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FACLSTM:ConvLSTM with focused attention for scene text recognition 认领
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作者 Qingqing WANG Ye HUANG +4 位作者 Wenjing JIA Xiangjian HE Michael BLUMENSTEIN Shujing LYU Yue LU 《中国科学:信息科学(英文版)》 SCIE EI CSCD 2020年第2期35-48,共14页
Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem,where traditional fully-connected-LSTM(FC-LSTM)has played a critical role.Owing to the limitation of FC-LSTM,existin... Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem,where traditional fully-connected-LSTM(FC-LSTM)has played a critical role.Owing to the limitation of FC-LSTM,existing methods have to convert 2-D feature maps into 1-D sequential feature vectors,resulting in severe damages of the valuable spatial and structural information of text images.In this paper,we argue that scene text recognition is essentially a spatiotemporal prediction problem for its2-D image inputs,and propose a convolution LSTM(Conv LSTM)-based scene text recognizer,namely,FACLSTM,i.e.,focused attention Conv LSTM,where the spatial correlation of pixels is fully leveraged when performing sequential prediction with LSTM.Particularly,the attention mechanism is properly incorporated into an efficient Conv LSTM structure via the convolutional operations and additional character center masks are generated to help focus attention on right feature areas.The experimental results on benchmark datasets IIIT5 K,SVT and CUTE demonstrate that our proposed FACLSTM performs competitively on the regular,low-resolution and noisy text images,and outperforms the state-of-the-art approaches on the curved text images with large margins. 展开更多
关键词 SCENE TEXT RECOGNITION convolutional LSTM FOCUSED ATTENTION spatial correlation SEQUENTIAL prediction
Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging 认领
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作者 Yongbo LI Xiaoqiang DU +2 位作者 Fangyi WAN Xianzhi WANG Huangchao YU 《中国航空学报:英文版》 SCIE EI CAS CSCD 2020年第2期427-438,共12页
Rotating machinery is widely applied in industrial applications.Fault diagnosis of rotating machinery is vital in manufacturing system,which can prevent catastrophic failure and reduce financial losses.Recently,Deep L... Rotating machinery is widely applied in industrial applications.Fault diagnosis of rotating machinery is vital in manufacturing system,which can prevent catastrophic failure and reduce financial losses.Recently,Deep Learning(DL)-based fault diagnosis method becomes a hot topic.Convolutional Neural Network(CNN)is an effective DL method to extract the features of raw data automatically.This paper develops a fault diagnosis method using CNN for InfRared Thermal(IRT)image.First,IRT technique is utilized to capture the IRT images of rotating machinery.Second,the CNN is applied to extract fault features from the IRT images.In the end,the obtained features are fed into the Softmax Regression(SR)classifier for fault pattern identification.The effectiveness of the proposed method is validated using two different experimental data.Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method. 展开更多
关键词 Convolutional neural network FEATURE extraction INFRARED thermography(IRT) Intelligent FAULT diagnosis ROTATING machinery
Web3D Learning Framework for 3D Shape Retrieval Based on Hybrid Convolutional Neural Networks 认领
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作者 Wen Zhou Jinyuan Jia +1 位作者 Chengxi Huang Yongqing Cheng 《清华大学学报自然科学版(英文版)》 SCIE EI CAS CSCD 2020年第1期93-102,共10页
With the rapid development of Web3 D technologies, sketch-based model retrieval has become an increasingly important challenge, while the application of Virtual Reality and 3 D technologies has made shape retrieval of... With the rapid development of Web3 D technologies, sketch-based model retrieval has become an increasingly important challenge, while the application of Virtual Reality and 3 D technologies has made shape retrieval of furniture over a web browser feasible. In this paper, we propose a learning framework for shape retrieval based on two Siamese VGG-16 Convolutional Neural Networks(CNNs), and a CNN-based hybrid learning algorithm to select the best view for a shape. In this algorithm, the AlexNet and VGG-16 CNN architectures are used to perform classification tasks and to extract features, respectively. In addition, a feature fusion method is used to measure the similarity relation of the output features from the two Siamese networks. The proposed framework can provide new alternatives for furniture retrieval in the Web3 D environment. The primary innovation is in the employment of deep learning methods to solve the challenge of obtaining the best view of 3 D furniture,and to address cross-domain feature learning problems. We conduct an experiment to verify the feasibility of the framework and the results show our approach to be superior in comparison to many mainstream state-of-the-art approaches. 展开更多
