Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network

The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.

Download Full-text

Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network

10.36227/techrxiv.13350866.v1 ◽

2020 ◽

Author(s):

Sorush Niknamian

Keyword(s):

Genetic Algorithm ◽

Wireless Sensor Network ◽

False Alarm ◽

False Alarm Rate ◽

Sensor Network ◽

Fitness Function ◽

Recognition Rate ◽

Accurate Method ◽

Wireless Sensor ◽

Vehicle Load

The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.

Download Full-text

Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network

10.31219/osf.io/6yrv5 ◽

2020 ◽

Author(s):

Sorush Niknamian

Keyword(s):

Genetic Algorithm ◽

Wireless Sensor Network ◽

False Alarm ◽

False Alarm Rate ◽

Sensor Network ◽

Fitness Function ◽

Recognition Rate ◽

Accurate Method ◽

Wireless Sensor ◽

Vehicle Load

Abstract: The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.

Download Full-text

Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network

10.31219/osf.io/x93mr ◽

2019 ◽

Author(s):

Sorush Niknamian

Keyword(s):

Genetic Algorithm ◽

Wireless Sensor Network ◽

False Alarm ◽

False Alarm Rate ◽

Sensor Network ◽

Fitness Function ◽

Recognition Rate ◽

Accurate Method ◽

Wireless Sensor ◽

Vehicle Load

The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.

Download Full-text

Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network

10.31219/osf.io/426na ◽

2019 ◽

Author(s):

Sorush Niknamian

Keyword(s):

Genetic Algorithm ◽

Wireless Sensor Network ◽

False Alarm ◽

False Alarm Rate ◽

Sensor Network ◽

Fitness Function ◽

Recognition Rate ◽

Accurate Method ◽

Wireless Sensor ◽

Vehicle Load

The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.

Download Full-text