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T*$\boldsymbol{\varepsilon}$—Bounded-Suboptimal Efficient Motion Planning for Minimum-Time Planar Curvature-Constrained Systems

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IEEE Robotics and Automation Letters 2022年第2期7卷 4102-4109页

作者： Doron Pinsky Petr Váňa Jan Faigl Oren Salzman Technion Autonomous Systems Program (TASP) Technion Haifa Israel Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Department of Computer Science Technion Haifa Israel

We consider the problem of finding collision-free paths for curvature-constrained systems in the presence of obstacles while minimizing execution time. Specifically, we focus on the setting where a planar system can travel at some range of speeds with unbounded acceleration. This setting can model many systems, such as fixed-wing drones. Unfortunately, planning for such systems might require evaluating many (local) time-optimal transitions connecting two close-by configurations, which is computationally expensive. Existing methods either pre-compute all such transitions in a preprocessing stage or use heuristics to speed up the search, thus foregoing any guarantees on solution quality. Our key insight is that computing all the time-optimal transitions is both (i) computationally expensive and (ii) unnecessary for many problem instances. We show that by finding bounded-suboptimal solutions (solutions whose cost is bounded by $\boldsymbol{1+\varepsilon}$ times the cost of the optimal solution for any user-provided $\boldsymbol{\varepsilon}$ ) and not time-optimal solutions, one can dramatically reduce the number of time-optimal transitions used. We demonstrate using empirical evaluation that our planning framework can reduce the runtime by several orders of magnitude compared to the state-of-the-art while still providing guarantees on the quality of the solution.

关键词： Costs Planning Robots Image edge detection Vehicle dynamics Runtime Heuristic algorithms

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Two-Dimensional Magnetic Exciton Polariton with Strongly Coupled Atomic and Photonic Anisotropies

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Physical Review Letters 2024年第26期133卷 266901-266901页

作者： Qiuyang Li Xin Xie Adam Alfrey Christiano W. Beach Nicholas McLellan Yang Lu Jiaqi Hu Wenhao Liu Nikhil Dhale Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Applied Physics Program University of Michigan Ann Arbor Michigan 48109 USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan 48109 USA Department of Physics The University of Texas at Dallas Richardson Texas 75080 USA

Anisotropy is a fundamental property of both material and photonic systems. The interplay between material and photonic anisotropies, however, has hardly been explored due to the vastly different length scales. Here we demonstrate exciton polaritons in a 2D antiferromagnet, CrSBr, coupled with an anisotropic photonic crystal cavity, where the spin, atomic, and photonic anisotropies are strongly correlated. Atomic anisotropy led to exceptionally strong coupling between anisotropic exciton and optical modes, which are stable against excitation densities a few orders of magnitude higher than polaritons in isotropic materials. The resulting polaritons feature anisotropic polarizations determined by the interplay of not only the anisotropies but also the dissipations and coupling of both exciton and photon modes, tunable by tens of degrees via many parameters. The work provides insights of excitons in CrSBr and demonstrates a prototype where atomic- and photonic-scale orders strongly couple, giving rise to unconventional properties in quantum materials and photonic devices.

关键词： Atoms

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Bilevel optimization for machine learning: Algorithm design and convergence analysis

arXiv

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arXiv 2021年

作者： Ji, Kaiyi Graduate Program in Department of Electrical and Computer Engineering Ohio State University

Bilevel optimization has become a powerful framework in a variety of machine learning applications including signal processing, meta-learning, hyperparameter optimization, reinforcement learning and network architecture search. There are generally two classes of bilevel optimization formulations for modern machine learning: 1) problem-based bilevel optimization, whose inner-level problem is formulated as finding a minimizer of a given loss function;and 2) algorithm-based bilevel optimization, whose inner-level solution is an output of a fixed algorithm. For the first problem class, two popular types of gradient-based algorithms have been proposed to estimate the gradient of the outer-level objective (hypergradient) via approximate implicit differentiation (AID) and iterative differentiation (ITD). Algorithms for the second problem class include the popular model-agnostic meta-learning (MAML) and almost no inner loop (ANIL). Although bilevel optimization algorithms have been widely used, their convergence rate and fundamental limitations have not been well explored. Copyright © 2021, The Authors. All rights reserved.

