强彦 - 太原理工大学 - 信息与计算机学院

个人简介

学习经历 1988-09至1992-07 太原工业大学计算机科学与技术，学士学位 1996-09至1999-07 太原理工大学计算机应用技术，硕士学位 2006-09至2010-07 太原理工大学计算机应用技术，博士学位 2011-08至2011-10 日本横滨国立大学，研修访问 2015-10至2016-10 美国德克萨斯大学，访问学者工作经历 1992-07至1995-06 太原工业大学计算机系分团委书记 1995-06至1996-09 山西省扶贫工作队队员 1999-12至2009-11 太原理工大学计算机学院讲师 2009-12至2014-07 太原理工大学计算机学院副教授（硕士生导师） 2010-06至2011-12 太原理工大学计算机学院计科系副主任 2014-08至2015-10 太原理工大学计算机学院教授（硕士生导师） 2015-11至今太原理工大学计算机学院教授（博士生导师） 2012-01至2017-12 太原理工大学计算机学院实验技术中心主任 2018-01 至今太原理工大学信息与计算机学院副院长

研究领域

云计算技术、数据库性能优化、人工智能、图像处理

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

[1]Qiang, Y, et al. An automatic detection method for lung nodules based on multi-scale enhancement filters and 3D shape features. KSII Transactions on Internet and Information Systems, 13, 1, (2019), 347-370. [2]Qiang, Y, et al. An Automated Segmentation Method for Lung Parenchyma Image Sequences Based on Fractal Geometry and Convex Hull Algorithm. Applied Sciences, 8(5), 832. [3]Qiang, Y, et al. Automatic diagnosis of pulmonary nodules using a hierarchical extreme learning machine model. International Journal of Bio-Inspired Computation, 11(3), 192. [4]Qiang, Y, et al. Juxta-Vascular Pulmonary Nodule Segmentation in PET-CT Imaging Based on an LBF Active Contour Model with Information Entropy and Joint Vector. Computational and Mathematical Methods in Medicine, 2018, 1–10. [5]Qiang, Y, et al. Automated Lung Nodule Segmentation Using an Active Contour Model Based on PET/CT Images.Journal of Computational and Theoretical Nanoscience, 12(8), 1972-1976. [6]Qiang, Y, et al. Coarse-to-Fine Lung Segmentation in Computed Tomography Images. Journal of Computational and Theoretical Nanoscience, 12(2), 330-334. [7]Qiang, Y, et al. An efficient cluster head selection approach for collaborative data processing in wireless sensor networks[J]. International Journal of Distributed Sensor Networks, 2015, 2015: 132. [8]Qiang, Y, et al. Improvement of path analysis algorithm in social networks based on HBase[J]. Journal of Combinatorial Optimization, 2014, 28(3): 588-599. [9]Qiang, Y, et al. An automated pulmonary parenchyma segmentation method based on an improved region growing algorithmin PET-CT imaging[J]. Frontiers of Computer Science, 2016, 10(1): 189-200. [10]Qiang, Y, et al. Solitary Pulmonary Nodule Segmentation Based on the Rolling Ball Method. Journal of Computational and Theoretical Nanoscience, 12(8), 1977-1983. [11]Qiang, Y, et al. A New Method of Detecting Pulmonary Nodules with PET/CT Based on an Improved Watershed Algorithm. PloS one, 10(4). [12]Qiang, Y, et al. A short-term trend prediction model of topic over Sina Weibo dataset. Journal of Combinatorial Optimization, 28(3), 613–625. [13]Qiang, Y, et al. A Bijection between Lattice-Valued Filters and Lattice-Valued Congruences in Residuated Lattices．MATHEMATICAL PROBLEMS IN ENGINEERING, 2013. [14]Qiang, Y, et al. A novel hybrid system for feature selection based on an improved gravitational search algorithm and k-NN method[J]. Applied Soft Computing, 2015, 31: 293-307. [15]Qiang, Y, et al. Rapid Retrieval of Lung Nodule CT Images Based on Hashing and Pruning Methods. BioMed Research International, 2016, 1–10. [16]Qiang, Y, et al. Automatic segmentation method for solitary pulmonary nodules based on PET/CT [J],Journal of Tsinghua University, 2013, 2(53):200-204. [17]Qiang, Y, et al. Improvement of path analysis algorithm in social networks based on HBase [J],Journal of Combinatorial Optimization,2013:1-12. [18]Qiang, Y, et al. Social network path analysis based on HBase [C],19th International Computing and Combinatorics Conference, COCOON 2013,2013,770-779. [19]Qiang, Y, et al. Computerized distinction of benign and malignant pulmonary nodules on PET-CT imageology character.Journal of Chemical and harmaceutical Research, v 5, n 12, p 183-187, 2013. [20]Qiang, Y, et al. Medical image denoising method based on PET/CT [J]. Journal of Tsinghua University,v 52,n 8,,p1056-1060,2012.