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作者： Deyun Yang Yingfeng Zhao Xiaolin Jin Bin Li Academy of Computer Simulation Technology National Key Laboratory of Science and Technology on Vacuum ElectronicsUniversity of Electronic Science and Technology of China

Scientific visualization is widely used in engineering design,finite element analysis visualization as an important application area is a significant tool to understand and analyze the result data of finite ***,large quantity of calculation results causes it hardly meet the real time requirement of finite element application,meanwhile,due to the complexity and irregularity of 3 D solid models and meshes,observing the fully data sets becomes *** paper designs a data structure suitable for handling finite element complex calculation results,which improves the efficiency of data execution,furthermore,designs and develops a finite element post-processing module for data *** module is developed based on the theory of Object-Oriented Programming and Microsoft Foundation Classes(MFC).The paper demonstrates the key technology regarding post-processing,including finite element meshes generation,data interpolation processing,nephogram rendering,contour line generation,vector data display and model-user *** scene rendering realization based on HOOPS 3 D Graphics system,including lighting,rendering mode,interacting with the *** paper evaluates the module performance through simulating the high frequency electric field and magnetic field distribution in the rectangular micro-strip *** the module is designed to be lean,fast,easy-to-use,and good visual quality,it satisfies the requirement of visualizing the result of the finite element simulation well.

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Variations of surface ozone (O3) pollution and its relationship with meteorological factors in Xinyang City during 2015-2019

Variations of surface ozone (O3) pollution and its relations...

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2021 International Conference on Statistics, Applied Mathematics, and Computing Science, CSAMCS 2021

作者： Yan, Wei Jiang, Ziyun Zhou, Feng Ning, Jia Liu, Minghua School of Geographic Sciences Xinyang Normal University Henan46400 China Xinyang Key Laboratory of Climate Change and Environmental Evolution Xinyang Normal University Xinyang46400 China College of Computer and Information Technology Xinyang Normal University Henan46400 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China

ISBN: (纸本)9781510652026

With the rapid development of the economy, surface ozone (O3) has become one of the most critical pollutants which disturb urban air quality improvement and standard management. It is important to clarify the variation characteristics of urban surface O3 concentration and its influence factors for scientifically formulating early warning of O3 pollution and prevention and control schemes. In this paper, based on datasets of surface O3 concentration and meteorological data of Xinyang City during 2015∼2019, the variation characteristics of surface O3 concentration and the influence of meteorological factors on it were analysed by using statistical analysis and path analysis. The results show that the annual mean O3 concentration of Xinyang City fluctuated "declining-rising-declining"from 2015 to 2019, and the annual average probability in exceedance of the Chinese grade I and II ambient air quality standards were 41.7% and 10.7%, respectively. The monthly variation of O3 concentration presented a significant M-Type fluctuation, with the peak values appearing in May and September, respectively. The meteorological factors had different degrees of influence on O3 concentration, with the influence degree in order air temperature © COPYRIGHT SPIE.

关键词： Regression analysis

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A Survey of Continuous Collision Detection 2

A Survey of Continuous Collision Detection

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2nd International Conference on Information technology and computer Application, ITCA 2020

作者： Nie, Quan Zhao, Yingfeng Xu, Li Li, Bin University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Vacuum Electronics Academy of Computer Simulation Technology Chengdu610054 China

ISBN: (纸本)9780738111414

Continuous collision detection (CCD) is a key technology in the field of virtual surgery, cloth simulation and robot motion planning. It can accurately detect the first time of contact between objects and returns collision information such as penetration depth, friction and repulsive force, etc., have a wide range of application and important research value. By analyzing the processing framework of continuous collision detection algorithm in detail, the current research status of continuous collision detection is systematically reviewed from perspectives of two phases respectively. In broad-phase, the recent achievements of space decomposition and sweep and prune are introduced. In narrow-phase, the research status of intelligent optimization based algorithm and image-space based algorithm is illustrated. Besides, the development of bounding volume hierarchy (BVH) is analyzed and discussed. After that, the performance and innovative achievements of self-collision detection in deformable objects are summarized and analyzed. Finally, the challenges and future trends of algorithm research are pointed out. © 2020 IEEE.

关键词： Motion planning

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DeepDir: a deep learning approach for API directive detection

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Science China(Information Sciences) 2021年第9期64卷 238-240页

作者： Jingxuan ZHANG He JIANG Shuai LU Ge LI Xin CHEN College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics State Key Laboratory for Novel Software Technology Nanjing University Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education School of Software Dalian University of Technology School of Electronic Engineering and Computer Science Peking University School of Computer Science and Technology Hangzhou Dianzi University

Dear editor,Software developers tend to reuse existing libraries to facilitate their development process and implement certain functionalities by invoking application programming interfaces(APIs) [1]. However, it remains a challenging task for developers to correctly use APIs [2], so they often consult API learning resources [3, 4]. As one of the most important API learning resources,

关键词： Information Systems and Communication Service

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Deep learning-based segmentation of breast masses using convolutional neural networks 12

Deep learning-based segmentation of breast masses using conv...

