In order to solve the linear variable differential transformer （LVDT） displacement sensor nonlinearity of overall range and extend its working range, a novel line-element based adaptively seg- menting method for pie...In order to solve the linear variable differential transformer （LVDT） displacement sensor nonlinearity of overall range and extend its working range, a novel line-element based adaptively seg- menting method for piecewise compensating correction was proposed. According to the mechanical structure of LVDT, the output equation was calculated, and then the theoretic nonlinear source of output was analyzed. By the proposed line-element adaptive segmentation method, the nonlinear output of LVDT was divided into linear and nonlinear regions with a given threshold. Then the com- pensating correction function was designed for nonlinear parts employing polynomial regression tech- nique. The simulation of LVDT validates the feasibility of proposed scheme, and the results of cali- bration and testing experiments fully prove that the proposed method has higher accuracy than the state-of-art correction algorithms.展开更多
Attribute reduction is an important process in rough set theory.Finding minimum attribute reduction has been proven to help the user-oriented make better knowledge discovery in some cases.In this paper,an efficient mi...Attribute reduction is an important process in rough set theory.Finding minimum attribute reduction has been proven to help the user-oriented make better knowledge discovery in some cases.In this paper,an efficient minimum attribute reduction algorithm is proposed based on the multilevel evolutionary tree with self-adaptive subpopulations.A model of multilevel evolutionary tree with self-adaptive subpopulations is constructed,and interacting attribute sets are better decomposed into subsets by the self-adaptive mechanism of elitist populations.Moreover it can self-adapt the subpopulation sizes according to the historical performance record so that interacting attribute decision variables are captured into the same grouped subpopulation,which will be extended to better performance in both quality of solution and competitive computation complexity for minimum attribute reduction.The conducted experiments show the proposed algorithm is better on both efficiency and accuracy of minimum attribute reduction than some representative algorithms.Finally the proposed algorithm is applied to magnetic resonance image(MRI)segmentation,and its stronger applicability is further demonstrated by the effective and robust segmentation results.展开更多
在图像语义分割领域,无监督领域自适应技术的发展有效降低了模型对标注数据的依赖,提升了自动驾驶等智能系统的效率和广泛适用性。针对无监督领域自适应技术在新场景泛化能力有限及在稀有类别中分割效果差的问题,文章提出了一种基于强...在图像语义分割领域,无监督领域自适应技术的发展有效降低了模型对标注数据的依赖,提升了自动驾驶等智能系统的效率和广泛适用性。针对无监督领域自适应技术在新场景泛化能力有限及在稀有类别中分割效果差的问题,文章提出了一种基于强弱一致性的无监督领域自适应语义分割算法。算法首先通过增加特征级别的增强,拓展图像增强空间的维度,改善了只利用图像级增强局限性。其次,采用基于能量分数的伪标签筛选方法,筛选出足够接近当前训练数据的样本赋予伪标签,避免了使用Softmax置信度方法在稀有类别中存在局限性,使模型更新更加稳健。最后,构建结合图像级别增强和特征级别增强的双重一致性框架,更充分的利用一致性训练,进一步提高模型的泛化能力。实验结果证明,提出的方法在GTA5-to-Cityscapes公开数据集中平均交并比指标(mean Intersection over Union,mIoU)可提升至52.6%,较PixMatch算法,性能提升了4.3%。展开更多
基金Supported by National High Technology Research and Development Program of China("863" Program)(2011AA041002)
文摘In order to solve the linear variable differential transformer （LVDT） displacement sensor nonlinearity of overall range and extend its working range, a novel line-element based adaptively seg- menting method for piecewise compensating correction was proposed. According to the mechanical structure of LVDT, the output equation was calculated, and then the theoretic nonlinear source of output was analyzed. By the proposed line-element adaptive segmentation method, the nonlinear output of LVDT was divided into linear and nonlinear regions with a given threshold. Then the com- pensating correction function was designed for nonlinear parts employing polynomial regression tech- nique. The simulation of LVDT validates the feasibility of proposed scheme, and the results of cali- bration and testing experiments fully prove that the proposed method has higher accuracy than the state-of-art correction algorithms.
基金Supported by the National Natural Science Foundation of China(61139002,61171132)the Natural Science Foundation of Jiangsu Education Department(12KJB520013)+2 种基金the Fundamental Research Funds for the Central Universitiesthe Funding of Jiangsu Innovation Program for Graduate Education(CXZZ110219)the Open Project Program of State Key Lab for Novel Software Technology in Nanjing University(KFKT2012B28)
文摘Attribute reduction is an important process in rough set theory.Finding minimum attribute reduction has been proven to help the user-oriented make better knowledge discovery in some cases.In this paper,an efficient minimum attribute reduction algorithm is proposed based on the multilevel evolutionary tree with self-adaptive subpopulations.A model of multilevel evolutionary tree with self-adaptive subpopulations is constructed,and interacting attribute sets are better decomposed into subsets by the self-adaptive mechanism of elitist populations.Moreover it can self-adapt the subpopulation sizes according to the historical performance record so that interacting attribute decision variables are captured into the same grouped subpopulation,which will be extended to better performance in both quality of solution and competitive computation complexity for minimum attribute reduction.The conducted experiments show the proposed algorithm is better on both efficiency and accuracy of minimum attribute reduction than some representative algorithms.Finally the proposed algorithm is applied to magnetic resonance image(MRI)segmentation,and its stronger applicability is further demonstrated by the effective and robust segmentation results.
文摘在图像语义分割领域,无监督领域自适应技术的发展有效降低了模型对标注数据的依赖,提升了自动驾驶等智能系统的效率和广泛适用性。针对无监督领域自适应技术在新场景泛化能力有限及在稀有类别中分割效果差的问题,文章提出了一种基于强弱一致性的无监督领域自适应语义分割算法。算法首先通过增加特征级别的增强,拓展图像增强空间的维度,改善了只利用图像级增强局限性。其次,采用基于能量分数的伪标签筛选方法,筛选出足够接近当前训练数据的样本赋予伪标签,避免了使用Softmax置信度方法在稀有类别中存在局限性,使模型更新更加稳健。最后,构建结合图像级别增强和特征级别增强的双重一致性框架,更充分的利用一致性训练,进一步提高模型的泛化能力。实验结果证明,提出的方法在GTA5-to-Cityscapes公开数据集中平均交并比指标(mean Intersection over Union,mIoU)可提升至52.6%,较PixMatch算法,性能提升了4.3%。
