检索结果-内蒙古大学图书馆

MSLEFC: A low-frequency focused underwater acoustic signal classification and analysis system

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期123卷

作者： Zhang, Yunqi Zeng, Qunfeng Xi An Jiao Tong Univ Key Lab Educ Minist Modern Design & Rotor Bearing Xian 710049 Peoples R China

Many feature extraction techniques are employed to study the classification of underwater acoustic, however, most of these techniques result in the loss of detailed information in this field. A Multi-scale short-time Fourier transform (MS-STFT) method was proposed to improve the low-frequency information and maintain the detailed information by increasing the number of channels. An effective data augmentation approach was proposed and a Ladder-like Encode (LE) architecture was built to increase the generalizability of the model and the classification accuracy for the features extracted. Finally, A Frequency-CAM (FC) method is proposed to analyze the frequency band locations that the neural network is interested in when conducting classification tasks on various classes of data. The integration of the above technologies is called MSLEFC. The performance of this system has been tested on two ship radiation noise datasets and obtained 82.94% and 96.06% accuracy, respectively. On the ShipsEar dataset, the proposed method increases the accuracy by 0.7% over the previous state-of-the-art method. The proposed model architecture has significant improvements in parameter size and computation compared to ResNet50.

关键词： Underwater acoustics encoder-decoder Multi-scale short-time fourier transform Frequency-CAM

来源：评论

学校读者我要写书评

暂无评论

Rethinking 1D convolution for lightweight semantic segmentation

引用

FRONTIERS IN NEUROROBOTICS 2023年 17卷 1119231页

作者： Zhang, Chunyu Xu, Fang Wu, Chengdong Xu, Chenglong Northeastern Univ Fac Robot Sci & Engn Shenyang Peoples R China Shenyang Siasun Robot & Automat Co Ltd Shenyang Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China

Lightweight semantic segmentation promotes the application of semantic segmentation in tiny devices. The existing lightweight semantic segmentation network (LSNet) has the problems of low precision and a large number of parameters. In response to the above problems, we designed a full 1D convolutional LSNet. The tremendous success of this network is attributed to the following three modules: 1D multi-layer space module (1D-MS), 1D multi-layer channel module (1D-MC), and flow alignment module (FA). The 1D-MS and the 1D-MC add global feature extraction operations based on the multi-layer perceptron (MLP) idea. This module uses 1D convolutional coding, which is more flexible than MLP. It increases the global information operation, improving features' coding ability. The FA module fuses high-level and low-level semantic information, which solves the problem of precision loss caused by the misalignment of features. We designed a 1D-mixer encoder based on the transformer structure. It performed fusion encoding of the feature space information extracted by the 1D-MS module and the channel information extracted by the 1D-MC module. 1D-mixer obtains high-quality encoded features with very few parameters, which is the key to the network's success. The attention pyramid with FA (AP-FA) uses an AP to decode features and adds a FA module to solve the problem of feature misalignment. Our network requires no pre-training and only needs a 1080Ti GPU for training. It achieved 72.6 mIoU and 95.6 FPS on the Cityscapes dataset and 70.5 mIoU and 122 FPS on the CamVid dataset. We ported the network trained on the ADE2K dataset to mobile devices, and the latency of 224 ms proves the application value of the network on mobile devices. The results on the three datasets prove that the network generalization ability we designed is powerful. Compared to state-of-the-art lightweight semantic segmentation algorithms, our designed network achieves the best balance between segmentation accuracy an

关键词： semantic segmentation lightweight network 1D convolution encoder-decoder feature alignment

来源：评论

学校读者我要写书评

暂无评论

AdamR-GRUs: Adaptive momentum-based Regularized GRU for HMER problems

引用

APPLIED SOFT COMPUTING 2023年 143卷

作者： Pal, Aniket Singh, Krishna Pratap Indian Inst Informat Technol Dept Informat Technol Machine Learning & Optimizat Lab Allahabad India

