Chen Song¹, Xu Cheng^{1, *}, Yongxiang Gu¹, Beijing Chen¹, Zhangjie Fu¹

Journal on Artificial Intelligence, Vol.2, No.2, pp. 59-77, 2020, DOI:10.32604/jai.2020.010193

Abstract Object detection is one of the most fundamental, longstanding and significant problems in the field of computer vision, where detection involves object classification and location. Compared with the traditional object detection algorithms, deep learning makes full use of its powerful feature learning capabilities showing better detection performance. Meanwhile, the emergence of large datasets and tremendous improvement in computer computing power have also contributed to the vigorous development of this field. In the paper, many aspects of generic object detection are introduced and summarized such as traditional object detection algorithms, datasets, evaluation metrics, detection frameworks based on deep learning and state-of-the-art… More >

Christopher A. Lemmon^1,2, Nathan J. Sniadecki³, Sami Alom Ruiz^1,3, John L. Tan, Lewis H. Romer^2,4,5, Christopher S. Chen^3,4

Molecular & Cellular Biomechanics, Vol.2, No.1, pp. 1-16, 2005, DOI:10.3970/mcb.2005.002.001

Abstract The interplay of mechanical forces between the extracellular environment and the cytoskeleton drives development, repair, and senescence in many tissues. Quantitative definition of these forces is a vital step in understanding cellular mechanosensing. Microfabricated post array detectors (mPADs) provide direct measurements of cell-generated forces during cell adhesion to extracellular matrix. A new approach to mPAD post labeling, volumetric imaging, and an analysis of post bending mechanics determined that cells apply shear forces and not point moments at the matrix interface. In addition, these forces could be accurately resolved from post deflections by using images of post tops and bases. Image… More >

Ruihan Zhang^1,2, Junhao Yang¹, Chunxiao Chen^1,*

Molecular & Cellular Biomechanics, Vol.15, No.3, pp. 177-187, 2018, DOI: 10.3970/mcb.2018.04292

Abstract The proportion of cells staining for the nuclear antigen Ki-67 is an important predictive indicator for assessment of tumor cell proliferation and growth in routine pathological investigation. Instead of traditional scoring methods based on the experience of a trained laboratory scientist, deep learning approach can be automatically used to analyze the expression of Ki-67 as well. Deep learning based on convolutional neural networks (CNN) for image classification and single shot multibox detector (SSD) for object detection are used to investigate the expression of Ki-67 for assessment of biopsies from patients with breast cancer in this study. The results focus on… More >

ShiXu Guo¹, Shili Chen¹, Xinjing Huang¹, Yu Zhang¹, Shijiu Jin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.101, No.1, pp. 59-80, 2014, DOI:10.3970/cmes.2014.101.059

Abstract Spherical leak detectors can detect very tiny leakage in pipelines and have low risk of blockage. In this paper the passing ability of the detector in the vertical segment of a pipe was studied using CFD simulations and experiments. The Reynolds number for the sphere exceeds 10⁴ at the economical velocity range for oil pipelines, and there were few researches related to the hydrodynamic force on the sphere by the pipe flow at high Reynolds number. For sphere with different sizes and density, and at different flow rates, more than 100 3-D steady numerical simulations were carried out. The simulation… More >