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adaptive Rough Entropy Clustering algorithms in Image Segmentation

FUNDAMENTA INFORMATICAE 2010年第2-3期98卷 199-231页

作者： Malyszko, Dariusz Stepaniuk, Jaroslaw Bialystok Tech Univ Dept Comp Sci PL-15351 Bialystok Poland

High quality performance of image segmentation methods presents one leading priority in design and implementation of image analysis systems. Incorporating the most important image data information into segmentation process has resulted in development of innovative frameworks such as fuzzy systems, rough systems and recently rough - fuzzy systems. Data analysis based on rough and fuzzy systems is designed to apprehend internal data structure in case of incomplete or uncertain information. Rough entropy framework proposed in [12, 13] has been dedicated for application in clustering systems, especially for image segmentation systems. We extend that framework into eight distinct rough entropy measures and related clustering algorithms. The introduced solutions are capable of adaptive incorporation of the most important factors that contribute to the relation between data objects and makes possible better understanding of the image structure. In order to prove the relevance of the proposed rough entropy measures, the evaluation of rough entropy segmentations based on the comparison with human segmentations from Berkeley and Weizmann image databases has been presented. At the same time, rough entropy based measures applied in the domain of image segmentation quality evaluation have been compared with standard image segmentation indices. Additionally, rough entropy measures seem to comprehend properly properties validated by different image segmentation quality indices.

关键词： adaptive algorithms rough entropy measure image segmentation image clustering

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An adaptive Tuning Mechanism for Phase-Locked Loop algorithms for Faster Time Performance of Interconnected Renewable Energy Sources

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2015年第2期51卷 1792-1804页

作者： Hadjidemetriou, Lenos Kyriakides, Elias Blaabjerg, Frede Univ Cyprus Dept Elect & Comp Engn KIOS Res Ctr Intelligent Syst & Networks CY-1678 Nicosia Cyprus Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

Interconnected renewable energy sources (RES) require fast and accurate fault ride through (FRT) operation, in order to support the power grid, when faults occur. This paper proposes an adaptive phase-locked loop (adaptive d alpha beta PLL) algorithm, which can be used for a faster and more accurate response of the grid-side converter (GSC) control of a RES, particularly under FRT operation. The adaptive d alpha beta PLL is based on modifying the tuning parameters of the d alpha beta PLL, according to the type and voltage characteristics of the grid fault, with the purpose of accelerating the performance of the PLL algorithm. The proposed adaptive tuning mechanism adjusts the PLL parameters in real time, according to the proposed fault classification unit, in order to accelerate the synchronization performance. The beneficial effect of the proposed adaptive tuning mechanism on the performance of d alpha beta PLL is verified through simulation and experimental results. Furthermore, the benefits of using a faster synchronization method on the control of the GSC of RES are also demonstrated in this paper. Additionally, a new synchronization technique named frequency-phase decoupling (FPD)-d alpha beta PLL is presented, which applies an FPD technique from the literature in the structure of d alpha beta PLL, with the purpose of improving the performance of d alpha beta PLL. Finally, the adaptive tuning mechanism proposed in this paper is combined with the FPD-d alpha beta PLL in order to introduce the adaptive FPD-d alpha beta PLL, which presents an even faster time performance and is an ideal solution for the synchronization of RES under grid faults.

关键词： adaptive algorithms dc-ac power converters fault ride through (FRT) operation phase-locked loops (PLLs) power system faults renewable energy sources (RES)

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adaptive Randomized Coordinate Descent for Sparse Systems: Lasso and Greedy algorithms

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第15期63卷 4091-4101页

作者： Onose, Alexandru Dumitrescu, Bogdan Tampere Univ Technol Dept Signal Proc Tampere 33720 Finland Univ Politehn Bucuresti Dept Automat Control & Comp Bucharest Romania

Coordinate descent (CD) is a simple optimization technique suited to low complexity requirements and also for solving large problems. In randomized version, CD was recently shown as very effective for solving least-squares (LS) and other optimization problems. We propose here an adaptive version of randomized coordinate descent (RCD) for finding sparse LS solutions, from which we derive two algorithms, one based on the lasso criterion, the other using a greedy technique. Both algorithms employ a novel way of adapting the probabilities for choosing the coordinates, based on a matching pursuit criterion. Another new feature is that, in the lasso algorithm, the penalty term values are built without knowing the noise level or using other prior information. The proposed algorithms use efficient computations and have a tunable trade-off between complexity and performance through the number of CD steps per time instant. Besides a general theoretical convergence analysis, we present simulations that show good practical behavior, comparable to or better than that of state of the art methods.

