Article
Computer Science, Information Systems
Guerric Chupin, Henrik Nilsson
Summary: Non-causal modelling is a powerful approach to modelling physical systems, but compiling non-causal languages modularly has its challenges. The order-parametric differentiation technique allows for truly modular compilation, enabling high-level and modular modelling. Performance evaluation of the technique shows its feasibility as a complement to existing implementation techniques.
Article
Automation & Control Systems
Avinash Malik
Summary: This research presents a novel efficient simulation/integration technique for general stochastic hybrid systems (SHS) modeled as a network of stochastic hybrid automata (SHA). By using adaptive step-size integration and leveraging mathematical principles, the step-size for each system component is calculated, thereby enhancing the simulation efficiency.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2022)
Article
Automation & Control Systems
Jun Fu, Zixiao Ma, Yue Fu, Tianyou Chai
Summary: This paper proposes a hybrid adaptive control method for a class of discrete-time MIMO systems with non-Lipschitz nonlinearities by introducing a novel operator and artificial neural network. The method aims to improve response performance and stability in the original nonlinear system, while achieving enhanced output tracking performance through a switching mechanism between linear and nonlinear controllers.
SYSTEMS & CONTROL LETTERS
(2021)
Article
Automation & Control Systems
Stefanie Winkler, Andreas Koerner, Felix Breitenecker
Summary: The text describes the use of hybrid approaches in various industry branches to solve complex application problems. Different tools for simulation and identification of hybrid systems have been developed over the last decades. The integration of artificial feed-forward neural networks into the modelling process of HDS allows for interdisciplinary exchange and introduces specific modelling methods and challenges.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2021)
Article
Management
M. Heemskerk, M. Mandjes, B. Mathijsen
Summary: The paper introduces a specific non-homogeneous Poisson process to address arrival processes with larger than anticipated fluctuations, a time varying rate, and temporal correlation. A staffing rule is developed for many-server systems facing such arrival processes, with system performance being stabilized under highly varying and strongly correlated conditions through a square-root staffing principle. Real data from an emergency department is used to fit the arrival stream model and demonstrate the performance of the novel staffing rule through simulation.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Rui Ding, Bowei Chen, Guibing Guo, Xiaochun Yang
Summary: This article introduces a novel adversarial path-based recommendation model, path2rec, to address the limitations of existing GAN-based methods in recommendation task by incorporating auxiliary information. Consisting of pathGAN and path2vec modules, the model utilizes a smart walk strategy and CBOW model to optimize path learning and improve performance in top-n item recommendation.
IEEE INTELLIGENT SYSTEMS
(2021)
Article
Automation & Control Systems
Zhanlue Liang, Xinzhi Liu
Summary: This paper investigates the problem of flocking control in heterogeneous multi-agent systems with multiple groups tracking various virtual leaders. A hybrid impulsive control protocol is designed based on partially discrete agents' information transmission, with full information exchange occurring only at each impulsive time instant. Braking and gyroscopic forces are utilized for collision avoidance. Conditions for multi-group formation are established based on coupling strength and impulse period length. The designed control protocols ensure asymptotic stability and collision-free motion in the multi-agent systems. Numerical examples and computer simulations are provided to validate the theoretical results.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Polymer Science
Tanner L. Grover, C. Allan Guymon
Summary: Control of phase separation during radical/cationic hybrid photopolymerization allows manipulation of polymer morphology and material properties. The cationic comonomer ratio influences reaction kinetics, network morphology, and bulk material properties. The ability to control polymer structure through comonomer content enables tailoring of mechanical properties in photocured films. This study demonstrates the importance of internally regulating cationic polymerization rate for controlling polymer structure and material properties in radical/cationic photopolymer systems.
Article
Automation & Control Systems
Mariana Ballesteros, Andrey Polyakov, Denis Efimov, Isaac Chairez, Alexander S. Poznyak
Summary: This study aims to design a non-parametric identifier for homogeneous systems based on a class of artificial neural networks with continuous dynamics. The main contributions include extending the universal approximation property of neural networks for continuous homogeneous systems and developing a differential non-parametric identifier based on homogeneous neural networks. The effectiveness of the proposed identifier is verified through simulations, showing faster convergence and less oscillations compared to a classical ANN identifier.
Article
Automation & Control Systems
Fenglan Sun, Chuan Lu, Wei Zhu, Juergen Kurths
Summary: This paper investigates the mean-square consensus of second-order hybrid multi-agent systems over jointly connected topologies with time-varying delay and multiplicative noise. The date sampling control technique is utilized. By employing matrix transformation, a positive definite matrix, which is transformed by the Laplacian matrix of the jointly connected topologies, is obtained. Sufficient conditions for the mean-square consensus and an upper bound for time delays are derived using graph theory, matrix theory, and Lyapunov stability theory. Finally, simulations are conducted to validate the proposed control method.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Xiang Xie, Haiyang Zhang, Xinzhi Liu, Honglei Xu, Xiaodi Li
Summary: This paper studies the input-to-state stabilization problem of nonlinear time-delay systems by proposing a novel event-triggered hybrid controller and using the Lyapunov-Krasovskii method to construct sufficient conditions for input-to-state stability. The obtained criteria are applicable to time-delay systems with various impulsive effects.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Engineering, Electrical & Electronic
Fubin Wang, Fang Yang, Jian Song, Zhu Han
Summary: This article reviews the current research on hybrid visible light communication (VLC) and radio frequency (RF) systems. It introduces the structures of hybrid access frameworks and discusses the technical characteristics and specific advantages of hybrid systems in different application scenarios. The future trends of hybrid VLC and RF systems are also discussed.
