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Pei-Ying ZHANG
China University of Petroleum

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The Application of BP Neural Network In Oil-Field Pei-Ying ZHANG; Meng-Meng ZHAO
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 9: September 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

Aiming at the situation that many techniques of production performance analysis acquire lots of data and are expensive considering the computational and human resources, and their applications are limited, this paper puts forward a new method to analyze the production performance of oil-field based on the BP neural network. It builds a dataset with some available measured data such as well logs and production history, then, builds a field-wide production model by neural network technique, a model will be used to predict. The technique is verified, which shows that the predicted results are consistent with the maximum error of rate of oil production lower than 7% and maximum error of rate of water production lower than 5%, having certain application and research value. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3280
A HowNet-Based Semantic Relatedness Kernel for Text Classification Pei-Ying Zhang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 4: April 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

The exploitation of the semantic relatedness kernel has always been an appealing subject in the context of text retrieval and information management. Typically, in text classification the documents are represented in the vector space using the bag-of-words (BOW) approach. The BOW approach does not take into account the semantic relatedness information. To further improve the text classification performance, this paper presents a new semantic-based kernel of support vector machine algorithm for text classification. This method firstly using CHI method to select document feature vectors, secondly calculates the feature vector weights using TF-IDF method, and utilizes the semantic relatedness kernel which involves the semantic similarity computation and semantic relevance computation to classify the document using support vector machines. Experimental results show that compared with the traditional support vector machine algorithm, the algorithm in the text classification achieves improved classification F1-measure. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2361
Co-Authors Meng-Meng ZHAO