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检索条件"任意字段=4th International Conference on Parallel and Distributed Methods for Image Processing IV"

共 46 条记录，以下是1-10 订阅

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引用

4th international conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Zhou, Ya Zhao, Ning Huang, Guimin Deng, Nanxiao Guo, Qingkai School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350337181

In sentence similarity research methods, sentence similarity is often calculated from semantic aspects, however, the influence of other features is ignored. For example, the influence of sentence syntactic structure and word order on sentence similarity is not considered. therefore, this paper proposes a method to measure the degree of similarity between sentences based on a multi-feature fusion of semantics, syntactic similarity and word order similarity, respectively. For the aspect of semantic similarity, in this paper, we first preprocess the sentence and obtain the word vectors, and then obtains sentence vector features through neural networks, so as to calculate the sentence semantic similarity;Finally, for the aspect of word order similarity, word order is considered as the auxiliary feature information to calculate sentence similarity since word order has a smaller effect on sentence similarity compared to the above information. the experimental results show that the proposed model has an accuracy of 77.4 % and an F1 value of 84.3% on the MRPC dataset. © 2023 IEEE.

关键词： Semantics

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U-shaped Dual Attention Transformer: An Efficient Transformer Based on Channel and Spatial Attention 4

U-shaped Dual Attention Transformer: An Efficient Transforme...

引用

4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Zhai, Zhaoyuan Qiao, Peng Li, Rongchun Zhou, Zhen National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798331531225

Transformer-based methods have demonstrated remarkable performance on image super-resolution tasks. Due to high computational complexity, researchers have been working to achieve a balance between computation costs and performance. Restormer has achieved commendable balance by utilizing global channel attention. However, the performance is limited by insufficient local pixel reconstruction. In this paper, we propose a U-shaped Dual Attention Transformer (UDAT) with local-global receptive field, addressing the limitation of Restormer in local pixel reconstruction. We propose a dense window channel attention to enhance the local feature representation, more efficient in computational complexity. Experiments demonstrate that our UDAT achieve superior performance compared with Restormer on benchmark datasets, surpassing Restormer by 0.57 dB on the Urban100 dataset. On-par with SwinIR, our method reduces computational complexity by 3.2 times and improves inference speed by 2 times, achieving a better balance between computational costs and performance. © 2024 IEEE.

关键词： Pixels

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A Comparative Study of SVMs Model Optimized by Machine Learning methods in Water Quality Assessment of Dongting Lake 4

A Comparative Study of SVMs Model Optimized by Machine Learn...

引用

4th international conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Xi, Zhen Xue, Yun Hunan Prov. Key Lab. of Remote Sensing Monitoring of Ecological Environment in Dongting Lake Area Hunan Changsha China School of Municipal and Surveying Engineering Hunan City University Hunan Yiyang China

ISBN: (纸本)9798350337181

this paper presents 15 SVM models optimized by machine learning methods in water quality assessment. through comparative analysis, we found that all the models reached reasonable accuracy during the training and testing process. In particular, SAA-SVM-PI, GWO-SVM-PI, GWO-SVM-TN, PSO-SVM-TP models have the highest accuracy, the highest squared correlation and the lowest mean squared error when testing the three indexes of PI, TN and TP. In order to study the spatial distribution characteristics of water quality in Dongting Lake, GWO-SVM-PI, GWO-SVM-TN and PSO-SVM-TP models were respectively used for single-factor water quality classification. the results showed that: As far as the PI is concerned, the water quality is mainly in grade I and II;As far as TN is concerned, the water quality is mainly of grade iv and V, with the worst water quality in the East Dongting Lake and the outlet profile;In terms of TP, the water quality is mainly of grade III, iv and V, with the best inlet water quality, the worst water quality in the South Dongting Lake. © 2023 IEEE.

关键词： Support vector machines

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Research on Low Dose CBCT image Reconstruction Algorithm Based on Deep Learning 4

Research on Low Dose CBCT Image Reconstruction Algorithm Bas...

