检索结果-内蒙古大学图书馆

Boosting the Electrochemical Performance of Li-and Mn-Rich Cathodes by a Three-in-One Strategy

Nano-Micro Letters 2021年第12期13卷 311-321页

作者： Wei He Fangjun Ye Jie Lin Qian Wang Qingshui Xie Fei Pei Chenying Zhang Pengfei Liu Xiuwan Li Laisen Wang Baihua Qu Dong-Liang Peng State Key Lab of Physical Chemistry of Solid Surface Collaborative Innovation Center of Chemistry for Energy MaterialsCollege of Materials and Pen-Tung Sah Institute of Micro-Nano Science and TechnologyXiamen UniversityXiamen 361005People’s Republic of China College of Chemistry and Chemical Engineering Xiamen UniversityXiamen 361005People’s Republic of China Zhengzhou Key Laboratory of Big Data Analysis and Application Henan Academy of Big DataZhengzhou UniversityZhengzhou 450002People’s Republic of China Fujian Provincial Key Laboratory of Light Propagation and Transformation College of Information Science and EngineeringHuaqiao UniversityXiamen 361021People’s Republic of China Shenzhen Research Institute of Xiamen University Shenzhen 518000People’s Republic of China

There are plenty of issues need to be solved before the practi-cal application of Li-and Mn-rich cathodes,including the detrimental voltage decay and mediocre rate capability,*** doping can e ectively solve the above problems,but cause the loss of *** introduction of appropriate defects can compensate the capacity loss;however,it will lead to structural mismatch and stress ***,a three-in-one method that combines cation–polyanion co-doping,defect construction,and stress engineering is *** co-doped Na^(+)/SO_(4)^(2-)can stabilize the layer framework and enhance the capacity and voltage *** induced defects would activate more reac-tion sites and promote the electrochemical ***,the unique alternately distributed defect bands and crystal bands structure can alleviate the stress accumulation caused by changes of cell parameters upon ***,the modified sample retains a capacity of 273 mAh g^(-1)with a high-capacity retention of 94.1%after 100 cycles at 0.2 C,and 152 mAh g^(-1)after 1000 cycles at 2 C,the corresponding voltage attenuation is less than 0.907 mV per cycle.

关键词： Li-and Mn-rich cathodes Cation–polyanion co-doping Defect and stress engineering Good structure stability Electrochemical performance

来源：评论

学校读者我要写书评

暂无评论

Detection of scab in wheat ears using in situ hyperspectral data and support vector machine optimized by genetic algorithm

引用

International Journal of Agricultural and Biological engineering 2020年第2期13卷 182-188页

作者： Linsheng Huang Hansu Zhang Chao Ruan Wenjiang Huang Tingguang Hu Jinling Zhao National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefeiChina Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina

A new method was proposed to extract sensitive features and to construct a monitoring model for wheat scab based on in situ hyperspectral data of wheat ears to achieve effective prevention and control and provide theoretical support for its large-scale *** sensitive features were selected through correlation analysis and wavelet *** features were as follows:three original bands of 350-400 nm,500-600 nm,and 720-1000 nm;three vegetation indices of modified simple ratio(MSR),normalized difference vegetation index,and structural independent pigment index;and two wavelet features of WF01 and *** combining the selected sensitive features with support vector machine(SVM)and SVM optimized by genetic algorithm(GASVM),a total of 16 monitoring models were built,and the monitoring accuracies of the two types of models were *** ability of the monitoring models built by GASVM to identify scab was better than that of SVM algorithm under the same characteristic *** the 16 models,MSR combined with GASVM had an overall accuracy of 75%and a Kappa coefficient of *** can be used to monitor wheat scab and its application can improve the accuracy of disease monitoring.

关键词： wheat scab hyperspectral data correlation analysis genetic algorithm wavelet transform support vector machine

来源：评论

学校读者我要写书评

暂无评论

Medical image super-resolution algorithm based on multi-scale feature aggregation

Medical image super-resolution algorithm based on multi-scal...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xinyu Han Xun Gong School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Manufacturing Industry Chains Collaboration and InformationSupport Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

Ultrasound images are vital for medical diagnostics but often suffer from information loss and blurred details due to limitations in imaging systems and sensor technologies. Many researchers have proposed super-resolution algorithms to enhance medical images. But existing super-resolution methods struggle with the uneven distribution of noise and clarity. To address this, we propose a super-resolution algorithm for medical images based on multi-scale feature aggregation Leveraging the architecture of Unet as our primary framework, our method enhances output details through multi-scale hole convolutions. Taking into consideration the characteristics of ultrasonic images, we propose a frequency domain-based module to enhances edge information while effectively denoising the image. Furthermore, to improve the quality of the output images, we introduce an image processing module grounded in global information. The module ensures clarity while considering the overarching context, thereby preserving global consistency and enhancing output quality. We experiment on three datasets to demonstrate the effectiveness of our model. Additionally, significant improvements in medical image segmentation are observed, proving the practicability of our proposed approach.

