Miracle-dz/UniFAR-FTUs huggingface dataset:
Ball Screw Fault Detection and Location Based on Outlier and Instantaneous Rotational Frequency Estimation

Ball screw, as a crucial component, is widely used in various rotating machines. Its health condition significantly influences the efficiency and position precision of rotating machines. Therefore, it is important to accurately detect faults and estimate fault location in a ball screw system to make sure that the ball screw system runs safely and effectively. However, there are few research studies concerning the topic. The aim of this paper is to fill the gap. In this paper, we propose a method to automatically detect and locate faults in a ball screw system. The proposed method mainly consists of two steps: fault time estimation and instantaneous rotational frequency extraction. In the first step, a statistics-based outlier detection method is proposed to involve the fault information mixing in vibration signals and estimate the fault time. In the second step, a parameterized time-frequency analysis method is utilized to extract the instantaneous rotational frequency of the ball screw system. Once the fault time and instantaneous rotational frequency are estimated, the fault location in a ball screw system is calculated through an integral operation. In order to verify the effectiveness of the proposed method, two fault location experiments under the constant and varying speed conditions are conducted in a ball screw failure simulation testbed. The results demonstrate that the proposed method is able to accurately detect the faults in a ball screw system and estimate the fault location within an error of 22%.
All-dielectric metasurface imaging platform applicable to laser scanning microscopy with enhanced axial resolution and wavelength selection

Metasurfaces composed of artificially fabricated nano-sized structures have shown extraordinary potential for the precise control of light. Here, we demonstrate for the first time, a metasurface application to reduce the axial size of the point spread function in laser scanning microscopy. The all-dielectric metasurface has wavelength selectivity over the whole visible range, and confinement of the excitation point spread function of the electromagnetic field. These two unique features allow the metasurface to be applied to laser scanning microscope systems. Numerical and experimental demonstrations of the proposed all-dielectric metasurface are reported, showing sharp implicit spectral filtering in the visible range and enhanced axial confinement by observing actin filaments in NIH3T3 cells. We believe that our approach can provide a useful insight on the practicality of using metasurfaces as an imaging platform.
Post-Fabrication Trimming of Silicon Ring Resonators via Integrated Annealing

We report post-fabrication trimming of silicon-on-insulator micro-ring resonators via annealing of lattice defects using integrated micro-heaters. Defects are introduced via an inert MeV boron ion implantation at doses ranging from <inline-formula> <tex-math notation="LaTeX">$3\times 10^{10}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$3\times 10^{13}$ </tex-math></inline-formula> cm<sup>−2</sup>. Ion implantation results in a stable red-shift ranging from 20 to 1200 pm, for the stated dose range. Post-implantation annealing produces a subsequent blue-shift ranging from 380 to 800 pm, dependent on the implantation dose, indicating partial recovery of the silicon lattice through removal of the implantation-induced defects. Moreover, evidence is shown for a resonance blue-shift associated with modification of the micro-ring, even without a prior ion implantation step. With the method described in this letter, we demonstrate precise trimming of a four-ring filter such that the resonances are separated by 50 GHz, despite the as-fabricated rings having a random resonance separation resulting from fabrication variances.
Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal 

Classification of economic journal articles has been done using the VSM (Vector Space Model) approach and the Cosine Similarity method. The results of previous studies are considered to be less optimal because Stopword Removal was carried out by using a dictionary of basic words (tuning). Therefore, the omitted words limited to only basic words. This study shows the improved performance accuracy of the Cosine Similarity method using frequency-based Stopword Removal. The reason is because the term with a certain frequency is assumed to be an insignificant word and will give less relevant results. Performance testing of the Cosine Similarity method that had been added to frequency-based Stopword Removal was done by using K-fold Cross Validation. The method performance produced accuracy value for 64.28%, precision for 64.76 %, and recall for 65.26%. The execution time after pre-processing was 0, 05033 second.
BLP-2LASSO for aggregate discrete choice models with rich covariates

We introduce the BLP-2LASSO model, which augments the classic BLP (Berry, Levinsohn, and Pakes, 1995) random-coefficients logit model to allow for data-driven selection among a high-dimensional set of control variables using the 'double-LASSO' procedure proposed by Belloni, Chernozhukov, and Hansen (2013). Economists often study consumers’ aggregate behaviour across markets choosing from a menu of differentiated products. In this analysis, local demographic characteristics can serve as controls for market-specific preference heterogeneity. Given rich demographic data, implementing these models requires specifying which variables to include in the analysis, an ad hoc process typically guided primarily by a researcher’s intuition. We propose a data-driven approach to estimate these models, applying penalized estimation algorithms from the recent literature in high-dimensional econometrics. Our application explores the effect of campaign spending on vote shares in data from Mexican elections.
Fast and Accurate Calculation of Electromagnetic Scattering/Radiation Fields

