检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Tang, Wenhao Huang, Sheng Zhang, Xiaoxian Huangfu, Luwen School of Big Data & Software Engineering Chongqing University Chongqing China School of Big Data & Software Engineering Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing China Fowler College of Business Center for Human Dynamics in the Mobile Age San Diego State University San DiegoCA United States

Automatic pavement distress classification facilitates improving the efficiency of pavement maintenance and reducing the cost of labor and resources. A recently influential branch of this task divides the pavement image into patches and infers the patch labels for addressing these issues from the perspective of multi-instance learning. However, these methods neglect the correlation between patches and suffer from a low efficiency in the model optimization and inference. As a representative approach of vision Transformer, Swin Transformer is able to address both of these issues. It first provides a succinct and efficient framework for encoding the divided patches as visual tokens, then employs self-attention to model their relations. Built upon Swin Transformer, we present a novel vision Transformer named Pavement Image Classification Transformer (PicT) for pavement distress classification. In order to better exploit the discriminative information of pavement images at the patch level, the Patch Labeling Teacher is proposed to leverage a teacher model to dynamically generate pseudo labels of patches from image labels during each iteration, and guides the model to learn the discriminative features of patches via patch label inference in a weakly supervised manner. The broad classification head of Swin Transformer may dilute the discriminative features of distressed patches in the feature aggregation step due to the small distressed area ratio of the pavement image. To overcome this drawback, we present a Patch Refiner to cluster patches into different groups and only select the highest distress-risk group to yield a slim head for the final image classification. We evaluate our method on a large-scale bituminous pavement distress dataset named CQU-BPDD. Extensive results demonstrate the superiority of our method over baselines and also show that PicT outperforms the second-best performed model by a large margin of +2.4% in P@R on detection task, +3.9% in F 1 on recogni

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Demand Response Scheme: Achieving Sustainability in Distributed data Processing

Demand Response Scheme: Achieving Sustainability in Distribu...

引用

IEEE International Conference on data Science in Cyberspace (DSC)

作者： Xueying Zhai Yunfeng Peng Xiuping Guo Liang Zhang Wei Zhang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Guizhou Key Laboratory of Electric Power Big Data Guizhou Institute of Technology Guizhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization.

关键词： Renewable energy sources Energy consumption Costs Electricity Simulation Carbon dioxide Spatiotemporal phenomena Internet of Things Sustainable development Optimization

来源：评论

学校读者我要写书评

暂无评论

Aspect-Based Capsule Network With Mutual Attention for Recommendations

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2021年第3期2卷 228-237页

作者： Yang, Zhenyu Wang, Xuesong Cheng, Yuhu Liu, GuoJing The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The Qilu University of Technology Shandong Academy of Sciences Software Integration Institute Jinan250353 China The Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The School of Computer Science and Technology Qilu University of Technology Jinan250353 China

Review text is a valuable source of information for recommendation systems and often contains rich semantics with user preferences and item attributes. Recently, mainstream recommendation approaches have been using deep learning techniques to utilize review text. These efforts employ an affinity matrix to model the correlations between users and items and in turn aggregate the contextual features of users and items to form latent representations with some interpretability. However, constructing this simple interaction does not capture the complex correlations between users and items. Furthermore, a single representation of latent features is not sufficient to express user preferences and item attributes in reviews with multiple granularities. To this end, we propose an aspect-based capsule network and mutual attention for recommendation systems, named Acapnet. We design a novel contextual mutual attention mechanism for modeling the fine-grained correlations in the contextual features of users and items. We employ reverse dynamic routing to aggregate user and item context features into aspect features (capsules) for rating predictions;the vector length of a capsule can be used as a basis for estimating aspect importance. Extensive experiments are conducted on five real-world datasets with different features. The results show that the proposed Acapnet is superior to recent state-of-the-art models in terms of performance. Further analysis reveals that our model effectively mitigates the homogeneity among various aspect capsules with strong interpretability. © 2021 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Deep Fusion of Shifted MLP and CNN for Medical Image Segmentation

