Spatial object has not only non-spatial attribute properties but also spatial properties related with space coordinates and topological structures. When using the traditional clustering methods to classify spatial obj...
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Spatial object has not only non-spatial attribute properties but also spatial properties related with space coordinates and topological structures. When using the traditional clustering methods to classify spatial objects, the objects of the same class may appear in non-adjacent spatial positions because spatial relationships are not been considered. The k-means adapts algorithm based on spatial contiguity relations regards spatial contiguities of the neighboring objects as a restrained condition. So the clustering result not only reflects the similarities of attributes but also reflects spatial adjacent relations, and furthermore reviews spatial distribution patterns of different classes. Therefore, this adapted algorithm is more suitable for the clustering analysis of spatial objects than the traditional k-means method.
Due to the inherent nonlinearity existed in the hydraulic system, the failure mechanism become complex and the failure characteristics are difficult to extract. Model-based fault diagnosis method depends heavily on th...
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ISBN:
(纸本)1424403316
Due to the inherent nonlinearity existed in the hydraulic system, the failure mechanism become complex and the failure characteristics are difficult to extract. Model-based fault diagnosis method depends heavily on the accuracy of mathematical model. An accuracy mathematical model of the process, however, is difficult to avail because of the nonlinearity and ripple coupling in actual hydraulic servo system. Therefore, Robustness of fault diagnosis method based on approximate linear model is worse. A failure observer based on RBF neural network is developed to realize failure detection. The Gaussian function is used for hidden node function, whose centers are adjusted by improved k-means clustering algorithm presented. The weights of output layer are obtained based on improved LS (least square) presented. The trained RBF observer, working concurrently with the,, actual system, accepts the input voltage signal to the servo valve and the measurements of the ram displacements, then rebuilds the system states. The output of the system is accurately estimated. By comparing the estimated output with the actual measurements, residual signal is generated and then analyzed to report the occurrence of faults. The experimental results demonstrate that the failure observer based on RBF neural network is effective in detecting the failure of the hydraulic position servo system.
The process of partitioning a large set of patterns into disjoint and homogeneous clusters is fundamental in knowledge acquisition. It is called clustering in the literature and it is applied in various fields includi...
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The process of partitioning a large set of patterns into disjoint and homogeneous clusters is fundamental in knowledge acquisition. It is called clustering in the literature and it is applied in various fields including data mining, statistical data analysis, compression and vector quantization. The k-means is a very popular algorithm and one of the best for implementing the clustering process. The k-means has a time complexity that is dominated by the product of the number of patterns, the number of clusters, and the number of iterations. Also, it often converges to a local minimum. In this paper, we present an improvement of the k-means clustering algorithm, aiming at a better time complexity and partitioning accuracy. Our approach reduces the number of patterns that need to be examined for similarity, in each iteration, using a windowing technique. The latter is based on well known spatial data structures, namely the range tree, that allows fast range searches. (C) 2002 Elsevier Science (USA).
Spectroscopic image analysis can be characterized as two different and distinct problems depending on the kind of information required from the solution. In its simplest form, the data can be decomposed into two subma...
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Spectroscopic image analysis can be characterized as two different and distinct problems depending on the kind of information required from the solution. In its simplest form, the data can be decomposed into two submatrices, each of which carries different aspects of pure component information. Typically, this means information about pure spectra and pure intensities are obtained from the solution. This is the well-known and well-characterized bilinear form that by itself cannot guarantee a unique solution due to the rotational ambiguity inherent in the mathematical solution. This problem has been addressed in a number of ways by different authors using novel constraints applied to the least-squares solution. This usually takes the form of natural constraints as suggested by Tauler as the standard methodology to improve the resolution of data during alternative least squares (ALS) iterative process. A second type of multivariate image analysis problem is proposed in this paper that is quite different from the tradition methods and in some ways potentially is more useful. This involves the solution as a class problem in which the relevant information is not necessarily contained in pure component information, but rather, in unique combinations of the pure components that are allowed to be spatially collocated. This discriminant image resolution (DIR) method theoretically can be treated as a more generalized solution to the problem because the distribution of components is allowed to freely mix in simplified combinations of solutions. The result is a constrained least-squares solution where the constraints are more limited and therefore less restrictive. The constraints in this case employ the results of probabilistic class partition information by applying Bayesian discriminant clustering to the intensity submatrix. This amounts to a spatial constraint because the probability of class association is used as a way of limiting the components that are allowed to appear
This paper considers the problem of ventricular segmentation and visualisation from dynamic (4D) MR cardiac data covering an entire patient cardiac cycle, in a format that is compatible with the web. Four different me...
