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Research on Question Answering System Based on BERT Model 3

Research on Question Answering System Based on BERT Model

引用

3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Yin, Jie Jiangsu University of Science and Technology School of Computer ZhenJiang China

ISBN: (纸本)9781665459112

The core of natural language processing is the science of how computers understand and respond to the influence of human language. This is also the main research direction in the field of machine intelligence development. Question answering system is one of the development directions. How to make the answer of question answering system more intelligent is the focus of research in recent years. Based on this, this thesis proposes a question answering system based on the BERT model that combines the co-attention mechanism and the self-attention mechanism. In order to better show the representation of questions and articles at their respective semantic levels, this article will choose BERT language to encode them. Attention and fusion are then performed horizontally and vertically at different levels of granularity between questions and paragraphs. Experiments show that the model has an average EM of 46.56 and an average F1 of 58.90 on all datasets. The average F1 for the development dataset is 54.85, and the average F1 for the test dataset is 62.95. The content answered by the question answering system based on the BERT model is basically reasonable, and the intelligence of the question answering system is effectively improved. © 2022 IEEE.

关键词： Semantics

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Prior-YOLO: Enhancing Intelligent Vehicle Small Object Detection with Driving Status-Informed YOLOv8 3

Prior-YOLO: Enhancing Intelligent Vehicle Small Object Detec...

引用

3rd international conference on computer Science, Electronic Information Engineering and Intelligent control Technology, CEI 2023

作者： Hu, Shuang Zhao, Baixuan Ding, Taojun Jiang, Hao Hu, Chuan Zhang, Xi School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350372052

In the realm of intelligent vehicles, the evolution of object detection algorithms is of paramount importance. Current deep learning-based methodologies excel in identifying medium to large-sized objects but often falter with smaller entities. A notable research gap exists in integrating key vehicular state data, such as velocity and steering angle, into generally designed object detection frameworks. To bridge these gaps, we present Prior-YOLO, a novel modification of YOLO v8, marked by advanced network structure and refined inference processes. This adaptation includes a dedicated head for small object detection and a new neck component. We have also curated a custom dataset, particularly for algorithm development and validation. This dataset, enriched with critical driving status data, is rigorously annotated to maximize precision. Furthermore, we introduce the Visual Center of Mass Method (VCM), a groundbreaking technique that integrates the driver's focal point to identify the region of interest (ROI) in each image, enhancing auxiliary inference. Our comprehensive experimental evaluations demonstrate significant improvements, with a notable increase of up to 6.04% in Average Precision (AP) and up to 5.44% in Average Precision for Small Objects (APs) across all objects. Most impressively, we observe a 9.24% increase in AP and a 12.59% rise in APs for critical objects, solidifying the effectiveness of Prior-YOLO in intelligent vehicle applications. © 2023 IEEE.

关键词： computer vision

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Simple modeling of a novel fuzzy memristor PID controller 3

Simple modeling of a novel fuzzy memristor PID controller

引用

3rd international conference on Electronic Information Engineering and computer, EIECT 2023

作者： Kuang, Xianyan Huan, Xianglan Xiao, Hongbiao Xu, Yaoming Jiangxi University of Science and Technology 108 Hongqi Road Jiangxi Ganzhou China

ISBN: (纸本)9798350357707

To address the issue of challenging adjustments and the lack of online adaptability for traditional analog PID controllers when dealing with varying controlled objects, an innovative fuzzy multiterminal memristor device is proposed. This innovation enables real-time adjustments of controller parameters. Additionally, a solution is introduced to tackle the complexity of determining the memristor's resistance value. A fuzzy algorithm is integrated with the memristor-based PID controller, resulting in the development of a fuzzy memristor PID control system. This system achieves adaptive parameter adjustments. Building on theoretical foundations, simulations using Multisim and practical circuit experiments are conducted. The outcomes demonstrate that the fuzzy memristor PID control system outperforms the conventional analog PID controller, offering robust online adaptability. © 2023 IEEE.

关键词： Three term control systems

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ADNet: A neural network model for adversarial example detection based on steganalysis and attention mechanism 3

ADNet: A neural network model for adversarial example detect...