A novel and general method for a fast calculation of scattering/radiation fields is proposed by expressing the source-field relationship through the method of moments (MoM). The scattering fields in the observation region are expanded by a set of SWGs defined on an auxiliary mesh. The associated expansion coefficients are obtained from matrix vector multiplication (MVM) of the impedance matrix in the MoM and the equivalent currents. The proposed method can enable an accurate near-field calculation without a special treatment of the singularity. Numerical experiments on targets with complex configurations are conducted to validate the performance of the proposed method.
USING FACTOR ANALYSIS AND MULTIPLE LINEAR REGRESSIONS: AN EXAMPLE IN SATISFACTION WITH LIFE IN SLOVENIA

This paper presents the research about satisfaction with life in Slovenia. There were analysed the impact of gender, age, level of education, employment status and the amount of salary on the satisfaction with life. The main goal of the research is to determine whether (and how) the impact of five independent variables (gender, age, level of education, employment status and the amount of salary) influences the satisfaction with life (dependent variable). A written “online” survey (with Google-drive) was conducted from 13/4/2017 to 7/5/2017. Based on the multilinear regression model (Rsquare is only 0,037) we found out that there is no statistically correlation between gender, age, level of education, employment status, the amount of salary and the satisfaction with life.
Deep Mixed Effect Models using Gaussian Process: A Personalized and Reliable Prediction Model for Healthcare

We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (EHR), by modeling two complementary components: i) a shared component that captures global trend across diverse patients and ii) a patient-specific component that models idiosyncratic variability for each patient. To this end, we propose a composite model of a deep neural network to learn complex global trends from the large number of patients, and Gaussian Processes (GP) to probabilistically model individual time-series given relatively small number of visits per patient. We evaluate our model on diverse and heterogeneous tasks from EHR datasets and show practical advantages over standard time-series models such as pure RNNs.
Methods for cluster cosmology and application to the SDSS in preparation for DES Year 1 release

We implement the first blind analysis of cluster abundance data to derive cosmological constraints from the abundance and weak lensing signal of redMaPPer clusters in the Sloan Digital Sky Survey (SDSS). We simultaneously fit for cosmological parameters and the richness–mass relation of the clusters. For a flat Λ cold dark matter cosmological model with massive neutrinos, we find $S_8 \equiv \sigma _{8}(\Omega _\mathrm{ m}/0.3)^{0.5}=0.79^{+0.05}_{-0.04}$. This value is both consistent and competitive with that derived from cluster catalogues selected in different wavelengths. Our result is also consistent with the combined probes analyses by the Dark Energy Survey (DES), the Kilo-Degree Survey (KiDS), and with the cosmic microwave background (CMB) anisotropies as measured by Planck. We demonstrate that the cosmological posteriors are robust against variation of the richness–mass relation model and to systematics associated with the calibration of the selection function. In combination with baryon acoustic oscillation data and big bang nucleosynthesis data (Cooke et al.), we constrain the Hubble rate to be h = 0.66 ± 0.02, independent of the CMB. Future work aimed at improving our understanding of the scatter of the richness–mass relation has the potential to significantly improve the precision of our cosmological posteriors. The methods described in this work were developed for use in the forthcoming analysis of cluster abundances in the DES. Our SDSS analysis constitutes the first part of a staged-unblinding analysis of the full DES data set.
Better Education through Improved Reinforcement Learning.

