检索结果-内蒙古大学图书馆

Optica Quantum 2.0 Conference and Exhibition, Quantum 2.0

作者： Dhara, Prajit Englund, Dirk R. Guha, Saikat Wyant College of Optical Sciences The University of Arizona TucsonAZ85721 United States NSF-ERC Center for Quantum Networks The University of Arizona TucsonAZ85721 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

We evaluate the quality of entangled states generated on quantum network links based on entanglement swapping. Network non-idealities in conjunction with physical memory decoherence models are incorporated to demonstr... 详细信息

ISBN: (纸本)9781957171272

关键词： Defect center materials Modes Optical fibers Photonic entanglement Quantum memories Quantum teleportation

来源：评论

学校读者我要写书评

暂无评论

High-throughput transport-of-intensity quantitative phase imaging with aberration correction

引用

Light(Advanced Manufacturing) 2024年第4期5卷 54-63页

作者： Linpeng Lu Shun Zhou Yefeng Shu Yanbo Jin Jiasong Sun Ran Ye Maciej Trusiak Peng Gao Chao Zuo Smart Computational Imaging Laboratory(SCILab) School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjingJiangsu Province 210094China Smart Computational Imaging Research Institute(SCIRI)of Nanjing University of Science and Technology NanjingJiangsu Province 210019China Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense NanjingJiangsu Province 210094China School of Computer and Electronic Information Nanjing Normal UniversityNanjing 210023China Institute of Micromechanics and Photonics Warsaw University of Technology8 Sw.A.Boboli St.Warsaw 02-525Poland School of Physics Xidian UniversityXi'anChina

The transport of intensity equation(TIE)is a well-established phase retrieval technique that enables incoherent diffraction limit-resolution imaging and is compatible with widely available brightfield microscopy ***,existing TIE methods encounter difficulties in decoupling the independent contributions of phase and aberrations to the measurements in the case of unknown pupil ***,spatially nonuniform and temporally varied aberrations dramatically degrade the imaging performance for long-term ***,it remains a critical challenge to realize the high-throughput quantitative phase imaging(QPI)with aberration correction under partially coherent *** address these issues,we propose a novel method for highthroughput microscopy with annular illumination,termed as transport-of-intensity QPI with aberration correction(TI-AC).By combining aberration correction and pixel super-resolution technique,TI-AC is made compatible with large pixel-size sensors to enable a broader field of ***,it surpasses the theoretical Nyquist-Shannon sampling resolution limit,resulting in an improvement of more than two *** results demonstrate that the half-width imaging resolution can be improved to~345 nm across a 10×field of view of 1.77 mm^(2)(0.4 NA).Given its high-throughput capability for QPI,TI-AC is expected to be adopted in biomedical fields,such as drug discovery and cancer diagnostics.

关键词： Transport of intensity Quantitative phase microscopy Aberration correction High-throughput imaging

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial: Special issue on computational methods and artificial intelligence applications in low-carbon energy systems

引用

IET Renewable Power Generation 2024年第3期18卷 303-305页

作者： Wang, Yishen Zhou, Fei Guerrero, Josep M. Baker, Kyri Chen, Yize Wang, Hao Xu, Bolun Xu, Qianwen Zhu, Hong Agwan, Utkarsha State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China Department of Energy Technology Aalborg University Aalborg Denmark Department of Civil Environmental and Architectural Engineering University of Colorado-Boulder BoulderCO United States Guangzhou China Department of Data Science and AI Monash University ClaytonVIC Australia Department of Earth and Environmental Engineering Columbia University New YorkNY United States Electric Power and Energy Systems Division KTH Royal Institute of Technology Stockholm Sweden State Grid Jiangsu Electric Power Co. Ltd. Nanjing China Department of Electrical Engineering and Computer Sciences University of California - Berkeley BerkeleyCA United States

