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Evolution of defects and deformation mechanisms in different tensile directions of solidified lamellar Ti-Al alloy

Chinese Physics B 2022年第4期31卷 518-524页

作者： Yutao Liu Tinghong Gao Yue Gao Lianxin Li Min Tan Quan Xie Qian Chen Zean Tian Yongchao Liang Bei Wang Institute of New Optoelectronic Materials and Technology College of Big Data and Information EngineeringGuizhou UniversityGuiyang 550025China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China

Two-phaseγ-TiAl/α_(2)-Ti_(3)Al lamellar intermetallics have attracted considerable attention because of their excellent strength and ***,the exact deformation mechanisms remain to be *** this paper,a solidified lamellar Ti-Al alloy with lamellar orientation at 0°,17°,and 73°with respect to the loading direction was stretched by utilizing molecular dynamics(MD)*** results show that the mechanical properties of the sample are considerably influenced by solidified defects and tensile *** structure deformation and fracture were primarily attributed to an intrinsic stacking fault(ISF)accompanied by the nucleated Shockley dislocation,and the adjacent extrinsic stacking fault(ESF)and ISF formed by solidification tend to form large HCP structures during the tensile process loading at 73°.Moreover,cleavage cracking easily occurs on theγ/α_(2)interface under tensile *** fracture loading mechanism at 17°is grain boundary slide whereas,at 73°and 0°,the dislocation piles up to form a dislocation junction.

关键词： molecular dynamics simulation solidified lamellar Ti-Al alloy tensile directions γ/α_(2)interface

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Discovering Frequency Bursting Patterns in Temporal Graphs 39

Discovering Frequency Bursting Patterns in Temporal Graphs

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Zhang, Qianzhen Guo, Deke Zhao, Xiang Yuan, Long Luo, Lailong National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China National University of Defense Technology Laboratory for Big Data and Decision China Nanjing University of Science and Technology China

ISBN: (纸本)9798350322279

A frequency bursting pattern (FBP) in temporal graphs represents some interaction behavior that accumulates its frequency at the fastest rate. Mining FBPs is essential to early warning of emergencies. However, existing studies on frequency-based pattern mining in graphs do not consider the temporal information and bursting features of a subgraph pattern. As a result, they may not provide effective and efficient mining algorithms for FBP discovery. In this paper, we study the problem of discovering top-k FBPs in temporal graphs. We present a novel model, referred to as maximal (m, θ)-bursting pattern, to describe FBPs in a temporal graph, which is a subgraph with a size larger than m that accumulates its frequency at the fastest rate during a time interval of length no less than θ. A naive solution for top-k FBPs discovery is to use the best-first search algorithm, where the burstiness threshold changes as more patterns are mined. However, this method will result in huge search space since we need to check every possible time interval for a candidate pattern in the temporal graph. To tackle this problem, we devise an online top-k framework in which k candidate results are maintained from the initial timestamp to the end in the temporal graph. Under the new framework, we further conceive two optimization strategies by exploiting incremental subgraph matching and Evolutionary Game Theory to boost the performance. Extensive experiment results on five real temporal graphs show that our algorithm has higher efficiency, effectiveness and scalability. © 2023 IEEE.

关键词： Game theory

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NAGNet: A Convolutional Neural Network for Real-Time Sentiment Analysis of Students 12

NAGNet: A Convolutional Neural Network for Real-Time Sentime...

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Zhu, Huihui Hu, Pengyun Tang, Xianpiao Xia, Daoxun Guizhou Normal University School of Big Data and Computer Science Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350310153

Students' emotional state during class can reflect their understanding of classroom knowledge. Analysing the emotional data of students is helpful for ensuring that teachers understand students' learning effect and improve their teaching quality. With the rapid development of information education, student emotion analysis has been widely studied in the field of educational technology. However, due to the complexity and inconsistency of educational scenes, traditional facial expression recognition algorithms have low accuracy, losing information while extracting facial expression features, and student emotion analysis faces several technical issues. Therefore, based on the theory of deep learning, this paper proposes a novel network model NAGNet for recognizing and analysing students' expressions in classroom environments, to assist teachers in carrying out reasonable teaching effect evaluations. In this model, Res2Net50 is used as the backbone network to enhance the feature extraction ability of the model. Moreover, non-local attention module is added to integrate the global facial expression feature information to achieve fine-grained sentiment analysis. In addition, we replace the original pooling algorithm with generalized mean (GeM) pooling to pool the features of the last layer. The experimental results on the public affection dataset FERPlus, which includes eight emotions, show that the NAGNet model achieves an accuracy of 89.3%. Thus, the proposed model has higher accuracy than existing methods. In a real-world application scenario, the NAGNet model can analyse and detect students' emotions in real time. What's more, it has good robustness. © 2022 IEEE.