When a new student comes to play an educational game, how can we determine what content to give them such that they learn as much as possible? When a frustrated customer calls in to a helpline, how can we determine what to say to best assist them? When an ill patient comes in to the clinic, how do we determine what tests to run and treatments to give to maximize their quality of life? These problems, though diverse, are all a seemingly natural choice for reinforcement learning, where an AI agent learns from experience how to make a sequence of decisions to maximize some reward signal. However, unlike many recent successes of reinforcement learning, in these settings the agent gains experience solely by interacting with humans (e.g. game players or patients). As a result, although the potential to directly impact human lives is much greater, intervening to collect new data is often expensive and potentially risky. Therefore, in this thesis I present several methods that allow us to evaluate candidate learning approaches offline using previously-collected data instead of actually deploying them. First, I present an unbiased evaluation methodology based on importance sampling that allows us to compare policies built on very different representations. I show how this approach enables us to improve student achievement by over 30\% on a challenging and important educational games problem with limited data but 4,500 features. Next, I examine the understudied problem of offline evaluation of algorithms that learn online. In the simplified case of bandits, I present a novel algorithm that is (often vastly) more efficient than the previously state-of-the-art approach. Next, for the first time I examine the more general reinforcement learning case, developing several new evaluation approaches, each with fairly strong theoretical guarantees. Using actual student data, we show that each method has different empirical tradeoffs and is useful in different settings. Further, I present new learning algorithms which ensure that, when we do choose to deploy algorithms to humans, the data we gather is maximally useful. I first examine the important real-world problem of delayed feedback in the bandit case. I present an exploration algorithm which is theoretically on par with the state-of-the-art but much more attractive empirically, as evaluated on real-world educational games data. I show how one can incorporate arbitrary heuristics to further improve reward without harming theoretical guarantees. Next I present Thompson Clustering for Reinforcement Learning (TCRL), a Bayesian clustering algorithm which addresses the key twin problems of exploration and generalization in a computationally-efficient and data-efficient manner. TCRL has gained traction in industry, being used by an educational startup to serve literacy content to students. Finally, I explore how reinforcement learning agents should best leverage human expertise to gradually extend the capabilities of the system, a topic which lies in the exciting area of…
Coordination Modes of Aminodiphosphane Ligands to the Cuboidal Mo3S4 Cluster Unit

The financial support of the Spanish Ministerio de Economia y Competitividad (Grant CTQ2015-65207), Universitat Jaume I (Research Project P1·1B2013-19) and Generalitat Valenciana (Prometeo/2014/022) is gratefully acknowledged. The authors also thank the Servei Central d Instrumentacio Cientifica (SCIC) of the University Jaume I for providing us with the mass spectrometry, NMR, elemental analysis and X-ray facilities. C.A. thanks the Spanish Ministerio de Economia y Competitividad for a predoctoral fellowship (FPI).
Robust visual tracking via multiscale deep sparse networks

Abstract. In visual tracking, deep learning with offline pretraining can extract more intrinsic and robust features. It has significant success solving the tracking drift in a complicated environment. However, offline pretraining requires numerous auxiliary training datasets and is considerably time-consuming for tracking tasks. To solve these problems, a multiscale sparse networks-based tracker (MSNT) under the particle filter framework is proposed. Based on the stacked sparse autoencoders and rectifier linear unit, the tracker has a flexible and adjustable architecture without the offline pretraining process and exploits the robust and powerful features effectively only through online training of limited labeled data. Meanwhile, the tracker builds four deep sparse networks of different scales, according to the target’s profile type. During tracking, the tracker selects the matched tracking network adaptively in accordance with the initial target’s profile type. It preserves the inherent structural information more efficiently than the single-scale networks. Additionally, a corresponding update strategy is proposed to improve the robustness of the tracker. Extensive experimental results on a large scale benchmark dataset show that the proposed method performs favorably against state-of-the-art methods in challenging environments.
A framework for evaluating the quality of modelling languages in MDE environments A framework for evaluating the quality of modelling languages in MDE environments

This thesis presents the Multiple Modelling Quality Evaluation Framework method (hereinafter MMQEF), which is a conceptual, methodological, and technological framework for evaluating quality issues in modelling languages and modelling elements by the application of a taxonomic analysis. It derives some analytic procedures that support the detection of quality issues in model-driven projects, such as the suitability of modelling languages, traces between abstraction levels, specification for model transformations, and integration between modelling proposals. MMQEF also suggests metrics to perform analytic procedures based on the classification obtained for the modelling languages and artifacts under evaluation. MMQEF uses a taxonomy that is extracted from the Zachman framework for Information Systems (Zachman, 1987; Sowa and Zachman, 1992), which proposed a visual language to classify elements that are part of an Information System (IS). These elements can be from organizational to technical artifacts. The visual language contains a bi-dimensional matrix for classifying IS elements (generally expressed as models) and a set of seven rules to perform the classification. As an evaluation method, MMQEF defines activities in order to derive quality analytics based on the classification applied on modelling languages and elements. The Zachman framework was chosen because it was one of the first and most precise proposals for a reference architecture for IS, which is recognized by important standards such as the ISO 42010 (612, 2011). This thesis presents the conceptual foundation of the evaluation framework, which is based on the definition of quality for model-driven engineering (MDE). The methodological and technological support of MMQEF is also described. Finally, some validations for MMQEF are reported.
Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks

