Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data.

Knowledge management in future factories

Technology in the manufacturing sector has seen rapid change, transforming from stand alone, manual processes to smart, integrated systems. We have witnessed the migration of relay-based systems to advance SCADA systems, manual pro-duction to fully automated, and hand written reports to interactive computer-based dashboards. We are now seeing the emergence of smart products manufactured in smart plants and the evolution of smart services in manufacturing. Future manu-facturing systems will be distinguished by intelligent machines, automation and human factors’ integration. This talk will focus on how knowledge can be embed-ded in processes and products through the use of simulation and modelling tools to streamline future smart production systems and improve product quality. The implications to future smart manufacturing enterprises are explored through a se-ries of case studies from aerospace, mining and small and medium manufacturing enterprises.

Selective, spontaneous one-way oil-transport fabrics and their novel use for gauging liquid surface tension

Thin porous materials that can spontaneously transport oil fluids just in a single direction have great potential for making energy-saving functional membranes. However, there is little data for the preparation and functionalities of this smart material. Here, we report a novel method to prepare one-way oil-transport fabrics and their application in detecting liquid surface tension. This functional fabric was prepared by a two-step coating process to apply flowerlike ZnO nanorods, fluorinated decyl polyhedral oligomeric silsesquioxanes, and hydrolyzed fluorinated alkylsilane on a fabric substrate. Upon one-sided UV irradiation, the coated fabric shows a one-way transport feature that allows oil fluid transport automatically from the unirradiated side to the UV-irradiated surface, but it stops fluid transport in the opposite direction. The fabric still maintains high superhydrophobicity after UV treatment. The one-way fluid transport takes place only for the oil fluids with a specific surface tension value, and the fluid selectivity is dependent on the UV treatment time. Changing the UV irradiation time from 6 to 30 h broadened the one-way transport for fluids with surface tension from around 22.3 mN/m to a range of 22.3-56.7 mN/m. We further proved that this selective one-way oil transport can be used to estimate the surface tension of a liquid simply by observing its transport feature on a series of fabrics with different one-way oil-transport selectivities. To our knowledge, this is the first example to use one-way fluid-transport materials for testing the liquid surface tension. It may open up further theoretical studies and the development of novel fluid sensors.

Influence of Excitation Signal on Impedance-based Structural Health Monitoring

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

On the performance of a dual-mode non-linear vibration energy harvesting device

The research trend for harvesting energy from the ambient vibration sources has moved from using a linear resonant generator to a non-linear generator in order to improve on the performance of a linear generator; for example, the relatively small bandwidth, intolerance to mistune and the suitability of the device for low-frequency applications. This article presents experimental results to illustrate the dynamic behaviour of a dual-mode non-linear energy-harvesting device operating in hardening and bi-stable modes under harmonic excitation. The device is able to change from one mode to another by altering the negative magnetic stiffness by adjusting the separation gap between the magnets and the iron core. Results for the device operating in both modes are presented. They show that there is a larger bandwidth for the device operating in the hardening mode compared to the equivalent linear device. However, the maximum power transfer theory is less applicable for the hardening mode due to occurrence of the maximum power at different frequencies, which depends on the non-linearity and the damping in the system. The results for the bi-stable mode show that the device is insensitive to a range of excitation frequencies depending upon the input level, damping and non-linearity.