Handwritten Mathematical Expression Recognition (HMER) is essential to online education and scientific research. However, discerning the length and characters of handwritten mathematical expressions remains a formidable challenge. In this study, we propose an encoder-decoder architecture incorporating a novel Adaptive momentum-based Regularized Gated Recurrent Unit (AdamR-GRU) to solve this problem. It incorporates a novel gate within the GRU framework to maintain input histories, enabling extended memory retention and faster convergence. To assess the performance of AdamR-GRU, we conducted a series of comprehensive experiments on the CROHME 2014, 2016, and 2019 datasets. The results indicate that the AdamR-GRU model exhibits competitive performance with existing state-of-the-art methods regarding Expression Recognition Rate (exprate). Moreover, our model achieves state-of-the-art accuracy concerning 1, 2, and 3 symbol-level errors (top 1, 2, 3 error accuracy) on the CROHME 2014 and 2016 datasets. For the CROHME 2019 dataset, the model attains state-of-the-art performance in top 2 and 3 error accuracy. AdamR-GRU demonstrates reduced computational complexity and memory usage compared to alternative contemporary models.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Handwritten Mathematical Expression Recognition encoder-decoder Gated recurrent unit Dropout Regularization Adaptive momentum

来源：评论

学校读者我要写书评

暂无评论

CrackF-Net: a pixel-level segmentation network for pavement crack detection

引用

JOURNAL OF ELECTRONIC IMAGING 2023年第6期32卷 063002-063002页

作者： Luan, Shen Gao, Xingen Wang, Chen Zhang, Hongyi Chao, Fei Lin, Juqiang Huang, Junqi Jiang, Huali Lin, Feng Xiamen Univ Technol Sch Optoelect & Commun Engn Xiamen Peoples R China Xiamen Univ Sch Informat Dept Artificial Intelligence Xiamen Peoples R China

Detecting pavement cracks from images is a complex computer vision task due to their varying shapes, backgrounds, and sizes. We propose CrackF-Net, an end-to-end convolutional neural network for automatic crack detection in road images. We construct the CrackF-Net network using an encoder-decoder architecture to extract image features in convolutional blocks with residuals and fuse the multiscale convolutional features produced by the decoder. Convolutional blocks with residuals are used to capture the strong semantic features of cracks, and an adaptive filter fusion module is proposed to assist the network make a selection of filter fusion features on the channels. CrackF-Net fuses the multiscale features in decoder to improve crack detection performance. The proposed CrackF-Net is compared to other advanced crack detection methods using three public datasets. The experimental results show that CrackF-Net achieves state-of-the-art performance, which obtains F-measures of 0.866, 0.737, and 0.852 on the three datasets. (c) 2023 SPIE and IS&T

关键词： crack detection encoder-decoder noise processing attention mechanism semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

Microaneurysm detection in fundus images based on a novel end-to-end convolutional neural network

引用

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2021年第2期41卷 589-604页

作者： Liao, Yinhan Xia, Haiying Song, Shuxiang Li, Haisheng Guangxi Normal Univ Coll Elect Engn Guilin 541004 Peoples R China

Microaneurysms are the earliest symptom of diabetic retinopathy and play an important role in the screening of diabetic retinopathy. However, because of the complex background, automatic detection microaneurysm in fundus images is a challenging task. Firstly, moti-vated by the characteristics of microaneurysm, a novel deep convolutional encoder-decoder network for microaneurysm detection is designed to locate the MAs by the differ-ences between the skip connection in the network. Then, a weighted dice loss, termed the smooth dice loss, is presented to put more focus on misclassified microaneurysms. Finally, an activation function with a long tail is used to produce an accurate probability map for MA detection. Plenty of experiments, conducted on the Retinopathy Online Challenge data-set and the e-ophtha-MA dataset, demonstrate that the proposed model achieves the com-parable performance to the existing state-of-the-art methods on microaneurysm detection with only one-hundredth the running time compared with its counterparts. The proposed method is simple and effective, guarantees the performance while shortening the test time. It indicates the potential application in the auxiliary diagnosis of diabetic retinopathy screening. (c) 2021 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Deep learning Computer-aided diagnosis Diabetic retinopathy screening Microaneurysm detection encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Deep Learning Models for Text Summarization on Hindi Corpus 5th

Comparative Analysis of Deep Learning Models for Text Summar...

