scholarly journals Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3293 ◽  
Author(s):  
Hongquan Qu ◽  
Tingliang Feng ◽  
Yuan Zhang ◽  
Yanping Wang
Keyword(s):  
Optical Fiber ◽  
Ensemble Learning ◽  
Oil And Gas ◽  
Recognition Performance ◽  
Small Sample ◽  
Signal Recognition ◽  
Bootstrap Sampling ◽  
Vibration Signals ◽  
Uniform Noise ◽  
Noisy Signals

Optical fiber pre-warning systems (OFPS) based on Φ-OTDR are applied to many different scenarios such as oil and gas pipeline protection. The recognition of fiber vibration signals is one of the most important parts of this system. According to the characteristics of small sample set, we choose stochastic configuration network (SCN) for recognition. However, due to the interference of environmental and mechanical noise, the recognition effect of vibration signals will be affected. In order to study the effect of noise on signal recognition performance, we recognize noisy optical fiber vibration signals, which superimposed analog white Gaussian noise, white uniform noise, Rayleigh distributed noise, and exponentially distributed noise. Meanwhile, bootstrap sampling (bagging) and AdaBoost ensemble learning methods are combined with original SCN, and Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN are proposed and compared for noisy signals recognition. Results show that: (1) the recognition rates of two classifiers combined with AdaBoost are higher than the other two methods over the entire noise range; (2) the recognition for noisy signals of AdaBoost-Bootstrap-SCN is better than other methods in recognition of noisy signals.

scholarly journals Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3663
Author(s):  
Zun Shen ◽  
Qingfeng Wu ◽  
Zhi Wang ◽  
Guoyi Chen ◽  
Bin Lin
Keyword(s):  
Diabetic Retinopathy ◽  
Prediction Model ◽  
Ensemble Learning ◽  
Positive Impact ◽  
Reduction Rate ◽  
Developed Countries ◽  
Small Sample ◽  
Feature Reduction ◽  
Physical Data ◽  
Diabetes Retinopathy

(1) Background: Diabetic retinopathy, one of the most serious complications of diabetes, is the primary cause of blindness in developed countries. Therefore, the prediction of diabetic retinopathy has a positive impact on its early detection and treatment. The prediction of diabetic retinopathy based on high-dimensional and small-sample-structured datasets (such as biochemical data and physical data) was the problem to be solved in this study. (2) Methods: This study proposed the XGB-Stacking model with the foundation of XGBoost and stacking. First, a wrapped feature selection algorithm, XGBIBS (Improved Backward Search Based on XGBoost), was used to reduce data feature redundancy and improve the effect of a single ensemble learning classifier. Second, in view of the slight limitation of a single classifier, a stacking model fusion method, Sel-Stacking (Select-Stacking), which keeps Label-Proba as the input matrix of meta-classifier and determines the optimal combination of learners by a global search, was used in the XGB-Stacking model. (3) Results: XGBIBS greatly improved the prediction accuracy and the feature reduction rate of a single classifier. Compared to a single classifier, the accuracy of the Sel-Stacking model was improved to varying degrees. Experiments proved that the prediction model of XGB-Stacking based on the XGBIBS algorithm and the Sel-Stacking method made effective predictions on diabetes retinopathy. (4) Conclusion: The XGB-Stacking prediction model of diabetic retinopathy based on biochemical and physical data had outstanding performance. This is highly significant to improve the screening efficiency of diabetes retinopathy and reduce the cost of diagnosis.

scholarly journals Review of Femtosecond-Laser-Inscribed Fiber Bragg Gratings: Fabrication Technologies and Sensing Applications

2021 ◽  
Author(s):  
Jun He ◽  
Baijie Xu ◽  
Xizhen Xu ◽  
Changrui Liao ◽  
Yiping Wang
Keyword(s):  
Femtosecond Laser ◽  
Optical Fiber ◽  
Oil And Gas ◽  
Optical Fiber Sensor ◽  
Phase Mask ◽  
Fiber Types ◽  
Direct Writing ◽  
Excellent Thermal Stability ◽  
Sensing Applications ◽  
Compact Size

AbstractFiber Bragg grating (FBG) is the most widely used optical fiber sensor due to its compact size, high sensitivity, and easiness for multiplexing. Conventional FBGs fabricated by using an ultraviolet (UV) laser phase-mask method require the sensitization of the optical fiber and could not be used at high temperatures. Recently, the fabrication of FBGs by using a femtosecond laser has attracted extensive interests due to its excellent flexibility in creating FBGs array or special FBGs with complex spectra. The femtosecond laser could also be used for inscribing various FBGs on almost all fiber types, even fibers without any photosensitivity. Such femtosecond-laser-induced FBGs exhibit excellent thermal stability, which is suitable for sensing in harsh environment. In this review, we present the historical developments and recent advances in the fabrication technologies and sensing applications of femtosecond-laser-inscribed FBGs. Firstly, the mechanism of femtosecond-laser-induced material modification is introduced. And then, three different fabrication technologies, i.e., femtosecond laser phase mask technology, femtosecond laser holographic interferometry, and femtosecond laser direct writing technology, are discussed. Finally, the advances in high-temperature sensing applications and vector bending sensing applications of various femtosecond-laser-inscribed FBGs are summarized. Such femtosecond-laser-inscribed FBGs are promising in many industrial areas, such as aerospace vehicles, nuclear plants, oil and gas explorations, and advanced robotics in harsh environments.

