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Minimum Initial Marking Estimation in Labeled Petri Nets With Unobservable Transitions Based on Minimal Explanations

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第6期54卷 3427-3438页

作者： Yue, Hao Xu, Yakun Xing, Keyi Hu, Hesuan Pang, Shanchen China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Syst Engn Inst Xian 710049 Peoples R China Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China Nanyang Technol Univ Coll Engn Sch Comp Sci & Engn Singapore 639798 Singapore

Marking estimation is crucial in the area of discrete event systems. This paper proposes algorithms for addressing the problem of minimum initial markings (MuIMs) estimation in a known Petri net (PN) structure. The existence of unobservable transitions makes this problem challenging since the number of transition sequences consistent with an observed label sequence can potentially be infinite. It is assumed that only minimal explanations can fire before the firing of each observable transition (OT). We present the definition of minimal explanation places, which allows potential minimal explanations to fire before the firing of each OT to obtain more minimal initial markings. Since the number of initial markings may be infinite, we aim to obtain the initial markings that not only enable at least one transition sequence consistent with both the observed label sequence and the PN structure, but also exhibit the minimum total number of tokens (i.e., the minimum total number of tokens when summed over all places). After developing the algorithms, we also propose two heuristic methods to reduce the computational cost, resulting in a subset or an approximation of the final MuIMs. An illustrative example is provided to indicate how the proposed algorithms and heuristics can be utilized to reveal the minimum number of resources required which are indispensable at the initial state for the completion of a specified task sequence.

关键词： Petri nets Heuristic algorithms Task analysis approximation algorithms Vectors Resource management Prognostics and health management Initial marking estimation labeled Petri net (LPN) minimal explanation unobservable transition (UT)

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Implementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第2期35卷 2364-2377页

作者： Li, YaoChong Zhou, Ri-Gui Xu, RuiQing Luo, Jia Hu, WenWen Fan, Ping Shanghai Maritime Univ Res Ctr Intelligent Informat Proc & Quantum Intel Shanghai 201306 Peoples R China Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China East China Jiaotong Univ Coll Informat Engn Nanchang 330013 Jiangxi Peoples R China

Feature selection plays a significant role in computer science;nevertheless, this task is intractable since its search space scales exponentially with the number of dimensions. Motivated by the potential advantages of near-term quantum computing, three graph-theoretic feature selection (GTFS) methods, including minimum cut (MinCut)-based, densest k-subgraph (DkS)-based, and maximal-independent set/minimal vertex cover (MIS/MVC)-based, are investigated in this article, where the original graph-theoretic problems are naturally formulated as the quadratic problems in binary variables and then solved using the quantum approximate optimization algorithm (QAOA). Specifically, three separate graphs are created from the raw feature set, where the vertex set consists of individual features and pairwise measure describes the edge. The corresponding feature subset is generated by deriving a subgraph from the established graph using QAOA. For the above three GTFS approaches, the solving procedure and quantum circuit for the corresponding graph-theoretic problems are formulated with the framework of QAOA. In addition, those proposals could be employed as a local solver and integrated with the Tabu search algorithm for solving large-scale GTFS problems utilizing limited quantum bit resource. Finally, extensive numerical experiments are conducted with 20 publicly available datasets and the results demonstrate that each model is superior to its classical scheme. In addition, the complexity of each model is only O(pn(2)) even in the worst cases, where p is the number of layers in QAOA and n is the number of features.

关键词： Feature extraction Quantum computing Optimization approximation algorithms Quantum state Search problems Quantum circuit Feature selection graph theory parameterized quantum circuit quantum approximation optimization algorithm quantum computing

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Dealing With Class Imbalance in Uplift Modeling-Efficient Data Preprocessing via Oversampling and Matching

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IEEE ACCESS 2024年 12卷 188993-189008页

作者： Vairetti, Carla Marfan, Maria Jose Maldonado, Sebastian Univ Andes Fac Ingn & Ciencias Aplicadas Santiago 7620086 Chile Inst Sistemas Complejos Ingn ISCI Santiago 1025000 Chile Univ Chile Sch Econ & Business Dept Management Control & Informat Syst Santiago 8370451 Chile

