检索结果-内蒙古大学图书馆

2022 International Conference on Smart Energy and Electrical Engineering, SEEE 2022

作者： Sun, Jinyu Chen, Fubing Huai'An Key Laboratory of Big Data Intelligent Computing and Analysis China The School of Computer Science and Technology Huaiyin Normal University Huaian223001 China

For the sake of the fixed magnitude of candidate control sets, the conventional model predictive current control (MPCC) for asymmetric six-phase permanent magnet motors suffers from huge harmonic currents. This article designs a novel MPCC that aims to improve operational performance. First, virtual-voltage vector (VV) sets are synthesized as the new candidate vectors to restrain harmonic currents. Then, a simplified method for evaluating voltage vectors is used to alleviate the computation in the enumeration process. Moreover, the duty cycle modulation with the deadbeat solution is introduced to optimize the magnitude of the VV. The modulation range can be expanded by utilizing null vectors, which will significantly reduce the torque fluctuations and flux ripples. The experiments are conducted to verify the designed control strategy. © Published under licence by IOP Publishing Ltd.

关键词： Vectors

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Signal Demodulation in Underwater Wireless Optical Communications

引用

China Communications 2024年第5期21卷 297-313页

作者： Ma Shuai Yang Lei Ding Wanying Li Hang Zhang Zhongdan Xu Jing Li Zongyan Xu Gang Li Shiyin Department of Strategic and Advanced Interdisciplinary Research Peng Cheng LaboratoryShenzhen 518055China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Data-driven Intelligent Information System Laboratory Shenzhen Research Institute of Big DataShenzhen 518172China Ocean College Zhejiang UniversityZhoushan 316021China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication *** other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental *** on such a platform,we first construct a real signal dataset with ten modulation ***,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature *** also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated ***,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other *** also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.

关键词： AdaBoost DBN machine learning signal demodulation

来源：评论

学校读者我要写书评

暂无评论

Task Separation and Knowledge Sharing for Class Incremental Learning 5

Task Separation and Knowledge Sharing for Class Incremental ...

引用

5th International Conference on intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Zhang, Jiali Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China School of Mathematics and Information Science Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China

ISBN: (纸本)9798350368284

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Inductive Relation Prediction by Disentangled Subgraph Structure

引用

Tsinghua Science and Technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

来源：评论

学校读者我要写书评

暂无评论

An attention-based cascade R-CNN model for sternum fracture detection in X-ray images

引用

CAAI Transactions on Intelligence Technology 2022年第4期7卷 658-670页

作者： Yang Jia Haijuan Wang Weiguang Chen Yagang Wang Bin Yang School of Computer Xi'an University of Posts and TelecommunicationsXi'anShaanxiChina Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and TelecommunicationsXi'anShaanxiChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anShaanxiChina Department of Radiology Xi'an Honghui HospitalXi'anChina

Fracture is one of the most common and unexpected *** not treated in time,it may cause serious consequences such as joint stiffness,traumatic arthritis,and nerve *** computer vision technology to detect fractures can reduce the workload and misdiagnosis of fractures and also improve the fracture detection ***,there are still some problems in sternum fracture detection,such as the low detection rate of small and occult *** this work,the authors have constructed a dataset with 1227 labelled X-ray images for sternum fracture *** authors designed a fully automatic fracture detection model based on a deep convolution neural network(CNN).The authors used cascade R-CNN,attention mechanism,and atrous convolution to optimise the detection of small fractures in a large X-ray image with big local *** authors compared the detection results of YOLOv5 model,cascade R-CNN and other state-of-the-art *** authors found that the convolution neural network based on cascade and attention mechanism models has a better detection effect and arrives at an mAP of 0.71,which is much better than using the YOLOv5 model(mAP=0.44)and cascade R-CNN(mAP=0.55).

关键词： attention mechanism cascade R-CNN fracture detection X-ray image

来源：评论

学校读者我要写书评

暂无评论

An ε-domination based two-archive 2 algorithm for many-objective optimization

引用

Journal of Systems Engineering and Electronics 2022年第1期33卷 156-169页

作者： WU Tianwei AN Siguang HAN Jianqiang SHENTU Nanying College of Mechanical and Electrical Engineering China Jiliang UniversityHangzhou 310018China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jilang UniversityHangzhou 310018China

The two-archive 2 algorithm(Two_Arch2) is a manyobjective evolutionary algorithm for balancing the convergence,diversity,and complexity using diversity archive(DA) and convergence archive(CA).However,the individuals in DA are selected based on the traditional Pareto dominance which decreases the selection pressure in the high-dimensional *** traditional algorithm even cannot converge due to the weak selection ***,Two_Arch2 adopts DA as the output of the algorithm which is hard to maintain diversity and coverage of the final solutions synchronously and increase the complexity of the *** increase the evolutionary pressure of the algorithm and improve distribution and convergence of the final solutions,an ε-domination based Two_Arch2 algorithm(ε-Two_Arch2) for many-objective problems(MaOPs) is proposed in this *** ε-Two_Arch2,to decrease the computational complexity and speed up the convergence,a novel evolutionary framework with a fast update strategy is proposed;to increase the selection pressure,ε-domination is assigned to update the individuals in DA;to guarantee the uniform distribution of the solution,a boundary protection strategy based on I_(ε+) indicator is designated as two steps selection strategies to update individuals in *** evaluate the performance of the proposed algorithm,a series of benchmark functions with different numbers of objectives is *** results demonstrate that the proposed method is competitive with the state-of-the-art multi-objective evolutionary algorithms and the efficiency of the algorithm is significantly improved compared with Two_Arch2.