引用

5th International Conference on Artificial Intelligence and Speech Technology, AIST 2023

作者： Sowpari, Niranjana Bansal, Poonam Sethi, Nandini Indira Gandhi Delhi Technical University for Women Kashmere Gate Delhi India

ISBN: (纸本)9783031751639

We often come across large texts for example, stories, news articles etc. Sometimes it is required to have a quick summary of the context to save our time in reading the long paragraphs. In literature we often know this process by the name of Text Summarization. Text summarization comes under a field in Data Science called as Natural Language Processing. Over the years, a lot of work towards Text Summarization has been presented on English Corpus, but not much work has been done for our Indian Languages like Hindi. Our goal for this research work is to find a model which is able to generate good Hindi summary out of Hindi texts and is efficient in terms of time and resources. To be able to achieve our goal, we have performed a comparative analysis of the Deep Learning Models for text summarization so that we can compare the models on various parameters and their results. In our paper, we have trained the models BART and T5 on the ‘Hindi Text Short Summarization’ Corpus where T5 performed better than BART and with a rouge-L (f-measure) score of 20.5. For experimentation, we have used the ‘Hindi Text Short Summarization’ Corpus available from Kaggle as not much work has been performed until now on this dataset and we wanted to learn about the essential data transformations or data pre-processing that can be done on a Hindi dataset so that our model yields us good results for the Hindi Corpus as most of times the industrial data are not very clean and understanding the data and performing data-preprocessing becomes a very crucial step. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： BART BERT Deep Learning encoder-decoder IndicBART mT5 Natural Language Processing Neural Networks T5 Transformers

来源：评论

学校读者我要写书评

暂无评论

Pseudo dense counterfactual augmentation for aspect-based sentiment analysis

引用

NEUROCOMPUTING 2023年 561卷

作者： Ouyang, Jihong Feng, Shi Wang, Bing Yang, Zhiyao Jilin Univ Coll Comp Sci & Technol Changchun 130022 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130022 Peoples R China

Aspect-based sentiment analysis (ABSA) is a fine-grained text classification task, and the cutting-edge ABSA models have achieved outstanding performance. Unfortunately, the robustness of these ABSA models is neglected. ABSA models must face numerous challenges to be robust, and we concentrate on one of these challenges caused by negation words, such as "not'', "un-". In the actual context, these negation words intuitively result in two problems: negative sensitivity and spurious correlation. First, a negation word tends to reverse the sentiment polarity of a sentence. Meanwhile, in the ABSA datasets, most sentences containing negation words express Negative polarities, which will lead the predictive model to learn the spurious correlation between negation words and polarities. To resolve these ambiguous issues, we are inspired by causal inference and propose a novel data augmentation framework, namely Pseudo Dense Counterfactual Augmentation (PDCaug) for ABSA. Specifically, we initialize a pseudo sequence and employ a multi-head multi layer attention network to achieve counterfactual augmentation for a vanilla sentence in the hidden space. This pseudo sequence will be adversarially trained. PDCaug is a plug-and-play method for various ABSA models, so we evaluate it on discriminative models and generative prompt-based models. Our extensive experiments show that our PDCaug can significantly and consistently outperform several data augmentation methods and ABSA models.

关键词： Aspect-based sentiment analysis encoder-decoder Data augmentation

来源：评论

学校读者我要写书评

暂无评论

MMAG: Mutually Motivated Attention Gates for Simultaneous Extraction of Contextual and Spatial Information from a Monocular Image 23

MMAG: Mutually Motivated Attention Gates for Simultaneous Ex...

