被引排行
Second Development for Fore Treatment of ABAQUS Using Python Language
ZHONG Tong-sheng~1,WEI Feng~1,WANG Zhi~2,ZHI You-hai~1 (1.Department of Mechanical Engineering,Northwestern Polytechnical University, Xi'an 710072,China;2.Department of Civill Engineering and Architecture, Northwestern Polytechnical University,Xi'an 710072,China)The fore treatment modular of ABAQUS software is developed using Python language.The function and calling procedure of the scenarios ports and target models in the second treatment of ABAQUS are discussed.The model building and ploting grid course of ABAQUS are controlled by the Python scenarios program.Therefore,it improves the fore treatment efficiency and solves the fussy problem that apprears during the manual model building of the model which includes a lot of repeated members.
A New Extreme Learning Machine Optimized by PSO
WANG Jie,BI Hao-yang(School of Electrical Engineering,Zhengzhou University,Zhengzhou 450001,China)Extreme learning machine(ELM) was a new type of feedforward neural network.Compared with traditional single hidden layer feedforward neural networks,ELM possessed higher training speed and smaller error.Due to random input weights and hidden biases,ELM might need numerous hidden neurons to achieve a reasonable accuracy.A new ELM learning algorithm,which was optimized by the particle swarm optimization(PSO),was proposed.PSO algorithm was used to select the input weights and bias of hidden layer,then the output weights could be calculated.To test the validity of proposed method,two simulation experiments were drawn on the approximation curves of the Sinc function.Experimental results showed that the proposed algorithm achieved better performance with less hidden neurons than other similar methods.
A Review on Image Mosaicing Techniques
PEI Hongxing;LIU Jinda;GE Jialong;ZHANG Bin;School of Physics and Engineering,Zhengzhou University;Image mosaicing was analyzed and summarized comprehensively. Based on the development of this technology the background and applications were described generally. The definition and steps of image mosaicing were indicated initially,and the algorithms of image registration were examined by classifying them into several groups. For each algorithm, especially the scale-invariant feature transform( SIFT),the basic theory and characteristics were demonstrated in detail. In the last part,the classifications of image fusion were illustrated and methods of this stage,specially the weighted average,were explained briefly. In addition,the challenges and tendencies of image mosaicing were also pointed out.
Study on the Stability of Aqueous Ferrate(VI) Solution
Jia Handong Ma Ning Sun Hongbin Liu Tongliang Yang Yong (Department of Chemistry, Zhengzhou University, Zhenzhou 450052)The catalytic behaviour of Fe(OH) 3 for the hydrolysis reaction of ferrate(VI) is reported.The effect of temperature, concentration and alkalinity on stability of aqueous ferrate(VI) solutions was studied. It has been descovered that the stability of FeO 2- 4 decreased with increasing amount of Fe(Ⅲ) and with increasing concentration of FeO 2- 4, that the solubility of FeO 2- 4 was not affected by alkalinity at the concentration of OH - below 1.0 mol/L, and that dilute solution of FeO 2- 4 is stable at the normal temperature . These important laws have been used to the synthesis of potassium ferrate(VI) with satisfactory results.
Review on Infrared Dim and Small Target Detection Technology
REN Xiangyang;WANG Jie;MA Tianlei;ZHU Xiaodong;BAI Ke;WANG Jiaqi;With the continuous development of infrared detection technology, the requirements for detection distance were increasingly high. The infrared dim and small target detection technology became the focus of infrared detection. The background and significance of infrared dim and small target detection were reviewed at first. Then the current research status and latest progress of various typical methods in the field of infrared dim and small target detection were trained. After that, experimental comparisons of several different types of infrared dim and small target detection methods were presented. Finally, the research on infrared dim and small target detection technology was summarized and prospected.
Study on Fast Color-to-Gray Image Transformation with High Fidelity
ZHANG Quan-fa,YANG Hai-bin,REN Chao-dong,LI Huan(School of Physics & Engineering,Zhengzhou University,Zhengzhou 450052,China)To transform color images into gray images quickly and with high fidelity,factors that affect the transformation speed and fidelity were analyzed by comparing existing algorithms.Several methods universal for improving the speed of image transforming programs were proposed,such as expressing gray images in the most suitable bitmap form,developing programs with Visual C++ 6.0 and making full use of its features that could speed up the programs,and so on.Appropriate approximations were introduced to the weight values of red,green and blue component for developing programs with these methods,and it was possible of fast transformation with high fidelity.These advantages of the new algorithm were demonstrated by experiment results of speed,error and visual effect.
