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The microseismic (MS) monitoring in the coal mining provides a powerful method to identify the coal mine geological hazards as well as to prevent against the illegal mining. The challenge of MS monitoring is to extract characteristics of interest from the collected raw MS signals and locate the MS events using effective algorithms. In order to improve the localization accuracy of microseismic sources,...
This study aims to meet requirement of rapid detection of the TBM (tunnel boring machine) tool's wear state. To this end, the local region-growing method based on normalized cross-correlation was proposed to detect the block slag on belt conveyor and the least-square method for ellipse fitting was employed to measure the size. Firstly, the monocular color image was decomposed into two gray-scale images...
Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis, and it is also one of the most challenging tasks due to the intrinsic complexity of remote sensing images and the lack of priori knowledge. In recent years, a number of EE methods have been developed, and several different optimization objectives have been proposed from different perspectives. In all of...
Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided intervention. However, the lack of clear boundary specifically at the apex and base, and huge variation of shape and texture between the images from different patients make the task very challenging. To overcome these problems, in this paper, we propose a deeply supervised convolutional neural network...
Labeling each instance in a large-scale data set is extremely labor- and time-consuming. One way to alleviate this problem is active learning, which aims to discover the most valuable instances for labeling to construct a powerful classifier with low generalization error. Considering both informativeness and representativeness provides a promising way to design a practical active learning. However,...
Pattern recognition tasks such as the data classification and clustering usually can be represented by the perspective of multiple views or feature spaces. Obviously, the accuracy of the classification and clustering should be greatly improved if we carefully consider the discriminabilities from multiple views and explore the complementary information among them. However, multiple features also bring...
Deep convolutional networks have achieved successful performance in data mining field. However, training large networks still remains a challenge, as the training data may be insufficient and the model can easily get overfitted. Hence the training process is usually combined with a model regularization. Typical regularizers include weight decay, Dropout, etc. In this paper, we propose a novel regularizer,...
Confronted with severe environment issues, large scale utilization and development of wind energy and solar energy that are regenerative, non-pollution, green and clean, the best way for power generation and grid synchronization of distributed energy is to develop intelligence micro-grid. Research should be conducted on micro-grid synchronization and isolation control strategy to ensure compliance...
Confronted with severe environment issues, large scale utilization and development of wind energy and solar energy that are regenerative, non-pollution, green and clean, the best way for power generation and grid synchronization of distributed energy is to develop intelligence micro-grid. Amongst micro-grid, batteries will be connected with super capacitors to form hybrid energy storage system. Batteries...
This paper presents an optimization control strategy of particle quality for drug fluidized bed granulation process. Firstly, a predictive model of average particle size is established to construct the optimization model. However, this quality optimization will be hampered by model uncertainty. The batch-to-batch optimization based on ILC method is utilized to overcome model uncertainty and the integrated...
Spectral unmixing is one of the most important techniques for analyzing hyperspectral images and many hyperspectral unmixing algorithms were developed under an assumption that pure pixels exist in recent years. However, the pure-pixel assumption may be seriously violated for highly mixed data. Endmember extraction can be regards as an optimization problem no matter whether pure-pixel exists or not...
This manuscript proposes a near-isometric linear embeddings (NILE) method to extract low-dimensional features from hyperspectral imagery (HSI). The NILE aims to isometrically preserve local nearest neighbors of all points in the HSI data with a linear deterministic projection matrix that has fewer rows. The problem is equally transformed as an affine rank minimization problem for constructing a matrix...
One obvious trend for scholars is seeking an appropriate data description in hyperspectral anomaly detection. However, a specific predetermined model may not be able to fit all the other cases of hyperspectral images (HSIs). Hence, in this paper, we propose a graphical score estimation based hyperspectral anomaly detector (GSEAD) that utilizes a graphical data description of the HSI to achieve a data-adaptive...
Deep networks like the convolutional neural network and its variants usually learn hierarchical features from labeled images, which is very expensive to obtain. How can we find an unsupervised way to effectively extract deep and abstract features from images without annotations? Even from large qualities of images with noise? In this paper, we propose a robust deep neural network, named as stacked...
Robust background representation is a key issue for detecting anomaly targets in hyperspectral imagery. Meanwhile, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection process. This paper for the first time aims to implement robust background representation, as well as to explore the intrinsic data structure of the hyperspectral imagery...
Gait recognition is a rising biometric technology which aims to distinguish people purely through the analysis of the way they walk, while the problem is that the dimensionality of the gait data is too high, so it is necessary to carry on dimensionality reduction task. Up to date, in the area of computer vision and pattern recognition, various dimensionality reduction algorithms have been employed...
In this paper, endmember extraction algorithm is described as a combinatorial optimization problem. A novel quantum-behaved particle swarm optimization (QPSO) approach which employs quantum-behaved particle swarm optimization to find endmembers with good performance is proposed. As far as our knowledge, it is the first time that quantum-behaved particle swarm optimization is introduced into hyperspectral...
Diffusion magnetic resonance imaging (dMRI), including diffusion tensor imaging (DTI) and various high-angular-resolution imaging (HARDI) techniques, has been widely used in the research of neuroscience and clinical applications. With the development of new acquisition and imaging schemes, numerous novel post-processing methods are also proposed. However, it remains unclear whether the newly proposed...
Because of the complex ocean environment, a seismic wave signal caused by a moving ship usually has a low signal to noise ratio and its wave field is complicated. Using the traditional theoretical methods of spectrum analysis and wave filtering usually cannot distinguish the useful signal from the noise, so it is difficult to obtain ideal results. A space polarization filtering method is proposed,...
Detecting certain targets from hyperspectral images (HSIs) is of great interest for both civilian and military applications, with the aim being to detect and identify target pixels based on specific spectral signatures. However, the classical algorithms are generally dependent on the specific statistical hypothesis test, and the algorithms may only perform well with certain assumptions. Therefore,...
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