引用

4th international conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Liu, Xiangyu Dong, Jiwen Zhu, Jian School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Department of Radiation Physics Technology Jinan250117 China

ISBN: (纸本)9798350337181

Radiation therapy, along with surgery and internal medicine therapy, is one of the three major methods of tumor treatment. To ensure that the actual treatment process is consistent with the radiotherapy plan and make the radiation accurately strikes the tumor, it is necessary to use the Cone Beam Computed Tomography (CBCT) device onboard the medical linear accelerator to quickly obtain CBCT images of the patient's treatment site before implementing radiotherapy. On the one hand, it is used to correct the patient's posture, and on the other hand, it can be used to observe the changes in the patient's tumor and normal tissue structure around the tumor, so as to timely revise the radiotherapy plan. Compared with CT images, CBCT has the characteristics of lower dose and more convenient access. At present, CBCT only has single level ray imaging, and the commonly used CBCT imaging energy is 80 keV, which can avoid patients receiving additional radiation exposure. However, the organ tissue edges of the 80keVCBCT image are relatively blurry and cannot effectively suppress the generation of metal artifacts, making it limited in observing changes in the patient's target area. the use of 140keV imaging can significantly suppress artifacts in the image, improve image quality, and thus improve radiotherapy accuracy, but at the same time, it increases the burden on patients. therefore, we propose a deep learning method. By collecting low-dose 80keVCBCT images, we can reconstruct and generate images closer to 140keV energy level, reduce the radiation exposure received by patients, and at the same time provide radiotherapy technicians with higher quality CBCT images, so as to improve the observation effect of tumors and their surrounding normal tissues, and achieve the purpose of improving the accuracy of radiotherapy. © 2023 IEEE.

关键词： image reconstruction

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Efficient Allocation of image Recognition and LLM Tasks on Multi-GPU System 15th

Efficient Allocation of Image Recognition and LLM Tasks on...

引用

15th international conference on parallel processing and Applied Mathematics, PPAM 2024

作者： Lawenda, Marcin Samborski, Krzesimir Khloponin, Kyrylo Szustak, Lukasz Poznan Supercomputing and Networking Center Jana Pawla II 10 Poznań61-139 Poland Czestochowa University of Technology Dąbrowskiego 69 Częstochowa42-201 Poland

ISBN: (纸本)9783031857027

this work is concerned with the evaluation of the performance of parallelization of learning and tuning processes for image classification and large language models. For machine learning model in image recognition, various parallelization methods are developed based on different hardware and software scenarios: simple data parallelism, distributed data parallelism, and distributed processing. A detailed description of presented strategies is given, highlighting the challenges and benefits of their application. Furthermore, the impact of different dataset types on the tuning process of large language models is investigated. Experiments show to what extent the task type affects the iteration time in a multi-GPU environment, offering valuable insights into the optimal data utilization strategies to improve model performance. Furthermore, this study leverages the built-in parallelization mechanisms of PyTorch that can facilitate these tasks. Furthermore, performance profiling is incorporated into the study to thoroughly evaluate the impact of memory and communication operations during the training/tuning procedure. Test scenarios are developed and tested with numerous benchmarks on the NVIDIA H100 architecture showing efficiency through selected metrics. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Research on parallel Detection Technology of Remote Sensing Object Based on Deep Learning 4

Research on Parallel Detection Technology of Remote Sensing ...

