Article
Physics, Fluids & Plasmas
Zbigniew Walczak, Jaroslaw H. Bauer
Summary: Parrondo's paradox refers to a paradoxical effect where a combination of biased random walks shows a counterintuitive reversal of the bias direction. This study reveals that Parrondo's paradox can occur not only in one-dimensional discrete-time quantum walks with a deterministic sequence of two quantum coins, but also with a deterministic sequence of three quantum coins. Additionally, it demonstrates how Parrondo's paradox influences the time evolution of quantum entanglement for such quantum walks.
Article
Mechanics
Zu-Yu Qian, Cheng Yuan, Jie Zhou, Shi-Ming Chen, Sen Nie
Summary: This study explores the incorporation of conformity behavior into network control and finds that controlling undirected networked systems with conformity becomes easier after the network connectivity reaches a critical point. The research also identifies key nodal structural characteristics and proposes an optimal control strategy to reduce energy consumption. These findings are validated in synthetic and real networks, highlighting their significance in describing control energy in networked systems.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2022)
Article
Multidisciplinary Sciences
Kang Hao Cheong, Tao Wen, Sean Benler, Jin Ming Koh, Eugene Koonin
Summary: In the competition between bacteriophages, the disadvantaged phage can win by alternating between lytic and lysogenic infections, analogous to a paradox in game theory. These findings help us understand the evolution of host-parasite interactions.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Computer Science, Artificial Intelligence
Wu Jing, Haiyan Kang
Summary: Studying the regularity of rumor spreading in networks is crucial as rumors can lead to social unrest and pose a threat to public security and interest. In this study, a new ISDPR rumor propagation model is proposed that incorporates the influence of fact spreading and punishment mechanism by combining network structure and game theory. The model is analyzed, and its stability is proven, demonstrating its rationality. Through experiments on different network types, including small world and scale-free networks, the model's correctness and effectiveness are validated.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Automation & Control Systems
Daniel A. Lazar, Samuel Coogan, Ramtin Pedarsani
Summary: This study presents a macroscopic model for studying routing on networks shared by human-driven and autonomous vehicles while considering the effects of autonomous vehicles forming platoons. The research finds that selfish routing may lead to inefficiency, but by establishing road capacity models and road delay functions, it is possible to bound the price of anarchy and other measures of inefficiency.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Computer Science, Cybernetics
Yuxuan Huang, Jiajing Wu, Chi K. Tse, Zibin Zheng
Summary: The study uses game theory to analyze the strategies of attackers and defenders in complex networks. By proposing a flexible attacker-defender game model, parameters and resources for network attack/defense are allocated accordingly.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Multidisciplinary Sciences
K. M. Ariful Kabir, M. D. Shahidul Islam, Sabawatara Nijhum
Summary: The typical framework of replicator dynamics in evolutionary game theory assumes that all mutations are equally likely, meaning that the mutation of an evolving inhabitant only contributes constantly. However, in natural systems in biological and social sciences, mutations can arise due to their repetitive regeneration. We identified a form of volatile mutation as a form of noise that, under certain situations, could be used to enhance cooperation in social systems and design strategies for promoting cooperation in networked environments.
Article
Engineering, Industrial
J. Lisowski
Summary: A model for safe control of ships in the vicinity of other ships was formulated, allowing for the synthesis of algorithms for safe path planning based on the state of the environment. Three possible algorithms - game non-cooperative path, game cooperative path, and optimal path - were used to map the state. Simulation results confirmed the effectiveness of the algorithms in representing the real traffic environment of multiple ships. The findings can be applied to optimize the control of other mobile objects.
INTERNATIONAL JOURNAL OF SIMULATION MODELLING
(2023)
Article
Business
Mee Sook Kim, Jessica R. Methot, Won-Woo Park, Stanley M. Gully
Summary: The research found that leaders' external brokerage can enhance team performance through team members' perceptions of organizational support, but it can also compromise team performance by affecting leaders' commitment to their teams.
JOURNAL OF ORGANIZATIONAL BEHAVIOR
(2022)
Article
Computer Science, Information Systems
Qian Li, Bojian Hu, Wei Xu, Yunpeng Xiao
Summary: This paper proposes a group behavior prediction model based on sparse representation and interaction of complex messages, which effectively explores the interaction between complex messages and accurately predicts group behavior. The model fuses different submodels to improve generalization ability and achieve dynamic group behavior prediction.