关键词： Students

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PC-CSP: An Efficient Partial Convolution in Cross Stage Partial Network for Small Targets Detection 7

PC-CSP: An Efficient Partial Convolution in Cross Stage Part...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Xue, Junmin Rao, Yunbo Wu, Jiangang Huang, Mei School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Operation Data Center of Postal Savings Bank of China Beijing Beijing610000 China Chengdu Aerospace Science and Industry Big Data Research Institute Corporation Limited Chengdu610299 China

ISBN: (纸本)9798350350890

The common approach for small object detection does not work well in complex scenes with objects of multiple sizes. This is because the detection of small objects and objects of other sizes often difficult to take into account at the same time. To overcome these limitations, we introduce an efficient Partial Convolution in Cross Stage Partial (PC-CSP) network for detecting small targets. First, the introduction of the DCNv3 module improves the feature extraction for detection, slimneck is utilized to enhance the attention mechanism suitable for objects of varying sizes. Then, PC-CSP is employed to streamline the detection backbone network. Last, the loss function is refined by incorporating the concept of Inner. Through extensive experiments, our method demonstrates a significant increase in accuracy of 6.8%, and mAP@0.5 by 8.97% on the standard dataset compared to the original model. Overall, the proposed method exhibits excellent performance for small objects, such as: mobile phones, food, and faces. © 2024 IEEE.

关键词： Feature extraction

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GRAFRAUD: Fraud Detection using Graph databases and Neural Networks 4

GRAFRAUD: Fraud Detection using Graph Databases and Neural N...

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4th International Informatics and Software Engineering Conference, IISEC 2023

作者： Sayar, Alperen Arslan, Suayip Raina, Ajeet Singh Ertugrul, Seyit Cakar, Tuna Tam Finans R and D Data Science Istanbul Turkey Massachusetts Institute of Technology Big Data and Data Governance Massachusetts United States Docker Containerized Software Docker Inc. CA United States MEF University Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350318036

The issue of fraud has become a significant concern for many companies, particularly in the finance sector, but the traditional methods of detecting fraud are no longer adequate. Innovative technologies are necessary to identify complex fraudulent activities, and RedisGraph, a high-performance graph database, may offer a solution. With the assistance of neural networks, RedisGraph can accurately and efficiently detect fraudulent transactions in vast and intricate environments. Companies typically use a combination of Python and Oracle databases to design fraud detection systems. which provide robust data management and real time AI processing capabilities. These technologies allow to create fraud detection systems that can determine fraudulent activities in real-Time. But according to advancements of fraud methods only using of these systems not efficient nowadays. This article presents a proof of concept based on an essential use case of RedisGraph-powered neural networks in detecting financial fraud. It demonstrates the value of carefully employing Python and Oracle database to construct and deploy real-Time systems that can efficiently detect fraudulent activities. © 2023 IEEE.

关键词： Decision making

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Multi-topography dataset for wind turbine detection from remote sensing image 4

Multi-topography dataset for wind turbine detection from rem...

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4th International Conference on Geology, Mapping, and Remote Sensing, ICGMRS 2023

作者： Mi, Hao Zhao, Jinghao Fang, Sheng Tian, Yajun College of Computer Science and Engineering Shandong University of Science and Technology Qingdao Shandong China Extended Energy Big Data and Strategy Research Center Qibebt Chinese Academy of Sciences Qingdao Shandong China

ISBN: (纸本)9781510672741

Owing to the global energy conservation and emission reduction, the wind power industry is developing rapidly in China because of abundant wind energy resources. Wind power has become the third largest power source and remain growing rapidly in China. Efficient and accurate measurement of the location of wind turbine is important for assessing local wind power development. Remote sensing and deep learning technologies offer technical possibilities for wind turbine identification. However, until now there is few publicly available land remote sensing wind power turbine object detection dataset. Aiming at this question, this paper designs a land remote sensing wind turbine dataset, which includes 4459 individual wind turbines in various environmental backgrounds, including sandy land, woodland, grassland, snowy land, and wasteland. In addition, the dataset is composed of YOLO and VOC markup formats to meet the experimental needs of researchers. This paper also evaluates the dataset under the various mainstream object detection frameworks and reach 92% in mAP. The dataset can be obtained from Zenodo website https://***/10.5281/zenodo.7808269. © 2024 COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Remote sensing

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Automatic Inspection and Reading System of Pointer Instrument Based on Computer Vision 3

Automatic Inspection and Reading System of Pointer Instrumen...