Uplift modeling is a widely recognized predictive approach used to identify individuals who are more likely to respond positively to an intervention or treatment, such as a marketing campaign. However, this approach can be negatively affected by the class-imbalance problem, which occurs when the distribution of target classes is highly skewed. For instance, in a class-imbalanced uplift modeling task, only a small fraction typically responds to a marketing campaign that leads to a purchase. In this paper, we propose a novel resampling scheme that addresses the class-imbalance issue by combining intelligent oversampling and propensity score matching (PSM). By leveraging intelligent oversampling in observational studies, we alleviate the class-imbalance problem and mitigate the negative effects of PSM in terms of information loss. We introduce two efficient resampling schemes that intelligently combine these approaches. To ensure scalability and effectiveness, we adopt a distributed framework based on MapReduce and utilize a hybrid spill trees algorithm for efficient nearest neighbor search. Our experimental results demonstrate the advantages of the proposed method, achieving statistically superior predictive performance compared to other resampling approaches while maintaining efficiency in terms of overall running times.

关键词： Predictive models Big Data Interpolation Data models Context modeling Prediction algorithms Computational modeling Biological system modeling approximation algorithms Sparks Computational and artificial intelligence uplift modeling class imbalance big data

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Construction of Robust Lightweight S-Boxes Using Enhanced Logistic and Enhanced Sine Maps

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IEEE ACCESS 2024年 12卷 63976-63994页

作者： Duong, Phuc-Phan Nguyen, Hieu Minh Dao, Ba-Anh Kieu-Do-Nguyen, Binh Tran, Thai-Ha Hoang, Trong-Thuc Pham, Cong-Kha Univ Electrocommun Chofu Shi Tokyo 1821885 Japan Acad Cryptog Tech Hanoi 100000 Vietnam

Substitution boxes (S-Boxes) are essential elements of modern block ciphers, serving as non-linear characteristics that enhance the resistance of these ciphers against cryptanalysis. This research presents a new approach for constructing lightweight S-Boxes that possess strong cryptographic characteristics by combining an enhanced logistic map and an enhanced sine map. The proposed novel algorithm has optimized multiple parameters according to the security threshold set. This study has conducted the most comprehensive evaluation of criteria for S-Boxes to date. The analysis results of the generated 4 x 4and 5 x 5 S-Boxes have achieved optimal criteria in terms of the Strict Avalanche Criterion (SAC) and the Bit Independence Criterion (BIC) that no previous S-Boxes have achieved. Furthermore, parameters related to side-channel attack resistance have been optimized in the design stage of S-Boxes. The constructed S-Boxes with high security can be applied to replace S-Boxes of the same size in existing algorithms or to develop lightweight block cipher algorithms. This research has made a significant contribution to the construction of S-Boxes.

关键词： Cryptography Side-channel attacks Resistance Logistics Chaos Boolean functions approximation algorithms S-Box logistic map sine map chaotic map side-channel attacks

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A Path Planning Method for Chargeable Sweep Coverage With Multiple Charging Stations

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IEEE ACCESS 2024年 12卷 34931-34941页

作者： Liang, Dieyan Feng, Baojian Liao, Xuefeng Huizhou Univ Sch Comp Sci & Engn Huizhou 516007 Peoples R China Huizhou Univ Network & Informat Ctr Huizhou 516007 Peoples R China Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

The energy limit would abbreviate the lifetime of sweep coverage network. Wireless charging technology might be the best way to extend the lifetime of sweep coverage networks, as sensors are required to constantly move to ensure periodic target coverage. This paper studies the sweep coverage facilitated by rechargeable sensors, which persistently navigate between target nodes and charging stations to fulfill the periodic coverage demands of the targets while ensuring they return to the charging stations before their energy runs out. This paper firstly proposes a general definition of the Chargeable Sweep Coverage (CSC) problem, and studies the CSC problem by examining various constraints. We also propose one CSC problem subject to a special constraint where the sensors must return to their initial charging stations to recharge. The CSC problem under constrain is also NP-hard. In this paper, the problem is modeled as Maximum nodes inclusion problem with unbound candidate paths. We discuss the effective paths of the problems by situation, bound the candidate paths to certain lengths using discretization technology, and employ existing approximation algorithms to get the approximation solutions. When the charging period is greater than the sweep period, we get a 2/5 -approximation algorithm. For the opposite, we get 1/6 -approximation algorithms for it. The validity and scalability of the proposed algorithms are proved by theoretical proofs and experimental simulations.

