JICE 2017 December

Showing 9–10 of 10 results

  • Developing A Simplified Maintenance & Rehabilitation Activity Prioritization Tool for Afghanistan Roads

    $15.00

    Abstract: Roads are one of the most important factor of life, and maintenance & rehabilitation of them are very vital and challenging for a country. Afghanistan is one of those countries which face the challenges of low-budget, computerized office works and skilled personnel. Regarding to budget limitation pavement maintenance and rehabilitation activity prioritization is obligatory. Currently, a technology based, and simplified maintenance activity prioritization tool are an essential need of the country. The aim of this research is to develop a tool which prioritize the maintenance and rehabilitation activities by considering some factors such as pavement condition index, road width, traffic volume, residential importance as well as maintenance and rehabilitation cost. Since characterizing a model that presents each one of those variables was difficult, a simplified model named TOPSIS was denoted for the issue of prioritization. TOPSIS model lets you have a more precise ranking for the outcome. Considering the problem, Visual Basic have the ability to easily code any type of model and present a graphical display of the model. A source code was developed and Visual Basic was used for computations coding, graphical display of results and generating reports. The developed model indicates that more than two criteria/weighs are very important for prioritizing the alternatives/activities. The developed tool can prioritize the maintenance and rehabilitation activities and generate different database for further use in ArcGIS.

  • A Comparative Study on the Performances of Q-Learning and Neural Q-Learning Agents toward Analysis of Emergency of Communication

    $15.00

    Abstract: In this paper, we suppose the gesture theory that is one theory on the origin of language, which tries to establish that speech originated from gestures. Based on the theory,  we assume that “actions” having some purposes can be used as “symbols” in the communication through a learning process. The purpose of this study is to clarify what abilities of agents and what conditions are necessary to acquire usages of the actions as the symbols. To investigate them, we adopt a collision avoidance game and compare the performances of Q-learning agents with that of Neural Q-learning agents. In our simulation, we found that the Neural Q-learning agent’s ability to reach the goal place is higher than the Q-learning agent’s one. In contrast, the Neural Q-learning agent’s ability to avoid collisions is lower than the Q-learning agent’s one. If the inconsistencies in the learning data sets of the Neural Q-learning agent, however, can be resolved, the agent has enough potential to improve its ability for collision avoidance. Therefore, we conclude that the most suitable agent to analyze the emergence of communication is the Neural Q-learning agent who changed a feed forward type neural network into a recurrent type neural network that can resolve the inconsistencies in the learning data sets.