关键词 WEB3D sketch-based model RETRIEVAL Convolutional NEURAL Networks(CNNs) best VIEW cross-domain
A flexible technique to select objects via convolutional neural network in VR space 认领
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作者 Huiyu LI Linwei FAN 《中国科学:信息科学(英文版)》 SCIE EI CSCD 2020年第1期53-72,共20页
Most studies on the selection techniques of projection-based VR systems are dependent on users wearing complex or expensive input devices, however there are lack of more convenient selection techniques.In this paper, ... Most studies on the selection techniques of projection-based VR systems are dependent on users wearing complex or expensive input devices, however there are lack of more convenient selection techniques.In this paper, we propose a flexible 3 D selection technique in a large display projection-based virtual environment. Herein, we present a body tracking method using convolutional neural network(CNN) to estimate3 D skeletons of multi-users, and propose a region-based selection method to effectively select virtual objects using only the tracked fingertips of multi-users. Additionally, a multi-user merge method is introduced to enable users’ actions and perception to realign when multiple users observe a single stereoscopic display.By comparing with state-of-the-art CNN-based pose estimation methods, the proposed CNN-based body tracking method enables considerable estimation accuracy with the guarantee of real-time performance. In addition, we evaluate our selection technique against three prevalent selection techniques and test the performance of our selection technique in a multi-user scenario. The results show that our selection technique significantly increases the efficiency and effectiveness, and is of comparable stability to support multi-user interaction. 展开更多
关键词 convolutional NEURAL network interaction techniques POSE estimation VIRTUAL REALITY 3D selection
Face Image Recognition Based on Convolutional Neural Network 认领
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作者 Guangxin Lou Hongzhen Shi 《中国通信:英文版》 SCIE CSCD 2020年第2期117-124,共8页
With the continuous progress of The Times and the development of technology,the rise of network social media has also brought the“explosive”growth of image data.As one of the main ways of People’s Daily communicati... With the continuous progress of The Times and the development of technology,the rise of network social media has also brought the“explosive”growth of image data.As one of the main ways of People’s Daily communication,image is widely used as a carrier of communication because of its rich content,intuitive and other advantages.Image recognition based on convolution neural network is the first application in the field of image recognition.A series of algorithm operations such as image eigenvalue extraction,recognition and convolution are used to identify and analyze different images.The rapid development of artificial intelligence makes machine learning more and more important in its research field.Use algorithms to learn each piece of data and predict the outcome.This has become an important key to open the door of artificial intelligence.In machine vision,image recognition is the foundation,but how to associate the low-level information in the image with the high-level image semantics becomes the key problem of image recognition.Predecessors have provided many model algorithms,which have laid a solid foundation for the development of artificial intelligence and image recognition.The multi-level information fusion model based on the VGG16 model is an improvement on the fully connected neural network.Different from full connection network,convolutional neural network does not use full connection method in each layer of neurons of neural network,but USES some nodes for connection.Although this method reduces the computation time,due to the fact that the convolutional neural network model will lose some useful feature information in the process of propagation and calculation,this paper improves the model to be a multi-level information fusion of the convolution calculation method,and further recovers the discarded feature information,so as to improve the recognition rate of the image.VGG divides the network into five groups(mimicking the five layers of AlexNet),yet it USES 3*3 filters and combines them as a conv 展开更多
