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Robust Lane Marking Detection Algorithm Using Drivable Area Segmentation and Extended SLT

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Robust Lane Marking Detection Algorithm Using Drivable Area ...

IEEE International Conference on robotics and Biomimetics

作者： Umar Ozgunalp Rui Fan Shanshan Cheng Yuxiang Sun Weixun Zuo Yilong Zhu Bohuan Xue Linwei Zheng Qing Liang Ming Liu Electrical and Electronics Engineering Cyprus International University North Cyprus Turkey Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong SAR China Guolu Gaoke Engineering Technology Institute Co. Ltd. Beijing China Robotics and Multiperception Laboratory Robotics Institute the Hong Kong University of Science and Technology Hong Kong SAR China

In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road area is segmented. Since the lane markings are on the road area and any feature point above the ground will be a noise source for the lane detection, a mask is created for the road area to remove some of the noise for lane detection. The estimated mask is multiplied by the lane feature map in a bird's eye view (BEV). The lane feature points are extracted by using an extended version of symmetrical local threshold (SLT), which not only considers dark light dark transition (DLD) of the lane markings, like (SLT), but also considers parallelism on the lane marking borders. The segmentation then uses only the feature points that are on the road area. A maximum of two linear lane markings are detected using an efficient 1D Hough transform. Then, the detected linear lane markings are used to create a region of interest (ROI) for parabolic lane detection. Finally, based on the estimated region of interest, parabolic lane models are fitted using robust fitting. Due to the robust lane feature extraction and road area segmentation, the proposed algorithm robustly detects lane markings and achieves lane marking detection with an accuracy of 91% when tested on a sequence from the KITTI dataset.

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Control for Multifunctionality: Bioinspired Control Based on Feeding in Aplysia californica

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arXiv 2020年

作者： Webster-Wood, Victoria A. Gill, Jeffrey P. Thomas, Peter J. Chiel, Hillel J. Department of Mechanical Engineering Department of Biomedical Engineering McGowan Institute for Regenerative Medicine Carnegie Mellon University 5000 Forbes Ave. PittsburghPA15213 United States Department of Biology Case Western Reserve University 2080 Adelbert Road ClevelandOH44106-7080 United States Department of Mathematics Applied Mathematics and Statistics Department of Biology Department of Cognitive Science Department of Electrical Computer and Systems Engineering Case Western Reserve University 10900 Euclid Avenue ClevelandOH44106-4901 United States Department of Biology Department of Neurosciences Department of Biomedical Engineering Case Western Reserve University 2080 Adelbert Road ClevelandOH44106-7080 United States

Animals exhibit remarkable feats of behavioral flexibility and multifunctional control that remain challenging for robotic systems. The neural and morphological basis of multifunctionality in animals can provide a source of bio-inspiration for robotic controllers. However, many existing approaches to modeling biological neural networks rely on computationally expensive models and tend to focus solely on the nervous system, often neglecting the biomechanics of the periphery. As a consequence, while these models are excellent tools for neuroscience, they fail to predict functional behavior in real time, which is a critical capability for robotic control. To meet the need for real-time multifunctional control, we have developed a hybrid Boolean model framework capable of modeling neural bursting activity and simple biomechanics at speeds faster than real time. Using this approach, we present a multifunctional model of Aplysia californica feeding that qualitatively reproduces three key feeding behaviors (biting, swallowing, and rejection), demonstrates behavioral switching in response to external sensory cues, and incorporates both known neural connectivity and a simple bioinspired mechanical model of the feeding apparatus. We demonstrate that the model can be used for formulating testable hypotheses and discuss the implications of this approach for robotic control and neuroscience. Copyright © 2020, The Authors. All rights reserved.

