelastic responses sharpened with increasing sintering cycles. This effect is attributed to a reduced core-shell effect. Doped ceramic exhibited a relaxation peak due to oxygen vacancy with a similar activation energy and relaxation time as pure material. The steep increase in dielectric constant and internal friction in the ferroelectric phase and the reduced dielectric and mechanical anomaly in the vicinity of the transformations in the fine grain ceramics are attributed to the internal stress built up at the grain boundar.
Channel predictive proportional fair scheduling

Recent work on channel modeling and prediction has shown the feasibility of predicting the mobile radio channel, quite accurately, several milliseconds ahead in time for realistic Doppler spreads. Motivated by these results we consider opportunistic scheduling algorithms that exploit both current and future channel estimates. We demonstrate how this extra channel information can be used to improve the scheduling. Simulations show that the proposed algorithm can improve the inherent tradeoff between throughput, fairness and delay. The current approach builds on proportional fair scheduling but can also be generalized to other criteria.
Energy and its double: a case-study in Critical Multimodal Discourse Analysis

This chapter provides a multimodal analysis of a printed advert campaign focusing on corporate identity. The paper’s main goal is to analyse how the interplay between visual and verbal modes of communication constructs complex layers of meaning in Total’s 2005 advertising campaign and how different affordances of verbal and visual communication are co-deployed in such a way as to enhance the adverts’ main claims. In particular, the paper investigates how these affordances multiply the layers of meaning, creating a polysemous message that can be interpreted in different ways by different groups of stakeholders. The main tenet of this study is that visual and verbal work together to contribute to the adverts’ claims and their construction of corporate identity. While foregrounding different aspects of semiotic resources and tapping into different but complementary cultural stereotypes, visual and verbal components create a synergic and integrated means to communicate ideological consensus and ‘narcotize’ potentially adverse aspects relating to the company’s practices. The study builds on work by Vasta (2000, 2001, 2002, 2004) in multimodal analysis and in the field of corporate advertising (2005 and this volume; see also Bortoluzzi & Vasta, 2007): the focus of these studies relates to companies manufacturing or dealing in goods potentially harmful to the environment or to local communities. The article begins by presenting the theoretical background, the framework of analysis and the type of data (the print advertisements of the 2005 Total campaign); this is folEnergy and its double: a case-study in Critical Multimodal Discourse Analysis
Diet-Induced Increases in Adiposity, but Not Plasma Lipids, Promote Macrophage Infiltration Into White Adipose Tissue

Obesity, hyperlipidemia, and insulin resistance are cardinal features of the metabolic syndrome and individually increase the risk for developing diabetes and cardiovascular disease, a risk that is amplified when they are simultaneously present. It is becoming increasingly clear that macrophages can infiltrate white adipose tissue (WAT) in the obese state, and their presence is associated with pathophysiological consequences of obesity, such as inflammation and insulin resistance. To determine whether hyperlipidemia could potentiate macrophage infiltration into WAT in the presence of obesity, obesity-prone agouti yellow mice (Ay/a) on a hyperlipidemia-prone LDL receptor (LDLR)–deficient (LDLR−/−) background were placed on chow or Western diet. In addition, Ay/a mice that were LDLR sufficient were also placed on Western diet. Both genetics and diet increased the degree of adiposity; however, plasma lipids were elevated only in the Western diet–fed LDLR−/− mice. The extent of macrophage accumulation in WAT correlated with the degree of adiposity. However, hyperlipidemia did not impact macrophage recruitment to WAT or the downstream metabolic consequences of macrophage accumulation in WAT, such as inflammation and insulin resistance. These data have important implications for the pathogenesis of diet-induced obesity in humans, even when plasma lipid abnormalities are not present.
Activation of Right Ventral Prefrontal Cortex Using a Predictive Cue during Visual Spatial Orienting of Attentional Processes: An fMRI Study

Visual spatial orienting of attention can be investigated with location-cueing paradigms in which a cue provides correct information about the location of the upcoming target. Target detection is facilitated when the target appears at the expected cued location. In this study, we examined the brain activation of the spatial orienting response based on attentional “benefits. ” During an fMRI experiment, two types of attentional tasks were used. Both predictive and nonpredictive cues were used and followed by an upcoming target. Behavioral data showed a faster reaction time with the predictive cue when compared with that of the nonpredictive cue. The fMRI results of these two tasks were compared, whereby isolated brain areas activated when the targets appeared at the attended position after a specific spatial expectation was induced by the cue were compared with when equivalent targets appeared after no spatial expectation was induced by the cue. The results showed that the right ventral prefrontal cortex was activated to a similar degree as the dorsal frontoparietal spatial attentional network.
DM-AMS: employing data mining techniques for alert management

