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检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

共 761 条记录，以下是741-750 订阅

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Vehicle Bearing Fault signal Detection Method Based on Long-Short-Term Memory Network 2021

Vehicle Bearing Fault Signal Detection Method Based on Long-...

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Pin Xia Chongqing Vocational College of Transportation China

ISBN: (纸本)9781450385046

As rolling bearings play an increasingly important role in mechanical equipment, the reliability requirements of rolling bearings are getting higher and higher. In this paper, an embedded system is used to amplify the weak signal submerged in noise, and extract its characteristic information, and finally realize the bearing fault detection, use the MEMS sensor to collect the vibration signal, and use the long and short-term memory network method to demodulate the signal. Demodulation processing, input the processed signal to the stochastic resonance system, and use the signal-to-noise ratio as an indicator to enhance the target signal. In this paper, the use of sound-assisted vibration weak signal enhancement detection can realize the enhancement of the characteristic frequency of weak bearing vibration fault under strong background noise, and improve the accuracy of bearing fault diagnosis. Both the peak index and impulse index fluctuate around 3, the margin index fluctuates around 1, and the kurtosis fluctuates around 3, which conforms to the theoretical analysis. The long- and short-term memory network can improve the accuracy of vehicle bearing fault signal detection.

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Mixup Augmentation for Deep Hashing 2021

Mixup Augmentation for Deep Hashing

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2021 3rd international conference on Video, signal and Image processing

作者： Weida Cao Futao Liu Xiaomin Yin Mingrui Chen Weizhi Lu Ran Song Liaocheng Power Supply Company State Grid Corporation of China China School of Control Science and Engineering Shandong University China

ISBN: (纸本)9781450385886

Deep hashing methods has gained growing popularity in approximate nearest neighbour search for large-scale image retrieval, but exhibit undesirable behaviors such as sensitivity to adversarial examples. In the paper, inspired by the success of mixup-based data augmentation in adversarial training, we for the first time apply this technique to deep hash codes-based image retrieval, and evaluate its performance on six kinds of typical deep hashing methods. Experiments show that the mixup augmentation indeed could provide stable performance gains, ranging from 0.2% to 1.3%.

关键词： image retrieval data augmentation mixup deep neural networks hash codes

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Application of computer Aided Design in the Major of Exhibition Design 2021

Application of Computer Aided Design in the Major of Exhibit...

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Yue Zhang Jiangxi University of Software Professional Technology China

ISBN: (纸本)9781450385046

With the upgrading of computers and the continuous development of science, computer-aided design (CAD) technology is also advancing. Especially the use of it in exhibition design has opened up new possibilities for the development of the exhibition design profession, and has now become an indispensable part of exhibition design. The purpose of this article is to study the application of CAD in exhibition design. Starting from the practical application of CAD in exhibition design, this article summarizes the characteristics and application scope of CAD, and summarizes and analyzes the application characteristics of CAD in exhibition design. This paper analyzes the application value and advantages of CAD in exhibitions through analysis and comparison with traditional exhibition design technology. Questionnaire survey data show that the use of CAD has achieved 72.1% satisfaction with the diversified display methods of exhibits, and 80.6% of the processing methods that the design can enrich the display space. computer-aided design is used in exhibition design. Satisfaction with the application effect in majors is high.

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Cross-modal and Semantics-Augmented Asymmetric CycleGAN for Data-Imbalanced Anime Style Face Translation 2021

Cross-modal and Semantics-Augmented Asymmetric CycleGAN for ...

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2021 3rd international conference on Video, signal and Image processing

作者： Shiping Deng Kaoru Uchida Zhengwei Yin School of Software Engineering University of Science and Technology of China China and Graduate School of Computer and Information Sciences Hosei University Japan Graduate School of Computer and Information Sciences Hosei University Japan Alibaba Group China

ISBN: (纸本)9781450385886

Human face to anime face translation has attracted the attention of many researchers in recent years, and various works have achieved high-quality style transfer on conventional tasks. However, existing works often have fatal shortcomings when the target domain training data is heavily insufficient, which is named as imbalanced setting. Here the imbalanced (low-resource) task, generally means there is no sufficient data on the training dataset compared with the conventional task, e.g. the training data size is less than 100. To solve this problem, we propose a multi-modal translation model for a specific style. Based on the cyclic adversarial network and class activation map, we import semantic modality to enhance data information and attention modules, which help the model focus more on the discriminative areas between source and target domain. The experimental results show that our method has superiority in low-resource settings compared with similar existing work.

