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| 1 | A Deep Adaptive Learning Method for Rolling Bearing Fault Diagnosis Using Immunity显示文摘The extraction of rolling bearing fault features using traditional diagnostic methods is not sufficiently comprehensive and the features are often chosen subjectively and depend on human experience. In this paper, an improved deep convolutional process is used to extract a set of features adaptively. The hidden multi-layer feature of deep convolutional neural networks is also exploited to improve the extraction features. A deterministic detection of low-confidence samples is performed to ensure the reliability of the recognition results and to decrease the rate of false positives by evaluating the diagnosis of the deep convolutional neural network. To improve the efficiency of the continuous learning elements of the rolling bearing fault diagnosis, a clone learning strategy based on cloning and mutation operations is proposed. The experimental results show that the proposed deep convolutional neural network model can extract multiple rolling bearing fault features, improve classification and detection accuracy by reducing the false positive rate when diagnosing rolling bearing faults, and accelerate learning efficiency when using low-confidence rolling bearing fault samples. | Yuling Tian Xiangyu Liu | 2019 | Tsinghua Science and Technology2019，24，6： | 18 |
| 2 | Discriminative graph regularized broad learning system for image recognition显示文摘Broad learning system(BLS) has been proposed as an alternative method of deep learning. The architecture of BLS is that the input is randomly mapped into series of feature spaces which form the feature nodes, and the output of the feature nodes are expanded broadly to form the enhancement nodes, and then the output weights of the network can be determined analytically. The most advantage of BLS is that it can be learned incrementally without a retraining process when there comes new input data or neural nodes. It has been proven that BLS can overcome the inadequacies caused by training a large number of parameters in gradient-based deep learning algorithms. In this paper, a novel variant graph regularized broad learning system(GBLS) is proposed. Taking account of the locally invariant property of data, which means the similar images may share similar properties, the manifold learning is incorporated into the objective function of the standard BLS. In GBLS, the output weights are constrained to learn more discriminative information,and the classification ability can be further enhanced. Several experiments are carried out to verify that our proposed GBLS model can outperform the standard BLS. What is more, the GBLS also performs better compared with other state-of-the-art image recognition methods in several image databases. | Junwei JIN Zhulin LIU C.L.Philip CHEN | 2018 | Science China(Information Sciences)2018，61，11： | 12 |
| 3 | Progress and Applications of Visual SLAM显示文摘Visual simultaneous localization and mapping (SLAM) provides mapping and self-localization results for a robot in an unknown environment based on visual sensors, that have the advantages of small volume, low power consumption, and richness of information acquisition. Visual SLAM is essential and plays a significant role in supporting automated and intelligent applications of robots. This paper presents the key techniques of visual SLAM, summarizes the current research status, and analyses the new trends of visual SLAM research and development. Finally, specific applications of visual SLAM in restricted environments, including deep space and indoor scenarios, are discussed. | Kaichang DI Wenhui WAN Hongying ZHAO Zhaoqin LIU Runzhi WANG Feizhou ZHANG | 2019 | Journal of Geodesy and Geoinformation Science2019，2，2： | 11 |
| 4 | Feature extraction of welding defect based on machine vision显示文摘There are many flaws in welding images such as noise, low contrast, and blurred edges, which affect feature extraction from welding defect regions and impede classification and recognition of welding defects. To deal with the complexity of welding defect images, this paper proposes an effective method for extracting the features of welding defect regions. Firstly, image preprocessing, image segmentation and image background removal are carried out to a welding image in order to extract welding defect region; and then an 8-connected-component labeling method is used to mark defect regions. Finally, it extracts geometric characteristic parameters including perimeter, area, circularity and others. The experimental result shows that the method proposed in the paper can accurately extract the features of welding defect regions. It has good adaptability and practicability. | 齐继阳 李金燕 | 2019 | China Welding2019，28，1： | 10 |
| 5 | A fast registration algorithm of rock point cloud based on spherical projection and feature extraction显示文摘Point cloud registration is an essential step in the process of 3D reconstruction.In this paper,a fast registration algorithm of rock mass point cloud is proposed based on the improved iterative closest point (ICP)algorithm.In our proposed algorithm,the point cloud data of single station scanner is transformed into digital images by spherical polar coordinates,then image features are extracted and edge points are removed,the features used in this algorithm is scale-invariant feature transform (SIFT).By analyzing the corresponding relationship between digital images and 3D points,the 3D feature points are extracted,from which we can search for the two-way correspondence as candidates. After the false matches are eliminated by the exhaustive search method based on random sampling,the transformation is computed via the Levenberg-Marquardt-Iterative Closest Point (LM-ICP)algorithm.Experiments on real data of rock mass show that the proposed algorithm has the similar accuracy and better registration efficiency compared with the ICP algorithm and other algorithms. | Yaru XIAN Jun XIAO Ying WANG | 2019 | Frontiers of Computer Science2019，13，1： | 8 |