Alert management plays a critical role in many application domains including homeland security and natural disaster management, to allow timely and well-informed decisions. The major challenge faced by these systems is that the number of incoming alarms is overwhelming and some of the alarms are false positives. In this paper, we present an alert management system (AMS) that generates meaningful alerts from alarms received from different sensors. The alert generation module of our system (i) flags and eliminates potential false positives by characterizing the region into uniformly behaving neighborhoods, (ii) generates aggregated alerts from the alarms by employing density based clustering techniques and identifying the overlap among clusters, and (iii) identifies the dynamic flow of the alerts by integrating scientific models that characterize the behavior of sensor parameters. Once the alerts are generated our customized dissemination module disperses the alerts on the need-to-know basis to the individuals and agencies involved. This module adheres to the National Incident Management System (NIMS) and the National Response plan (NRP) protocols. To implement these protocols, we utilize the Common Alerting Protocol (CAP), which is an XML nonproprietary data interchange format. Finally, our GIS module displays the alerts through a user-friendly interface.
Local Tissue Growth Patterns Underlying Normal Fetal Human Brain Gyrification Quantified In Utero

Existing knowledge of growth patterns in the living fetal human brain is based upon in utero imaging studies by magnetic resonance imaging (MRI) and ultrasound, which describe overall growth and provide mainly qualitative findings. However, formation of the complex folded cortical structure of the adult brain requires, in part, differential rates of regional tissue growth. To better understand these local tissue growth patterns, we applied recent advances in fetal MRI motion correction and computational image analysis techniques to 40 normal fetal human brains covering a period of primary sulcal formation (20–28 gestational weeks). Growth patterns were mapped by quantifying tissue locations that were expanding more or less quickly than the overall cerebral growth rate, which reveal increasing structural complexity. We detected increased local relative growth rates in the formation of the precentral and postcentral gyri, right superior temporal gyrus, and opercula, which differentiated between the constant growth rate in underlying cerebral mantle and the accelerating rate in the cortical plate undergoing folding. Analysis focused on the cortical plate revealed greater volume increases in parietal and occipital regions compared to the frontal lobe. Cortical plate growth patterns constrained to narrower age ranges showed that gyrification, reflected by greater growth rates, was more pronounced after 24 gestational weeks. Local hemispheric volume asymmetry was located in the posterior peri-Sylvian area associated with structural lateralization in the mature brain. These maps of fetal brain growth patterns construct a spatially specific baseline of developmental biomarkers with which to correlate abnormal development in the human.
On Artificial Structural Unemployment and Endogenous Minimum Wage

We construct a novel model of equilibrium unemployment. Specifically, in a dynamic micro based model, we illustrate that rational and profit maximizing firms can choose to pay wages that exceed the market clearing level to restrict competition. Resulting unemployment and above market clearing wages are both shown to be a part of equilibrium and to persist idifinitely. In addition, we provide a new rationale for the prevalence of minimum wage legislation in most economies. In particular, we show that minimum wage regulation can be be universally favored by all producers while being resnted by employees.
Preferred - Habitat Investors and the US Term Structure of Real Rates

We estimate structurally a model of the term structure of interest rates that is consistent with no arbitrage but allows for demand pressures. The term structure in our model is determined through the interaction of risk-averse arbitrageurs and preferred-habitat investors with preferences for specific maturities. The model is estimated on US real rates during the 2000s and allows for two factors: one corresponding to the short rate and one to preferred - habitat demand. We find that the puzzling drop in long rates during 2004-05 (Greenspan conundrum) is driven by the demand factor. International reserves, foreign official holdings of longer-term US Treasuries may all be proxies for the preferred - habitat demand factor. For example, foreign purchases in the year to July 2004 appear to have lowered the ten-year rate by about 100 basis points. Foreign purchases have larger effects following periods when arbitrageurs have lost money.
Anima Studio: A Platform for Character Motion Synthesis and Interaction

The content generation of character animation is a hard task, because it has so many time consuming processes, and needs highly skilled animators. In this paper, an integrated visual platform has been proposed, which contains two key components, one is motion synthesis adapting to off-line fields, the other is motion interaction for real-time simulation in dynamic environments. Supplied with some secondary toolkits, which support WYSWYG (what you see is what you get), this platform becomes a full pipeline for character animation generations. The application fields contain digital games, visual effects, simulation, education, so on and forth.
Discovering Common Ground: How Future Search Conferences Bring People Together to Achieve Breakthrough Innovation, Empowerment, Shared Vision and Collaborative Action