关键词： Image segmentation Ultrasonic imaging Image edge detection Frequency-domain analysis Superresolution Noise reduction Noise Medical diagnosis Artificial intelligence Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Causal Deep Learning for Real-Time Detection of Cardiac Surgery-Associated Acute Kidney Injury: Derivation and Validation in Seven Time-Series Cohorts

SSRN

引用

SSRN 2024年

作者： Zhong, Qin Cheng, Yuxiao Li, Zongren Wang, Dongjin Rao, Chongyou Jiang, Yi Li, Lianglong Wang, Ziqian Liu, Pan Che, Hebin Li, Pei Lv, Xin Suo, Jinli He, Kunlun Medical Big Data Research Center Chinese PLA General Hospital Beijing China National Engineering Research Center of Medical Big Data Application Technology Chinese PLA General Hospital Beijing China Department of Automation Tsinghua University Beijing China Department of Cardiovascular Surgery Nanjing Drum Tower Hospital Chinese Academy of Medical Science Peking Union Medical College Nanjing China College of Systems Engineering National University of Defense Technology Changsha China Beijing Key Laboratory of Chronic Heart Failure Precision Medicine China

Background: Cardiac surgery-associated Acute Kidney Injury (CSA-AKI) is a complex complication significantly contributing to an increased risk of mortality. Effective CSA-AKI management relies on timely diagnosis and interventions. However, many cases are detected too late. Despite the advancements in novel biomarkers and data-driven predictive models, existing practices are primarily constrained due to the limited discriminative and generalization capabilities and stringent application requirements, presenting significant challenges to the timely and effective diagnosis and interventions in CSA-AKI management. Methods: This retrospective model development and prospective validation study aimed to develop a causal deep learning architecture, named REACT, to achieve precise and dynamic predictions of CSA-AKI within the subsequent 48 hours. This study included adult'patients (aged ≥18 years) from seven distinct cohorts undergoing major open heart surgery for model training and validation. data for model development and internal validation were sourced from electronic health records (EHR) of two large centers in Beijing, China, from January 2000 to December 2022. External validation was conducted on the EHRs of three independent centers in China between January 2000 and December 2022, along with cross-national data from the public databases MIMIC-IV and eICU in the United States. To facilitate implementation, we also developed a publicly accessible web calculator and applet. The model's prospective application was validated from June to October 2023 at two centers in Beijing and Nanjing. Findings: The final derivation cohort included 14,513 eligible patients with no critical information missing, a median age of 56 years, 38·0% women, and 21·0% developed CSA-AKI. The external validation dataset included 20,813 patients from China and 28,023 from the United States. REACT reduced 1,325 input variables to six essential "causal" factors for CSA-AKI prediction. In internal v

关键词： Cardiovascular surgery

来源：评论

学校读者我要写书评

暂无评论

EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced Graph Sample and Aggregate Inductive Learning Algorithm

EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced ...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuejia Wu Jian-Tao Zhou Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

关键词： Training Knowledge engineering Convolution Aggregates Optimization methods Knowledge graphs Learning (artificial intelligence)

来源：评论

学校读者我要写书评

暂无评论

Finding Simplex Items in data Streams

Finding Simplex Items in Data Streams

引用

International Conference on data engineering

作者： Zhuochen Fan Jiarui Guo Xiaodong Li Tong Yang Yikai Zhao Yuhan Wu Bin Cui Yanwei Xu Steve Uhlig Gong Zhang School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China Theory Lab Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. Hong Kong SAR China School of Electronic Engineering and Computer Science Queen Mary University of London London UK

In this paper, we propose a new type of item in data streams, called simplex items. Simplex items have frequencies in consecutive p windows that can be approximated by a polynomial of degree at most k, where k = 0, 1, 2. These low-order representable simplex items have a wide range of potential applications. For example, when k = 1, we can leverage these items whose frequency has obvious linear increase or decrease to speed up the running time of a class of machine learning models and detect network attacks such as distributed denial-of-service (DDoS), etc. To find k-degree simplex items in real time, we propose a novel sketch, namely X-Sketch, to accurately record simplex items in a compact space. The key idea of X-Sketch is to effectively filter out non-simplex items with less memory overhead, and then monitor the remaining potential simplex items and keep those items with more consecutive windows. We conduct extensive experiments, and the experimental results show that the F1 Score of X-Sketch is on average 68.6%, 57.9%, and 42.2% higher than the baseline solution for k = 0, 1, 2, respectively. Finally, we also provide a case study that applies X-Sketch to "accelerate" the two machine learning models through end-to-end experiments. We have released our source code at GitHub.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning

arXiv

引用

arXiv 2024年

作者： Wang, Tianfu Shen, Li Fan, Qilin Xu, Tong Liu, Tongliang Xiong, Hui Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science University of Science and Technology of China Hefei230027 China The School of Big Data and Software Engineering Chongqing University ChinaScience Chongqing401331 China JD Explore Academy The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney DarlingtonNSW2008 Australia Thrust of Artificial Intelligence Nansha Guangdong Guangzhou511400 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

