论画网络图的规则

网络计划技术有个重要的步骤是画网络图。当工序间的关系复杂时 ,画网络图就有一定的困难 ,画出的图 (以及画图的过程 )也往往是杂乱无章 ,缺少经验的人甚至干脆就画不出来。因此 ,需要找出画图的规律 ,使画图工作变得程序化 :无须费多少脑筋便能容易地画出清晰美观的网络图。本文主要说明如何用“分级”的方法来画网络图 ,使作图的过程简单易行 ,画出的图逻辑清晰。

EFFECTIVE FEATURE ANALYSIS FOR COLOR IMAGE SEGMENTATION

An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.

3-D visual tracking based on CMAC neural network and Kalman filter

In this paper, the Kalman filter is used to predict image feature positionaround which an image-processing window is then established to diminish feature-searching area andto heighten the image-processing speed. According to the fundamentals of image-based visual servoing(IBVS), the cerebellar model articulation controller (CMAC) neural network is inserted into thevisual servo control loop to implement the nonlinear mapping from the error signal in the imagespace to the control signal in the input space instead of the iterative adjustment and complicatedinverse solution of the image Jacobian. Simulation results show that the feature point can bepredicted efficiently using the Kalman filter and on-line supervised learning can be realized usingCMAC neural network; end-effector can track the target object very well.

Multi-sensors Image Fusion via NSCT and GoogLeNet

In order to improve the detail preservation and target information integrity of different sensor fusion images,an image fusion method of different sensors based on non-subsampling contourlet transform(NSCT)and GoogLeNet neural network model is proposed. First,the different sensors images,i. e.,infrared and visible images,are transformed by NSCT to obtain a low frequency sub-band and a series of high frequency sub-bands respectively.Then,the high frequency sub-bands are fused with the max regional energy selection strategy,the low frequency subbands are input into GoogLeNet neural network model to extract feature maps,and the fusion weight matrices are adaptively calculated from the feature maps. Next,the fused low frequency sub-band is obtained with weighted summation. Finally,the fused image is obtained by inverse NSCT. The experimental results demonstrate that the proposed method improves the image visual effect and achieves better performance in both edge retention and mutual information.

Towards universal access to all knowledge——Internet Archive

Advances in computing and communications mean that we can cost-effectively store every book, sound recording, movie, software package, and public Web page ever created, and provide access to these collections via the Intemet to students and adults all over the world. By mostly using existing institutions and funding sources, we can build this as well as compensate authors within the current worldwide library budget. We should take advantage of our new technologies and our open societies to make a Universal Library again, and go the next step and make all knowledge easily available to every man, woman and child around the world.

EFFECT OF ELECTROACUPUNCTURE OF ACUPOINTS OF THE HEART MERIDIAN ON ECG, SMALL INTESTINAL ELECTROGRAM AND EEG IN THE RABBIT WITH MYOCARDIAL ISCHEMIA

In the present paper, effects of electroacupuncture (EA) of 3 points of the Heart Meridian and other 3 points of the Lung Meridian on changes of electrocardiogram (ECG), small intestinal electrogram (SIG) and electroencephalogram (EEG) in intravenous drip of pituitrin induced myocardial ischemia rabbits. The three points of the Heart Meridian are "Shenmen" (HT 7), "Lingdao" (HT 4) and one point between HT 7 and HT 4, the 3 points of the Lung Meridian are "Taiyuan"(LU 9), Lieque (LU 7) and one point between LU 9 and LU 7. These points are punctured with filiform needles and stimulated electrically by setting the parameters being frequency of 2.5 Hz, dense sparse waves and duration of 10 min. Results display that the regulative effect of EA of the Heart Meridian is superior to that of EA of the Lung Meridian on the three indexes, showing a closer correlation between the whole Heart Meridian and activities of ECG, SIG and EEG.

Heterogeneous graph construction and node representation learning method of Treatise on Febrile Diseases based on graph convolutional network

Objective To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)dataset and explore an optimal learning method represented with node attributes based on graph convolutional network(GCN).Methods Clauses that contain symptoms,formulas,and herbs were abstracted from Treatise on Febrile Diseases to construct symptom-formula-herb heterogeneous graphs,which were used to propose a node representation learning method based on GCN−the Traditional Chinese Medicine Graph Convolution Network(TCM-GCN).The symptom-formula,symptom-herb,and formula-herb heterogeneous graphs were processed with the TCM-GCN to realize high-order propagating message passing and neighbor aggregation to obtain new node representation attributes,and thus acquiring the nodes’sum-aggregations of symptoms,formulas,and herbs to lay a foundation for the downstream tasks of the prediction models.Results Comparisons among the node representations with multi-hot encoding,non-fusion encoding,and fusion encoding showed that the Precision@10,Recall@10,and F1-score@10 of the fusion encoding were 9.77%,6.65%,and 8.30%,respectively,higher than those of the non-fusion encoding in the prediction studies of the model.Conclusion Node representations by fusion encoding achieved comparatively ideal results,indicating the TCM-GCN is effective in realizing node-level representations of heterogeneous graph structured Treatise on Febrile Diseases dataset and is able to elevate the performance of the downstream tasks of the diagnosis model.

Research on single image super-resolution based on very deep super-resolution convolutional neural network

Single image super-resolution(SISR)is a fundamentally challenging problem because a low-resolution(LR)image can correspond to a set of high-resolution(HR)images,while most are not expected.Recently,SISR can be achieved by a deep learning-based method.By constructing a very deep super-resolution convolutional neural network(VDSRCNN),the LR images can be improved to HR images.This study mainly achieves two objectives:image super-resolution(ISR)and deblurring the image from VDSRCNN.Firstly,by analyzing ISR,we modify different training parameters to test the performance of VDSRCNN.Secondly,we add the motion blurred images to the training set to optimize the performance of VDSRCNN.Finally,we use image quality indexes to evaluate the difference between the images from classical methods and VDSRCNN.The results indicate that the VDSRCNN performs better in generating HR images from LR images using the optimized VDSRCNN in a proper method.

Multimodality image registration and fusion using neural network

Multimodality image registration and fusion are essential steps in building 3-D models from remotesensing data. We present in this paper a neural network technique for the registration and fusion of multimodali-ty remote sensing data for the reconstruction of 3-D models of terrain regions. A FeedForward neural network isused to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data arepresented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusionresults.

THE METHOD OF COLORING IN GRAPHS AND ITS APPLICATION

Graph coloring has interesting real life applications in optimization and network design. In this paper some new results on the acyclic-edge coloring, f-edge coloring, g-edge cover coloring, （g, f）-coloring and equitable edge-coloring of graphs are introduced. In particular, some new results related to the above colorings obtained by the authors are given. Some new problems and conjectures are presented.