EU-China collaboration in design: research in Web-enabled collaborative design supported by the Asia-Link and Asia IT&C projects

The research of Web-enabled collaboration in total design supported by the European Union's Asia Link project [1] and Asia IT&C project is reported in this paper. The two projects both aim at enhancing research collaboration between the EU and China. The Virtual Research Institute (VRI) is described first, which is the platform for the collaboration for the Asia Link project and is established by utilizing the advanced Web techniques; and then, the framework for the collaboration and the Web techniques involved in the research are presented which represent the major research of the Asia IT&C project. The effective collaboration between the project partners and the impacts of the project outcome on the partnership are also discussed

Hypergraph Neural Networks

In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-the-art methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.Comment: Accepted in AAAI'201

Understanding best practices in control engineering education using the concept of TPACK

This study aimed to design an integrated pedagogical approach to advance introductory Process Control Engineering Education through the application of the Technological Pedagogical Content Knowledge (TPACK) framework, and evaluating its impact on student learning. The research is initially being undertaken at Nottingham Trent University, UK but we will next adapt it to a case study in Libya. This paper aims to strengthen the teaching of introductory Process Control by using appropriate approach es in universities to improve the learning outcomes for students. From this work a new schematic for teaching Process Control ha s be en developed and, moreover, a thoughtful best practice in introducing Process Control in engineering education can be developed

Experimental and analytical performance investigation of air to air two phase closed thermosyphon based heat exchangers

In recent years, the use of wickless heat pipes (thermosyphons) in heat exchangers has been on the rise, particularly in gas to gas heat recovery applications due to their reliability and the level of contingency they offer compared to conventional heat exchangers. Recent technological advances in the manufacturing processes and production of gravity assisted heat pipes (thermosyphons) have resulted in significant improvements in both quality and cost of industrial heat pipe heat exchangers. This in turn has broadened the potential for their usage in industrial waste heat recovery applications. In this paper, a tool to predict the performance of an air to air thermosyphon based heat exchanger using the ε-NTU method is explored. This tool allows the predetermination of variables such as the overall heat transfer coefficient, effectiveness, pressure drop and heat exchanger duty according to the flow characteristics and the thermosyphons configuration within the heat exchanger. The new tool's predictions were validated experimentally and a good correlation between the theoretical predictions and the experimental data, was observed. © 2014 Elsevier Ltd. All rights reserved

Unsupervised Learning of Long-Term Motion Dynamics for Videos

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, Depth, and RGB-D videos.Comment: CVPR 201