A C-ITS based lane changing advisory for weaving sections

This study examines the benefits of Cooperative Intelligent Transport Systems (C-ITS) in weaving sections. The research proposes a lane-changing advisory application to alleviate the lane-changing concentration in weaving sections by coordinating weaving vehicles. While non-weaving vehicles travel as normal, weaving vehicles are monitored and advised through personalized messages based on their destination lane. The findings of this research, derived from a microscopic simulation in AIMSUN, reveal that the proposed strategy has the potential to improve delay significantly and that it can be applied to any existing one-sided weaving sections.

Searching for semantic person queries using channel representations

It is not uncommon to hear a person of interest described by their height, build, and clothing (i.e. type and colour). These semantic descriptions are commonly used by people to describe others, as they are quick to relate and easy to understand. However such queries are not easily utilised within intelligent surveillance systems as they are difficult to transform into a representation that can be searched for automatically in large camera networks. In this paper we propose a novel approach that transforms such a semantic query into an avatar that is searchable within a video stream, and demonstrate state-of-the-art performance for locating a subject in video based on a description.

On the use of optical flow for scene change detection and description

We propose the use of optical flow information as a method for detecting and describing changes in the environment, from the perspective of a mobile camera. We analyze the characteristics of the optical flow signal and demonstrate how robust flow vectors can be generated and used for the detection of depth discontinuities and appearance changes at key locations. To successfully achieve this task, a full discussion on camera positioning, distortion compensation, noise filtering, and parameter estimation is presented. We then extract statistical attributes from the flow signal to describe the location of the scene changes. We also employ clustering and dominant shape of vectors to increase the descriptiveness. Once a database of nodes (where a node is a detected scene change) and their corresponding flow features is created, matching can be performed whenever nodes are encountered, such that topological localization can be achieved. We retrieve the most likely node according to the Mahalanobis and Chi-square distances between the current frame and the database. The results illustrate the applicability of the technique for detecting and describing scene changes in diverse lighting conditions, considering indoor and outdoor environments and different robot platforms.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Sense of modern heritage

As the society matures, there was an increasing pressure to preserve historic buildings. The economic cost in maintaining these important heritage legacies has become the prime consideration of every state. Dedicated intelligent monitoring systems supplementing the traditional building inspections will enable the stakeholder to carry out not only timely reactive response but also plan the maintenance in a more vigilant approach; thus, preventing further degradation which was very costly and difficult to address if neglected. The application of the intelligent structural health monitoring system in this case studies of ‘modern heritage’ buildings is on its infancy but it is an innovative approach in building maintenance. ‘Modern heritage’ buildings were the product of technological change and were made of synthetic materials such as reinforced concrete and steel. Architectural buildings that was very common in Oceania and The Pacific. Engineering problems that arose from this type of building calls for immediate engineering solution since the deterioration rate is exponential. The application of this newly emerging monitoring system will improve the traditional maintenance system on heritage conservation. Savings in time and resources can be achieved if only pathological results were on hand. This case study will validate that approach. This publication will serve as a position paper to the on-going research regarding application of (Structural Health Monitoring) SHM systems to heritage buildings in Brisbane, Australia. It will be investigated with the application of the SHM systems and devices to validate the integrity of the recent structural restoration of the newly re-strengthened heritage building, the Brisbane City Hall.