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Indoor Uncertain Semantic Trajectory Similarity Join

Journal of Computer Science & Technology 2024年第6期39卷 1441-1465页

作者： Hong-Bo Yin Dong-Hua Yang Kai-Qi Zhang Hong Gao Jian-Zhong Li Faculty of Computing Harbin Institute of TechnologyHarbin 150001China Center of Analysis Measurement and ComputingHarbin Institute of TechnologyHarbin 150001China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

With the widespread deployment of indoor positioning systems, an unprecedented scale of indoor trajectories is being produced. By considering the inherent uncertainties and the text information contained in such an indoor trajectory, a new definition named Indoor Uncertain Semantic Trajectory is defined in this paper. In this paper, we focus on a new primitive, yet quite essential query named Indoor Uncertain Semantic Trajectory Similarity Join (IUST-Join for short), which is to match all similar pairs of indoor uncertain semantic trajectories from two sets. IUST-Join targets a number of essential indoor applications. With these applications in mind, we provide a purposeful definition of an indoor uncertain semantic trajectory similarity metric named IUS. To process IUST-Join more efficiently, both an inverted index on indoor uncertain semantic trajectories named 3IST and the first acceleration strategy are proposed to form a filtering-and-verification framework, where most invalid pairs of indoor uncertain semantic trajectories are pruned at quite low computation cost. And based on this filtering-and-verification framework, we present a highly-efficient algorithm named Indoor Uncertain Semantic Trajectory Similarity Join Processing (USP for short). In addition, lots of novel and effective acceleration strategies are proposed and embedded in the USP algorithm. Thanks to these techniques, both the time complexity and the time overhead of the USP algorithm are further reduced. The results of extensive experiments demonstrate the superior performance of the proposed work.

关键词： filtering-and-verification framework indoor uncertain semantic trajectory inverted index trajectory similarity join

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Generating diverse clothed 3D human animations via a generative model

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Computational Visual Media 2024年第2期10卷 261-277页

作者： Min Shi Wenke Feng Lin Gao Dengming Zhu School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Data-driven garment animation is a current topic of interest in the computer graphics *** approaches generally establish the mapping between a single human pose or a temporal pose sequence,and garment deformation,but it is difficult to quickly generate diverse clothed human *** address this problem with a method to automatically synthesize dressed human animations with temporal consistency from a specified human motion *** the heart of our method is a twostage ***,we first learn a latent space encoding the sequence-level distribution of human motions utilizing a transformer-based conditional variational autoencoder(Transformer-CVAE).Then a garment simulator synthesizes dynamic garment shapes using a transformer encoder-decoder *** the learned latent space comes from varied human motions,our method can generate a variety of styles of motions given a specific motion *** means of a novel beginning of sequence(BOS)learning strategy and a self-supervised refinement procedure,our garment simulator is capable of efficiently synthesizing garment deformation sequences corresponding to the generated human motions while maintaining temporal and spatial *** verify our ideas *** is the first generative model that directly dresses human animation.

关键词： Transformer garment animation conditional variational autoencoder(CVAE) computer graphics

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Motion-Inspired Real-Time Garment Synthesis with Temporal-Consistency

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Journal of Computer Science & Technology 2023年第6期38卷 1356-1368页

作者：魏育坤石敏冯文科朱登明毛天露 School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Synthesizing garment dynamics according to body motions is a vital technique in computer ***-based simulation depends on an accurate model of the law of kinetics of cloth,which is time-consuming,hard to implement,and complex to *** data-driven approaches either lack temporal consistency,or fail to handle garments that are different from body *** this paper,we present a motion-inspired real-time garment synthesis workflow that enables high-level control of garment *** a sequence of body motions,our workflow is able to gen-erate corresponding garment dynamics with both spatial and temporal *** that end,we develop a transformer-based garment synthesis network to learn the mapping from body motions to garment ***-level attention is employed to capture the dependency of garments and body ***,a post-processing procedure is further tak-en to perform penetration removal and ***,textured clothing animation that is collision-free and tempo-rally-consistent is *** quantitatively and qualitatively evaluated our proposed workflow from different *** experiments demonstrate that our network is able to deliver clothing dynamics which retain the wrinkles from the physics-based simulation,while running 1000 times ***,our workflow achieved superior synthesis perfor-mance compared with alternative *** stimulate further research in this direction,our code will be publicly available soon.

