Knowledge Graph Enhanced Transformers for Diagnosis Generation of Chinese Medicine

Chinese medicine(CM)diagnosis intellectualization is one of the hotspots in the research of CM modernization.The traditional CM intelligent diagnosis models transform the CM diagnosis issues into classification issues,however,it is difficult to solve the problems such as excessive or similar categories.With the development of natural language processing techniques,text generation technique has become increasingly mature.In this study,we aimed to establish the CM diagnosis generation model by transforming the CM diagnosis issues into text generation issues.The semantic context characteristic learning capacity was enhanced referring to Bidirectional Long Short-Term Memory(BILSTM)with Transformer as the backbone network.Meanwhile,the CM diagnosis generation model Knowledge Graph Enhanced Transformer(KGET)was established by introducing the knowledge in medical field to enhance the inferential capability.The KGET model was established based on 566 CM case texts,and was compared with the classic text generation models including Long Short-Term Memory sequence-to-sequence(LSTM-seq2seq),Bidirectional and Auto-Regression Transformer(BART),and Chinese Pre-trained Unbalanced Transformer(CPT),so as to analyze the model manifestations.Finally,the ablation experiments were performed to explore the influence of the optimized part on the KGET model.The results of Bilingual Evaluation Understudy(BLEU),Recall-Oriented Understudy for Gisting Evaluation 1(ROUGE1),ROUGE2 and Edit distance of KGET model were 45.85,73.93,54.59 and 7.12,respectively in this study.Compared with LSTM-seq2seq,BART and CPT models,the KGET model was higher in BLEU,ROUGE1 and ROUGE2 by 6.00–17.09,1.65–9.39 and 0.51–17.62,respectively,and lower in Edit distance by 0.47–3.21.The ablation experiment results revealed that introduction of BILSTM model and prior knowledge could significantly increase the model performance.Additionally,the manual assessment indicated that the CM diagnosis results of the KGET model used in this study were highly consistent with the practical diagnosis results.In conclusion,text generation technology can be effectively applied to CM diagnostic modeling.It can effectively avoid the problem of poor diagnostic performance caused by excessive and similar categories in traditional CM diagnostic classification models.CM diagnostic text generation technology has broad application prospects in the future.

DEFORMATION ENHANCED FERRITE TRANSFORMATION IN PLAIN LOW CARBON STEEL

The microstructure evolution during deformation enhanced transformation of undercooled austenite of a plain low carbon steel has been investigated by means of hot compression simulation experiment under various conditions of strain rate, deformation temperature and strain. The effect of austenite grain size on the strain enhanced ferrite transformation has been studied. The ferrite dynamic recrystallization involved in successive hot deformation has been explored.

Solving shock wave with discontinuity by enhanced differential transform method(EDTM)

An enhanced differential transform method （EDTM）, which introduces the Pad@ technique into the standard differential transform method （DTM）, is proposed. The enhanced method is applied to the analytic treatment of the shock wave. It accelerates the convergence of the series solution and provides an exact Dower series solution.

Localization method of subsynchronous oscillation source based on high-resolution time-frequency distribution image and CNN

The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features,such as frequency drift,caused by the random volatility of wind farms when oscillations occur.This paper proposes a subsynchronous oscillation sourcelocalization method that involves an enhanced short-time Fourier transform and a convolutional neural network(CNN).First,an enhanced STFT is performed to secure high-resolution time-frequency distribution(TFD)images from the measured data of the generation unit ports.Next,these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model.Ultimately,the trained CNN model realizes the online localization of subsynchronous oscillation sources.The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform.Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms,thus providing a foundation for oscillation suppression in practical engineering scenarios.

Precipitation and Hetero-nucleation Effect of V(C,N) in V-Microalloyed Steel

The precipitation behavior of V（C, N） in steels microalloyed with vanadium was researched using a thermal simulator during single-pass deformation at 800-750 ℃. The V（C, N） precipitates and its nucleation effect on ferrite were investigated by TEM and EDS. The experimental results show that there are two remarkable heterogeneous nucleation effects of V（C, N） particles precipitated before γ →/ α phase change： primary reason is that high coherency between V（C, N） and ferrite promotes V（C, N） to become a nucleating center of intragranular ferrite; secondary reason is that the coarsening of V（C, N） causes locally solute-poor region in austenite, thus expedites the nucleation of intragranular ferrites further. Furthermore, the relationship between the size and shape of V（C, N） was studied, and identification method was provided for distinguishing interphase precipitation and general precipitation to avoid erroneous judgment and misguide.