| 6 | Method of Modulation Recognition Based on Combination Algorithm of K-Means Clustering and Grading Training SVM显示文摘For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition. | Faquan Yang Ling Yang Dong Wang Peihan Qi Haiyan Wang 无 | 2018 | China Communications2018，15，12： | 7 |
| 7 | HRRP target recognition based on kernel joint discriminant analysis显示文摘With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on kernel joint discriminant analysis(KJDA)is proposed.Compared with the traditional feature extraction methods,KJDA possesses stronger discriminative ability in the kernel feature space.K-nearest neighbor(KNN)and kernel support vector machine(KSVM)are applied as feature classifiers to verify the classification effect.Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality,and improve target recognition performance. | LIU Wenbo YUAN Jiawen ZHANG Gong SHEN Qian | 2019 | Journal of Systems Engineering and Electronics2019，30，4： | 7 |
| 8 | CLASSIFICATION OF GEAR FAULTS USING HIGHER-ORDER STATISTICS AND SUPPORT VECTOR MACHINES显示文摘Gears alternately mesh and detach in driving process, and then working conditions of gears are alternately changing, so they are easy to be spalled and worn. But because of the effect of additive gaussian measurement noises, the signal-to-noises ratio is low; their fault features are difficult to extract. This study aims to propose an approach of gear faults classification,using the cumulants and support vector machines. The cumulants can eliminate the additive gaussian noises, boost the signal-to-noises ratio. Generalisation of support vector machines as classifier, which is employed structural risk minimisation principle, is superior to that of conventional neural networks, which is employed traditional empirical risk minimisation principle. Support vector machines as the classifier, and the third and fourth order cumulants as input, gears faults are successfully recognized. The experimental results show that the method of fault classification combining cumulants with support vector machines is very effective. | Lai Wuxing Zhang Guicai Shi Tielin Yang ShuziSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology,Wuhan 430074, China | 2002 | Chinese Journal of Mechanical Engineering2002，15，3： | 6 |
| 9 | Multi-layer Contribution Propagation Analysis for Fault Diagnosis显示文摘The recent development of feature extraction algorithms with multiple layers in machine learning and pattern recognition has inspired many applications in multivariate statistical process monitoring. In this work, two existing multi-layer linear approaches in fault detection are reviewed and a new one with extra layer is proposed in analogy. To provide a general framework for fault diagnosis in succession, this work also proposes the contribution propagation analysis which extends the original definition of contribution of variables in multivariate statistical process monitoring. In fault diagnosis stage, the proposed contribution propagation analysis for multilayer linear feature extraction algorithms is compared with the fault diagnosis results of original contribution plots associated with single layer feature extraction approach. Plots of variable contributions obtained by the aforementioned approaches on the data sets collected from a simulated benchmark case study(Tennessee Eastman process) as well as an industrial scale multiphase flow facility are presented as a demonstration of the usage and performance of the contribution propagation analysis on multi-layer linear algorithms. | Ruo-Mu Tan Yi Cao | 2019 | International Journal of Automation and computing2019，16，1： | 6 |
| 10 | Quantitative Damage Detection for Planetary Gear Sets Based on Physical Models显示文摘Planetary gear set is the critical component in helicopter transmission train,and an important problem in condition monitoring and health management of planetary gear set is quantitative damage detection.In order to resolve this problem,an approach based on physical models is presented to detect damage quantitatively in planetary gear set.A particular emphasis is put on a feature generation and selection method,which is used for sun gear tooth breakage damage detection quantitatively in planetary gear box of helicopter transmission system.In this feature generation procedure,the pure torsional dynamical models of 2K-H planetary gear set is established for healthy case and sun gear tooth-breakage case.Then,a feature based on the spectrum of simulation signals of the dynamical models is generated.Aiming at selecting the best feature suitable for quantitative damage detection,a two-sample Z-test procedure is used to analyze the performance of features on damage evolution tracing.A feature named SR,which had better performance in tracking damage,is proposed to detect damage in planetary gear set.Meanwhile,the sun gear tooth-chipped seeded experiments with different severity are designed to validate the method above,and then the test vibration signal is picked up and used for damage detection.With the results of several experiments for quantitative damage detection,the feasibility and the effect of this approach are verified.The proposed method can supply an effective tool for degradation state identification in condition monitoring and health management of helicopter transmission system. | CHENG Zhe HU Niaoqing | 2012 | Chinese Journal of Mechanical Engineering2012，25，1： | 5 |