关键词： many-objective optimization ε-domination boundary protection strategy two-archive algorithm

来源：评论

学校读者我要写书评

暂无评论

Gradient-Based Differential kWTA Network With Application to Competitive Coordination of Multiple Robots

引用

IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1452-1463页

作者： Mei Liu Xiaoyan Zhang Mingsheng Shang Long Jin School of Information Science and Engineering Lanzhou UniversityLanzhou 730000 State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining 810016China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Shenzhen Research Institute Lanzhou UniversityShenzhen 518063China

Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)*** obtaining the network,theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA ***,numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ***,the GD-k WTA network,backed with a consensus filter,is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination,thereby further demonstrating its effectiveness and feasibility.

关键词： Consensus filter k-winners-take-all(kWTA) multirobot coordination noise resistance

来源：评论

学校读者我要写书评

暂无评论

IMPRESS: an importance-informed multi-tier prefix KV storage system for large language model inference 25

IMPRESS: an importance-informed multi-tier prefix KV storage...

引用

Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Weijian Chen Shuibing He Haoyang Qu Ruidong Zhang Siling Yang Ping Chen Yi Zheng Baoxing Huai Gang Chen The State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security and Zhejiang Key Laboratory of Big Data Intelligent Computing The State Key Laboratory of Blockchain and Data Security Zhejiang University Huawei Cloud

ISBN: (纸本)9781939133458

Modern advanced large language model (LLM) applications often prepend long contexts before user queries to improve model output quality. These contexts frequently repeat, either partially or fully, across multiple queries. Existing systems typically store and reuse the keys and values of these contexts (referred to as prefix KVs) to reduce redundant computation and time to first token (TTFT). When prefix KVs need to be stored on disks due to insufficient CPU memory, reusing them does not always reduce TTFT, as disk I/O latency is high. In this paper, we propose IMPRESS, an importance-informed multi-tier prefix KV storage system to reduce I/O delay for LLM inference by only loading important prefix KVs. IMPRESS first leverages the insight that there is significant similarity in important token index sets across attention heads and introduces an I/O-efficient important KV identification algorithm. It then optimizes prefix KV storage and caching through importance-informed KV management, reducing TTFT during model inference. Our experimental results show that IMPRESS can reduce TTFT by up to 2.8× compared to state-of-the-art systems, while maintaining comparable inference accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Numerical study of microorganisms swimming near a convex wall in a Giesekus fluid

引用

Physical Review E 2025年第1期111卷 015103-015103页

作者： Chenlin Zhu Fangyuan Peng Dingyi Pan Zhaosheng Yu Zhaowu Lin Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou 310018 China

The motion of microorganisms in complex fluids stands out as a prominent subject within fluid mechanics. In our study, we utilize the fictitious domain method to investigate the locomotion of squirmers along a convex wall in Giesekus viscoelastic fluids. This study examines the influence of fluid elasticity and wall curvature on squirmer particles, analyzing their movement patterns in detail. Near the convex wall, three distinct behavioral characteristics emerge: scattering, orbiting forward, and orbiting backward. The findings reveal that, compared with Newtonian fluids, squirmers exhibit a stronger tendency to be attracted toward the wall in viscoelastic fluids. This behavior is attributed to the elastic stress of the fluid, which generates a reverse torque on microbial particles, altering their movement direction and hindering their escape from the wall. Notably, as the wall curvature decreases, the likelihood of particles escaping diminishes.

关键词： Forward scattering

来源：评论

学校读者我要写书评

暂无评论

Reliability Analysis of Heterogeneous Sensor-Cloud Systems against Targeted Attacks

引用

China Communications 2022年第8期19卷 181-197页

作者： Hao Peng Zhen Qian Guangquan Xu Kejie Mao Kangtian Li Dandan Zhao Department of Computer Science and Engineering Zhejiang Normal UniversityJinhua Zhejiang 321004China Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal UniversityJinhua Zhejiang 321004China The Big Data School Qingdao Huanghai UniversityQingdao Shandong 266427China College of Intelligence and Computing Tianjin UniversityTianjin 300072China

Based on the wide application of cloud computing and wireless sensor networks in various fields,the Sensor-Cloud System(SCS)plays an indispensable role between the physical world and the network ***,due to the close connection and interdependence between the physical resource network and computing resource network,there are security problems such as cascading failures between systems in the *** this paper,we propose a model with two interdependent networks to represent a sensor-cloud ***,based on the percolation theory,we have carried out a formulaic theoretical analysis of the whole process of cascading *** the system’s subnetwork presents a steady state where there is no further collapse,we can obtain the largest remaining connected subgroup components and the penetration ***,this result is the critical maximum that the coupled SCS can *** verify the correctness of the theoretical results,we further carried out actual simulation *** results show that a scale-free network priority attack’s percolation threshold is always less than that of ER network which is priority ***,when the scale-free network is attacked first,adding the power law exponentλcan be more intuitive and more effective to improve the network’s reliability.

关键词： sensor-cloud systems targeted attacks heterogeneous networks reliability analysis system security

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：