K-Means algorithm is one of the most popular clustering analysis *** the algorithm can be easily understood and implemented,and its execution is more efficient than common clustering algorithm,it has been used *** the...
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ISBN:
(纸本)9781467399050
K-Means algorithm is one of the most popular clustering analysis *** the algorithm can be easily understood and implemented,and its execution is more efficient than common clustering algorithm,it has been used *** the same time,with the increasing size of the data sets processed,CPUbasedserial K-Means implementation has been unable to meetthe people's need of data *** computing is considered well with the large data sets ***-based concurrent computation can accelerate common tasks,and especially for accelerating the compute-intensive ***(Compute Unified Device Architecture) is one of the methods that achieving the GPU-based concurrent *** the paper,the author hope to achieve a K-Means algorithm implementation can handle larger data sets via CUDA and the algorithm can be used on a common computer with NVIDIA graphics cards.
—This paper studies the problem of how to choose good viewpoints for taking photographs of architectures. We achieve this by learning from professional photographs of world famous landmarks that are available on the ...
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Due to the personalized needs for specific aspect evaluation on product quality, these years have witnessed a boom of researches on aspect rating prediction, whose goal is to extract ad hoc aspects from online reviews...
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This paper studies the problem of how to choose the viewpoint for taking good photographs for architecture. We achieve this by learning from professional photographs of world famous landmarks that are available in the...
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This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combinin...
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Consider that a set of balls of n different colors are thrown into m bins with the assumption that the ball number of each color is constant and the number of balls in each bin is also constant. Our optimal goal is to...
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ISBN:
(纸本)9781479966653
Consider that a set of balls of n different colors are thrown into m bins with the assumption that the ball number of each color is constant and the number of balls in each bin is also constant. Our optimal goal is to find a feasible placement such that the distinct colors of remaining balls should be at least c after removing any k bins (k ≤ m) with the minimum number of balls. We present that the optimal colored bins in bins is equivalent to the optimization of Fractional Repetition (FR) codes in distributed storage systems. Here balls correspond to coded packets and bins correspond to storage nodes. This problem can be represented as biregualr graph and then deduced to the Zarankiewicz problem, which is a well-known extremal graph theoretic problem. We present the problem with the relation to combinatorial design theory, especially t-designs and propose the explicit construction algorithm for the optimization problem from t-designs. Some constructions of the optimized FR codes by 2-designs are analyzed to tolerate the desired k fault-tolerance with c=n- 1.
The Minimum Spanning Tree(MST) is of crucial importance for Communication Networks(CNs),which can solve problems of unconstrained CNs ***,in practical CNs,the degree of the spanning tree is *** such a case,the problem...
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ISBN:
(纸本)9781467397155
The Minimum Spanning Tree(MST) is of crucial importance for Communication Networks(CNs),which can solve problems of unconstrained CNs ***,in practical CNs,the degree of the spanning tree is *** such a case,the problem is very difficult,which has been proved to be *** this paper,an improved genetic algorithm(I-GA) is proposed for solving the problem of Degree Constrained Minimum Spanning Tree(DCMST).We use Prufer number as the chromosome,and improve the crossover and mutation processes of the existing genetic algorithm for obtaining high locality,heritability,and *** results show that our mechanism of I-GA can get relatively better results.
Single particle analysis, which can be regarded as an average of signals from thousands or even millions of particle projections, is an efficient method to study the three-dimensional structures of biological macro- m...
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Single particle analysis, which can be regarded as an average of signals from thousands or even millions of particle projections, is an efficient method to study the three-dimensional structures of biological macro- molecules. An intrinsic assumption in single particle analysis is that all the analyzed particles must have identical composition and conformation. Thus specimen heterogeneity in either composition or conformation has raised great challenges for high-resolution analysis. For particles with multiple conformations, inaccurate align- ments and orientation parameters will yield an averaged map with diminished resolution and smeared density. Besides extensive classification approaches, here based on the assumption that the macromolecular complex is made up of multiple rigid modules whose relative orien- tations and positions are in slight fluctuation around equilibriums, we propose a new method called as local optimization refinement to address this conformational heterogeneity for an improved resolution. The key idea is to optimize the orientation and shift parameters of each rigid module and then reconstruct their three-dimen- sional structures individually. Using simulated data of 80S/70S ribosomes with relative fluctuations between the large （60S/50S） and the small （40S/30S） subunits, we tested this algorithm and found that the resolutions of both subunits are significantly improved. Our method provides a proof-of-principle solution for high-resolutionsingle particle analysis of macromolecular complexes with dynamic conformations.
In embedded Internet of Things(IOT) environment, there are the troubles such as complex background, illumination changes, shadows and other factors for detecting moving object, so we put forward a new detection servic...
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In embedded Internet of Things(IOT) environment, there are the troubles such as complex background, illumination changes, shadows and other factors for detecting moving object, so we put forward a new detection service method through mixing Gaussian Mixture Model(GMM), edge detection service method and continuous frame difference method in this paper. In time domain, the new method uses GMM to model and updates the background. In spatial domain, it uses the hybrid detection service method which mixes edge detection service method, continuous frame difference method and GMM to get initial contour of moving object, and gets ultimate moving object. This method not only can well adapt to the illumination gradients and background disturbance occurred on scene, but also can well solve some problems such as inaccurate target detection, incomplete edge detection, cavitation and ghost which usually appears in traditional method. As experimental result showing, this method holds better real-time and robustness. It is not only easily implemented, but also can accurately detect moving object.
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