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A Modified Park Transformation Algorithm for Phase-Locked Loop Applications in Unbalanced Three-Phase Systems

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A Modified Park Transformation Algorithm for Phase-Locked Lo...

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Hsueh-Ju Wu Yaow-Ming Chen Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper proposes a novel modified Park transformation (MPT) algorithm for phase-locked loop (PLL) applications in unbalanced three-phase systems. Unlike the conventional Park transformation, the proposed MPT algorithm directly incorporates unbalanced characteristics into its transformation matrix, thereby streamlining control strategies without requiring supplementary filtering. The effectiveness of the proposed MPT is verified through rigorous mathematical derivations, comparative simulations, and practical implementations using a digital signal processor (DSP). The results demonstrate that the MPT algorithm significantly improves the stability and accuracy of PLL systems under unbalanced conditions, providing an effective solution for handling phase unbalances in various power systems. Theoretical derivations and practical results, including crucial waveform comparisons, confirm the superior performance of the MPT algorithm and establish a robust framework for future research and applications.

关键词： Technological innovation Accuracy Heuristic algorithms Digital signal processors Signal processing algorithms adaptive algorithms Power system stability Stability analysis Phase locked loops Power harmonic filters

Comparative Analysis of Autonomous Indoor Exploration Strategies: Floodfill algorithm vs. Frontier-Based Method

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Comparative Analysis of Autonomous Indoor Exploration Strate...

International Conference on Control, Automation and Systems ( ICCAS)

作者： Naufaldo Hsiu-Ming Wu Graduate Institute of Mechatronic Engineering National Taipei University of Technology Taipei Taiwan Department of Intelligent Automation Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

This paper investigates the efficiency of autonomous indoor exploration utilizing simulation testing environments in Gazebo. Two exploration methods, Floodfill algorithm and Frontier-based algorithm, using the 2D LiDAR sensor are compared. The Floodfill algorithm employs a systematic traversal approach, while the Frontier-based method dynamically detects and navigates towards frontiers. Results indicate that the Frontier-based approach outperforms Flood-fill Algorithm in terms of efficiency and map completeness, particularly in complex environments. The study underscores the importance of the Frontier-based strategy for autonomous indoor exploration and paves the way for enhanced robotic applications in diverse domains.

关键词： Systematics Laser radar Automation Heuristic algorithms Indoor navigation adaptive algorithms Robot sensing systems Control systems Indoor environment Testing

Tailoring Semantic Communication at Network Edge: A Novel Approach Using Dynamic Knowledge Distillation

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arXiv 2024年

作者： Albaseer, Abdullatif Abdallah, Mohamed Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

Semantic Communication (SemCom) systems, empowered by deep learning (DL), represent a paradigm shift in data transmission. These systems prioritize the significance of content over sheer data volume. However, existing SemCom designs face challenges when applied to diverse computational capabilities and network conditions, particularly in time-sensitive applications. A key challenge is the assumption that diverse devices can uniformly benefit from a standard, large DL model in SemCom systems. This assumption becomes increasingly impractical, especially in high-speed, high-reliability applications such as industrial automation or critical healthcare. Therefore, this paper introduces a novel SemCom framework tailored for heterogeneous, resource-constrained edge devices and computation-intensive servers. Our approach employs dynamic knowledge distillation (KD) to customize semantic models for each device, balancing computational and communication constraints while ensuring Quality of Service (QoS). We formulate an optimization problem and develop an adaptive algorithm that iteratively refines semantic knowledge in edge devices, resulting in better models tailored to their resource profiles. This algorithm strategically adjusts the granularity of distilled knowledge, enabling devices to maintain high semantic accuracy for precise inference tasks, even under unstable network conditions. Extensive simulations demonstrate that our approach significantly reduces model complexity for edge devices, leading to better semantic extraction and achieving the desired QoS. © 2024, CC BY.