关键词： Network architecture

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Comparative performance of machine learning models for temperature prediction in vanadium redox flow batteries

Comparative performance of machine learning models for tempe...

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International Conference on Green Energy, Computing and Intelligent Technology 2024 (GEn-CITy 2024)

作者： Anxiu Zhang Filbert H. Juwono Hongfei Li Erick Purwanto W. K. Wong Linfeng Liu Telecommunications Engineering Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Applied Mathematics Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China

ISBN: (数字)9781837242863

Vanadium Redox Flow Batteries (VRFB) are promising for large-scale energy storage due to their long life and environmental benefits. Accurate temperature prediction is key to optimizing VRFB performance and longevity. This study compares the performance of four machine learning models, i.e., 1D CNN, Particle Swarm Optimization - Support Vector Regressor (PSO-SVR), Decision Tree (DT), and K-Nearest Neighbors (KNN), using a publicly available dataset. Results show that KNN achieves the best results with test Root Mean Square Error (RMSE) of 0.0424 (average) and test R2 of 0.9746 (average), demonstrating strong predictive accuracy. 1D CNN, however, shows poor generalization. These findings suggest that non-parametric models like KNN and DT are highly effective for VRFB temperature prediction.

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Agus Pratondo Astri Novianty Budi Laksono Putro Siska Komala Sari Department of Multimedia Engineering School of Applied Sciences Telkom University Bandung Indonesia Department of Computer Engineering School of Electrical Engineering Telkom University Bandung Indonesia Department of Computer Science Education Indonesia University of Education Bandung Indonesia Diploma Program of Information System School of Applied System Telkom University Bandung Indonesia

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice.

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Physarum Route Optimization: Bio-inspired Strategies for Mobile Robot Navigation

Physarum Route Optimization: Bio-inspired Strategies for Mob...

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IEEE Latin American Robotics Symposium, LARS

作者： Dieisson Martinelli André Schneider de Oliveira Vivian Cremer Kalempa Graduate Program in Electrical and Computer Engineering Universidade Tecnológica Federal do Paraná (UTFPR) Curitiba Brazil Department of Information Systems Universidade do Estado de Santa Catarina (UDESC) São Bento do Sul Brazil

ISBN: (数字)9798331508807

ISBN: (纸本)9798331508814

The objective of this project is to explore the applicability of a bioinspired approach, derived from the behavior of the Physarum polycephalum fungus, in optimizing trajectory navigation for mobile robot structures. The precise organism Physarum, which has the capability to quick locate efficient paths among factors in complicated environments, serves as a herbal version for constructing inspired trajectory algorithms. In this project, the development of a trajectory set of rules is proposed, that’s as compared to conventional trajectory introduction techniques, aiming to introduce a novel approach to cellular robot navigation. This work is presented thru a aggregate of experimental observations and computational simulations that show the effectiveness of this bioinspired approach in trajectory choice. As a end result, an set of rules is developed that mimics the behavior of the Physarum and, similarly to conventional navigation algorithms, paves the manner for research and improvement.

