From the viewpoint of mathematical morphology, an atomic force microscopy (AFM) image contains the distortion effect of the tip convolution on a real sample surface. If tip shape can be characterized accurately, mathematical deconvolution can be applied to reduce the distortion to obtain more precise AFM images. AFM image reconstruction has practical significance in nanoscale observation and manipulation technology. Among recent tip modeling algorithms, the blind tip evaluation algorithm based on mathematical morphology is widely used. However, it takes considerable computing time, and the noise threshold is hard to optimize. To tackle these problems, a new blind modeling method is proposed in this paper to accelerate the computation of the algorithm and realize the optimum threshold estimation to build a precise tip model. The simulation verifies the efficiency of the new algorithm by comparing the computing time with the original one. The calculated tip shape is also validated by comparison with the SEM image of the tip. Finally, the reconstruction of a carbon nanotube image based on the precise tip model illustrates the feasibility and validity of the proposed algorithm.
With the rapid development of the Internet of Things (IoT) and smart cities, more and more types of applications have been emerging. In fact, different applications have different features and different requirements on services. In order to satisfy users' Quality of Service (QoS) requirements, the application-awareness technique should be leveraged to distinguish different applications for providing the differentiated services. However, the traditional Internet only can obtain the local network view, which belongs to the offline awareness mode and cannot adapt to the dynamical network environment. At the right time, Software-Defined Networking (SDN) has been accepted as a new networking paradigm thanks to its network awareness on the global status information, which can greatly facilitate the online applicationawareness. At present, three ways, i.e., port number, depth packet inspection and deep learning can be used for the application-awareness. To the best of our knowledge, the deep learning based application-awareness method is the most cutting-edge technique. In spite of this, the previous related schemes fail to effectively guarantee the correctness and stability. To this end, this paper proposes a Convolutional Neural Network (CNN) based deep learning mechanism to do the application-awareness, including three phases, i.e., traffic collection, data pre-processing and application-awareness. The SDN environment is implemented based on the MiniNet and the simulation experiments are made based on the TensorFlow. The experimental results show that the proposed application-awareness mechanism outperforms three benchmarks on recall ratio, precision ratio, F value and stability.
A well-established method for diagnosis of glaucoma is the examination of the optic nerve head based on fundus image as glaucomatous patients tend to have larger cup-to-disc ratios. The difficulty of optic segmentation is due to the fuzzy boundaries and peripapillary atrophy (PPA). In this paper a novel method for optic nerve head segmentation is proposed. It uses template matching to find the region of interest (ROI). The method of vessel erasing in the ROI is based on PDE inpainting which will make the boundary smoother. A novel optic disc segmentation approach using image texture is explored in this paper. A cluster method based on image texture is employed before the optic disc segmentation step to remove the edge noise such as cup boundary and vessels. We replace image force in the snake with image texture and the initial contour of the balloon snake is inside the optic disc to avoid the PPA. The experimental results show the superior performance of the proposed method when compared to some traditional segmentation approaches. An average segmentation dice coefficient of 94% has been obtained.
The automatic width control of the strip that is used in vertical rolling is a key issue in the hot strip rolling process. It is the first time that the internal deformation power and the friction power are obtained by using angular bisector yield criterion and Pavlov projection principle in the vertical rolling. At the same time, the shear power is determined by use of the integral mean value theorem. Then the vertical rolling force and shape parameters are obtained by minimizing the total power functional. The relative error of rolling forces is less than 8.29 % compared with those measured on-line in a hot strip rolling plant. Moreover, the formula forms of the three powers mentioned above are greatly simplified and improved.
Abstract. In this work, the effect of the quenching media (brine,
water, and two types of naphthenic mineral oils) and the tempering
temperature (200, 400, 600 ∘C) on the static mechanical properties
and the fatigue life has been investigated using 300 fatigue and 36 static
tension tests. S–N curves and standard deviations of fatigue life under each
heat treatment condition were calculated and shown. The fracture surfaces of
the selected 11 specimens were observed by the scanning electron microscope
and the reasons of affecting the fatigue life were discussed. To estimate
the mean fatigue life under the conditions of any given tempering
temperature and cycle stress amplitude based on 300 fatigue tests, the mean
fatigue life estimation method based on RBF neural network was presented and
verified by 12 other fatigue tests. The test results have shown that (1) the
mean fatigue life decreases with the increase of tempering temperature for
the same quenching media, (2) the mean fatigue life using brine is more than
water which is more than naphthenic mineral oils for the same tempering
temperature, and (3) the proposed method based on RBF neural network could
accurately estimate the mean fatigue life when the tempering temperature and
cyclic stress amplitude are given for each quenching medium.
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