关键词： Sensors Charging stations approximation algorithms Wireless sensor networks Routing Inductive charging Batteries Path planning Scalability mobile sensors sweep coverage approximation algorithm chargeable

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Scalable Analytic Eigenvalue Extraction Algorithm

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IEEE ACCESS 2024年 12卷 166652-166659页

作者： Khattak, Faizan A. Proudler, Ian K. Weiss, Stephan Univ Strathclyde Dept Elect & Elect Engn Glasgow City G1 1XW Scotland

Broadband sensor array problems can be formulated using parahermitian polynomial matrices, and the optimal solution to these problems can be based on the eigenvalue decomposition (EVD) of these matrices. An algorithm has been proposed in the past to extract analytic eigenvalues of parahermitian matrices, but it does not scale well with the temporal and spatial dimensions of the parahermitian matrix. This paper introduces a scalable analytical eigenvalue extraction algorithm for parahermitian polynomial matrices. The proposed algorithm operates in the discrete Fourier transform (DFT) domain, where an EVD is computed in each bin. Associations across bins are established based on properties of the analytic eigenvectors. The need to avoid problems with non-trivial algebraic multiplicities and control time-domain aliasing leads to an iterative algorithm that increases the DFT size until a suitable error criterion is satisfied. The algorithm can be shown to converge. Benchmarked against the existing algorithm, it performs accurately and with lower cost, and can successfully decompose matrices with dimensions much larger than previously had been feasible.

关键词： Eigenvalues and eigenfunctions Discrete Fourier transforms Matrix decomposition Interpolation Data mining Coherence Polynomials Computational efficiency Bandwidth approximation algorithms Analytic functions algebraic multiplicities space-time covariance discrete Fourier transform eigenvalue decomposition parahermitian matrix scalability

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Distributed Nonconvex Optimization: Gradient-Free Iterations and ε-Globally Optimal Solution

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2024年第4期11卷 2239-2251页

作者： He, Zhiyu He, Jianping Chen, Cailian Guan, Xinping Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Shanghai Engn Res Ctr Intelligent Control & Manage Shanghai 200240 Peoples R China Automat Control Lab CH-8092 Zurich Switzerland

Distributed optimization utilizes local computation and communication to realize a global aim of optimizing the sum of local objective functions. This article addresses a class of constrained distributed nonconvex optimization problems involving univariate objectives, aiming to achieve global optimization without requiring local evaluations of gradients at every iteration. We propose a novel algorithm named Chebyshev-proxy-and-consensus-based algorithm, exploiting the notion of combining Chebyshev polynomial approximation, average consensus, and polynomial optimization. The proposed algorithm is able to obtain epsilon-globally optimal solutions for any arbitrarily small given accuracy epsilon, efficient in both zeroth-order queries (i.e., evaluations of function values) and interagent communication, and distributed terminable when the specified precision requirement is met. The key insight is to use polynomial approximations to substitute for general local objectives, distribute these approximations via average consensus, and solve an easier approximate version of the original problem. Due to the nice analytic properties of polynomials, this approximation not only facilitates efficient global optimization, but also allows the design of gradient-free iterations to reduce cumulative costs of queries and achieve geometric convergence for solving nonconvex problems. We provide a comprehensive analysis of the accuracy and complexities of the proposed algorithm.