来源：评论

学校读者我要写书评

暂无评论

Multi-Stage Vehicle Dispatch for Community Group-buying Logistics via Deep Reinforcement Learning

Multi-Stage Vehicle Dispatch for Community Group-buying Logi...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Lingyi Xu Xingyuan Liang Xiaolei Zhou Tian He School of Computer Science and Engineering Southeast University The Sixty-Third Research Institute National University of Defense Technology Department of Technology and Data Intelligence JD Logistics

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Community group-buying is an emerging shopping model in which individuals within the same community collectively purchase daily necessities at lower prices. The logistics transportation in this scenario requires higher turnover rates and timeliness compared to traditional online shopping. Therefore, making efficient vehicle dispatch decisions is necessary to improve delivery efficiency and reduce transportation costs. However, uncertainties in orders and complex dependencies between multi-stage decisions in the community group-buying scenario challenge the scheduling of delivery vehicles. In this paper, we propose a vehicle dispatch system for community group-buying logistics based on a predict-then-optimize process, which incorporates a demand prediction module and a vehicle dispatch module. In the prediction module, we categorize goods and predict the total quantity of each category using a multi-layer perceptron (MLP). In the vehicle dispatch module, we model the vehicle dispatch process as a reinforcement learning problem and use an action masking mechanism to prune the policy search space. Experimental results demonstrate that the vehicle dispatch system achieves state-of-the-art performance compared to several popular baselines across three different datasets. Our system reduces transportation costs by at least 8.89% and improves decision efficiency by at least 10%. Moreover, it behaves close to the approximate optimal solution in most cases.

关键词： Space vehicles Costs Uncertainty Transportation Predictive models Search problems Sensors

来源：评论

学校读者我要写书评

暂无评论

Efficient Detection of DDoS Attacks in E-Government Clouds Using Sparse Neural Networks 4

Efficient Detection of DDoS Attacks in E-Government Clouds U...

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Bixapathi, A. Ali, H. Mohammed Maranan, R. Sudhakar, A.V.V. Beschi, I.S. Ramya, M. T.S Medchal district501301 India Srm Institute of Science and Technology Department Mechanical Engineering Ramapuram Campus Chennai89 India Simats Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India Sr University Department of Electrical and Electronics Engineering Telangana Warangal506371 India Department of Computer Applications Kovur Chennai India Lovely Professional University Division of Research and Development Jalandhar - Delhi G.T.Road Punjab Jalandhar144411 India

ISBN: (纸本)9798331518981

E-government (Electronic Government) refers to the use of information and communication technology (ICT) by governmental bodies to provide public services, manage government operations, and facilitate communication with citizens, businesses, and other government entities. E-government platforms have expanded significantly in recent years, offering a range of services such as online tax filing, license renewals, healthcare services, and more. These services, often hosted on cloud-based infrastructures, benefit from improved scalability, efficiency, and accessibility through the use of cloud computing technologies. However, the growing volume of legitimate traffic from a diverse range of users on e-government platforms makes distinguishing between normal and malicious traffic, such as DDoS (Distributed Denial of Service) attacks, increasingly difficult. To address this challenge, this manuscript proposes the use of Sparse Neural Networks (SNN) for DDoS attack detection in cloud-based e-government systems. Initially, data from the CSE-CIC-IDS2018 dataset is collected and processed through a feature selection phase. The selected features are selected using the Energy Valley Optimizer. These optimized features are fed into the Sparse Neural Network, which categorizes traffic as benign, DDoS, or DoS. To further enhance the accuracy of DDoS detection, the Ladybug Beetle Optimization (LBOA) is employed to optimize the Sparse Neural Network's parameters, ensuring optimal performance in classifying attacks. This approach aims to safeguard cloud-based e-government systems from cyber threats while maintaining reliable public service delivery. In comparison to current methods such as data clustering for the detection of DDoS attacks in the cloud for e-government (DBSCAN), e-government impact factor analysis cleverly using enhanced machine learning (ML-EGOV), and Problems with Machine Learning in Cloud Computing E-Government Models in Developing Nations, the proposed DCNN-QSA-PDO