关键词： clothing animation computer graphics transformer temporal consistency

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Accurate Robotic Grasp Detection with Angular Label Smoothing

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Journal of Computer Science & Technology 2023年第5期38卷 1149-1161页

作者：石敏路昊李兆歆朱登明王兆其 School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Grasp detection is a visual recognition task where the robot makes use of its sensors to detect graspable objects in its *** the steady progress in robotic grasping,it is still difficult to achieve both real-time and high accuracy grasping *** this paper,we propose a real-time robotic grasp detection method,which can accurately predict potential grasp for parallel-plate robotic grippers using RGB *** work employs an end-to-end convolutional neural network which consists of a feature descriptor and a grasp *** for the first time,we add an attention mechanism to the grasp detection task,which enables the network to focus on grasp regions rather than ***,we present an angular label smoothing strategy in our grasp detection method to enhance the fault tolerance of the *** quantitatively and qualitatively evaluate our grasp detection method from different aspects on the public Cornell dataset and Jacquard *** experiments demonstrate that our grasp detection method achieves superior performance to the state-of-the-art *** particular,our grasp detection method ranked first on both the Cornell dataset and the Jacquard dataset,giving rise to the accuracy of 98.9%and 95.6%,respectively at realtime calculation speed.

关键词： robotic grasp detection attention mechanism angular label smoothing anchor box deep learning

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Online coal dust suppression system for opencast coal mines

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International Journal of Coal Science & Technology 2023年第5期10卷 178-186页

作者： Senju Panicker S.S.Shankar S.Jithin S.Sandeep Muhammed Irshad Jerry Daniel Tarique Sajad Control and Instrumentation Group Centre for Development of Advanced ComputingThiruvananthapuramKeralaIndia Mining Electronics Department Central Mine Planning and Design InstituteRanchiJharkhandIndia

Coal is the major source of power in India and world *** mining is an essential industry which has a major role in the economic development of the *** major mining activities contribute directly or indirectly to air *** dust is a major air pollutant which affects the personal working in the mines and also people residing in villages near the *** pollution due to coal particulates can affect human health and cause damages to the *** effective pollution control mechanisms are needed to keep the pollution levels within permissible *** easiest and most common method employed for dust suppression worldwide is sprinkling of *** majority of mines,water sprinklers are operated manually and can lead to wastage of water due to over *** can also prove to be ineffective in dust suppression if sprinkling is not done *** paper proposes a system which can be deployed to automate the dust suppressions *** system will monitor the concentration of PM_(10) and PM_(2.5) in the air and initiate sprinkling operation when the particulate matter content exceeds preconfigured limits.

关键词： Coal dust Dust suppression Sprinkling Automation Particulate matter PMio-PM2.5

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THE STUDY BY SPLINES OF NORM INEQUALITIES FOR RIEMANN–LIOUVILLE OPERATORS IN WEIGHTED BESOV SPACES

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Journal of Mathematical Sciences (United States) 2024年第5期280卷 758-820页

作者： Ushakova, Elena P. V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Computing Center of Far Eastern Branch of Russian Academy of Sciences

The work is devoted to the study of boundedness properties of integration and differentiation operators of Riemann–Liouville type on the real axis and semi–axis. The operators are acting in smoothness function spaces of Besov type with Muckenhoupt weight functions (weights) of standard and local type. The problem posed is solved by decomposing elements of the function spaces with respect to spline wavelet systems, which are the main solution tools. The article presents in detail a scheme for constructing such systems. In their terms, the corresponding decomposition theorems are established in the paper. The main results of the study are conditions on weights for the fulfilment of inequalities connecting the norms of images and pre–images of Riemann–Liouville integration operators. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： B-spline Battle–Lemarié wavelet system Besov space Decomposition theorem Local Muckenhoupt weight Muckenhoupt weight Riemann–Liouville operator

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High-precision shape approximation low-thrust trajectory optimization method satisfying bi-objective index

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Chinese Journal of Aeronautics 2022年第1期35卷 436-457页

作者： Ruiye JIANG Ming YANG Songyan WANG Tao CHAO Rajan SHANKARAN Control and Simulation Center Harbin Institute of TechnologyHarbin 150080China Department of Computing Macquarie UniversitySydneyNSW 2109Australia

The shape approximation method has been proven to be rapid and practicable in resolving low-thrust trajectory;however,it still faces the challenges of large deviation from the optimal solution and inability to satisfy the specific flight time and fuel mass *** this paper,a modified shape approximation low-thrust model is presented,and a novel constrained optimization algorithm is developed to solve this *** proposed method aims at settling the bi-objective optimization orbit involving the twin objectives of minimum flight time and low fuel consumption and enhancing the accuracy of optimized *** particular,a transformed high-order polynomial model based on finite Fourier series is proposed,which can be characterized as a multi-constraint optimization ***,a novel optimization algorithm is specifically developed to optimize the large-scale multi-constraint dynamical equations of shape *** key performance indicators of the index include minimum flight time,low fuel consumption and bi-objective optimization of the *** results prove that this approach possesses both the high precision achievable by numerical methods and low computational complexity offered by shape approximation ***,the Pareto front of the fuel-time bi-objective optimization orbit is firstly introduced to analyze an intact optimal solution ***,we have demonstrated that our proposed approach is appropriate to generate the preliminary orbit for pseudo-spectral method.