关键词 convolutional NEURAL network FACE image RECOGNITION machine learning artificial INTELLIGENCE MULTILAYER information FUSION
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Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios 认领
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作者 Shilian Zheng Shichuan Chen +2 位作者 Peihan Qi Huaji Zhou Xiaoniu Yang 《中国通信:英文版》 SCIE CSCD 2020年第2期138-148,共11页
Spectrum sensing is a key technology for cognitive radios.We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.We normalize the received signal pow... Spectrum sensing is a key technology for cognitive radios.We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.We normalize the received signal power to overcome the effects of noise power uncertainty.We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals.We also use transfer learning strategies to improve the performance for real-world signals.Extensive experiments are conducted to evaluate the performance of this method.The simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method.In addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals.Furthermore,the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning.Finally,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method. 展开更多
关键词 SPECTRUM SENSING DEEP learning convolutional NEURAL network COGNITIVE RADIO SPECTRUM management
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Complex Network Classification with Convolutional Neural Network 认领
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作者 Ruyue Xin Jiang Zhang Yitong Shao 《清华大学学报:自然科学版(英文版)》 SCIE EI CAS CSCD 2020年第4期447-457,共11页
Classifying large-scale networks into several categories and distinguishing them according to their fine structures is of great importance to several real-life applications.However,most studies on complex networks foc... Classifying large-scale networks into several categories and distinguishing them according to their fine structures is of great importance to several real-life applications.However,most studies on complex networks focus on the properties of a single network and seldom on classification,clustering,and comparison between different networks,in which the network is treated as a whole.Conventional methods can hardly be applied on networks directly due to the non-Euclidean properties of data.In this paper,we propose a novel framework of Complex Network Classifier(CNC)by integrating network embedding and convolutional neural network to tackle the problem of network classification.By training the classifier on synthetic complex network data,we show CNC can not only classify networks with high accuracy and robustness but can also extract the features of the networks automatically.We also compare our CNC with baseline methods on benchmark datasets,which shows that our method performs well on large-scale networks. 展开更多
关键词 complex NETWORK NETWORK CLASSIFICATION DEEP WALK Convolutional NEURAL Network(CNN)
Term-Based Pooling in Convolutional Neural Networks for Text Classification 认领
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作者 Shuifei Zeng Yan Ma +1 位作者 Xiaoyan Zhang Xiaofeng Du 《中国通信:英文版》 SCIE CSCD 2020年第4期109-124,共16页
To achieve good results in convolutional neural networks(CNN) for text classification task, term-based pooling operation in CNNs is proposed. Firstly, the convolution results of several convolution kernels are combine... To achieve good results in convolutional neural networks(CNN) for text classification task, term-based pooling operation in CNNs is proposed. Firstly, the convolution results of several convolution kernels are combined by this method, and then the results after combination are made pooling operation, three sorts of CNN models(we named TBCNN, MCT-CNN and MMCT-CNN respectively) are constructed and then corresponding algorithmic thought are detailed on this basis. Secondly, relevant experiments and analyses are respectively designed to show the effects of three key parameters(convolution kernel, combination kernel number and word embedding) on three kinds of CNN models and to further demonstrate the effect of the models proposed. The experimental results show that compared with the traditional method of text classification in CNNs, term-based pooling method is addressed that not only the availability of the way is proved, but also the performance shows good superiority. 展开更多
关键词 convolutional NEURAL Networks term-based pooling TEXT Classification
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Consideration of the Local Correlation of Learning Behaviors to Predict Dropouts from MOOCs 认领
