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A MILP Based Framework for the Hydro Unit Commitment Considering Irregular Forbidden Zone Related Constraints

IEEE TRANSACTIONS ON POWER SYSTEMS 2021年第3期36卷 1819-1832页

作者： Zhao, Zhipeng Cheng, Chuntian Liao, Shengli Li, Yapeng Lu, Quan Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China Dalian Univ Technol Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Dept Elect & Elect Engn Dalian 116024 Peoples R China

The irregular forbidden zone (FZ) is a common phenomenon with giant hydropower plants developed in the past two decades in China. Irregular shapes of FZs significantly challenge hydro unit commitment (HUC). This paper proposes a novel MILP based framework for HUC considering irregular FZ related constraints including the FZ constraint, effects of linearization errors in both the net head and the output, and the FZ crossing constraint. In the framework, the FZ constraint is handled by the optimal convex partitioning algorithm and the common structure-based formulation method. Inspired by the planar translating robot placement problem, linearization errors are considered by the Minkowski sum method. To handle the FZ crossing constraint, we then propose a graph theory-based approximate formulation method. The framework is integrated into a HUC model with an objective of peak shaving. The model is then tested with a batch of real-world instances of a cascade hydropower system formed by ten giant units with highly irregular FZs. The results show our framework can effectively consider the irregular FZ related constraints. The major advantage of our framework is its ability to handle the irregular shapes of FZs without any tedious and error-prone manual processing.

关键词： Hydroelectric power generation Shape Vibrations partitioning algorithms Robots Reservoirs irregular forbidden zone hydro unit commitment MILP convex partition graph theory

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Kernel-based Fuzzy Co-clustering in Feature Space with Automated Variable Weighting

Kernel-based Fuzzy Co-clustering in Feature Space with Autom...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： De Sa, Jose Nataniel A. Ferreira, Marcelo R. P. De Carvalho, Francisco De A. T. Univ Fed Pernambuco Centro Informat CIn UFPE Ave Jornalista Anibal FernandesS-N BR-50740560 Recife PE Brazil Univ Fed Paraiba Dept Estatist DE UFPB Conj Pres Castelo Branco III BR-58050585 Joao Pessoa Paraiba Brazil

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

Kernel functions have been used successfully in clustering algorithms to deal with the separability of clusters efficiently. Bringing this idea to co-clustering, we propose two kernel-based fuzzy co-clustering algorithms based on the fuzzy double Kmeans (FDK). The first proposed algorithm, the Gaussian kernel fuzzy double Kmeans (GKFDK), is based on FDK and computes the cluster prototypes in the original feature space. The second algorithm, the Weighted gaussian kernel fuzzy double Kmeans (WGKFDK), is an extension of the GKFDK with automated variable weighting, that distinguishes the relevance of the variables in each cluster. Experiments performed with both synthetic and real data, in comparison with previous state-of-the-art co-clustering algorithms, showed the effectiveness of the proposed algorithms.

关键词： Measurement Clustering algorithms Prototypes Entropy partitioning algorithms Indexes Kernel

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Modified Bron–Kerbosch-Based Sensor Deployment Algorithm for Target Coverage in Wireless Sensor Networks

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IEEE Access 2025年 13卷 95692-95705页

作者： P. Surya Bharathi R. Pavithra Vellore Institute of Technology Chennai Tamil Nadu India

The growing adoption of Wireless Sensor Network (WSN) technologies has highlighted several critical factors that are vital for their efficiency and functionality. Optimal sensor node deployment emerges as a critical factor that significantly impacts the performance and overall effectiveness of the network. To address these challenges, a new deployment algorithm is proposed that substantially reduce the number of sensor requirement to ensure maximum coverage based on the geometric relationship between the targets. This paper proposes a novel Modified Bron-Kerbosch based Sensor Deployment (MBK-SD) algorithm that partition the vertex set as maximal clique and an iterative circle intersection method is introduced to find the optimal sensor placement. Theorem is proven that a central point exists within a specified distance R from all vertices forming a circle with radius R. The experimental results demonstrate that the proposed algorithm requires minimum number of sensors compared to Vertex Coloring based Optimal Sensor Placement (VC-OSP) and achieves better coverage compared to the Artificial Bee Colony (ABC) algorithm and Firefly Algorithm (FA). Statistical analysis is conducted for the proposed algorithm in comparison with ABC and FA. The results demonstrate the significance of the proposed algorithm over ABC and FA.