Comparative analysis of different methods for image enhancement

Image enhancement technology plays a very important role to improve image quality in image processing. By enhancing some information and restraining other information selectively, it can improve image visual effect. The objective of this work is to implement the image enhancement to gray scale images using different techniques. After the fundamental methods of image enhancement processing are demonstrated, image enhancement algorithms based on space and frequency domains are systematically investigated and compared. The advantage and defect of the above-mentioned algorithms are analyzed. The algorithms of wavelet based image enhancement are also deduced and generalized. Wavelet transform modulus maxima(WTMM) is a method for detecting the fractal dimension of a signal, it is well used for image enhancement. The image techniques are compared by using the mean(μ),standard deviation(?), mean square error(MSE) and PSNR(peak signal to noise ratio). A group of experimental results demonstrate that the image enhancement algorithm based on wavelet transform is effective for image de-noising and enhancement. Wavelet transform modulus maxima method is one of the best methods for image enhancement.

Influence of prior austenite grain size on the critical strain for completion of DEFT through hot compression test

A low carbon steel was used to determine the critical strain εc for completion of deformation enhanced ferrite transformation （DEFT） through a series of hot compression tests. In addition, the influence of prior austenite grain size （PAGS） on the critical strain was systematically investigated. Experimental results showed that the critical strain is affected by PAGS. When γ→α transformation completes, the smaller the PAGS is, the smaller the critical strain is. The ferrite grains obtained through DEFT can be refined to about 3 μm when the DEFT is completed.

Multi-level denoising and enhancement method based on wavelet transform for mine monitoring

Based on low illumination and a large number of mixed noises contained in coal mine, denoising with one method usually cannot achieve good results, so a multi-level image denoising method based on wavelet correlation relevant inter-scale is presented. Firstly, we used directional median filter to effectively reduce impulse noise in the spatial domain, which is the main cause of noise in mine. Secondly, we used a Wiener filtration method to mainly reduce the Gaussian noise, and then finally used a multi-wavelet transform to minimize the remaining noise of low-light images in the transform domain. This multi-level image noise reduction method combines spatial and transform domain denoising to enhance benefits, and effectively reduce impulse noise and Gaussian noise in a coal mine, while retaining good detailed image characteristics of the underground for improving quality of images with mixing noise and effective low-light environment.

Phase Transformation and Enhancing Electron Field Emission Properties in Microcrystalline Diamond Films Induced by Cu Ion Implantation and Rapid Annealing

Cu ion implantation and subsequent rapid annealing at 500℃ in N2 result in low surface resistivity of 1.611 ohm/sq with high mobility of 290 cm2 V-1S-1 for microcrystalline diamond （MCD） films. Its electrical field emission behavior can be turned on at Eo = 2.6 V/μm, attaining a current density of 19.5μA/cm2 at an applied field of 3.5 V/#m. Field emission scanning electron microscopy combined with Raman and x-ray photoelectron mi- croscopy reveal that the formation of Cu nanoparticles in MCD films can catalytically convert the less conducting disorder/a-C phases into graphitic phases and can provoke the formation of nanographite in the films, forming conduction channels for electron transportation.

Distance Transform Based Enhancement for Linear Interpolated Images

An approach of distane map based imageenhancement (DMIE) is proposed. It is applied toconventional interpolations to get sharp images. Edgedetection is performed after images are interpolatedby linear interpolations. To meet the two conditionsset for DMIE, i. e., no abrupt changes and no over-boosting, different boosting rate should be used inadjusting pixel intensities. When the boosting rate isdetermined by using the distance from enhancedpixels to nearest edges, edge-oriented imageenhancement is obtained. By using Erosion technique,the range for pixel intensity adiustment is set.Over-enhancement is avoided by limiting the pixel iutensities in enhancement within the range. A unifled linear-time algoritiml for disance transform is adopted to deal with the calculation of Euelidean distance of the images.Its computation complexity is 0(N).After the preparation,i.e.,distance transforming and erosion,the images get more and more sharpened while no over.boosting.Occurs by repeating the enhancement procedure ,The simplicity of the enhancement operation makes DMIE suitable for enhancement rate adjusting

Improved single image dehazing using dark channel prior

An improved single image dehazing method based on dark channel prior and wavelet transform is proposed. This proposed method employs wavelet transform and guided filter instead of the soft matting procedure to estimate and refine the depth map of haze images. Moreover, a contrast enhancement method based on just noticeable difference(JND) and quadratic function is adopted to enhance the contrast for the dehazed image, since the scene radiance is usually not as bright as the atmospheric light,and the dehazed image looks dim. The experimental results show that the proposed approach can effectively enhance the haze image and is well suitable for implementing on the surveillance and obstacle detection systems.