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PV System Installation Assessment Based on Power Measurement for Balcony Power Plant Applications

IEEE JOURNAL OF PHOTOVOLTAICS 2024年第4期14卷 571-582页

作者： Goegelein, David von Schwerin, Marianne Herbort, Volker Ulm Univ Appl Sci Dept Elect Engn & Informat Technol D-89075 Ulm Germany Ulm Univ Appl Sci Dept Comp Sci D-89075 Ulm Germany

Photovoltaic systems for balcony power plant applications are rapidly growing, and with it comes the need for holistic concepts for energy utilization. Since these systems are intended for users without deep technical knowledge, the applications need to work and optimize themselves automatically without the user having to interfere. Therefore, this article presents a method to estimate the azimuth and the inclination of photovoltaic modules by analyzing the on-site power data and comparing it to a power forecast by Forecast. Solar, thereby making it unnecessary for users to provide these values themselves. The information gained hereby can be used to obtain and optimize a power forecast considering prevailing on-site conditions for energy management purposes. This is done by using low-volume power data at hourly resolution, where a single day of data is enough to make an estimation. The results of this method provide PV installation parameters with a resolution of the azimuth in 45(degrees)-steps and the inclination in 15(degrees)-steps, which are matched to the best-fitting power forecast.

关键词： approximation algorithms forecasting photovoltaic systems solar power generation

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Noisy One-Bit Compressed Sensing With Side-Information

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 3792-3804页

作者： Kafle, Swatantra Wimalajeewa, Thakshila Varshney, Pramod K. Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

We consider the problem of sparse signal reconstruction from noisy one-bit compressed measurements when the receiver has access to side-information (SI). We assume that compressed measurements are corrupted by additive white Gaussian noise before quantization and sign-flip error after quantization. A generalized approximate message passing-based method for signal reconstruction from noisy one-bit compressed measurements is proposed which is then extended for the case where the receiver has access to a signal that aids signal reconstruction, i.e., side-information. Two different scenarios of side-information are considered-a) side-information consisting of support information only, and b) side information consisting of support and amplitude information. SI is either a noisy version of the signal or a noisy estimate of the support of the signal. We develop reconstruction algorithms from one-bit measurements using noisy SI available at the receiver. Laplacian distribution and Bernoulli distribution are used to model the noise which when applied to the signal yields the SI for the above two cases. The Expectation-Maximization algorithm is used to estimate the noise parameter using noisy one-bit compressed measurements and the SI. We show that one-bit compressed measurement-based signal reconstruction is quite sensitive to noise, and the reconstruction performance can be significantly improved by exploiting side-information at the receiver when available.

关键词： Noise measurement Receivers Quantization (signal) Signal processing algorithms Compressed sensing Signal reconstruction approximation algorithms Sparse signal reconstruction one-bit compressed measurements generalized approximate message passing side-information

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Dynamic Leader-Follower Output Containment Control of Heterogeneous Multiagent Systems Using Reinforcement Learning

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第9期54卷 5307-5316页

作者： Zhang, Huaipin Zhao, Wei Xie, Xiangpeng Yue, Dong Nanjing Univ Posts & Telecommun Inst Adv Technol Carbon Neutral Nanjing 210023 Peoples R China Nanjing Univ Finance & Econ Coll Informat Engn Nanjing 210023 Peoples R China

This article addresses the optimal containment problem of heterogeneous multiagent systems (MASs) with dynamic leaders via reinforcement learning (RL), where the dynamics of all agents are all completely unknown. A distributed model-free observer is constructed for each follower to estimate the leaders' dynamics and the output trajectories inside the convex hull formed by the leaders. Based on the designed observers, the optimal containment problem is formulated as an optimal tracking control issue. Then the discounted performance functions are introduced to obtain algebraic Riccati equations (AREs). And a model-free RL algorithm is developed to learn the AREs online. To implement this algorithm, we design a single critic neural network structure for each follower to approximate Q -function, and estimate optimal control policy and worst-case adversarial input policy. Finally, a numerical simulation is provided to demonstrate the effectiveness of the proposed algorithm.