Virtual samples based robust block-diagonal dictionary learning for face recognition

2021 ◽  
Vol 25 (5) ◽  
pp. 1273-1290
Author(s):  
Shuangxi Wang ◽  
Hongwei Ge ◽  
Jinlong Yang ◽  
Shuzhi Su
Keyword(s):  
Face Recognition ◽  
Dictionary Learning ◽  
Recognition Performance ◽  
Small Sample Size ◽  
Small Sample ◽  
Low Rank ◽  
Negative Effects ◽  
Linear Classifier ◽  
Virtual Samples ◽  
Block Diagonal

It is an open question to learn an over-complete dictionary from a limited number of face samples, and the inherent attributes of the samples are underutilized. Besides, the recognition performance may be adversely affected by the noise (and outliers), and the strict binary label based linear classifier is not appropriate for face recognition. To solve above problems, we propose a virtual samples based robust block-diagonal dictionary learning for face recognition. In the proposed model, the original samples and virtual samples are combined to solve the small sample size problem, and both the structure constraint and the low rank constraint are exploited to preserve the intrinsic attributes of the samples. In addition, the fidelity term can effectively reduce negative effects of noise (and outliers), and the ε-dragging is utilized to promote the performance of the linear classifier. Finally, extensive experiments are conducted in comparison with many state-of-the-art methods on benchmark face datasets, and experimental results demonstrate the efficacy of the proposed method.

scholarly journals An Improved High-Intelligence Method of Gas and Oil Pipeline Prewarning System in Real Soil Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fang Wang ◽  
Jichuan Xing ◽  
Jinxin Li ◽  
Feng Zhao ◽  
Shufeng Zhang
Keyword(s):  
Optical Fiber ◽  
Real Time ◽  
Recognition Accuracy ◽  
Signal Acquisition ◽  
Soil Environment ◽  
Long Distance ◽  
Vibration Signals ◽  
Oil Pipeline ◽  
Time Performance ◽  
High Intelligence

With the development of technology, the total extent of global pipeline transportation is also increased. However, the traditional long-distance optical fiber prewarning system has poor real-time performance and high false alarm rate when recognizing events threatening pipeline safety. The same vibration signal would vary greatly when collected in different soil environments and this problem would reduce the signal recognition accuracy of the prewarning system. In this paper, we studied this effect theoretically and analyzed soil vibration signals under different soil conditions. Then we studied the signal acquisition problem of long-distance gas and oil pipeline prewarning system in real soil environment. Ultimately, an improved high-intelligence method was proposed for optimization. This method is based on the real application environment, which is more suitable for the recognition of optical fiber vibration signals. Through experiments, the method yielded high recognition accuracy of above 95%. The results indicate that the method can significantly improve signal acquisition and recognition and has good adaptability and real-time performance in the real soil environment.

Applied Research of Optical Fiber Sensor in Oil and Gas Pipe Corrosion Monitoring

2014 ◽  
Vol 51 (2) ◽  
pp. 020604 ◽  
Author(s):  
杨牧 Yang Mu ◽  
刘秀红 Liu Xiuhong ◽  
刘伟 Liu Wei ◽  
李再春 Li Zaichun
Keyword(s):  
Optical Fiber ◽  
Applied Research ◽  
Oil And Gas ◽  
Optical Fiber Sensor ◽  
Fiber Sensor ◽  
Corrosion Monitoring

scholarly journals TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1724
Author(s):  
Zilu Ying ◽  
Chen Xuan ◽  
Yikui Zhai ◽  
Bing Sun ◽  
Jingwen Li ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Network Architecture ◽  
Learning Strategy ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Speckle Noise ◽  
Small Sample ◽  
Operating Conditions ◽  
Small Samples

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model’s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.

Ensemble learning based on approximate reducts and bootstrap sampling

2021 ◽  
Vol 547 ◽  
pp. 797-813
Author(s):  
Feng Jiang ◽  
Xu Yu ◽  
Junwei Du ◽  
Dunwei Gong ◽  
Youqiang Zhang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Bootstrap Sampling

Optical fiber intrusion signal recognition method based on TSVD-SCN

2019 ◽  
Vol 48 ◽  
pp. 270-277 ◽  
Author(s):  
Zhiyong Sheng ◽  
Zhiqiang Zeng ◽  
Hongquan Qu ◽  
Yuan Zhang
Keyword(s):  
Optical Fiber ◽  
Signal Recognition ◽  
Recognition Method
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