Deep Fusion of Shifted MLP and CNN for Medical Image Segment...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chengyu Yuan Hao Xiong Guoqing Shangguan Hualei Shen Dong Liu Haojie Zhang Zhonghua Liu Kun Qian Bin Hu Björn W. Schuller Yoshiharu Yamamoto Shlomo Berkovsky College of Computer and Information Engineering Henan Normal University China Key Laboratory of Artificial Intelligence and Personalized Learning in Education Henan China Big Data Engineering Lab of Teaching Resources & Assessment of Education Quality Henan China Centre for Health Informatics Australian Institute of Health Innovation Macquarie University Australia Key Laboratory of Brain Health Intelligent Evaluation and Intervention (BIT) Ministry of Education China School of Medical Technology Beijing Institute of Technology China School of Information Engineering Zhejiang Ocean University China GLAM – Group on Language Audio & Music Imperial College London U.K. Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Educational Physiology Laboratory Graduate School of Education The University of Tokyo Japan

Medical image segmentation is an important task in modern analysis of medical images. Current methods tend to extract either local features with convolutions or global features with Transformers. However, few of them are able to effectively fuse global and local features to facilitate segmentation. In this work, we propose a novel hybrid network that involves three main branches: the Multi-Layer Perception (MLP) branch, the Convolutional Neural Network (CNN) branch, and a Fusion branch. The MLP and CNN branches aim to learn global and local features, respectively. To fuse these, the fusion branch introduces a novel hierarchical fusion that performs multi-layered fusions that generate high-level representations to enhance segmentation. Our evaluation with two datasets shows strong performance of the proposed method compared to state-of-the-art baselines.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserved Neural Graph Similarity Learning

arXiv

引用

arXiv 2022年

作者： Hou, Yupeng Zhao, Wayne Xin Li, Yaliang Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China China Alibaba Group China Beijing Key Laboratory of Big Data Management and Analysis Methods China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education

To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years. Although GSL models are frequently deployed in privacy-sensitive scenarios, the user privacy protection of neural GSL models has not drawn much attention. To comprehensively understand the privacy protection issues, we first introduce the concept of attackable representation to systematically characterize the privacy attacks that each model can face. Inspired by the qualitative results, we propose a novel Privacy-Preserving neural Graph Matching network model, named PPGM, for graph similarity learning. To prevent reconstruction attacks, the proposed model does not communicate node-level representations between devices. Instead, we learn multi-perspective graph representations based on learnable context vectors. To alleviate the attacks to graph properties, the obfuscated features that contain information from both graphs are communicated. In this way, the private properties of each graph can be difficult to infer. Based on the node-graph matching techniques while calculating the obfuscated features, PPGM can also be effective in similarity measuring. To quantitatively evaluate the privacy-preserving ability of neural GSL models, we further propose an evaluation protocol via training supervised black-box attack models. Extensive experiments on widely-used benchmarks show the effectiveness and strong privacy-protection ability of the proposed model PPGM. The code is available at: https://***/RUCAIBox/PPGM. © 2022, CC BY.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Optimal Sizing of Ev Charging Stations Considering Charging Scheduling and Renewable Energy

SSRN

引用

SSRN 2022年

作者： Zhou, Kaile Hu, Dingding Yang, Shanlin School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education Hefei University of Technology Hefei230009 China National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai China

The construction of electric vehicle (EV) charging stations is critical for the widespread use of EVs. Appropriate design of EV charging stations should fulfill charging requests while considering various economic issues. In this regard, we present an optimization model to address the sizing problem of EV charging stations with integration of renewable energy resources, energy storage system and multi-types of chargers. The proposed model aims at reducing the investment cost and improving the profitability of EV charging stations. In order to accurately estimate EV charging demand, the characteristics of EV charging behaviors are derived based on real-world charging data. Furthermore, a charging scheduling strategy that responds to time-of-use (TOU) price and charging demand levels is proposed to improve the utilization efficiency of chargers. Results show that the design of EV charging stations with integrated consideration of these components and charging scheduling strategy can achieve optimal economic efficiency. © 2022, The Authors. All rights reserved.

