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.

scholarly journals An emotion classification method from electroencephalogram based on 1/f fluctuation theory

2020 ◽  
Vol 53 (5-6) ◽  
pp. 824-832
Author(s):  
Hao Li ◽  
Xia Mao ◽  
Lijiang Chen
Keyword(s):  
Real Time ◽  
Classification Accuracy ◽  
Support Vector Machine Classifier ◽  
Fluctuation Theory ◽  
Classification Method ◽  
Support Vector ◽  
Signal Acquisition ◽  
Emotion Classification ◽  
The Real ◽  
Time Performance

Electroencephalogram data are easily affected by artifacts, and a drift may occur during the signal acquisition process. At present, most research focuses on the automatic detection and elimination of artifacts in electrooculograms, electromyograms and electrocardiograms. However, electroencephalogram drift data, which affect the real-time performance, are mainly manually calibrated and abandoned. An emotion classification method based on 1/f fluctuation theory is proposed to classify electroencephalogram data without removing artifacts and drift data. The results show that the proposed method can still achieve a great classification accuracy of 75% in cases in which artifacts and drift data exist when using the support vector machine classifier. In addition, the real-time performance of the proposed method is guaranteed.

scholarly journals Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

2021 ◽  
Author(s):  
Linghui Xu ◽  
Jiansong Chen ◽  
Fei Wang ◽  
Yuting Chen ◽  
Wei Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Plantar Pressure ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Gait Recognition ◽  
Recognition System ◽  
Experimental Results ◽  
Time Performance ◽  
Pathological Gait

Abstract Background: Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information.Methods: In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of seventeen children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe in, toe out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified 10-fold cross-validation with recall, precision, and a time cost as metrics.Results: The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% recognition accuracy respectively in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing.Conclusions: In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.

Backbone Optical Fiber Analysis at 1310 nm and 1550 nm

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Nandhakumar P ◽  
Arun Kumar
Keyword(s):  
Optical Fiber ◽  
Real Time ◽  
Optical Fibers ◽  
Single Mode ◽  
Optical Fiber Communication ◽  
Long Distance ◽  
Fiber Communication ◽  
The Real ◽  
Optical Time Domain Reflectometer ◽  
Optic Communication

AbstractOptical fiber communication is the backbone of the entire telecommunication industries in the world. In this work, the real-time backbone long-distance optical fibers (single mode) are tested and analyzed with two different wavelengths (1,310 nm and 1,550 nm) with the help of optical time domain reflectometer. Using these two different wavelengths, how the losses and events of the backbone optical fibers are changing are compared and analyzed. This work will give a way to study the nature of long-distance backbone optical fiber and understand the real-time application of the fiber optic communication.

Design of an Information Integration Control Network for Hybrid Electric Trucks

2013 ◽  
Vol 694-697 ◽  
pp. 2572-2575
Author(s):  
Jun Xu ◽  
Gang Yan Li ◽  
Fei Yang
Keyword(s):  
Real Time ◽  
Information Integration ◽  
Control Strategies ◽  
Bench Test ◽  
Control Network ◽  
Signal Acquisition ◽  
Time Performance ◽  
Load Rate ◽  
Hybrid Electric ◽  
And Control

In view of the requirements on multi-target information interaction and implementing complicated control strategies among different ECUs in hybrid electric trucks, an information integration control network based on CAN bus is proposed. A HCS12 dual-core processor MC9S12XDG128 is adopted to design an information integration module for sensor signal acquisition and data transmission in the network. Application layer protocol is defined to optimize the message transmission mechanism. Meanwhile, Rate Monotonic Analysis (RMA) method is adopted to analyze bus load rate and real-time performance of the network. Bench test results have shown that the designed information integration control network for hybrid electric trucks owns good real-time performance and is reliable to implement communication and control tasks with low bus load rate.

scholarly journals Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Linghui Xu ◽  
Jiansong Chen ◽  
Fei Wang ◽  
Yuting Chen ◽  
Wei Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Plantar Pressure ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Gait Recognition ◽  
Recognition System ◽  
Experimental Results ◽  
Time Performance ◽  
Pathological Gait

Abstract Background Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information. Methods In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of 17 children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe-in, toe-out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified tenfold cross-validation with recall, precision, and a time cost as metrics. Results The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% intra-subject recognition accuracy, and 85.78% and 78.81% inter-subject recognition accuracy, respectively, in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing. Conclusions In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.

scholarly journals Processing chain for 3D histogram of gradients based real-time object recognition

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142097836
Author(s):  
Cristian Vilar ◽  
Silvia Krug ◽  
Benny Thörnberg
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Real Time ◽  
Recognition Accuracy ◽  
3D Object Recognition ◽  
Learning Approaches ◽  
Data Set ◽  
3D Object ◽  
Time Performance ◽  
Voxel Grid

3D object recognition has been a cutting-edge research topic since the popularization of depth cameras. These cameras enhance the perception of the environment and so are particularly suitable for autonomous robot navigation applications. Advanced deep learning approaches for 3D object recognition are based on complex algorithms and demand powerful hardware resources. However, autonomous robots and powered wheelchairs have limited resources, which affects the implementation of these algorithms for real-time performance. We propose to use instead a 3D voxel-based extension of the 2D histogram of oriented gradients (3DVHOG) as a handcrafted object descriptor for 3D object recognition in combination with a pose normalization method for rotational invariance and a supervised object classifier. The experimental goal is to reduce the overall complexity and the system hardware requirements, and thus enable a feasible real-time hardware implementation. This article compares the 3DVHOG object recognition rates with those of other 3D recognition approaches, using the ModelNet10 object data set as a reference. We analyze the recognition accuracy for 3DVHOG using a variety of voxel grid selections, different numbers of neurons ( Nh) in the single hidden layer feedforward neural network, and feature dimensionality reduction using principal component analysis. The experimental results show that the 3DVHOG descriptor achieves a recognition accuracy of 84.91% with a total processing time of 21.4 ms. Despite the lower recognition accuracy, this is close to the current state-of-the-art approaches for deep learning while enabling real-time performance.

Sign in / Sign up

Export Citation Format

Share Document