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检索条件"任意字段=2022 International Conference on Algorithms, Network and Computer Technology, ICANCT 2022"

共 1024 条记录，以下是411-420 订阅

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Rolling Bearing Fault Diagnosis Based on Multiscale Block Convolution Neural network 4

Rolling Bearing Fault Diagnosis Based on Multiscale Block Co...

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2022 4th international conference on Modeling, Simulation, Optimization and Algorithm, ICMSOA 2022

作者： Wang, Peng Luo, Yuting Gong, Liming Zhou, Yiren School of Mechanical Engineering of Tianjin University Tianjin China International Institute for Innovative Design and Intelligent Manufacturing of Tianjin University in Zhejiang Zhejiang Shaoxing China School of Mechanical Engineering Nanchang Institute of Technology Jiangxi Nanchang China

The working conditions of rolling bearings are always non-stationary, which will degrade the performance of the traditional intelligent fault diagnosis algorithm can achieves better results under constant working conditions. Aiming to the problems mentioned above, a novel fault diagnosis algorithm based on Multiscale Block Convolutional Neural network (MBCNN) has been proposed in this paper. Compared with other intelligent fault diagnosis algorithms, the proposed algorithm achieves the reuse of effective features while simultaneously extracting global and local fault features end-to-end synchronously. Owing to the original one-dimensional vibration signal can effectively reveal the non-stationarity of bearing fault signal, it is selected as the input of MBCNN and the output of multiple fault categories. Finally, a experiment is conducted to verify the validity of the model, and the advantages of the model are analyzed through the visualization of features. The results show that compared with other methods, this method has higher prediction performance under variable working conditions. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Failure analysis

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Design of Intelligent Recognition English Translation System Based on Machine Learning Algorithm

Design of Intelligent Recognition English Translation System...

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2022 international conference on Education, network and Information technology, ICENIT 2022

作者： Zhang, Yifan Lei, Min Xi'An Jiaotong University City College Department of Foreign Languages Shaanxi Xi'an China School of Anti-Terrorism Law Northwest University of Politics and Law Shaanxi Xi'an China

ISBN: (纸本)9781665463072

In recent years, with the continuous innovation and development of communication technology, different countries have become more closely connected, especially under the system of trade globalization, communication between different countries is more frequent, among which English is the One of the international common languages, the use of English language as an international communication reflects the importance of English translation (ET). This paper focuses on the research on translation systems that can only recognize English. ML (machine learning) algorithms can make feasible and effective results for large data sources in a relatively short period of time. Therefore, in the translation process, it is necessary to call internal and external resources, and use various translation strategies to understand the original text and output the translation. The ability to verify data plays an important role in this understanding and output process. This paper can greatly improve the efficiency and accuracy of ET through ML algorithms. After extracting a large number of translation rules, it is necessary to establish a probability model for the translation rules. The general methods include counting-based maximum likelihood estimation, EM method, discriminant training and other methods. The final results of the research show that the ML algorithm scores 91.7, 93.6, and 94.5 in terms of recognition speed, recognition accuracy, and update ability, respectively. Compared with the GLA algorithm, the ML algorithm has obvious advantages. The accuracy, and the ability to speed up the update are better. © 2022 IEEE.

关键词： international trade

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Rotation-Invariant Self-Supervised Representation Learning for Object Detection in Autonomous Driving 2

Rotation-Invariant Self-Supervised Representation Learning f...

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2nd international conference on Computational Intelligence and network Systems, CINS 2024

作者： Marathe, Aboli Roy, Sidharth Singh, Devansh Walambe, Rahee Kotecha, Ketan Machine Learning Dept. Carnegie Mellon University Pittsburgh United States Dept. of Computer and Information Science University of Pennsylvania Philadelphia United States Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International University Pune India

ISBN: (纸本)9798331504106

This paper describes a self supervised representation learning approach that can perform robust object detection in out-of-distribution rotated images for autonomous driving task. Keeping in mind the limitations of vehicle sensors we incorporate self-supervised rotation invariance for correcting rotational corruptions. We present a comprehensive analysis of observed label shifts, validate the results using holdout sets and provide explainability for the performance uisng GradCAM appaorach. We demonstrate results on real-world adversarial examples: (Autonomous Scenes - DAWN dataset) that significantly outperform common methods. The proposed framework outperforms the rotation-affected benchmark by 14.73 points of mAP for the OD task in autonomous driving applications. ©2024 IEEE.

关键词： Self-supervised learning

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A Review of Gray-scale Image Recoloring Methods With Neural network Based Model 7

A Review of Gray-scale Image Recoloring Methods With Neural ...