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

research on recognition algorithm for gesture page turning based on wireless sensing

引用

Intelligent and Converged Networks 2023年第1期4卷 15-27页

作者： Lin Tang Sumin Wang Meng Zhou Yinfan Ding Chao Wang Shengbo Wang Zhen Sun Jie Wu Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceSchool of Communication and Information EngineeringShanghai UniversityShanghai 200444China School of Electronic Information Engineering Gannan University of Science and TechnologyGanzhou 341000China Affiliated High School of Peking University Beijing 100086China Peking University&National Engineering Laboratory for Big Data Analysis and Applications Beijing 100871China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122-6096USA

When a human body moves within the coverage range of Wi-Fi signals,the reflected Wi-Fi signals by the various parts of the human body change the propagation path,so analysis of the channel state data can achieve the perception of the human *** extracting the Channel State Information(CSI)related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm,the human motion in the spatial environment can be *** the basis of this theory,this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide *** algorithm collects the environmental information containing upward or downward wave in a conference room scene,uses the local outlier factor detection algorithm to segment the actions,and then the time domain features are extracted to train Support Vector Machine(SVM)and eXtreme Gradient Boosting(XGBoost)classification *** experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%,and the SVM module can reach 89%,so the module could be extended to the field of smart classroom and significantly improve speech efficiency.

关键词： Wi-Fi signal Channel State Information(CSI) wireless sensing human behavior recognition

来源：评论

学校读者我要写书评

暂无评论

Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention

Few-Shot Point Cloud Semantic Segmentation via Contrastive S...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiahui Wang Haiyue Zhu Haoren Guo Abdullah Al Mamun Cheng Xiang Tong Heng Lee College of Design and Engineering Electrical and Computer Engineering National University of Singapore Singapore Agency for Science Technology and Research (A*STAR) Singapore Institute of Manufacturing Technology (SIMTech) Singapore Republic of Singapore

This paper presents an effective few-shot point cloud semantic segmentation approach for real-world applications. Existing few-shot segmentation methods on point cloud heavily rely on the fully-supervised pretrain with large annotated datasets, which causes the learned feature extraction bias to those pretrained classes. However, as the purpose of few-shot learning is to handle unknown/unseen classes, such class-specific feature extraction in pretrain is not ideal to generalize into new classes for few-shot learning. Moreover, point cloud datasets hardly have a large number of classes due to the annotation difficulty. To address these issues, we propose a contrastive self-supervision framework for few-shot learning pretrain, which aims to eliminate the feature extraction bias through class-agnostic contrastive supervision. Specifically, we implement a novel contrastive learning approach with a learnable augmentor for a 3D point cloud to achieve point-wise differentiation, so that to enhance the pretrain with managed overfitting through the self-supervision. Furthermore, we develop a multi-resolution attention module using both the nearest and farthest points to extract the local and global point information more effectively, and a center-concentrated multi-prototype is adopted to mitigate the intra-class sparsity. Comprehensive experiments are conducted to evaluate the proposed approach, which shows our approach achieves state-of-the-art performance. Moreover, a case study on practical CAM/CAD segmentation is presented to demonstrate the effectiveness of our approach for real-world applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Universal Optics with Programmable Multimode Interference

Universal Optics with Programmable Multimode Interference

引用

Frontiers in Optics + Laser science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Larocque, Hugo Englund, Dirk Research Laboratory of Electronics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

We show that multimode interference programmable optics benefit from the simple algorithmic construction and length scaling of state-of-the-art programmable photonic circuits, while featuring orders of magnitude reduc...