关键词： Wireless sensor networks Monitoring Sensors Sensor placement partitioning algorithms Clustering algorithms Temperature sensors Silicon Optimization Iterative algorithms

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$k$-Shape Clustering Enhances Group Lasso for Gene Selection and Sample Classification

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第3期22卷 1176-1188页

作者： Shunjie Chen Pei Wang Jinhu Lü School of Mathematics and Statistics Henan University Kaifeng China Henan Engineering Research Center for Industrial Internet of Things Henan University Zhengzhou China School of Automation Science and Electrical Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China

The surge in high-throughput biological data necessitates efficient tools for knowledge discovery. Group Lasso for logistic regression, a powerful model for sample classification and gene selection in correlated data, relies on robust clustering. This paper addresses the instability of traditional $k$ -means variants by introducing $k$ -shape clustering into the group Lasso for logistic regression framework. Comparative analyses using simulated and real-world datasets demonstrate that group Lasso for logistic regression with $k$ -shape (GLKSH) outperforms $k$ -means variants in accuracy and robustness. GLKSH also uniquely identifies informative genes and achieves superior sample classification, highlighting the impact of clustering on group Lasso and offering a valuable approach for gene selection.

关键词： Clustering algorithms Logistic regression partitioning algorithms Kernel Vectors Euclidean distance Classification algorithms Training Shape measurement Gene expression

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TIDY: Publishing a Time Interval Dataset With Differential Privacy

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2021年第5期33卷 2280-2294页

作者： Jung, Woohwan Kwon, Suyong Shim, Kyuseok Seoul Natl Univ Dept Elect & Comp Engn Seoul Gwanak POB 55 Seoul 08755 South Korea

Log data from mobile devices generally contain a series of events with temporal information including time intervals which consist of the start and finish times. However, the problem of releasing differentially private time interval datasets has not been tackled yet. A time interval dataset can be represented by a two dimensional (2D) histogram. Most of the methods to publish 2D histograms partition the data into rectangular spaces to reduce the aggregated noise error for range queries. However, the existing algorithms to publish 2D histograms suffer from the structural error when applied to time interval datasets. To reduce the aggregated noise errors and suppress the increase in the structural error, we propose the TIDY (publishing Time Intervals via Differential privacY) algorithm. We use the frequency vectors as a compact representation of the time interval dataset. After applying the Laplace mechanism to the frequency vectors, we improve the utility of the frequency vectors based on a maximum likelihood estimation. We also develop a new partitioning method adapted for the frequency vectors to balance the trade-off between the noise and structural errors. Our empirical study on real-life and synthetic datasets confirms that TIDY outperforms the existing algorithms for 2D histograms.

关键词： Data privacy Publishing Histograms Privacy Time-frequency analysis Two dimensional displays partitioning algorithms Privacy-preserving data publishing differential privacy time interval dataset

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Sparse Subspace Clustering via Two-Step Reweighted L1-Minimization: Algorithm and Provable Neighbor Recovery Rates

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第2期67卷 1216-1263页

作者： Wu, Jwo-Yuh Huang, Liang-Chi Yang, Ming-Hsun Liu, Chun-Hung Natl Chiao Tung Univ Inst Commun Engn Dept Elect & Comp Engn Hsinchu 30010 Taiwan Natl Chiao Tung Univ Coll Elect Engn Hsinchu 30010 Taiwan Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39762 USA

