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| 1 | Convolutional neural networks for time series classification显示文摘Time series classification is an important task in time series data mining, and has attracted great interests and tremendous efforts during last decades. However, it remains a challenging problem due to the nature of time series data: high dimensionality,large in data size and updating continuously. The deep learning techniques are explored to improve the performance of traditional feature-based approaches. Specifically, a novel convolutional neural network(CNN) framework is proposed for time series classification. Different from other feature-based classification approaches,CNN can discover and extract the suitable internal structure to generate deep features of the input time series automatically by using convolution and pooling operations. Two groups of experiments are conducted on simulated data sets and eight groups of experiments are conducted on real-world data sets from different application domains. The final experimental results show that the proposed method outperforms state-of-the-art methods for time series classification in terms of the classification accuracy and noise tolerance. | Bendong Zhao Huanzhang Lu Shangfeng Chen Junliang Liu Dongya Wu | 2017 | Journal of Systems Engineering and Electronics2017，28，1： | 33 |
| 2 | Photogrammetry and Deep Learning显示文摘Deep learning has become popular and the mainstream technology in many researches related to learning,and has shown its impact on photogrammetry.According to the definition of photogrammetry,that is,a subject that researches shapes,locations,sizes,characteristics and inter-relationships of real objects from optical images,photogrammetry considers two aspects,geometry and semantics.From the two aspects,we review the history of deep learning and discuss its current applications on photogrammetry,and forecast the future development of photogrammetry.In geometry,the deep convolutional neural network(CNN)has been widely applied in stereo matching,SLAM and 3D reconstruction,and has made some effects but needs more improvement.In semantics,conventional methods that have to design empirical and handcrafted features have failed to extract the semantic information accurately and failed to produce types of“semantic thematic map”as 4D productions(DEM,DOM,DLG,DRG)of photogrammetry.This causes the semantic part of photogrammetry be ignored for a long time.The powerful generalization capacity,ability to fit any functions and stability under types of situations of deep leaning is making the automatic production of thematic maps possible.We review the achievements that have been obtained in road network extraction,building detection and crop classification,etc.,and forecast that producing high-accuracy semantic thematic maps directly from optical images will become reality and these maps will become a type of standard products of photogrammetry.At last,we introduce our two current researches related to geometry and semantics respectively.One is stereo matching of aerial images based on deep learning and transfer learning;the other is precise crop classification from satellite spatio-temporal images based on 3D CNN. | Jianya GONG Shunping JI | 2018 | Journal of Geodesy and Geoinformation Science2018，1，1： | 19 |
| 3 | Deep Learning Based Single Image Super-resolution:A Survey显示文摘Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recently, deep learning techniques have emerged and blossomed, producing ' the state-of-the-art” in many domains. Due to their capability in feature extraction and mapping, it is very helpful to predict high-frequency details lost in low-resolution images. In this paper, we give an overview of recent advances in deep learning-based models and methods that have been applied to single image super-resolution tasks. We also summarize, compare and discuss various models from the past and present for comprehensive understanding and finally provide open problems and possible directions for future research. | Viet Khanh Ha Jin-Chang Ren Xin-Ying Xu Sophia Zhao Gang Xie Valentin Masero Amir Hussain | 2019 | International Journal of Automation and computing2019，16，4： | 17 |
| 4 | Satellite Image Matching Method Based on Deep Convolutional Neural Network显示文摘This article focuses on the first aspect of the album of deep learning: the deep convolutional method. The traditional matching point extraction algorithm typically uses manually designed feature descriptors and the shortest distance between them to match as the matching criterion. The matching result can easily fall into a local extreme value, which causes missing of the partial matching point. Targeting this problem, we introduce a two-channel deep convolutional neural network based on spatial scale convolution, which performs matching pattern learning between images to realize satellite image matching based on a deep convolutional neural network. The experimental results show that the method can extract the richer matching points in the case of heterogeneous, multi-temporal and multi-resolution satellite images, compared with the traditional matching method. In addition, the accuracy of the final matching results can be maintained at above 90%. | Dazhao FAN Yang DONG Yongsheng ZHANG | 2019 | Journal of Geodesy and Geoinformation Science2019，2，2： | 16 |
