A modified BP network using malsburg learning for rotation and location invariant fingerprint recognition and localization with and without occlusion

Background: Systems of the Internet of Things are actively implementing biometric systems. For fast and high-quality recognition in sensory biometric control and management systems, skeletonization methods are used at the stage of fingerprint recognition. The analysis of the known skeletonization methods of Zhang-Suen, Hilditch, Ateb-Gabor with the wave skeletonization method has been carried out and it shows a good time and qualitative recognition results. Methods: The methods of Zhang-Suen, Hildich and thinning algorithm based on Ateb-Gabor filtration, which form the skeletons of biometric fingerprint images, are considered. The proposed thinning algorithm based on Ateb-Gabor filtration showed better efficiency because it is based on the best type of filtering, which is both a combination of the classic Gabor function and the harmonic Ateb function. The combination of this type of filtration makes it possible to more accurately form the surroundings where the skeleton is formed. Results: Along with the known ones, a new Ateb-Gabor filtering algorithm with the wave skeletonization method has been developed, the recognition results of which have better quality, which allows to increase the recognition quality from 3 to 10%. Conclusion: The Zhang-Suen algorithm is a 2-way algorithm, so for each iteration, it performs two sets of checks during which pixels are removed from the image. Zhang-Suen's algorithm works on a plot of black pixels with eight neighbors. This means that the pixels found along the edges of the image are not analyzed. Hilditch thinning algorithm occurs in several passages, where the algorithm checks all pixels and decides whether to replace a pixel from black to white if certain conditions are satisfied. This Ateb-Gabor filtering will provide better performance, as it allows to obtain more hollow shapes, organize a larger range of curves. Numerous experimental studies confirm the effectiveness of the proposed method.

Download Full-text

Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096511666180629152127 ◽

2019 ◽

Vol 12 (3) ◽

pp. 277-283

Author(s):

Jiatang Cheng ◽

Yan Xiong

Keyword(s):

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Wind Turbine ◽

Bp Neural Network ◽

Quantum Particle ◽

Particle Swarm ◽

Wind Turbine Gearbox ◽

Bp Network ◽

Swarm Optimization ◽

Quantum Particle Swarm Optimization

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

Download Full-text

Damaged Fingerprint Recognition by Convolutional Long Short-Term Memory Networks for Forensic Purposes

2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP) ◽

10.1109/csp51677.2021.9357588 ◽

2021 ◽

Author(s):

Jaouhar Fattahi ◽

Mohamed Mejri

Keyword(s):

Short Term Memory ◽

Fingerprint Recognition ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Download Full-text

Grassland resource evaluation based on improved bp network model and analytic hierarchy process

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189540 ◽

2020 ◽

pp. 1-12

Author(s):

Zhou Jiang ◽

Zhenwu Wei

Keyword(s):

Sustainable Development ◽

Analytic Hierarchy Process ◽

Carrying Capacity ◽

Index System ◽

Evaluation Index System ◽

Analytic Hierarchy ◽

Bp Network ◽

Resource Evaluation ◽

Environmental Carrying Capacity ◽

Hierarchy Process

Grassland resources are an important part of land resources. Moreover, it has the functions of regulating the climate, windproof and sand fixation, conserving water sources, maintaining water and soil, raising livestock, providing food, purifying the air, and beautifying the environment in terrestrial ecosystems. Grassland resource evaluation is of great significance to the sustainable development of grassland resources. Therefore, this paper improves the BP neural network, uses the comprehensive index method to calculate the weights in the analytic hierarchy process, and constructs a water resources carrying capacity research and analysis system based on the entropy weight extension decision theory. Meanwhile, this paper analyzes different levels of resource and environmental carrying capacity to achieve the purpose of comprehensive evaluation of resource and environmental carrying capacity. In addition, based on the theory of sustainable development, under the guidance of the principle of index system construction, this paper studies the actual situation of grassland resources and the availability and operability of data, and combines with the opinions given by experts to form an evaluation index system of grassland resources and environmental carrying capacity. Finally, through the actual case study analysis, it is concluded that the model constructed in this paper has a certain effect.

Download Full-text

P‐77: LTPS TDDI with Full‐screen Fingerprint Recognition

SID Symposium Digest of Technical Papers ◽

10.1002/sdtp.14959 ◽

2021 ◽

Vol 52 (1) ◽

pp. 1368-1371

Author(s):

Bozhi Liu ◽

Xuanxian Cai ◽

Jiaqian Wu ◽

Xiaoxiao Wu ◽

Binbin Chen ◽

...

Keyword(s):

Fingerprint Recognition ◽

Full Screen

Download Full-text

P‐74: Large‐Area Optical Fingerprint‐Recognition Scheme under LCD Screen

SID Symposium Digest of Technical Papers ◽

10.1002/sdtp.14956 ◽

2021 ◽

Vol 52 (1) ◽

pp. 1358-1360

Author(s):

Xiaowei Ye ◽

Guangkun Liu ◽

Zhou Zhang ◽

Guowei Zha ◽

Guanghui Liu

Keyword(s):

Fingerprint Recognition ◽

Large Area

Download Full-text

Financial market trend analysis based on autoregressive conditional heteroscedasticity model and BP neural network prediction

International Journal of Electrical Engineering Education ◽

10.1177/0020720920988497 ◽

2021 ◽

pp. 002072092098849

Author(s):

Xinzhe Yin ◽

Jinghua Li

Keyword(s):

Neural Network ◽

Trend Analysis ◽

Financial Market ◽

Prediction Method ◽

Good Effect ◽

Trend Prediction ◽

Bp Network ◽

Conditional Heteroscedasticity ◽

Bp Model ◽

Autoregressive Conditional Heteroscedasticity

Many experts and scholars at home and abroad have studied this topic in depth, laying a solid foundation for the research of financial market prediction. At present, the mainstream prediction method is to use neural network and autoregressive conditional heteroscedasticity to build models, which is a more scientific way, and also verified the feasibility of the way in many studies. In order to improve the accuracy of financial market trend prediction, this paper studies in detail the neural network system represented by BP and the autoregressive conditional heterogeneous variance model represented by GARCH. Analyze its structure and algorithm, combine the advantages of both, create a GARCH-BP model, and transform its combination structure and optimize the algorithm according to the uniqueness of the financial market, so as to meet the market as much as possible Characteristics. The novelty of this paper is the construction of the autoregressive conditional heteroscedasticity model, which lays the foundation for the prediction of financial market trends through the construction of the model. However, there are some shortcomings in this article. The overall overview of the financial market is not very clear, and the prediction of the BP network is not so comprehensive. Finally, through the actual data statistics of market transactions, the effectiveness of the GARCH-BP model was tested, analyzed and researched. The final results show that model has a good effect on the prediction and trend analysis of market, and its accuracy and availability greatly improved compared with the previous conventional approach, which is worth further study and extensive research It is believed that the financial market prediction model will become one of the mainstream tools in the industry after its later improvement.

Download Full-text