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12th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2023

作者： Anghelache Nastase, I.-N. Moldovanu, S. Moraru, L. The Modelling & Simulation Laboratory Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania Emil Racovita Theoretical Highschool 12-14 Regiment 11 Siret Street Galati800332 Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania Department of Chemistry Physics & Environment Faculty of Sciences and Environment Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania

Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast cancer and, thus, can help decrease the mortality rate. In order to assist medical professionals in breast cancer investigation a computerized system based on two encoder-decoder architectures for breast tumor segmentation has been developed. Two pre-trained DeepLabV3+ and U-Net models are proposed. The encoder generates a high-dimensional feature vector while the decoder analyses the low-resolution feature vector provided by the encoder and generates a semantic segmentation mask. Semantic segmentation based on deep learning techniques can overcome the limitations of traditional algorithms. To assess the efficiency of breast ultrasound image segmentation, we compare the segmentation results provided by CNNs against the Local Graph Cut technique (a semi-automatic segmentation method) in the Image Segmenter application. The output segmentation results have been evaluated by using the Dice similarity coefficient that compares the ground truth images provided by the specialists against the predicted segmentation results provided by the CNNs and Local Graph Cut algorithm. The proposed approach is validated on 780 breast ultrasonographic images of the BUSI public database of which 437 are benign and 210 are malignant. The BUSI database provides classification (benign or malignant) labels for ground truth in binary mask images. The average Dice scores computed between the ground truth images against CNNs were as follows: 0.9360 (malignant) and 0.9325 (benign) for the DeepLabV3+ architecture and of 0.6251 (malignant) and 0.6252 (benign) for the U-Net, respectively. When the segmentation results provided by CNNs were compared with the Local Graph Cut segmented images, the Dice scores were 0.9377 (malignant) and 0.9204 (benign) for DeepLabV3+ architecture and 0.61

关键词： Graphic methods

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Research on Severe Weather Prediction Model Based on Multi-view Learning

Research on Severe Weather Prediction Model Based on Multi-v...

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2021 International Conference on Electronic Information Engineering and computer Science, EIECS 2021

作者： Sun, Wenjie Zhang, Suli Zheng, Guoxun Zhang, Weixuan Changchun University of Technology School of Computer Science Engineering Jilin Prov. Changbai Mt. Ecol. Culture and Virtual Reality Simulation Engineering Research Center Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Changchun China

ISBN: (纸本)9781665416740

In the study of accurate prediction of severe weather, the regional climate characteristics are often not considered for targeted prediction. For the purpose of predicting severe weather more scientifically and accurately, and further accelerate the development of tourism in the Changbai Mountain area, we collect weather data in the Changbai Mountain area, redivision of severe weather based on regional characteristics, and propose Tri-Training severe weather forecasting model based Multi-view Semi-Supervised Learning. The experimental results show that the model proposed in this paper can accurately predict severe weather based on a very small number of labeled weather samples. When the proportion of labeled data samples is 20%, the model accuracy can reach more than 85%, and when the labeled data reaches 80%, the prediction accuracy can reach 95%. © 2021 IEEE.

关键词： Weather forecasting

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Electronic band energy of a bent ZnO piezoelectric semiconductor nanowire

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Applied Mathematics and Mechanics(English Edition) 2020年第6期41卷 833-844页

作者： Wanli YANG Yuantai HU E.N.PAN Department of Mechanics Hubei Key Laboratory of Engineering Structural Analysis and Safety AssessmentHuazhong University of Science and TechnologyWuhan 430074China Computer Modeling and Simulation Group College of EngineeringUniversity of AkronAkronOhio 44325U.S.A.

The electric band energy variation in a bent piezoelectric semiconductor(PSC) nanowire of circular cross-section induced by the mechanical force is analyzed based on a six-band k · p method. The electric-mechanical fields are first obtained analytically in a cantilever bent PSC nanowire by solving the fully-coupled electro-mechanical equations. Then, the band energy is acquired numerically via the six-band *** considering further the nonlinear coupling between the piezoelectric and semiconducting quantities, the contribution of the redistribution carriers to the electric field is analyzed from the Gauss’ s law. Numerical examples are carried out for an n-type Zn O nanowire in different locations induced by an applied concentrated end force. They include the electric potential, heavy hole(HH), light hole(LH), spin-orbit split-off(SO),and conduction band(CB) edges along the axial and thickness directions. Our results show that the applied force has a significant effect on the band energies. For instance, on the bottom surface along the axial direction, the bandgaps near the fixed end are greater than those near the loading end, and this trend is reversed on the top surface. Moreover,at a fixed axial location, the energy level of the lower side can be enhanced by applying a bending force at the end. The present results could be of significant guidance to the electronic devices and piezotronics.