引用

3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Zhang, Xinrui Wang, Yong Beijing University of Technology College of Computer Science and Technology Beijing China

ISBN: (纸本)9781665459112

The vulnerability of deep learning to adversarial attacks has brought many security risks to its development. However, the currently proposed adversarial attack detection methods are ineffective in defending large-size images. Studies have shown that detection methods based on steganalysis have apparent advantages in defense of large-scale adversarial examples. Still, their detection results are very dependent on the quality of manually extracted features. This paper proposes an end-to-end convolutional neural network (CNN) model based on steganalysis for adversarial examples detection. We found that adversarial attacks tend to embed perturbations in high-texture regions. To focus more on vulnerable areas during feature learning, we use the original image as information to learn the attention distribution, which improves the model's accuracy. And an adversarial loss function is proposed to fine-tune the model so that the normal images and adversarial examples are more separated in the spatial distribution, reducing the probability of misclassifying normal images. Experiments show that this model has a high defense effect against the current state-of-the-art attack methods. © 2022 IEEE.

关键词： Neural network models

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Distant Blurred Pedestrian Detection: Based on SSD Network with SRGAN Image Super Resolution 3

Distant Blurred Pedestrian Detection: Based on SSD Network w...

引用

3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Zhang, Naifeng Beijing Institute of Technology School of Computer Science Beijing China

ISBN: (纸本)9781665459112

Traditional target detection methods have poor accuracy when processing lower resolution images with many pedestrians, particularly for small and blurry targets whose features are not manifested as significantly as the larger, clearer ones. This paper proposes an SSD pedestrian detection method with super-resolution image enhancement to address this problem. Because the small blurry pedestrians in the distance usually appear in the central part of the image, the model this paper proposed crops the central part of the screen and enhances it using SRGAN. After that, this paper use SSD to detect the enhanced image again. The detection results are fused with the normal detection results to obtain higher pedestrian detection accuracy. For the experiments, the AP of the proposed method reaches 0.693, and F1 comes to 0.709 in the street scene, which is 0.031 and 0.061 higher than the original SSD network. Meanwhile, the AP reaches 0.722 and F1 reaches 0.737 in the denser pedestrian scene, which is 0.044 and 0.085 more elevated than the original SSD network, reflecting the significant improvements in our proposed method's detection accuracy. © 2022 IEEE.

关键词： Image enhancement

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Assessing the 3D Position of a Car with a Single 2D Camera Using Siamese Networks 3rd

Assessing the 3D Position of a Car with a Single 2D Camera U...

引用

3rd international conference on Optimization, Learning Algorithms and Applications (OL2A)

作者： Haj Yahia, Youssef Bel Lopes, Julio Castro Bezerra, Eduardo Rodrigues, Pedro Joao Lopes, Rui Pedro Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal CEFET RJ Rio De Janeiro Brazil Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Reg Monta Braganca Portugal

ISBN: (纸本)9783031530357;9783031530364

Using computer vision for the classification of an object's 3D position using a 2D camera is a topic that has received some attention from researchers over the years. Visual data is interpreted by the computer to recognize the objects found. In addition, it is possible to infer their orientation, evaluating their spatial arrangement, rotation, or alignment in the scene. The work presented in this paper describes the training and selection of a siamese neural network for classifying the 3D orientation of cars using 2D images. The neural network is composed of an initial phase for feature selection through convolutional neural networks followed by a dense layer for embedding generation. For feature selection, four architectures were tested: VGG16, VGG19, ResNet18 and ResNet50. The best result of 95.8% accuracy was obtained with the VGG16 and input images preprocessed for background removal.

关键词： computer vision Maintenance support Siamese networks Object Orientation

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Frequency Decomposition Network for Fast Joint Image Demosaic, Denoising and Super-Resolution 3

Frequency Decomposition Network for Fast Joint Image Demosai...

