At present, in the mainstream sentiment analysis methods represented by the Support Vector Machine, the vocabulary and the latent semantic information involved in the text are not well considered, and sentiment analysis of text is dependent overly on the statistics of sentiment words. Thus, a Fisher kernel function based on Probabilistic Latent Semantic Analysis is proposed in this paper for sentiment analysis by Support Vector Machine. The Fisher kernel function based on the model is derived from the Probabilistic Latent Semantic Analysis model. By means of this method, latent semantic information involving the probability characteristics can be used as the classification characteristics, along with the improvement of the effect of classification for support vector machine, and the problem of ignoring the latent semantic characteristics in text sentiment analysis can be addressed. The results show that the effect of the method proposed in this paper, compared with the comparison method, is obviously improved.
In the construction of a smart marine, marine big data mining has a significant impact on the growing maritime industry in the Beibu Gulf. Clustering is the key technology of marine big data mining, but the conventional clustering algorithm cannot achieve the efficient clustering of marine data. According to the characteristics of marine big data, a marine big data clustering scheme based on self-organizing neural network (SOM) algorithm is proposed. First, the working principle of SOM algorithm is analyzed, and the algorithm's two-dimensional network model, similarity model and competitive learning model are focused. Secondly, combining with the working principle of algorithm, the marine big data clustering process and algorithm achievement based on SOM algorithm are developed; finally, experiments show that all vectors in marine big data clustering are stable, and the neurons in the output layer of clustering result have obvious consistency with the data itself, which shows the effectiveness of SOM algorithm in marine big data clustering.
ASME B31G provides the most basic and widespread method in assessing the remaining strength of corroded pipelines. The third edition B31G (ASME B31G-2009) is the latest revision issued by the American National Standards Institute (ANSI) and is used as the basis for this study. This article discusses the development process of ASME B31G, and presents the comparative analysis of ASME B31G, RSTRENG, and DNV RP-F101. The predicted failure pressures are calculated by each standard mentioned above, based on 35 groups of data for full-size pipe tests collected from the literature. The deviations between the predicted values and the actual experiment results are discussed. Finally, practical applications are compared among the assessment methods. The investigation showed that predictions based on ASME B31G-2009 are much more accurate than predictions based on the previous editions of B31G. The applications of ASME B31G-2009 and RSTRENG 0.85 dL effectively improve the pipe's conveying efficiency and optimize the cost of managing the piping system. However, they both are applicable only for evaluating the medium-and lowstrength pipe steels. In contrast, DNV RP-F101 is applicable to the medium-and high-strength pipe steels, but its results are often not safe for application to the lowerstrength pipe steels.
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