关键词： adaptive algorithms coordinate descent randomization sparse filters

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adaptive noise control algorithms for active headrest system

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CONTROL ENGINEERING PRACTICE 2004年第9期12卷 1101-1112页

作者： Pawelczyk, M Silesian Tech Univ Inst Automat Control PL-44101 Gliwice Poland

In this paper a prototype of an active headrest system is studied. First, the plant is discussed and its basic characteristics are presented. Then, adaptive control techniques for acoustic noise control are employed. An efficient two-stage algorithm generating zones of highest noise attenuation at desired locations is proposed and compared with other control systems. Both SISO and MIMO systems are considered. The latter are found to be more appropriate. Obtained results are illustrated in different forms. Distribution of the zones of quiet is presented and discussed. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： active noise control adaptive algorithms multivariable feedback control

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adaptive Linear Minimum BER Reduced-Rank Interference Suppression algorithms Based on Joint and Iterative Optimization of Filters

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IEEE COMMUNICATIONS LETTERS 2013年第4期17卷 633-636页

作者： Cai, Yunlong de Lamare, Rodrigo C. Zhejiang Univ Dept Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Univ York Dept Elect Commun Res Grp York YO10 5DD N Yorkshire England

In this letter, we propose a novel adaptive reduced-rank strategy based on joint iterative optimization (JIO) of filters according to the minimization of the bit error rate (BER) cost function. The proposed optimization technique adjusts the weights of a subspace projection matrix and a reduced-rank filter jointly. We develop stochastic gradient (SG) algorithms for their adaptive implementation and introduce a novel automatic rank selection method based on the BER criterion. Simulation results for direct-sequence code-division-multiple-access (DS-CDMA) systems show that the proposed adaptive algorithms significantly outperform the existing schemes.

关键词： Reduced-rank techniques adaptive algorithms BER cost function multiuser detection

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Grouped signed power-of-two algorithms for low-complexity adaptive equalization

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2005年第12期52卷 816-820页

作者： Chen, KH Chen, CN Chiuch, TD Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan

With increasing demand for higher data rate, modern communication systems have grown more complex. Equalization has become more and more important as it is effective in mitigating the multipath fading often occurred in high-data-rate communication systems. However, the implementation complexity of adaptive equalizers is usually too high for mobile communication applications. In this paper, a novel adaptive equalization algorithm and its low-complexity architecture are proposed. This algorithm employs a new grouped signed power-of-two (GSPT) number representation. The GSPT algorithm and several enhanced versions are simulated as adaptive equalizers in a phase-shift keying communication receiver for several practical channels and the GSPT-based equalizers perform as well as the least mean square (LMS) equalizer. Moreover, for comparison, two GSPT-based equalizers and two other equalizers are implemented in field-programmable gate arrays. The GSPT-based equalizers require only about 25%-30% of the hardware resources needed in the LMS equalizer. Also the GSPT-based equalizers are more than twice as fast as the LMS equalizer.

关键词： adaptive algorithms adaptive filters low circuit complexity power-of-two (PT)

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Optimizing Network Traffic Prediction for Network Digital Twins: The Impact of Look-Back Period and Forecast Horizon

Optimizing Network Traffic Prediction for Network Digital Tw...

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IEEE Conference on Wireless Communications and Networking

作者： John Sengendo Fabrizio Granelli Dept. of Information Engineering and Computer Science University of Trento Trento ITALY

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Over recent years, many advancements in networks have taken place with now the advent of 6G. With these growing advancements, challenges in managing networks also emerge. Network Digital Twins (NDTs) is one of the potential technology in solving many of these challenges because of the capability to virtually replicate the physical network elements. One of the key challenges is predicting network traffic especially with the exponentially growing number of devices in the network. In this paper, we study and show how varying the “look-back period” and “forecast horizon” significantly affects traffic predictions. Look-back period or window size is how much of the historical data is used by a prediction algorithm to make predictions. Forecast horizon is how far in the future we can predict. These highly impact how accurately traffic is predicted in networks and as well significantly determine how well Digital Twins (DTs) for networks accurately reflect traffic of the physical network.