INFORMATION SCIENCES
(2022)
Article
Mechanics
Hao Wang, Xiao-Yong Yan, Jinshan Wu
Summary: The social gravity law is widely observed in various domains, such as travel, migration, trade, communication, and collaboration. The law is explained by individual interaction and bounded rationality, with a free utility model proposed to provide a mathematical explanation. The model not only explains existing models but also extends to network interactions.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Mathematics
Robert Simon, Grzegorz Tomkowicz
Summary: Given a probability space (X, B, m), measure preserving transformations g(1),..., g(k) of X, and a colour set C, a colouring rule is a way to colour the space with C, where the allowed colours for a point x are determined by its location in X and the colours of its descendants. A paradoxical colouring rule can be satisfied almost everywhere but not in a measurable way that respects the transformations and probability measure. We show that a paradoxical colouring rule can exist when certain conditions are met, and that it has a stability property where deviations from the rule make it unmeasurable according to any finitely additive measure that respects the information structure of a game.
MATHEMATISCHE ANNALEN
(2023)
Article
Mathematics, Applied
Joel Weijia Lai, Kang Hao Cheong
Summary: Individuals' choices can have either positive or negative effects on the entire group. This article introduces a variant of Parrondo's paradox called PAPP, which shows how two losing choices can combine to yield a winning outcome, and explores its application in social networks.
Article
Mechanics
S. Bittihn, A. Schadschneider
Summary: The Braess paradox highlights that adding new roads to a network can increase travel times for all users. Recent research shows that even with intelligent route choices, the paradox can still occur.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Ecology
Pedro H. T. Schimit, Fabio H. Pereira, Mark Broom
Summary: This paper investigates the evolution of a population over a non-homogeneous structure and explores the fixation probability and various evolutionary dynamics. The study finds temperature and mean group size to be good predictors and identifies unique features of a specific type of graph.
ECOLOGICAL COMPLEXITY
(2022)
Article
Automation & Control Systems
Weiye Zheng, David J. Hill
Summary: In this article, a distributed method for real-time IEHS dispatch is proposed, ensuring feasibility and system security through techniques such as network reduction and feasibility cut generation.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Electrical & Electronic
Lipeng Zhu, David J. Hill
Summary: This paper proposes a networked time series shapelet learning approach for interpretable transient stability assessment (TSA). By introducing a network impedance-based adjacency matrix to characterize spatial networked correlations, and incorporating it as spatial constraints, the method learns critical sequential features, i.e., networked shapelets, from time series trajectories acquired from multiple buses. The obtained data-driven TSA model performs highly reliable and interpretable online TSA, as demonstrated by numerical test results on real-world power systems.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Computer Science, Information Systems
Lipeng Zhu, David J. Hill, Chao Lu
Summary: This article presents an intelligent data-driven approach for performing PMU data anomaly identification in IoT-enabled power grids. By utilizing the spatial-temporal correlations in PMU measurements, the proposed approach effectively identifies and labels abnormal data, leading to improved reliability and accuracy in power grid monitoring.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Lipeng Zhu, David J. Hill, Chao Lu
Summary: This paper proposes a semi-supervised ensemble learning framework for accelerating the computation process of stability knowledge base generation. By performing detailed simulations for a minority of cases and fast simulations for the majority ones, the total computation time is reduced. Two concise feature descriptors are introduced to extract transient features from multiplex system trajectories, and a series of semi-supervised support vector machines are trained to derive an enhanced SSEL model.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Automation & Control Systems
Lipeng Zhu, David J. Hill
Summary: This article proposes a novel data/model jointly driven framework to generate high-quality cases for power system DSA applications. By utilizing model-driven numerical simulations and CycleGAN learning, refined cases highly resembling actual historical ones can be produced. With the combination of LSTM-based semisupervised learning scheme, all refined cases can be reliably labeled to mitigate the small sample size and class-imbalance problems.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Electrical & Electronic
Hesamoddin Marzooghi, Mehdi Garmroodi, Gregor Verbic, Ahmad Shabir Ahmadyar, Ruidong Liu, David J. Hill
Summary: This study evaluates the system stability of future high-renewable energy grids using a novel framework that considers scenario evolution and sensitivity. Based on proposed future scenarios and sensitivities for the Australian grid, the impact of grid strength, level of prosumers, and utility storage on stability is studied. The results provide insights into the underlying stability issues of future grids.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Yue Song, David J. Hill, Tao Liu, Tianlun Chen
