检索结果-内蒙古大学图书馆

Lecture Notes in Networks and Systems 2023年 748 LNNS卷 v-vii页

作者： Rolle, José Luis Calvo de Pisón, Francisco Javier Martínez Bringas, Pablo García García, Hilde Pérez Álvarez, Francisco Martínez Lora, Alicia Troncoso Herrero, Álvaro Quintián, Héctor Corchado, Emilio Faculty of Engineering University of Deusto Bilbao Spain Department of Mechanical Engineering University of La Rioja Logroño Spain Data Science and Big Data Lab Pablo de Olavide University Seville Spain Department of Industrial Engineering University of A Coruña A Coruña Spain Faculty of Science University of Salamanca Salamanca Spain School of Industrial Computer and Aerospace Engineering University of Leon León Spain Applied Computational Intelligence University of Burgos Burgos Burgos Spain

来源：评论

学校读者我要写书评

暂无评论

Itg-Yolov7-Based Temperature Extraction Method for Group-Raised Pigs from Thermal Images

SSRN

引用

SSRN 2024年

作者： Liu, Xiaowen Zeng, Xueting Chu, Mengyuan Li, Qian Liu, Gang Li, Tao Ding, Xiangdong Mi, Yang Key Lab of Smart Agriculture Systems Ministry of Education China Agricultural University Beijing100083 China Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture and Rural Affairs of China China Agricultural University Beijing100083 China Henan Fengyuan Hepu Agricultural and Animal Husbandry Co. Ltd Xinyang464000 China College of Animal Science and Technology China Agricultural University Beijing100193 China National Engineering Laboratory for Animal Breeding Laboratory of Animal Genetics Breeding and Reproduction Ministry of Agriculture and Rural Affairs Beijing100193 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China College of Information and Electrical Engineering China Agricultural University China

Body temperature detection in pigs is vital for effective pig breeding, particularly in large-scale farming where traditional infrared thermal imaging methods typically measure only one pig at a time, leading to inefficient monitoring. This study introduces a novel approach for detecting the body temperature of group-raised pigs by leveraging an image super-resolution (SR) algorithm alongside an instantaneous posture recognition technique. The Porcine-ESRGAN was employed to preprocess thermal infrared images, addressing issues related to low resolution and improving the characterization of target areas. Additionally, an improved You Only Look Once v7 (YOLOv7) based on pig posture classification was proposed and named ITG-YOLOv7. ITG-YOLOv7 was developed to identify the instantaneous posture of pigs and measure their ear root or eye temperature. This approach effectively resolves the challenge of obtaining the temperatures of pigs in a lying position. The ITG-YOLOv7 model integrates multi-scale features through the Scale Sequence Feature Fusion (SSFF) module and optimizes parameters with the Generalized Efficient Layer Aggregation Network (GELAN). The incorporation of Shape-IoU as the anchor box loss function enhances bounding box regression accuracy, thus improving detection precision. Performance assessment of Porcine-ESRGAN using thermal infrared images before and after preprocessing. The processed images significantly improve the detection precision (P), recall (R), and mean average precision (mAP) for lying pigs by 3.1%, 11.5%, and 6.9%, indicating that the method is effective in enhancing the temperature detection accuracy for lying pigs. The proposed ITG-YOLOv7 model achieves an accuracy of 93.2%, a R of 95.4%, and an mAP of 97.5% with a model size of 61.9 MB when used in conjunction with the Porcine-ESRGAN model. By correlating maximum head temperatures with ear root and eye temperatures, the maximum head temperature was identified as the body temperature ind

关键词： Thermography (imaging)

来源：评论

学校读者我要写书评

暂无评论

DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN

arXiv

引用

arXiv 2022年

作者： Zhao, Chenwei Ye, Miao Xue, Xingsi Lv, Jianhui Jiang, Qiuxiang Wang, Yong School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Peng Cheng Lab. Guangdong Shenzhen518038 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multi-objective optimization problem, and an intelligent multicast routing algorithm DRL-M4MR based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in SDN. First, the multicast tree state matrix, link bandwidth matrix, link delay matrix, and link packet loss rate matrix are designed as the state space of the DRL agent by combining the global view and control of the SDN. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree under four cases. Third, single-step and final reward function forms are designed to guide the intelligence to make decisions to construct the optimal multicast tree. The experimental results show that, compared with existing algorithms, the multicast tree construct by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Atmospheric fine particles in a typical coastal port of Yangtze River Delta

引用

Journal of Environmental sciences 2020年第12期32卷 62-70页

作者： Qi Yuan Xiaomi Teng Shaoxuan Tu Binxin Feng Zhiyu Wu Hang Xiao Qiuliang Cai Yinxiao Zhang Qiuhan Lin Zhaoce Liu Mengmeng He Xiaokun Ding Weijun Li Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province Department of Atmospheric SciencesSchool of Earth SciencesZhejiang UniversityHangzhou 310027China Center for Excellence in Regional Atmospheric Environment&Key Lab of Urban Environment and Health Institute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China Key Laboratory of Urban Environmental Processes and Pollution Control of Zhejiang Province Ningbo Urban Environment Observation and Research StationChinese Academy of SciencesNingbo 315830China School of Earth Science and Engineering Hebei University of EngineeringHandan 056038China Department of Chemistry Zhejiang UniversityHangzhou 310027China