关键词： Observers Vectors Heuristic algorithms Trajectory Multi-agent systems approximation algorithms Adaptive observers containment control heterogeneous multiagent systems (MASs) neural network (NN) approximation reinforcement learning (RL)

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Joint Dataset Reconstruction and Power Control for Distributed Training in D2D Edge Network

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第1期21卷 132-147页

作者： Wu, Jiaxin Wu, Jigang Chen, Long Sun, Yifei Wu, Yalan Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China

The intrinsic nature of non-independent and identically distributed datasets on heterogeneous devices slows down the distributed model training process and reduces the training accuracy. To settle this problem, we propose a dataset reconstruction scheme to transform the data distribution of training device's dataset into independent and identically distributed dataset via data exchange among trusted devices. For energy efficiency, we further consider power control for the devices. We then formulate an optimization problem, which is a mixed integer non-linear programming problem, to minimize the total energy consumption for each round of distributed training. Due to the NP-hardness and coupling property of the optimization problem, we decompose it into two subproblems for dataset reconstruction and power control, respectively. An approximation algorithm is designed to obtain a near-optimal auxiliary devices set for dataset reconstruction with minimum energy consumption, while meeting the variance constraint of the optimization problem. We prove that approximation algorithm has a worst-case approximation ratio of 1+ln|Omega i(t)|, where |Omega i(t)| is the required data samples for dataset reconstruction of each training device. For power control, we design a dynamic programming algorithm to further reduce the energy consumption. For comparison, we propose three benchmark schemes that adopt either one of the algorithms or neither. We also customize three baseline algorithms based on the state-of-the-arts to compare with our proposed algorithm. Numerical results show that, our proposed algorithm outperforms three benchmarks on the average energy consumption for one round for different cases. When varying the labels that each device owns, our proposed algorithm outperforms the other three baseline algorithms on training accuracy. Besides, when setting a target accuracy, our proposed algorithm always has the lowest energy consumption.

关键词： Training Distributed databases Device-to-device communication Data models approximation algorithms Adaptation models Servers D2D distributed training NP-hard weighted set cover approximation algorithm dynamic programming

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A Simple 1.5-approximation Algorithm for a Wide Range of Maximum Size Stable Matching Problems 23

A Simple 1.5-approximation Algorithm for a Wide Range of Max...

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Csáji, Gergely HUN-REN Centre for Economic and Regional Studies Budapest Hungary ELTE Eötvös Loránd University Budapest Hungary

We give a simple approximation algorithm for a common generalization of many previously studied extensions of the maximum size stable matching problem with ties. These generalizations include the existence of critical vertices in the graph, amongst whom we must match as much as possible, free edges, that cannot be blocking edges and Δ-stabilities, which mean that for an edge to block, the improvement should be large enough on one or both sides. We also introduce other notions to generalize these even further, which allows our framework to capture many existing and future applications. We show that the edge duplicating technique allows us to treat these different types of generalizations simultaneously, while also making the algorithm, the proofs and the analysis much simpler and shorter than in previous approaches. In particular, we answer an open question by Askalidis et al. [1] about the existence of a 32 -approximation algorithm for the Max-SMTI problem with free edges. This demonstrates that this technique can grasp the underlying essence of these problems quite well and have the potential to be able to solve many future applications. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： approximation algorithms

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Robust Source Positioning Method With Accurate and Simplified Worst-Case approximation

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第2期71卷 1891-1900页

作者： Qin, Shuang Guo, Xiansheng Sichuan Normal Univ Coll Phys & Elect Engn Key Lab Wireless Sensor Network Univ Sichuan Prov Chengdu 610101 Peoples R China Univ Elect Sci & Technol China UESTC Dept Elect Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Quzhou Quzhou 324000 Peoples R China

Worst-case robust approximation was proven to be efficient in alleviating the non-line-of-sight (NLOS) influence for source positioning. However, the existing time-difference-of-arrival (TDOA)-based worst-case solutions still have two issues: 1) Inaccurate objective transformations are introduced in some algorithms, which reduce the accuracy;2) A method with higher accuracy is computationally intensive. This study proposes an accurate and simplified worst-case approximation method to tackle the troubles. Precisely, we first prove that the nonconvex worst-case objective is piecewise monotone to the NLOS bias. We further use monotonicity to derive an accurate and convex expression of the worst-case objective. Then, we propose simplified transformations to redefine the worst-case approximation problem with fewer constraints. Besides, we prove the effectiveness of the simplified transformations. Simulations and experiments demonstrate that the proposed method with moderate computation exhibits better performance than the state-of-the-art worst-case approximation algorithms.

关键词： Location awareness Wireless sensor networks approximation algorithms Gaussian noise Noise measurement Linear programming Synchronization Global optimization non-line-of-sight (NLOS) time-difference-of-arrival (TDOA) wireless sensor networks (WSNs) localizaiton worst-case robust approximation

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Online and Offline Parameter Optimization of SCR Denitration Control in Power Plants

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IEEE ACCESS 2024年 12卷 163481-163490页

作者： Liu, Jianbo Wang, Yaping Zhao, Nan Wang, Jiadong Guoneng Beijing Gas Fired Cogenerat Beijing 100018 Peoples R China

The optimization control of the selective catalytic reduction (SCR) denitrification system holds significant importance in the power generation industry. This paper presents an engineering fastest controller combined with active disturbance rejection control (EFC-ADRC) to enhance the control performance in SCR denitrification process. Compared with EFC, EFC-ADRC strategy has better performance in disturbances rejection when using the extended state observer(ESO) to compensate for uncertain disturbances. Two optimization methods are adopted including online Simultaneous Perturbation Stochastic approximation (SPSA) and offline whale optimization algorithm (WOA), to tune the filter parameters in EFC by matching the time-varying characteristics of the process. Finally, the superior performance of the proposed control strategy in terms of set point tracking and disturbance rejection is verified through simulation experiments.

