Universiti Teknologi Malaysia

About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Adsorption. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.

A comprehensive study on pathfinding techniques for robotics and video games

Abstract: This survey provides an overview of popular pathfinding algorithms and techniques based on graph generation problems. We focus on recent developments and improvements in existing techniques and examine their impact on robotics and the video games industry. We have categorized pathfinding algorithms based on a 2D/3D environment search. The aim of this paper is to provide researchers with a thorough background on the progress made in the last 10 years in this field, summarize the principal techniques, and describe their results. We also give our expectations for future trends in this field and discuss the possibility of using pathfinding techniques in more extensive areas.

A combination of the ICA-ANN model to predict air-overpressure resulting from blasting

Abstract: Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. This paper presents three non-linear methods, namely empirical, artificial neural network (ANN), and imperialist competitive algorithm (ICA)-ANN to predict AOp induced by blasting operations in Shur river dam, Iran. ICA as a global search population-based algorithm can be used to optimize the weights and biases of the network connection for training by ANN. In this study, 70 blasting operations were investigated and relevant blasting parameters were measured. The most influential parameters on AOp, namely maximum charge per delay and the distance from the blast-face, were considered as input parameters or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R2), root mean square error and value account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models were selected among all constructed models. It was found that the ICA-ANN approach can provide higher performance capacity in predicting AOp compared to other predictive methods.