The Role of Crosslink Density in Influencing the Electrical Resistance Behaviour of Polymeric Coatings

Paint films used to protect metalic surfaces are commonly polymeric in nature. The extent of protection offered by film depends on many factors including the characteristic electrical resistance behaviou and its effect on impeding local electrochemical processes. In the present work a range of polymeric coatings have been produced with systematically varied crosslinked density using an ultra-violet light curing technique. Their electrical resistance behaviour in an environment of varying concentrations of KC1 electrolyte has been examined. It is demonstrated that there are signs of the beginnings of a mechanism changeover from “D-type” to “I-type” behaviour at higher levels of crosslink density thus giving some tenuous support to previously unproven hypotheses in this area.     

Closed loop torque SVM-DTC based on robust super twisting speed controller for induction motor drive with efficiency optimization

This paper presents a space vector modulation (SVM) based Direct Torque Control strategy (DTC) for induction motor (IM) in order to overcome the drawbacks of the classical DTC. SVM can reduce the high torque and flux ripples by preserving a fixed switching frequency. This technique is known by the closed loop torque SVM-DTC. Moreover, the control scheme performance is improved by inserting a second order sliding mode super twisting controller in the outer loop for speed regulation. This nonlinear technique ensures a good dynamic and high robustness against external disturbance. Furthermore, the IM energy optimization is treated in the second objective of this paper. A proposed model based loss minimization strategy is presented for efficiency optimization. This strategy chooses an optimal flux magnitude for each applied load torque. The proposed optimized SVM-DTC algorithm will be investigated by simulation and real time implementation using Matlab/Simulink with real time interface based on dSpace 1104 signal card.     

Surface modifications of silicon nitride for cellular biosensor applications

Thin films of silicon nitride (Si₃N₄) can be used in several kinds of micro-sized biosensors as a material to monitor fine environmental changes related to the process of bone formation in vitro. We found however that Si₃N₄ does not provide optimal conditions for osseointegration as osteoblast-like MG-63 cells tend to detach from the surface when cultured over confluence. Therefore Si₃N₄ was modified with self-assembled monolayers bearing functional end groups of primary amine (NH₂) and carboxyl (COOH) respectively. Both these modifications enhanced the interaction with confluent cell layers and thus improve osseointegration over Si₃N₄. Furthermore it was observed that the NH₂ functionality increased the adsorption of fibronectin (FN), promoted cell proliferation, but delayed the differentiation. We also studied the fate of pre-adsorbed and secreted FN from cells to learn more about the impact of above functionalities for the development of provisional extracellular matrix on materials interface. Taken together our data supports that Si₃N₄ has low tissue integration but good cellular biocompatibility and thus is appropriate in cellular biosensor applications such as the ion-sensitive field effect transistor (ISFET). COOH and NH₂ chemistries generally improve the interfacial tissue interaction with the sensor and they are therefore suitable substrates for monitoring cellular growth or matrix deposition using electrical impedance spectroscopy.     

Learning with partly labeled data

Learning with partly labeled data aims at combining labeled and unlabeled data in order to boost the accuracy of a classifier. This paper outlines the two main classes of learning methods to deal with partly labeled data: pre-labeling-based learning and semi-supervised learning. Concretely, we introduce and discuss three methods from each class. The first three ones are two-stage methods consisting of selecting the data to be labeled and then training the classifier using the pre-labeled and the originally labeled data. The last three ones show how labeled and unlabeled data can be combined in a symbiotic way during training. The empirical evaluation of these methods shows: (1) pre-labeling methods tend be better than semi-supervised learning methods, (2) both labeled and unlabeled have positive effect on the classification accuracy of each of the proposed methods, (3) the combination of all the methods improve the accuracy, and (4) the proposed methods compare very well with the state-of-art methods.     

Towards Incremental Fuzzy Classifiers

Fuzzy classification systems (FCS) are traditionally built from observations (data points) in an off-line one shot-experiment. Once the learning phase is exhausted, the classifier is no more capable to learn further knowledge from new observations nor is it able to update itself in the future. This paper investigates the problem of incremental learning in the context of FCS. It shows how, in contrast to off-line or batch learning, incremental learning infers knowledge in the form of fuzzy rules from data that evolves over time. To accommodate incremental learning, appropriate mechanisms are applied in all steps of the FCS construction: (1) Incremental supervised clustering to generate granules in a progressive manner, (2) Systematic and automatic update of fuzzy partitions, (3) Incremental feature selection using an incremental version of Fisher’s interclass separability criterion. The effect of incrementality on various aspects is demonstrated via a numerical evaluation.