关键词： Renewable energy resources

来源：评论

学校读者我要写书评

暂无评论

Effects of NGF-chitosan on alleviating secondary degeneration and repairing primary degeneration after expanded partial optic nerve transection

引用

Science China Life Sciences 2025年

作者： Xiao Liu Hongmei Duan Limin Gao Linhao Yuan Peng Hao Wen Zhao Yudan Gao Zitian Huang Xi Wang Wenjianlong Zhou Shunchang Ma Ningli Wang Kwok-Fai So Zhaoyang Yang Xiaoguang Li Wang Jia School of Engineering Medicine Beijing Key Laboratory for Biomaterials and Neural Regeneration Beihang University Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural Regeneration Beijing Advanced Innovation Center for Big Data-Based Precision MedicineBeihang University China National Clinical Research Center for Neurological Diseases (NCRC-ND) Department of Neurosurgery Beijing Tiantan Hospital National Center for Neurological Disorders Capital Medical University Department of Neurobiology School of Basic Medical Sciences Capital Medical University Department of Neurotomy Beijing Neurosurgical Institute Capital Medical University Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Guangdong-Hongkong-Macau Institute of CNS Regeneration Ministry of Education CNS Regeneration Collaborative Joint Laboratory Jinan University Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) Department of Ophthalmology and State Key Laboratory of Brain and Cognitive Sciences The University of Hong Kong Center for Brain Science and Brain-Inspired Intelligence Guangdong-Hong Kong-Macao Greater Bay Area Co-innovation Center of Neuroregeneration Nantong University

Optic neuropathy is one of the main causes of irreversible blindness in the world, and there is no effective treatment in clinic. Both primary degeneration and secondary degeneration play an important role in the injury caused by optic neuropathy. Partial optic nerve transection(PONT) model can be used to study these two kinds of degeneration simultaneously. However, there is currently no measure that can effectively intervene in both types of injuries concurrently. Here, we constructed an expanded partial optic nerve transection(EPONT) model. Nerve growth factor(NGF)-chitosan locally implanted into the injured area could simultaneously intervene in the secondary and primary degeneration, not only protecting the ventral part of the injured optic nerve, but also promoting the regeneration of the dorsal part. Visual functions, including pupillary light reflex and depth perception, were also well preserved. NGF-chitosan exerted biological effects by enhancing the expression of NGF and tyrosine kinase A(Trk A) in the optic nerve and retinal ganglion cells(RGCs). Furthermore, NGFchitosan played a protective and repairing role by inhibiting the activation of microglia in the ventral area of the injured optic nerve and increasing the expression of mammalian target of rapamycin(m TOR) in RGCs. Our results demonstrate that the local use of NGF-chitosan in the injured area effectively repaired the optic nerve, which provides a new measure for the clinical treatment of optic nerve injury.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary 39

Multi-Granularity Open Intent Classification via Adaptive Gr...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Yanhua Ouyang, Xiaocao Pan, Chaofan Zhang, Jie Zhao, Sen Xia, Shuyin Yang, Xin Wang, Guoyin Li, Tianrui School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China Engineering Research Center of Intelligent Finance Ministry of Education Chengdu611130 China Key Laboratory of Big Data Intelligent Computing Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China National Center for Applied Mathematics in Chongqing Chongqing Normal University Chongqing401331 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)157735897X

Open intent classification is critical for the development of dialogue systems, aiming to accurately classify known intents into their corresponding classes while identifying unknown intents. Prior boundary-based methods assumed known intents fit within compact spherical regions, focusing on coarse-grained representation and precise spherical decision boundaries. However, these assumptions are often violated in practical scenarios, making it difficult to distinguish known intent classes from unknowns using a single spherical boundary. To tackle these issues, we propose a Multi-granularity Open intent classification method via adaptive Granular-Ball decision boundary (MOGB). Our MOGB method consists of two modules: representation learning and decision boundary acquiring. To effectively represent the intent distribution, we design a hierarchical representation learning method. This involves iteratively alternating between adaptive granular-ball clustering and nearest sub-centroid classification to capture fine-grained semantic structures within known intent classes. Furthermore, multi-granularity decision boundaries are constructed for open intent classification by employing granular-balls with varying centroids and radii. Extensive experiments conducted on three public datasets demonstrate the effectiveness of our proposed method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fusion of Terahertz Spectroscopy and Raman Spectroscopy Combined with Support Vector Machine to Distinguish Different Pericarpium Citri Reticulatae Species

