Abstract of this computer paper-presentations : (Neural network)
Automobiles have become an integrated part of our daily life. The development of technology has improved the automobile industry in both cost & efficiency. Still, accidents prove as challenge to technology. Highway accident news are frequently found in the newspapers. The automobile speed has increased with development in technology through years and the complexity of the accidents has also increased. Higher speeds the accidents prove to be more fatal. Man is intelligent with reasoning power and can respond to any critical situation. But under stress and tension he falls as a prey to accidents. The manual control of speed & braking of a car fails during anxiety. Thus automated speed control & braking system is required to prevent accidents. This automation is possible only with the help of Artificial Intelligence (Fuzzy Logic).
In this paper we are proposing a method of intelligent controlling of injection molding using neural network based control unit. Neural networks have been recently expanding into many areas of engineering science and commerce is an appropriate tool for this task. The characteristics of neural networks that make them promising for manufacturing applications are –
After being trained by observed data, neural networks offer pattern recognition based intelligent control.
In this paper, we describe the artificial evolution of adaptive neural controllers for an outdoor mobile robot equipped with a mobile camera. The robot can dynamically select the gazing direction by moving the body and/or the camera. The neural control system, which maps visual information to motor commands, is evolved online by means of a genetic algorithm, but the synaptic connections (receptive fields) from visual photoreceptors to internal neurons can also be modified by Hebbian plasticity while the robot moves in the environment. We show that robots evolved in physics based simulations with Hebbian visual plasticity display more robust adaptive behavior when transferred to real outdoor environments as compared to robots evolved without visual plasticity.
Face Recognition is the inherent capability of human beings. Identifying a person by face is one of the most fundamental human functions since time immemorial. Face recognition by computer is to endow a machine with capability to approximate in some sense, a similar capability in human beings. To impart this basic human capability to a machine has been a subject of interest over the last few years. Such a machine would find considerable utility in many commercial transactions, personal management, security and the law enforcement applications, especially in criminal identification, authentication in secure system etc. Enough research has not been carried out on identification of human faces. However a number of automated or semi-automated recognition studies have been reported.
Automobiles have become an integrated part of our daily life. The development of technology has improved the automobile industry in both cost & efficiency. Still, accidents prove as challenge to technology. Highway accident news are frequently found in the newspapers. The automobile speed has increased with development in technology through years and the complexity of the accidents has also increased. Higher speeds the accidents prove to be more fatal. Man is intelligent with reasoning power and can respond to any critical situation. But under stress and tension he falls as a prey to accidents. The manual control of speed & braking of a car fails during anxiety. Thus automated speed control & braking system is required to prevent accidents. This automation is possible only with the help of Artificial Intelligence (Fuzzy Logic).
Neural networks applications
ABSTRACTIn this paper we are proposing a method of intelligent controlling of injection molding using neural network based control unit. Neural networks have been recently expanding into many areas of engineering science and commerce is an appropriate tool for this task. The characteristics of neural networks that make them promising for manufacturing applications are –
After being trained by observed data, neural networks offer pattern recognition based intelligent control.
Active Vision and Receptive Field Development In Evolutionary Robots
Abstract:In this paper, we describe the artificial evolution of adaptive neural controllers for an outdoor mobile robot equipped with a mobile camera. The robot can dynamically select the gazing direction by moving the body and/or the camera. The neural control system, which maps visual information to motor commands, is evolved online by means of a genetic algorithm, but the synaptic connections (receptive fields) from visual photoreceptors to internal neurons can also be modified by Hebbian plasticity while the robot moves in the environment. We show that robots evolved in physics based simulations with Hebbian visual plasticity display more robust adaptive behavior when transferred to real outdoor environments as compared to robots evolved without visual plasticity.
Paper presentation: Neural networks and Face recognition
HEM.
ABSTRACT:
Face Recognition is the inherent capability of human beings. Identifying a person by face is one of the most fundamental human functions since time immemorial. Face recognition by computer is to endow a machine with capability to approximate in some sense, a similar capability in human beings. To impart this basic human capability to a machine has been a subject of interest over the last few years. Such a machine would find considerable utility in many commercial transactions, personal management, security and the law enforcement applications, especially in criminal identification, authentication in secure system etc. Enough research has not been carried out on identification of human faces. However a number of automated or semi-automated recognition studies have been reported.