| 5 | Forecasting Different Types of Convective Weather: A Deep Learning Approach显示文摘A deep learning objective forecasting solution for severe convective weather(SCW) including short-duration heavy rain(HR), hail, convective gusts(CG), and thunderstorms based on numerical weather prediction(NWP) data was developed. We first established the training datasets as follows. Five years of severe weather observations were utilized to label the NCEP final(FNL) analysis data. A large number of labeled samples for each type of weather were then selected for model training. The local temperature, pressure, humidity, and winds from 1000 to 200 h Pa, as well as dozens of convective physical parameters, were taken as predictors in our model. A six-layer convolutional neural network(CNN) model was then built and trained to obtain optimal model weights. After that, the trained model was used to predict SCW based on the Global Forecast System(GFS) forecast data as input. The performances of the CNN model and other traditional methods were compared. The results show that the deep learning algorithm had a higher classification accuracy on HR and hail than support vector machine, random forests, and other traditional machine learning algorithms. The objective forecasts by use of the deep learning algorithm also showed better forecasting skills than the subjective forecasts by the forecasters. The threat scores(TSs) of thunderstorm, HR, hail, and CG were increased by 16.1%, 33.2%, 178%, and 55.7%, respectively. The deep learning forecast model is currently used in the National Meteorological Center of China to provide guidance for the operational SCW forecasting over China. | Kanghui ZHOU Yongguang ZHENG Bo LI Wansheng DONG Xiaoling ZHANG | 2019 | Journal of Meteorological Research2019，33，5： | 12 |
| 6 | Application of artificial intelligence in gastroenterology显示文摘Artificial intelligence(AI) using deep-learning(DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis,prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias(class imbalance) have the possibility of overestimating the accuracy,external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification,prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability.Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed. | Young Joo Yang Chang Seok Bang | 2019 | World Journal of Gastroenterology2019，25，14： | 12 |
| 7 | Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China显示文摘Forest fires have caused considerable losses to ecologies, societies, and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network(CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, we proposed a spatial prediction model for forest fire susceptibility using a CNN. Past forest fire locations in Yunnan Province, China, from 2002 to 2010, and a set of 14 forest fire influencing factors were mapped using a geographic information system. Oversampling was applied to eliminate the class imbalance, and proportional stratified sampling was used to construct the training/validation sample libraries. A CNN architecture that is suitable for the prediction of forest fire susceptibility was designed and hyperparameters were optimized to improve the prediction accuracy. Then, the test dataset was fed into the trained model to construct the spatial prediction map of forest fire susceptibility in Yunnan Province.Finally, the prediction performance of the proposed model was assessed using several statistical measures—Wilcoxon signed-rank test, receiver operating characteristic curve,and area under the curve(AUC). The results confirmed the higher accuracy of the proposed CNN model(AUC 0.86)than those of the random forests, support vector machine,multilayer perceptron neural network, and kernel logistic regression benchmark classifiers. The CNN has stronger fitting and classification abilities and can make full use of neighborhood information, which is a promising alternative for the spatial prediction of forest fire susceptibility. This research extends the application of CNN to the prediction of forest fire susceptibility. | Guoli Zhang Ming Wang Kai Liu | 2019 | International Journal of Disaster Risk Science2019，10，3： | 12 |
| 8 | Multi-scale object detection by top-down and bottom-up feature pyramid network显示文摘While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection of objects with multiple aspect ratios and scales is still a key problem. This paper proposes a top-down and bottom-up feature pyramid network(TDBU-FPN),which combines multi-scale feature representation and anchor generation at multiple aspect ratios. First, in order to build the multi-scale feature map, this paper puts a number of fully convolutional layers after the backbone. Second, to link neighboring feature maps, top-down and bottom-up flows are adopted to introduce context information via top-down flow and supplement suboriginal information via bottom-up flow. The top-down flow refers to the deconvolution procedure, and the bottom-up flow refers to the pooling procedure. Third, the problem of adapting different object aspect ratios is tackled via many anchor shapes with different aspect ratios on each multi-scale feature map. The proposed method is evaluated on the pattern analysis, statistical modeling and computational learning visual object classes(PASCAL VOC)dataset and reaches an accuracy of 79%, which exhibits a 1.8% improvement with a detection speed of 23 fps. | ZHAO Baojun ZHAO Boya TANG Linbo WANG Wenzheng WU Chen | 2019 | Journal of Systems Engineering and Electronics2019，30，1： | 11 |