关键词： Accuracy Costs Heuristic algorithms Memory management adaptive algorithms Telecommunication traffic Traffic control Predictive models Prediction algorithms Digital twins

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Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leiblier Maillard Sampling

arXiv

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arXiv 2025年

作者： Qin, Hao Jun, Kwang-Sung Zhang, Chicheng University of Arizona United States

We study the problem of Multi-Armed Bandits (MAB) with reward distributions belonging to a One-Parameter Exponential Distribution (OPED) family. In the literature, several criteria have been proposed to evaluate the performance of such algorithms, including Asymptotic Optimality (A.O.), Minimax Optimality (M.O.), Sub-UCB, and variance-adaptive worst-case regret bound. Thompson Sampling (TS)-based and Upper Confidence Bound (UCB)-based algorithms have been employed to achieve some of these criteria. However, none of these algorithms simultaneously satisfy all the aforementioned criteria. In this paper, we design an algorithm, Exponential Kullback-Leibler Maillard Sampling (abbrev. EXP-KL-MS), that can achieve multiple optimality criteria simultaneously, including A.O., M.O. with a logarithmic factor, Sub-UCB, and variance-adaptive worst-case regret bound. Copyright © 2025, The Authors. All rights reserved.

关键词： adaptive algorithms

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adaptive Local Clustering over Attributed Graphs

arXiv

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arXiv 2025年

作者： Zheng, Haoran Yang, Renchi Xu, Jianliang Hong Kong Baptist University Hong Kong

Given a graph G and a seed node vs, the objective of local graph clustering (LGC) is to identify a subgraph Cs ∈ G (a.k.a. local cluster) surrounding vs in time roughly linear with the size of Cs. This approach yields personalized clusters without needing to access the entire graph, which makes it highly suitable for numerous applications involving large graphs. However, most existing solutions merely rely on the topological connectivity between nodes in G, rendering them vulnerable to missing or noisy links that are commonly present in real-world graphs. To address this issue, this paper resorts to leveraging the complementary nature of graph topology and node attributes to enhance local clustering quality. To effectively exploit the attribute information, we first formulate the LGC as an estimation of the bidirectional diffusion distribution (BDD), which is specialized for capturing the multi-hop affinity between nodes in the presence of attributes. Furthermore, we propose LACA, an efficient and effective approach for LGC that achieves superb empirical performance on multiple real datasets while maintaining strong locality. The core components of LACA include (i) a fast and theoretically-grounded preprocessing technique for node attributes, (ii) an adaptive algorithm for diffusing any vectors over G with rigorous theoretical guarantees and expedited convergence, and (iii) an effective three-step scheme for BDD approximation. Extensive experiments, comparing 17 competitors on 8 real datasets, show that LACA outperforms all competitors in terms of result quality measured against ground truth local clusters, while also being up to orders of magnitude faster. Copyright © 2025, The Authors. All rights reserved.

关键词： adaptive algorithms

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Big Data Clustering algorithms: Improving Efficiency and Scalability for Massive Data Mining and Pattern Recognition

Big Data Clustering Algorithms: Improving Efficiency and Sca...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Shanmugam Muthu G Ramesh Kalyan V. Samuthira Pandi Shobana D Lakshmi Priya J Jaishika S Shipt Inc Birmingham USA Department of Computer Science and Engineering SNS College of Technology Coimbatore Tamilnadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Mechatronics Engineering Rajalakshmi Engineering College Chennai Department of ECE Saveetha Engineering College Chennai Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The emergence of big data has completely changed the way data analysis is done. As a result, there is a pressing need for effective clustering algorithms that can handle large datasets from different fields. Efficiency, scalability, and finding meaningful patterns in enormous data are some of the important difficulties that this research tackles in relation to big data clustering. We thoroughly examine and evaluate a range of clustering algorithms, from more conventional ones like hierarchical and K-Means clustering to more cutting-edge ones like density-based spatial clustering (DBSCAN) and model-based clustering. To further improve the performance and scalability of clustering algorithms, we investigate new methods that make use of distributed computing frameworks like Apache Spark and MapReduce. We offer a thorough assessment of their efficacy in terms of clustering quality, computational efficiency, and resource use by using and evaluating these methods on large-scale datasets. While conventional clustering algorithms may fail miserably when faced with extremely big data sets, our research shows that newer methods that make use of optimization techniques and distributed processing greatly outperform their predecessors. The effect of feature selection and dimensionality reduction techniques on improving clustering results is also shown. By shedding light on how to choose and implement clustering algorithms for effective data mining and pattern identification, this study adds to the continuing endeavors in the domain of big data analytics. Progress in areas like customer segmentation, anomaly detection, and social network analysis can be achieved with the help of adaptive algorithms that can handle large datasets while keeping clustering results accurate and dependable, according to the study.

关键词： Dimensionality reduction Machine learning algorithms Accuracy Social networking (online) Scalability Heuristic algorithms Clustering algorithms adaptive algorithms Big Data Computational efficiency

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