IEEE COMMUNICATIONS MAGAZINE
(2022)
Article
Automation & Control Systems
Zhiguang Liu, Quanxin Zhu
Summary: This article discusses a class of nonlinear hybrid stochastic differential delay equations with Poisson jump and different structures. The jump makes the analysis more complex due to the discontinuity of its sample paths compared to Brownian motion. Moreover, the coefficients meet a novel nonlinear growth condition and different structures in different switch modes. By using M-matrices and Lyapunov functions, the article proves the existence-uniqueness, asymptotic boundedness, and exponential stability of the solution. Finally, two examples are provided to demonstrate the usefulness of the theory.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Computer Science, Information Systems
Amadou Ba, Karol Lynch, Joern Ploennigs, Ben Schaper, Christopher Lohse, Fabio Lorenzi
Summary: Efficient training of deep learning models for IoT systems requires understanding domain knowledge. Heterogeneous graph neural networks (HGNNs) are a promising approach to incorporate domain knowledge and improve model performance. However, encoding domain knowledge into HGNNs for IoT systems is challenging and manual. To overcome this, we propose a framework to automatically derive HGNN features by parsing equations in publications, and validate our approach with IoT use cases. Our approach significantly outperforms other techniques.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Tao Bai, Jinqi Luo, Jun Zhao
Summary: Deep-learning-based image-recognition systems on mobile devices are vulnerable to adversarial examples. To tackle this issue, this study proposes a method for generating inconspicuous adversarial patches with one single image. The patches are produced in a coarse-to-fine manner and encouraged to be consistent with the background images while retaining strong attack abilities. The approach demonstrates high attack success rates in white-box and black-box settings, with minimal risk of being detected and evading human observations.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Hardware & Architecture
Flavia Bonomo-Braberman, Nick Brettell, Andrea Munaro, Daniel Paulusma
Summary: This article discusses the convexity and mim-width of bipartite graphs, and it proves that for certain families of graphs 7-t, the 7-t-convex graphs can be solved in polynomial time for NP-complete problems. It also explores the bounded and unbounded mim-width of 7-t-convex graphs for different sets 7-t.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Keqin Li
Summary: In this paper, we propose a computation offloading strategy to satisfy all UEs served by an MEC and develop an efficient method to find such a strategy. By using Markov chains to characterize UE mobility and calculating the joint probability distribution of UE locations, we can obtain the average response time of UEs and predict the overall average response time of tasks. Additionally, we solve the power constrained MEC speed setting problem.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Correction
Computer Science, Hardware & Architecture
Peter L. Bartlett, Philip M. Long
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Philipp Czerner, Roland Guttenberg, Martin Helfrich, Javier Esparza
Summary: This paper presents a construction method that produces population protocols with a small number of states, while achieving near-optimal expected number of interactions, for deciding Presburger predicates.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Katharina T. Huber, Leo van Iersel, Remie Janssen, Mark Jones, Vincent Moulton, Yukihiro Murakami, Charles Semple
Summary: This paper investigates the relationship between undirected and directed phylogenetic networks, and provides corresponding algorithms. The study reveals that the directed phylogenetic network is unique under specific conditions. Additionally, an algorithm for directing undirected binary networks is described, applicable to certain classes of directed phylogenetic networks.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Francesco Betti Sorbelli, Alfredo Navarra, Lorenzo Palazzetti, Cristina M. Pinotti, Giuseppe Prencipe
Summary: This study discusses the deployment of IoT sensors in an area that needs to be monitored. Drones are used to collect data from the sensors, but they have energy and storage constraints. To maximize the overall reward from the collected data and ensure compliance with energy and storage limits, an optimization problem called Multiple-drone Data-collection Maximization Problem (MDMP) is proposed and solved using an Integer Linear Programming algorithm.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Carla Binucci, Emilio Di Giacomo, William J. Lenhart, Giuseppe Liotta, Fabrizio Montecchiani, Martin Nollenburg, Antonios Symvonis
Summary: In this study, we investigate the problem of representing a graph as a storyplan, which is a model for dynamic graph visualization. We prove the NP-completeness of this problem and propose two parameterized algorithms as solutions. We also demonstrate that partial 3-trees always admit a storyplan and can be computed in linear time. Additionally, we show that even if the vertex appearance order is given, the problem of choosing how to draw the frames remains NP-complete.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Leszek Gasieniec, Tomasz Jurdzinski, Ralf Klasing, Christos Levcopoulos, Andrzej Lingas, Jie Min, Tomasz Radzik
Summary: This passage describes the Bamboo Garden Trimming Problem and presents approximation algorithms for both Discrete BGT and Continuous BGT.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)