引用

3rd international conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Niu, Guangxiao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350331578

Image denoising (DN), demosaicing (DM) and super-resolution (SR) are the key tasks of the low-level vision. Joint demosaicing, denoising and Super-resolution (JDDSR) can effectively improve the image quality. However, the previous methods explored the feasibility of tasks more than the characteristics of DM, DN and SR. Meanwhile, the joint training also brought computational burden and the three tasks process information at different frequencies. DN and DM pay more attention to low-frequency (LF) information, while SR is used to recover the lost high-frequency (HF) information. In this work, we use the way of Laplace pyramid to separate the HF and LF of the image, and use different branches to learn the information of different frequencies. In order to reduce the computational burden, we redesign the network architecture and use the form of non-parametric up-sampling to generate the results. Experiments demonstrate that our method can achieve results similar to existing methods with very small computational effort and storage. © 2023 IEEE.

关键词： Image denoising

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Research on a Algorithm of Medicine Box Tracing Code Location 3

Research on a Algorithm of Medicine Box Tracing Code Locatio...

引用

3rd international conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Hu, Dan Pan, Mengsha Taizhou Vocational Collage of Science Technology Zhejiang Taizhou China

ISBN: (纸本)9798350331578

Aiming at the identification problem of medicine box traceability code, according to the principle that the relative position of each character in the medicine box remains unchanged, this paper proposes an efficient and accurate barcode identification method based on barcode neighborhood template matching algorithm. First, create a variable shape template of fixed elements in the kit, and then obtain the edge of the variable model, so as to determine the transformation matrix parameters of the collected image. Through geometric transformation, complete the positioning of the kit traceability code, and finally achieve the extraction of the traceability code. In order to verify the accuracy of the proposed algorithm, a test platform is built to verify the application of the proposed method. The experimental results show that the recognition rate of different kit samples is more than 99.4%, the algorithm has good robustness, high positioning accuracy, and fast recognition speed, which provides a new solution for the identification of kit traceability code on the production line, and has high practical significance. © 2023 IEEE.

关键词： computer vision

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Nonlinear model predictive control method for carbon fiber angle link weaving machine tension based on neural network 3

Nonlinear model predictive control method for carbon fiber a...

引用

2023 IEEE 3rd international conference on Data Science and computer Application, ICDSCA 2023

作者： Zhu, Zhihua Wang, Yufeng Tiangong University School of Mechanical Engineering Tianjin China

ISBN: (纸本)9798350341546

In this paper, a neural network-based nonlinear model predictive control strategy for carbon fiber angle link weaving machine tension is proposed. Firstly, the tension nonlinear model considering the opening disturbance is established. Secondly, considering the existence of nonlinear terms in the system, a radial basis function (RBF) neural network is proposed to approximate the nonlinear terms online to improve the control accuracy of the system. A tension nonlinear model predictive control(NMPC) is designed to achieve constant tension control under the driving torque constraint. And compared with the conventional nonlinear model predictive controller, the RBFNMPC improves the time to reach the steady state in the system state variation curve by 0.2s, reduces the overshoot of the three states in the state response curve by 19.1%, 6.1% and 12.3%, respectively, and effectively reduces the time to reach the steady state. © 2023 IEEE.

关键词： carbon fiber Angle loom neural network nonlinear model predictive control tension control

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An improved multi-resolution 2D/3D registration method 3

An improved multi-resolution 2D/3D registration method

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3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Cao, Yipei He, Fei Qu, Feng Wang, Tiejun Yang, Chen Shi, Weili Jiang, Zhengang Changchun University of Science and Technology School of Computer Science and Technology Changchun China Zhongshan Institute of Changchun University of Science and Technology Zhongshan China The First Hospital of Jilin University Department of Orthopaedic Traumatology Changchun China The First Hospital of Jilin University Bone and Joint Surgery Changchun China

ISBN: (纸本)9781665459112

2D/3D image registration is one of the key technologies to realize pose estimation in computer-aided surgery. In order to improve the global and local search performance of the model in the pose parameter space, an improved multi-resolution 2D/3D registration method is proposed in this paper. Firstly, aiming at the problem that the intensity-based similarity measure is not sensitive to small offset between images, the gradient information with strong sensitivity to image texture edge is introduced, and the Intensity and Gradient Weighted Correlation (IGWC) coefficient similarity measure is proposed;Secondly, aiming at the problem of slow convergence of global optimization algorithm and small capture range of local optimization algorithm, a global-local combined registration optimization strategy is proposed. The experimental results show that this method improves the registration accuracy and success rate. © 2022 IEEE.

关键词： Numerical methods

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