Vector control of a position sensorless SPMSM drive with RNN based stator flux estimator

A position sensorless Surface Permanent Magnet Synchronous Motor (SPMSM) drive based on single layer Recurrent Neural Network (RNN) is presented in this paper. The motor equations are written in rotor fixed d-q reference frame. A PID controller is used to process the speed error to generate the reference torque current keeping the magnetizing current fixed. The RNN estimator is used to estimate flux components along the stator fixed stationary axes. The flux angle and the reference current phasor angle are used in vector rotator to generate the reference phase currents. Hysteresis current controller block controls the switching of the three phase inverter to apply voltage to the motor stator. Simulation studies on different operating conditions indicate the acceptability of the drive system. The proposed estimator can be used to accurately measure the motor fluxes and rotor angle over a wide speed range. The proposed control scheme is robust under load torque disturbances and motor parameter variations. It is also simple and low cost to implememnt in a practical environment

Learning latent activities from social signals with hierarchical dirichlet processes

Understanding human activities is an important research topic, most noticeably in assisted-living and healthcare monitoring environments. Beyond simple forms of activity (e.g., an RFID event of entering a building), learning latent activities that are more semantically interpretable, such as sitting at a desk, meeting with people, or gathering with friends, remains a challenging problem. Supervised learning has been the typical modeling choice in the past. However, this requires labeled training data, is unable to predict never-seen-before activity, and fails to adapt to the continuing growth of data over time. In this chapter, we explore the use of a Bayesian nonparametric method, in particular the hierarchical Dirichlet process, to infer latent activities from sensor data acquired in a pervasive setting. Our framework is unsupervised, requires no labeled data, and is able to discover new activities as data grows. We present experiments on extracting movement and interaction activities from sociometric badge signals and show how to use them for detecting of subcommunities. Using the popular Reality Mining dataset, we further demonstrate the extraction of colocation activities and use them to automatically infer the structure of social subgroups. © 2014 Elsevier Inc. All rights reserved.

Associations of parental rules and socioeconomic position with preschool children's sedentary behaviour and screen time

There is little current understanding of the influences on sedentary behaviour and screen time in preschool children. This study investigated socioeconomic position (SEP) and parental rules as potential correlates of preschool children's sedentary behaviour and screen time.

Dietary patterns and depressive symptoms over time: examining the relationships with socioeconomic position, health behaviours and cardiovascular risk

Recent research suggests that diet quality influences depression risk; however, a lack of experimental evidence leaves open the possibility that residual confounding explains the observed relationships. The aim of this study was to document the cross-sectional and longitudinal associations between dietary patterns and symptoms of depression and to undertake a detailed examination of potential explanatory factors, particularly socioeconomic circumstances, in the diet-depression relationship.

Biotemplated fabrication of a novel hierarchical porous C/LiFePO4/C composite for Li-ion batteries

Hierarchical porous composites are a potentially attractive material for high-rate cathode. This work presents a facile sol-gel process for the fabrication of a hierarchical porous C/LiFePO4/bio-C composite by using artemia cyst shells as natural biological carbon templates. The C/LiFePO4/bio-C composite exhibits a superior electrochemical performance with discharge capacities of 105 mA h g-1, 93 mA h g-1 and 80 mA h g-1 at 5 C, 10 C and 20 C, respectively. Remarkably, it produces a high discharge capacity of 69.1 mA h g-1 and no fading after 50 cycles even at a high current density of 6800 mA g-1. This journal is

Iterative construction of hierarchical classifiers for phishing website detection

This article is devoted to a new iterative construction of hierarchical classifiers in SimpleCLI for the detection of phishing websites. Our new construction of hierarchical systems creates ensembles of ensembles in SimpleCLI by iteratively linking a top-level ensemble to another middle-level ensemble instead of a base classifier so that the top-level ensemble can generate a large multilevel system. This new construction makes it easy to set up and run such large systems in SimpleCLI. The present article concentrates on the investigation of performance of the iterative construction of such classifiers for the example of detection of phishing websites. We carried out systematic experiments evaluating several essential ensemble techniques as well as more recent approaches and studying their performance as parts of the iterative construction of hierarchical classifiers. The results presented here demonstrate that the iterative construction of hierarchical classifiers performed better than the base classifiers and standard ensembles. This example of application to the classification of phishing websites shows that the new iterative construction combining diverse ensemble techniques into the iterative construction of hierarchical classifiers can be applied to increase the performance in situations where data can be processed on a large computer. © 2014 ACADEMY PUBLISHER.

Sulfur-impregnated, sandwich-type, hybrid carbon nanosheets with hierarchical porous structure for high-performance lithium-sulfur batteries

Sandwich-type hybrid carbon nanosheets (SCNMM) consisting of graphene and micro/mesoporous carbon layer are fabricated via a double template method using graphene oxide as the shape-directing agent and SiO2 nanoparticles as the mesoporous guide. The polypyrrole synthesized in situ on the graphene oxide sheets is used as a carbon precursor. The micro/mesoporous strcutures of the SCNMM are created by a carbonization process followed by HF solution etching and KOH treatment. Sulfur is impregnated into the hybrid carbon nanosheets to generate S@SCNMM composites for the cathode materials in Li-S secondary batteries. The microstructures and electrochemical performance of the as-prepared samples are investigated in detail. The hybrid carbon nanosheets, which have a thickness of about 10-25 nm, high surface area of 1588 m2 g-1, and broad pore size distribution of 0.8-6.0 nm, are highly interconnected to form a 3D hierarchical structure. The S@SCNMM sample with the sulfur content of 74 wt% exhibits excellent electrochemical performance, including large reversible capacity, good cycling stability and coulombic efficiency, and good rate capability, which is believed to be due to the structure of hybrid carbon materials with hierarchical porous structure, which have large specific surface area and pore volume.

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management.

Hierarchical Bayesian models of subtask learning

 The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking task, which logged participant actions, enabling measurement of strategy use and subtask performance. Model comparison was performed using deviance information criterion (DIC), posterior predictive checks, plots of model fits, and model recovery simulations. Results showed that although learning tended to be monotonically decreasing and decelerating, and approaching an asymptote for all subtasks, there was substantial inconsistency in learning curves both at the group- and individual-levels. This inconsistency was most apparent when constraining both the rate and the ratio of learning to asymptote to be equal across subtasks, thereby giving learning curves only 1 parameter for scaling. The inclusion of 6 strategy covariates provided improved prediction of subtask performance capturing different subtask learning processes and subtask trade-offs. In addition, strategy use partially explained the inconsistency in subtask learning. Overall, the model provided a more nuanced representation of how complex tasks can be decomposed in terms of simpler learning mechanisms.

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.

A Bayesian nonparametric model for hierarchical sequence of images

 Scale features are useful for a great number of applications in computer vision. However, it is difficult to tolerate diversities of features in natural scenes by parametric methods. Empirical studies show that object frequencies and segment sizes follow the power law distributions which are well generated by Pitman-Yor (PY) processes. Based on mid-level segments, we propose a hierarchical sequence of images to obtain scale information stored in a hierarchical structure through the hierarchical Pitman-Yor (HPY) model which is expected to tolerate uncertainty of natural images. We also evaluate our representation by the application of segmentation.

Australia’s position on medicines policy in international forums: Intellectual property protection and public health

Universal access to affordable medicines, which are safe, efficacious and of high quality, and which are appropriately used, depends on national legislation that is in turn constrained by a range of international agreements. This regulatory configuration also affects the profitability of the pharmaceutical industry, domestic and international. Tensions and contradictions between industry profitability and public health objectives relate to access, innovation and regulation.