检索结果-内蒙古大学图书馆

A Novel CNN-based Model for Medical Image Registration

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第11期14卷 1125-1136页

作者： Gao, Hui Liang, Mingliang Zhengzhou Railway Vocat & Tech Coll Zhengzhou 451460 Henan Peoples R China

The registration of the deformable image is applied widely to image diagnosis, the monitoring of the disease, and the navigation of the surgery with the aim of learning the correspondence of the anatomist among an image of motion and an image of static. The procedure of the registration of an image mainly includes three steps: the creation of a model of the deformation, a function design for the mensuration of the similarity, and the step of learning for the optimization of the parameter. In the current article, 2-stream architecture is designed, which has the ability to sequentially estimate the fields of the registration of the multi-level by a couple of the pyramids of the feature. In this paper, a 3D network of the encoder decoder with the 2-stream is designed, which calculates 2 pyramids of the feature of the convolutional as separately by 2 volumes of the input. Also, the registration of the pyramid of the sequential is proposed, which in it, a trail of the modules of the pyramid registration (PR) for the prediction of the fields of the registration of the multi-level is designed as straight by the pyramids of the feature of the decoding. In addition, the modules of PR can be augmented with the computation of the 3D correlations of the local among the pyramids of the feature, which this work leads to the further improvement of the presented approach. Thus, it is capable of collecting the detailed anatomical structure of the brain. The proposed method is tested in three criterion datasets about the registration of MRI of the brain. The evaluation outcomes display that the presented approach outperforms the advanced approaches with a big value.

关键词： Image registration convolutional neural network Pyramid Registration (PR) encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

Long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries

引用

PATTERNS 2023年第6期4卷 100732页

作者： Cui, Binghan Wang, Han Li, Renlong Xiang, Lizhi Du, Jiannan Zhao, Huaian Li, Sai Zhao, Xinyue Yin, Geping Cheng, Xinqun Ma, Yulin Huo, Hua Zuo, Pengjian Han, Guokang Du, Chunyu Harbin Inst Technol Sch Chem & Chem Engn MIIT Key Lab Crit Mat Technol New Energy Convers & Harbin 150001 Peoples R China

Accurate early detection of internal short circuits (ISCs) is indispensable for safe and reliable application of lithium-ion batteries (LiBs). However, the major challenge is finding a reliable standard to judge whether the battery suffers from ISCs. In this work, a deep learning approach with multi-head attention and a multi-scale hierarchical learning mechanism based on encoder-decoder architecture is developed to accurately forecast voltage and power series. By using the predicted voltage without ISCs as the standard and detecting the consistency of the collected and predicted voltage series, we develop a method to detect ISCs quickly and accurately. In this way, we achieve an average percentage accuracy of 86% on the dataset, including different batteries and the equivalent ISC resistance from 1,000 U to 10 U, indicating successful application of the ISC detection method.

关键词： lithium-ion battery internal short circuit detection voltage prediction power prediction time-series forecasting deep learning encoder-decoder attention model

来源：评论

学校读者我要写书评

暂无评论

ED-ConvLSTM: A Novel Global Ionospheric Total Electron Content Medium-Term Forecast Model

引用

SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS 2022年第8期20卷 e2021SW002959-e2021SW002959页

作者： Xia, Guozhen Zhang, Fubin Wang, Cheng Zhou, Chen Wuhan Univ Dept Space Phys Wuhan Peoples R China Beihang Univ Res Inst Frontier Sci Beijing Peoples R China

