检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Pu, Wenqiang Liu, Ya-Feng Luo, Zhi-Quan Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

An important preliminary procedure in multi-sensor data fusion is sensor registration, and the key step in this procedure is to estimate sensor biases from their noisy measurements. There are generally two difficulties in this bias estimation problem: one is the unknown target states which serve as the nuisance variables in the estimation problem, and the other is the highly nonlinear coordinate transformation between the local and global coordinate systems of the sensors. In this paper, we focus on the 3-dimensional asynchronous multi-sensor scenario and propose a weighted nonlinear least squares (NLS) formulation by assuming that there is a target moving with a nearly constant velocity. We propose two possible choices of the weighting matrix in the NLS formulation, which correspond to classical and weighted NLS estimation and maximum likelihood (ML) estimation, respectively. To address the intrinsic nonlinearity, we propose a block coordinate descent (BCD) algorithm for solving the formulated problem, which alternately updates different kinds of bias estimates. Specifically, the proposed BCD algorithm involves solving linear LS problems and nonconvex quadratically constrained quadratic program (QCQP) problems with special structures. Instead of adopting the semidefinite relaxation technique, we develop a much more computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) to solve the nonconvex QCQP subproblems. The convergence of the ADMM to the global solution of the QCQP subproblems is established under mild conditions. The effectiveness and efficiency of the proposed BCD algorithm are demonstrated via numerical simulations. Copyright © 2021, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

Efficient Low-Resolution Face Recognition via Bridge Distillation

arXiv

引用

arXiv 2024年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Zhang, Yu Li, Jia The Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China SenseTime Group Limited 100084 China The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The Peng Cheng Laboratory Shenzhen518055 China

Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability. In this paper, we propose a bridge distillation approach to turn a complex face model pretrained on private high-resolution faces into a light-weight one for low-resolution face recognition. In our approach, such a cross-dataset resolution-adapted knowledge transfer problem is solved via two-step distillation. In the first step, we conduct cross-dataset distillation to transfer the prior knowledge from private high-resolution faces to public high-resolution faces and generate compact and discriminative features. In the second step, the resolution-adapted distillation is conducted to further transfer the prior knowledge to synthetic low-resolution faces via multi-task learning. By learning low-resolution face representations and mimicking the adapted high-resolution knowledge, a light-weight student model can be constructed with high efficiency and promising accuracy in recognizing low-resolution faces. Experimental results show that the student model performs impressively in recognizing low-resolution faces with only 0.21M parameters and 0.057MB memory. Meanwhile, its speed reaches up to 14,705, 934 and 763 faces per second on GPU, CPU and mobile phone, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi-task learning

来源：评论

学校读者我要写书评

暂无评论

Personalized Food Recommendations: A Machine Learning Model for Enhanced Dining Choices

Personalized Food Recommendations: A Machine Learning Model ...

引用

IEEE International Conference on Control System, computing and Engineering (ICCSCE)

作者： Mohammed Al-Hubaishi Musab A. M. Ali Nooritawati Md Tahir Department of Computer Engineering Halic University Istanbul Turkey College of Computing and Information Sciences University of Technology and Applied Sciences Sultanate of Oman Department of Computer Engineering and Information Security International Information Technology University Kazakhstan Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Malaysia College of Engineering Universiti Teknologi MARA Malaysia Applied College Princess Nourah Bint Abdul Rahman University (PNU) Riyadh Saudi Arabia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

This research introduces an innovative approach to enhance dining experiences through a personalized meal suggestion system. Our method leverages decision tree-based machine learning to predict user preferences by analyzing various attributes. The aim is to assist individuals in making optimal food choices at restaurants, thereby enriching their culinary journey. QR codes are integrated into an Android restaurant application, facilitating convenient access to the food menu. User inquiries are efficiently managed through a MySQL database accessible across multiple platforms, including a Java GUI, an Android app, and a dedicated website catering to both user and administrator roles.

关键词： Java Codes databases Machine learning QR codes Control systems Decision trees Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

Photovoltaic Power Forecasting with Weather Conditioned Attention Mechanism

SSRN

引用

SSRN 2023年

作者： Jiang, Xuetao Jiang, Meiyu Luo, Lihui Shen, Jun Zhou, Qingguo Zhou, Rui School of Information Science and Engineering Lanzhou University Tianshui South Road No.222 Gansu Lanzhou730000 China Gansu Provincial Industry Technology Center of Intelligent Equipment & Big Data for Disaster Prevention Northwest Institute of Eco-Environment and Resources Chinese Academy of Science Gansu Lanzhou730000 China School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia