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作者 Yimin Wen Ye Tian +3 位作者 Boxi Wen Qing Zhou Guoyong Cai Shaozhong Liu 《清华大学学报自然科学版(英文版)》 SCIE EI CAS CSCD 2020年第3期336-347,共12页
Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout predictio... Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning,this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network(CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected. 展开更多
关键词 MASSIVE Open Online Courses(MOOCs) DROPOUT prediction local correlation of learning BEHAVIORS Convolutional Neural Network(CNN) EDUCATIONAL data mining
Extraction of gravitational wave signals with optimized convolutional neural network 认领
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作者 Hua-Mei Luo Wenbin Lin +1 位作者 Zu-Cheng Chen Qing-Guo Huang 《物理学前沿:英文版》 SCIE CSCD 2020年第1期135-140,共6页
Gabbard et al.have demonstrated that convolutional neural networks can achieve the sensitivity of matched filtering in the recognization of the gravitational-wave signals with high efficiency[Phys.Rev.Lett.120,141103(... Gabbard et al.have demonstrated that convolutional neural networks can achieve the sensitivity of matched filtering in the recognization of the gravitational-wave signals with high efficiency[Phys.Rev.Lett.120,141103(2018)].In this work we show that their model can be optimized for better accuracy.The convolutional neural networks typically have alternating convolutional layers and max pooling layers,followed by a small number of fully connected layers.We increase the stride in the max pooling layer by 1,followed by a dropout layer to alleviate overfitting in the original model.We find that these optimizations can effectively increase the area under the receiver operating characteristic curve for various tests on the same dataset. 展开更多
关键词 GRAVITATIONAL WAVE convolutional NEURAL networks DEEP learning
Hybrid first and second order attention Unet for building segmentation in remote sensing images 认领
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作者 Nanjun HE Leyuan FANG Antonio PLAZA 《中国科学:信息科学(英文版)》 SCIE EI CSCD 2020年第4期65-76,共12页
Recently,building segmentation(BS)has drawn significant attention in remote sensing applications.Convolutional neural networks(CNNs)have become the mainstream analysis approach in this field owing to their powerful re... Recently,building segmentation(BS)has drawn significant attention in remote sensing applications.Convolutional neural networks(CNNs)have become the mainstream analysis approach in this field owing to their powerful representative ability.However,owing to the variation in building appearance,designing an effective CNN architecture for BS still remains a challenging task.Most of CNN-based BS methods mainly focus on deep or wide network architectures,neglecting the correlation among intermediate features.To address this problem,in this paper we propose a hybrid first and second order attention network(HFSA)that explores both the global mean and the inner-product among different channels to adaptively rescale intermediate features.As a result,the HFSA can not only make full use of first order feature statistics,but also incorporate the second order feature statistics,which leads to more representative feature.We conduct a series of comprehensive experiments on three widely used aerial building segmentation data sets and one satellite building segmentation data set.The experimental results show that our newly developed model achieves better segmentation performance over state-of-the-art models in terms of both quantitative and qualitative results. 展开更多
关键词 BUILDING segmentation(BS) convolutional neural networks(CNNs) remote sensing high order pooling ATTENTION
Person Re-Identification with Effectively Designed Parts 认领
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作者 Yali Zhao Yali Li Shengjin Wang 《清华大学学报自然科学版(英文版)》 SCIE EI CAS CSCD 2020年第3期415-424,共10页
Person re-IDentification(re-ID) is an important research topic in the computer vision community, with significance for a range of applications. Pedestrians are well-structured objects that can be partitioned, although... Person re-IDentification(re-ID) is an important research topic in the computer vision community, with significance for a range of applications. Pedestrians are well-structured objects that can be partitioned, although detection errors cause slightly misaligned bounding boxes, which lead to mismatches. In this paper, we study the person re-identification performance of using variously designed pedestrian parts instead of the horizontal partitioning routine typically applied in previous hand-crafted part works, and thereby obtain more effective feature descriptors. Specifically, we benchmark the accuracy of individual part matching with discriminatively trained Convolutional Neural Network(CNN) descriptors on the Market-1501 dataset. We also investigate the complementarity among different parts using combination and ablation studies, and provide novel insights into this issue. Compared with the state-of-the-art, our method yields a competitive accuracy rate when the best part combination is used on two large-scale datasets(Market-1501 and CUHK03) and one small-scale dataset(VIPeR). 展开更多