In this paper, we proposed an innovative encoder-decoder structure with a convolution long short-term memory (ED-ConvLSTM) network to forecast global total electron content (TEC) based on the International GNSS Service (IGS) TEC maps from 2005 to 2018 with 1-hr time cadence. The ED-ConvLSTM model is used to forecast TEC maps 1-7 days in advance through iterations. To investigate the model's performance, we compared the model with International Reference Ionosphere (IRI2016) model in 2014 and 2018, and compared the model with 1-day Beijing University of Aeronautics and Astronautics (BUAA) model in 2018. The results show that our 7-day ED-ConvLSTM model (ED-ConvLSTM model that forecasts 7 days in advance) outperforms IRI2016 in 2014 and 2018, and our 5-day ED-ConvLSTM model (ED-ConvLSTM model that forecasts 5 days in advance) outperforms 1-day BUAA model. Furthermore, the root mean square error (RMSE) from the 1-day ED-ConvLSTM model with respect to the IGS TEC maps decreases by 51.5% and 43%, respectively, in 2014 and 2018 compared with that from IRI2016 model. The RMSE from the 1-day ED-ConvLSTM model is 20.3% lower than that from the 1-day BUAA model in 2018. In addition, our model has the highest RMSE in the Equatorial Ionospheric Anomaly (EIA) region, but can roughly predict the features and locations of EIA. However, the model fails to forecast localized TEC enhancement and the sudden ionospheric response to the geomagnetic storms. Overall, the model shows competitive performance in medium-term global TEC maps prediction during geomagnetic quiet periods.

关键词： ionospheric TEC medium-term global prediction encoder-decoder CNN ConvLSTM

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI

引用

SENSORS 2020年第11期20卷 3183.-3183.页

作者： Khan, Zia Yahya, Norashikin Alsaih, Khaled Ali, Syed Saad Azhar Meriaudeau, Fabrice Univ Teknol PETRONAS Ctr Intelligent Signal & Imaging Res CISIR Dept Elect & Elect Engn Seri Iskandar 32610 Perak Malaysia Univ Burgundy ImViA ITFIM F-21078 Dijon France

In this paper, we present an evaluation of four encoder-decoder CNNs in the segmentation of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected CNNs are FCN, SegNet, U-Net, and DeepLabV3+, which was originally proposed for the segmentation of road scene, biomedical, and natural images. Segmentation of prostate in T2W MRI images is an important step in the automatic diagnosis of prostate cancer to enable better lesion detection and staging of prostate cancer. Therefore, many research efforts have been conducted to improve the segmentation of the prostate gland in MRI images. The main challenges of prostate gland segmentation are blurry prostate boundary and variability in prostate anatomical structure. In this work, we investigated the performance of encoder-decoder CNNs for segmentation of prostate gland in T2W MRI. Image pre-processing techniques including image resizing, center-cropping and intensity normalization are applied to address the issues of inter-patient and inter-scanner variability as well as the issue of dominating background pixels over prostate pixels. In addition, to enrich the network with more data, to increase data variation, and to improve its accuracy, patch extraction and data augmentation are applied prior to training the networks. Furthermore, class weight balancing is used to avoid having biased networks since the number of background pixels is much higher than the prostate pixels. The class imbalance problem is solved by utilizing weighted cross-entropy loss function during the training of the CNN model. The performance of the CNNs is evaluated in terms of the Dice similarity coefficient (DSC) and our experimental results show that patch-wise DeepLabV3+ gives the best performance with DSC equal to 92.8%. This value is the highest DSC score compared to the FCN, SegNet, and U-Net that also competed the recently published state-of-the-art method of prostate segmentation.

关键词： encoder-decoder CNNs DNN FCN SegNet U-Net DeepLabV3+

来源：评论

学校读者我要写书评

暂无评论

Trace2trace-A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories

引用

SENSORS 2020年第12期20卷 3503.-3503.页

作者： Crivellari, Alessandro Beinat, Euro Univ Salzburg Dept Geoinformat Z GIS A-5020 Salzburg Austria

Neural machine translation is a prominent field in the computational linguistics domain. By leveraging the recent developments of deep learning, it gave birth to powerful algorithms for translating text from one language to another. This study aims to assess the feasibility of transferring the neural machine translation approach into a completely different context, namely human mobility and trajectory analysis. Building a conceptual parallelism between sentences (sequences of words) and motion traces (sequences of locations), we aspire to translate individual trajectories generated by a certain category of users into the corresponding mobility traces potentially generated by a different category of users. The experiment is inserted in the background of tourist mobility analysis, with the goal of translating the motion behavior of tourists belonging to a specific nationality into the motion behavior of tourists belonging to a different nationality. The model adopted is based on the seq2seq approach and consists of an encoder-decoder architecture based on long short-term memory (LSTM) neural networks and neural embeddings. The encoder turns an input location sequence into a corresponding hidden vector;the decoder reverses the process, turning the vector into an output location sequence. The proposed framework, tested on a real-world large-scale dataset, explores an effective attempt of motion transformation between different entities, arising as a potentially powerful source of mobility information disclosure, especially in the context of crowd management and smart city services.

