The influence of indenter tip rounding on the indentation size effect

A model was developed to interpret the indentation size effect. The model considers the tip wear effect, causing a rounded tip, the plastic zone size and various strengthening contributions, including geometrically necessary dislocations, preexisting statistically stored dislocations and grain size. It is shown that the shape of the worn tip can be effectively determined through calibration experiments. The model is applied to predict dislocation densities, and shows a good correspondence with published data on dislocation densities in copper single crystals. Predicted ISE is shown to be in good correspondence with published data on a range of metals, and an improvement over existing models is demonstrated

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm

Exemplar-based Linear Discriminant Analysis for Robust Object Tracking

Tracking-by-detection has become an attractive tracking technique, which treats tracking as a category detection problem. However, the task in tracking is to search for a specific object, rather than an object category as in detection. In this paper, we propose a novel tracking framework based on exemplar detector rather than category detector. The proposed tracker is an ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each detector is quite specific and discriminative, because it is trained by a single object instance and massive negatives. To improve its adaptivity, we update both object and background models. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our tracking algorithm.Comment: ICIP201

Influence of orientation-dependent grain boundary oxidation on fatigue cracking behaviour in an advanced Ni-based superalloy

Fatigue tests have been conducted on an advanced disc Ni-based superalloy [low solvus, high refractory (LSHR) alloy] at 650°C in air under three-point bend loading to investigate the role of orientation-dependent grain boundary (GB) oxidation in crack initiation and early propagation. It is found that crack initiation occurs mainly from bulged GB oxides, and cracks then predominantly propagate along the oxidised grain boundaries. These bulged oxides are extremely enriched in Co and preferentially form at the boundaries between high and low Schmid factor grains which are inclined normal to the applied tensile stress direction. Meanwhile, relatively flat/thin Ni/ Ti/Al-rich oxide complexes also form at other grain boundaries, but they appear to be much less detrimental in fatigue crack initiation and propagation compared with the bulged GB Co-rich oxide complexes

Orbital Angular Momentum Mode Multiplexer Based on Multimode Micro-Ring Resonator with Angular Gratings

We demonstrate silicon photonic orbital angular momentum multiplexing devices based on mulitmode microring resonator. Up to four optical beams carrying different orbital angular momentum states can be selectively excited from different input ports

3D hydrodynamic analysis of a biomimetic robot fish

This paper presents a three-dimensional (3D) computational fluid dynamic simulation of a biomimetic robot fish. Fluent and user-defined function (UDF) is used to define the movement of the robot fish and the Dynamic Mesh is used to mimic the fish swimming in water. Hydrodynamic analysis is done in this paper too. The aim of this study is to get comparative data about hydrodynamic properties of those guidelines to improve the design, remote control and flexibility of the underwater robot fish

Microembossing of ultrafine grained Al: microstructural analysis and finite element modelling

Ultra fine grained (UFG) Al-1050 processed by equal channel angular pressing (ECAP) and UFG Al-Mg-Cu-Mn processed by high pressure torsion (HPT) were embossed at both room temperature and 300 °C, with the aim of producing micro-channels. The behaviour of Al alloys during the embossing process was analysed using finite element (FE) modelling. The cold embossing of both Al alloys is characterised by a partial pattern transfer, a large embossing force, channels with oblique sidewalls and a large failure rate of the mould. The hot embossing is characterised by straight channel sidewalls, fully transferred patterns and reduced loads which decrease the failure rate of the mould. Hot embossing of UFG Al-Mg-Cu-Mn produced by HPT shows a potential of fabrication of microelectromechanical system (MEMS) components with micro channels

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm