Lijun Jiang

Zu-Hui Ma, Weng Cho Chew, Lijun Jiang

In this paper we present a novel fast method to solve Poisson equation in an arbitrary two dimensional region with Neumann boundary condition. The basic idea is to solve the original Poisson problem by a two-step procedure: the first one finds the electric displacement field $\mathbf{D}$ and the second one involves the solution of potential $ϕ$. The first step exploits loop-tree decomposition technique that has been applied widely in integral equations within the computational electromagnetics (CEM) community. We expand the electric displacement field in terms of a tree basis. Then, coefficients of the tree basis can be found by the fast tree solver in O(N) operations. Such obtained solution, however, fails to expand the exact field because the tree basis is not completely curl free. Despite of this, the accurate field could be retrieved by carrying out a procedure of divergence free field removal. Subsequently, potential distribution $ϕ$ can be found rapidly at the second stage with another fast approach of O(N) complexity. As a result, the method dramatically reduces solution time comparing with traditional FEM with iterative method. Numerical examples including electrostatic simulations are presented to demonstrate the efficiency of the proposed method.

Jiajiong Ma, Guihua Wen, Yang Hu et al.

Background and Object: In China, body constitution is highly related to physiological and pathological functions of human body and determines the tendency of the disease, which is of great importance for treatment in clinical medicine. Tongue diagnosis, as a key part of Traditional Chinese Medicine inspection, is an important way to recognize the type of constitution.In order to deploy tongue image constitution recognition system on non-invasive mobile device to achieve fast, efficient and accurate constitution recognition, an efficient method is required to deal with the challenge of this kind of complex environment. Methods: In this work, we perform the tongue area detection, tongue area calibration and constitution classification using methods which are based on deep convolutional neural network. Subject to the variation of inconstant environmental condition, the distribution of the picture is uneven, which has a bad effect on classification performance. To solve this problem, we propose a method based on the complexity of individual instances to divide dataset into two subsets and classify them separately, which is capable of improving classification accuracy. To evaluate the performance of our proposed method, we conduct experiments on three sizes of tongue datasets, in which deep convolutional neural network method and traditional digital image analysis method are respectively applied to extract features for tongue images. The proposed method is combined with the base classifier Softmax, SVM, and DecisionTree respectively. Results: As the experiments results shown, our proposed method improves the classification accuracy by 1.135% on average and achieves 59.99% constitution classification accuracy. Conclusions: Experimental results on three datasets show that our proposed method can effectively improve the classification accuracy of tongue constitution recognition.