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FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting FDNet:一种具有两个平行交叉编码路径的降水临近预报深度学习方法
IF 1.9 3区 计算机科学
Journal of Computer Science and Technology Pub Date : 2023-09-30 DOI: 10.1007/s11390-021-1103-8
Bi-Ying Yan, Chao Yang, Feng Chen, Kohei Takeda, Changjun Wang
{"title":"FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting","authors":"Bi-Ying Yan, Chao Yang, Feng Chen, Kohei Takeda, Changjun Wang","doi":"10.1007/s11390-021-1103-8","DOIUrl":"https://doi.org/10.1007/s11390-021-1103-8","url":null,"abstract":"<p>With the goal of predicting the future rainfall intensity in a local region over a relatively short period time, precipitation nowcasting has been a long-time scientific challenge with great social and economic impact. The radar echo extrapolation approaches for precipitation nowcasting take radar echo images as input, aiming to generate future radar echo images by learning from the historical images. To effectively handle complex and high non-stationary evolution of radar echoes, we propose to decompose the movement into optical flow field motion and morphologic deformation. Following this idea, we introduce Flow-Deformation Network (FDNet), a neural network that models flow and deformation in two parallel cross pathways. The flow encoder captures the optical flow field motion between consecutive images and the deformation encoder distinguishes the change of shape from the translational motion of radar echoes. We evaluate the proposed network architecture on two real-world radar echo datasets. Our model achieves state-of-the-art prediction results compared with recent approaches. To the best of our knowledge, this is the first network architecture with flow and deformation separation to model the evolution of radar echoes for precipitation nowcasting. We believe that the general idea of this work could not only inspire much more effective approaches but also be applied to other similar spatio-temporal prediction tasks.</p>","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138540056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Accurate Robotic Grasp Detection with Angular Label Smoothing 基于角标记平滑的精确机器人抓取检测
IF 1.9 3区 计算机科学
Journal of Computer Science and Technology Pub Date : 2023-09-30 DOI: 10.1007/s11390-022-1458-5
Min Shi, Hao Lu, Zhao-Xin Li, Deng-Ming Zhu, Zhao-Qi Wang
{"title":"Accurate Robotic Grasp Detection with Angular Label Smoothing","authors":"Min Shi, Hao Lu, Zhao-Xin Li, Deng-Ming Zhu, Zhao-Qi Wang","doi":"10.1007/s11390-022-1458-5","DOIUrl":"https://doi.org/10.1007/s11390-022-1458-5","url":null,"abstract":"<p>Grasp detection is a visual recognition task where the robot makes use of its sensors to detect graspable objects in its environment. Despite the steady progress in robotic grasping, it is still difficult to achieve both real-time and high accuracy grasping detection. In this paper, we propose a real-time robotic grasp detection method, which can accurately predict potential grasp for parallel-plate robotic grippers using RGB images. Our work employs an end-to-end convolutional neural network which consists of a feature descriptor and a grasp detector. And for the first time, we add an attention mechanism to the grasp detection task, which enables the network to focus on grasp regions rather than background. Specifically, we present an angular label smoothing strategy in our grasp detection method to enhance the fault tolerance of the network. We quantitatively and qualitatively evaluate our grasp detection method from different aspects on the public Cornell dataset and Jacquard dataset. Extensive experiments demonstrate that our grasp detection method achieves superior performance to the state-of-the-art methods. In particular, our grasp detection method ranked first on both the Cornell dataset and the Jacquard dataset, giving rise to the accuracy of 98.9% and 95.6%, respectively at real-time calculation speed.</p>","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138540007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Reinvent Cloud Software Stacks for Resource Disaggregation 为资源分解重新设计云软件堆栈
IF 1.9 3区 计算机科学
Journal of Computer Science and Technology Pub Date : 2023-09-30 DOI: 10.1007/s11390-023-3272-0
Chen-Xi Wang, Yi-Zhou Shan, Peng-Fei Zuo, Hui-Min Cui
{"title":"Reinvent Cloud Software Stacks for Resource Disaggregation","authors":"Chen-Xi Wang, Yi-Zhou Shan, Peng-Fei Zuo, Hui-Min Cui","doi":"10.1007/s11390-023-3272-0","DOIUrl":"https://doi.org/10.1007/s11390-023-3272-0","url":null,"abstract":"<p>Due to the unprecedented development of low-latency interconnect technology, building large-scale disaggregated architecture is drawing more and more attention from both industry and academia. Resource disaggregation is a new way to organize the hardware resources of datacenters, and has the potential to overcome the limitations, e.g., low resource utilization and low reliability, of conventional datacenters. However, the emerging disaggregated architecture brings severe performance and latency problems to the existing cloud systems. In this paper, we take memory disaggregation as an example to demonstrate the unique challenges that the disaggregated datacenter poses to the existing cloud software stacks, e.g., programming interface, language runtime, and operating system, and further discuss the possible ways to reinvent the cloud systems.</p>","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138540011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Ontology Driven Semantic Campus Map Application for NJUST 基于本体驱动的语义校园地图应用