关键词： seq2seq encoder-decoder neural networks LSTM trajectories motion behavior smart tourism

来源：评论

学校读者我要写书评

暂无评论

ResneSt-Transformer: Joint attention segmentation-free for end-to-end handwriting paragraph recognition model

引用

ARRAY 2023年 19卷

作者： Hamdan, Mohammed Cheriet, Mohamed Univ Quebec ETS Synchromedia Lab Syst Engn 1100 Notre Dame St W Montreal PQ H3C 1K3 Canada

Offline handwritten text recognition (HTR) typically relies on segmented text-line images for training and transcription. However, acquiring line-level position and transcript information can be challenging and time-consuming, while automatic line segmentation algorithms are prone to errors that impede the recognition phase. To address these issues, we introduce a state-of-the-art solution that integrates vision and language models using efficient split and multi-head attention neural networks, referred to as joint attention (ResneSt-Transformer), for end-to-end recognition of handwritten paragraphs. Our proposed novel one-stage, segmentation-free pipeline employs joint attention mechanisms to process paragraph images in an end-to-end trainable manner. This pipeline comprises three modules, with the output of one serving as the input for the next. Initially, a feature extraction module employing a CNN with a split attention mechanism (ResneSt50) is utilized. Subsequently, we develop an encoder module containing four transformer layers to generate robust representations of the entire paragraph image. Lastly, we designed a decoder module with six transformer layers to construct weighted masks. The encoder and decoder modules incorporate a multi-head self-attention mechanism and positional encoding, enabling the model to concentrate on specific feature maps at the current time step. By leveraging joint attention and a segmentation-free approach, our neural network calculates split attention weights on the visual representation, facilitating implicit line segmentation. This strategy signifies a substantial advancement toward achieving end-to-end transcription of entire paragraphs. Experiments conducted on paragraph-level benchmark datasets, including RIMES, IAM, and READ 2016 test datasets, demonstrate competitive results compared to recent paragraph-level models while maintaining reduced complexity. The code and pre-trained models are available on our GitHub repository

关键词： Handwritten text recognition ResneSt Transformer Self attention Segmentation-free Lexicon-free Paragraph transcription OCR encoder-decoder Image-seq

来源：评论

学校读者我要写书评

暂无评论

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

引用

GIGASCIENCE 2019年第11期8卷 giz123页

作者： Yasrab, Robail Atkinson, Jonathan A. Wells, Darren M. French, Andrew P. Pridmore, Tony P. Pound, Michael P. Univ Nottingham Sch Comp Sci Jubilee CampusWollaton Rd Nottingham NG8 1BB England Univ Nottingham Sch Biosci Sutton Bonington Campus Nottingham LE12 SRD England

Background: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. Results: We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. Conclusions: We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to

关键词： convolutional neural network (CNN) plant phenotyping computer vision encoder-decoder root system

来源：评论

学校读者我要写书评

暂无评论

Enhancing image caption generation through context-aware attention mechanism

引用

HELIYON 2024年第17期10卷 e36272页

作者： Bhuiyan, Ahatesham Hossain, Eftekhar Hoque, Mohammed Moshiul Dewan, M. Ali Akber Chittagong Univ Engn & Technol Dept Elect & Telecommun Engn Chittagong 4349 Bangladesh Chittagong Univ Engn & Technol Dept Comp Sci & Engn Chittagong 4349 Bangladesh Athabasca Univ Fac Sci & Technol Sch Comp & Informat Syst Athabasca AB T9S 3A3 Canada

