Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener...Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener region location method based on online learning strategy was proposed,which can locate fastener regions according to the prior knowledge of track image and template matching method.Online learning strategy is used to update the template library dynamically,so that the method not only can locate fastener regions in the track images of multi railways,but also can automatically collect and annotate fastener samples.Secondly,a fastener defect recognition method based on deep convolutional neural network was proposed.The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region.The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.Findings–Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways.Specifically,fastener location module has achieved an average detection rate of 99.36%,and fastener defect recognition module has achieved an average precision of 96.82%.Originality/value–The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways,which has high reliability and strong adaptability to multi railways.展开更多
This paper investigates a multi-cell uplink network,where the orthogonal frequency division multiplexing(OFDM)protocol is considered to mitigate the intra-cell interference.An optimization problem is formulated to max...This paper investigates a multi-cell uplink network,where the orthogonal frequency division multiplexing(OFDM)protocol is considered to mitigate the intra-cell interference.An optimization problem is formulated to maximize the user sup-porting ratio for the uplink multi-cell system by optimizing the transmit power.This paper adopts the user supporting ratio as the main performance metric.Our goal is to improve the user supporting ratio of each cell.Since the formulated optimization problem is non-convex,it cannot be solved by using traditional convex-based optimi-zation methods.Thus,a distributed method with low complexity and a small amount of multi-cell interaction is proposed.Numerical results show that a notable perfor-mance gain achieved by our proposed scheme compared with the traditional one is without inter-cell interaction.展开更多
People usually travel together with others in groups for different purposes, such as family members for visiting relatives, colleagues for business, friends for sightseeing and so on. Especially, the family groups, as...People usually travel together with others in groups for different purposes, such as family members for visiting relatives, colleagues for business, friends for sightseeing and so on. Especially, the family groups, as a kind of the most com- mon consumer units, have a considerable scale in the field of passenger transportation market. Accurately identifying family groups can help the carriers to provide passengers with personalized travel services and precise product recommendation. This paper studies the problem of finding family groups in the field of civil aviation and proposes a family group detection method based on passenger social networks. First of all, we construct passenger social networks based on their co-travel behaviors extracted from the historical travel records; secondly, we use a collective classification algorithm to classify the social relationships between passengers into family or non-family relationship groups; finally, we employ a weighted com- munity detection algorithm to find family groups, which takes the relationship classification results as the weights of edges. Experimental results on a real dataset of passenger travel records in the field of civil aviation demonstrate that our method can effectively find family groups from historical travel records.展开更多
基金funded by the Key Research and Development Project of China Academy of Railway Sciences Corporation Limited(2021YJ310).
文摘Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener region location method based on online learning strategy was proposed,which can locate fastener regions according to the prior knowledge of track image and template matching method.Online learning strategy is used to update the template library dynamically,so that the method not only can locate fastener regions in the track images of multi railways,but also can automatically collect and annotate fastener samples.Secondly,a fastener defect recognition method based on deep convolutional neural network was proposed.The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region.The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.Findings–Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways.Specifically,fastener location module has achieved an average detection rate of 99.36%,and fastener defect recognition module has achieved an average precision of 96.82%.Originality/value–The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways,which has high reliability and strong adaptability to multi railways.
文摘This paper investigates a multi-cell uplink network,where the orthogonal frequency division multiplexing(OFDM)protocol is considered to mitigate the intra-cell interference.An optimization problem is formulated to maximize the user sup-porting ratio for the uplink multi-cell system by optimizing the transmit power.This paper adopts the user supporting ratio as the main performance metric.Our goal is to improve the user supporting ratio of each cell.Since the formulated optimization problem is non-convex,it cannot be solved by using traditional convex-based optimi-zation methods.Thus,a distributed method with low complexity and a small amount of multi-cell interaction is proposed.Numerical results show that a notable perfor-mance gain achieved by our proposed scheme compared with the traditional one is without inter-cell interaction.
基金the Fundamental Research Funds for the Central Universities of China, the National Natural Science Foundation of China under Grant No. 61403023, the Beijing Committee of Science and Technology under Grant No. Z131110002813118, and the Program for Changjiang Scholars and Innovative Research Team in University of China under Grant No. IRT201206.
文摘People usually travel together with others in groups for different purposes, such as family members for visiting relatives, colleagues for business, friends for sightseeing and so on. Especially, the family groups, as a kind of the most com- mon consumer units, have a considerable scale in the field of passenger transportation market. Accurately identifying family groups can help the carriers to provide passengers with personalized travel services and precise product recommendation. This paper studies the problem of finding family groups in the field of civil aviation and proposes a family group detection method based on passenger social networks. First of all, we construct passenger social networks based on their co-travel behaviors extracted from the historical travel records; secondly, we use a collective classification algorithm to classify the social relationships between passengers into family or non-family relationship groups; finally, we employ a weighted com- munity detection algorithm to find family groups, which takes the relationship classification results as the weights of edges. Experimental results on a real dataset of passenger travel records in the field of civil aviation demonstrate that our method can effectively find family groups from historical travel records.
