@article{luan_liu_sun_zhang_hu_fang_ming_song_2019, title={High strain rate compressive response of the C-f/SiC composite}, volume={45}, ISSN={["1873-3956"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85059100831&partnerID=MN8TOARS}, DOI={10.1016/j.ceramint.2018.12.174}, abstractNote={Carbon fiber reinforced ceramic owns the properties of lightweight, high fracture toughness, excellent shock resistance, and thus overcomes ceramic's brittleness. The researches on the advanced structure of astronautics, marine have exclusively evaluated the quasi-static mechanical response of carbon fiber reinforced ceramics, while few investigations are available in the open literature regarding elastodynamics. This paper reports the dynamic compressive responses of a carbon fiber reinforced silicon carbide (Cf/SiC) composite (CFCMC) tested by the material test system 801 machine (MTS) and the split Hopkinson pressure bar (SHPB). These tests were to determine the rate dependent compression response and high strain rate failure mechanism of the Cf/SiC composite in in-plane and out-plane directions. The in-plane compressive strain rates are from 0.001 to 2200 s−1, and that of the out-plane direction are from 0.001 to 2400 s−1. The compressive stress-strain curves show the Cf/SiC composite has a property of strain rate sensitivity in both directions while under high strain rate loadings. Its compressive stiffness, compressive stress, and corresponding strain are also strain rate sensitive. The compressive damage morphologies after high strain rate impacting show different failure modes for each loading direction. This study provides knowledge about elastodynamics of fiber-reinforced ceramics and extends their design criterion with a reliable evaluation while applying in the scenario of loading high strain rate.}, number={6}, journal={CERAMICS INTERNATIONAL}, author={Luan, Kun and Liu, Jianjun and Sun, Baozhong and Zhang, Wei and Hu, Jianbao and Fang, Xiaomeng and Ming, Chen and Song, Erhong}, year={2019}, month={Apr}, pages={6812–6818} }
 @article{li_xiong_liu_gao_shamey_2018, title={Determining the colorimetric attributes of multicolored materials based on a global correction and unsupervised image segmentation method}, volume={57}, ISSN={["2155-3165"]}, url={https://doi.org/10.1364/AO.57.007482}, DOI={10.1364/AO.57.007482}, abstractNote={Fast and accurate measurement of colors in multicolored prints using commercial instruments or existing computer vision systems remains a challenge due to limitations in image segmentation methods and the size and complexity of the colored patterns. To determine the colorimetric attributes (L*a*b*) of multicolored materials, an approach based on global color correction and an effective unsupervised image segmentation is presented. The colorimetric attributes of all patches in a ColorChecker chart were measured spectrophotometrically, and an image of the chart was also captured. Images were segmented using a modified Chan-Vese method, and the sRGB values of each patch were extracted and then transformed into L*a*b* values. In order to optimize the transformation process, the performance of 10 models was examined by minimizing the average color differences between measured and calculated colorimetric values. To assess the performance of the model, a set of printed samples was employed and the color differences between the predicted and measured L*a*b* values of samples were compared. The results show that the modified Chan-Vese method, with suitable settings, generates satisfactory segmentation of the printed images with mean and maximum ΔE00 values of 2.43 and 4.28 between measured and calculated values.}, number={26}, journal={APPLIED OPTICS}, author={Li, Zhongjian and Xiong, Nian and Liu, Jiajun and Gao, Weidong and Shamey, Renzo}, year={2018}, month={Sep}, pages={7482–7491} }
 @article{liu_hughes-oliver_menius_2007, title={Domain-enhanced analysis of microarray data using GO annotations}, volume={23}, ISSN={["1367-4803"]}, DOI={10.1093/bioinformatics/btm092}, abstractNote={AbstractMotivation: New biological systems technologies give scientists the ability to measure thousands of bio-molecules including genes, proteins, lipids and metabolites. We use domain knowledge, e.g. the Gene Ontology, to guide analysis of such data. By focusing on domain-aggregated results at, say the molecular function level, increased interpretability is available to biological scientists beyond what is possible if results are presented at the gene level.Results: We use a ‘top–down’ approach to perform domain aggregation by first combining gene expressions before testing for differentially expressed patterns. This is in contrast to the more standard ‘bottom–up’ approach, where genes are first tested individually then aggregated by domain knowledge. The benefits are greater sensitivity for detecting signals. Our method, domain-enhanced analysis (DEA) is assessed and compared to other methods using simulation studies and analysis of two publicly available leukemia data sets.Availability: Our DEA method uses functions available in R (http://www.r-project.org/) and SAS (http://www.sas.com/). The two experimental data sets used in our analysis are available in R as Bioconductor packages, ‘ALL’ and ‘golubEsets’ (http://www.bioconductor.org/).Contact:  jliu6@stat.ncsu.eduSupplementary information: Supplementary data are available at Bioinformatics online.}, number={10}, journal={BIOINFORMATICS}, author={Liu, Jiajun and Hughes-Oliver, Jacqueline M. and Menius, Alan, Jr.}, year={2007}, month={May}, pages={1225–1234} }