In recent decades, coastal ports have experienced rapid development and become an important economic and ecological hub in China. Atmospheric particle is a research hotspot in atmospheric environmental sciences in inland regions. However, few studies on the atmospheric particle were conducted in coastal port areas in China, which indeed suffers atmospheric particle pollution. Lack of the physicochemical characteristics of fine particles serves as an obstacle toward the accurate control for air pollution in the coastal port area in China. Here, a field observation was conducted in an important coastal port city in Yangtze River Delta from March 6 to March 19, 2019. The average PM2.5 concentration was 63.7 ±27.8 μg/m^3 and NO3^-, SO4^2-, NH4^+, and organic matter accounted for ?60% of PM 2.5. Fe was the most abundant trace metal element and V as the ship emission indicator was detected. Transmission electron microscopy images showed that SK-rich, soot, Fe, SK-soot and SK-Fe were the major individual particles in the coastal port. V and soluble Fe were detected in sulfate coating of SK-Fe particles. We found that anthropogenic emissions, marine sea salt, and secondary atmosphere process were the major sources of fine particles. Backward trajectory analysis indicated that the dominant air masses were marine air mass, inland air mass from northern Zhejiang and inland-marine mixed air mass from Shandong and Shanghai during the sampling period. The findings can help us better understand the physicochemical properties of atmospheric fine particles in the coastal port of Eastern China.

关键词： Coastal port Water-soluble ion Organic matter Trace metal Individual particle

来源：评论

学校读者我要写书评

暂无评论

A self-supervised mixed-curvature graph neural network

arXiv

引用

arXiv 2021年

作者： Sun, Li Zhang, Zhongbao Ye, Junda Peng, Hao Zhang, Jiawei Su, Sen Yu, Philip S. School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science Beijing University of Posts and Telecommunications China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Ifm Lab Department of Computer Science University of California DavisCA United States Department of Computer Science University of Illinois at Chicago IL United States

Graph representation learning received increasing attentions in recent years. Most of the existing methods ignore the complexity of the graph structures and restrict graphs in a single constant-curvature representation space, which is only suitable to particular kinds of graph structure indeed. Additionally, these methods follow the supervised or semisupervised learning paradigm, and thereby notably limit their deployment on the unlabeled graphs in real applications. To address these aforementioned limitations, we take the first attempt to study the self-supervised graph representation learning in the mixed-curvature spaces. In this paper, we present a novel Self-supervised Mixed-curvature Graph Neural Network (SELFMGNN). To capture the complex graph structures, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces, and design hierarchical attention mechanisms for learning and fusing graph representations across these component spaces. To enable the self-supervisd learning, we propose a novel dual contrastive approach. The constructed mixed-curvature space actually provides multiple Riemannian views for the contrastive learning. We introduce a Riemannian projector to reveal these views, and utilize a welldesigned Riemannian discriminator for the single-view and cross-view contrastive learning within and across the Riemannian views. Finally, extensive experiments show that SELFMGNN captures the complex graph structures and outperforms state-of-the-art baselines. © 2021, CC BY-NC-SA.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

A precision diagnostic framework of renal cell carcinoma on whole-slide images using deep learning

arXiv

引用

arXiv 2021年

作者： Wu, Jialun Zhang, Haichuan Gao, Zeyu Bao, Xinrui Gong, Tieliang Wang, Chunbao Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China School of Electrical Engineering and Computer Science Pennsylvania State University University Park PA United States School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Department of Pathology The First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treatment. Tumor region detection, subtype and grade classification are the fundamental diagnostic indicators for renal cell carcinoma (RCC) in whole-slide images (WSIs). However, pathological diagnosis is subjective, differences in observation and diagnosis between pathologists is common in hospitals with inadequate diagnostic capacity. The main challenge for developing deep learning based RCC diagnostic system is the lack of large-scale datasets with precise annotations. In this work, we proposed a deep learning-based framework for analyzing histopathological images of patients with renal cell carcinoma, which has the potential to achieve pathologist-level accuracy in diagnosis. A deep convolutional neural network (InceptionV3) was trained on the high-quality annotated dataset of The Cancer Genome Atlas (TCGA) whole-slide histopathological image for accurate tumor area detection, classification of RCC subtypes, and ISUP grades classification of clear cell carcinoma subtypes. These results suggest that our framework can help pathologists in the detection of cancer region and classification of subtypes and grades, which could be applied to any cancer type, providing auxiliary diagnosis and promoting clinical consensus. © 2021, CC BY.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

PIMIP: An open source platform for pathology information management and integration

arXiv

引用

arXiv 2021年

作者： Wu, Jialun Mao, Anyu Bao, Xinrui Zhang, Haichuan Gao, Zeyu Wang, Chunbao Gong, Tieliang Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA United States Department of Pathology First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

