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IEEE Conference on Decision and Control

作者： Vignesh Kottayam Viswanathan Andreas Papadimitriou Avijit Banerjee Sina Sharif Mansouri George Nikolakopolous Department of Computer Electrical and Space Engineering Robotics and AI Team Luleå University of Technology Sweden Autonomous Driving Lab in Scania Group Sweden

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this article, we propose the implementation of a Nonlinear Moving Horizon Estimation (NMHE) framework to estimate exogenous disturbances acting on a spacecraft for autonomous navigation around Small Celestial Bodies (SCBs). The estimation framework is coupled with a Nonlinear Model Predictive Control (NMPC) to promote robust autonomous operations in Space. The NMHE based exogenous disturbance estimation formulates a finite horizon optimization problem, while incorporating the lumped disturbances as an additional augmented state vector. Next, the estimated disturbance is utilized by the NMPC controller in a feed-forward manner. Numerous closed-loop simulations have been conducted to assess the validity of the proposed estimation and disturbance rejection framework by considering: a) Inertial hovering around two different asteroid bodies namely 433 Eros and Ryugu and b) unavailability of two primary accurate asteroid characterization data, the asteroid rotation rate n a and the gravitational parameter µ. The presented results are compared with an Extended Kalman Filter (EKF) and NMPC formulation to validate the efficacy of the proposed framework.

关键词： Space vehicles Navigation Estimation Predictive models Data models Orbits Solar system

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FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration

arXiv

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arXiv 2023年

作者： Stathoulopoulos, Nikolaos Koval, Anton Agha-Mohammadi, Ali-Akbar Nikolakopoulos, George Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå971 87 Sweden Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots’ poses. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework’s main pipeline, offer a fast and robust region overlap estimation, hence eliminating the need for the time-consuming global feature extraction and feature matching process that is typically used in 3D map integration. The region overlap estimation provides a homogeneous rigid transform that is applied as an initial condition in the point cloud registration algorithm Fast-GICP, which provides the final and refined alignment. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments, where both ground and aerial robots are deployed, with different sensor configurations. Copyright © 2023, The Authors. All rights reserved.

关键词： Antennas

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Cross-Task and Time-Aware Adversarial Attack Framework for Perception of Autonomous Driving

SSRN

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SSRN 2023年

作者： Lu, Yantao Liu, Ning Li, Yilan Chen, Jinchao Velipasalar, Senem Northwestern Polytechnical University School of Computer Science Shaanxi Xi’an China Midea Group AI Innovation Center Beijing China Xi’an University of Technology School of Computer Science and Engineering Shaanxi Xi’an China Syracuse University Department of Electrical Engineering and Computer Science NY13244 United States

Despite the rapid advances in adversarial machine learning, state-of-the-art attack methods encounter practical limitations in the field of onboard perception that require real-time and multi-task processing. Conventional attacks target on a specific perception task such as object detection or segmentation, which is difficult to simultaneously penetrate the entire multi-task perception module. Moreover, the real-time processing requirement further restrict the existing attacks to be deployed on practical autonomous driving platforms. To address these limitations, we propose Perception Streaming Attack (PSA), which is a sequence-based cross-task adversarial attack framework. We firstly propose Priori Perturbation Generator (PPG) to calculate a priori perturbation by leveraging the perturbation of previous frame as well as the motion information between the previous and current frames. Then, we propose Posterior Perturbation Updater (PPU) to refine the priori perturbation and obtain the final adversarial example for current frame. Comprehensive experimental evaluations on BDD100k and NuImages datasets demonstrate that the proposed PSA, compared with the state-of-the-art attacks, can effectively and efficiently attack across different tasks used in onboard perception. We also deploy our Perception Streaming Attack framework on a single-board computer (NVIDIA Jetson AGX Xavier) to validate the on-board performance. The experimental results show that the proposed PSA can successfully run at 12 Hz and effectively erase at least 76% objects that should be sensed. © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Retrieving Soil Organic Matter and Soil Moisture Profiles of the Arctic Foothills Tundra Using P-band Polarimetric SAR Imagery

Retrieving Soil Organic Matter and Soil Moisture Profiles of...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Kazem Bakian-Dogaheh Yuhuan Zhao John S. Kimball Mahta Moghaddam Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA Numerical Terradynamic Simulation Group W. A. Franke College of Forestry & Conservation University of Montana Missoula MT

This paper presents a physics-based radar modeling framework that enables joint retrievals of the Arctic tundra permafrost active layer soil organic matter content and soil moisture profile using P-band polarimetric SAR. Initially, an extensive set of field observations are used to model the subsurface soil moisture and organic matter profiles with independent model parameters. Then a new organic soil dielectric model is used to translate the soil profile properties into equivalent dielectric properties that bridge the soil field properties to their manifestation in radar measurements. Finally, a multi-layered dielectric structure is adopted by an electromagnetic scattering computational model that predicts equivalent backscattering coefficients resulting from the soil profile state parameters. These pieces together construct the forward model, which is at the heart of a physics-based radar retrieval algorithm. Finally, we integrate the developed model into a retrieval scheme, in which the soil moisture and organic profile model parameters are estimated using radar backscattering coefficients measured by airMOSS P-band radar. We show retrieved soil moisture and soil organic matter profiles, derived pixel-wise by the algorithm providing the first airborne-driven soil organic carbon map.