SSRN

引用

SSRN 2024年

作者： Yang, Qiyu Xia, Congzi Cui, Yuanwu Zhang, Xiaoqiuyan Zhang, Tianyu Huang, Yong Liu, Diwei Wei, Yanyu Gong, Yubin Fang, Guangyou Wang, Zhenyou Chen, Xuequan Hu, Min Terahertz Center School of Electronic Science and Engineering University of Electronic Science and Technology Sichuan611731 China Key Laboratory of Terahertz Technology Ministry of Education Sichuan611731 China Institute of Big Data for Vocational Education Guangdong Polytechnic of Science and Technology Zhuhai519000 China GBA Branch of Aerospace Information Research Institute Chinese Academy of Sciences Guangzhou510700 China Guangdong Provincial Key Laboratory of Terahertz Quantum Electromagnetics Guangzhou51 0700 China Shenzhen Authentic Traditional Chinese Medicine Treatment Hospital Shenzhen518100 China

Pericarpium citri reticulatae (PCR, also called Chenpi) is an important medical and food resource in China. The medicinal value and market price of Chenpi varies greatly among different origins and years. Traditional sensory identification methods involve a great deal of subjectivity, while chemical methods are cumbersome and have low repeatability. In recent years, the classification method using electromagnetic spectrum combined with machine learning has become a new means of identifying the classification of PCR, but problems such as the limited accuracy and the unclear criterion of classification hinder their practical usage. This paper explores the classification of PCR based on the fusion of terahertz and Raman spectral data combined with machine learning methods. The fused spectral data from the two frequency bands will be used together to build a model for the classification of seven PCR species of different ages, origins, and artificial skins in Jiangmen, Guangdong, China. Model selection includes K-nearest neighbor (KNN) classification algorithms, convolutional neural networks (CNN), and support vector machine (SVM). We initially obtain a good classification model by analyzing the model's metrics. These performance metrics include the classification accuracy of the model and the area under receiver operating characteristic curve (AUC). To further improve the overall performance of the model, we use an innovative Manhattan data preprocessing approach. Through the Manhattan distance of the data between different species, the method further attenuates the data errors within the same species of PCR due to instrumental, environmental and human factors, which makes the indicators of the model improved. Ultimately, we built a PCR classification model with high AUC metrics as well as a high accuracy of 96.8%. We used the T-distributed stochastic neighbor embedding (t-SNE) algorithm to realize the downscaling visualization of the data, which intuitively obtains the

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Dft Calculations for Leaching Mechanism of Spent Hds Catalysts with Acidic or Alkaline Leaching Agents

SSRN

引用

SSRN 2023年

作者： Ma, Liwen Shi, Tiantian Nie, Zhuanghua Xi, Xiaoli Key Laboratory of Advanced Functional Materials Ministry of Education Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China Collaborative Innovation Center of Capital Resource-Recycling Material Technology Beijing University of Technology Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China

The leaching mechanism of the spent hydrodesulfurization (HDS) catalyst was investigated by density functional theory (DFT) calculations and experiments. The ad-molecular (ADM) model of NiO-MoO3/Al2O3 was created to simulate the spent HDS catalyst. The model shows a stable structure with the length of chemical bonds varied greatly and the low symmetry. The adsorption configurations and state densities of different leaching agents on the NiO-MoO3/Al2O3 model were calculated. The results showed that NaOH increased the bond lengths of Mo-O3 and Mo-O4 by 0.062 Å and 0.052 Å;increased the bond lengths of Ni-O4, Ni-O5 and Ni-O6 by 0.092 Å, 0.023 Å and 0.058 Å, respectively. HCl increased the bond length of Mo-O3, Ni-O4 and Ni-O5 by 0.016 Å, 0.043 Å and 0.041 Å, respectively;HNO3 increased the length of Ni-O4 bond by 0.127 Å;H2SO4 increased the bond length of Mo-O1 and Ni-O4 by 0.036 Å and 0.165 Å. The influencing order of agents on Mo leaching was H2SO4>NaOH>HNO3>HCl and H2SO4>NaOH>HNO3≈HCl on Ni leaching according to the bond length increasing value. The leaching experimental results showed that the leaching efficiency of Mo and Ni was almost consistent with the calculated influence order. © 2023, The Authors. All rights reserved.

关键词： Density functional theory

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：