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检索条件"主题词=Iterative algorithms"

共 16609 条记录，以下是481-490 订阅

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A Cholesky QR type algorithm for computing tall-skinny QR factorization with column pivoting

A Cholesky QR type algorithm for computing tall-skinny QR fa...

引用

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Takeshi Fukaya Yuji Nakatsukasa Yusaku Yamamoto Hokkaido University Sapporo Hokkaido Japan University of Oxford Oxford UK The University of Electro-Communications Chofu Tokyo Japan

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

We consider computing the QR factorization with column pivoting (QRCP) for a tall and skinny matrix, which has important applications including low-rank approximation and rank determination. Motivated by recent progresses of Cholesky QR type algorithms for tall-skinny QR factorization (without pivoting), we propose a new Cholesky QR type algorithm for tall-skinny QRCP, which we call iterative Cholesky QR with Column Pivoting (Ite-CholQR-CP). Through performance evaluation, it is confirmed that Ite-CholQR-CP provides a solution as accurate as that by Householder QR with column pivoting (HQR-CP), which is a widely-used conventional algorithm. In addition, Ite-CholQR-CP outperforms HQR-CP in execution time in single node and distributed parallel computations: up to 45x (single node computation) and 27x (distributed parallel computation) speedup.

关键词： Performance evaluation Distributed processing Accuracy High performance computing Approximation algorithms iterative algorithms Computational efficiency

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Spherical Projection Based Clustering Algorithm for Cooperative Sweep Coverage in Crowdsourcing

Spherical Projection Based Clustering Algorithm for Cooperat...

引用

IEEE International Conference on Web Services (ICWS)

作者： Yucen Gao Xikai Wei Hui Gao Qun Li Xiaofeng Gao Guihai Chen Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China Didi Chuxing Technology Co. Beijing China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

The sweep coverage problem is one of the important issues in spatial crowdsourcing, which requires task participants to monitor a series of Points of Interest (PoIs) periodically. In this paper, we study the Cooperative Sweep Coverage (CSC) problem with the objective of minimizing the maximum sweep period. We propose an iterative clustering algorithm based on spherical projection, called SP-Cycle. The algorithm firstly projects the points in the 2D space to a spherical surface in the 3D space. It then utilizes a new balancing clustering algorithm and uses an iterative coordinate updating method in the 3D spherical space based on the gradient descent, the aim of which is to use the simple minimum spanning tree length computation in the spherical surface to replace the complex Traveling Salesman Problem (TSP) cycle computation in the original 2D space, which improves the performance while keeping a low computational complexity. After we get the clusters, we can compute the lengths of TSP cycles and enter a new iteration. Experimental results based on synthetic and real-world datasets demonstrate the effectiveness of our proposed algorithm. The code is available at https://***/GaoYucen/CSC.

关键词： Crowdsourcing Three-dimensional displays Codes Web services Clustering algorithms Traveling salesman problems iterative algorithms Space exploration Computational complexity Monitoring

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FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning

FedSelect: Personalized Federated Learning with Customized S...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Rishub Tamirisa Chulin Xie Wenxuan Bao Andy Zhou Ron Arel Aviv Shamsian Lapis Labs University of Illinois Urbana-Champaign Bar-Ilan University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Standard federated learning approaches suffer when client data distributions have sufficient heterogeneity. Recent methods addressed the client data heterogeneity issue via personalized federated learning (PFL) - a class of FL algorithms aiming to personalize learned global knowledge to better suit the clients' local data distributions. Existing PFL methods usually decouple global updates in deep neural networks by performing personalization on particular layers (i.e. classifier heads) and global aggregation for the rest of the network. However, preselecting network layers for personalization may result in suboptimal storage of global knowledge. In this work, we propose FEDSELECT,a novel PFL algorithm inspired by the iterative subnetwork discovery procedure used for the Lottery Ticket Hypothesis. FEDSELECT incrementally expands subnetworks to personalize client parameters, concurrently conducting global aggregations on the remaining parameters. This approach enables the personalization of both client parameters and subnetwork structure during the training process. Finally, we show that FEDSELECT outperforms recent state-of-the-art PFL algorithms under challenging client data heterogeneity settings and demonstrates robustness to various real-world distributional shifts. Our code is available at https://***/lapisrocks/fedselect.

关键词： Training Knowledge engineering Adaptation models Costs Federated learning Robustness iterative algorithms

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Hybrid Quantum Search Algorithm for Solving the Multi-Dimensional Knapsack Problem

Hybrid Quantum Search Algorithm for Solving the Multi-Dimens...