Synthesis of ZSM-5 with Inorganic Amine
Chen Bingyi 1,2, Yang Xinli 2, Du Baoshi 2, Wei Dongwei 2, Guo Yiqun 2 (1. Henan Urban Construction College,Pingdingshan,Henan 467001; 2.Institute of Industrial Catalysis, Zhengzhou University, Zhengzhou 450052)The ZSM-5 zeolite sieve is synthesized using ammonia as templating agent. The influence of synthetic temperature and crystallizing time on the synthesis of the zeolite are studied .It is found that ZSM-5 zeolite sieve is synthesized using ammonia as templating agent at temperature ranging from 142℃ to 177℃.The lower the synthetic temperature, the longer the synthetic time .The structures of ZSM-5 zeolites synthesized using ammonia are characterized by the method of XRD, comparing with the structure of ZSM-5 zeolite synthesized using n-propyl amine.
Barrage Text Sentiment Analysis Based on ALBERT-CRNN
ZENG Cheng;WEN Chaodong;SUN Yumin;PAN Lie;HE Peng;The barrage text sentiment analysis model ALBERT-CRNN based on ALBERT pre-training language model and convolutional recurrent neural network(CRNN) was proposed. Firstly, the ALBERT pre-training language model was used to obtain the dynamic feature representations of barrage texts, so that the same word had different word vector expressions in different contexts. Then, these feature vectors were trained by CRNN, which made the local features and context semantic correlation to be fully considered. Finally, the sentiment polarity of barrage texts was obtained by the Softmax function. Experiments were carried out on the barrage text datasets of Bilibili, iQiYi and Tencent video platforms. The experimental results showed that the accuracy of ALBERT-CRNN on the above three datasets reached 94.3%, 93.5% and 94.8% respectively, which were better than some traditional models.
Research of K-means Clustering Algorithm for Images Based on Weighted Euclidean Distance
LI Cui,FENG Dong-qing(School of Electrical Engineering,Zhengzhou University,Zhengzhou 450001,China)In order to achieve accurate segmentation for color image,a K-means clustering algorithm for color image based on weighted Euclidean distance was studied in HLS color space.It obtained the optimized initial clustering centers which were computed from the mathematical statistics results of large pictures to resolve the problems that the clustering results of K-means were likely to be trapped into local optimums firstly.Then the weighted Euclidean distance by use of the different color properties was introduced to measure the relevance between objects in HLS color space.Experimental results showed this algorithm could take advantage of the concise and fast convergence features of K-means clustering,and produce more better clustering results to realized the accurate color image segmentation.
Construction of Chinese Medical Knowledge Graph Based on Multi-source Corpus
ZAN Hongying;DOU Huayi;JIA Yuxiang;GUAN Tongfeng;ODMAA Byambasuren;ZHANG Kunli;SUI Zhifang;The Chinese medical knowledge graph(CMeKG) was a structured description of professional medical knowledge,which was also an actual need for various types of intelligent medical applications. With medical texts from multiple sources collected by this study, structural features of corpus were analyzed; semantic features of medical knowledge were combined; the annotation system of medicine named entities and entity relations was formulated and the platform was developed accordingly. Under the guidance of medical experts, 106 high-incidence diseases were selected and labeled. The consistency rate of named entity and entity relationship were achieved 87.3% and 82.9% respectively. On the basis of manual annotation, the entity and relation were extracted automatically. Along with more than 200 000 medical entities and over one million conceptual relationship examples and attribute triples, 6 310 diseases, 19 853 drugs(western medicine, Chinese patent medicine and Chinese herbal medicine), and 1 237 diagnosis and treatment technologies were included in the CMeKG version 1.0. Thus, A solid foundation could be laid by CMeKG in certain fields, such as medical question answering system and intelligent assisted diagnosis.