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检索条件"任意字段=International Conference on Pattern Analysis and Intelligence Robotics"

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Radiation patterns analysis of Monopulse Antenna System 3

Radiation Patterns Analysis of Monopulse Antenna System

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3rd international conference on Mechatronics and Intelligent robotics (ICMIR)

作者： Zong, Bin Feng Zeng, Hui Yong Cao, Nai Sen Geng, Lin Air Force Engn Univ Air & Missile Def Coll Xian 710051 Peoples R China Air Force Engn Univ Aviat Maintenance NCO Acad Xian 710051 Peoples R China

Monopulse antenna can provide multiple beams simultaneously and use single pulse echo signal to form sum and difference signals for ranging and angle measurement. As the front end of the monopulse radar system, the performance of the monopulse antenna is crucial to the range detection and angle measurement accuracy of the radar system. For the monopulse antenna system, the pattern characteristics determine its performance. In this paper, the factors affecting the pattern characteristics of monopulse antenna are studied. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Radiation pattern monopulse antenna

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25th international conference on pattern Recognition Workshops, ICPR 2020

25th International Conference on Pattern Recognition Worksho...

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25th international conference on pattern Recognition Workshops, ICPR 2020

ISBN: (纸本)9783030687793

The proceedings contain 414 papers. The special focus in this conference is on pattern Recognition. The topics include: Developing a Segmentation Model for Microscopic Images of Microplastics Isolated from Clams;foreword by general chairs;preface;finding Relevant Flood Images on Twitter Using Content-Based Filters;a Machine Learning Approach to Chlorophyll a Time Series analysis in the Mediterranean Sea;plankton Recognition in Images with Varying Size;Environment Object Detection for Marine ARGO Drone by Deep Learning;unsupervised Learning of High Dimensional Environmental Data Using Local Fractality Concept;spatiotemporal Air Quality Inference of Low-Cost Sensor Data;Application on a Cycling Monitoring Network;How Do Deep Convolutional SDM Trained on Satellite Images Unravel Vegetation Ecology?;latent Space Geometric Statistics;improving Neural Network Robustness Through Neighborhood Preserving Layers;metric Learning on the Manifold of Oriented Ellipses: Application to Facial Expression Recognition;An OCR Pipeline and Semantic Text analysis for Comics;natural Disaster Classification Using Aerial Photography Explainable for Typhoon Damaged Feature;manga Vocabulometer, A New Support System for Extensive Reading with Japanese Manga Translated into English;Automatic Landmark-Guided Face Image Generation for Anime Characters Using C2 GAN;text Block Segmentation in Comic Speech Bubbles;hierarchical Consistency and Refinement for Semi-supervised Medical Segmentation;BVTNet: Multi-label Multi-class Fusion of Visible and Thermal Camera for Free Space and Pedestrian Segmentation;multimodal Emotion Recognition Based on Speech and Physiological Signals Using Deep Neural Networks;cross-modal Deep Learning Applications: Audio-Visual Retrieval;exploiting Word Embeddings for Recognition of Previously Unseen Objects;visual Word Embedding for Text Classification.

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25th international conference on pattern Recognition Workshops, ICPR 2020

25th International Conference on Pattern Recognition Worksho...

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25th international conference on pattern Recognition Workshops, ICPR 2020

ISBN: (纸本)9783030687625

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25th international conference on pattern Recognition Workshops, ICPR 2020

25th International Conference on Pattern Recognition Worksho...

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25th international conference on pattern Recognition Workshops, ICPR 2020

ISBN: (纸本)9783030688202

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Spatial-Temporal Graph Neural Networks Fusing Multiple Data

Spatial-Temporal Graph Neural Networks Fusing Multiple Data

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： Haonan Xu Wenfang Xue Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Recently, quantitative investment has become a new way of investment finance, which combines the model with the computer to predict the investment stock trend more efficiently and accurately. Stock trend forecasting is one of the difficult and important tasks in modern quantitative investment systems. With the development of artificial intelligence technology, many deep learning models have emerged. The application of deep learning in the stock market not only reduces the difficulty of stock analysis and trend prediction, but also introduces new investment methods and ideas for investors. However, most of the models are based on volume and price data, and the consideration of stock relationships are relatively simple, so we propose a Spatial-Temporal Graph Neural Network model fusing Barra factors (BSTGNN), which integrates factor knowledge, mines potential inter-stock relationships, and fully consider the heterogeneity of the relationship. The experiments conducted on the real-world stock market dataset prove that BSTGNN perform better than the baseline methods.

关键词： Deep learning Solid modeling Computational modeling Time series analysis Market research Graph neural networks Data models

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Class-distribution-aware pseudo-labeling for semi-supervised multi-label learning 23

Class-distribution-aware pseudo-labeling for semi-supervised...

