A distributed online fiber sensing system based on the phase-sensitive optical time domain reflectometer(Φ-OTDR)enhanced by the drawing tower fiber Bragg grating(FBG)array is presented and investigated experimentally...A distributed online fiber sensing system based on the phase-sensitive optical time domain reflectometer(Φ-OTDR)enhanced by the drawing tower fiber Bragg grating(FBG)array is presented and investigated experimentally for monitoring the galloping of overhead transmission lines.The chirped FBG array enhanced Φ-OTDR sensing system can be used to measure the galloping behavior of the overhead transmission lines(optical phase conductor or optical power ground wire),which are helpful for monitoring the frequency response characteristics of the ice-induced galloping,evaluating the motion tendencies of these cables,and avoiding the risk of flashover during galloping.The feasibility of the proposed online monitoring system is demonstrated through a series of experiments at the Special Optical Fiber Cable Laboratory of State Grid Corporation of China(Beijing,China).Results show that the proposed system is effective and reliable for the monitoring of galloping shape and characteristic frequency,which can predict the trend of destructive vibration behavior and avoid the occurrence of cable breaking and tower toppling accidents,and these features are essential for the safety operation in smart grids.展开更多
Rice variety selection and quality inspection are key links in rice planting.Compared with two-dimensional images,three-dimensional information on rice seeds shows the appearance characteristics of rice seeds more com...Rice variety selection and quality inspection are key links in rice planting.Compared with two-dimensional images,three-dimensional information on rice seeds shows the appearance characteristics of rice seeds more comprehensively and accurately.This study proposed a rice variety classification method using three-dimensional point cloud data of the surface of rice seeds combined with a deep learning network to achieve the rapid and accurate identification of rice varieties.First,a point cloud collection platform was set up with a Raytrix light field camera as the core to collect three-dimensional point cloud data on the surface of rice seeds;then,the collected point cloud was filled,filtered and smoothed;after that,the point cloud segmentation is based on the RANSAC algorithm,and the point cloud downsampling is based on a combination of random sampling algorithm and voxel grid filtering algorithm.Finally,the processed point cloud was input to the improved PointNet network for feature extraction and species classification.The improved PointNet network added a cross-level feature connection structure,made full use of features at different levels,and better extracted the surface structure features of rice seeds.After testing,the improved PointNet model had an average classification accuracy of 89.4%for eight varieties of rice,which was 1.2%higher than that of the PointNet model.The method proposed in this study combined deep learning and point cloud data to achieve the efficient classification of rice varieties.展开更多
Rice quality directly affects the final rice yield.In order to achieve rapid,non-destructive testing of rice seeds,this paper combines the three-dimensional laser scanning technology and back propagation(BP)neural net...Rice quality directly affects the final rice yield.In order to achieve rapid,non-destructive testing of rice seeds,this paper combines the three-dimensional laser scanning technology and back propagation(BP)neural network algorithm to build a rice seeds identification platform.The information on rice seed surface is collected from four angles and processed using Geomagic Studio software.Based on the noise filtering,smoothing of the point cloud,vulnerability repair,and downsampling,the three-dimensional(3D)morphological characteristics of a rice seed surface,and the projection features of the main plane cross-section are obtained through the calculation of the features.The experiments were performed on five rice varieties,including Da Hua aromatic glutinous,Hong ShiⅠ,Tian You VIII,Xin Dao X,and Yu Jing VI.The resulting input vector consisted respectively of:(1)nine 3D morphological surface features,(2)nine projection features of the main cross-section plane of rice,and(3)all of the above features.The results showed that for an input vector consisting of nine surface 3D morphological features,the recognition rate of the five rice varieties was 95%,96%,87%,93%,and 89%,respectively;for an input vector consisting of nine projection features of the main cross-section plane of rice seeds,the recognition rate was 96%,96%,90%,92%,and 89%,respectively;and lastly,for an input vector consisting of all the features,the highest recognition rate of 96%,97%,91%,94%,and 90%,respectively,was achieved.The analysis showed that rice varieties could be identified by using 3D laser scanning.Therefore,the proposed method can improve the accuracy of rice varieties identification.展开更多
Due to its high-temperature and high-pressure operating environment,food/feed puffing machines are prone to faults such as cavity blockage and cutter wear.This paper presents the design of a fault diagnosis system for...Due to its high-temperature and high-pressure operating environment,food/feed puffing machines are prone to faults such as cavity blockage and cutter wear.This paper presents the design of a fault diagnosis system for puffing machines(food/feed processing equipment that expands or puffs agricultural products),based on a convolutional neural network and a multi-head attention mechanism model,which incorporates Bayesian optimization.The system combines multi-source information fusion,capturing patterns and characteristics associated with fault states by monitoring various sources of information,such as temperature,noise signals,main motor current and vibration signals from key components.Hyperparameters are optimized through Bayesian optimization to obtain the optimal parameter model.The integration of convolutional neural networks and multi-head attention mechanisms enables the simultaneous capture of both local and global information,thereby enhancing data comprehension.Experimental results demonstrate that the system successfully diagnoses puffing machine faults,achieving an average recognition accuracy of 98.8%across various operating conditions,highlighting its high accuracy,generalization ability and robustness.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61775173,61975157,and 52071245)the Science and Technology Project of State Grid Corporation of China(Research on the basic technology of the next generation intelligent optical cable based on grating array fiber sensor,Grant No.5442XX190009).