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DAGAM: Data Augmentation with Generation And Modification

arXiv

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arXiv 2022年

作者： Jo, Byeong-Cheol Heo, Tak-Sung Park, Yeongjoon Yoo, Yongmin Cho, Won Ik Kim, Kyungsun AI R&D Group NHN Diquest Korea Republic of Department of Electrical and Computer Engineering and INMC Seoul National University Korea Republic of

Text classification is a representative downstream task of natural language processing, and has exhibited excellent performance since the advent of pre-trained language models based on Transformer architecture. However, in pre-trained language models, under-fitting often occurs due to the size of the model being very large compared to the amount of available training data. Along with significant importance of data collection in modern machine learning paradigm, studies have been actively conducted for natural language data augmentation. In light of this, we introduce three data augmentation schemes that help reduce underfitting problems of large-scale language models. Primarily we use a generation model for data augmentation, which is defined as Data Augmentation with Generation (DAG). Next, we augment data using text modification techniques such as corruption and word order change (Data Augmentation with Modification, DAM). Finally, we propose Data Augmentation with Generation And Modification (DAGAM), which combines DAG and DAM techniques for a boosted performance. We conduct data augmentation for six benchmark datasets of text classification task, and verify the usefulness of DAG, DAM, and DAGAM through BERT-based fine-tuning and evaluation, deriving better results compared to the performance with original datasets. © 2022, CC BY.

关键词： Natural language processing systems

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Versatile Video Coding Performance Evaluation for Tiled 360° Videos 28

Versatile Video Coding Performance Evaluation for Tiled 360...

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28th European Wireless Conference, EW 2023

作者： Mahmoud, Moatasim Rizou, Stamatia Kantartzis, Nikolaos V. Valiandi, Ioanna Lazaridis, Pavlos I. Zaharis, Zaharias D. Panayides, Andreas S. Karagiannidis, George K. Singular Logic Aristotle University of Thessaloniki Greece R&D Department Singular Logic Athens14564 Greece School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece VIDEOMICS Group CYENS Centre of Excellence Nicosia Cyprus School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom Cyber Security Systems and Applied AI Research Center Lebanese American University Lebanon

ISBN: (纸本)9783800762262

Recently, virtual reality (VR) and 360° videos have been increasingly adopted in a wide range of applications. However, the huge sizes of 360° videos necessitate the use of powerful compression tools to allow for efficient storage and streaming of these videos. In this article, we compare the performance of four different video codecs, including AV1 and versatile video coding (VVC), when applied to a dataset of 360° videos. We conduct our evaluation according to peak signal-to-noise ratio (PSNR)-based 360° video quality assessment (VQA) metrics. As omnidirectional videos are usually streamed as independent tiles, we also investigate the tiling effect on VVC compression performance by comparing full-frame-based compression to tiled segment compression using grids of 8x8 and 16x16 tiles. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Virtual reality

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A Light Gradient Residual Encoder-Decoder Network for Multimodal Image Fusion

A Light Gradient Residual Encoder-Decoder Network for Multim...

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Intelligent Computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Muhammad Ishfaq Hussain Zubia Naz Linh Van Ma Jeonghwan Gwak Moongu Jeon School of Electrical Engineering and Computer Sciene Gwangju Institute of Science and Technology (GIST) South Korea Large-scale AI Research Group Korea Institute of Science and Technology Information (KISTI) South Korea Department of Software Korea National University of Transportation South Korea

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

Image fusion combines the complementary traits of source images into a single output, enhancing both human visual observation and machine vision perception. The existing fusion algorithms typically prioritize visual enhancement, often overlooking the real-time needs for critical surveillance applications. To address these real-time deployment needs, we present a compact fusion network for combining infrared and visible image representations, named Light-weight Fusion (LightFusion). This network employs incremental semantic integration and scene recognition accuracy constraints by incorporating three different bands of images (IR, RGB, and Grayscale) to fuse the data. Our approach includes a sparse semantic perception branch that captures critical semantic features, which are then integrated into the fusion network through a semantic injection module. This ensures that high-level vision tasks are adequately addressed. The scene fidelity path ensures that fusion features preserve all details required to reconstruct the original images. The importance and applicability of the proposed network are enhanced by employing an extra input in the form of a grayscale image, obtained by converting the RGB image for improved contrast, along with prominent target masks to enhance the visual quality of the fusion results. Our extensive analysis shows that the lightweight LightFusion network outperforms existing methods in both visual quality and semantic integrity, even under challenging conditions. The source code will be released at https://***/MI-HussainiLightFusion.