Dear readers, when you are hunting the new book collection to read this day, discovering common ground how future search conferences bring people together to achieve breakthrough innovation empowerment shared vision and collaborative action can be your referred book. Yeah, even many books are offered, this book can steal the reader heart so much. The content and theme of this book really will touch your heart. You can find more and more experience and knowledge how the life is undergone.
Surface Classification Using Conformal Structures

3D surface classification is a fundamental problem in computer vision and computational geometry. Surfaces can be classified by different transformation groups. Traditional classification methods mainly use topological transformation groups and Euclidean transformation groups. This paper introduces a novel method to classify surfaces by conformal transformation groups. Conformal equivalent class is refiner than topological equivalent class and coarser than isometric equivalent class, making it suitable for practical classification purposes. For general surfaces, the gradient fields of conformal maps form a vector space, which has a natural structure invariant under conformal transformations. We present an algorithm to compute this conformal structure, which can be represented as matrices, and use it to classify surfaces. The result is intrinsic to the geometry, invariant to triangulation and insensitive to resolution. To the best of our knowledge, this is the first paper to classify surfaces with arbitrary topologies by global conformal invariants. The method introduced here can also be used for surface matching problems.
A Tree-Based Kernel for Graphs

This paper proposes a new tree-based kernel for graphs. Graphs are decomposed into multisets of ordered Directed Acyclic Graphs (DAGs) and a family of kernels computed by application of tree kernels extended to the DAG domain. We focus our attention on the efficient development of one member of this family. A technique for speeding up the computation is given, as well as theoretical bounds and practical evidence of its feasibility. State of the art results on various benchmark datasets prove the effectiveness of our
Hierarchical English Emphatic Speech Synthesis Based on HMM with Limited Training Data

Emphasis is an important form of expressiveness in speech. Hidden Markov model (HMM) based speech synthesis has shown great flexibility in generating expressive speech. This paper proposes a hierarchical model based on HMMs aiming at synthesizing emphatic speech of both high emphasis quality and high naturalness with the limited amount of data. Decision trees (DTs) are constructed with non-emphasis-related questions using both neutral and emphasis corpora. The data in each leaf node of the DTs are classified into 6 emphasis categories according to the emphasis-related questions. The data in the same emphasis category are grouped into one sub-node and are used to train one HMM. As there might be no data of some specific emphasis categories in the leaf nodes of the DTs, a method based on cost calculation is proposed to select a suitable HMM in the same leaf node for predicting parameters. Further a compensation model is proposed to adjust the predicted parameters. Experiments show that the proposed hierarchical model can synthesize emphatic speech with high quality for both naturalness and emphasis, using limited amount of training data.
A Composite Transition Model for Brand Choiceand Purchase Timing

A new approach for analyzing duration state data in brand-choice studies is explored. This approach not only incorporates the correlation among repeated purchases for a subject, it also models the purchase timing and the brand decision jointly. The former is accomplished by applying transition model approaches from longitudinal studies. The latter is done by conditioning on the brand choice variable. Then mixed multinomial logit models and Cox proportional hazards models are employed to model the marginal densities of the brand choice and the conditional densities of the interpurchase time given the brand choice. We will illustrate our ndings by applying this new method to Nielsen household scanner panel data. 1 In brand-choice studies, we are interested in factors innuencing both the brand-switching patterns and purchase-timing decisions. Usually, panel data are collected for a period of time from N households where we can extract a series of interpurchase times and brands chosen for each purchase of a household. Moreover, we collect a list of covariates at each purchase. The covariates may include characteristics of both the consumers (chooser-speciic) and the brands (choice-speciic). For example, the choice-speciic explanatory variables may include the price of each brand or an indicator of a special display for each brand; the chooser-speciic variables may contain household income and size. Most of the older approaches to analyzing this kind of multiple-spell and multiple-destination data are restricted to marginal approaches where attention is given only to the state-space part (brand choice) of the data or the interpurchase-time part of the data. For example, the former includes the discrete brand-choice models of McFadden (1974) and Jain et al. (1994); the latter includes the proportional hazards model (Cox 1972) for the interpurchase time studied by Jain and Vilcassim (1991). The more advanced studies in this area include (1) the conditional model (for the interpurchase time given the transition states) proposed by Vilcassim and Jain (1991), and GG on ul and Srinivasan (1993), and (2) the joint model for the interpurchase time and the brand choice given by Chin-tagunta and Prasad (1998). Vilcassim and Jain derive a competing risk model where each latent interpurchase time between transitions is modeled by the Cox regression model with frailty. They found that the probability distribution of interpurchase times is not the same for various switching between brands. GG on ul and Srinivasan (1993) apply a similar approach except that they restrict the slopes to …