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Gordon Cui Rei Sato Kazuhiro Saito Rodney Van Meter Hideyuki Kawashima Faculty of Environment and Information Studies Keio University Fujisawa Kanagawa Japan Quantum Computing Group KDDI Research Inc. Fujimino Saitama Japan

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

The Multi-Dimensional Knapsack Problem (MDKP) is a strongly NP-Hard combinatorial optimization problem that extends the Knapsack Problem (KP) to multiple constraints. While effective heuristic classical MDKP algorithms have been studied, there remains no optimal exact classical algorithm and only few contributions using quantum algorithms exist. In this study, we leverage Grover's search and pre-processing techniques to propose an iterative quantum search algorithm and circuit design that integrates classical adjustments to the oracle, progressively refining solutions until optimal. Evaluations show that our method reduces depth, width, iterations, and runtime.

关键词： Runtime Quantum algorithm Circuit optimization Heuristic algorithms Scalability Refining Search problems iterative algorithms Circuit synthesis Optimization

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Cooperative Perception of Connected Autonomous Vehicles: A Stackelberg Game Approach

Cooperative Perception of Connected Autonomous Vehicles: A S...

引用

International Conference on Communication Technology (ICCT)

作者： Haozhong Xue Ke Zhang Supeng Leng Jianhua He School of Information and Communication Engineering University of Electronic Science and Technology of China University of Essex Colchester UK

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

The rise of connected autonomous vehicles demands a wider range of more accurate environment perception information. However, individual perception is hindered by blind spots and limited detection precision. Cooperative perception enhances vehicles’ environment perception capacity by sharing data with other vehicles or roadside infrastructure via vehicle-to-everything (V2X) communication, effectively addressing individual perception problems. Despite the critical importance of cooperative perception technology, the quality of transmitted data may decrease in environments with limited communication and computation resources, leading to a low coverage area and detection precision. To address the above issues and meet the perception utility demands of all vehicles in the environment, we propose a multi agent cooperative perception framework. Based on this framework, we propose a multi-leader multi-follower Stackelberg game model and design an iterative optimization algorithm to enhance perception utility while reducing system delay and overhead. Numerical results demonstrate that our proposed approach significantly improves the environment perception utility of vehicles compared to benchmark approaches.

关键词： Information sharing Games Receivers iterative algorithms Convex functions Numerical models Delays Optimization Autonomous vehicles Vehicle-to-everything

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ISAR Imaging with Envelope Information

ISAR Imaging with Envelope Information

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Min Tang Jiang Qian Haitao Lyu Jiayang Wu School of Resources and Environment University of Electronic Science and Technology of China Chengdu China Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of China Huzhou P. R. China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

To achieve high-quality inverse synthetic aperture radar (ISAR) imaging, measuring amplitude and phase precisely is crucial. However, for moving targets with complex motions such as rotation or rocking, the coherence between pulses deteriorates, making it difficult to estimate phase information accurately. This leads to azimuth defocusing issues with the traditional range-Doppler (RD) algorithm. To overcome this obstacle, we propose a novel ISAR imaging method based on the expectation maximization (EM) algorithm. This method accurately estimates the motion trajectory of the target by using only the envelope information of time-varying amplitudes and the dynamic observation model established within the Bayesian framework. The parameters are adjusted with great precision through iterative optimization of the EM algorithm, which includes both the expectation and maximization steps, to achieve high-quality ISAR images and track target trajectory effectively. The effectiveness of the proposed algorithm is verified using simulation data based on the millimeter-wave (MMW) radar on the Yak-42 aircraft model.

关键词： Trajectory tracking Heuristic algorithms Dynamics Imaging Coherence Radar imaging iterative algorithms

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The Harvesting Line Fitting for Intelligent Oolong Tea Harvesting Robot

The Harvesting Line Fitting for Intelligent Oolong Tea Harve...

引用

International Conference on Robotics and Automation Engineering (ICRAE)

作者： Ruidong Yu Yuqiong You Zhou Fang Yinhui Xie Qiming Li Zhiqin Guo Jiyou Zhang Jun Li School of Advanced Manufacturing Fuzhou University Quanzhou China Quanzhou Institute of Equipment Manufacturing Chinese Academy of Sciences Quanzhou China Fujian College University of Chinese Academy of Sciences Fuzhou China FuJian Wuyishan Yongsheng Tea Industrial Co. LTD Wuyishan China

ISBN: (数字)9798331518301

ISBN: (纸本)9798331518318

Aimed to improve the quality of picked tea leaves and the efficiency of tea harvesting, an intelligent oolong tea harvesting robot with a cutter adjustment module and a harvesting curve fitting algorithm is proposed. A cutter adjustment module is designed to adapt to different shapes and densities of tea bushes, ensuring precise tea harvesting. An adaptive density-weighted RANSAC algorithm is proposed to fit a harvesting line that matches the contour of cutter. Our algorithm enhances the robustness of point cloud recognition by weighting the samples based on sparsity and improves response speed by dynamically updating the iterative threshold. Experiments were conducted using a dataset of 30,000 frames of tea bush point clouds collected by us in a tea garden in Wuyi Mountain. Our algorithm takes only 0.081 seconds on average to fit a single frame of tea bush point cloud, achieving a coefficient of determination of 0.7270. The experimental results show that our algorithm exhibits great robustness and responsiveness in handling sparse point clouds of tea bushes.

