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Tactile texture is an important factor to determine to impression of an object. To measure a feeling of an object, a numerical evaluation method for tactile texture is required. In this paper, we propose a novel recognition method for tactile texture using deep convolutional neural networks. In proposed framework, the tactile texture information is obtained by analyzing a time-series data of a pressure...
Accurate models for electric power load forecasting are essential to the operation and planning of the electricity in the energy management system(EMS). In the economic dispatch problem, load forecasting helps an electric utility to make important decisions including the battery charging schedule and the generation of electric power. This paper considers several methods for load forecasting based...
Biotechnological processes are very complex systems for modeling, due to the presence of live microorganisms, nonlinear behavior and time-variant features. Many parameters of the process are uncertain and the industrial operating conditions are determined empirically for them. This paper presents a robust dynamic simulator in Matlab/Simulink environment from the work of Scaglia and the study of experimental...
Though the classical robotics is highly proficient in accomplishing a lot of complex tasks, still it is far from exhibiting the human-like natural intelligence in terms of flexibility and reliability to work in dynamic scenarios. In order to render these qualities in the robots, reinforcement learning could prove to be quite effective. By employing learning based training provided by reinforcement...
Unintended lane departure accidents are due to driver's inattention, incapacitation, and drowsiness. Lane departure warning systems have been developed to enhance traffic safety by predicting/detecting driving situation and alerting drivers to avoid or mitigate traffic accidents. This paper explores effectiveness of a three-layer perceptron neural network in predicting an unintentional lane departure,...
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