Sparse subspace clustering (SSC) relies on sparse regression for accurate neighbor identification. Inspired by recent progress in compressive sensing, this paper proposes a new sparse regression scheme for SSC via two-step reweighted l(1)-minimization, which also generalizes a two- step l(1)-minimization algorithm introduced by E. J. Candes et al. in [The Annals of Statistics, vol. 42, no. 2, pp. 669-699, 2014] without incurring extra algorithmic complexity. To fully exploit the prior information offered by the computed sparse representation vector in the first step, our approach places a weight on each component of the regression vector, and solves a weighted LASSO in the second step. We propose a data weighting rule suitable for enhancing neighbor identification accuracy. Then, under the formulation of the dual problem of weighted LASSO, we study in depth the theoretical neighbor recovery rates of the proposed scheme. Specifically, an interesting connection between the locations of nonzeros of the optimal sparse solution to the weighted LASSO and the indexes of the active constraints of the dual problem is established. Afterwards, under the semirandom model, analytic probability lower/upper bounds for various neighbor recovery events are derived. Our analytic results confirm that, with the aid of data weighting and if the prior neighbor information is accurate enough, the proposed scheme with a higher probability can produce many correct neighbors and few incorrect neighbors as compared to the solution without data weighting. Computer simulations are provided to validate our analytic study and evidence the effectiveness of the proposed approach.

关键词： Clustering algorithms Minimization Noise measurement Eigenvalues and eigenfunctions Classification algorithms partitioning algorithms Indexes Subspace clustering sparse representation sparse subspace clustering neighbor recovery discovery rate performance guarantees weighted LASSO compressive sensing

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A Study of Using Bethe/Kikuchi Approximation for Learning Directed Graphic Models

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IEEE ACCESS 2021年 9卷 125428-125438页

作者： Lin, Peng Neil, Martin Fenton, Norman Capital Univ Econ & Business Sch Stat Beijing 100070 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Agena Ltd Cambridge CB23 7NU England

This paper applies the variational methods to learn the parameters and the probability of evidence of directed graphic models (also known as Bayesian networks (BNs)) when data contains missing values. One class of variational methods, the Bethe/Kikuchi approximate algorithm, is combined with Expectation-Maximization (EM) to learn BN model parameters from data using an upper limit on cluster size. Our proposed method is novel since the Bethe/Kikuchi algorithms are typically only used to approximate marginal distributions. We review popular Bethe/Kikuchi approximate algorithms, including Belief Propagation (BP), Iterative Join Graph Propagation (IJGP), and Fundamental Cycle Base (FCB) algorithms for discrete BNs, and conduct experiments to test their learning performance using a wide range of discrete BNs in practice. Experiments show that by using IJGP or FCB algorithms, with EM, we can obtain more accurate results than the conventional approximate algorithms, such as Evidence Pre-propagation Importance Sampling and Variational Inference. When exact methods are intractable the IJGP/FCB with EM can be an effective alternative solution.

关键词： Inference algorithms Approximation algorithms Clustering algorithms partitioning algorithms Data structures Data models Graphics Bayesian networks Bethe Kikuchi empirical data EM algorithm generalized belief propagation parameter learning

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Motion Recognition Based on Sum of the Squared Errors Distribution

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IEEE ACCESS 2021年 9卷 37116-37130页

作者： Yang, Wenkang Du, Qiaoling Cui, Jianchao Wang, Yankai Lu, Xinpo Qi, Chunxiao Zhang, Guiping Jilin Univ Coll Elect Sci & Engn State Key Lab Integrated Optoelect Changchun 130012 Peoples R China