| 11 | Advances in urban information extraction from high-resolution remote sensing imagery显示文摘The study of urban area is one of the hottest research topics in the field of remote sensing. With the accumulation of high-resolution(HR) remote sensing data and emerging of new satellite sensors, HR observation of urban areas has become increasingly possible, which provides us with more elaborate urban information. However, the strong heterogeneity in the spectral and spatial domain of HR imagery brings great challenges to urban remote sensing. In recent years, numerous approaches were proposed to deal with HR image interpretation over complex urban scenes, including a series of features from low level to high level, as well as state-of-the-art methods depicting not only the urban extent, but also the intra-urban variations. In this paper, we aim to summarize the major advances in HR urban remote sensing from the aspects of feature representation and information extraction. Moreover, the future trends are discussed from the perspectives of methodology, urban structure and pattern characterization, big data challenge, and global mapping. | Jianya GONG Chun LIU Xin HUANG | 2020 | Science China Earth Sciences2020，63，4： | 4 |
| 12 | 脚型三维测量与特征参数自动提取显示文摘脚型自动测量的目的是提取与制鞋相关的特征参数,并用这些特征参数指导鞋楦的设计,制造出合脚的鞋.使用脚型三维扫描仪,获得了脚的三维数字模型.详细描述了主要特征参数的定义,提出了在未做标记的情况下特征参数的提取方法,结合实际脚型测量给出了测量结果.特征参数的自动提取可为个体化鞋楦的设计和量脚制鞋提供必要的数据支持. | 胡安铎 苏显渝 向立群 刘成林 | 2009 | 四川大学学报(自然科学版)2009，46，6： | 4 |
| 13 | A NEW DIGITAL MODULATION RECOGNITION METHOD USING FEATURES EXTRACTED FROM GAR MODEL PARAMETERS显示文摘Based on the features extracted from generalized autoregressive (GAR) model parameters of the received waveform, and the use of multilayer perceptron(MLP) neural network classifier, a new digital modulation recognition method is proposed in this paper. Because of the better noise suppression ability of the GAR model and the powerful pattern classification capacity of the MLP neural network classifier, the new method can significantly improve the recognition performance in lower SNR with better robustness. To assess the performance of the new method, computer simulations are also performed. | Lu Mingquan Xiao Xianci Li Lemin (University of Electronic Science and Technology of China, Chengdu 610054) | 1999 | Journal of Electronics(China)1999，16，3： | 3 |
| 14 | Feature Extraction of Kernel Regress Reconstruction for Fault Diagnosis Based on Self-organizing Manifold Learning显示文摘The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension.Currently,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddings,such as manifold learning.However,these methods are all based on manual intervention,which have some shortages in stability,and suppressing the disturbance noise.To extract features automatically,a manifold learning method with self-organization mapping is introduced for the first time.Under the non-uniform sample distribution reconstructed by the phase space,the expectation maximization(EM) iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention.After that,the local tangent space alignment(LTSA) algorithm is adopted to compress the high-dimensional phase space into a more truthful low-dimensional representation.Finally,the signal is reconstructed by the kernel regression.Several typical states include the Lorenz system,engine fault with piston pin defect,and bearing fault with outer-race defect are analyzed.Compared with the LTSA and continuous wavelet transform,the results show that the background noise can be fully restrained and the entire periodic repetition of impact components is well separated and identified.A new way to automatically and precisely extract the impulsive components from mechanical signals is proposed. | CHEN Xiaoguang LIANG Lin XU Guanghua LIU Dan | 2013 | Chinese Journal of Mechanical Engineering2013，26，5： | 3 |
| 15 | Convolutional Neural Network Based on Spatial Pyramid for Image Classification显示文摘A novel convolutional neural network based on spatial pyramid for image classification is proposed.The network exploits image features with spatial pyramid representation.First,it extracts global features from an original image,and then different layers of grids are utilized to extract feature maps from different convolutional layers.Inspired by the spatial pyramid,the new network contains two parts,one of which is just like a standard convolutional neural network,composing of alternating convolutions and subsampling layers.But those convolution layers would be averagely pooled by the grid way to obtain feature maps,and then concatenated into a feature vector individually.Finally,those vectors are sequentially concatenated into a total feature vector as the last feature to the fully connection layer.This generated feature vector derives benefits from the classic and previous convolution layer,while the size of the grid adjusting the weight of the feature maps improves the recognition efficiency of the network.Experimental results demonstrate that this model improves the accuracy and applicability compared with the traditional model. | Gaihua Wang Meng Lu Tao Li Guoliang Yuan Wenzhou Liu | 2018 | Journal of Beijing Institute of Technology2018，27，4： | 2 |
