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Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine
摘要： A pipeline's safe usage is of critical concern. In our previous work, a fiber Bragg grating hoop strain sensor was developed to measure the hoop strain variation in a pressurized pipeline. In this paper, a support vector machine (SVM) learning method is applied to identify pipeline leakage accidents from different hoop strain signals and then further locate the leakage points along a pipeline. For leakage identification, time domain features and wavelet packet vectors are extracted as the input features for the SVM model. For leakage localization, a series of terminal hoop strain variations are extracted as the input variables for a support vector regression (SVR) analysis to locate the leakage point. The parameters of the SVM/SVR kernel function are optimized by means of a particle swarm optimization (PSO) algorithm to obtain the highest identification and localization accuracy. The results show that when the RBF kernel with optimized C and γ values is applied, the classification accuracy for leakage identification reaches 97.5% (117/120). The mean square error value for leakage localization can reach as low as 0.002 when the appropriate parameter combination is chosen for a noise‐free situation. The anti‐noise capability of the optimized SVR model for leakage localization is evaluated by superimposing Gaussian white noise at different levels. The simulation study shows that the average localization error is still acceptable (≈500 m) with 5% noise. The results demonstrate the feasibility and robustness of the PSO–SVM approach for pipeline leakage identification and localization.
关键词： pipeline leakage localization,method of characteristics (MOC),FBG hoop strain sensor,support vector regression (SVR),particle swarm optimization (PSO) algorithm,support vector machine (SVM)
更新于2025-09-04 15:30:14
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[IEEE 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Bangalore (2018.2.9-2018.2.10)] 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Determination of Absolute Heart Beat from Photoplethysmographic Signals in the Presence of Motion Artifacts
摘要： In Wireless Body Area Networks (WBANs), accurate monitoring of heart rate (HR) using Photoplethysmography (PPG) signals is always a difficult task, especially when the subjects are under radical exercises. This is due to the signals corrupted by severely strong Motion Artifacts (MA) caused by the subject’s body movements. In this work, a novel approach has been proposed consisting of signal decomposition for denoising using principal component analysis (PCA), spare signal reconstruction (SSR), peak detection and tracking and support vector machine (SVM) classifier for accurate estimation of HR, based on the wrist type PPG signals. With this approach, we are able to achieve high accuracy and also, it is strong enough to remove MA. Experiments were conducted on 12 subjects and their datasets are obtained from 2015 IEEE Signal Processing CUP, running on a threadmill with varying speeds ranging from 0 to a maximum speed of 15 km/hour. From the results, it is observed that the average absolute error of heart rate estimation is 1.66 beats per minute (BPM).
关键词： SVM classifier,PCA,HR,Wireless Body Area Networks (BAN),SSR,Accelerometer,PPG
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC) - Maui, HI, USA (2018.11.4-2018.11.7)] 2018 21st International Conference on Intelligent Transportation Systems (ITSC) - Machine Learning-based Stereo Vision Algorithm for Surround View Fisheye Cameras
摘要： Recently, automated emergency brake systems for pedestrian have been commercialized. However, they cannot detect crossing pedestrians when turning at intersections because the field of view is not wide enough. Thus, we propose to utilize a surround view camera system becoming popular by making it into stereo vision which is robust for the pedestrian recognition. However, conventional stereo camera technologies cannot be applied due to fisheye cameras and uncalibrated camera poses. Thus we have created the new method to absorb difference of the pedestrian appearance between cameras by machine learning for the stereo vision. The method of stereo matching between image patches in each camera image was designed by combining D-Brief and NCC with SVM. Good generalization performance was achieved by it compared with individual conventional algorithms. Furthermore, feature amounts of the point cloud reconstructed by the stereo pairs are utilized with Random Forest to discriminate pedestrians. The algorithm was evaluated for the actual camera images of crossing pedestrians at various intersections, and 96.0% of pedestrian tracking rate with high position detection accuracy was achieved. They were compared with Faster R-CNN as the best pattern recognition technique, and our proposed method indicated better detection performance.
关键词： NCC,automated emergency brake systems,machine learning,SVM,Faster R-CNN,stereo vision,pedestrian detection,D-Brief,Random Forest,surround view camera system
更新于2025-09-04 15:30:14