关键词： Shortest path problem Navigation Heuristic algorithms Memory management MIMICs Dynamic scheduling Organisms Trajectory Mobile robots Resource management

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Subwavelength-Engineered Antislot Photonic Crystals in a Silicon Photonics Foundry for On-chip Communications

Subwavelength-Engineered Antislot Photonic Crystals in a Sil...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Kellen P. Arnold Joshua A. Allen Sami I. Halimi Landen D. Ryder Francis O. Afzal Yusheng Bian Abdelsalam Aboketaf Takako Hirokawa Kevin Dezfulian Michal Rakowski Rod Augur Karen Nummy Sharon M. Weiss Interdisciplinary Materials Science Program Vanderbilt University Nashville TN USA Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA NASA Goddard Space Flight Center Greenbelt MD US GlobalFoundries Malta NY USA

We report scalable foundry fabrication and characterization of photonic crystal nanobeam waveguides incorporating subwavelength-scale dielectric antislot unit cells. This work enables enhanced light-matter interaction... 详细信息

ISBN: (纸本)9798350369311

关键词： Mach-Zehnder interferometers Optical device fabrication Photonic crystals Waveguide lasers Lasers and electrooptics Electro-optic modulators Silicon photonics Foundries System-on-chip Electro-optical waveguides

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A Comprehensive Survey of Time Series Forecasting: Architectural Diversity and Open Challenges

arXiv

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arXiv 2024年

作者： Kim, Jongseon Kim, Hyungjoon Kim, HyunGi Lee, Dongjun Yoon, Sungroh Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of R&D Department LG Chem R&D Department Samsung SDI

Time series forecasting is a critical task that provides key information for decision-making across various fields, such as economic planning, supply chain management, and medical diagnosis. After the use of traditional statistical methodologies and machine learning in the past, various fundamental deep learning architectures such as MLPs, CNNs, RNNs, and GNNs have been developed and applied to solve time series forecasting problems. However, the structural limitations caused by the inductive biases of each deep learning architecture constrained their performance. Transformer models, which excel at handling long-term dependencies, have become significant architectural components for time series forecasting. However, recent research has shown that alternatives such as simple linear layers can outperform Transformers. These findings have opened up new possibilities for using diverse architectures. In this context of exploration into various models, the architectural modeling of time series forecasting has now entered a renaissance. This survey not only provides a historical context for time series forecasting but also offers comprehensive and timely analysis of the movement toward architectural diversification. By comparing and re-examining various deep learning models, we uncover new perspectives and presents the latest trends in time series forecasting, including the emergence of hybrid models, diffusion models, Mamba models, and foundation models. By focusing on the inherent characteristics of time series data, we also address open challenges that have gained attention in time series forecasting, such as channel dependency, distribution shift, causality, and feature extraction. This survey explores vital elements that can enhance forecasting performance through diverse approaches. These contributions lead to lowering the entry barriers for newcomers to the field of time series forecasting, while also offering seasoned researchers broad perspectives, new opportuniti

关键词： Supply chain management

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Smart Home Security System Using Face Recognition Based on IoT- CNN

Smart Home Security System Using Face Recognition Based on I...

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Information Technology Research and Innovation (ICITRI), International Conference on

作者： M. Aldiki Febriantono Alvin Zuhair Khaeruddin Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Electrical Engineering Department University of Islam Blitar Blitar Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Malang Indonesia

Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning computer Vision holds tremendous benefits for homeowners.

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Multi-Level Branched Regularization for Federated Learning

arXiv

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arXiv 2022年

作者： Kim, Jinkyu Kim, Geeho Han, Bohyung Computer Vision Laboratory Department of Electrical and Computer Engineering & ASRI Seoul National University Korea Republic of Interdisciplinary Program of Artificial Intelligence Seoul National University Korea Republic of

A critical challenge of federated learning is data heterogeneity and imbalance across clients, which leads to inconsistency between local networks and unstable convergence of global models. To alleviate the limitations, we propose a novel architectural regularization technique that constructs multiple auxiliary branches in each local model by grafting local and global subnetworks at several different levels and that learns the representations of the main pathway in the local model congruent to the auxiliary hybrid pathways via online knowledge distillation. The proposed technique is effective to robustify the global model even in the non-iid setting and is applicable to various federated learning frameworks conveniently without incurring extra communication costs. We perform comprehensive empirical studies and demonstrate remarkable performance gains in terms of accuracy and efficiency compared to existing methods. The source code is available in our project page1 Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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