| 16 | Hidden feature extraction for unstructured agricultural environment based on supervised kernel locally linear embedding modeling显示文摘An online hidden feature extraction algorithm is proposed for unknown and unstructured agricultural environments based on a supervised kernel locally linear embedding (SKLLE) algorithm. Firstly, an online obtaining method for scene training samples is given to obtain original feature data. Secondly, Bayesian estimation of the a posteriori probability of a cluster center is performed. Thirdly, nonlinear kernel mapping function construction is employed to map the original feature data to hyper-high dimensional kernel space. Fourthly, the automatic deter mination of hidden feature dimensions is performed using a local manifold learning algorithm. Then, a low-level manifold computation in hidden space is completed. Finally, long-range scene perception is realized using a 1-NN classifier. Experiments are conducted to show the effectiveness and the influence of parameter selection for the proposed algorithm. The kernel principal component analysis (KPCA), locally linear embedding (LLE), and supervised locally linear embedding (SLLE) methods are compared under the same experimental unstructured agricultural environment scene. Test results show that the proposed algorithm is more suitable for unstructured agricultural environments than other existing methods, and that the computational load is significantly reduced. | Zhong-Hua Miao Chen-Hui Ma Zhi-Yuan Gao Ming-Jun Wang Cheng-Liang Liu | 2018 | Advances in Manufacturing2018，6，4： | 2 |
| 17 | Research of Algorithm for Judgement of Joint Elongation of Conveyer Belt显示文摘By analyzing the feature of the joint image of the conveyer belt with steel ropes, a quick judgement algorithm based on the gradient search for the joint ’s elongation is put forward. And at the same time its experiment result is also given in this paper. | 和志强 王柏艳 高毓麟 | 1999 | International Journal of Mining Science and Technology1999，20，1： | 2 |
| 18 | Novel and Comprehensive Approach for the Feature Extraction and Recognition Method Based on ISAR Images of Ship Target显示文摘This paper proposes a novel and comprehensive method of automatic target recognition based on real ISAR images with the aim to recognize the non-cooperative ship targets. The special characteristics of the ISAR images for the real data compared with the simulated ISAR images are analyzed firstly. Then,the novel technique for the target recognition is proposed,and it consists of three steps,including the preprocessing,feature extraction and classification. Some segmentation and morphological methods are used in the preprocessing to obtain the clear target images. Then,six different features for the ISAR images are extracted.By estimating the features' conditional probability, the effectiveness and robustness of these features are demonstrated. Finally,Fisher's linear classifier is applied in the classification step. The results for the allfeature space are provided to illustrate the effectiveness of the proposed method. | Yong Wang Pengkai Zhu | 2017 | Journal of Harbin Institute of Technology(New Series)2017，24，5： | 1 |
| 19 | An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals显示文摘This paper addresses an advanced analysis system for the identification of alcoholic brain states from electroencephalogram(EEG) data in an automatic way. This study introduces an optimum allocation based sampling(OAS) scheme to discover the most favourable representative data points from every single time-window of each EEG signal considering the minimal variability of the observations. Combining all representative samples of each time-window in a set, some statistical features are extracted from every set of each class. The Mann-Whitney U test is used to assess whether each of the features is significant between the two classes(e.g., alcoholic and control). In order to evaluate the effectiveness of the OAS-based features, four well-known machine learning methods(decision table,support vector machine(SVM), k-nearest neighbor(k-NN) and logistic regression) are considered for identification of alcoholic brain state. The experimental results on the UCI KDD(i.e., UCI knowledge discovery in databases) database demonstrate that the OAS based decision table algorithm yields the highest accuracy of 99.58% with a low false alarm rate 0.40%, which is an improvement of up to9.58% over the existing algorithms. A proposed analysis system can be used to detect alcoholism and also to determine the level of alcoholism-related changes in EEG signals. | Siuly Siuly Varun Bajaj Abdulkadir Sengur Yanchun Zhang | 2019 | International Journal of Automation and computing2019，16，6： | 1 |
| 20 | Semantic Rule Based Image Visual Feature Ontology Creation显示文摘Multimedia is one of the important communication channels for mankind. Due to the advancement in technology and enormous growth of mankind, a vast array of multimedia data is available today. This has resulted in the obvious need for some techniques for retrieving these data. This paper will give an overview of ontology-based image retrieval system for asteroideae flower family domain. In order to reduce the semantic gap between the low-level visual features of an image and the high-level domain knowledge, we have incorporated a concept of multi-modal image ontology. So, the created asteroideae flower domain specific ontology would have the knowledge about the domain and the visual features. The visual features used to define the ontology are prevalent color,basic intrinsic pattern and contour gradient. In prevalent color extraction, the most dominant color from the images was identified and indexed. In order to determine the texture pattern for a particular flower, basic intrinsic patterns were used. The contour gradients provide the information on the image edges with respect to the image base. These feature values are embedded in the ontology at appropriate slots with respect to the domain knowledge. This paper also defines some of the query axioms which are used to retrieve appropriate information from the created ontology. This ontology can be used for image retrieval system in semantic web. | R. I. Minu K. K. Thyagharajan | 2014 | International Journal of Automation and computing2014，11，5： | 1 |
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