Modeling and Experimental Aspects of Self-healing Bolted Joint through Shape Memory Alloy Actuators

Bolted joints are a form of mechanical coupling largely used in machinery due to their reliability and low cost. Failure of bolted joints can lead to catastrophic events, such as leaking, train derailments, aircraft crashes, etc. Most of these failures occur due to the reduction of the pre-load, induced by mechanical vibration or human errors in the assembly or maintenance process. This article investigates the application of shape memory alloy (SMA) washers as an actuator to increase the pre-load on loosened bolted joints. The application of SMA washer follows a structural health monitoring procedure to identify a damage (reduction in pre-load) occurrence. In this article, a thermo-mechanical model is presented to predict the final pre-load achieved using this kind of actuator, based on the heat input and SMA washer dimension. This model extends and improves on the previous model of Ghorashi and Inman [2004, "Shape Memory Alloy in Tension and Compression and its Application as Clamping Force Actuator in a Bolted Joint: Part 2 - Modeling," J. Intell. Mater. Syst. Struct., 15:589-600], by eliminating the pre-load term related to nut turning making the system more practical. This complete model is a powerful but complex tool to be used by designers. A novel modeling approach for self-healing bolted joints based on curve fitting of experimental data is presented. The article concludes with an experimental application that leads to a change in joint assembly to increase the system reliability, by removing the ceramic washer component. Further research topics are also suggested.

Impedance-based structural health monitoring with artificial neural networks

This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically >30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, multiple sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with experimental examples, investigations on a massive quarter scale model of a steel bridge section and a space truss structure, in order to verify the performance of this proposed methodology.

Toward pH-controllable bioelectrocatalysis for hydrogen peroxide based on polymer brushes

In the present work, a biosensor was built with smart material based on polymer brushes. The biosensor demonstrated a pH-sensitive on-off property, and it was further used to control or modulate the electrochemical responses of the biosensor. This property could be used to realize pH-controlled electrochemical reaction of hydrogen peroxide and HRP immobilized on polymer brushes. The composite film also showed excellent amperometric i-t response toward hydrogen peroxide in the concentration range of 0-13 μM. In future, this platform might be used for self-regulating targeted diagnostic, drug delivery and biofuel cell based on controllable bioelectrocatalysis. © 2013 Elsevier B.V.

Controle da variação do arqueamento de um aerofólio utilizando atuadores de memória de forma

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Polymer brush modified electrode with switchable selectivity triggered by pH changes enhanced by gold nanoparticles

In this work, a sensor was built up with smart material based on polymer brush and gold nanoparticles. The modified electrode functionalized with polyacrylic acid (PAA) tethered to indium tin oxide (ITO) and covered with gold nanoparticle (ITO/PAA/Au) demonstrated switchable interfacial properties discriminating different pHs. The switchable electrochemical and plasmonic process was characterized by cyclic voltammetry (CV), electrochemistry impedance spectroscopy (EIS), and localized surface plasmon resonance (LSPR).

Detecção e localização de danos em materiais compósitos aplicado em aeronaves utilizando redes neurais artificiais

Pós-graduação em Engenharia Mecânica - FEIS

Application of passive control to energy harvester efficiency using a nonideal portal frame structural support system

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Design and implementation of wireless sensor networks for impedance-based structural health monitoring using ZigBee and Global System for Mobile Communications

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Computational and experimental characterization of a low-cost piezoelectric valveless diaphragm pump

Flow pumps act as important devices in areas such as Bioengineering, Medicine, and Pharmacy, among other areas of Engineering, mainly for delivering liquids or gases at small-scale and precision flow rate quantities. Principles for pumping fluids based on piezoelectric actuators have been widely studied, since they allow the construction of pump systems for displacement of small fluid volumes with low power consumption. This work studies valveless piezoelectric diaphragm pumps for flow generation, which uses a piezoelectric ceramic (PZT) as actuator to move a membrane (diaphragm) up and down as a piston. The direction of the flow is guaranteed by valveless configuration based on a nozzle-diffuser system that privileges the flow in just one pumping direction. Most research efforts on development of valveless flow pump deal either with computational simulations based on simplified models or with simplified physical approaches based on analytical models. The main objective of this work is the study of a methodology to develop a low-cost valveless piezoelectric diaphragm flow pump using computational simulations, parametric study, prototype manufacturing, and experimental characterization. The parametric study has shown that the eccentricity of PZT layer and metal layer plays a key role in the performance of the pump.