Digital pathology plays a crucial role in the development of artificial intelligence in the medical field. The digital pathology platform can make the pathological resources digital and networked, and realize the permanent storage of visual data and the synchronous browsing processing without the limitation of time and space. It has been widely used in various fields of pathology. However, there is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections, as well as the management and structured description of relevant patient information. Most platforms cannot integrate image viewing, annotation and analysis, and text information management. To solve the above problems, we propose a comprehensive and extensible platform, PIMIP (Pathology Information Management & Integration Platform). Our PIMIP has developed the image annotation functions based on the visualization of digital pathological sections. Our annotation functions support multi-user collaborative annotation and multi-device annotation, and realize the automation of some annotation tasks. In the annotation task, we invited a professional pathologist for guidance. We introduce a machine learning module for image analysis. The data we collected included public data from local hospitals and clinical examples. Our platform is more clinical and suitable for clinical use. In addition to image data, we also structured the management and display of text information. So our platform is comprehensive. The platform framework is built in a modular way to support users to add machine learning modules independently, which makes our platform extensible. © 2021, CC BY.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

A personalized diagnostic generation framework based on multi-source Heterogeneous data

arXiv

引用

arXiv 2021年

作者： Wu, Jialun Gao, Zeyu Zhang, Haichuan Zhang, Ruonan Gong, Tieliang Wang, Chunbao Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA United States Library of Xi’an Jiaotong University Xi’an China Department of Pathology First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

Personalized diagnoses have not been possible due to sear amount of data pathologists have to bear during the day-today routine. This lead to the current generalized standards that are being continuously updated as new findings are reported. It is noticeable that these effective standards are developed based on a multi-source heterogeneous data, including whole-slide images and pathology and clinical reports. In this study, we propose a framework that combines pathological images and medical reports to generate a personalized diagnosis result for individual patient. We use nuclei-level image feature similarity and content-based deep learning method to search for a personalized group of population with similar pathological characteristics, extract structured prognostic information from descriptive pathology reports of the similar patient population, and assign importance of different prognostic factors to generate a personalized pathological diagnosis result. We use multi-source heterogeneous data from TCGA (The Cancer Genome Atlas) database. The result demonstrate that our framework matches the performance of pathologists in the diagnosis of renal cell carcinoma. This framework is designed to be generic, thus could be applied for other types of cancer. The weights could provide insights to the known prognostic factors and further guide more precise clinical treatment protocols. © 2021, CC BY.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Superpage-Friendly Page Table Design for Hybrid Memory Systems

Superpage-Friendly Page Table Design for Hybrid Memory Syste...

引用

2020国际计算机前沿大会

作者： Xiaoyuan Wang Haikun Liu Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Service Computing Technology and System Lab Huazhong University of Science and Technology Cluster and Grid Computing Lab Huazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology

Page migration has long been adopted in hybrid memory systems comprising dynamic random access memory(DRAM) and non-volatile memories(NVMs), to improve the system performance and energy ***, page migration introduces some side effects, such as more translation lookaside buffer(TLB) misses, breaking memory contiguity, and extra memory accesses due to page table updating. In this paper, we propose superpagefriendly page table called SuperPT to reduce the performance overhead of serving TLB misses. By leveraging a virtual hashed page table and a hybrid DRAM allocator, SuperPT performs address translations in a flexible and efficient way while still remaining the contiguity within the migrated pages.

关键词： Pagetable Hybridmemorysystem Pagemigration Multiplepagesizes Addresstranslation

来源：评论

学校读者我要写书评

暂无评论

A Locality-Aware Energy-Efficient Accelerator for Graph Mining Applications

A Locality-Aware Energy-Efficient Accelerator for Graph Mini...

引用

IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Pengcheng Yao Long Zheng Zhen Zeng Yu Huang Chuangyi Gui Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering UNSW Sydney Australia

ISBN: (数字)9781728173832

ISBN: (纸本)9781728173849

Graph mining is becoming increasingly important due to the ever-increasing demands on analyzing complex structures in graphs. Existing graph accelerators typically hold most of the randomly-accessed data in an on-chip memory to avoid off-chip communications. However, graph mining exhibits substantial random accesses from not only vertex dimension but also edge dimension (with the latter being excessively more complex than the former), leading to significant degradations in terms of both performance and energy *** observe that the most random memory requests arising in graph mining come from accessing a small fraction of valuable (vertex and edge) data when handling real-world graphs. To exploit this extension locality with maximum parallelism, we architect GRAMER, the first graph mining accelerator. GRAMER contains a specialized memory hierarchy, where the valuable data (precisely identified through a cost-efficient heuristic) is permanently resident in a high-priority memory while others are maintained in a cache-like memory under a lightweight replacement policy. The specific pipelined processing units are carefully designed to maximize computational parallelism. GRAMER is also equipped with a work-stealing mechanism to reduce load imbalance. We have implemented GRAMER on a Xilinx Alveo U250 accelerator card. Compared with two state-of-the-art CPU-based graph mining systems, Fractal and RStream, running on a 14-core Intel E5-2680 v4 processor, GRAMER achieves not only considerable speedups (1.11 × ~ 129.95 ) but also significant energy savings (5.79 × ~ 678.34×)

关键词： Memory management Parallel processing Feature extraction Energy efficiency Fractals System-on-chip data mining

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：