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检索条件"主题词=approximation Algorithms"

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Ground-Density Clustering for Approximate Agricultural Field Segmentation

Ground-Density Clustering for Approximate Agricultural Field...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Henry J. Nelson Nikolaos Papanikolopoulos Department of Computer Science and Engineering University of Minnesota

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Instance and semantic segmentation form the backbone of robotic perception and are crucial to many tasks. While most research in the area focuses on improving segmentation quality metrics, there are plenty of applications where approximate methods are adequate as long as they are fast, especially in applications with large amounts of data like precision agriculture. In order to apply the recent successes of machine learning and computer vision on a large scale using robotics, efficient and general algorithms must be designed to intelligently split point clouds into small, yet actionable, portions that can then be processed by more complex algorithms. In this paper, we capitalize on a similarity between the current state-of-the-art for roughly segmenting corn plants and a commonly used density-based clustering algorithm, Quickshift. Exploiting this similarity we propose a novel algorithm, Ground-Density Quickshift++, with the goal of producing a general and scalable field segmentation algorithm that segments individual plants and their stems. This algorithm produces quantitatively better results than the current state-of-the-art on both plant separation and stem segmentation while being less sensitive to input parameters and maintaining the same algorithmic time complexity. When incorporated into field-scale phenotyping systems, the proposed algorithm should work as a drop-in replacement that can greatly improve the accuracy of results while ensuring that performance and scalability remain undiminished.

关键词： Precision agriculture Machine learning algorithms Semantic segmentation Plants (biology) Scalability Clustering algorithms approximation algorithms Throughput Time complexity Synthetic data

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Experimental Validation of User Experience-Focused Dynamic Onboard Service Orchestration for Software Defined Vehicles

Experimental Validation of User Experience-Focused Dynamic O...

引用

International Conference on Intelligent Transportation

作者： Pierre Laclau Stéphane Bonnet Bertrrand Ducourthial Trista Lin Xiaoting Li Heudiasyc CNRS Université de Technologie de Compiègne France Stellantis Poissy France

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

In response to the growing need for dynamic software features in automobiles, Software Defined Vehicles (SDVs) have emerged as a promising solution. They integrate dynamic onboard service management to handle the large variety of user-requested services during vehicle operation. Allocating onboard resources efficiently in this setting is a challenging task, as it requires a balance between maximizing user experience and guaranteeing mixed-criticality Quality-of-Service (QoS) network requirements. Our previous research introduced a dynamic resource-based onboard service or-chestration algorithm. This algorithm considers real-time in-vehicle and V2X network health, along with onboard resource constraints, to globally select degraded modes for onboard applications. It maximizes the overall user experience at all times while being embeddable onboard for on-the-fly decision-making. A key enabler of this approach is the introduction of the Automotive eXperience Integrity Level (AXIL), a metric expressing runtime priority for non-safety-critical applications. While initial simulation results demonstrated the algorithm's effectiveness, a comprehensive performance assessment would greatly contribute in validating its industrial feasibility. In this current work, we present experimental results obtained from a dedicated test bench. These results illustrate, validate, and assess the practicality of our proposed solution, providing a solid foundation for the continued advancement of dynamic onboard service orchestration in SDVs.

关键词： Runtime Heuristic algorithms Software algorithms Quality of service Dynamic scheduling approximation algorithms User experience Software Vehicle dynamics Vehicle-to-everything

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A Hybrid Index-Based Block Construction and Retrieval Algorithm for Efficient Data Retrieval on Blockchain

A Hybrid Index-Based Block Construction and Retrieval Algori...

引用

Computer Science and Blockchain (CCSB), International Conference on

作者： Lei Mu Minqiang Lv Shanshan Wang Hui Cao Xi'an Institute of Aeronautical Computing Technology Aviation Industry Corporation of China (AVIC) Xi'an China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

Blockchain, as a decentralized, secure, programmable, traceable, and tamper-resistant distributed ledger technology, has witnessed rapid development in various fields in recent years. This paper addresses the need for rapid data retrieval on blockchain and proposes a block construction and retrieval algorithm based on hybrid indexing. The algorithm aims to alter the underlying structure of blocks by utilizing data structures such as Merkle trees, linked lists, and hashes to facilitate data retrieval. Building upon this, block construction and retrieval algorithms are introduced to ensure efficient retrieval of corresponding transactions, maintaining low time complexity.

关键词： Computer science Filters Distributed ledger Buildings approximation algorithms Data structures Blockchains Time complexity Optimization Indexing

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Approximating Closed Queueing Networks in Semi-Markov Random Environments

Approximating Closed Queueing Networks in Semi-Markov Random...