关键词： piezoelectric semiconductor(PSC) mechanical bending energy band ZnO

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Melanoma Detection using a Random Forest Algorithm 10

Melanoma Detection using a Random Forest Algorithm

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Damian, Felicia-Anisoara Moldovanu, Simona Moraru, Luminita The Modelling & Simulation Laboratory University Dunarea de Jos of Galati UGAL Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics University Dunarea de Jos of Galati UGAL Galati Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment University Dunarea de Jos of Galati UGAL Galati Romania

ISBN: (纸本)9781665485579

In recent years, statistical evidence has identified melanoma as a major cause of skin cancer deaths. In this study, we propose a skin analysis lesions based on colour features (C) and Hu (H) moments to differentiate benign (nevi) and malignant (melanoma) lesions and, a classification based on Random Forest algorithm. Colour features are found as follows. Pre-processing for hair removing using the Dull-razor method;image segmentation based on the optimal colour threshold set on each RGB colour channel (red, green, blue);23 colour features extraction using the minimum and maximum RGB intensity and Hu moments;Random Forest (RF) algorithm to accurately differentiate benign and malignant skin lesions. This study used three datasets: 7-Point, Med-Node, PH2. RF classifier performance in terms of accuracy was: 94% for the 7-point dataset with H5, H7, H6, H2, C08 selected as relevant features;98.1% for the Med-Node dataset and H7, H5, H6, H2, H1 ACC the relevant features;and, 96.5% for the PH2 dataset and H5, H6, H7, C03, H1 selected as relevant features, respectively. Shape features based on Hu moments are found to improve the classification performance of the skin lesions compared to the colour features. © 2022 IEEE.

关键词： Image segmentation

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Classification of skin lesions using morpho-granulometry features derived from color clusters

Classification of skin lesions using morpho-granulometry fea...

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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics "Dunarea de Jos" University of Galati Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics The Modelling & Simulation Laboratory "Dunarea de Jos" University of Galati Galati Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The skin lesion can be thought of as a biological system, so the morpho-granulometry of significant color clusters found in skin lesions is one of the elements that reproduce in a natural way the structure of the lesion, this novelty is highlighted in this study. Important features of skin lesions can be modulated by fusing neural networks (NN) and machine learning (ML). By choosing the nevus and melanoma classes, the primary goal was accomplished, and three databases were used to test the methodology. The characteristics based on morpho-granulometry allowed for the identification of microstructure within the images, which can be very helpful in characterizing the biological system. Based on random forest (RF) and extreme gradient boosting (XGboost) classifiers, this work aimed to improve the classification performance of important feature selection. The selected features from three free image databases with three NNs were classified. In a binary classification of nevus vs. melanoma, the results showed that the pattern recognition neural network (PRNN), according to the PH2 database, provided an accuracy of 0.923 and an F1-score of 0.876. The classification is interpretable if it is not validated. In our study, the best results were verified with a logistic regression (LR) classifier.

关键词： Accuracy Recurrent neural networks Image color analysis Melanoma Biological systems Artificial neural networks Feature extraction Skin Lesions Classification tree analysis

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Improved Classification of Benign and Malignant Breast Lesions Based on Contrast Manipulation and Gradient Map 10

Improved Classification of Benign and Malignant Breast Lesio...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Nastase, Iulia-Nela Anghelache Moldovanu, Simona Moraru, Luminita The Modelling & Simulation Laboratory University Dunarea de Jos of Galati UGAL Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics University Dunarea de Jos of Galati UGAL Galati Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment University Dunarea de Jos of Galati UGAL Galati Romania

ISBN: (纸本)9781665485579

This study proposes an approach to enhance the contrast breast ultrasound (BUS) images with the preservation of the finer details. The main idea is to use the relevant and most important features in the classification of BUS images as benign or malignant. Among the contrast manipulation techniques, both linear under- and overexposure methods, contrast limiting adaptive histogram equalization (CLAHE) method and homomorphic filtering are used. The gradient maps of original images are generated for further analysis. Also, the gradient maps of original images are improved by the local mean subtraction and divisive normalization. Eight structural features are extracted from each gradient map image type. The extracted features feed a Random Forest (RF) classifier to determine their relevance for BUS images classification. An accuracy of 97.12% has been obtained for improved gradient map images and 93.03% for gradient map images. © 2022 IEEE.

关键词： Image classification

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