Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection abil...Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection ability, Aircraft Communications Addressing and Reporting System(ACARS) data can be used. However, owing to the characteristics of high dimension, complex correlations between parameters, and large noise content, it is difficult for existing methods to detect faults effectively by using ACARS data. To solve this problem, a novel engine fault detection method based on original ACARS data is proposed. First, inspired by computer vision methods, all variables were divided into separated groups according to their correlations. Then, an improved convolutional denoising autoencoder was used to extract the features of each group. Finally, all of the extracted features were fused to form feature vectors. Thereby, fault samples could be identified based on these feature vectors. Experiments were conducted to validate the effectiveness and efficiency of our method and other competing methods by considering real ACARS data as the data source. The results reveal the good performance of our method with regard to comprehensive fault detection and robustness. Additionally, the computational and time costs of our method are shown to be relatively low.展开更多
1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,Ch...1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China 3 Shenzhen Key Laboratory of Intelligent Bioinformatics,Shenzhen Institute of Advanced Technology,Chinese Academy of Science,Shenzhen 518055,China E-mail:xjlei@snnu.edu.cn;yalichen@snnu.edu.cn;yi.pan@siat.ac.cn Received December 9,2022;accepted July 29,2024.Abstract Identifying microbes associated with diseases is important for understanding the pathogenesis of diseases as well as for the diagnosis and treatment of diseases.In this article,we propose a method based on a multi-source association network to predict microbe-disease associations,named MMHN-MDA.First,a heterogeneous network of multimolecule associations is constructed based on associations between microbes,diseases,drugs,and metabolites.Second,the graph embedding algorithm Laplacian eigenmaps is applied to the association network to learn the behavior features of microbe nodes and disease nodes.At the same time,the denoising autoencoder(DAE)is used to learn the attribute features of microbe nodes and disease nodes.Finally,attribute features and behavior features are combined to get the final embedding features of microbes and diseases,which are fed into the convolutional neural network(CNN)to predict the microbedisease associations.Experimental results show that the proposed method is more effective than existing methods.In addition,case studies on bipolar disorder and schizophrenia demonstrate good predictive performance of the MMHN-MDA model,and further,the results suggest that gut microbes may influence host gene expression or compounds in the nervous system,such as neurotransmitters,or metabolites that alter the blood-brain barrier.展开更多
Object detection,one of the core research topics in computer vision,is extensively used in various industrial activities.Although there have been many studies of daytime images where objects can be easily detected,the...Object detection,one of the core research topics in computer vision,is extensively used in various industrial activities.Although there have been many studies of daytime images where objects can be easily detected,there is relatively little research on nighttime images.In the case of nighttime,various types of noises,such as darkness,haze,and light blur,deteriorate image quality.Thus,an appropriate process for removing noise must precede to improve object detection performance.Although there are many studies on removing individual noise,only a few studies handle multiple noises simultaneously.In this paper,we pro-pose a convolutional denoising autoencoder(CDAE)-based architecture trained on various types of noises.We also present various composing modules for each noise to improve object detection performance for night images.Using the exclusively dark(ExDark)Image dataset,experimental results show that the Sequentialfiltering architecture showed superior mean average precision(mAP)compared to other architectures.展开更多
为了提升入侵检测的准确率,鉴于自编码器在学习特征方面的优势以及残差网络在构建深层模型方面的成熟应用,提出一种基于特征降维的改进残差网络入侵检测模型(improved residual network intrusion detection model based on feature dim...为了提升入侵检测的准确率,鉴于自编码器在学习特征方面的优势以及残差网络在构建深层模型方面的成熟应用,提出一种基于特征降维的改进残差网络入侵检测模型(improved residual network intrusion detection model based on feature dimensionality reduction,IRFD),进而缓解传统机器学习入侵检测模型的低准确率问题。IRFD采用堆叠降噪稀疏自编码器策略对数据进行降维,从而提取有效特征。利用卷积注意力机制对残差网络进行改进,构建能提取关键特征的分类网络,并利用两个典型的入侵检测数据集验证IRFD的检测性能。实验结果表明,IRFD在数据集UNSW-NB15和CICIDS 2017上的准确率均达到99%以上,且F1-score分别为99.5%和99.7%。与基线模型相比,提出的IRFD在准确率、精确率和F1-score性能上均有较大提升。展开更多
基金co-supported by the Key Program of National Natural Science Foundation of China (No. U1533202)the Civil Aviation Administration of China (No. MHRD20150104)Shandong Independent Innovation and Achievements Transformation Fund (No. 2014CGZH1101)
文摘Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection ability, Aircraft Communications Addressing and Reporting System(ACARS) data can be used. However, owing to the characteristics of high dimension, complex correlations between parameters, and large noise content, it is difficult for existing methods to detect faults effectively by using ACARS data. To solve this problem, a novel engine fault detection method based on original ACARS data is proposed. First, inspired by computer vision methods, all variables were divided into separated groups according to their correlations. Then, an improved convolutional denoising autoencoder was used to extract the features of each group. Finally, all of the extracted features were fused to form feature vectors. Thereby, fault samples could be identified based on these feature vectors. Experiments were conducted to validate the effectiveness and efficiency of our method and other competing methods by considering real ACARS data as the data source. The results reveal the good performance of our method with regard to comprehensive fault detection and robustness. Additionally, the computational and time costs of our method are shown to be relatively low.
