As the supporting supplier of the main engine plant, the general air filter manufacturers have insufficient technical reserves. The structural optimization of air filter is often based on the bench experiment, which h...As the supporting supplier of the main engine plant, the general air filter manufacturers have insufficient technical reserves. The structural optimization of air filter is often based on the bench experiment, which has high implementation cost and poor performance. In view of this, taking computational fluid dynamics(CFD) as the basic technical means, an optimization design method based on parametric sensitivity combined with equidistant search was proposed. Specifically, the sensitivity of local structure parameters to pressure loss was analyzed by taking local structure of air filter as the object. According to the sensitivity, the method of equidistant search was used to optimize the parameters in order, so as to achieve the goal of overall optimization. After optimization, the pressure loss decreased by 45.13% and the effect was remarkable.展开更多
The existing query expansion(QE) methods cannot find the most users-requested source code version at times due to the over-expansion resulting from noises. To solve this problem, we propose a QE method based on evolvi...The existing query expansion(QE) methods cannot find the most users-requested source code version at times due to the over-expansion resulting from noises. To solve this problem, we propose a QE method based on evolving contexts(EC) that are added/deleted terms and their dependent terms during code evolution. On expanding a query, we appended the added terms as relevant terms, and excluded the deleted terms as noisy terms. We also developed a QE-integrating framework based on the Support Vector Machine(SVM) Ranking, called QESR, to simultaneously integrate multiple QE methods. Our experiment shows that QESR outperforms the state-of-the-art QE methods CodeHow and Query Expansion based on Crowd Knowledge(QECK) by 13%-16% in terms of precision when the first query result is inspected.展开更多
基金Supported by the General Plan Projects of Science and Technology of Jiangxi Provincial Department of Education(GJJ151161,GJJ180976)the Plan Projects of Science and Technology of Jiangxi Provincial Department of Science and Technology(20161BBE50053)the Foundation of the Center of Collaboration and Innovation(18XTKFYB03)
文摘As the supporting supplier of the main engine plant, the general air filter manufacturers have insufficient technical reserves. The structural optimization of air filter is often based on the bench experiment, which has high implementation cost and poor performance. In view of this, taking computational fluid dynamics(CFD) as the basic technical means, an optimization design method based on parametric sensitivity combined with equidistant search was proposed. Specifically, the sensitivity of local structure parameters to pressure loss was analyzed by taking local structure of air filter as the object. According to the sensitivity, the method of equidistant search was used to optimize the parameters in order, so as to achieve the goal of overall optimization. After optimization, the pressure loss decreased by 45.13% and the effect was remarkable.
基金Supported by the Science and Technology Project of Jiangxi Education Department(GJJ161151)the School-Level Team Building Project(JXTD1404)
文摘The existing query expansion(QE) methods cannot find the most users-requested source code version at times due to the over-expansion resulting from noises. To solve this problem, we propose a QE method based on evolving contexts(EC) that are added/deleted terms and their dependent terms during code evolution. On expanding a query, we appended the added terms as relevant terms, and excluded the deleted terms as noisy terms. We also developed a QE-integrating framework based on the Support Vector Machine(SVM) Ranking, called QESR, to simultaneously integrate multiple QE methods. Our experiment shows that QESR outperforms the state-of-the-art QE methods CodeHow and Query Expansion based on Crowd Knowledge(QECK) by 13%-16% in terms of precision when the first query result is inspected.
