On the Warning System of Obstacle Avoidance of Embedded Electronic Guide Dogs
Issue:
Volume 1, Issue 5, October 2013
Pages:
99-102
Received:
4 August 2013
Published:
10 September 2013
Abstract: With the rapid development of China's transportation, the frequency of traffic accidents is also high. This not only restricted the development of China's transportation greatly, but also threatened to people's safety seriously. In particular, the accidents caused by the blind due to there are more frequent reproduction, so their traffic safety has become a big issue to solve urgently. In the program, a new type of “warning system of obstacle avoidance of embedded electronic guide dog” has been developed on the basis of careful analysis of all kinds of present anti-collision warning systems, which has a core micro-controller, 32-bit ARM7 microprocessor, and takes the embedded operating system uCLinux as its platform. Such warning system of obstacle avoidance of embedded electronic guide dog can effectively eliminate the impact of the traffic environment and the subjective factors of the blind, warning in advance for the travelling blind in time, effectively avoiding obstacles such as vehicles, to reduce traffic accidents caused by the their blindness. This humane technology innovation is the specific embodiment of environmental science and technology aesthetic theory in the field of scientific and technological innovation. It has a positive and promoting role to the development of transportation and blind-man welfare in China.
Abstract: With the rapid development of China's transportation, the frequency of traffic accidents is also high. This not only restricted the development of China's transportation greatly, but also threatened to people's safety seriously. In particular, the accidents caused by the blind due to there are more frequent reproduction, so their traffic safety has...
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Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm
Reza Narimani,
Ahmad Narimani
Issue:
Volume 1, Issue 5, October 2013
Pages:
103-112
Received:
18 August 2013
Published:
20 September 2013
Abstract: This paper describes a credit risk evaluation system that uses supervised probabilistic neural network (PNN) models based on the Dynamic Decay learning algorithm (DDA). The PNN-DDA has two parameters called positive and negative threshold. This learning algorithm trains very quickly. Thus it makes sense that we use a meta-heuristic algorithm such as particle swarm optimization to optimize these parameters. When using the meta-heuristic algorithm such PSO, the tuning process of parameters is implemented wisely. Thus in this paper we also obtained optimum threshold. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the proposed model. The result shows that this new hybrid algorithm outperforms the most common used algorithm such as multi-layer neural network.
Abstract: This paper describes a credit risk evaluation system that uses supervised probabilistic neural network (PNN) models based on the Dynamic Decay learning algorithm (DDA). The PNN-DDA has two parameters called positive and negative threshold. This learning algorithm trains very quickly. Thus it makes sense that we use a meta-heuristic algorithm such a...
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A Comparative Evolutionary Models for Solving Sudoku
A. A. Ojugo.,
D. Oyemade.,
R. E. Yoro.,
A. O. Eboka.,
M. O. Yerokun,
E. Ugboh
Issue:
Volume 1, Issue 5, October 2013
Pages:
113-120
Received:
8 July 2013
Published:
10 November 2013
Abstract: Evolutionary algorithms have become robust tool in data processing and modeling of dynamic, complex and non-linear processes due to their flexible mathematical structure to yield optimal results even with imprecise, ambiguity and noise at its input. The study investigates evolutionary algorithms for solving Sudoku task. Various hybrids are presented here as veritable algorithm for computing dynamic and discrete states in multipoint search in CSPs optimization with application areas to include image and video analysis, communication and network design/reconstruction, control, OS resource allocation and scheduling, multiprocessor load balancing, parallel processing, medicine, finance, security and military, fault diagnosis/recovery, cloud and clustering computing to mention a few. Solution space representation and fitness functions (as common to all algorithms) were discussed. For support and confidence model adopted 1=0.2 and 2=0.8 respectively yields better convergence rates – as other suggested value combinations led to either a slower or non-convergence. CGA found an optimal solution in 32 seconds after 188 iterations in 25runs; while GSAGA found its optimal solution in 18seconds after 402 iterations with a fitness progression achieved in 25runs and consequently, GASA found an optimal solution 2.112seconds after 391 iterations with fitness progression after 25runs respectively.
Abstract: Evolutionary algorithms have become robust tool in data processing and modeling of dynamic, complex and non-linear processes due to their flexible mathematical structure to yield optimal results even with imprecise, ambiguity and noise at its input. The study investigates evolutionary algorithms for solving Sudoku task. Various hybrids are presente...
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