In this study, two empirical correlations of the Nusselt number, based on two artificial neural networks (ANN), were developed to determine the heat transfer coefficients for each section of a vertical helical double-pipe evaporator with water as the working fluid. Each ANN was obtained using an experimental database of 1109 values obtained from an evaporator coupled to an absorption heat transformer with energy recycling. The Nusselt number in the annular section was estimated based on the modified Wilson plot method solved by an ANN. This model included the Reynolds and Prandtl numbers as input variables and three neurons in their hidden layer. The Nusselt number in the inner section was estimated based on the Rohsenow equation, solved by an ANN. This ANN model included the numbers of the Prandtl and Jackob liquids as input variables and one neuron in their hidden layer. The coefficients of determination were R2>0.99 for both models. Both ANN models satisfied the dimensionless condition of the Nusselt number. The Levenberg–Marquardt algorithm was chosen to determine the optimum values of the weights and biases. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and the linear function in the output layer. The Nusselt numbers, determined by the ANNs, proved adequate to predict the values of the heat transfer coefficients of a vertical helical double-pipe evaporator that considered biphasic flow with an accuracy of ±0.2 for the annular Nusselt and ±4 for the inner Nusselt.
High harmonic generation with Laguerre–Gaussian beams

We summarize the development of high harmonic generation (HHG) with linearly polarized Laguerre–Gaussian (LG) beams and their superpositions to explain the non-perturbative aspects of HHG. Furthermore, we show that circularly polarized extreme ultraviolet vortices with well-defined orbital angular momentum (OAM) can be generated by HHG with bicircular LG beams. We introduce photon diagrams in order to explain how to calculate the OAM and the polarization of the generated harmonics by means of simultaneous conservation of spin angular momentum and OAM. Moreover, we show how the intensity ratio of the driving fields in HHG with bicircular LG beams further enhances the generation of circularly polarized twisted attosecond pulse trains.
Experimental study on single point incremental forming of Inconel 718

The contemporary sheet metal industry employs forming methods that use a precise die and punch to form components with precise tolerances. In mass production, the high cost of manufacturing dies and punches is absorbed by the number of components formed, whereas this is not the case for low volume production where it increases the manufacturing cost of the products. To overcome the demand for the development of manufacturing technologies that are both agile and meet industrial requirements, an incremental sheet metal forming experiment was carried out on Inconel 718. The incremental sheet metal forming process in the research stage needs to be improved both in terms of accuracy and product quality. The main process parameters involved in the study are incremental depth, rotational speed, and feed rate of the forming tool. Based on the process parameters, experiments are conducted using the Taguchi L9 orthogonal array design and responses such as geometrical accuracy, surface roughness, and thinning are studied. The forming limit and micro-structural study allow us to understand the forming behavior of Inconel 718, which will enhance the applicability of the material.
Multi-sequence segmentation via score and higher-criticism tests

We propose local segmentation of multiple sequences sharing a common time- or location-index, building upon the single sequence local segmentation methods of Niu and Zhang (2012) and Fang, Li and Siegmund (2016). We also propose reverse segmentation of multiple sequences that is new even in the single sequence context. We show that local segmentation estimates change-points consistently for both single and multiple sequences, and that both methods proposed here detect signals well, with the reverse segmentation method outperforming a large number of known segmentation methods on a commonly used single sequence test scenario. We show that on a recent allele-specific copy number study involving multiple cancer patients, the simultaneous segmentations of the DNA sequences of all the patients provide information beyond that obtained by segmentation of the sequences one at a time.
PROGRAMMABLE CONTROLLERS

For qualified staff only This manual is only intended for use by properly trained and qualified electrical technicians who are fully acquainted with automation technology safety standards. All work with the hardware described, including system design, installation, setup, maintenance, service and testing, may only be performed by trained electrical technicians with approved qualifications who are fully acquainted with the applicable automation technology safety standards and regulations.
Optimal Window Length and Performance Analysis of FEDS Based Adaptive Beam Forming Approach over Rayleigh Fading Channel

To the best of my knowledge, the concept of the Fast Euclidean Direction Search (FEDS) for the wireless communication systems has not been fully exploited and only a few researchers who are working on this topic. This paper deals with study and analysis overall FEDS approach performance and try to find its optimal window length (L) for adaptive beam forming applications. The channel used is a Rayleigh fading model with Jake's power spectral density, which is a popular choice for wireless communications systems. This channel model has Doppler frequency and the user's mobility parameters. The current investigation may be extended to include other adaptive algorithms such as Least Mean Square (LMS), Normalized LMS (NLMS), and Recursive Least Square (RLS) algorithms. Performance enhancement of FEDS approach can be obtained when the optimum window length (L) is a carefully selected and its value is related to or depends on the number of array elements (M) whether the channel is AWGN or Rayleigh fading models.