关键词： adaptive algorithms

Interventional Causal Discovery in a Mixture of DAGs

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arXiv 2024年

作者： Varıcı, Burak Katz-Rogozhnikov, Dmitriy A. Wei, Dennis Sattigeri, Prasanna Tajer, Ali Carnegie Mellon University United States IBM Research Rensselaer Polytechnic Institute United States

Causal interactions among a group of variables are often modeled by a single causal graph. In some domains, however, these interactions are best described by multiple co-existing causal graphs, e.g., in dynamical systems or genomics. This paper addresses the hitherto unknown role of interventions in learning causal interactions among variables governed by a mixture of causal systems, each modeled by one directed acyclic graph (DAG). Causal discovery from mixtures is fundamentally more challenging than single-DAG causal discovery. Two major difficulties stem from (i) an inherent uncertainty about the skeletons of the component DAGs that constitute the mixture and (ii) possibly cyclic relationships across these component DAGs. This paper addresses these challenges and aims to identify edges that exist in at least one component DAG of the mixture, referred to as the true edges. First, it establishes matching necessary and sufficient conditions on the size of interventions required to identify the true edges. Next, guided by the necessity results, an adaptive algorithm is designed that learns all true edges using O(n2) interventions, where n is the number of nodes. Remarkably, the size of the interventions is optimal if the underlying mixture model does not contain cycles across its components. More generally, the gap between the intervention size used by the algorithm and the optimal size is quantified. It is shown to be bounded by the cyclic complexity number of the mixture model, defined as the size of the minimal intervention that can break the cycles in the mixture, which is upper bounded by the number of cycles among the ancestors of a node. Copyright © 2024, The Authors. All rights reserved.

关键词： adaptive algorithms

GOAL-ORIENTED ERROR ESTIMATION AND ADAPTIVITY FOR STOCHASTIC COLLOCATION FEM

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arXiv 2024年

作者： Bespalov, Alex Praetorius, Dirk Round, Thomas Savinov, Andrey School of Mathematics University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Institute of Analysis and Scientific Computing TU Wien Wiedner Hauptstraße 8–10 Vienna1040 Austria

We propose and analyze a general goal-oriented adaptive strategy for approximating quantities of interest (QoIs) associated with solutions to linear elliptic partial differential equations with random inputs. The QoIs are represented by bounded linear or continuously Gâteaux differentiable nonlinear goal functionals, and the approximations are computed using the sparse grid stochastic collocation finite element method (SC-FEM). The proposed adaptive strategy relies on novel reliable a posteriori estimates of the errors in approximating QoIs. One of the key features of our error estimation approach is the introduction of a correction term into the approximation of QoIs in order to compensate for the lack of (global) Galerkin orthogonality in the SC-FEM setting. Computational results generated using the proposed adaptive algorithm are presented in the paper for representative elliptic problems with affine and nonaffine parametric coefficient dependence and for a range of linear and nonlinear goal functionals. © 2024, CC BY.

关键词： adaptive algorithms

On an optimal AFEM for elastoplasticity

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arXiv 2024年

作者： Schönauer, Miriam Schröder, Andreas Paris Lodron University of Salzburg Department of Mathematics Salzburg Austria

In this paper, optimal convergence for an adaptive finite element algorithm for elastoplasticity is considered. To this end, the proposed adaptive algorithm is established within the abstract framework of the axioms of adaptivity [Comput. Math. Appl., 67(6) (2014), 1195-1253], which provides a specific proceeding to prove the optimal convergence of the scheme. The proceeding is based on verifying four axioms, which ensure the optimal convergence. The verification is done by using results from [Numer. Math., 132(1) (2016), 131-154], which presents an alternative approach to optimality without explicitly relying on the *** Codes 65N30 Copyright © 2024, The Authors. All rights reserved.