IF 1.9 3区 计算机科学
Journal of Computer Science and Technology Pub Date : 2023-09-08 DOI: 10.57237/j.cst.2023.03.004
Jiayuan Wang, Qianru Zhou
{"title":"Ontology Driven Semantic Campus Map Application for NJUST","authors":"Jiayuan Wang, Qianru Zhou","doi":"10.57237/j.cst.2023.03.004","DOIUrl":"https://doi.org/10.57237/j.cst.2023.03.004","url":null,"abstract":"","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76237765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Towards Low-light Image Restoration via Color Correction Matrix Learning 基于颜色校正矩阵学习的弱光图像恢复
IF 1.9 3区 计算机科学
Journal of Computer Science and Technology Pub Date : 2023-09-08 DOI: 10.57237/j.cst.2023.03.003
Muhammad Tahir Rasheed, Daming Shi
{"title":"Towards Low-light Image Restoration via Color Correction Matrix Learning","authors":"Muhammad Tahir Rasheed, Daming Shi","doi":"10.57237/j.cst.2023.03.003","DOIUrl":"https://doi.org/10.57237/j.cst.2023.03.003","url":null,"abstract":"","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74291212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Disaggregated Datacenters for Future Cloud Computing 未来云计算的分散数据中心
IF 1.9 3区 计算机科学
Journal of Computer Science and Technology Pub Date : 2023-09-01 DOI: 10.1007/s11390-023-0006-2
Zhi-Wei Xu
{"title":"Disaggregated Datacenters for Future Cloud Computing","authors":"Zhi-Wei Xu","doi":"10.1007/s11390-023-0006-2","DOIUrl":"https://doi.org/10.1007/s11390-023-0006-2","url":null,"abstract":"","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139343965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
CA-DTS: A Distributed and Collaborative Task Scheduling Algorithm for Edge Computing Enabled Intelligent Road Network CA-DTS：边缘计算智能路网的分布式协作任务调度算法
IF 1.9 3区 计算机科学
Journal of Computer Science and Technology Pub Date : 2023-09-01 DOI: 10.1007/s11390-023-2839-0
Shi-Hong Hu, Qu-Yuan Luo, Guang-Hui Li, Weisong Shi, Bao-Liu Ye
{"title":"CA-DTS: A Distributed and Collaborative Task Scheduling Algorithm for Edge Computing Enabled Intelligent Road Network","authors":"Shi-Hong Hu, Qu-Yuan Luo, Guang-Hui Li, Weisong Shi, Bao-Liu Ye","doi":"10.1007/s11390-023-2839-0","DOIUrl":"https://doi.org/10.1007/s11390-023-2839-0","url":null,"abstract":"","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139344404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Identification and Optimization of Big Data Mining Technology for Digital Economy Characteristics 面向数字经济特征的大数据挖掘技术识别与优化
IF 1.9 3区 计算机科学
Journal of Computer Science and Technology Pub Date : 2023-08-29 DOI: 10.57237/j.cst.2023.03.002
Yanchao Li
{"title":"Identification and Optimization of Big Data Mining Technology for Digital Economy Characteristics","authors":"Yanchao Li","doi":"10.57237/j.cst.2023.03.002","DOIUrl":"https://doi.org/10.57237/j.cst.2023.03.002","url":null,"abstract":"","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82558487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation 利用自系综和自蒸馏改进BERT微调
3区 计算机科学
Journal of Computer Science and Technology Pub Date : 2023-07-01 DOI: 10.1007/s11390-021-1119-0
Yige Xu, Xipeng Qiu, Ligao Zhou, Xuanjing Huang
{"title":"Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation","authors":"Yige Xu, Xipeng Qiu, Ligao Zhou, Xuanjing Huang","doi":"10.1007/s11390-021-1119-0","DOIUrl":"https://doi.org/10.1007/s11390-021-1119-0","url":null,"abstract":"Fine-tuning pre-trained language models like BERT has become an effective way in NLP and yields state-of-the-art results on many downstream tasks. Recent studies on adapting BERT to new tasks mainly focus on modifying the model structure, re-designing the pre-train tasks, and leveraging external data and knowledge. The fine-tuning strategy itself has yet to be fully explored. In this paper, we improve the fine-tuning of BERT with two effective mechanisms: self-ensemble and self-distillation. The experiments on text classification and natural language inference tasks show our proposed methods can significantly improve the adaption of BERT without any external data or knowledge.","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135846006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 56
Real-Time Underwater Image Enhancement Using Adaptive Full-Scale Retinex 使用自适应全尺寸视网膜的实时水下图像增强
3区 计算机科学
Journal of Computer Science and Technology Pub Date : 2023-07-01 DOI: 10.1007/s11390-022-1115-z
Xing-Gui Xu, Xiang-Suo Fan, Yong-Li Liu
{"title":"Real-Time Underwater Image Enhancement Using Adaptive Full-Scale Retinex","authors":"Xing-Gui Xu, Xiang-Suo Fan, Yong-Li Liu","doi":"10.1007/s11390-022-1115-z","DOIUrl":"https://doi.org/10.1007/s11390-022-1115-z","url":null,"abstract":"","PeriodicalId":50222,"journal":{"name":"Journal of Computer Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135856841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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