关键词： Optimization approximation algorithms Convergence Polynomials Chebyshev approximation Linear programming Control systems Chebyshev polynomial approximation consensus distributed optimization nonconvex optimization

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A Stochastic Particle Variational Bayesian Inference Inspired Deep-Unfolding Network for Sensing Over Wireless Networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第10期42卷 2832-2846页

作者： Hu, Zhixiang Liu, An Xu, Wenkang Quek, Tony Q. S. Zhao, Minjian Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Singapore Univ Technol & Design Dept Informat Syst Technol & Design Singapore 487372 Singapore

Future wireless networks are envisioned to provide ubiquitous sensing services, driving a substantial demand for multi-dimensional non-convex parameter estimation. This entails dealing with non-convex likelihood functions containing numerous local optima. Variational Bayesian inference (VBI) provides a powerful tool for modeling complex estimation problems and leveraging prior information, but poses a long-standing challenge on computing intractable posterior distributions. Most existing variational methods depend on specific distribution assumptions for obtaining closed-form solutions, and are difficult to apply in practical scenarios. Given these challenges, firstly, we propose a parallel stochastic particle VBI (PSPVBI) algorithm. Due to innovations like particle approximation, added updates of particle positions, and parallel stochastic successive convex approximation (PSSCA), PSPVBI can flexibly drive particles to fit the posterior distribution with acceptable complexity, yielding high-precision estimates of the target parameters. Furthermore, additional speedup can be obtained by deep-unfolding this algorithm. Specifically, superior hyperparameters are learned to dramatically reduce iterations. In this PSPVBI-induced deep-unfolding network, some techniques related to gradient computation, data sub-sampling, differentiable sampling, and generalization ability are also employed to facilitate the practical deployment. Finally, we apply the learnable PSPVBI (LPSPVBI) to solve two important positioning/sensing problems over wireless networks. Simulations indicate that the LPSPVBI algorithm outperforms existing solutions.

关键词： Inference algorithms Bayes methods approximation algorithms Convergence Sensors Optimization Stochastic processes Non-convex parameter estimation variational Bayesian inference deep-unfolding particle approximation stochastic successive convex approximation

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An improved approximation for MAXIMUM k-DEPENDENT SET on bipartite graphs

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DISCRETE APPLIED MATHEMATICS 2022年 307卷 95-101页

作者： Hosseinian, Seyedmohammadhossein Butenko, Sergiy Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77843 USA

We present a (1 + k/k+2)-approximation algorithm for the Maximum k-dependent Set problem on bipartite graphs for any k >= 1. For a graph with n vertices and m edges, the algorithm runs in O(km root n) time and improves upon the previously best-known approximation ratio of 1 + k/k+1 established by Kumar et al. (2014). Our proof also indicates that the algorithm retains its approximation ratio when applied to the (more general) class of Konig-Egervary graphs. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Maximum k-dependent set approximation algorithms Bipartite graphs Konig-Egervary graphs

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Broad Learning System approximation-Based Adaptive Optimal Control for Unknown Discrete-Time Nonlinear Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第8期52卷 5028-5038页

作者： Yuan, Liang'en Li, Tieshan Tong, Shaocheng Xiao, Yang Shan, Qihe Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Liaoning Univ Technol Coll Sci Jinzhou 121001 Peoples R China Univ Alabama Dept Comp Sci Tuscaloosa AL 35487 USA

This article investigates optimal control problem for a class of discrete-time (DT) nonlinear systems with unknown dynamics. With the help of a broad learning system (BLS), a novel online adaptive dynamic programming (ADP) controller is presented. First, to approximate the unknown system dynamics, an approximator based on BLS is presented. The connection weights are calculated by the data of the system by using the ridge regression algorithm. Then, two BLSs are adopted to approximate the optimal cost function and optimal control law, respectively. The connection weights of these two BLSs are updated using the given weights tuning law at each sampling instant. The proposed optimal controller is proved to ensure that all the system states and estimation errors are uniform ultimate bounded. Finally, simulation examples are carried out to further demonstrate the effectiveness of the proposed BLS-based approximator and optimal controller.

关键词： Optimal control Artificial neural networks approximation algorithms Learning systems Heuristic algorithms Cost function Adaptive systems Adaptive dynamic programming (ADP) broad learning system (BLS) discrete-time (DT) systems optimal control

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