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Ming-Kun Xie Jia-Hao Xiao Hao-Zhe Liu Gang Niu Masashi Sugiyama Sheng-Jun Huang Nanjing University of Aeronautics and Astronautics and MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China RIKEN Center for Advanced Intelligence Project RIKEN Center for Advanced Intelligence Project and The University of Tokyo Tokyo Japan

Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional pseudo-labeling methods encounter difficulties when dealing with instances associated with multiple labels and an unknown label count. These limitations often result in the introduction of false positive labels or the neglect of true positive ones. To overcome these challenges, this paper proposes a novel solution called Class-Aware Pseudo-Labeling (CAP) that performs pseudo-labeling in a class-aware manner. The proposed approach introduces a regularized learning framework incorporating class-aware thresholds, which effectively control the assignment of positive and negative pseudo-labels for each class. Notably, even with a small proportion of labeled examples, our observations demonstrate that the estimated class distribution serves as a reliable approximation. Motivated by this finding, we develop a class-distribution-aware thresholding strategy to ensure the alignment of pseudo-label distribution with the true distribution. The correctness of the estimated class distribution is theoretically verified, and a generalization error bound is provided for our proposed method. Extensive experiments on multiple benchmark datasets confirm the efficacy of CAP in addressing the challenges of SSMLL problems. The implementation is available at https://***/milkxie/SSMLL-CAP.

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25th international conference on pattern Recognition Workshops, ICPR 2020

25th International Conference on Pattern Recognition Worksho...

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25th international conference on pattern Recognition Workshops, ICPR 2020

ISBN: (纸本)9783030687861

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24th international conference on Distributed and Computer and Communication Networks, DCCN 2021

24th International Conference on Distributed and Computer an...

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24th international conference on Distributed and Computer and Communication Networks, DCCN 2021

ISBN: (纸本)9783030925062

The proceedings contain 29 papers. The special focus in this conference is on Distributed and Computer and Communication Networks. The topics include: The Simulation of Finite-Source Retrial Queueing Systems with Two-Way Communication and Impatient Customers;Asymptotic Waiting Time analysis of a M/GI/1 RQ System;computational Algorithm for an analysis of a Single-Line Queueing System with Arrived Alternating Poisson Flow;analysis of a Batch Service Queueing System Associated with Inventory Transport;the Analytical Method of Transient Behavior of the M|M|1|n Queuing System for Piece-Wise Constant Information Flows;analysis of Multi-server Loss Queueing System with the Batch Marked Markov Arrival Process;two Types of Single-Server Queueing Systems with Threshold-Based Renovation Mechanism;Resource Queueing System M/ GI/ ∞ in a Random Environment;numerical analysis of a Retrial System with Unreliable Servers Based on Laplace Domain Description;recent Advances in Scheduling Theory and Applications in robotics and Communications;scaling Limits of a Tandem Retrial Queue with Common Orbit and Poisson Arrival Process;on Regenerative Estimation of Extremal Index in Queueing Systems;evaluation of the Performance Parameters of a Closed Queuing Network Using Artificial Neural Networks;example of Degrading Network Slicing System in Two-Service Retrial Queueing System;durability Evaluation of a Distributed Communication Network of Weather Stations;unreliable Retrial Queueing System with a Backup Server;on k-out-of-n System Under Full Repair and Arbitrary Distributed Repair Time;using a Machine Learning Approach for analysis of Polling Systems with Correlated Arrivals;statistical analysis of psychological Results Tests;reliability Model of a Homogeneous Hot-Standby k-Out-of-n: G System;the PageRank Vector of a Scale-Free Web Network Growing by Preferential Attachment;traffic Management Algorithm for V2X-Based Flying Fog System;investigation of Wireless Hybrid Communication System

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25th international conference on pattern Recognition Workshops, ICPR 2020

25th International Conference on Pattern Recognition Worksho...

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25th international conference on pattern Recognition Workshops, ICPR 2020

ISBN: (纸本)9783030687922

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Grading Problem-Solving for Clustering Students' Score Using Dynamic Programming Procedure in The Context of Dynamic Time Warping

Grading Problem-Solving for Clustering Students' Score Using...

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international conference on Informatics and Computing (ICIC)

作者： Mochamad Nizar Palefi Ma'ady Tabina Shafa Nabila Syahda Muhammad Nasrullah Anindya Salwa Salsabila Ully Asfari Hawwin Mardhiana Department of Information Systems Institut Teknologi Telkom Surabaya Surabaya Indonesia

ISBN: (纸本)9798350345728

Clustering is the solution for grouping a plenty of data when there is no knowledge of the classes. Clustering is typically a well-known algorithm in the origins of machine learning or artificial intelligence. However, artificial intelligence is, unfortunately, relatively hard to understand for some reasons, hence, followed by the rise of explainable artificial intelligence research. In this paper, we propose an alternative approach to grouping problem-solving by using a dynamic programming procedure in the context of dynamic time warping. We conduct a cluster analysis in stagewise optimization with the following four steps; (1) define an optimal value function, (2) formulate recurrence relation, (3) set the boundary condition, and (4) answer is given by. We also demonstrate a numerical example along with its table-filling solution in the case of grading students' scores into $k$ clusters. The results conclude that dynamic time warping algorithm may have preferences to be an alternative solution for certain machine learning tasks beyond traditional unsupervised methods instead.

关键词： Machine learning algorithms Heuristic algorithms Clustering algorithms Machine learning Programming Dynamic programming pattern recognition

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