| 9 | Geospatial Data to Images: A Deep-Learning Framework for Traffic Forecasting显示文摘Traffic forecasting has been an active research field in recent decades, and with the development of deeplearning technologies, researchers are trying to utilize deep learning to achieve tremendous improvements in traffic forecasting, as it has been seen in other research areas, such as speech recognition and image classification. In this study, we summarize recent works in which deep-learning methods were applied for geospatial data-based traffic forecasting problems. Based on the insights from previous works, we further propose a deep-learning framework,which transforms geospatial data to images, and then utilizes the state-of-the-art deep-learning methodologies such as Convolutional Neural Network(CNN) and residual networks. To demonstrate the simplicity and effectiveness of our framework, we present a formulation of the New York taxi pick-up/drop-off forecasting problem, and show that our framework significantly outperforms traditional methods, including Historical Average(HA) and AutoRegressive Integrated Moving Average(ARIMA). | Weiwei Jiang Lin Zhang | 2019 | Tsinghua Science and Technology2019，24，1： | 10 |
| 10 | Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review显示文摘BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algorithm similar to deep learning,has demonstrated its capability to recognise specific features that can detect pathological lesions.AIM To assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODS The databases PubMed,EMBASE,and the Web of Science and research books were systematically searched using related keywords.Studies analysing pathological anatomy,cellular,and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer,differentiating cancer from other lesions,or staging the lesion.The data were extracted as per a predefined extraction.The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed.The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTS A total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified.The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions(n=6),HCC from cirrhosis or development of new tumours(n=3),and HCC nuclei grading or segmentation(n=2).The CNNs showed satisfactory levels of accuracy.The studies aimed at detecting lesions(n=4),classification(n=5),and segmentation(n=2).Several methods were used to assess the accuracy of CNN models used.CONCLUSION The role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies.While a few limitations have been identified in these studies,overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liver cancers images. | Samy A Azer | 2019 | World Journal of Gastrointestinal Oncology2019，11，12： | 10 |
| 11 | Convolutional Neural Networks Based Indoor Wi-Fi Localization with a Novel Kind of CSI Images显示文摘Indoor Wi-Fi localization of mobile devices plays a more and more important role along with the rapid growth of location-based services and Wi-Fi mobile devices.In this paper,a new method of constructing the channel state information(CSI)image is proposed to improve the localization accuracy.Compared with previous methods of constructing the CSI image,the new kind of CSI image proposed is able to contain more channel information such as the angle of arrival(AoA),the time of arrival(TOA)and the amplitude.We construct three gray images by using phase differences of different antennas and amplitudes of different subcarriers of one antenna,and then merge them to form one RGB image.The localization method has off-line stage and on-line stage.In the off-line stage,the composed three-channel RGB images at training locations are used to train a convolutional neural network(CNN)which has been proved to be efficient in image recognition.In the on-line stage,images at test locations are fed to the well-trained CNN model and the localization result is the weighted mean value with highest output values.The performance of the proposed method is verified with extensive experiments in the representative indoor environment. | Haihan Li Xiangsheng Zeng Yunzhou Li Shidong Zhou Jing Wang | 2019 | China Communications2019，16，9： | 7 |
| 12 | De-scattering and edge-enhancement algorithms for underwater image restoration显示文摘Image restoration is a critical procedure for underwater images, which suffer from serious color deviation and edge blurring. Restoration can be divided into two stages: de-scattering and edge enhancement. First, we introduce a multi-scale iterative framework for underwater image de-scattering, where a convolutional neural network is used to estimate the transmission map and is followed by an adaptive bilateral filter to refine the estimated results. Since there is no available dataset to train the network, a dataset which includes 2000 underwater images is collected to obtain the synthetic data. Second, a strategy based on white balance is proposed to remove color casts of underwater images. Finally, images are converted to a special transform domain for denoising and enhancing the edge using the non-subsampled contourlet transform. Experimental results show that the proposed method significantly outperforms state-of-the-art methods both qualitatively and quantitatively. | Pan-wang PAN Fei YUAN En CHENG | 2019 | Frontiers of Information Technology & Electronic Engineering2019，20，6： | 5 |