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3rd International Symposium on Computer Engineering and Intelligent Communications, ISCEIC 2022

作者： Xue, Liming Qin, Junping Gao, Tong Wang, Chao Inner Mongolia University of Technology College of Data Science and Application Inner Mongolia Hohhot China Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service Inner Mongolia Hohhot China

ISBN: (纸本)9781510660298

Aiming at the problems of background interference and incorrect angles in the images collected by inspection robots, a computer vision-based automatic inspection and reading system for pointer-type instruments is proposed, which can automatically obtain pointer readings on the basis of instrument image detection and correction. First, use the Centernet algorithm to detect the target of the pointer instrument image, and cut out the instrument image with the background removed according to the detected position information;then, perform key point detection, select a pair of symmetrical key points to rotate the instrument using affine transformation Correction, and then select two pairs of symmetrical key points through template matching to correct the inclination of the meter using perspective transformation;finally, complete the reading of the meter by using Otsu segmentation and Hough transform circle and line detection. The experimental results show that the proportion of images to be corrected is 93%, the average error rate of the corrected instrument image is reduced by 10.19%, and the average accuracy of readings reaches 97.02%, which can meet the practical application. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

关键词： Hough transforms

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A study of stock price analysis algorithms for new energy companies based on random forest and genetic algorithms 3

A study of stock price analysis algorithms for new energy co...

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3rd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2022

作者： Song, Shiyang Gao, Junhan Zhao, Anran Li, Yu Li, Chengyifan Zhu, Yuhang Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China College of Global Talents Beijing Institute of Technology Zhuhai China

ISBN: (纸本)9781510663473

China is the world's largest emerging energy market, but due to the rapid development of China's new energy industry special, the value of new energy companies are more susceptible to sharp increases and decreases brought about by external factors in the market compared to other *** this paper, we propose an algorithm that uses an effective cluster learning strategy (Random Forest) contrasted with a genetic algorithm. A new financial forecasting model GSRF is constructed by parameter optimization of random forest model through grid search algorithm,and the model is applied to short-term stock forecasting. This paper builds a stock price trend forecasting model based on both algorithms and uses the model to determine whether the cost of a stock will be higher than its cost at a given date. The experimental results show that the model built using GSRF stochastic forest has the highest return and the lowest risk compared to the return based on the traditional genetic algorithm trading strategy. © 2023 SPIE.

关键词： Genetic algorithms

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Deep Time-Series Prediction of Complications and Deterioration in ICU Patients Based on the MSIPA Model

Deep Time-Series Prediction of Complications and Deteriorati...

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2023 Cross Strait Radio science and Wireless technology Conference, CSRSWTC 2023

作者： Feng, Duanyang Wang, Wennan Yan, Junjie Song, Shiyang Liu, Yiwen Guo, Xiaomin City University of Macau Faculty of Data Science Macau China Academy of Management Guangdong University of Science and Technology Dongguan China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China Information Technology School Hebei University of Economics and Business Shijiazhuang China

ISBN: (纸本)9798350358971

In this research, a novel deep time-series model, MSIPA, is introduced for predicting complications and deterioration in ICU patients. Unlike conventional models, MSIPA efficiently handles data across varied time intervals, offering enhanced accuracy, particularly with discontinuous time series. It outperforms other deep learning models in multiple tasks, emphasizing the promise of deep time series prediction in healthcare. © 2023 IEEE.

关键词： Deep Temporal Prediction Disease Deterioration Forecasting MSIPA Model

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A new network intrusion detection system based on auto-encoding and variance analysis 5

A new network intrusion detection system based on auto-encod...

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5th IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2023

作者： Chen, Dequ Zhang, Ming Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian Fuzhou China

ISBN: (纸本)9798350310603

In today's big data era, ensuring the network security of information technology (IT) communication facilities is one of the most challenging issues. In fact, with progress in technology, hackers have developed increasingly complex and risky network attacks, making malicious access identification a very tired task. Under various threats, the existing analysis methods encounter many challenges in detecting and mitigating these illegal accesses. This work presents a new network intrusion detection system (IDS) which combines deep learning method and mathematical strategy. Indeed, our IDS makes full use of data analysis, mathematical modeling, and deep learning to select and optimize features that are more relevant for classification. We use NSL-KDD dataset to test the performance of the IDS. The experimental results show that our proposed method has better performance compared with traditional deep learning, machine learning and recently proposed advanced methods. © 2023 IEEE.

关键词： Network security

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