关键词 PERSON re-IDentification(re-ID) Convolutional NEURAL Network(CNN) part model
Effect of Image Noise on the Classification of Skin Lesions Using Deep Convolutional Neural Networks 认领
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作者 Xiaoyu Fan Muzhi Dai +5 位作者 Chenxi Liu Fan Wu Xiangda Yan Ye Feng Yongqiang Feng Baiquan Su 《清华大学学报自然科学版(英文版)》 SCIE EI CAS CSCD 2020年第3期425-434,共10页
Skin lesions are in a category of disease that is both common in humans and a major cause of death.The classification accuracy of skin lesions is a crucial determinant of the success rate of curing lethal diseases.Dee... Skin lesions are in a category of disease that is both common in humans and a major cause of death.The classification accuracy of skin lesions is a crucial determinant of the success rate of curing lethal diseases.Deep Convolutional Neural Networks(CNNs)are now the most prevalent computer algorithms for the purpose of disease classification.As with all algorithms,CNNs are sensitive to noise from imaging devices,which often contaminates the quality of the images that are fed into them.In this paper,a deep CNN(Inception-v3)is used to study the effect of image noise on the classification of skin lesions.Gaussian noise,impulse noise,and noise made up of a compound of the two are added to an image dataset,namely the Dermofit Image Library from the University of Edinburgh.Evaluations,based on t-distributed Stochastic Neighbor Embedding(t-SNE)visualization,Receiver Operating Characteristic(ROC)analysis,and saliency maps,demonstrate the reliability of the Inception-v3 deep CNN in classifying noisy skin lesion images. 展开更多
关键词 SKIN LESION DEEP convolutional NEURAL NETWORK IMAGE noise
基于深度学习的全景片中颌骨疾病分类研究 认领
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作者 任家银 郭际香 《现代计算机》 2020年第14期35-38,共4页
作为当前人工智能领域的主流技术,深度学习技术在医学图像处理如分割、检测、分类及识别领域取得成功的应用。颌骨疾病是一种人类常见、多发的口腔疾病,口腔全景片是其主要检查方式之一。及时从全景片中检测颌骨疾病,有助于提高医生工... 作为当前人工智能领域的主流技术,深度学习技术在医学图像处理如分割、检测、分类及识别领域取得成功的应用。颌骨疾病是一种人类常见、多发的口腔疾病,口腔全景片是其主要检查方式之一。及时从全景片中检测颌骨疾病,有助于提高医生工作效率,能为病情诊断提供一定的参考。提出一种基于深度学习的颌骨疾病四分类模型,能识别和分类正常全景片和三种常见的颌骨疾病。实验结果较好,四分类模型在囊肿病变的精确率达90%。 展开更多
关键词 卷积神经网络 口腔全景片 颌骨疾病 分类
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Deterministic conversion rule for CNNs to efficient spiking convolutional neural networks 认领
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作者 Xu YANG Zhongxing ZHANG +3 位作者 Wenping ZHU Shuangming YU Liyuan LIU Nanjian WU 《中国科学:信息科学(英文版)》 SCIE EI CSCD 2020年第2期196-214,共19页
This paper proposes a general conversion theory to reveal the relations between convolutional neural network(CNN)and spiking convolutional neural network(spiking CNN)from structure to information processing.Based on t... This paper proposes a general conversion theory to reveal the relations between convolutional neural network(CNN)and spiking convolutional neural network(spiking CNN)from structure to information processing.Based on the conversion theory and the statistical features of the activations distribution in CNN,we establish a deterministic conversion rule to convert CNNs into spiking CNNs with definite conversion procedure and the optimal setting of all parameters.Included in conversion rule,we propose a novel"nscaling"weight mapping method to realize high-accuracy,low-latency and power efficient object classification on hardware.For the first time,the minimum dynamic range of spiking neuron’s membrane potential is studied to help to balance the trade-off between representation range and precise of the data type adopted by dedicated hardware when spiking CNNs run on it.The simulation results demonstrate that the converted spiking CNNs perform well on MNIST,SVHN and CIFAR-10 datasets.The accuracy loss over three datasets is no more than 0.4%.39%of processing time is shortened at best,and less power consumption is benefited from lower latency achieved by our conversion rule.Furthermore,the results of noise robustness experiments indicate that spiking CNN inherits the robustness from its corresponding CNN. 展开更多