| 13 | Application of convolutional neural networks to large-scale naphtha pyrolysis kinetic modeling显示文摘System design and optimization problems require large-scale chemical kinetic models. Pure kinetic models of naphtha pyrolysis need to solve a complete set of stiff ODEs and is therefore too computational expensive. On the other hand, artificial neural networks that completely neglect the topology of the reaction networks often have poor generalization. In this paper, a framework is proposed for learning local representations from largescale chemical reaction networks. At first, the features of naphtha pyrolysis reactions are extracted by applying complex network characterization methods. The selected features are then used as inputs in convolutional architectures. Different CNN models are established and compared to optimize the neural network structure.After the pre-training and fine-tuning step, the ultimate CNN model reduces the computational cost of the previous kinetic model by over 300 times and predicts the yields of main products with the average error of less than 3%. The obtained results demonstrate the high efficiency of the proposed framework. | Feng Hua Zhou Fang Tong Qiu | 2018 | Chinese Journal of Chemical Engineering2018，26，12： | 5 |
| 14 | Transferring deep neural networks for the differentiation of mammographic breast lesions显示文摘Machine learning can help differentiating benign and malignant lesions seen on mammographic images. Conventional models require handcrafting features for lesion representation. Due to insufficient medical instances, the performance of convolutional neural networks(CNNs) can be further increased. This study makes use of transfer learning for mammographic breast lesion diagnosis and deep neural network(DNN) models pre-trained with large-scale natural images are employed. The diagnosis performance is evaluated with the prediction accuracy(ACC) and the area under the curve(AUC) on average. A histologically verified database is analyzed which contains 406 lesions(230 benign and 176 malignant). Involved models include transferred DNNs(GoogLeNet and AlexNet), shallow CNNs(CNN2 and CNN3) that are fully trained with medical instances and boosted by support vector machine(SVM), and two conventional methods which combine handcrafted features and SVM for lesion diagnosis. Experimental results indicate that GoogLeNet achieves the best performance(ACC=0.81, AUC=0.88), followed by AlexNet(ACC=0.79, AUC=0.83) and CNN3(ACC=0.73, AUC=0.82). Knowledge transfer can improve the mammographic breast cancer diagnosis, while its wide application still requires further verification in medical imaging domain. | YU ShaoDe LIU LingLing WANG ZhaoYang DAI GuangZhe XIE YaoQin | 2019 | Science China(Technological Sciences)2019，62，3： | 5 |
| 15 | CNN Based Classification of Rigid Targets in Space Using Radar Micro-Doppler Signatures显示文摘Micro-motion characteristics play an important role in some applications of radar target classification.In this paper,a classification method of rigid targets in space using radar micro-Doppler signatures is proposed.Based on the attitude kinematics of rigid targets,we analyze feasibility of classification using micro-Doppler signatures by the relationship among inertial properties of typical rigid targets,their micro-motion characteristics,and corresponding modulation to radar echoes.According to the micro-Doppler time-frequency distribution of echoes and the scale of training sample set,Convolutional neural network(CNN)based feature extraction method and softmax Classifier are designed.Simulations are carried out to validate its effectiveness and discuss the impact of observation duration,composition of training data and size of convolutional kernels on its classification robustness and computational cost. | WANG Jun ZHU He LEI Peng ZHENG Tong GAO Fei | 2019 | Chinese Journal of Electronics2019，28，4： | 4 |
| 16 | Simultaneous Denoising and Interpolation of Seismic Data via the Deep Learning Method显示文摘Utilizing data from controlled seismic sources to image the subsurface structures and invert the physical properties of the subsurface media is a major effort in exploration geophysics. Dense seismic records with high signal-to-noise ratio(SNR) and high fidelity helps in producing high quality imaging results. Therefore, seismic data denoising and missing traces reconstruction are significant for seismic data processing. Traditional denoising and interpolation methods rarely occasioned rely on noise level estimations, thus requiring heavy manual work to deal with records and the selection of optimal parameters. We propose a simultaneous denoising and interpolation method based on deep learning. For noisy records with missing traces, we adopt an iterative alternating optimization strategy and separate the objective function of the data restoring problem into two sub-problems. The seismic records can be reconstructed by solving a least-square problem and applying a set of pre-trained denoising models alternatively and iteratively.We demonstrate this method with synthetic and field data. | GAO Han ZHANG Jie | 2019 | Earthquake Research in China2019，33，1： | 4 |
| 17 | Deep feature extraction and motion representation for satellite video scene classification显示文摘Satellite video scene classification(SVSC)is an advanced topic in the remote sensing field,which refers to determine the video scene categories from satellite videos.SVSC is an important and fundamental step for satellite video analysis and understanding,which provides priors for the presence of objects and dynamic events.In this paper,a two-stage framework is proposed to extract spatial features and motion features for SVSC.More specifically,the first stage is designed to extract spatial features for satellite videos.Representative frames are firstly selected based on the blur detection and spatial activity of satellite videos.Then the fine-tuned visual geometry group network(VGG-Net)is transferred to extract spatial features based on spatial content.The second stage is designed to build motion representation for satellite videos.The motion representation of moving targets in satellite videos is first built by the second temporal principal component of principal component analysis(PCA).Second,features from the first fully connected layer of VGG-Net are used as high-level spatial representation for moving targets.Third,a small network of long and short term memory(LSTM)is further designed for encoding temporal information.Two-stage features respectively characterize spatial and temporal patterns of satellite scenes,which are finally fused for SVSC.A satellite video dataset is built for video scene classification,including 7209 video segments and covering 8 scene categories.These satellite videos are from Jilin-1 satellites and Urthecast.The experimental results show the efficiency of our proposed framework for SVSC. | Yanfeng GU Huan LIU Tengfei WANG Shengyang LI Guoming GAO | 2020 | Science China(Information Sciences)2020，63，4： | 4 |