Human motion recognition is playing an increasingly important role in modern society. Direct recognition of complex motions has great limitations, so we usually study basic motions first. The key to the establishment of the basic motion set is how to determine the number of basic motions. Sum of the Squared Errors Distribution (SSED) is therefore proposed to determine the number of clustering classes of Self Organizing Maps (SOM). Secondly, the Weighted Tangent Segmentation (WTS) is also proposed to segment complex motions into simple ones, and the sequence with the time stamp is generated. Finally, Back Propagation with the time stamp (BPTS) is proposed to classify the simple motions according to the basic motion set, and the complex motion is recognized according to the time stamp. The motions of human upper limbs are used to verify the effectiveness of this method. SSED determines that the number of clusters of human upper limb motion is 10. The experimental results show that the correct segmentation rate (CSR) is 98.13%. The recognition rate of basic motions is 98.67% and 99.33% in user independent (UI) and user dependent (UD) experiments, respectively. The recognition rate of complex motions is 96.37%. Experiments on UI and UD recognitions verified the effectiveness of our algorithm. Compared with other recognition algorithms, the complex motion recognition method proposed in this article has better recognition performance.

关键词： Clustering algorithms Neural networks Motion segmentation Classification algorithms Self-organizing feature maps partitioning algorithms Eigenvalues and eigenfunctions Clustering algorithms multi-layer neural network object recognition object segmentation self organizing feature maps

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Randomly Penalized Symbol Flipping Decoding of Non-Binary LDPC Codes

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第1期70卷 639-654页

作者： Zhao, Zhanzhan Jiao, Xiaopeng Mu, Jianjun He, Yu-Cheng Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Huaqiao Univ Xiamen Key Lab Mobile Multimedia Commun Xiamen 361021 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Symbol flipping decoding (SFD) of non-binary low-density parity-check (NB-LDPC) codes has lower implementation complexity at a cost of performance degradation, when compared with other belief propagation decoding algorithms. This paper proposes modified SFD algorithms based on prediction (SFDP) for NB-LDPC codes by introducing random penalty items into the flipping metrics. Two types of noise with uniform and Gaussian distributions are considered. A probabilistic analysis shows theoretically that the randomly penalized SFDP algorithms can be advantageous in the correct flipping probability over their original versions. Simulation results show that the proposed penalizing methods exhibit significant performance gains for both regular and irregular NB-LDPC codes with different rates and alphabet sizes in both the additive white Gaussian noise (AWGN) channel and the binary symmetric channel (BSC).

关键词： Decoding Measurement Prediction algorithms partitioning algorithms Reliability Iterative decoding Probabilistic logic Gaussian noise non -binary LDPC codes random penalty symbol flipping decoding uniform noise

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Analysis of Students' Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks

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IEEE ACCESS 2021年 9卷 132592-132608页

作者： Delgado, Soledad Moran, Federico San Jose, Jose Carlos Burgos, Daniel Univ Politecn Madrid Dept Sistemas Informat Madrid 28031 Spain Univ Complutense Madrid Dept Bioquim & Biol Mol Madrid 28040 Spain Univ Int La Rioja UNIR Res Inst Innovat & Technol Educ UNIR iTED Logrono 26006 Spain

An accurate analysis of user behaviour in online learning environments is a useful means of early follow up of students, so that they can be better supported to improve their performance and achieve the expected competences. However, that task becomes challenging due to the massive data that learning management systems store and categories. With the COVID-19 pandemic still on-going, face-to-face learning settings have migrate into online and blended ones, meaning an increase of online students and teachers in need for a tailored and effective support to their needs. A novel unsupervised clustering technique based on the Self-Organizing Map (SOM) artificial neural network model is used in this research to analyse 1,709,189 records of online students enrolled from 2015 to 2019 at Universidad Internacional de La Rioja (UNIR), a fully online Higher Education institution. SOM performs a precise and diverse user clustering based on those records. Results highlight that specific clusters are linked to the intake average profile at the university, with a clear relation between user interaction and a higher performance. Further, results show that, out of a targeted desk research compared to the analysis in this paper, face-to-face and online settings are connected through the methodological approach beyond the technology-based environment, which presents a similar behaviour in both contexts.

关键词： Self-organizing feature maps Neurons Clustering algorithms Training Shape Data mining partitioning algorithms Artificial neural networks data science applications in education distance education and online learning pattern analysis self-organizing map (SOM) student behaviour unsupervised learning

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