This article investigates distributed adaptive algorithms for intralayer synchronization of multiplex networks, both with and without pinning control. Two types of distributed adaptive algorithms are considered based ...
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This article investigates distributed adaptive algorithms for intralayer synchronization of multiplex networks, both with and without pinning control. Two types of distributed adaptive algorithms are considered based on the parameters being adjusted: 1) node-based algorithms, which adapt the coupling strength of each node using the relative information from its neighborhood and itself, and 2) edge-based algorithms, which update the coupling weight of each edge based on the relative information between the two connected nodes. Using the Lyapunov function method, we prove that, under mild conditions on the uncoupled node dynamics, the proposed adaptive strategies guarantee intralayer synchronization for any multiplex network with strongly connected intralayer topologies.
The authors propose software for the analysis and solution to the algebraic eigenvalue problem using an MIMD computer with GPUs, which includes parallel algorithms and programs with the functions of automatic adaptive...
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The authors propose software for the analysis and solution to the algebraic eigenvalue problem using an MIMD computer with GPUs, which includes parallel algorithms and programs with the functions of automatic adaptive configuration of the variable computer environment (multilevel parallelism, variable topology of interprocessor communications, mixed word length, caching, etc.) on the mathematical properties of the problem identified in the computer and the architectural features to ensure the reliability of the solution results and the efficient use of computing resources.
In this paper we revisit the fixed-confidence identification of the Pareto optimal set in a multi-objective multi-armed bandit model. As the sample complexity to identify the exact Pareto set can be very large, a rela...
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Efficiently synchronizing data with external sources, such as social media feeds, while minimizing well-timed requests is a challenge in various domains. This research investigates prediction algorithms for determinin...
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ISBN:
(纸本)9798400704093
Efficiently synchronizing data with external sources, such as social media feeds, while minimizing well-timed requests is a challenge in various domains. This research investigates prediction algorithms for determining appropriate update intervals for Facebook and Twitter feeds, considering metrics such as the delay (time between a post's publication and retrieval) and requests per post. Due to variations in update intervals, different algorithms yield diverse results. Selecting the most suitable algorithm for each feed is a time-consuming but crucial task for achieving optimal resource usage. We propose three strategies for algorithm selection: baseline (using a single algorithm per feed), optimum (calculating the best algorithm for each feed), and classification (identifying algorithms through classification). Real-world data from Facebook and Twitter are used to evaluate the strategies, comprehensively assessing their strengths and weaknesses. Findings demonstrate that the strategy optimum identifies the best algorithms, while the strategy classification selects fairly good algorithms at significantly reduced computational effort.
This study aims to optimize adaptive algorithms for cross-country vehicle configuration comparison by using deep learning techniques, especially multi-column Deep Convolutional network (MDCNN) vehicle recognition mode...
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This paper proposes the use of adaptive algorithms for real-time data analysis. adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety...
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Model order selection problems are important for signal processing and its various applications in wireless communications, radar theory, navigation, control theory, and others. We described three model order selectio...
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The article considers the task of developing a software and hardware architecture for the synthesis of adaptive algorithms and methods of interaction with collaborative robotic complexes. The architecture of a distrib...
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Ambient energy-powered sensors are becoming increasingly crucial for the sustainability of the Internet-of-Things (IoT). In particular, batteryless sensors are a cost-effective solution that require no battery mainten...
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Ambient energy-powered sensors are becoming increasingly crucial for the sustainability of the Internet-of-Things (IoT). In particular, batteryless sensors are a cost-effective solution that require no battery maintenance, last longer and have greater weatherproofing properties due to the lack of a battery access panel. In this work, we study adaptive transmission algorithms to improve the performance of batteryless IoT sensors based on the LoRa protocol. First, we characterize the device power consumption during sensor measurement and/or transmission events. Then, we consider different scenarios and dynamically tune the most critical network parameters, such as inter-packet transmission time, data redundancy and packet size, to optimize the operation of the device. We design appropriate capacity-based storage, considering a renewable energy source (e.g., photovoltaic panel), and we analyze the probability of energy failures by exploiting both theoretical models and real energy traces. The results can be used as feedback to re-design the device to have an appropriate amount energy storage and meet certain reliability constraints. Finally, a cost analysis is also provided for the energy characteristics of our system, taking into account the dimensioning of both the capacitor and solar panel.
The primary objective of this research is to develop adaptive online algorithms for solving the Canadian Traveler Problem (CTP), which is a well-studied problem in the literature that has important applications in dis...
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The primary objective of this research is to develop adaptive online algorithms for solving the Canadian Traveler Problem (CTP), which is a well-studied problem in the literature that has important applications in disaster scenarios. To this end, we propose two novel approaches, namely Maximum Likely Node (MLN) and Maximum Likely Path (MLP), to address the single-agent single-destination variant of the CTP. Our computational experiments demonstrate that the MLN and MLP algorithms together achieve new best-known solutions for 10,715 instances. In the context of disaster scenarios, the CTP can be extended to the multiple-agent multiple-destination variant, which we refer to as MAD-CTP. We propose two approaches, namely MAD-OMT and MAD-HOP, to solve this variant. We evaluate the performance of these algorithms on Delaunay and Euclidean graphs of varying sizes, ranging from 20 nodes with 49 edges to 500 nodes with 1500 edges. Our results demonstrate that MAD-HOP outperforms MAD-OMT by a considerable margin, achieving a replan time of under 9 seconds for all instances. Furthermore, we extend the existing state-of-the-art algorithm, UCT, which was previously shown by Eyerich et al. (2010) to be effective for solving the single-source single-destination variant of the CTP, to address the MAD-CTP problem. We compare the performance of UCT and MAD-HOP on a range of instances, and our results indicate that MAD-HOP offers better performance than UCT on most instances. In addition, UCT exhibited a very high replan time of around 10 minutes. The inferior results of UCT may be attributed to the number of rollouts used in the experiments but increasing the number of rollouts did not conclusively demonstrate whether UCT could outperform MAD-HOP. This may be due to the benefits obtained from using multiple agents, as MAD-HOP appears to benefit to a greater extent than UCT when information is shared among agents.
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