关键词： Feeding

Searching for GEMS: TOI-6383Ab, a giant planet transiting an M3-dwarf star in a binary system

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arXiv 2024年

作者： Bernabò, Lia Marta Kanodia, Shubham CaÑas, Caleb I. Cochran, William D. Csizmadia, Szilárd Mahadevan, Suvrath StefÁnsson, Gudhmundur Gupta, Arvind F. Monson, Andrew Kobulnicky, Henry A. Larsen, Alexander K. Cotter, Ethan G. Birkholz, Alexina Swaby, Tera N. Zeimann, Gregory Bender, Chad F. Diddams, Scott A. Libby-Roberts, Jessica E. Lin, Andrea S.J. Ninan, Joe P. Rauer, Heike Reji, Varghese Robertson, Paul Roy, Arpita Schwab, Christian Rutherfordstrasse 2 Berlin12489 Germany McDonald Observatory Department of Astronomy The University of Texas Austin United States Earth and Planets Laboratory Carnegie Science 5241 Broad Branch Road NW WashingtonDC20015 United States NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Center for Planetary Systems Habitability The University of Texas Austin United States ELKH-SZTE Stellar Astrophysics Research Group Szegedi út Kt. 766 BajaH-6500 Hungary Department of Astronomy & Astrophysics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States ETH Zurich Institute for Particle Physics & Astrophysics Switzerland Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands U.S. National Science Foundation National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States Steward Observatory The University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States Department of Physics & Astronomy University of Wyoming LaramieWY82070 United States Hobby Eberly Telescope University of Texas AustinTX78712 United States Electrical Computer & Energy Engineering University of Colorado 425 UCB BoulderCO80309 United States Department of Physics University of Colorado 2000 Colorado Avenue BoulderCO80309 United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Homi Bhabha Road Colaba Mumbai400005 India Institut für Geologische Wissenschaften Freie Universität Berlin Berlin12249 Germany Department of Physics & Astronomy University of California Irvine IrvineCA92697 United States Astrophysics & Space Institute Schmidt Sciences New YorkNY10011 United States School of Mathematical and Physical Sciences Macq

We report on the discovery of a transiting giant planet around the 3500 K M3-dwarf star TOI-6383A located 172 pc from Earth. It was detected by the Transiting Exoplanet Survey Satellite (TESS) and confirmed by a combination of ground-based follow-up photometry and precise radial velocity measurements. This planet has an orbital period of ∼1.791 days, mass of 1.040±0.094 MJ and a radius of (Equation presented), resulting in a mean bulk density of (Equation presented). TOI-6383A has an M-dwarf companion star, TOI-6383B, which has a stellar effective temperature Teff ∼ 3100 K and a projected orbital separation of 3100 AU. TOI-6383A is a low-mass dwarf star hosting a giant planet and is an intriguing object for planetary evolution studies due to its high planet-to-star mass ratio. This discovery is part of the Searching for Giant Exoplanets around M-dwarf Stars (GEMS) Survey, intending to provide robust and accurate estimates of the occurrence of GEMS and the statistics on their physical and orbital parameters. This paper presents an interesting addition to the small number of confirmed GEMS, particularly notable since its formation necessitates massive, dust-rich protoplanetary discs and high accretion efficiency (> 10%). © 2024, CC BY.

关键词： Orbits

Representative task self-selection for flexible clustered lifelong learning

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arXiv 2019年

作者： Sun, Gan Cong, Yang Wang, Qianqian Zhong, Bineng Fu, Yun State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Xidian University. Xian Shanxi710071 China Huaqiao University Xiamen Fujian361021 China Department of Electrical and Computer Engineering Khoury College of Computer and Information Sciences Northeastern University BostonMA02115 United States

Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep networks for most recent lifelong learning models are with prescribed size, and can degenerate the performance for both learned tasks and coming ones when facing with a new task environment (cluster). To address this challenge, we propose a novel incremental clustered lifelong learning framework with two knowledge libraries: feature learning library and model knowledge library, called Flexible Clustered Lifelong Learning (FCL3). Specifically, the feature learning library modeled by an autoencoder architecture maintains a set of representation common across all the observed tasks, and the model knowledge library can be self-selected by identifying and adding new representative models (clusters). When a new task arrives, our proposed FCL3 model firstly transfers knowledge from these libraries to encode the new task, i.e., effectively and selectively soft-assigning this new task to multiple representative models over feature learning library. Then, 1) the new task with a higher outlier probability will be judged as a new representative, and used to redefine both feature learning library and representative models over time;or 2) the new task with lower outlier probability will only refine the feature learning library. For model optimization, we cast this lifelong learning problem as an alternating direction minimization problem as a new task comes. Finally, we evaluate the proposed framework by analyzing several multi-task datasets, and the experimental results demonstrate that our FCL3 model can achieve better performance than most lifelong learning frameworks, even batch clustered multi-task learning models. Copyright © 2019, The Authors. All rights reserved.

关键词： Libraries

Hamilton–Jacobi–Bellman Equations for Q-Learning in Continuous Time∗

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arXiv 2019年

作者： Kim, Jeongho Yang, Insoon Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of

In this paper, we introduce Hamilton–Jacobi–Bellman (HJB) equations for Q-functions in continuous time optimal control problems with Lipschitz continuous controls. The standard Q-function used in reinforcement learning is shown to be the unique viscosity solution of the HJB equation. A necessary and sufficient condition for optimality is provided using the viscosity solution framework. By using the HJB equation, we develop a Q-learning method for continuous-time dynamical systems. A DQN-like algorithm is also proposed for high-dimensional state and control spaces. The performance of the proposed Q-learning algorithm is demonstrated using 1-, 10- and 20-dimensional dynamical systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

Federated variance-reduced stochastic gradient descent with robustness to Byzantine attacks

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arXiv 2019年

作者： Wu, Zhaoxian Ling, Qing Chen, Tianyi Giannakis, Georgios B. School of Data and Computer Science Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University GuangzhouGuangdong510006 China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical and Computer Engineering Digital Technology Center University of Minnesota MinneapolisMN55455 United States

This paper deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, most resilient approaches so far combine stochastic gradient descent (SGD) with different robust aggregation rules. However, the sizeable SGD-induced stochastic gradient noise makes it challenging to distinguish malicious messages sent by the Byzantine attackers from noisy stochastic gradients sent by the 'honest' workers. This motivates us to reduce the variance of stochastic gradients as a means of robustifying SGD in the presence of Byzantine attacks. To this end, the present work puts forth a Byzantine attack resilient distributed (Byrd-) SAGA approach for learning tasks involving finite-sum optimization over networks. Rather than the mean employed by distributed SAGA, the novel Byrd-SAGA relies on the geometric median to aggregate the corrected stochastic gradients sent by the workers. When less than half of the workers are Byzantine attackers, the robustness of geometric median to outliers enables Byrd-SAGA to attain provably linear convergence to a neighborhood of the optimal solution, with the asymptotic learning error determined by the number of Byzantine workers. Numerical tests corroborate the robustness to various Byzantine attacks, as well as the merits of Byrd-SAGA over Byzantine attack resilient distributed SGD. Copyright © 2019, The Authors. All rights reserved.

关键词： Optimization

An Open Natural Language Processing (NLP) Framework for EHR-based Clinical Research: A Case Demonstration Using the National COVID Cohort Collaborative (N3C)

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arXiv 2021年

作者： Liu, Sijia Wen, Andrew Wang, Liwei He, Huan Fu, Sunyang Miller, Robert Williams, Andrew Harris, Daniel Kavuluru, Ramakanth Liu, Mei Abu-El-rub, Noor Schutte, Dalton Zhang, Rui Rouhizadeh, Masoud Osborne, John D. He, Yongqun Topaloglu, Umit Hong, Stephanie S. Saltz, Joel H. Schaffter, Thomas Pfaff, Emily Chute, Christopher G. Duong, Tim Haendel, Melissa A. Fuentes, Rafael Szolovits, Peter Xu, Hua Liu, Hongfang Department of Artificial Intelligence and Informatics Mayo Clinic United States Tufts Clinical and Translational Science Institute Tufts Medical Center United States Department of Internal Medicine University of Kentucky United Kingdom Department of Internal Medicine University of Kansas Medical Center United States Department of Pharmaceutical Care & Health Systems University of Minnesota Twin Cities United States Department of Pharmaceutical Outcomes & Policy University of Florida United States Department of Computer Science University of Alabama at Birmingham United States University of Michigan Medical School United States Wake Forest Baptist Medical Center United States Department of Medicine Johns Hopkins University United States Department of Biomedical Informatics Stony Brook University United States Sage Bionetwork United States Department of Medicine University of North Carolina Chapel Hill United States Albert Einstein College of Medicine United States Center for Health AI University of Colorado Anschutz Medical Campus United States Alex Informatics United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States School of Biomedical Informatics University of Texas Health Science Center Houston United States

