In recent years, deep learning has revolutionized fields such as computer vision, speech recognition, and natural language processing, primarily through techniques applied to data in Euclidean spaces. However, many re...
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In recent years, deep learning has revolutionized fields such as computer vision, speech recognition, and natural language processing, primarily through techniques applied to data in Euclidean spaces. However, many real world applications involve data from non-Euclidean domains, where graphs naturally represent entities and their complex interdependencies. Traditional machine learning methods have often struggled to process such data in an effective manner. Graph Neural Networks represent a crucial advance in the use of deep learning to interpret and extract knowledge from graph-based data. They have opened up new possibilities for tasks such as node categorization, link inference, and comprehensive graph analysis. This paper provides a detailed analysis of Graph Neural Network (GNN) methodologies, emphasizing their architectural diversity and wide ranging applications. GNN models are systematically categorized into fundamental frameworks such as message passing paradigms, spectral and spatial methods, and advanced extensions such as hypergraph neural networks and multigraph approaches. This paper also explores domains such as social network analysis, molecular biology, traffic forecasting, and recommendation systems. In addition, it emphasizes some critical open challenges, including scalability, dynamic graph modeling, and robustness against noisy or incomplete data. The paper concludes with a proposal for future research directions to improve the scalability, interpretability, and adaptability of GNNs in this fast-evolving field.
The limit behavior of a semi-Markov process, depending on a small parameter, is important for the analysis and optimization of telecommunication systems. Semi-Markov processes are an extension of Markov processes that...
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Time delay is an inherent characteristic of real-world phenomena which may affect the system's characteristic. The systems including delay are known as time-delay systems, they are represented using delay differen...
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Time delay is an inherent characteristic of real-world phenomena which may affect the system's characteristic. The systems including delay are known as time-delay systems, they are represented using delay differential equations. modeling, discretisation, stability and control design for time-delay systems are still challenging in modern control theory. This paper systematically overviews available discretisation methods of linear and nonlinear time-delay systems. Emphasis is placed on illustrating fundamental results and recent progress on discretisation methods for delay systems. Numerous methods for the discretisation of linear and nonlinear systems considering input delays, state or output delays in the system's dynamics have been presented. A particular attention will be paid to illustrate effects of the discretisation process on the stability of discretised systems. Examples of mathematical descriptions, problems, and performance analysis for delay systems are presented. The presentation of discretisation methods is as easy as possible, focussing more on the main ideas and mathematical concepts by analogy. Finally, some possible future research directions to be tackled by researchers in this field are discussed.
With advancements in reusable liquid rocket engine technology to meet the diverse demands of space missions, engine systems have become increasingly complex. In most cases, these engines rely on stable open-loop contr...
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With advancements in reusable liquid rocket engine technology to meet the diverse demands of space missions, engine systems have become increasingly complex. In most cases, these engines rely on stable open-loop control and closed-loop regulation systems. However, due to the high degree of coupling and nonlinear dynamics within the system, most transient adjustments still depend on open-loop control. Open-loop control often fails to provide the optimal control strategy when encountering external disturbances. To address this issue, we introduce the intrinsically motivated twin delayed deep deterministic (TD3) algorithm, specifically designed for the startup process of LOX/Kerosene high-pressure staged combustion engine. This approach leverages intrinsic motivation to enable the algorithm to adapt to the abrupt parameter changes during the start-up process. A series of comprehensive experiments were conducted to verify the effectiveness of our method. The experimental results demonstrate that our method outperforms both the PID method and previous researchers' reinforcement learning methods based on the TD3 algorithm and DDPG, achieving a faster and more stable start-up process and significantly enhancing engine performance.
A residual deep reinforcement learning (RDRL) based on an approximate-model-driven optimization approach is proposed for inverter-based volt-var control (IB-VVC) in active distribution networks. A modified Markov deci...