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This paper considers the problem of ventricular segmentation and visualisation from dynamic (4D) MR cardiac data covering an entire patient cardiac cycle, in a format that is compatible with the web. Four different methods are evaluated for the process of segmentation of the objects of interest: The k-means clustering algorithm, the fuzzy k-means (FkM) algorithm, self-organizing maps (SOMs) and seeded region growing algorithm. The technique of active surface is then subsequently applied to refine the segmentation results, employing a deformable generalised cylinder as geometric primitive. The final ventricular models are presented in VRML 2.0 format. The same process is repeated for all the 3D volumes of the cardiac cycle. The radial displacement between end systole and end diastole is calculated for each point of the active surface and is encoded in colour on the VRML vertex, using the RGB colour model. Using the VRML 2.0 specifications, morphing is performed showing all cardiac phases in real time. The expert has the ability to view the objects and interact with them using a simple internet browser. Preliminary results of normal and abnormal cases indicate that very important pathological situations (such as infarction) can be visualised and thus easily diagnosed and localised with the assistance of the proposed technique. (C) 1999 Elsevier Science B.V. All rights reserved.
The ASIC for multi-speaker speech recognition is design in this paper. The LPC-derived cepstral coefficients are chosen as speech features. Templates are trained by k-means clustering algorithm. Two stage recognition ...
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ISBN:
(纸本)7543909405
The ASIC for multi-speaker speech recognition is design in this paper. The LPC-derived cepstral coefficients are chosen as speech features. Templates are trained by k-means clustering algorithm. Two stage recognition system can not only improve recognition accuracy, but also reduce the delay. The first stage of recognition system uses speech spectrum difference(SSD) algorithm. The second stage uses DTW. The whole recognition system is design into ASIC on high level with VHDL and simulated in Powerview.
A feasibility study was conducted to segment 1.5T fMRIs into gray matter and large veins using individual pixel intensity and temporal phase delay as two correlated parameters in gradient echo images. The time-course ...
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ISBN:
(纸本)0819417815
A feasibility study was conducted to segment 1.5T fMRIs into gray matter and large veins using individual pixel intensity and temporal phase delay as two correlated parameters in gradient echo images. The time-course of each pixel in gradient echo images acquired during visual stimulation with a checkerboard flashing at 8Hz was correlated to the stimulation 'on'-'off' sequence to identify activated pixels. The temporal delay of each activated pixels was estimated by fitting its time-course to a reference sinusoidal function. The mean signal intensity difference of the activated pixels was computed by subtracting the average of the 'on' images from the average of the 'off' images. After replacing each activated pixel with 2D features (i.e., intensity and time-delay), a clustering method based on a k-meansalgorithm was employed to classify vein and tissue pixels. Good demarcation between large veins and activated gray matter was achieved with this method.
In agriculture, paddy crop monitoring placed a crucial role because it supports food security control. Water shortage, high cost of fertilizers, and soil deterioration were identified as some of the difficulties encou...
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In agriculture, paddy crop monitoring placed a crucial role because it supports food security control. Water shortage, high cost of fertilizers, and soil deterioration were identified as some of the difficulties encountered when monitoring rice crops through satellite images acquired by remote sensing. This study developed a deep learning method-assisted clusteringalgorithm (DLCA) which helps to improve the paddy growth identification process and enables the transparency of agricultural activity. Convolution neural network (CNN) has been utilized to identify crop growth which helps to understand drip irrigation and water scarcity for a particular crop. The experimental research shows that the proposed model is improved in identifying the paddy growth, soil availability, high cost of fertilizers, and soil degradation in monitoring paddy crop production through the satellite image process. Overall, the findings of the experiments have been carried out, and the proposed DLCA to achieve a lower error rate of 0.03 and high accuracy of 98.52%, MCC attains 98.43%, and F1-score 99.02% compared to other popular methods.
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