Despite recent methodology advancements in clinical natural language processing (NLP), adoption of clinical NLP models within the clinical and translational research community remains hindered by issues with ETL process heterogeneity and human factor variations. In this study, we proposed an open NLP development framework with the aim of addressing these issues. The viability of such a platform was evaluated on a COVID-19 use case through sites participating in the National COVID Cohort Collaborative (N3C). As part of our assessment of the impact of single vs. multi-site NLP algorithm development, we evaluated the performance of both an NLP ruleset developed solely using a single site’s clinical narratives as well as one further refined using a synthetic derived dataset sourced from three sites (Mayo, UKen, and UMN). The single-site ruleset resulted in performances of 0.876, 0.706, and 0.694 in F-scores for Mayo, Minnesota, and Kentucky test datasets, respectively, while the multi-site NLP ruleset improved performances to 0.884, 0.769 and 0.806. The results of our use case test run inform us of the importance of a multisite federated development, evaluation, and implementation framework. As such, we aim to meet this need with our framework by providing the tools necessary to conduct NLP development in a collaborative manner through consensus building, process coordination, and best practice sharing. © 2021, CC BY.

关键词： Natural language processing systems

A Review of Artificial Fish Swarm Algorithms: Recent Advances and Applications

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arXiv 2020年

作者： Pourpanah, Farhad Wang, Ran Lim, Chee Peng Wang, Xi-Zhao Yazdani, Danial College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Department of Electrical and Computer Engineering University of Windsor Canada College of Mathematics and Statistics Shenzhen Key Lab. of Advanced Machine Learning and Applications Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Institute for Intelligent Systems Research and Innovation Deakin University Australia College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China School of Computer Science and Engineering Southern University of Science and Technology China

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the continuous AFSA, encompassing the original ASFA, its improvements and hybrid models, as well as their associated applications. We focus on articles published in high-quality journals since 2013. Our review provides insights into AFSA parameters modifications, procedure and sub-functions. The main reasons for these enhancements and the comparison results with other hybrid methods are discussed. In addition, hybrid, multi-objective and dynamic AFSA models that have been proposed to solve continuous optimization problems are elucidated. We also analyse possible AFSA enhancements and highlight future research directions for advancing AFSA-based models. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

Big Data Governance for Building A Smart Cities

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Big Data Governance for Building A Smart Cities

International Conference on ICT For Smart Society (ICISS)

作者： Novan Zulkarnain Raymond Kosala Benny Ranti Suhono Harso Supangkat Computer Science Department BINUS Graduate Program Doctor of Computer Science Information Systems Department Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia Computer Science Department Universitas Indonesia Depok Indonesia School of Electrical and Informatics Engineering Institute of Technology Bandung Bandung Indonesia

There have been many studies conducted related to Smart City, IT Governance and Big Data. In this study aims to find out how the relationship between the three and how to form a framework to explain it. The methodology used is qualitative by looking for some literature on smart city framework, IT Governance framework, and a Big Data framework. From these results, an overall picture of the relationship between the three is concluded, where Big Data has a role in IT Governance. and also the relationship of IT Governance to the realization of Smart City. And the final results of this study produce a framework to explain the relationship between Smart City, IT Governance and Big Data.

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