In this paper, a recognition algorithm for the class of tree languages generated by linear, monadic context-free tree grammars (LM-CFTGs) is proposed. LM-CFTGs define an important class of tree languages because LM-CF...
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In this paper, a recognition algorithm for the class of tree languages generated by linear, monadic context-free tree grammars (LM-CFTGs) is proposed. LM-CFTGs define an important class of tree languages because LM-CFTGs are weakly equivalent to tree adjoining grammars (TAGs). The algorithm uses the CKY algorithm as a subprogram and recognizes whether an input tree can be derived from a given LM-CFTG in O(n(4)) time, where n is the number of nodes of the input tree.
Parallel multiple context-free grammar (PMCFG) and multiple context-free grammar (MCFG) were introduced to denote the syntax of natural languages. By the known fastest algorithm, the recognition problem for multiple c...
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Parallel multiple context-free grammar (PMCFG) and multiple context-free grammar (MCFG) were introduced to denote the syntax of natural languages. By the known fastest algorithm, the recognition problem for multiple context-free language (MCFL) and parallel multiple context-free language (PMCFL) can be solved in O(n(e)) time and O(n(e+1)) time, respectively, where e is a constant which depends only on a given MCFG or PMCFG. In this paper. we propose the following two algorithms. (1) An algorithm which reduces the recognition problem for MCFL to the boolean matrices multiplication problem. (2) An algorithm which reduces the recognition problem for PMCFL to the recognition problem for MCFL. The time complexity of these algorithms is O(n(e'-3i'+1).M(n(i'))) where e' and i' are constants which depend only on a given MCFG or PMCFG, and M(k) is the time needed for multiplying two k x k boolean matrices. The proposed algorithms are faster than the known fastest algorithms unless e' = e, i' = 1 for MCFG, and e' = e, i' = 0 for PMCFG.
The determination of network equipment weaknesses and the discovery of intrusion intention is one of the difficulties that troubled network security management personnel. Based on previous studies, further proposed a ...
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The determination of network equipment weaknesses and the discovery of intrusion intention is one of the difficulties that troubled network security management personnel. Based on previous studies, further proposed a double attack graph based on domain equipment. By the underlying network topology data collected and analyzed, using Bayesian theory to complete the quantify for the double attack graph and generation strategy in minimal power key set, with the cost of calculation of key equipment in the automatic recognition network topology, we provide an important basis for network maintenance. Experimental results show that the measure of using quantitative domain equipment double attack graph to recognize the intrusion intention is not only effective and feasible, but also has the feature of easy promotion.
In studies on agricultural robot vision systems, data used to evaluate algorithm performance, such as successful recognition rates, vary because of various factors. If the variation is too large, representation of the...
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In studies on agricultural robot vision systems, data used to evaluate algorithm performance, such as successful recognition rates, vary because of various factors. If the variation is too large, representation of the actual performance of algorithms by the data is bound to be poor. Here we present a method for analysing the quality of data used to evaluate the performance of a recognition algorithm for occluded tomatoes based on measurement system analysis. The measurement system included a soft measurement tool (a counting method for the number of successful recognitions), appraisers, measured objects (recognition results of 300 occluded tomato images), the usage method for the soft measurement tool and measurement environments. The measurement system was analysed on the basis of its repeatability and reproducibility. Repeatability and reproducibility were both evaluated based on Fleiss's Kappa values, free-marginal multirater Kappa values and Kendall coefficients. Test results showed that repeatability was excellent or fair to good based on Fleiss's Kappa values and excellent based on free-marginal multirater Kappa values and Kendall coefficients for the three appraisers. Further improvement in the soft type of measurement tool is necessary. Reproducibility was fair to good with Fleiss's Kappa values and free-marginal multirater Kappa values, and good with Kendall coefficients. Large values of measured feature resulted in inferior repeatability and reproducibility. (C) 2017 IAgrE. Published by Elsevier Ltd. All rights reserved.
A theorem concerning the correctness of the linear closure of the set of recognition algorithms based on the separation of objects by hyperplanes is proved.
A theorem concerning the correctness of the linear closure of the set of recognition algorithms based on the separation of objects by hyperplanes is proved.
In this paper, we study and analyse the recognition methods for digital image. In the digital image preprocessing, the conservative smoothing, mean filtering, Gaussian sharpening and binarisation are used together so ...
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In this paper, we study and analyse the recognition methods for digital image. In the digital image preprocessing, the conservative smoothing, mean filtering, Gaussian sharpening and binarisation are used together so as to guarantee the effectiveness of the digital feature extraction. We propose an edge-tracking method, a distance feature information method and a feature information extraction method for recognising digital images. And, those three methods are evaluated and compared by experimental tests.
The geometric parameters of the flute profile of micro-mills have a great influence on both the strength characteristics of the tool and the cutting process. This explains the need for high-precision control of the sh...
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ISBN:
(纸本)9781510657052;9781510657045
The geometric parameters of the flute profile of micro-mills have a great influence on both the strength characteristics of the tool and the cutting process. This explains the need for high-precision control of the shape of the flute profile. In modern CNC controlling and measuring machines, a non-contact measurement technique is implemented using high-precision reflected light cameras operating based on the principle of contrast autofocus. The existing methods of recognition of the flute profile have large errors associated with the low efficiency of the contrast focusing method in the case of control of a section of several subsections. The proposed recognition algorithm is based on the search for local focus points obtained as a result of the analysis of images from the reflected light camera. This method is shown to provide high accuracy control of the flute profile of micro-mills.
With the development of intelligent transportation technology, which all countries are suitable for their own license plate recognition system is developed. But because of the CCD camera Angle problem will make licens...
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ISBN:
(纸本)9783037859728
With the development of intelligent transportation technology, which all countries are suitable for their own license plate recognition system is developed. But because of the CCD camera Angle problem will make license plate image tilt;Segmentation after do not match the characters in size and character discontinuity, led to license plate recognition rate is not high, speed slow, reduce the real-time performance of the system. In order to improve the rate of convergence, the recognition rate presents a license plate recognition algorithm based on BP neural network. First put the image correction, segmentation of character normalization processing and eliminate the unfavorable factors, then puts forward characteristics of characters input for training the BP neural network. By setting the network weights and training transfer function, improved algorithm to improve the recognition rate of the system, as well as the real-time performance.
This paper proposes an efficient methodology that is able to accurately recognize nondeterministic signals generated by Stochastic Processes (SPs). This technique is based on (i) a training algorithm, which iterativel...
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ISBN:
(纸本)9780780397538
This paper proposes an efficient methodology that is able to accurately recognize nondeterministic signals generated by Stochastic Processes (SPs). This technique is based on (i) a training algorithm, which iteratively extracts suitable parameter collections;(d) a recognition procedure that measures the trajectory-proximities by means of an ad-hoc metric, in order to associate the unknown signal to an SP by using a representation based on Karhunen-Lo&e Transform (KLT). The recognition algorithm exploits a modelling of several signal classes based on KLT inasmuch this representation effectively characterizes projections of every SP signal in terms of nondeterministic trajectories defined on associated spaces. The methodology is able to recognize SPs without probability density function (pdf) estimation, and with low-computational complexity: exhaustive experimentations on specific case-studies have shown high recognition performance. As application examples, SPs generated by Stochastic Nonlinear-Differential-Equations (SNDEs), with different initial conditions and coefficients being random variables (RVs), have been considered.
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