引用

4th international conference on Intelligent Autonomous Systems (ICoIAS)

作者： Zhang, Chengguang Zhang, Xuebo Jiang, Min Space Engn Univ Sch Grad Beijing Peoples R China Space Engn Univ Sch Space Informat Beijing Peoples R China

ISBN: (纸本)9781665441957

In recent years, methods based on deep learning have become a hot spot and trend in the field of remote sensing object detection, and a series of encouraging results have been achieved. With the development of science and technology, people's technology and ability to acquire remote sensing data have been comprehensively improved, and high-resolution large-scale remote sensing image data has increased dramatically. However, the current mainstream object detection models cannot directly input large-scale high-resolution images for prediction. this paper proposes a remote sensing object parallel detection algorithm, which uses the mpi4py module to realize multi-CPU+GPU distributed parallel processing. Based on the yolov4 object detection model, it can detect remote sensing images of any scale without reducing the prediction accuracy. And shorten the object detection time according to the number of distributed nodes. the experimental results show that the parallel algorithm has a high speedup ratio, and the parallel detection technology has a good development prospect in the field of remote sensing image object detection.

关键词： remote sensing object detention deep learning parallel processing

来源：评论

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引用

Information Science, parallel and distributed Systems (ISPDS), international conference on

作者： Ya Zhou Ning Zhao Guimin Huang Nanxiao Deng Qingkai Guo Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

In sentence similarity research methods, sentence similarity is often calculated from semantic aspects, however, the influence of other features is ignored. For example, the influence of sentence syntactic structure and word order on sentence similarity is not considered. therefore, this paper proposes a method to measure the degree of similarity between sentences based on a multi-feature fusion of semantics, syntactic similarity and word order similarity, respectively. For the aspect of semantic similarity, in this paper, we first preprocess the sentence and obtain the word vectors, and then obtains sentence vector features through neural networks, so as to calculate the sentence semantic similarity; Finally, for the aspect of word order similarity, word order is considered as the auxiliary feature information to calculate sentence similarity since word order has a smaller effect on sentence similarity compared to the above information. the experimental results show that the proposed model has an accuracy of 77.4 % and an F1 value of 84.3% on the MRPC dataset.

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A Comparative Study of SVMs Model Optimized by Machine Learning methods in Water Quality Assessment of Dongting Lake

A Comparative Study of SVMs Model Optimized by Machine Learn...

引用

Information Science, parallel and distributed Systems (ISPDS), international conference on

作者： Zhen Xi Yun Xue Hunan Provincial Key Laboratory of Remote Sensing Monitoring of Ecological Environment in Dongting Lake Area Changsha Hunan China School of Municipal and Surveying Engineering Hunan City University Yiyang Hunan China

this paper presents 15 SVM models optimized by machine learning methods in water quality assessment. through comparative analysis, we found that all the models reached reasonable accuracy during the training and testing process. In particular, SAA-SVM-PI, GWO-SVM-PI, GWO-SVM-TN, PSO-SVM-TP models have the highest accuracy, the highest squared correlation and the lowest mean squared error when testing the three indexes of PI, TN and TP. In order to study the spatial distribution characteristics of water quality in Dongting Lake, GWO-SVM-PI, GWO-SVM-TN and PSO-SVM-TP models were respectively used for single-factor water quality classification. the results showed that: As far as the PI is concerned, the water quality is mainly in grade I and II; As far as TN is concerned, the water quality is mainly of grade iv and V, with the worst water quality in the East Dongting Lake and the outlet profile; In terms of TP, the water quality is mainly of grade III, iv and V, with the best inlet water quality, the worst water quality in the South Dongting Lake.

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Research on Low Dose CBCT image Reconstruction Algorithm Based on Deep Learning

Research on Low Dose CBCT Image Reconstruction Algorithm Bas...

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Information Science, parallel and distributed Systems (ISPDS), international conference on

作者： Xiangyu Liu Jiwen Dong Jian Zhu School of Information Science and Engineering University of Jinan Jinan China Department of Radiation Physics Technology Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences Jinan China

关键词：

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U-shaped Dual Attention Transformer: An Efficient Transformer Based on Channel and Spatial Attention

U-shaped Dual Attention Transformer: An Efficient Transforme...

引用

Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE international conference on

作者： Zhaoyuan Zhai Peng Qiao Rongchun Li Zhen Zhou National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

关键词： Visualization Superresolution Noise reduction Transform coding Transformers image restoration Computational complexity Spatial resolution image reconstruction Robots

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