Observation in absolute darkness and daytime under every atmospheric situation is one of the advantages of thermal imaging systems. In spite of increasing trend of using these systems, there are still lots of difficulties in analysing thermal images due to the variable features of pedestrians and atmospheric situations. In this paper an efficient method is proposed for detecting pedestrians in ...

In this paper, we investigated interactive learning between human subjects and robot experimentally, and its essential characteristics are examined using the dynamical systems approach. Our research concentrated on the navigation system of a specially developed humanoid robot called Robovie and seven human subjects whose eyes were covered , making them dependent on the robot for directions. We ...

Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems help ensure compliance with the quality of drinking wastewater treatment. Therefore, modelling predicting to control pollution has been widely researched. The novelty proposed system is presented develop an efficient operation monitoring a sustainable friendly green environment. In this work, ada...

The higher share of renewable energy sources in the electrical grid and electrification significant sectors, such as transport heating, are imposing a tremendous challenge on operation system due to increase complexity, variability uncertainties associated with these changes. recent advances computational technologies ever-growing data availability allowed development sophisticated efficient al...

Fruit classification is found to be one of the rising fields in computer and machine vision. Many deep learning-based procedures worked out so far classify images may have some ill-posed issues. The performance scheme depends on range captured images, volume features, types characters, choice features from extracted type classifiers used. This paper aims propose a novel learning approach consis...

In the oil industry, productivity of wells depends on performance sub-surface equipment system. These systems often have problems stemming from sand, corrosion, internal pressure variation, or other factors. order to ensure high and avoid high-cost losses, it is essential identify source possible failures in early stage. However, this requires additional maintenance fees human power. Moreover, ...

in this paper, the application of neural networks for simulation and optimization of the cogeneration systems has been presented. cgam problem, a benchmark in cogeneration systems, is chosen as a casestudy. thermodynamic model includes precise modeling of the whole plant. for simulation of the steadysate behavior, the static neural network is applied. then using dynamic neural network, plant is...

Aerodynamic parameter estimation is critical in the aviation sector, especially design and development programs of defense-military aircraft. In this paper, new results application Artificial Neural Networks (ANN) to field aircraft are presented. The performances Feedforward Network (FFNN) with Backpropagation FFNN using Recursive Least Square (RLS) investigated for aerodynamic estimation. meth...

Short-term traffic flow prediction (TFP) is an important area in intelligent transportation system (ITS), which used to reduce congestion. But the avail of data with temporal features and periodic are susceptible weather conditions, making TFP a challenging issue. process significantly influenced by several factors like accident weather. Particularly, inclement conditions may have extreme impac...

the main purpose of this paper is prediction of tse corporate financial bankruptcy using artificial neural networks. the mean values of key ratios reported in past bankruptcy studies were selected for neural network inputs (working capital to total assets, net income to total assets, total debt to total assets, current assets to current liabilities, quick assets to current liabilities). the neu...