引用

Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing

作者： Aditya Singh Saloni Mohan Vandana Kushwaha G C Nandi IIIT Allahabad IN

ISBN: (纸本)9798400716256

In order to effectively interact with its environment, an agent must possess the ability to comprehend the ’what’ and the ’where.’ Two vision-based approaches, namely semantic segmentation and depth estimation, can be employed to provide this information. This paper introduces a unified model that combines these two tasks and utilizes a shared latent space. The chosen approach for the model involves an encoder-decoder architecture accompanied by a novel attention gate mechanism called MMAG, which has been proven to be highly effective. Furthermore, a common skip connection method, in conjunction with the proposed attention gate, is included to emphasize crucial features for both prediction purposes. By leveraging the complementary information within the shared representations, the model is capable of generating more accurate predictions compared to other joint-prediction models, all while utilizing fewer network parameters. To enhance performance, dilated layers are incorporated, allowing for focused attention through different receptive fields. The model’s performance has been evaluated on the NYU Depth v2 and Camvid datasets, demonstrating improved results on both tasks when compared to other state-of-the-art models. With its reduced parameter count, this model is particularly suitable for low-cost robots and has undergone successful testing for inference on the LoCoBot robot.

关键词： attention gate mechanism encoder-decoder monocular depth estimation semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

The Code Generation Method Based on Gated Attention and InterAction-LSTM 1

引用

18th Web Information Systems and Applications Conference (WISA)

作者： Wang, Yuxuan Wu, Junhua Nanjing Tech Univ Sch Comp Sci & Technol Nanjing 211816 Peoples R China

ISBN: (数字)9783030875718

ISBN: (纸本)9783030875718;9783030875701

Code generation is an important research field of software engineering, aiming to reduce development costs and improve program quality. Nowadays, more and more researchers intend to implement code generation by natural language understanding. In this paper, we propose a generation method to convert natural language descriptions to the program code based on deep learning. We use an encoder-decoder model with gated attention mechanism. Here, the decoder is an InterAction-LSTM. The gated attention combines the previous decoding cell state with source representations to improve the limitation of invariant source representations. The decoder makes the information interact each other before putting them into the gate of the LSTM. The code generation is verified on two datasets, Conala and Django. Compared with known models, our model outperforms the baselines both in Accuracy and Bleu.

关键词： Code generation encoder-decoder Gated attention InterAction-LSTM

来源：评论

学校读者我要写书评

暂无评论

A Transformer-based Radical Analysis Network for Chinese Character Recognition 25

A Transformer-based Radical Analysis Network for Chinese Cha...

引用

25th International Conference on Pattern Recognition (ICPR)

作者： Yang, Chen Wang, Qing Du, Jun Zhang, Jianshu Wu, Changjie Wang, Jiaming Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc Hefei Anhui Peoples R China

ISBN: (纸本)9781728188089

Recently, a novel radical analysis network (RAN) has the capability of effectively recognizing unseen Chinese character classes and largely reducing the requirement of training data by treating a Chinese character as a hierarchical composition of radicals rather than a single character class. However, when dealing with more challenging issues, such as the recognition of complicated characters, low-frequency character categories, and characters in natural scenes, RAN still has a lot of room for improvement. In this paper, we explore options to further improve the structure generalization and robustness capability of RAN with the Transformer architecture, which has achieved start-of-the-art results for many sequence-to-sequence tasks. More specifically, we propose to replace the original attention module in RAN with the transformer decoder, which is named as a transformer-based radical analysis network (RTN). The experimental results show that the proposed approach can significantly outperform the RAN on both printed Chinese character database and natural scene Chinese character database. Meanwhile, further analysis proves that RTN can be better generalized to complex samples and low-frequency characters, and has better robustness in recognizing Chinese characters with different attributes.

关键词： Chinese characters Transformer network encoder-decoder Attention

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：