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A residual deep reinforcement learning (RDRL) based on an approximate-model-driven optimization approach is proposed for inverter-based volt-var control (IB-VVC) in active distribution networks. A modified Markov decision process is introduced to formulate the model-based and RDRL-based IB-VVC simultaneously, and then RDRL learns a residual action based on the action of the model-based approach with an approximate model. It inherits the control capability of the approximate-model-based optimization and enhances the policy optimization capability by residual policy learning. Since the approximate model acquired by operators is generally relatively reliable, the action solved by model-based optimization approaches is not far away from the optimal one. This allows RDRL to search for the residual action in a smaller residual action space, which further improves the approximation accuracy of the critic and reduces the search difficulties of the actor. Simulations demonstrate that RDRL improves the optimization performance considerably throughout the learning stage and verifies their three rationales for superior performance point-by-point on 69 and 141 bus balanced distribution networks.
This paper addresses the modeling of astaxanthin accumulation in the microal-gae Haematococcus pluvialis, a well-known source of natural astaxanthin, under fluctuating environmental conditions. For biomass growth and ...
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While conducting large-depth vertical drilling, correcting well trajectory deviations is a critical and challenging task. Designing a feasible deviation-correction trajectory becomes an expensive constrained multi-obj...
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While conducting large-depth vertical drilling, correcting well trajectory deviations is a critical and challenging task. Designing a feasible deviation-correction trajectory becomes an expensive constrained multi-objective optimization problem due to the need for refined modeling of large-depth wellbore stability analysis. There is a pressing need for advanced drilling trajectory planning methods designed to handle robust constraints and to consider refined geological formation modeling, as current surrogate model-assisted optimization algorithms lack efficiency and balance among feasibility, convergence, and diversity. A Gaussian process-assisted Bayesian Multi-Objective Evolutionary Algorithm (MOEA) based on the reference point-based Non-dominated Sorting Genetic Algorithm (NSGA-III) is developed to manage the expensive wellbore stability objective. While surrogate models can effectively mitigate the computational expense, they may not adequately satisfy the stringent trajectory planning constraints. To enhance the constraint handling ability, an intricately devised infill criterion, Feasibility-oriented Bi-objective Acquisition Function (FBAF), tends to select promising feasible solutions to infill into the next generation. The deviation-correction trajectory planning simulation experiment was carried out under limited evaluations with real vertical well data. The results of empirical attainment function analysis demonstrate that the proposed FB-NSGA-III reduces the number of evaluations and exhibits superior performance compared to 11 other traditional surrogate-assisted MOEAs, particularly in terms of feasibility. FB-NSGA-III successfully prevents the back-hook by avoiding constraint violations and maintaining curvature within the specified safety and directional drilling tool build-up range.
Epileptic seizure propagation is a dynamic process that can be triggered by local abnormal discharges, leading to widespread network abnormalities in the brain. Understanding the causal relationship between the change...
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Epileptic seizure propagation is a dynamic process that can be triggered by local abnormal discharges, leading to widespread network abnormalities in the brain. Understanding the causal relationship between the changes in brain network characteristics and the diverse propagation dynamics of epileptic seizures is crucial. We gather stereo-EEG data from 17 patients with temporal lobe epilepsy and utilize cross-channel phase amplitude coupling to extract the dynamic functional networks. Further, the patterns of brain network changes during seizure in patients with different surgeries are assessed using Hidden Markov Model. And characteristics of state transitions under different seizure periods are explored. Results show that the frequency of state transitions increases with seizures, and all epilepsy patients have a main state network with weakly connected network structure centered on the epileptogenic zone. The occupancy ratio of main state is inversely proportional to state transition frequency, where the emergence of strongly connected networks facilitates the seizure propagation. Variability in state characteristics is observed cross patients with different surgeries. The heterogeneous epileptor network model driven by the state transition is developed to simulate seizure propagation. Results show that state transition frequency and relationships affect seizure onset time and spread range. Under the main state network, seizures occur only in the epileptogenic zone and do not propagate to surrounding regions. Additionally, increasing the proportion of the main state network delays the onset of seizures. This suggests that the characteristics of the state network and its transitions may play a role in controlling the propagation of epileptic seizures.
This paper presents a comprehensive analytical review of contemporary mathematical models of information influence and control in social networks, emphasizing the integration of agent-level factors such as trust, repu...
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We are on the cusp of holistically analyzing a variety of data being collected in every walk of life in diverse ways. For this, current analytics and science are being extended (Big data Analytics/Science) along with ...
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