关键词： image translation low-resource Deep neural networks

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Anti Fraud Detection Model Using Deep Learning Approach

Anti Fraud Detection Model Using Deep Learning Approach

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： VNLN Murthy A Bhanu Prasad BJV Varma Hariharan Shanmugasundaram Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9781665474979

ISBN: (纸本)9781665474986

Recently, Internet finance has become more and more popular. However, bad debts becamea serious threat to online finance corporations. A commonly used fraud detection models by the traditional monetary companies is logistic regression. In this paper we use dataset consisting large publicloans data of a financial company i.e., Lending Club to check the potential of deep neural networks in fraud detection. Once this dataset is loaded we dealtwith the missing values and data pre-processing. With this pre-processed data, we extracted important features using the XGBoost algorithm and developed a CNN deep neural network to detect loan fraud on the Internet. Extensive experiments were conducted to prove that deep neural networks are superior tocommonly used models. This easy and effective model can give enlightenment for the utilization of deep learning to combat online loan fraud, which can profit the financial engineers of tiny and medium-sized financial corporations.

关键词： Deep learning Neural networks Finance Companies Feature extraction Fraud Internet

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A Methodology of Customized Dataset for Cotton Disease Detection Using Deep Learning Algorithms

A Methodology of Customized Dataset for Cotton Disease Detec...

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Frontiers of Information Technology (FIT)

作者： Maryam Saba Tahir Ayesha Yaqoob Haiqa Hamid Rana M. Amir Latif Department of Computer Science Barani Institute of Sciences Sahiwal Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Pakistan

ISBN: (纸本)9798350345940

Agribusiness occupies the lion’s share of Pakistan’s land. It also supports Pakistan’s financial situation. Approximately 62% of Pakistan’s population lives in rural regions and relies on agriculture for a portion of their income. Pakistan is now the 5th-largest producer of cotton and the 3rd-largest consumer/manufacturer of cotton yarn worldwide. Cotton is grown on 6.0 million acres by 1.3 million of the country’s 5 million farmers, or around 15% of the country’s total cultivated land. Cotton fields are plagued by various illnesses that may have a devastating effect on the quality and quantity of the crop. Detection of these disorders has become more common because of image processing. Pathogens often cause plant diseases like germs, fungi, and microbes that thrive in an unsanitary environment. The farmer suffers a significant setback as a result of this. The main purpose of this research is to get to know the disease in a cotton field. We identify plant/leaf disease using the convolutional neural networks (CN) technique with Pooling, Flatten, Dense, and dropout layers to analyze picture data using TensorFlow and Kera’s support. Our dataset has six classes, including 1965 photos of five sick cotton plant classes and one healthy class. We compared three Kera’s applications to get the algorithm’s best accuracy. The applications we used are Xception, InceptionV3 and InceptionRestNetV2. The Xception model shows us the best accuracy, an average of 90.34%.

关键词： Training Deep learning Plant diseases Pathogens Machine learning algorithms Computational modeling Cotton

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Tracked-Vehicle Retrieval by Natural Language Descriptions With Domain Adaptive Knowledge

Tracked-Vehicle Retrieval by Natural Language Descriptions W...

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IEEE computer Society conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Huy Dinh-Anh Le Quang Qui-Vinh Nguyen Vuong Ai Nguyen Thong Duy-Minh Nguyen Nhat Minh Chung Tin-Trung Thá i Synh Viet-Uyen Ha School of Computer Science and Engineering International University Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper introduces our solution for Track 2 in AI City Challenge 2022. The task is Tracked-Vehicle Retrieval by Natural Language Descriptions with a real-world dataset of various scenarios and cameras. We mainly focus on developing a robust natural language-based vehicle retrieval system to address the domain bias problem due to unseen scenarios and multi-view multi-camera vehicle tracks. Specifically, we apply CLIP [16] to effectively extract both visual and textual representations for contrastive representation learning. Furthermore, for new scenarios in the test set, we pro-pose a novel Domain Adaptive Training method that utilizes information from labeled data and transfers it to the unseen domain by generating pseudo labels. By using this simple and effective strategy, we not only bridge the domain gap between the training set and test set, but also require less computational cost and data compared to previous top performance methods. Finally, we employ a context-sensitive post-processing method to address model’s uncertainty and eliminate the wrong retrieved vehicle track. Taking one step further, we also investigate the impact of different text formats and the number of pseudo labels data for the fine-tuning process. Our proposed method has achieved 3rd place in the AI City Challenge 2022, yielding a competitive performance of 47.73% MRR accuracy on the private test set, which verified the effectiveness of the proposed solution.