关键词： adaptive algorithms

Quantitative Group Testing with Tunable Adaptation

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Quantitative Group Testing with Tunable Adaptation

IEEE International Symposium on Information Theory

作者： Mahdi Soleymani Tara Javidi Hahcioğlu Data Science Institute University of California San Diego CA USA Department of Electrical and Computer Engineering University of California San Diego CA USA

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

The aim of quantitative group testing problem is to recover $k$ defective items from a set of $n$ items using minimal number of quantitative/additive group tests, where each test reveals the number of defective items present in the group. Fully adaptive strategies have been proposed and shown to, in general, require significantly fewer measurements. In the sublinear regime, where $k=n^{\alpha}$ for $0 < \alpha < 1$ , for instance, this means a gain proportional to $\alpha \log n$ . However, this gain is obtained at the cost of significant complexity associated with adapting tests to previous outcomes. This paper introduces a family of low-complexity strategies with efficient construction and decoding. These strategies adapt tests only in stages, allowing for a flexible trade-off between the number of stages, i.e., the complexity cost of adaptation, and the overall number of tests i.e., the adaptivity gain. Specifically, our algorithm matches the state-of-the-art adaptive algorithm in terms of the number of measurements while reducing the required number of stages by a multiplicative factor of $1-\alpha$ . Our results demonstrates that a small number of stages can lead to substantial improvements over non-adaptive strategies. Furthermore, in comparison to existing non-adaptive algorithms, our algorithm achieves a 47% improvement in the overall number of tests, with the addition of just one stage.

关键词： Costs adaptive algorithms Complexity theory Decoding Testing

adaptive Point Cloud Resampling Study Based on Unbalanced Optimal Transport

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Adaptive Point Cloud Resampling Study Based on Unbalanced Op...

Computer Science and Blockchain (CCSB), International Conference on

作者： Zizheng Li Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

3D point cloud resampling optimizes data representation and improves usage efficiency by adjusting point cloud density, but limitations of scanning equipment lead to uneven curvature distribution and the presence of noise and outliers. Therefore, an adaptive algorithm based on non-equilibrium optimal transport is proposed herein. First, the point cloud structure is optimized using centroidal Voronoi tessellation; second, the resampling problem is converted into an optimization problem, where RG scattering and entropy regularization are employed to control the probability distribution, and the point cloud position is updated by the stochastic Newton iteration method. Finally, the algorithm's performance is quantified using mean-square error and kernel density estimation. The experimental results show that the algorithm proposed herein outperforms existing methods in terms of resampling accuracy and efficiency.

关键词： Point cloud compression Three-dimensional displays Heuristic algorithms Stochastic processes Scattering adaptive algorithms Sampling methods Entropy Robustness Optimization

Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation

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arXiv 2024年

作者： Li, Yiwei Mi, Fei Li, Yitong Wang, Yasheng Sun, Bin Feng, Shaoxiong Li, Kan School of Computer Science & Technology Beijing Institute of Technology China Huawei Noah’s Ark Lab Hong Kong Huawei Technologies Ltd.

Stochastic sampling strategies such as top-k and top-p have been widely used in dialogue generation task. However, as an open-domain chatting system, there will be two different conversation scenarios, i.e. chit-chat and knowledge-based question answering. In the former situation, responses diversity is essential due to the one-to-many nature in dialogue. The latter, on the other hand, requires less randomness given that stochastic decoding strategy entails the risk of generating incorrect information. As a result, an adaptive and flexible decoding strategy is needed to cope with these two scenarios simultaneously. To this end, we propose the dynamic decoding strategy (DDS), which can adjust the decoding space w.r.t. different contexts. In DDS, both sequence-level and token-level adaptive search can be achieved to adjust the decoding process in a unified framework. Besides, our adaptive algorithm can not only be used during model inference, but it can also be applied during the model training stage to further enhance the performance. Comprehensive experiments indicate that the proposed decoding strategy can consistently improve the performance of pre-trained dialogue models when coupled with four well-used stochastic decoding algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： adaptive algorithms