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intelligent sensor selection for object recognition using simulated annealing and neural network

Intelligent sensor selection for object recognition using si...

引用

2011 3rd IEEE International Conference on Information Management and Engineering(ICIME 2011)

作者： Abdolhossein Alipoor Mehdi Fesharaki Islamic Azad University CE Department Babol Maleke Ashtar University CE Department Tehran

ISBN: (纸本)9781612842387

Today object recognition by using sensor fusion systems is one of the main issues for target detection and environmental *** intelligent sensor selection for monitoring operations is the serious subject to reduce information processing time and increasing information fusion *** condition and target characteristic are two parameters which can change sensor value in target recognition so for recognizing targets,a group of sensors which have more recognition rates,must be selected *** paper attempts to design an intelligent sensor selection service by using optimization algorithms and neural *** service specifies the best group of sensors having highest recognition rate in each *** propose two different optimization algorithms including simulated annealing and hill climbing to help accessing the best sensory configuration for a definite *** network is used to perform as fitness function of optimization *** define 500 different scenarios for 6 different sensors in different *** recognition rate of each sensor group is *** rates are used for neural networks training *** defining new scenarios and run optimization algorithms in these scenarios we find which sensors by which recognition ability must be used to reach the highest recognition *** contribution of this paper is the use of neural network as an estimator to evaluate the fitness value of each optimization algorithms but in all previous methods at first made a fitness function and then use optimization approach to find the optimal solution.

关键词： intelligent sensor selection Objects Recognition Rate Simulated Annealing Hill Climbing Neural Network.

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Multi objective optimization for object recognition

Multi objective optimization for object recognition

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2010 2nd International Conference on Education Technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

作者： Abdolhossein Alipoor Mehdi Fesharaki Science & Research Branch Islamic Azad University CE Department Tehran Iran Maleke Ashtar University CE Department Tehran Iran

ISBN: (纸本)9781424463671;9781424463688;9781424463701

Relevant with some important subjects like target recognition, sensor fusion systems can be considered as one of the main issues highlighting here. Environmental condition, target characteristic and sensor efficiency are three parameters which can impress on sensor value in target recognition so for recognizing targets, a group of sensors which have more recognition rates, must be selected intelligently. Utilizing many sensors to acquire the highest object recognition rate would have extra cost and decrease energy of mobile sensors rapidly. Therefore make a tradeoff between sensor numbers and object recognition rate would be imperatively. This paper attempts to design a multi objective optimization service by using optimization algorithm and neural network. This service specifies highest recognition rate for each distinct sensor numbers. We propose multi objective optimization algorithm to help accessing the best sensory configuration for a definite environment regarding to the environmental conditions, sensors performance, and object features. Our multi objective optimization algorithm has two functions. Genetic algorithm is used to perform as one of functions to specify object recognition rates of each sensor group. Neural network is used to perform as fitness function of each genetic algorithm chromosomes. Another function is sensor numbers determinant. Highest recognition rate and lowest sensor numbers are two objects which multi objective optimization algorithm wants to make a balance between them. We define 500 different scenarios for 6 different sensors in different conditions. Object recognition rate of each sensor is collected. These rates are used for neural networks training process. By defining new scenario and run multi objective optimization algorithm in this scenario, this algorithm makes a Pareto front between sensor numbers and object recognition rate. Finally this algorithm finds, by distinct numbers of sensor, which sensors by which recognition ab

关键词： Automatic sensor Management intelligent sensor selection Objects Recognition Rate multi objective optimization Genetic Algorithm Neural Network

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Multi Criteria Decision Making for Optimal sensor selection

Multi Criteria Decision Making for Optimal Sensor Selection

引用

International Conference on Computer Communication and Management（ICCCM 2011）

作者： Abdolhossein Alipoor Farhad Hadinejad CE Department Islamic Azad University Babol Branch Iran IE Department Shomal University Amol Iran

Today object recognition by using sensor fusion systems is one of the main issues for target detection and environmental monitoring. Environmental condition and target characteristic are two parameters which can change sensor value in target recognition and there are many factors which can impact on the sensor numbers utilizing for object recognition. Object recognition rate, object recognition time, operation cost and system’s reinforcement rate are four important criteria help for optimal sensor selection. sensor selection system designers according to Environmental condition and target characteristic, weighting these four factors in every situation. Therefore optimal sensor selection according to criteria preference decides which sensors group must be utilized to obtain the best consequence. This paper attempts to design an optimization service by using Multi Criteria Decision Making （MCDM） techniques like AHP and PROMETHEE and their related soft ware’s EXPERT CHOICE and DECISION LAB for criteria weighting. For Achieving the object recognition rate and object recognition time of every sensory group, genetic algorithm and neural network are used. This service specifies highest recognition rate for each sensory group. The contribution of this paper is the use of neural network as an estimator to evaluate the fitness value of each optimization algorithms and after that select optimal sensory group by utilizing MCDM method based on four significant factors.

关键词： intelligent sensor selection Multi Criteria Decision Making Genetic Algorithm Neural Network.

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