BP-GA Data Fusion Algorithm Studies Oriented to Smart Home

Wang, Tichun; Xie, Yuzhu

doi:10.18280/mmep.030304

Cited by 11 publications

(10 citation statements)

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“…an N2×N3 matrix. Next, the GA was selected to calculate the weight matrix from the hidden layer to the output layer [42]. Inspired by natural selection and genetic mechanism, the GA can converge to the global optimal solution by fitness function with the probability of 1, without requiring derivative or other information.…”

Section: Figure 1 Topology Of the Rbf Networkmentioning

confidence: 99%

Signal Separation of Phase-sensitive Optical Time-domain Reflectometry Considering Thermo-mechanical Coupling and 3D Data Matching

Wang¹,

Hu²,

Liu³

2019

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This paper aims to enhance the signal separation accuracy of phase-sensitive optical timedomain reflectometry (Φ-OTDR) under thermo-mechanical coupling (TMC). For this purpose, the Φ-OTDR signal was firstly separated by genetic algorithm-empirical mode decompositionnonnegative matrix factorization (GA-EMD-NMF). Then, the Kalman-proportional-integralderivative (PID) method was introduced to further separate the data. Taking the TMC as the input, a feedback model was constructed by the genetic algorithm-radial basis function (GA-RBF) to decouple the TMC. To tackle the 3D Φ-OTDR data, a 3D speeded up robust features (SURF) method was developed to reconstruct the box filter and Gaussian pyramid based on the features of the 3D data, thereby enhancing the positioning accuracy of Φ-OTDR signal. Later, several experiments were carried out in a semi-anechoic room, which proved that the proposed method outperforms the traditional methods. The research findings shed new light on signal separation of Φ-OTDR.

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Section: Figure 1 Topology Of the Rbf Networkmentioning

confidence: 99%

Signal Separation of Phase-sensitive Optical Time-domain Reflectometry Considering Thermo-mechanical Coupling and 3D Data Matching

Wang¹,

Hu²,

Liu³

2019

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“…Raw data, or multiple sets of data of the upper layer can be input to the input layer of the next neuron at the same time, data can be transmitted among input, hidden, and output layers in one-way manner, and the data processed by the upper layer can be transmitted to any neuron in the next layer. The hidden layer is typically multilayered and finally output at the output layer (Gleitsmann et al, 2007;D íaz et al, 2008;Wang and xie, 2016). Error back propagation algorithm is the main characteristic of BP neural network, and mainly embodied in the self-learning, organization and training of the implicit layer (Tarasov, 2006;Liu et al, 2013).…”

Section: Ga-bp Neural Networkmentioning

confidence: 99%

Energy Consumption Control and Optimization of Large Power Grid Operation Based on Artificial Neural Network Algorithm

Gong¹

2018

Neuroquantology

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Green, energy-saving, efficient and reliable smart grids have become one of the most important development areas in the world. An artificial neural network algorithm based on cranial nerve principle proposed in this paper provides reference for power flow analysis, reasonable dispatching and control decision-making of smart grids. At the same time, new EMS (Energy Management System) structure and strategy are introduced to optimize resource allocation and energy management. Based on the single-layer network control theory of large power grid, BP neural network including genetic algorithm is constructed and combined with regression analysis to realize the optimization of power analysis. In addition, a neuron controller is set up in the distributed unit to collect, monitor and control the parameters and send them to the high-level central controller, forming a multi-level threedimensional network to analyze and make decisions, accurately predict the energy consumption of power grids, and improve the control level of the energy consumption of smart grids.

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“…WoT enables information interaction between humans and things, things and things, people and spaces with perceptive and wireless communication technologies [1][2][3]. As a plurality of applications have sprung up, such as 3D printing, smart wearable devices and unmanned technologies, the WoT has ushered in a new era of rapidly developed 2.0 version.…”

Section: Introductionmentioning

confidence: 99%

RFID Anti-Collision Technology in Big Data Environment

Bu-ying¹

2018

Int. J. Onl. Eng.

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Abstract-Along with the rapid development of Web of Things, the RFID technology is widely applied in every field, but today, the great challenge we face is how to avoid information conflict and collision in the process of acquisition and treatment of massive information. It is the keystone of the study. This paper conducts a comparative analysis on different Tree-based algorithms as improved, and integrated with multiple sub-cycle response mechanism, a MultiResponse Collision Tree algorithm is proposed. And beyond that, this paper simulates and analyzed this algorithm and other improved ones. The results reveal that, compared to other algorithms, MRCT algorithm features a better performance, less recognition cycles, least query time slots on average, and ceiling throughput rate.Keywords-RFID; anti-collision; Tree-based algorithm; Manchester code IntroductionThe popularity of Web of Things attributes to the spread of Internet. WoT enables information interaction between humans and things, things and things, people and spaces with perceptive and wireless communication technologies [1][2][3]. As a plurality of applications have sprung up, such as 3D printing, smart wearable devices and unmanned technologies, the WoT has ushered in a new era of rapidly developed 2.0 version. This technology began to develop on a large scale, collaboratively and intelligently, and has infiltrated into all fields of our daily life, so that it plays a strategic role in the development of China's economy. However, the first level of the WoT, i.e. the perceptive technology, faces a severe challenge [4]. The performance of perceived information is poor under the complex scale of the WoT, while the energy and time consumptions of data transmission hit the mark. Whence, how to improve the reliability and availability of perceptive technology in big data environment has become the focus the current WoT technology should conquer [5][6].In relation to other recognition technologies, Radio Frequency Identification (RFID) technology is finding wider and wider application due to its advantages of non-contact, multi-target recognition and strong anti-interference. In 2003, the United States began to vigorously spread FRID; after 2007, the FRID industry showed a development in super express and on a large scale; in 2016, China's market volume 42

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BP-GA Data Fusion Algorithm Studies Oriented to Smart Home

Cited by 11 publications

References 0 publications

Signal Separation of Phase-sensitive Optical Time-domain Reflectometry Considering Thermo-mechanical Coupling and 3D Data Matching

Signal Separation of Phase-sensitive Optical Time-domain Reflectometry Considering Thermo-mechanical Coupling and 3D Data Matching

Energy Consumption Control and Optimization of Large Power Grid Operation Based on Artificial Neural Network Algorithm

RFID Anti-Collision Technology in Big Data Environment

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