| 18 | Characterizing robustness and sensitivity of convolutional neural networks for quantitative analysis of mitochondrial morphology显示文摘Background:Quantitative analysis of mitochondrial morphology plays important roles in studies of mitoehondrial biology.The analysis depends critically on segmentation of mitochondria,the image analysis process of extracting mitochondrial morphology from images.The main goal of this study is to characterize the performance of eonvolutional neural networks (CNNs)in segmentation of mitochondria from fluorescence microscopy images. Recently,CNNs have achieved remarkable success in challenging image segmentation tasks in several disciplines.So far,however,our knowledge of their performance in segmenting biological images remains limited.In particular,we know little about their robustness,which defines their capability of segmenting biological images of different conditions,and their sensitivity,which defines their capability of detecting subtle morphological changes of biological objects. Methods:We have developed a method that uses realistic synthetic images of different conditions to characterize the robustness and sensitivity of CNNs in segmentation of mitochondria.Using this method,we compared performance of two widely adopted CNNs:the fully convolutional network (FCN)and the U-Net.We further compared the two networks against the adaptive active-mask (AAM)algorithm,a representative of high-performance conventional segmentation algorithms. Results:The FCN and the U-Net consistently outperformed the AAM in accuracy,robustness,and sensitivity,often by a significant margin.The U-Net provided overall the best performance. Conclusions:Our study demonstrates superior performance of the U-Net and the FCN in segmentation of mitochondria.It also provides quantitative measurements of the robustness and sensitivity of these networks that are essential to their applications in quantitative analysis of mitochondrial morphology. | Xiaoqi Chai Qinle Ba Ge Yang | 2018 | Frontiers of Electrical and Electronic Engineering in China2018，6，4： | 3 |
| 19 | Multi-label Image Classification via Coarse-to-Fine Attention显示文摘Great efforts have been made by using deep neural networks to recognize multi-label images.Since multi-label image classification is very complicated,many studies seek to use the attention mechanism as a kind of guidance.Conventional attention-based methods always analyzed images directly and aggressively,which is difficult to well understand complicated scenes.We propose a global/local attention method that can recognize a multi-label image from coarse to fine by mimicking how human-beings observe images.Our global/local attention method first concentrates on the whole image,and then focuses on its local specific objects.We also propose a joint max-margin objective function,which enforces that the minimum score of positive labels should be larger than the maximum score of negative labels horizontally and vertically.This function further improve our multi-label image classification method.We evaluate the effectiveness of our method on two popular multi-label image datasets(i.e.,Pascal VOC and MS-COCO).Our experimental results show that our method outperforms state-of-the-art methods. | LYU Fan LI Linyan Victor S.Sheng FU Qiming HU Fuyuan | 2019 | Chinese Journal of Electronics2019，28，6： | 3 |
| 20 | ia-PNCC: Noise Processing Method for Underwater Target Recognition Convolutional Neural Network显示文摘Underwater target recognition is a key technology for underwater acoustic countermeasure.How to classify and recognize underwater targets according to the noise information of underwater targets has been a hot topic in the field of underwater acoustic signals.In this paper,the deep learning model is applied to underwater target recognition.Improved anti-noise Power-Normalized Cepstral Coefficients(ia-PNCC)is proposed,based on PNCC applied to underwater noises.Multitaper and normalized Gammatone filter banks are applied to improve the anti-noise capacity.The method is combined with a convolutional neural network in order to recognize the underwater target.Experiment results show that the acoustic feature presented by ia-PNCC has lower noise and are wellsuited to underwater target recognition using a convolutional neural network.Compared with the combination of convolutional neural network with single acoustic feature,such as MFCC(Mel-scale Frequency Cepstral Coefficients)or LPCC(Linear Prediction Cepstral Coefficients),the combination of the ia-PNCC with a convolutional neural network offers better accuracy for underwater target recognition. | Nianbin Wang Ming He Jianguo Sun Hongbin Wang Lianke Zhou Ci Chu Lei Chen | 2019 | Computers, Materials & Continua2019，，1： | 3 |
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