As an essential resource management problem in network virtualization, virtual network embedding (VNE) aims to allocate the finite resources of physical network to sequentially arriving virtual network requests (VNRs) with different resource demands. Since this is an NP-hard combinatorial optimization problem, many efforts have been made to provide viable solutions. However, most existing approaches have either ignored the admission control of VNRs, which has a potential impact on long-term performances, or not fully exploited the temporal and topological features of the physical network and VNRs. In this paper, we propose a deep Hierarchical Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for VNE, named HRL-ACRA. Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the requirement of VNR through the HRL approach. Considering the proximal policy optimization as the basic training algorithm, we also adopt the average reward method to address the infinite horizon problem of the upper-level agent and design a customized multi-objective intrinsic reward to alleviate the sparse reward issue of the lower-level agent. Moreover, we develop a deep feature-aware graph neural network to capture the features of VNR and physical network and exploit a sequence-to-sequence model to generate embedding actions iteratively. Finally, extensive experiments are conducted in various settings, and show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue. Our code is available at https://***/GeminiLight/hrl-acra. Copyright © 2024, 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

Research of mask wearing detection method using Improved YOLO network

Research of mask wearing detection method using Improved YOL...

引用

Chinese Control and Decision Conference, CCDC

作者： Fei Xiao Zixi Liu Shengbin Zhao Shuai Yuan School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China Daqing Oilfield Limited Company DaQing China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Mask wearing detection technology is a practical and research-oriented area within object detection. Due to the complexity of mask wearing detection scenarios, issues such as small target detection and low image clarity often result in missed detections and false alarms. The YOLO algorithm, known for its fast detection speed, is commonly used in mask wearing detection. However, the original YOLO algorithm struggles to achieve satisfactory results in dense crowd scenarios. Therefore, this paper proposes an improved YOLOv5 algorithm based on the BiFPN structure and incorporating attention mechanisms to enhance the accuracy of mask wearing detection while maintaining real-time performance. The proposed approach firstly introduces the Bidirectional Feature Pyramid (BiFPN) structure into the backbone network to enhance the feature extraction capability. Additionally, attention mechanisms are incorporated to filter out irrelevant information, conserve computational resources, and improve the detection performance for small targets. Experimental results on a custom dataset demonstrate that the YOLOv5_BiFPN algorithm achieves a 4% improvement in accuracy and an 11.7% increase in mAP50 compared to the original YOLOv5. Moreover, the inclusion of attention mechanisms further boosts the accuracy by an additional 1.6%. These findings confirm that the improved YOLOv5s model exhibits superior performance in the task of mask wearing detection.

关键词： YOLO Attention mechanisms Accuracy Filtering algorithms Feature extraction Information filters Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Learning Synergistic Attention for Light Field Salient Object Detection 32

Learning Synergistic Attention for Light Field Salient Objec...

引用

32nd British Machine Vision Conference, BMVC 2021

作者： Zhang, Yi Chen, Geng Chen, Qian Sun, YuJia Xia, Yong Deforges, Olivier Hamidouche, Wassim Zhang, Lu France National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University China School of Information Science and Technology University of Science and Technology of China China School of Computer Science Inner Mongolia University China

In this work, we propose Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms. Our SA-Net exploits the rich information of focal stacks via 3D convolutional neural networks, decodes the high-level features of multi-modal light field data with two cascaded synergistic attention modules, and predicts the saliency map using an effective feature fusion module in a progressive manner. Extensive experiments on three widely-used benchmark datasets show that our SA-Net outperforms 28 state-of-the-art models, sufficiently demonstrating its effectiveness and superiority. Our code is available at https://***/PanoAsh/SA-Net. © 2021. The copyright of this document resides with its authors.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Dual Attention Guided R2 U-Net Architecture for Right Ventricle Segmentation in MRI Images 11th

Dual Attention Guided R2 U-Net Architecture for Right Ventri...

引用

11th International Conference on Image and Graphics, ICIG 2021

作者： Jiang, Lei Cui, Hengfei Yuwen, Chang Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground -Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710072 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9783030873578

Right ventricle segmentation plays an important role in the computer-aided diagnosis of heart diseases. However, due to the small area of right ventricle and limited dataset, the performances of the existing deep learning segmentation methods are not good enough. For some small areas of right ventricle that are difficult to segment, we apply a novel dual attention module on the decoding path of Dilated R2 U-net to extract better feature representations in this work. The dual attention module in this work is divided into position attention module and channel attention module. The positional attention module suppresses the irrelevant feature representations in the feature map and enhances the useful feature representations to improve the sensitivity and prediction accuracy of the model. The channel attention module enhances the interdependence of the feature representation of channels by gathering the information of the associated channels in the feature map. We use dilated convolutions to expand the receptive field of the model. By adding dual attention modules, our model shows higher precision than Dilated U-net on the Right Ventricle Segmentation Challenge (RVSC) test dataset. © 2021, Springer Nature Switzerland AG.

关键词： Computer aided diagnosis

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：