Detection of cross-channel anomalies

The data deluge has created a great challenge for data mining applications wherein the rare topics of interest are often buried in the flood of major headlines. We identify and formulate a novel problem: cross-channel anomaly detection from multiple data channels. Cross-channel anomalies are common among the individual channel anomalies and are often portent of significant events. Central to this new problem is a development of theoretical foundation and methodology. Using the spectral approach, we propose a two-stage detection method: anomaly detection at a single-channel level, followed by the detection of cross-channel anomalies from the amalgamation of single-channel anomalies. We also derive the extension of the proposed detection method to an online settings, which automatically adapts to changes in the data over time at low computational complexity using incremental algorithms. Our mathematical analysis shows that our method is likely to reduce the false alarm rate by establishing theoretical results on the reduction of an impurity index. We demonstrate our method in two applications: document understanding with multiple text corpora and detection of repeated anomalies in large-scale video surveillance. The experimental results consistently demonstrate the superior performance of our method compared with related state-of-art methods, including the one-class SVM and principal component pursuit. In addition, our framework can be deployed in a decentralized manner, lending itself for large-scale data stream analysis.     

Interval functional observers for time-delay systems with additive disturbances

This article considers the design of interval functional observers to estimate a linear function of the state vector of time-delay systems subject to both input and output additive disturbances. Two novel functional observers are proposed and designed such that they bound the set of all admissible values of a linear function of the state vector at each instant of time. By contrast to interval observers currently available in the literature, both observers proposed in this article utilize multiple delayed output measurement and have a more general structure. This trade-off feature overcomes some drawbacks in previous work and enables interval functional observers to be designed for a wider class of time-delay systems. Conditions for the existence of interval functional observers are derived and an effective design algorithm for computing unknown observer matrices is provided. Two illustrative examples are given to show the advantages and effectiveness of our design method.     

Regularized nonnegative shared subspace learning

Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining.     

A novel inverse modeling control for piezo positioning stage

Journal of Mechanical Science and Technology - This paper focuses on the application of a novel inverse nonlinear autoregressive with exogenous input (NARX) structure and a fuzzy inference system...     

Polyacrylonitrile‐carbon Nanotube‐polyacrylonitrile: A Versatile Robust Platform for Flexible Multifunctional Electronic Devices in Medical Applications

Flexible multifunctional electronic devices are of high interest for a wide range of applications including thermal therapy and respiratory devices in medical treatment, safety equipment, and structural health monitoring systems. This paper reports a scalable and efficient strategy of manufacturing a polyacrylonitrile‐carbon nanotube‐polyacrylonitrile (PAN‐CNT‐PAN) robust flexible platform for multifunctional electronic devices including flexible heaters, temperature sensors, and flexible thermal flow sensors. The key advantages of this platform include low cost, porosity, mechanical robustness, and electrical stability under mechanical bending, enabling the development of fast‐response flexible heaters with a response time of ≈1.5 s and relaxation time of ≈1.7 s. The temperature‐sensing functionality is also investigated with a range of temperature coefficient of resistances from ?650 to ?900 ppm K^?1. A flexible hot‐film sensing concept is successfully demonstrated using PAN‐CNT‐PAN with a high sensitivity of 340 mV (m s^?1)^?1. The sensitivity enhancement of 50% W^?1 is also observed with increasing supply power. The low cost, porosity, versatile, and robust properties of the proposed platform will enable the development of multifunctional electronic devices for numerous applications such as flexible thermal management, temperature stabilization in industrial processing, temperature sensing, and flexible/wearable devices for human healthcare applications.     

Synthesis, characterization and electrochemical properties of 4.8 V LiNi0.5Mn1.5O4 cathode material in lithium-ion batteries

In this work the synthesis of a nickel doped cubic manganese spinel has been studied for application as cathode material in secondary lithium batteries. Six different experimental approaches have been tested in order to carry out a screening of the various possible synthetic routes. The used synthetic strategies were wet chemistry (WC), solid state (SS), combustion synthesis (CS), cellulose-based sol-gel synthesis (SG-C), ascorbic acid-based sol-gel synthesis (SG-AA) and resorcinol/formaldehyde-based sol-gel synthesis (SG-RF). The goal of our study is to obtain insights about how the synthesis conditions can be modified in order to achieve a material with improved electrochemical performances in such devices, especially in high current operating regimes. The synthesized materials have been characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS), atomic absorption, inductively coupled plasma (ICP-MS) atomic emission spectroscopy, surface area measurements and tested as high voltage cathodes in Li-ion electrochemical devices.