关键词： Visualization Image recognition Surveillance Source coding Semantics Gray-scale Real-time systems Robustness Image reconstruction Image fusion

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GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations

arXiv

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arXiv 2023年

作者： Ranasinghe, Nisal Senanayake, Damith Seneviratne, Sachith Premaratne, Malin Halgamuge, Saman AI Optimization and Pattern Recognition Research Group Dept. of Mechanical Eng. University of Melbourne Australia Melbourne School of Design University of Melbourne Australia Department of Electrical and Computer Systems Engineering Monash University Australia

Traditional machine learning is generally treated as a black-box optimization problem and does not typically produce interpretable functions that connect inputs and outputs. However, the ability to discover such interpretable functions is desirable. In this work, we propose GINN-LP, an interpretable neural network to discover the form and coefficients of the underlying equation of a dataset, when the equation is assumed to take the form of a multivariate Laurent Polynomial. This is facilitated by a new type of interpretable neural network block, named the "power-term approximator block", consisting of logarithmic and exponential activation functions. GINN-LP is end-to-end differentiable, making it possible to use backpropagation for training. We propose a neural network growth strategy that will enable finding the suitable number of terms in the Laurent polynomial that represents the data, along with sparsity regularization to promote the discovery of concise equations. To the best of our knowledge, this is the first model that can discover arbitrary multivariate Laurent polynomial terms without any prior information on the order. Our approach is first evaluated on a subset of data used in SRBench, a benchmark for symbolic regression. We first show that GINN-LP outperforms the state-of-the-art symbolic regression methods on datasets generated using 48 real-world equations in the form of multivariate Laurent polynomials. Next, we propose an ensemble method that combines our method with a high-performing symbolic regression method, enabling us to discover non-Laurent polynomial equations. We achieve state-of-the-art results in equation discovery, showing an absolute improvement of 7.1% over the best contender, by applying this ensemble method to 113 datasets within SRBench with known ground-truth equations. © 2023, CC BY-NC-ND.

关键词： Polynomials

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RETRACTED ARTICLE: Distributed energy management for networked microgrids in a three-phase unbalanced distribution network

引用

Frontiers in Energy 2023年第3期17卷 446-446页

作者： Guodong LIU Thomas BOLLIS Maximiliano F.FERRARIL Aditya SUNDARARAJAN Yang CHEN Mohammed M.OLAMA Kevin TOMSOVIC Grid Components and Controls Group Oak Ridge National LaboratoryOak RidgeTN 37831USA Water Resource Science and Engineering Group Oak Ridge National LaboratoryOak RidgeTN 37831USA Modeling and Simulation Group Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Electrical Engineering and Computer Science The University of TennesseeKnoxvilleTN 37996USA

The Editor-in-Chief has retracted this article because of a substantial overlap with a different article by the same authors that was under submission at the same time[1]*** authors did not explicitly state whether they agree to this ***,'one of the authors'name was spelled wrong in the original article,i.e.,Maximiliano *** should be Maximiliano *** online version of this article contains the full text of the retracted article as electronic supplementary material.

关键词： material. network Ferrari

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Rethinking Self-Supervised Learning for Time Series Forecasting: A Temporal Perspective

SSRN

引用

SSRN 2024年

作者： Zhao, Shubao Li, Zengxiang Zhou, Xinxing Jin, Ming Hou, Zhaoxiang Yang, Chengyi Wen, Qingsong Wang, Yi Wen, Yanlong Yuan, Xiaojie Digital Research Insitute ENN Group Beijing China College of Computer Science Nankai University Tianjin China School of Information and Communication Technology Griffith University Nathan Australia Squirrel AI Bellevue United States Department of Electrical and Engineering The University of Hong Kong Hong Kong

Self-supervised learning, including contrastive learning and masked modeling, has garnered significant attention for its ability to learn meaningful representations. Recent efforts have enhanced time series forecasting by introducing advanced self-supervised pre-training methods. However, these efforts have faced limitations due to two primary drawbacks. Firstly, these approaches often borrow techniques from vision and language domains without adequately addressing the unique temporal dependencies inherent in time series data. Secondly, different pre-training strategies lack an organic combination to build a more powerful feature extractor. To address these issues, we propose TempSSL, a self-supervised learning framework for time series forecasting. TempSSL divides the time series data into context (history data) and target (future data), employing two pre-training strategies: (1) Temporal Masked Modeling (TMM) designed to capture temporal dependencies by reconstructing future time series based on historical context;(2) Temporal Contrastive Learning (TCL) employs context and target as positive samples to enhance discriminative representations and mitigate distribution shifts within the time series. TempSSL's innovation lies in two key aspects. Firstly, it underscores the importance of temporal dependencies for time series forecasting by designing specific pre-training tasks. Secondly, it effectively integrates contrastive learning and masked modeling, leveraging their respective strengths to develop time series representation with strong instance discriminability and local perceptibility. Extensive experiments conducted on seven mainstream datasets demonstrate that TempSSL significantly outperforms contemporary self-supervised and end-to-end learning methods by a considerable margin of 1.92~78.12\%. The effectiveness of TempSSL is further demonstrated through its application in natural gas demand forecasting. © 2024, The Authors. All rights reserved.

关键词： Time series

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