Most of the studies about tilting pad journal bearings are for load-on-pad or load-between-pad tilting pad journal bearings, and for the other loading forms, the performance are often estimated by the performance of the two limited conditions, that may reduce the reliablity of bearing design or lead to waste materials in design. To obtained the influence of the load directions on the static and dynamic characteristics of the tilting pad journal bearing, which is called eccentric load effect in this papers, the performance calculation of the tilting pad journal bearing in different load directions is operated with a self-designed program. The results show that the load directions have considerable effects both on the static and dynamic characteristics of the tilting pad journal bearing, for the operating condition that load direction changed rapidly, it need performance analysis of the bearing in its special loading forms to enhance the precision and efficiency of bearing design, espacially where the dynamic performance of the tilting pad journal bearing is demanding.
Technical debt expresses the use of non-optimal solutions for short-term gains. Self-admitted technical debt is a technical debt that is deliberately introduced and recognized by developers in source code comments, including design debt, requirement debt, and defect debt, etc. Currently, many methods are proposed to detect SATD. However, these methods are limited to the identification of SATD or non-SATD. In this paper, we propose a CNN-BiLSTM method to detect and classify SATD. Through our cross-project experiments on 10 projects, it is shown that our method can not only effectively detect SATD, but also classify design debt, requirement debt, and defect debt in SATD.
Requirements are important in software development. Ambiguous requirements cause inconsistent understanding by developers, which leads to rework, delayed delivery, and other problems, and may even have devastating effects on the project. A large number of requirements text written in natural language are not concise, intuitive, and accurate. This condition increases the workload of designers and the difficulty of their tasks. An effective solution for the aforementioned problems is to extract actors and use cases from the requirement texts. This study proposes a model for extracting actors and using cases automatically, which combines bi-directional long short-term memory (BiLSTM) and conditional random fields. BiLSTM is used to capture the contextual information of the texts, and CRF is used to calculate the tag transfer score and determine the most accurate tag sequence, which aims to extract actors and use cases. Results show that the accuracy of extraction is significantly improved compared with the baseline method, which verifies the effectiveness of the proposed method in extracting actors and use cases.
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