Search

In view of the problem that it is difficult to ensure high efficiency and high accuracy in circle fitting algorithm in the same time, a circle fitting algorithm based on multi-level optimization is proposed in this study. The 3σ criterion is used to remove the coarse error points, and the random sampling consistency is improved by reducing the randomness of subset selection, the descending operation of the circle model and the threshold transformation of the number of adaptive iterations, so as to extract the high quality internal group points. The iterative weighted least square method with iteration termination condition is applied to achieve the great processing of point groups. In this study, the effectiveness of the algorithm is verified from the three aspects of defect circle, impurity interference and other noise, and the proposed method is compared with other mainstream circle fitting algorithms. The results show that the fitting accuracy of the proposed algorithm is less than 0.7 pixels under different degrees of circular defect and impurity interference, and the algorithm performs better than other algorithms in fitting effect. In the case of the noise interference of about 20%~265%, the fitting accuracy of the algorithm does not exceed 1 pixel, and the running time is less than 0.7 s. These results indicate that the proposed algorithm can resist a large number of salt-and-pepper noise interference and maintain high accuracy and detection efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since the left ventricle pressure waveform（LVPW）estimation is based on the model with numerous patient specific parameters, there is a great need to reduce the computation cost. A parameter subset selection strategy based on sensitivity analysis is proposed to solve these problems. LVPW features are chosen as outputs of a systemic circulation model. The meta model is created by adaptive sparse polynomial chaos expansion algorithm, and then Sobol sensitivity index is computed. Finally, the parameters which have large impact on LVPW features are selected as parameter subset. The parameter subset selection proposed in this paper can provide help for applying model to patient specific situations in clinical applications. Meantime, the results indicate that the reducing size of parameter subset can decrease the complex computation of parameter optimization significantly. Furthermore, LVPW estimation based on the proposed method has high correlation with that based on full model parameters. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, the existing feature selection methods based on ant colony algorithm optimization mostly use attribute dependence and information entropy attribute importance as the heuristic search factor on the path. However, this kind of search method has premature convergence in some decision tables or the searched feature subset contains redundant features, which leads to a significant decrease in selection accuracy. Aiming at such problems, this paper proposed an attribute importance measurement method based on the proportion of conditional attributes in the discernibility matrix. Taking the importance of the discernibility matrix as the heuristic factor on the path, this article designed a feature subset search method based on the discernibility matrix and ant colony algorithm optimization. The algorithm started from the feature core, and the ant colony selected features with high probability in turn to add to the feature core set, until the ant found the smallest feature subset. Validation of examples and experimental results of UCI data set show that compared with the feature selection method based on attribute dependence and information entropy attribute importance, under normal circumstances, this algorithm can find the smallest feature subset at a lower cost. [ABSTRACT FROM AUTHOR]

Concerning the high-dimensional biomedical data contains a large number of irrelevant or weak correlation features, affecting the efficiency of disease diagnosis, this paper proposed a high-dimensional biomedical data feature selection method based on improved shuffled frog leaping algorithm. This method introduced chaos memory weighting factor and balance group strategy into the shuffled frog leaping algorithm. The method enhanced the diversity of the algorithm, maintained the balance between global and local optimization of the algorithm, reduced the probability of the algorithm falling into local optimality and further improved the exploring ability of the shuffled frog leaping algorithm in feature space. The simulation results show that compared with the improved GA and PSO algorithm, the improved shuffled frog leaping algorithm feature selection method can significantly enhance the recognition and classification accuracy of related feature subsets. [ABSTRACT FROM AUTHOR]

This paper studied the problem of secure subset which most of the existing protocols that solve such problems can only keep the elements of one set private to. Under the semi-honest model, this paper constructed a secure subset protocol by using Bloom filter and Goldwasser-Micah homomorphic encryption algorithm, and proved the security of the protocol by using common simulation examples in secure multi-party computing. It used the Bloom filter to map a data set with a large number or large number field elements into a smaller data set, improved the efficiency and range of the protocol, at the same time, it used the Goldwasser-Micali homomorphic encryption algorithm to ensure the security of the protocol. Most of the relevant researches were based on the difficult problems such that secondary residuals, it is impossible to resist quantum attacks, and the secure subset computation which can resist quantum attacks is a further research direction. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The discrete binary particle swarm optimization algorithm has many advantages in various discrete optimization problems, but it is very easy to fall into the local optimal solution due to the nonlinear problem, and the best feature subset cannot be obtained. The noise reduction self-encoder can be mapped and reconstructed through a multi-layer nonlinear network, which has a good effect on Chinese medicine data. This paper proposed a feature combination method of fusion noise reduction self-encoder and BPSO. This method mainly used noise reduction self-encoder to perform nonlinear mapping of features to form super-complete basis, and then searched in super-complete basis through BPSO. To get the best feature subset, it used the clinical diabetes datasets and UCI datasets for analysis and processing. The experimental results show th at the combination of fusednoise self-encoder and BPSO features has a good adaptability to clinical experimental data of TCM. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the low execution efficiency in statistical optimal sample size algorithm to determine sample size for sampling large datasets, especially high-dimensional datasets and the importance of each dimension for high-dimensional datasets is different, moreover, there may be redundant attributes, this paper proposed statistical optimal sample size algorithm based on feature selection. The algorithm made use of the entropy theory. It constructed an entropy measure of similarity between objects to evaluate the importance of each dimension, then obtained important feature subsets according to design a kind of evaluation standard, finally executed statistical optimal sample size algorithm in the feature subsets. Experimental results show that the improved algorithm not only can receive higher accuracy in the clustering algorithm, but also can obviously reduce the execution time of the algorithm, so the improved algorithm is efficacious and feasible. [ABSTRACT FROM AUTHOR]

To fulfill this task that should tackle several primary challenges in microblogs, such as high dimensional data, noise information, and rapid topic evolution. This paper proposed a novel online topic detection model for tweets, called DLM. DLM first selected a discriminative feature subset and then detected interesting topics with a unigram language model. Experimental results show that DLM clearly outperforms the state-of-the-art models in terms of both MACRO_F1 and AVG_CDET. [ABSTRACT FROM AUTHOR]

The traditional proofs for Gaussian coefficient identities are of ten algebraic or mimic subset proofs. Interpreting Gaussian coefficients as the generalized binomial coefficients of the second kind with weight W = (w1 w2 ... wn)(wi; = qi) defined by Konvalina, combining dual selection, which is a selection of k boxes with repetition from the set {1,2, ..., n - k + 1}, can be uniquely associated with an n - k selection with repetition from the set {1,2, ..., k +1}, and the results show that a selection and its dual have the same weight to give a combinatorial proof for a Gaussian coefficient identity. The sequence representation of a selection consisting of 0, 1, 0, 1, plays an important role in the proof. When q = 1, this Gaussian coefficient identity changes the corresponding binomial coefficient identity. This method reveals the combinatorial connections between the binomial coefficients and the Gaussian coefficients. [ABSTRACT FROM AUTHOR]