关键词 convolutional NEURAL networks(CNN) SPIKING NEURAL networks(SNN) image classification CONVERSION RULE noise robustness neuromorphic hardware
Arbitrary-oriented target detection in large scene sar images 认领
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作者 Zi-shuo Han Chun-ping Wang Qiang Fu 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2020年第4期933-946,共14页
Target detection in the field of synthetic aperture radar(SAR) has attracted considerable attention of researchers in national defense technology worldwide,owing to its unique advantages like high resolution and large... Target detection in the field of synthetic aperture radar(SAR) has attracted considerable attention of researchers in national defense technology worldwide,owing to its unique advantages like high resolution and large scene image acquisition capabilities of SAR.However,due to strong speckle noise and low signal-to-noise ratio,it is difficult to extract representative features of target from SAR images,which greatly inhibits the effectiveness of traditional methods.In order to address the above problems,a framework called contextual rotation region-based convolutional neural network(RCNN) with multilayer fusion is proposed in this paper.Specifically,aimed to enable RCNN to perform target detection in large scene SAR images efficiently,maximum sliding strategy is applied to crop the large scene image into a series of sub-images before RCNN.Instead of using the highest-layer output for proposal generation and target detection,fusion feature maps with high resolution and rich semantic information are constructed by multilayer fusion strategy.Then,we put forwards rotation anchors to predict the minimum circumscribed rectangle of targets to reduce redundant detection region.Furthermore,shadow areas serve as contextual features to provide extraneous information for the detector identify and locate targets accurately.Experimental results on the simulated large scene SAR image dataset show that the proposed method achieves a satisfactory performance in large scene SAR target detection. 展开更多
关键词 Target detection Convolutional neural network Multilayer fusion Context information Synthetic aperture radar
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A new bearing fault diagnosis method based on modified convolutional neural networks 认领
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作者 Jiangquan ZHANG Yi SUN +3 位作者 Liang GUO Hongli GAO Xin HONG Hongliang SONG 《中国航空学报:英文版》 SCIE EI CAS CSCD 2020年第2期439-447,共9页
Fault diagnosis is vital in manufacturing system.However,the first step of the traditional fault diagnosis method is to process the signal,extract the features and then put the features into a selected classifier for ... Fault diagnosis is vital in manufacturing system.However,the first step of the traditional fault diagnosis method is to process the signal,extract the features and then put the features into a selected classifier for classification.The process of feature extraction depends on the experimenters’experience,and the classification rate of the shallow diagnostic model does not achieve satisfactory results.In view of these problems,this paper proposes a method of converting raw signals into twodimensional images.This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process.And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network(CNN),which can automatically accomplish the process of the feature extraction and fault diagnosis.The effect of this method is verified by bearing data.The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed.The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis. 展开更多
关键词 BEARING Convolutional NEURAL networks Different load DOMAINS FAULT identification RAW SIGNALS FAULT diagnosis
Automatic ocular artifact removal from EEG data using a hybrid CAE-RLS approach 认领
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作者 Wang Zhongmin Tian Meng +1 位作者 Liang Chen Song Hui 《中国邮电高校学报:英文版》 EI CSCD 2020年第1期81-91,共11页