关键词： Training Representation learning Visualization Adaptive systems Uncertainty Computational modeling Urban areas

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Fourier ptychographic microscopy based on Gaussian apodization coherent transfer function constraints 3

Fourier ptychographic microscopy based on Gaussian apodizati...

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3rd international Computing Imaging conference, CITA 2023

作者： Cheng, Hong Li, Ju Liu, Hong Zhang, Hongyi Li, Yang School of Electronic and Information Engineering Anhui University Anhui Hefei230601 China Key Laboratory of Intelligent Computing & Signal Processing Anhui University Anhui Hefei230601 China

ISBN: (纸本)9781510671409

Fourier ptychographic microscopy technique combines the concept of phase retrieval algorithm and synthetic aperture, which can solve the problem that large field of view and high resolution cannot coexist. However, describing the linear transfer process of the imaging system by coherent transfer function in conventional calculations leads to ringing artifacts. Gaussian apodization coherent transfer function constraints is proposed and embedded into the software platform, and a high-resolution Fourier ptychographic microscopy imaging system is constructed together with the designed hardware. The proposed algorithm can reduce the ringing artifacts and ensure the accuracy and resolution of the reconstructed results by simulating experiments and acquiring real images. © 2023 SPIE.

关键词： Imaging systems

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A Multi-scale Framework for Visual Grounding 2021

A Multi-scale Framework for Visual Grounding

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2021 3rd international conference on Video, signal and Image processing

作者： Baosheng Li Peng Qi Jian Wang Chong Tan Runze Qi Xiaolei Li State Grid Shandong Electric Power Company Laiwu Power Supply Company China School of Control Science and Engineering Shandong University China

ISBN: (纸本)9781450385886

Visual Grounding aims to ground the right region of the image according to the query that describes the target. Generally, this task can be formalized as selecting the target with the highest matching score between the image regions and the language queries. This task is based on object detection, word embedding and multi-modal data fusion. At present, this task has a wide range of needs in real life. For example, it is necessary to search the matching content of surveillance video based on text information to check whether certain violations have occurred. Based on the current advanced one-stage algorithm ReSC, this paper studies the algorithm details of the object detection algorithm, word embedding and multi-modal data fusion. On this basis, this paper exploits multi-scale features to improve performance.

关键词： Word Embedding Object Detection Visual Grounding Multi-scale features

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Design and Implementation of Financial Learning System Based on computer Simulation Technology 2021

Design and Implementation of Financial Learning System Based...

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Yanhong Wu Zhanjiang Science and Technology College China

ISBN: (纸本)9781450385046

With the rapid development of computer network technology, its popularity and application have become stronger and stronger. Information technology has already become one of the important forces of economic development and social progress, and has been highly successful and applied in various fields. The purpose of this paper is to study the design of financial learning system based on computer simulation technology. Using computer simulation technology, JSP Application Technology, JavaBean database access technology and database technology, this paper designs and implements a financial learning system that can effectively meet the actual needs of enterprise employees and school teachers and students. The system can effectively reduce the difficulty of enterprises and schools in financial education, improve the learning initiative and enthusiasm of employees and students, and improve the teaching quality of teachers. In order to make the financial learning system of computer simulation system more vivid, a system combining computer simulation technology with computer-aided teaching is proposed. By combining computer simulation technology with computer-aided teaching, the application of computer-based financial learning system based on computer simulation in education is further explored. Provide students with a clearer learning direction and apply computer simulation to computer teaching. The experimental research shows that the test fluctuation of the system is relatively stable, the average value of the maximum transaction response time is about 0.84 seconds, and the probability of successful transaction processing is 100%.

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