As the demand for power load forecasting continues to grow in modern society, this paper proposes an improved D-KAN model that integrates Kolmogorov-Arnold Networks (KAN) into the DLinear power load time series predic...
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this paper presents a knowledge graph representation learning framework based on Horn clause rules, designed to efficiently integrate logical information into knowledge graphs (KGs) in continuous vector spaces. Due to...
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ISBN:
(纸本)9798350374353;9798350374346
this paper presents a knowledge graph representation learning framework based on Horn clause rules, designed to efficiently integrate logical information into knowledge graphs (KGs) in continuous vector spaces. Due to the challenge of rule uncertainty, it is difficult to devise a principled framework in continuous vector spaces where encoding the logical background knowledge of rules is usually not straightforward. therefore, we propose a solution that calculates the Horn rule constraint among relations, obtained through iterative optimizationlearning with labeled triplets, objective score functions, and relation modeling. this method enables us to achieve better regulation of rule-based effects, merely enforcing relation representations to satisfy constraints introduced by Horn rules. Finally, we analyze the proposed method on several FB15K datasets. the analysis results demonstrate that our scheme effectively improves the performance of link prediction evaluation on public datasets.
Transmission lines are an important part of the safe and reliable operation of the power grid, and the monitoring of transmission lines is an important task for the safe operation of the power grid. therefore, the mai...
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this research presents an innovative optimization approach that combines data mining and deep learning techniques to enhance anti-freezing protection in direct air-cooled thermoelectric systems operating in China'...
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the rapid advancements in digital assessment systems have highlighted the need for efficient, automated methods of evaluating written examination scripts. the paper presents a new framework that leverages Optical Char...
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Reinforcement learning (RL) enables agents to make decisions through interactions withtheir environment and feedback in the form of rewards or penalties. the distinction between single-objective reinforcement learnin...
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ISBN:
(数字)9789819990375
ISBN:
(纸本)9789819990368;9789819990375
Reinforcement learning (RL) enables agents to make decisions through interactions withtheir environment and feedback in the form of rewards or penalties. the distinction between single-objective reinforcement learning (SORL) and multi-objective reinforcement learning (MORL) is established, emphasizing the latter's ability to optimize multiple conflicting objectives simultaneously. the study explores various algorithms and approaches within the MORL framework, focusing on track navigation optimization. Key components of the implementation are detailed, including states, actions, rewards, and tracks used for training. the proposed algorithm, Pareto Q-learning, is highlighted as a powerful approach to simultaneously optimize multiple objectives. the architecture and methodology of the learning agent are presented, outlining the training process and the impact of hyper-parameters. Results from experimentation are discussed, revealing the agent's learning curve, crash avoidance, and successful achievement of multiple objectives. the study underlines the significance of MORL in enabling agents to manage complex decision-making scenarios, leading to more robust and optimal policies. the paper concludes by emphasizing the practical implications of the MORL approach in navigating challenging tracks with conflicting goals, such as minimizing steps, maximizing rewards, and avoiding collisions.
In the current context of an energy transition, solar potential is an invaluable resource for producing renewable energy in Morocco. However, the efficiency for installation of solar panels requires accurate predictio...
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ISBN:
(纸本)9783031686740;9783031686757
In the current context of an energy transition, solar potential is an invaluable resource for producing renewable energy in Morocco. However, the efficiency for installation of solar panels requires accurate prediction of temperature. this paper purports to research new information about Geographic Information Systems coupled with Machine learning techniques for temperature forecasting in Morocco. In this work, we compare two models: the Random Forest (RF) one withthe XGBoost one, based on a set of factors: PVOUT, GIT, OPTA, GHI, DNI, DIF, and DEM. Our results present a promising outlook for optimizing the solar panel installation process, using the value that a pixel has as a target for our prediction. Initial results indicate that the RF model has some promising levels of precision up to a level of 0.9971 R2, whereas XGBoost reached 0.977775. these results give good insights into the optimization for the solar panel installation at the pixel level for our purpose of predictions.
the efficacy as well as the standards of innovative manufacturing processes can be greatly enhanced by using deep learning models. Creating a hybrid artificial intelligence strategy for recognizing faulty casting prod...
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COVID-19, or coronavirus, is a virus that causes a pandemic where the severe acute respiratory syndrome virus 2 (SARS-Cov-2) affects the respiratory organs such as the lungs. As of 4th February 2024, there are over 77...
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Quadruped robots are commonly used for navigating difficult terrains. However, they remain vulnerable to falls in unpredictable and unstructured environments. To ensure continuous operation, a self-recovery behavior i...
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