关键词： Constrained optimization Intelligent optimization Low-thrust trajectory Multi-objective optimization Shape approximation method

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Research on UAV-Based Mars Topography Mapping and 3D Reconstruction System 11th

Research on UAV-Based Mars Topography Mapping and 3D Reconst...

引用

11th International Conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： Chen, Long Zhao, Pin Chen, Huajun Liu, Xintan Beijing Institute of Control Engineering Beijing China North China Institute of Computing Technology Langfang China Beijing Institute of Spacecraft Environment Engineering Beijing China

ISBN: (纸本)9789819721153

The 3D reconstruction technology is of great significance for the research of Martian topography. In this paper, an unmanned aerial vehicle (UAV) based Mars topography mapping and 3D reconstruction system is proposed. In the system, an on-board range-visual-inertial odometry (on-board range-VIO) is applied to solve UAV navigation and flight path planning problems. The pictures of simulated Mars topography are photted. Images are processed and 3D model is reconstructed by applying high-performance computing unit (HPCU). By building an experimental system on the ground and conducting flight simulation and 3D reconstruction experiments, the effectiveness of the system was verified. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Topography

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OAFuser: Toward Omni-Aperture Fusion for Light Field Semantic Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6225-6239页

作者： Teng, Fei Zhang, Jiaming Peng, Kunyu Wang, Yaonan Stiefelhagen, Rainer Yang, Kailun Hunan University School of Robotics National Engineering Research Center of Robot Visual Perception and Control Technology Changsha410082 China Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Karlsruhe76131 Germany Institute for Visual Computing ETH Zurich Zurich8092 Switzerland

Light field cameras are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation. However, two significant issues arise: 1) The extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resources of intelligent agents. 2) A relative displacement difference exists in the data collected by different microlenses. To address these issues, we propose an omni-aperture fusion model (OAFuser) that leverages dense context from the central view and extracts the angular information from subaperture images to generate semantically consistent results. To simultaneously streamline the redundant information from the light field cameras and avoid feature loss during network propagation, we present a simple yet very effective subaperture fusion module (SAFM). This module efficiently embeds subaperture images in angular features, allowing the network to process each subaperture image with a minimal computational demand of only (∼1\rm GFlops). Furthermore, to address the mismatched spatial information across viewpoints, we present a center angular rectification module (CARM) to realize feature resorting and prevent feature occlusion caused by misalignment. The proposed OAFuser achieves state-of-the-art performance on four UrbanLF datasets in terms of all evaluation metrics and sets a new record of 84.93% in mIoU on the UrbanLF-Real Extended dataset, with a gain of +3.69%. The source code for OAFuser is available at https://***/FeiBryantkit/OAFuser. © 2020 IEEE.

关键词： Semantic Segmentation

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Machine learning and deep learning tools for the automated capture of cancer surveillance data

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Journal of the National Cancer institute - Monographs 2024年第65期2024卷 145-151页

作者： Hsu, Elizabeth Hanson, Heidi Coyle, Linda Stevens, Jennifer Tourassi, Georgia Penberthy, Lynne Surveillance Research Program Division of Cancer Control and Population Sciences National Cancer Institute Advanced Computing for Health Sciences Computing and Computational Sciences Directorate Oak Ridge National Laboratory Information Management Services Inc Computing and Computational Sciences Directorate Oak Ridge National Laboratory

The National Cancer institute and the Department of Energy strategic partnership applies advanced computing and predictive machine learning and deep learning models to automate the capture of information from unstructured clinical text for inclusion in cancer registries. Applications include extraction of key data elements from pathology reports, determination of whether a pathology or radiology report is related to cancer, extraction of relevant biomarker information, and identification of recurrence. With the growing complexity of cancer diagnosis and treatment, capturing essential information with purely manual methods is increasingly difficult. These new methods for applying advanced computational capabilities to automate data extraction represent an opportunity to close critical information gaps and create a nimble, flexible platform on which new information sources, such as genomics, can be added. This will ultimately provide a deeper understanding of the drivers of cancer and outcomes in the population and increase the timeliness of reporting. These advances will enable better understanding of how real-world patients are treated and the outcomes associated with those treatments in the context of our complex medical and social environment. © 2024 Published by Oxford University Press.

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