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International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS)

作者： Yaqi Zhou Matthew Sheldon Giuliano Casale Department of Computing Imperial College London UK

ISBN: (数字)9798331531300

ISBN: (纸本)9798331531317

Modeling queueing networks in random environments is a challenging problem that finds application in routing, reliability analysis, and evaluation of systems processing bursty workloads. In this paper, we introduce a method to evaluate closed queueing networks in random environments that can deal with non-Markovian transitions among environment stages and accurately approximate time-averaged performance metrics. Our technique supports both state-independent and state-dependent random environments, the latter allowing us to analyze challenging queueing network models with bursty service processes. Using simulation on representative case studies, we show that our technique provides low approximation errors. We further demonstrate the applicability of our result to a mobility case study demonstrating the applicability of the proposed approach.

关键词： Measurement Analytical models Computational modeling Switched mode power supplies approximation algorithms Routing Telecommunications Telecommunication network reliability Iterative methods Queueing analysis

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Open-Loop and Feedback Nash Trajectories for Competitive Racing with iLQGames

Open-Loop and Feedback Nash Trajectories for Competitive Rac...

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International Conference on Intelligent Transportation

作者： Matthias Rowold Alexander Langmann Boris Lohmann Johannes Betz Chair of Automatic Control TUM School of Engineering and Design Technical University of Munich Germany Professorship of Autonomous Vehicle Systems TUM School of Engineering and Design Technical University of Munich Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Interaction-aware trajectory planning is crucial for closing the gap between autonomous racing cars and human racing drivers. Prior work has applied game theory as it provides equilibrium concepts for non-cooperative dynamic problems. With this contribution, we formulate racing as a dynamic game and employ a variant of iLQR-called iLQGames-to solve the game. iLQGames finds trajectories for all players that satisfy the equilibrium conditions for a linear-quadratic approximation of the game and has been previously applied in traffic scenarios. We analyze the algorithm's applicability for trajectory planning in racing scenarios and evaluate it based on interaction awareness, competitiveness, and safety. With the ability of iLQGames to solve for open-loop and feedback Nash equilibria, we compare the behavioral outcomes of the two equilibrium concepts in simple scenarios on a straight track section.

关键词： Trajectory planning Heuristic algorithms Games approximation algorithms Trajectory Safety Automobiles Game theory Intelligent transportation systems

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Identification of Quasi-Periodically Varying Systems Using the Local Basis Function Approach

Identification of Quasi-Periodically Varying Systems Using t...

引用

European Signal Processing Conference (EUSIPCO)

作者： Artur Gańcza Maciej Niedźwiecki Department of Signals and Systems Gdansk University of Technology Gdansk Poland

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In this paper we propose a solution to the problem of tracking quasi-periodically varying systems based on the local basis function (LBF) approach. Within this framework, parameter trajectories are locally approximated using linear combinations of specific functions of time known as basis functions. We derive both bias and variance characteristics of LBF estimators. Additionally, we demonstrate that the computational burden associated with LBF estimation algorithms can be significantly reduced, without sacrificing high estimation accuracy, by employing the computationally fast, approximate version of the LBF scheme.

关键词： Accuracy Signal processing algorithms Estimation Europe Signal processing Benchmark testing approximation algorithms Trajectory Computational efficiency

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Efficient Nyström Method for Solving Ultrasonic Scattering Problems

Efficient Nyström Method for Solving Ultrasonic Scattering ...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Jiahui Yu Runze Liu Yang Bao Praveen Gurrala College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing China Micron Technology Inc. Boise ID USA

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

Elasticodynamic scattering plays an important role in ultrasonic scattering problems, especially in evaluating the far-field scattering amplitude of defects. To address the elasticodynamic scattering problem, we employ the high-order Nyström method (NM). This approach utilizes high-order surface triangles for meshing and employs high-order interpolation to define the basis functions on each surface triangle unit. Within the impedance matrix generated by the high-order NM model based on the conventional boundary integral equation (CBIE), the diagonal and near blocks are stored in a full matrix, while the far blocks are compressed and stored using adaptive cross approximation (ACA) technique. The results predicted by the ACA based solver are compared with those predicted by pure NM to verify the accuracy and effectiveness of this method.

关键词： Surface impedance Interpolation Integral equations Electromagnetic scattering approximation algorithms Prediction algorithms Acoustics Robustness Mathematical models Impedance

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Scalable Reinforcement Learning for Linear-Quadratic Control of Networks

Scalable Reinforcement Learning for Linear-Quadratic Control...