Accurate photovoltaic (PV) generation forecasts can reduce power redeploy from the grid, thus increasing the supplier's profit in the day-ahead electricity market. However, the PV process is affected differently by various factors under different weather conditions, resulting in significantly different energy output curves. In this context, this paper proposes a day-ahead PV power forecasting method with weather conditioned attention mechanism. We propose a multi-stream attention fusion network (MSAFN) which utilizes an algorithm to derive the optimal decomposition algorithm for different weather conditions. The proposed conditional decomposition (CD) algorithm searches for the decomposition algorithms and corresponding hyperparameters of the prediction model, aiming to achieve the optimal prediction performance. The MSAFN incorporates multiple attention modules to learn the energy output patterns under various weather conditions. Notably, the attention modules adeptly learn patterns under diverse conditions, while simultaneously, the sharing of weights among the remaining components of the model effectively enhances prediction accuracy and facilitates a reduction in training time. We compare the state-of-the-art decomposition algorithms (VMD, EEMD and MSTL, etc.) and prediction models (BPN, LSTM, XGBoost and transformer, etc.) commonly used in PV prediction. The results show that the MSAFN model is more accurate than the models above, which has a noticeable improvement compared to other recent day-ahead PV predictions on DKASC. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

ALRP: An Adaptive Link State Routing Protocol for Multi-Identifier Network

ALRP: An Adaptive Link State Routing Protocol for Multi-Iden...

引用

International Conference on Computer and Communications (ICCC)

作者： Feng Wang Hui Li Zhengqi Wu Ping Lu Bin Wang Yong Yang He Bai Xinyuan Pei Shenzhen Graduate School Peking University Shenzhen China Shenzhen Graduate School Peking University Peng Cheng Laboratory Shenzhen China The Cloud Computing and IT Institute of ZTE Corporation Shenzhen China China United Television Shenzhen China School of Mathematics and Big Data Foshan University Foshan China

With the emergence of novel network applications., the traditional push-based TCP/IP architecture cannot satisfy diverse user demands. To solve this issue, Multi-Identifier Network (MIN) is put forward as a promising network architecture with excellent features. It introduces multiple identifiers and communication semantics to support various transmission scenarios. However, this neoteric network architecture poses new challenges to the design of intra-domain dynamic routing. In this paper, we propose an Adaptive Link-state Routing Protocol (ALRP) for MIN, which empowers active link-state notification and multipath calculation, inspired by the existing solutions. ALRP disseminates routing reachability information using push-pull converged semantics to reduce network traffic overhead. Meanwhile, it calculates multiple alternative paths to provide abundant guidance for the forwarding plane. We have implemented ALRP in Go programming language, and conducted simulation experiments to verify the effectiveness of ALRP. The experimental results show that ALRP can effectively reduce the network traffic overhead and data retrieval delay.

关键词： Computer languages Computational modeling Semantics Telecommunication traffic TCPIP Computer architecture Network architecture

来源：评论

学校读者我要写书评

暂无评论

Accurate Latent Factor Analysis via Particle Swarm Optimizers

Accurate Latent Factor Analysis via Particle Swarm Optimizer...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Jia Chen Xin Luo MengChu Zhou School of Cyber Science and Technology Beihang University Beijing China Chongqing Key Lab. of Big Data and Intelligent Computing and the Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chongqing School University of Chinese Academy of Sciences Chongqing China New Jersey Institute of Technology Newark NJ USA

ISBN: (纸本)9781665442084

A stochastic-gradient-descent-based Latent Factor Analysis (LFA) model is highly efficient in representative learning of a High-Dimensional and Sparse (HiDS) matrix. Its learning rate adaptation is vital in ensuring its efficiency. Such adaptation can be realized with an evolutionary computing algorithm. However, a resultant model tends to suffer from two issues: a) the pre-mature convergence of the swarm of learning rates as caused by an adopted evolution algorithm, and b) the pre-mature convergence of the LFA model as caused jointly by evolution-based learning rate adaptation and an optimization algorithm. This paper focuses on the methods to address such issues. A Hierarchical Particle-swarm-optimization-incorporated Latent factor analysis (HPL) model with a two-layered structure is proposed, where the first layer pre-trains desired latent factors with a position-transitional particle-swarm-optimization-based LFA model, and the second layer performs latent factor refining with a newly-proposed mini-batch particle swarm optimizer. With such design, an HPL model can well handle the pre-mature convergence, which is supported by the positive experimental results achieved on HiDS matrices from industrial applications.