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7th international conference on Image, Vision and Computing, ICIVC 2022

作者： Wang, Yuyang Ren, Tianli Zhang, Zhuoyi Chen, Zheyu Xu, Jinfeng Ma, Zixiao Zhu, Pai Zhang, Mingjia Beijing University of Technology Beijing China University College Dublin Beijing-Dublin International College Dublin Ireland Northeastern University at Qinhuangdao Department of Computer Science China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Gray-scale image recolor or computer-assisted color restoration is a computer-assisted process of adding color in-formation to grayscale photographs, which is crucial in perceiving visual information by humans. An important feature of this technique is the ability to restore the original style of old images. Neural network-based black and grayscale image coloring technology is gradually becoming a prevalent topic in current research with the development of computer vision and data science. Models with DNN, CNN, GAN, and DCGAN were successively proposed in the recent few years, indicating a fresh-new hot spot of image processing. Several popular image re-coloring methods have been discussed in this essay, including a review of recent research and then a detailed analysis of the current deployment method. Finally, the essay compares the existing application, demonstrating several real-world examples and providing information and statistics on how to apply this technique to the industry. We also summarized current limitations and future trends of this research field to provide an outlook for other researchers. © 2022 IEEE.

关键词： Restoration

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An Enhanced System for Wrong-Way Driving Vehicle Detection with Road Boundary Detection Algorithm

An Enhanced System for Wrong-Way Driving Vehicle Detection w...

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2022 international conference on Industry Sciences and computer Science Innovation, iSCSi 2022

作者： Suttiponpisarn, Pintusorn Charnsripinyo, Chalermpol Usanavasin, Sasiporn Nakahara, Hiro TAIST Tokyo Tech ICTES Program Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathum Thani Thailand School of ICT Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Dept. of Information and Communications Engineering Tokyo Institute of Technology Tokyo Japan

Driving in the wrong direction is one of the main reasons that cause road accidents in Thailand. To efficiently detect wrong direction driving vehicles, we proposed a system that can track those moving vehicles from CCTVs using deep learning and image processing techniques. Our proposed system is composed of two main algorithms: Road Lane Boundary detection from CCTV algorithm (RLB-CCTV) and Majority-Based Correct Direction Detection algorithm (MBCDD). In this paper, we introduced the RLB-CCTV algorithm and the enhanced version of MBCDD algorithms for detecting road boundaries and wrong-direction driving vehicles. Based on our experiments, our system can identify the road lane boundary and track vehicles driving in the wrong direction on each lane with an accuracy of 96.61%. © 2022 Elsevier B.V.. All rights reserved.

关键词： Hough transforms

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Application of computer Aided Translation System in Online Learning

Application of Computer Aided Translation System in Online L...

引用

2022 international conference on Education, network and Information technology, ICENIT 2022

作者： Guo, Lin Heilongjiang International University Heilongjiang Harbin China

ISBN: (纸本)9781665463072

With the gradual acceleration of the globalization process, the exchanges between various countries around the world have become more frequent, and the demand for translation between different languages is also increasing. Under this demand, the development of computer aided translation (CAT) has also accelerated the reissue. Online learning, especially in the field of translation, is inseparable from CAT. Based on this, this article launches the application research of CAT system in online learning. This article quotes the noise channel model, analyzes the corpus-based CAT method, and introduces the translation process of the CAT system. This article designs a CAT system based on online learning, designs the architecture and functional modules of the system, and introduces functional modules such as user interaction, user request, translation display, and online translation. This article found through a survey that 73.97% of the respondents said that they often use CAT software for translation, and only 6.85% have never used CAT software to complete translation tasks, indicating that the popularity of CAT software in translation professional online learning is relatively high. © 2022 IEEE.

关键词： E-learning

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Privacy Preserving and Secured Clustering of Distributed Data Using Self Organizing Map 1

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1st international conference on Cyber Warfare, Security and Space Research, SpacSec 2021

作者： Gadepaka, Latha Surampudi, Bapi Raju Computational Intelligence Lab School of Computer and Information Sciences University of Hyderabad Hyderabad India Cognitive Science Lab International Institute of Information Technology Hyderabad Hyderabad India

ISBN: (数字)9783031157844

ISBN: (纸本)9783031157837

Preserving privacy of data in data mining domain has been a challenging objective to achieve. There has been lot of progress in applying secure algorithms in order to achieve privacy preserving of sensitive information. In distributed data mining perspective, there is necessity to share the information among the different parties aiming for combined outcomes. Privacy preserving methods and algorithms helps in preserving privacy of data when it is compulsory to share the information between the collaborated parties to perform various data mining operations like Classification and Clustering. It is always important to address the privacy issues of an individual when the data is shared among different parties to achieve a common goal. It is observed that there has been great development in addressing the privacy issues and designing privacy preserving algorithms applied to achieve a secured model. Adopting neural network learning for privacy preserving of data has became very much promising approach. In this paper we adopt self organizing map (SOM) neural network trained in order to preserve the privacy of data of two different parties, in combined environment to perform combined clustering. We present two novel algorithms to preserve privacy of horizontally and vertically partitioned data while combined clustering is performed using SOM. We apply perturbation of data and training the SOM for horizontal partitioning and Cryptography based solutions for vertical partitioning. The proposed algorithms, Horizontal-PPSOM and Vertical-PPSOM are novel contributions in this paper and all the experiments for both algorithms shows good results with an assured privacy level. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Self organizing maps

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Challenges' Strategies in Virtual Robocup Standard Platform League 7

Challenges' Strategies in Virtual Robocup Standard Platform ...