关键词： Denitrification Ammonia Uncertainty approximation algorithms Power generation Inductors Catalysts Tuning Process control Delays Selective catalytic reduction (SCR) engineering fastest control (EFC) ADRC EFC-ADRC controller simultaneous perturbation stochastic approximation (SPSA) whale optimization algorithm (WOA)

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A Fast Hop-Biased approximation Algorithm for the Quadratic Group Steiner Tree Problem 24

A Fast Hop-Biased Approximation Algorithm for the Quadratic ...

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33rd ACM Web Conference, WWW 2024

作者： Wang, Xiaoqing Cheng, Gong State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China

ISBN: (纸本)9798400701719

Knowledge Graph (KG) exploration helps Web users understand the contents of a large and unfamiliar KG and extract relevant insights. The task has recently been formulated as a Quadratic Group Steiner Tree Problem (QGSTP) to search for a semantically cohesive subgraph connecting entities that match query keywords. However, on large graphs, existing algorithms for this NP-hard problem cannot meet the performance need. In this paper, we propose a novel approximation algorithm for QGSTP called HB. It finds and merges an optimal set of paths according to a Hop-Biased objective function, which not only leads to a guaranteed approximation ratio but is also decomposable by paths to enable efficient dynamic programming based search. Accompanied by a set of pruning heuristics, HB outperformed the state of the art by 1-2 orders of magnitude, empirically reducing the average time for answering a query on a million-scale graph from about one minute to one second. © 2024 ACM.

关键词： approximation algorithms

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Fast Bicriteria approximation Algorithm for Minimum Cost Submodular Cover Problem 12th

Fast Bicriteria Approximation Algorithm for Minimum Cost Su...

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12th International Conference on Computational Data and Social Networks, CSoNet 2023

作者： Pham, Canh V. Phu, Quat V. Ha, Dung T. K. ORLab Faculty of Computer Science Phenikaa University Hanoi Viet Nam Faculty of Information Technology VNU University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9789819706686

This paper studies the Minimum Cost Submodular Cover (MCSC) problem over the ground set of size n, which aims at finding a subset with the minimal cost required so that the utility submodular function exceeds a given threshold. The issue has recently attracted a lot of attention due to its applications in various domains of artificial intelligence and combination optimization. However, the best approximation algorithm for the problem requires an expensive query complexity of O(n2) that may become infeasible for some applications with large data. In this work, we propose a bicriteria approximation algorithm that keeps the performance guarantees of the state-of-the-art ones but reduces the required number of queries to O(nlogn). Besides the theoretical, the experiment results on two applications, Twitter feed threshold summarization, and threshold influence in social networks, further show the superiority of our algorithm with the state-of-the-art in terms of both solution quality and query complexity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： approximation algorithms

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Clustering Complexity and an approximation Algorithm for a Version of the Cluster Editing Problem 23rd

Clustering Complexity and an Approximation Algorithm for ...

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23rd International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2024

作者： Il’ev, Artyom Il’ev, Victor Sobolev Institute of Mathematics SB RAS Omsk Russia Dostoevsky Omsk State University Omsk Russia

ISBN: (纸本)9783031627910

In graph clustering problems, one has to partition the vertex set of a given undirected graph into pairwise disjoint subsets (clusters). Vertices of the graph correspond to some objects, edges connect the pairs of similar objects. In cluster editing (CE) problems the goal is to find a nearest to a given graph G=(V,E) cluster graph, i.e., a graph on the same vertex set V each connected component of which is a complete graph. The distance between graphs is understood as the number of their non-coinciding edges. The distance between a graph G and a nearest to G cluster graph is called clustering complexity of G. We consider a version of CE problem in which the size of each cluster is bounded from above by a positive integer s. This problem is NP-hard for any fixed s≥3. In 2015, Puleo and Milenkovic proposed a 6-approximation algorithm for this problem. For the version of the problem with s=5 we propose a polynomial-time approximation algorithm with better performance guarantee and prove an upper bound on clustering complexity of a graph that is better than earlier known one. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： approximation algorithms

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