Last decades have seen a lot of research on Analog Design Automation. the most recent approaches are based on Reinforcement learning (RL). this paper describes a new learning strategy enhancing the most recent Proxima...
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Artificial Intelligence (AI) is becoming popular in the field of histopathological imaging. there isn’t a lot of annotated medical image data, and it is time consuming task to get this data annotated. the self-superv...
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Visually sorted grid layouts provide an efficient method for organizing high-dimensional vectors in two-dimensional space by aligning spatial proximity with similarity relationships. this approach facilitates the effe...
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ISBN:
(纸本)9798400706028
Visually sorted grid layouts provide an efficient method for organizing high-dimensional vectors in two-dimensional space by aligning spatial proximity with similarity relationships. this approach facilitates the effective sorting of diverse elements ranging from data points to images, and enables the simultaneous visualization of a significant number of elements. However, sorting data on two-dimensional grids is a challenge due to its high complexity. Even for a small 8-by-8 grid with 64 elements, the number of possible arrangements exceeds 1.3.10(89) - more than the number of atoms in the universe - making brute-force solutions impractical. Although various methods have been proposed to address the challenge of determining sorted grid layouts, none have investigated the potential of gradient-based optimization. In this paper, we present a novel method for grid-based sorting that exploits gradient optimization for the first time. We introduce a novel loss function that balances two opposing goals: ensuring the generation of a "valid" permutation matrix, and optimizing the arrangement on the grid to reflect the similarity between vectors, inspired by metrics that assess the quality of sorted grids. While learning-based approaches are inherently computationally complex, our method shows promising results in generating sorted grid layouts with superior sorting quality compared to existing techniques.
Research shows that heuristic optimizationalgorithms can find solutions for complex problems in the physical world. Pairing these algorithms with machine learning models for predictive building control applications h...
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Research shows that heuristic optimizationalgorithms can find solutions for complex problems in the physical world. Pairing these algorithms with machine learning models for predictive building control applications has been widely explored. this study investigates the efficacy of determining optimal ventilation rates using a particle swarm optimization (PSO) algorithm, a genetic algorithm (GA), and a hybridized genetic particle swarm optimization (GPSO) algorithm developed using MATLAB within an EnergyPlus building energy simulation model. Weather data from Vancouver is used to exemplify a marine climate, where free-cooling opportunities are relatively abundant. the algorithm performance results are collected for boththe heating and cooling seasons and are compared against each other for run times, energy savings, and indoor air quality performance. the results are compared against simulation results using a conventional demand control ventilation (DCV) system. Results indicate that when compared to the DCV controller with an economizer mode, heuristic optimization control methods are capable of reducing HVAC energy consumption during a cooling season with free-cooling opportunities by up to 14.1%. Additionally, all three optimizationalgorithms are capable of minimizing HVAC energy consumption with zero unmet hours for the indoor carbon dioxide concentration setpoint.
the 1°C rise in global temperature since the pre-industrial era is mainly attributed to the use of fossil fuels and human activities. To mitigate this temperature increase, it is crucial to reduce greenhouse gas ...
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the paper presents a comprehensive in-depth analysis of big data, machine learning (ML), and deep learning (DL) methodologies in predictive healthcare analytics, with a focus on their comparative strengths, research g...
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Operation and maintenance system is an important platform to support the deployment, operation and evaluation of distributed simulation system. the operation monitoring tool is to manage the simulation node, control t...
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In recent years, the arrival of the big data era has driven the rapid development of machine learning technology. Cluster analysis is one of the commonly used methods in traditional machine learningalgorithms. Due to...
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In this paper, we design a cooperative UAV maneuver decision-making task and use multi-agent reinforcement learning to solve it. In this task, two UAVs must learn cooperating with each other to defeat a stronger enemy...
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With its Pay-As-You-Use model that allows users to share resources remotely, cloud computing ushers in a new era of network-based computing. However, because user services and sophisticated applications generate vast ...
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