引用

7th Asian conference on Artificial Intelligence technology, ACAIT 2023

作者： Wang, Zijian Liu, Hongjiang Chen, Pingchuan He, Yinuo Zhu, Lingyun Liangjiang International College ChongQing University of Technology ChongQing China College of Computer Science And Engineering ChongQing University of Technology ChongQing China

ISBN: (纸本)9798350359145

The RoboCup, an international competition, necessitated an innovative approach in 2022 due to its transition to an online format in China, prompted by an epidemic. This shift demanded the development of more specialized algorithms for the competition. In response to this, we devised a unique algorithm for each challenge. For Challenge 1, we implemented a streamlined greedy algorithm, which rounded the target points to minimize the total time expenditure. In Challenge 2, we adopted an automated obstacle avoidance strategy. For Challenge 3, we calculated the kick angle in real-time to enhance the synergy between the two robots, employing the Sector-angle method to select the optimal shot or pass angle. Our algorithm showed excellent results in the competition, ranking first in the country. In the practical test, our cumulative scores for the three challenges remained consistent at 163.18, 33.65, and 4.8, significantly improving from the pre-optimization scores of 196.2, 51.78, and 3.5, respectively. This indicates the precise and reliable nature of our algorithms. In particular, the sector angle method was notably influential in Challenge 3, swiftly calculating the ideal angle with exceptional accuracy. Before improvements, the algorithm in this project only managed to score 3.5, which was substantially improved post-optimization. Moving forward, these algorithms can be further optimized and adapted to meet the demands of future scenarios. © 2023 IEEE.

关键词： Competition

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Challenges of Medical Text and Image Processing

Challenges of Medical Text and Image Processing

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2022 international conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022

作者： Das, Meghna Sambodhi, Pragya Parmita Khare, Ayushi Naik, Sachin A. Symbiosis Institute of Computer Studies and Research Symbiosis International Deemed University Pune India

ISBN: (纸本)9781665461092

The processing of text and images has experienced expansion, and has been such a research area which attracts expertise from various domains, for example, medical, mathematics and computer. The advancement of Machine Learning and Deep Learning models has positively affected the medical domain. computer processing with the help of diagnostics has already become a foremost part of clinical routine. As the use of technology advances, greater challenges appear. Some examples are the processing and analysis of a significant volume of images, the inadequacy of annotated data and the unavailability of algorithms on public domains for the use of information in the disease detection process. Other challenges include the suspicion of the community on the automatic technology and lack of awareness in the community. All these challenges need to be overcome for the successful implementation of medical applications. This paper discusses the challenges of medical text and image processing and emphasizes the need to conquer these challenges for the betterment of the community. The principal objective of this research is to discuss the challenges in medical text and image processing amongst the public and to promote interest in further research in this field. © 2022 IEEE.

关键词： Medical applications

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Experimental research on the colour interpolation algorithm of the full colour image 3

Experimental research on the colour interpolation algorithm ...

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3rd international conference on computers, Information Processing and Advanced Education, CIPAE 2022

作者： Tang, Haoran Wang, Zhuoyang Yang, Jianhui Zhou, Sichen Leeds College Southwest Jiaotong University Chengdu611756 China International College Macao University of Science and Technology China College of Computer and Engineering Tianshi College Tianjin301723 China Xi'An Jiaotong University Faculty of Electronic and Information Engineering Xi'an710049 China

ISBN: (纸本)9781665468121

With the rapid development of multimedia technology, images have become the main data form of people's daily communication and information expression. In order to ensure fast data transmission, much effort has been made to compress the three-channel color image into the 2D matrix and then recover the image from the 2D matrix through interpolation algorithms. However, there usually exists different degrees of edge blurring or noise generation when reconstructing images, which significantly affects the accuracy of reconstruction. More importantly, we find that different interpolation algorithms may have drastic performance differences when dealing with different scene images. All of these aspects inspire us to explore the application boundary of interpolation algorithms. In this paper, we compare the representative bilinear interpolation, cok interpolation and Hibbard interpolation from the degree of edge blurring, the amount of noise generated and the processing speed. With extensive experiments, both visual and quantitative analysis shows an interesting but meaningful result. Three algorithms obtain a similar accuracy for the image with a single color or simple lines, while the Hibbard algorithm could restore the most realistic image when the image structure is complex. © 2022 IEEE.

关键词： Image reconstruction

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