文摘A distributed online fiber sensing system based on the phase-sensitive optical time domain reflectometer(Φ-OTDR)enhanced by the drawing tower fiber Bragg grating(FBG)array is presented and investigated experimentally for monitoring the galloping of overhead transmission lines.The chirped FBG array enhanced Φ-OTDR sensing system can be used to measure the galloping behavior of the overhead transmission lines(optical phase conductor or optical power ground wire),which are helpful for monitoring the frequency response characteristics of the ice-induced galloping,evaluating the motion tendencies of these cables,and avoiding the risk of flashover during galloping.The feasibility of the proposed online monitoring system is demonstrated through a series of experiments at the Special Optical Fiber Cable Laboratory of State Grid Corporation of China(Beijing,China).Results show that the proposed system is effective and reliable for the monitoring of galloping shape and characteristic frequency,which can predict the trend of destructive vibration behavior and avoid the occurrence of cable breaking and tower toppling accidents,and these features are essential for the safety operation in smart grids.
基金supported by the National Natural Science Foundation of China Youth Fund Project(Grant No.51305182)the Ministry of Agriculture Key Laboratory of Modern Agricultural Equipment(Grant No.201602004).
文摘Rice variety selection and quality inspection are key links in rice planting.Compared with two-dimensional images,three-dimensional information on rice seeds shows the appearance characteristics of rice seeds more comprehensively and accurately.This study proposed a rice variety classification method using three-dimensional point cloud data of the surface of rice seeds combined with a deep learning network to achieve the rapid and accurate identification of rice varieties.First,a point cloud collection platform was set up with a Raytrix light field camera as the core to collect three-dimensional point cloud data on the surface of rice seeds;then,the collected point cloud was filled,filtered and smoothed;after that,the point cloud segmentation is based on the RANSAC algorithm,and the point cloud downsampling is based on a combination of random sampling algorithm and voxel grid filtering algorithm.Finally,the processed point cloud was input to the improved PointNet network for feature extraction and species classification.The improved PointNet network added a cross-level feature connection structure,made full use of features at different levels,and better extracted the surface structure features of rice seeds.After testing,the improved PointNet model had an average classification accuracy of 89.4%for eight varieties of rice,which was 1.2%higher than that of the PointNet model.The method proposed in this study combined deep learning and point cloud data to achieve the efficient classification of rice varieties.
基金The authors are very grateful for the support provided by the N ational Natural Science Foundation of China(Grant No.51507081)the Fundamental Research Funds for the Central Universities(KJ QN201623)the National Key Research and Development Pro gram of China(2017YFD0700800).
文摘Rice quality directly affects the final rice yield.In order to achieve rapid,non-destructive testing of rice seeds,this paper combines the three-dimensional laser scanning technology and back propagation(BP)neural network algorithm to build a rice seeds identification platform.The information on rice seed surface is collected from four angles and processed using Geomagic Studio software.Based on the noise filtering,smoothing of the point cloud,vulnerability repair,and downsampling,the three-dimensional(3D)morphological characteristics of a rice seed surface,and the projection features of the main plane cross-section are obtained through the calculation of the features.The experiments were performed on five rice varieties,including Da Hua aromatic glutinous,Hong ShiⅠ,Tian You VIII,Xin Dao X,and Yu Jing VI.The resulting input vector consisted respectively of:(1)nine 3D morphological surface features,(2)nine projection features of the main cross-section plane of rice,and(3)all of the above features.The results showed that for an input vector consisting of nine surface 3D morphological features,the recognition rate of the five rice varieties was 95%,96%,87%,93%,and 89%,respectively;for an input vector consisting of nine projection features of the main cross-section plane of rice seeds,the recognition rate was 96%,96%,90%,92%,and 89%,respectively;and lastly,for an input vector consisting of all the features,the highest recognition rate of 96%,97%,91%,94%,and 90%,respectively,was achieved.The analysis showed that rice varieties could be identified by using 3D laser scanning.Therefore,the proposed method can improve the accuracy of rice varieties identification.
基金sponsored by the Belt and Road Innovative Cooperation Project(BZ2022003).
文摘Due to its high-temperature and high-pressure operating environment,food/feed puffing machines are prone to faults such as cavity blockage and cutter wear.This paper presents the design of a fault diagnosis system for puffing machines(food/feed processing equipment that expands or puffs agricultural products),based on a convolutional neural network and a multi-head attention mechanism model,which incorporates Bayesian optimization.The system combines multi-source information fusion,capturing patterns and characteristics associated with fault states by monitoring various sources of information,such as temperature,noise signals,main motor current and vibration signals from key components.Hyperparameters are optimized through Bayesian optimization to obtain the optimal parameter model.The integration of convolutional neural networks and multi-head attention mechanisms enables the simultaneous capture of both local and global information,thereby enhancing data comprehension.Experimental results demonstrate that the system successfully diagnoses puffing machine faults,achieving an average recognition accuracy of 98.8%across various operating conditions,highlighting its high accuracy,generalization ability and robustness.
