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Canada-0-LOGISTICS कंपनी निर्देशिकाएँ
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कंपनी समाचार :
- Machine Learning in Additive Manufacturing: A Review - JOM
In this review article, the latest applications of machine learning (ML) in the additive manufacturing (AM) field are reviewed These applications, such as parameter optimization and anomaly detection, are classified into different types of ML tasks, including regression, classification, and clustering The performance of various ML algorithms in these types of AM tasks are compared and
- Evaluating Artificial Intelligence-Based Industrial Wastewater . . .
This study integrates life cycle assessment (LCA) and system dynamics (SD) modeling to evaluate the potential of Artificial Intelligence (AI)-enhanced anaerobic ammonium oxidation (anammox) technology in industrial wastewater treatment The research examines the environmental, economic, and social benefits of AI optimization, with a focus on its long-term implications for sustainable
- Machine learning-powered, high-affinity modification strategies for . . .
More importantly, these AI models only generate de novo, naturally-unmodified aptamers or DNA fragments, which are ineffective in the affinity optimization of the selected aptamer To date, there have been no machine learning-powered, high-affinity modification strategies in aptamer discovery
- Framing Artificial Intelligence (AI) Additive Manufacturing (AM)
Nowadays AM is a rapidly growing and emerging discipline in manufacturing, as well as AI is in informational applications Both are related to logistical and self-referential -copying concepts which make them scalable What is in AM osmotic mass production spreading is in AI-related Cyber-Physical Systems (CPS) the osmotic computational approach AI-AM self-propagatedly framed is itself an
- Smart additive manufacturing: Current artificial intelligence-enabled . . .
Additive manufacturing (AM) has been increasingly used in production Because of its rapid growth, the efficiency and robustness of AM-based product development processes should be improved Artificial intelligence (AI) is a powerful tool that has outperformed humans in numerous complex tasks AI-enabled intelligent agents can reduce the workforce required to scale up AM production and achieve
- Adaptive fabrication of material extrusion-AM process using machine . . .
The optimization process, named NSGA-II, employs a search-based approach inspired by genetics and natural selection This population-based algorithm iteratively optimizes input parameters by adhering to the survival of the fittest principle The algorithm begins by randomizing an initial set of populations
- Design Optimization Method Based on Artificial Intelligence . . . - MDPI
It also focuses on the methods of design optimization of this restoration process by adopting the AI technique, including using the hybrid method It also explores and identifies all the means and possibilities that lead to maximum benefits of design optimization-based AI in AM applications by analyzing previous research in this field
- Marginal Price Optimization A new framework for arbitrage and routing . . .
Abstract We introduce a new framework for optimal routing and arbitrage in AMM driven markets This framework improves on the original best-practice convex optimization by restricting the search to the boundary of the optimal space We can parameterize this boundary using a set of prices, and a potentially very high dimensional optimization problem (2 optimization variables per curve) gets
- Intelligent optimization: Literature review and state-of-the-art . . .
This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories Then, the most comprehensive review and recent methods (during 1965–2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories
- QLAMMP: A Q-Learning Agent for Optimizing Fees on Automated Market . . .
We propose an RL framework to optimize the fees collected on an AMM protocol In particular, we develop a Q-Learning Agent for Market Making Protocols (QLAMMP) that learns the optimal fee rates and leverage coefficients for a given AMM protocol and maximizes the expected fee collected under a range of different market conditions
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