Ferromagnetism in Fe-doped Ba6Ge25 Chiral Clathrate

We have successfully synthesized a Ba6Ge25 clathrate, substituting 3 Fe per formula unit by Ge. This chiral clathrate has Ge sites forming a framework of closed cages and helical tunnel networks. Fe atoms randomly occupy these sites, and exhibit high-spin magnetic moments. A ferromagnetic transition is observed with Tc = 170 K, the highest observed Tc for a magnetic clathrate. However, the magnetic phase is significantly disordered, and exhibits a transformation to a re-entrant spin glass phase. This system has a number of features in common with other dilute magnetic semiconductors.Comment: Submitted to Applied Physics Letters. Fig. 1 resolution reduced for online archive versio

Observer-biased bearing condition monitoring: from fault detection to multi-fault classification

Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.Grant number: 145602

Generative Adversarial Networks Selection Approach for Extremely Imbalanced Fault Diagnosis of Reciprocating Machinery

At present, countless approaches to fault diagnosis in reciprocating machines have been proposed, all considering that the available machinery dataset is in equal proportions for all conditions. However, when the application is closer to reality, the problem of data imbalance is increasingly evident. In this paper, we propose a method for the creation of diagnoses that consider an extreme imbalance in the available data. Our approach first processes the vibration signals of the machine using a wavelet packet transform-based feature-extraction stage. Then, improved generative models are obtained with a dissimilarity-based model selection to artificially balance the dataset. Finally, a Random Forest classifier is created to address the diagnostic task. This methodology provides a considerable improvement with 99% of data imbalance over other approaches reported in the literature, showing performance similar to that obtained with a balanced set of data.National Natural Science Foundation of China, under Grant 51605406National Natural Science Foundation of China under Grant 7180104

Characterizing mixed mode oscillations shaped by noise and bifurcation structure

Many neuronal systems and models display a certain class of mixed mode oscillations (MMOs) consisting of periods of small amplitude oscillations interspersed with spikes. Various models with different underlying mechanisms have been proposed to generate this type of behavior. Stochastic versions of these models can produce similarly looking time series, often with noise-driven mechanisms different from those of the deterministic models. We present a suite of measures which, when applied to the time series, serves to distinguish models and classify routes to producing MMOs, such as noise-induced oscillations or delay bifurcation. By focusing on the subthreshold oscillations, we analyze the interspike interval density, trends in the amplitude and a coherence measure. We develop these measures on a biophysical model for stellate cells and a phenomenological FitzHugh-Nagumo-type model and apply them on related models. The analysis highlights the influence of model parameters and reset and return mechanisms in the context of a novel approach using noise level to distinguish model types and MMO mechanisms. Ultimately, we indicate how the suite of measures can be applied to experimental time series to reveal the underlying dynamical structure, while exploiting either the intrinsic noise of the system or tunable extrinsic noise.Comment: 22 page

Bound states of Θ+\Theta^+ in nuclei

We study the binding energy and the width of the $\Theta^+$ in nuclei, associated to the $K N$ and $K \pi N$ components. The first one leads to negligible contributions while the second one leads to a sizeable attraction, enough to bind the $\Theta^+$ in nuclei. Pauli blocking and binding effects on the $K N$ decay reduce considerably the $\Theta^+$ decay width in nuclei and medium effects associated to the $K \pi N$ component also lead to a very small width, as a consequence of which one finds separation between the bound levels considerably larger than the width of the states.Comment: Presentation in the 10th International Baryon Conference BARYON0