引用

American Control Conference (ACC)

作者： Johan Olsson Runyu Cathy Zhang Emma Tegling Na Li The Department of Automatic Control and the ELLIIT Strategic Research Area Lund University Sweden The John A. Paulson School of Engineering and Applied Sciences Harvard University MA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such problems where distributed state feedback controllers can give near-optimal performance. More specifically, we consider networked linear-quadratic controllers with decoupled costs and spatially exponentially decaying dynamics. We aim to exploit the structure in the problem to design a scalable reinforcement learning algorithm for learning a distributed controller. Recent work has shown that the optimal controller can be well approximated only using information from a kq -neighborhood of each agent. Motivated by these results, we show that similar results hold for the agents' individual value and Q-functions. We continue by designing an algorithm, based on the actor-critic framework, to learn distributed controllers only using local information. Specifically, the Q-function is estimated by modifying the Least Squares Temporal Difference for Q-functions method to only use local information. The algorithm then updates the policy using gradient descent. Finally, we evaluate the algorithm through simulations that indeed suggest near-optimal performance.

关键词： State feedback Regulators Costs Heuristic algorithms Optimal control Reinforcement learning approximation algorithms

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Overmind: Fast and Scalable Decentralized Learning Simulation for Mobile Environments

Overmind: Fast and Scalable Decentralized Learning Simulatio...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Sangsu Lee Haoxiang Yu Xi Zheng Christine Julien Department of Electrical and Computer Engineering University of Texas at Austin School of Computing Macquarie University

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Recent progress in decentralized machine learning provides a foundation for a future in which mobile devices collaboratively learn from the vast amount of crowdsourced data they produce without necessarily relying on a centralized server for coordination. However, testing novel decentralized learning algorithms requires many iterations that are very costly in terms of time and budget. We present Overmind, a fast simulation framework for testing the performance of decentralized learning algorithms in dynamic networks. The design of Overmind allows users to easily scale their scenarios in terms of both simulated nodes and the number of physical machines on which the simulation runs. Overmind supports an operational mode that expedites the simulation by approximating the interaction time between two nodes, which allows skipping events that are unlikely to happen (e.g, two nodes exchanging big model parameters on a connection that is maintained for a very short time period). Users can also configure Overmind with a dataset of connection traces of simulated nodes, which is especially helpful to test algorithms given realistic dynamic networks in which devices join and leave unpredictably. To leverage Overmind, users just need to implement the generic behavior that is invoked on participating nodes when they connect to or disconnect from other nodes. The implemented behaviors are invoked in Overmind according to a provided encounter trace. This node-centric programming model that Overmind provides, where users define node behavior based on an individual node's perspective, is an intuitive design for decentralized settings. In this paper, we discuss the design of Overmind and demonstrate that it enables fast, scalable, and easily configurable decentralized learning simulations.

关键词： Performance evaluation Machine learning algorithms Heuristic algorithms Machine learning Programming Smart systems approximation algorithms Mobile handsets Servers Testing

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Simultaneous Optimization of Placement Planning and Motion Planning for a Single Robotic Arm Using Genetic Algorithm

Simultaneous Optimization of Placement Planning and Motion P...

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IEEE International Conference on Industrial Engineering and Engineering Management

作者： T. Taniguchi T. Nishi Z. Liu T. Fujiwara Faculty of Environmental Life Natural Science and Technology Okayama University Okayama Japan

ISBN: (数字)9798350386097

ISBN: (纸本)9798350386103

Industrial robots are commonly used in factories for efficient high-mix low-volume production. It is required to determine the optimal placement and posture of a robot arm to pick up and place a workpiece to minimize the total operation time. We propose an efficient optimization approach for solving the placement planning and motion planning problems of a six-axis robot arm using a genetic algorithm (GA). To obtain a feasible solution to the problem of minimizing the total operation time, the proposed method conducts placement planning using GA and motion planning using Robot Operation System (ROS) simulation with a Rapidly-exploring Random Tree Star (RRT*) for a six-axis robot arm. The performance of the proposed algorithm is compared with that of a conventional method that uses particle swarm optimization. The computational results show that the proposed algorithm can reduce approximately 26% of the motion planning time compared to the conventional method. The proposed method can provide a near-optimal layout and motion planning solution within a practical computing time.

关键词： Service robots Stars Manipulators approximation algorithms Production facilities Planning Particle swarm optimization Robots Optimization Genetic algorithms

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