基金supported by the National Natural Science Foundation of China under Grant Nos.62272288 and U22A2041the Fundamental Research Funds for the Central Universities of China,and Shaanxi Normal University under Grant No.GK202302006.
文摘1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China 3 Shenzhen Key Laboratory of Intelligent Bioinformatics,Shenzhen Institute of Advanced Technology,Chinese Academy of Science,Shenzhen 518055,China E-mail:xjlei@snnu.edu.cn;yalichen@snnu.edu.cn;yi.pan@siat.ac.cn Received December 9,2022;accepted July 29,2024.Abstract Identifying microbes associated with diseases is important for understanding the pathogenesis of diseases as well as for the diagnosis and treatment of diseases.In this article,we propose a method based on a multi-source association network to predict microbe-disease associations,named MMHN-MDA.First,a heterogeneous network of multimolecule associations is constructed based on associations between microbes,diseases,drugs,and metabolites.Second,the graph embedding algorithm Laplacian eigenmaps is applied to the association network to learn the behavior features of microbe nodes and disease nodes.At the same time,the denoising autoencoder(DAE)is used to learn the attribute features of microbe nodes and disease nodes.Finally,attribute features and behavior features are combined to get the final embedding features of microbes and diseases,which are fed into the convolutional neural network(CNN)to predict the microbedisease associations.Experimental results show that the proposed method is more effective than existing methods.In addition,case studies on bipolar disorder and schizophrenia demonstrate good predictive performance of the MMHN-MDA model,and further,the results suggest that gut microbes may influence host gene expression or compounds in the nervous system,such as neurotransmitters,or metabolites that alter the blood-brain barrier.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2021S1A5A2A01061459).
文摘Object detection,one of the core research topics in computer vision,is extensively used in various industrial activities.Although there have been many studies of daytime images where objects can be easily detected,there is relatively little research on nighttime images.In the case of nighttime,various types of noises,such as darkness,haze,and light blur,deteriorate image quality.Thus,an appropriate process for removing noise must precede to improve object detection performance.Although there are many studies on removing individual noise,only a few studies handle multiple noises simultaneously.In this paper,we pro-pose a convolutional denoising autoencoder(CDAE)-based architecture trained on various types of noises.We also present various composing modules for each noise to improve object detection performance for night images.Using the exclusively dark(ExDark)Image dataset,experimental results show that the Sequentialfiltering architecture showed superior mean average precision(mAP)compared to other architectures.
文摘为了提升入侵检测的准确率,鉴于自编码器在学习特征方面的优势以及残差网络在构建深层模型方面的成熟应用,提出一种基于特征降维的改进残差网络入侵检测模型(improved residual network intrusion detection model based on feature dimensionality reduction,IRFD),进而缓解传统机器学习入侵检测模型的低准确率问题。IRFD采用堆叠降噪稀疏自编码器策略对数据进行降维,从而提取有效特征。利用卷积注意力机制对残差网络进行改进,构建能提取关键特征的分类网络,并利用两个典型的入侵检测数据集验证IRFD的检测性能。实验结果表明,IRFD在数据集UNSW-NB15和CICIDS 2017上的准确率均达到99%以上,且F1-score分别为99.5%和99.7%。与基线模型相比,提出的IRFD在准确率、精确率和F1-score性能上均有较大提升。