关键词： Adaptation models Analytical models Computational modeling Refining Predictive models Sparse matrices Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Narrow bandwidth gamma comb from nonlinear Compton scattering using the polarization gating technique

arXiv

引用

arXiv 2020年

作者： Valialshchikov, Maksim A. Yu. Kharin, V. Rykovanov, S.G. High Performance Computing and Big Data Laboratory Skolkovo Institute of Science and Technology Moscow Russia

Nonlinear Compton scattering is a promising source of bright gamma-rays. Using readily available intense laser pulses to scatter off the energetic electrons, on the one hand, allows to significantly increase the total photon yield, but on the other hand, leads to a dramatic spectral broadening of fundamental emission line as well as its harmonics due to the laser pulse shape induced ponderomotive effects. In this paper we propose to avoid ponderomotive broadening in harmonics by using the polarization gating technique - a well-known method to construct a laser pulse with temporally varying polarization. We show that by restricting harmonic emission only to the region near the peak of the pulse, where the polarization is linear, it is possible to generate a bright narrow bandwidth comb in the gamma region. Copyright © 2020, The Authors. All rights reserved.

关键词： Gamma rays

来源：评论

学校读者我要写书评

暂无评论

Cross-View Fuzziness and Intra-view Uncertainty-Based Weight Reconstruction for Multi-view Feature Selection 11th

Cross-View Fuzziness and Intra-view Uncertainty-Based Weigh...

引用

11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Xiang, Biao Chen, Hongmei Mi, Yong Sang, Binbin Horng, Shi-Jinn Li, Tianrui Institute of Artificial Intelligence School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China College of Computer and Information Science Chongqing Normal University Chongqing401331 China Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan

ISBN: (纸本)9783031927430

Weight assignment is a universal and indispensable process in multi-view feature selection. However, most existing methods overlook the fuzziness and uncertainty implied in multi-view data. This may result in improper weight assignment, degrading the quality of selected features. A cross-view fuzziness and intra-view uncertainty-based weight reconstruction method (CFIUWR) is proposed for multi-view feature selection. With the fuzzy rough set theory, fuzzy approximation operators recognize cross-view fuzziness and intra-view uncertainty, which are evaluated by classification-related measurement. Cross-view fuzziness and intra-view uncertainty are incorporated to develop a reconstruction mechanism for view and feature weights. An optimization algorithm is derived, guaranteeing rapid convergence. Comparisons with state-of-the-art methods on several real-world datasets demonstrate the superiority of the CFIUWR. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Rough set theory

来源：评论

学校读者我要写书评

暂无评论

APPLICATION OF THE DIGITAL ANNEALER UNIT IN OPTIMIZING CHEMICAL REACTION CONDITIONS FOR ENHANCED PRODUCTION YIELDS

arXiv

引用

arXiv 2024年

作者： Li, Shih-Cheng Wang, Pei-Hwa Su, Jheng-Wei Chiang, Wei-Yin Huang, Shih-Hsien Lin, Yen-Chu Ou, Chia-Ho Chen, Chih-Yu Insilico Medicine Taiwan Ltd. Taipei City110208 Taiwan Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Quantum Information Center Chung Yuan Christian University Taoyuan City320314 Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City320314 Taiwan Department of Computer Science and Information Engineering National Pingtung University Pingtung City900392 Taiwan

Finding appropriate reaction conditions that yield high product rates in chemical synthesis is crucial for the chemical and pharmaceutical industries. However, due to the vast chemical space, conducting experiments for each possible reaction condition is impractical. Consequently, models such as QSAR (Quantitative Structure-Activity Relationship) or ML (Machine Learning) have been developed to predict the outcomes of reactions and illustrate how reaction conditions affect product yield. Despite these advancements, inferring all possible combinations remains computationally prohibitive when using a conventional CPU. In this work, we explore using a Digital Annealing Unit (DAU) to tackle these large-scale optimization problems more efficiently by solving Quadratic Unconstrained Binary Optimization (QUBO). Two types of QUBO models are constructed in this work: one using quantum annealing and the other using ML. Both models are built and tested on four high-throughput experimentation (HTE) datasets and selected Reaxys datasets. Our results suggest that the performance of models is comparable to classical ML methods (i.e., Random Forest and Multilayer Perceptron (MLP)), while the inference time of our models requires only seconds with a DAU. Additionally, in campaigns involving active learning and autonomous design of reaction conditions to achieve higher reaction yield, our model demonstrates significant improvements by adding new data, showing promise of adopting our method in the iterative nature of such problem settings. Our method can also accelerate the screening of billions of reaction conditions, achieving speeds millions of times faster than traditional computing units in identifying superior conditions. Therefore, leveraging the DAU with our developed QUBO models has the potential to be a valuable tool for innovative chemical synthesis. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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

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：