Traditional methods for removing ocular artifacts(OAs) from electroencephalography(EEG) signals often involve a large number of EEG electrodes or require electrooculogram(EOG) as the reference, these constraints make ... Traditional methods for removing ocular artifacts(OAs) from electroencephalography(EEG) signals often involve a large number of EEG electrodes or require electrooculogram(EOG) as the reference, these constraints make subjects uncomfortable during the acquisition process and increase the complexity of brain-computer interfaces(BCI). To address these limitations, a method combining a convolutional autoencoder(CAE) and a recursive least squares(RLS) adaptive filter is proposed. The proposed method consists of offline and online stages. In the offline stage, the peak and local mean of the four-channel EOG signals are automatically extracted to obtain the CAE model. Once the model is trained, the EOG channels are no longer needed. In the online stage, by using the CAE model to identify the OAs from a single-channel raw EEG signal, the identified OAs and the given raw EEG signal are used as the reference and input for an RLS adaptive filter. Experiments show that the root mean square error(RMSE) of the CAE-RLS algorithm and independent component analysis(ICA) are 1.253 3 and 1.254 6 respectively, and the power spectral density(PSD) curve for the CAE-RLS is similar to the original EEG signal. These experimental results indicate that by using only a couple of EEG channels, the proposed method can effectively remove OAs without parallel EOG records and accurately reconstruct the EEG signal. In addition, the processing time of the CAE-RLS is shorter than that of ICA, so the CAE-RLS algorithm is very suitable for BCI system. 展开更多
关键词 electroencephalography(EEG) electrooculogram(EOG) OCULAR artifacts(OAs) recursive least squares(RLS) convolutional autoencoder(CAE)
Deep Learning and Time Series-to-Image Encoding for Financial Forecasting 认领
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作者 Silvio Barra Salvatore Mario Carta +2 位作者 Andrea Corriga Alessandro Sebastian Podda Diego Reforgiato Recupero 《自动化学报:英文版》 SCIE EI CSCD 2020年第3期683-692,共10页
In the last decade,market financial forecasting has attracted high interests amongst the researchers in pattern recognition.Usually,the data used for analysing the market,and then gamble on its future trend,are provid... In the last decade,market financial forecasting has attracted high interests amongst the researchers in pattern recognition.Usually,the data used for analysing the market,and then gamble on its future trend,are provided as time series;this aspect,along with the high fluctuation of this kind of data,cuts out the use of very efficient classification tools,very popular in the state of the art,like the well known convolutional neural networks(CNNs)models such as Inception,Res Net,Alex Net,and so on.This forces the researchers to train new tools from scratch.Such operations could be very time consuming.This paper exploits an ensemble of CNNs,trained over Gramian angular fields(GAF)images,generated from time series related to the Standard&Poor's 500 index future;the aim is the prediction of the future trend of the U.S.market.A multi-resolution imaging approach is used to feed each CNN,enabling the analysis of different time intervals for a single observation.A simple trading system based on the ensemble forecaster is used to evaluate the quality of the proposed approach.Our method outperforms the buyand-hold(B&H)strategy in a time frame where the latter provides excellent returns.Both quantitative and qualitative results are provided. 展开更多
关键词 Convolutional neural networks(CNNs) ENSEMBLE of CNNS financial forecasting Gramian ANGULAR fields(GAF)imaging
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Detection of ocean internal waves based on Faster R-CNN in SAR images 认领
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作者 BAO Sude MENG Junmin +1 位作者 SUN Lina LIU Yongxin 《海洋湖沼学报(英文)》 SCIE CAS CSCD 2020年第1期55-63,共9页
Ocean internal waves appear as irregular bright and dark stripes on synthetic aperture radar(SAR)remote sensing images.Ocean internal waves detection in SAR images consequently constituted a difficult and popular rese... Ocean internal waves appear as irregular bright and dark stripes on synthetic aperture radar(SAR)remote sensing images.Ocean internal waves detection in SAR images consequently constituted a difficult and popular research topic.In this paper,ocean internal waves are detected in SAR images by employing the faster regions with convolutional neural network features(Faster R-CNN)framework;for this purpose,888 internal wave samples are utilized to train the convolutional network and identify internal waves.The experimental results demonstrate a 94.78%recognition rate for internal waves,and the average detection speed is 0.22 s/image.In addition,the detection results of internal wave samples under different conditions are analyzed.This paper lays a foundation for detecting ocean internal waves using convolutional neural networks. 展开更多
关键词 ocean internal waves FASTER REGIONS with convolutional neural network features (Faster R-CNN) synthetic APERTURE radar (SAR) image region PROPOSAL (RPN)
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