Image captioning, the process of generating natural language descriptions based on image content, has garnered attention in AI research for its implications in scene understanding and human-computer interaction. While much prior research has focused on caption generation for English, addressing low-resource languages like Bengali presents challenges, particularly in producing coherent captions linking visual objects with corresponding words. This paper proposes a context-aware attention mechanism over semantic attention to accurately diagnose objects for image captioning in Bengali. The proposed architecture consists of an encoder and a decoder block. We chose ResNet-50 over the other pre-trained models for encoding the image features due to its ability to solve the vanishing gradient problem and recognize complex object features. For decoding generated captions, a bidirectional Gated Recurrent Unit (GRU) architecture combined with an attention mechanism captures contextual dependencies in both directions, resulting in more accurate captions. The paper also highlights the challenge of transferring knowledge between domains, especially with culturally specific images. Evaluation of three Bengali benchmark datasets, namely BAN-Cap, , BanglaLekhaImageCaption, , and Bornon, , demonstrates significant performance improvement in METEOR score over existing methods by approximately 30%, 18%, and 45%, respectively. The proposed context-aware, attention-based image captioning system significantly outperforms current state-of-the-art models in Bengali caption generation despite limitations in reference captions on certain datasets.

关键词： Image captioning encoder-decoder Computer vision Cross-domain transfer Natural language processing Attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Application of Recurrent Neural Network to Modeling Earth's Global Electron Density

引用

JOURNAL OF GEOPHYSICAL RESEARCH-SPACE PHYSICS 2022年第9期127卷 e2022JA030695-e2022JA030695页

作者： Huang, Sheng Li, Wen Shen, Xiao-Chen Ma, Qianli Chu, Xiangning Ma, Donglai Bortnik, Jacob Capannolo, Luisa Nishimura, Yukitoshi Goldstein, Jerry Boston Univ Ctr Space Phys Boston MA 02215 USA Univ Calif Los Angeles Dept Atmospher & Ocean Sci Los Angeles CA USA Univ Colorado Lab Atmospher & Space Phys Boulder CO 80309 USA Southwest Res Inst Space Sci & Engn Div San Antonio TX USA Univ Texas San Antonio Dept Phys & Astron San Antonio TX USA

The total electron density is a fundamental quantity in the Earth's magnetosphere and plays an important role in a number of physical processes, but its dynamic global evolution is not fully quantified yet. We present an implementation of a specific type of recurrent neural network (encoder-decoder), which is distinct from previous models, to construct global electron density based on the multiyear data from Van Allen Probes. The history of geomagnetic indices is first encoded into a hidden state H, then together with auxiliary information (satellite location), they are decoded into the quantity of interest (total electron density in this study). In this process the input of historical geomagnetic indices is detangled from the satellite location and is processed chronologically by the encoder. As a result, time evolution of geomagnetic indices is explicitly embedded in the structure and the encoded hidden state H can be viewed as the representation of the inner magnetospheric state. The magnetospheric state is then decoded to predict global electron density evolution. Our results show that the model can capture the dynamical evolution of total electron density with the formation and evolution of stable and evident plume configurations that roughly agree with global observations. Our findings demonstrate the importance of applying recurrent neural networks to specify the inner magnetospheric state in a novel way, which will potentially improve our fundamental understanding of wave and particle dynamics in the Earth's magnetosphere.

关键词： electron density deep learning LSTM encoder-decoder plume plasmasphere

来源：评论

学校读者我要写书评

暂无评论

Face From Voice: PyTorch Adaptation of Speech2Face Framework

引用

Procedia Computer Science 2024年 246卷 2892-2901页

作者： Kacper Pietkun Bogna Gondek Marta Browarczyk Faculty of Electronics Telecommunications and Informatics Multimedia Systems Department Gdansk University of Technology Narutowicza 11/12 80-233 Gdańsk Poland

This paper focuses on the problem of face images reconstruction from short audio segments. Built in PyTorch, the speech-to-face pipeline retains the core methodology of the others presented in the previous works, but with the introduction of a few key modifications. Leveraging a comprehensive dataset of internet audio recordings, a deep neural network undergoes training to discern correlations between voice and facial features. Through self-supervised learning, various physical attributes such as age, gender and ethnicity are captured without the need for explicit feature analysis. The evaluation process quantifies the fidelity of reconstructions, measuring the resemblance to the actual facial images solely from audio data through numerical metrics.

关键词： Speech-to-Face encoder-decoder Face generation Audio Spectrogram Transformer Convolutional Neural Networks

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：