Summary: This paper develops a convexification method for the AC optimal power flow problem with flexible line impedances. The method improves the solution tractability and optimality by reformulating the problem as a semi-definite program using a circuit equivalent of the flexible-impedance line.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Tianlun Chen, Yue Song, David J. Hill, Albert Y. S. Lam
Summary: This paper proposes a chance constrained optimization model to estimate the flexibility area at a transmission-distribution interface and introduces power flow routers to facilitate the flexibility of the distribution system. Case studies show that power flow routers significantly enlarge the flexibility area, indicating that network flexibility is an important supplement to node power flexibility in transmission-distribution interaction.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Automation & Control Systems
Bin Liu, David J. Hill, Zhijie Sun
Summary: This paper investigates the input-to-state stability (ISS) of time-varying delayed systems (TVDS) in Halanay-type inequality forms. By introducing the concept of a uniform M-matrix, exponential ISS theorems are established for continuous-time, discrete-time, and zero-order TVDS. The convergence rates of exponential ISS, ISS gains, and their relationship are estimated. These ISS theorems are less conservative and extend the existing results on stability and ISS for Halanay-type inequalities. Furthermore, necessary conditions for ISS of TVDS in Halanay-type equality forms are given, and the necessary and sufficient conditions for ISS are derived for linear time-invariant delayed systems. Three examples are provided to illustrate the theoretical findings.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Engineering, Electrical & Electronic
Tianlun Chen, Yue Song, David J. Hill, Albert Y. S. Lam
Summary: This paper proposes a chance-constrained optimal power flow problem with power flow routers to regulate the voltage profile in microgrids. The proposed method uses partial linearization and an iterative algorithm to solve the subproblem in a convex form. Experimental results show that power flow routers significantly reduce the standard deviations of voltage magnitudes and mitigate voltage volatility, leading to a more economic and secure system operation.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Engineering, Electrical & Electronic
Tong Han, David J. Hill, Yue Song
Summary: This paper focuses on the issue of network connectedness (NC) in security-constrained optimal transmission switching problems. Two criteria are proposed to preserve NC within reasonable limits. Mathematical formulas for the NC criteria are derived by extending the electrical flow based NC constraints and associating them with the optimum of a linear program. The proposed approach is demonstrated to be effective through case studies on various networks and SCOTS models.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Tong Han, David J. Hill
Summary: This paper proposes a learning-based solution approach for optimal transmission switching (OTS) and distribution network reconfiguration (DNR) in power network transitions. The proposed approach utilizes a parameterized function designed with gated graph neural network and multilayer perceptrons, replacing the original hand-crafted and short-sighted evaluation function. A learning algorithm combining the double deep Q-network algorithm and multi-step learning is designed to learn the parameterized function without training labels. Numerical studies demonstrate the superiority of the learning-based heuristic algorithm in both solution optimality and computational efficiency.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Automation & Control Systems
Yue Song, David J. Hill, Tao Liu, Xinran Zhang
Summary: This article investigates the concept of the impasse surface in the differential-algebraic equation model of power systems. It establishes a necessary condition for a system trajectory hitting the impasse surface and identifies a class of static load parameters that prevent power systems from collapsing. The obtained results have important implications for early indicators of voltage collapse and inductive compensation in power networks.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Tong Han, Yue Song, David J. J. Hill
Summary: This paper proposes a novel methodology to address the topology transition problem of transmission networks. The methodology utilizes various eligible control resources in transmission networks to cooperate with the optimization of line-switching sequence, aiming at achieving a bumpless topology transition regarding both static and dynamic performance. The effectiveness and superiority of the proposed methodology to achieve bumpless topology transition is demonstrated through numerical studies.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Tong Han, David J. Hill, Yue Song
Summary: Network topology has a significant impact on the operational performance of power systems. The problem of transitioning from an initial topology to the desired optimal topology requires study. To address this problem, the concept of optimal topology transition (OTT) is proposed, aiming to find the transition trajectory that optimizes transition performance and satisfies operational constraints. The OTT problem is further formulated as a mixed-integer program, and an efficient problem-specific solution algorithm is developed.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