关键词： Point cloud compression Location awareness Shape Heuristic algorithms Fitting Robustness Real-time systems iterative algorithms Curve fitting Robots

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Comparative Analysis of Glottal and Vocal Tract Features in Dysarthria

Comparative Analysis of Glottal and Vocal Tract Features in ...

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Geeta Sai Sahasra Kadwasra Swapna Arushi Srivastava Aditya Pusuluri Hemant A. Patil Koneru Lakshmaiah Education Foundation Hyderabad Telangana Dhirubhai Ambani Institute of Information and Communication Technology Gandhinagar Gujarat

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Dysarthria is a neurological disorder associated with the muscles in the vocal tract system that is caused by problems with coordination. It affects the vocal tract system and the glottis with different levels of severity. This research deals with the role of vocal tract (i.e., filter) and glottal excitation source in dysarthric speech, using Glottal Flow Model with iterative Adaptive Inverse Filtering (GFM-IAIF). By decomposing the vocal tract and glottal source into two separate components, we were able to identify which component (source or filter) is affected the most. Our research showed that the vocal tract system is the most affected part, determining a better classification of features the magnitude spectrum-based Mel frequency cepstral coefficient (MFCC) resulted in 95.75% accuracy for vocal tract components and lower accuracy of 86.5% for glottal source components. In the same way, Modified Group Delay Cepstral Coefficients (MGDCC) correlated with a test accuracy of 94.43% of the vocal tract in comparison with 88.70% of the glottal source. These outcomes reveal that the vocal tract gets most damaged due to dysarthria and thus, emphasis the use of specific diagnostic and therapy interventions targeted at this area. The research not only explores feature fusion but also recommends further efforts to refine dysarthria diagnosis and treatment.

关键词： Neurological diseases Adaptation models Accuracy Filtering Medical treatment Information processing Muscles iterative algorithms Delays Mel frequency cepstral coefficient

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Comparison of recursive and nonrecursive linearization-based algorithms for one class of nonlinear estimation problems*

Comparison of recursive and nonrecursive linearization-based...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Oleg A. Stepanov Yulia A. Litvinenko Alexey M. Isaev Concern CSRI Elektropribor JSC ITMO University Saint Petersburg Russia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Two schemes of suboptimal estimation algorithms synthesized using the Bayesian approach and based on the linearization of functions describing the behavior of the estimated state vector and measurement model are compared for one class of nonlinear estimation problems. One of them is traditional, in which the estimate is found recursively with respect to measurements, and the other one, nonrecursive, involves the simultaneous use of the full set of all available measurements. The advantages and disadvantages of the analyzed algorithms are discussed and illustrated by an example.

关键词： Accuracy Estimation Vectors iterative algorithms Bayes methods Information technology

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Pruning-Based Ensemble Tree for Multi-Class Classification

Pruning-Based Ensemble Tree for Multi-Class Classification

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International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Maliha Mehjabin Mitu Sadot Arefin Zulkarnain Saurav Md. Afanur Hasan Dewan Md. Farid Department of Computer Science and Engineering United International University Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Decision tree is one of the most popular classifiers in supervised learning to address classification tasks. It is easy to build and easy to understand. It is a divide-and-conquer algorithm and provides a clear path for making decisions. Decision tree can be used for both regression and classification problems. It finds the best splitting features as a root node and create sub-datasets. A leaf node of decision tree represents a decision where all and majority of the instances of a sub-dataset belongs to a particular decision or class-value. The commonly used algorithms are ID3 (iterative Dichotomiser 3), C4.5 (improvement over ID3 algorithm), and CART (Classification And Regression Tree). Decision tree can be easily scalable and apply for Big Data mining. In this paper, we have proposed a new pruning-based tree ensemble method where each tree is build from the each input feature of the dataset. Therefore, $N$ number of trees will be created for $N$ number of features. Then, we apply post pruning technique on each tree and select the best trees according to their cost-complexity. We have considered the size and accuracy of the tree to select in the ensemble process. The proposed approach surpasses the performance of the C4.5 classifier by almost 11 % for the selected benchmark datasets taken from the UCI machine learning repository.

关键词： Training Machine learning algorithms Supervised learning Training data Big Data Sensitivity and specificity iterative algorithms

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