ISBN: (纸本)9781557528209

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mitigating Voltage Sag in Power Systems with Proportional Integral Control using Tangent Search Algorithm and Multi-Fidelity Bayesian Neural Network 8

Mitigating Voltage Sag in Power Systems with Proportional In...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Meena, Radhey Shyam Singh, Harsimran Vasudevan, K. Ekambaram, Saranya Katiyar, Atul Kaliappan, S. Indian Institute of Technology Industry Coordinator Odl Uttar Pradesh Kanpur208016 India Guru Kashi University Department of Electrical Engineering Punjab Bathinda151302 India Parul University Department of Electrical Engineering Gujarat Vadodara391760 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Mjp Rohilkhand University Bareilly Faculty of Engineering and Technology Department of Electrical Engineering Uttar Pradesh 243006 India Lovely Professional University Division of Research and Development Delhi Jalandhar144411 India

ISBN: (纸本)9798350376425

Mitigation strategies for voltage sag in power systems using dynamic voltage restorers (DVRs) involve compensating for voltage dips by injecting the required voltage to maintain stability. However, DVRs present problems such as high initial costs in installation and subsequent costs in maintenance and replacement;it only corrects voltage sag within its capability to handle severe or long periods of voltage sags. Moreover, DVRs may need complex control algorithms and most accurate tuning for optimal performance under various grid conditions. To overcome these drawbacks, this manuscript proposes a hybrid approach for voltage sag mitigation in power systems with Proportional Integral (PI) controller. The proposed method is the combined execution of both Tangent Search Algorithm (TSA) and Multi-fidelity Bayesian Neural Network (MFBNN). Hence, it is named as hybrid TSA-MFBNN approach. The major aim is to minimize the impact of voltage sags on sensitive equipment and maintain consistent power quality across the network and reduce the integral squared error (ISE) and Total Harmonic Distortion (THD) of the system. The PI controller's gain parameters are optimized by the proposed TSA, and its optimum values are predicted by the MFBNN. By then, the proposed technique is executed in MATLAB and compared with different existing approaches. The proposed method outperforms all existing approaches such as Artificial Neural Network (ANN), Salp Swarm Algorithm (SSA), and Adaptive-Network Fuzzy Inference System (ANFIS). The proposed technique demonstrates a THD of 2.38% and an ISE of 0.00410, indicating superior performance in reducing harmonic distortions and minimizing error. © 2024 IEEE.

关键词： Harmonic distortion

来源：评论

学校读者我要写书评

暂无评论

Study on Improving Ship Punctuality to Port Based on Response Surface Methodology

Study on Improving Ship Punctuality to Port Based on Respons...

引用

computer, Internet of Things and Smart City (CIoTSC), International Conference on

作者： Jinlong Wei Shunmin Li Junhua Zhao Ruihan Luo Zhili Wang Xiaoning Feng College of Computer Science and Technology Harbin Engineering University Harbin China System Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China

ISBN: (数字)9798350306569

ISBN: (纸本)9798350306576

The punctuality rate of vessels arriving at ports is a pivotal operational indicator in the maritime industry, exerting a direct influence on cargo efficiency and economic benefits. Therefore, the exploration of effective approaches to enhance ship punctuality to ports holds significant practical implications and economic value for the maritime industry. In this study, the response surface methodology is employed to establish a predictive model for ship punctuality to ports. Firstly, a substantial amount of simulated shipping data was collected and subjected to exploratory data analysis to identify the primary factors influencing ship punctuality to ports. Subsequently, leveraging the modeling technique based on response surface methodology, a relationship model between ship punctuality to ports and different factor conditions was established. By subjecting the response surface equation to response surface analysis and residual analysis, the accuracy and reliability of the predictive model were validated. Concurrently, a comprehensive analysis of the significance of various influencing factors on different levels of ship punctuality to ports was conducted. Finally, through residual analysis and validation of predictive outcomes, the enhancement of ship punctuality to ports was evaluated.

关键词： Industries Economics Analytical models Computational modeling Seaports Predictive models Mathematical models Response surface methodology Marine vehicles Optimization

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：