To improve automation, increase efficiency, and maintain high quality in the production of steel, applying modern machine learning techniques to help detect steel defects has been the research focus in the steel indus...
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ISBN:
(纸本)9781450376822
To improve automation, increase efficiency, and maintain high quality in the production of steel, applying modern machine learning techniques to help detect steel defects has been the research focus in the steel industry, since an unprecedented revolution in image semantic segmentation has been witnessed in the past few years. In the traditional production process of steel materials, localizing and classifying surface defects manually on a steel sheet is inefficient and error-prone. Therefore, it's a key challenge to achieve automated detection of steel surface defects in image pixel level, leaving an urgent and critical issue to be addressed. In this paper, to accomplish this crucial task, we apply a series of machine learning algorithms of real-time semantic segmentation, utilizing neuralnetworks with encoder-decoder architectures based on Unet and feature pyramid network (FPN). The image dataset of steel defects is provided by Severstal, the largest steel company in Russia, through a featured code competition in the Kaggle community. The results show that the ensemble algorithm of several neuralnetworks with encoder-decoder architectures has a decent performance regarding both time cost and segmentation accuracy. Our machine learning algorithms achieve dice coefficients over 0.915 and 0.905 at a speed of over 1.5 images per second on the public test set and private test set on the Kaggle platform, respectively, which locates at the top 2% among all teams in the competition.
In this paper, we present a deep reinforcement learning(DRL) based strategy for optimizing the scheduling of satellite on-orbit services. The orbital maneuvers necessitate the servicing satellite to consecutively rend...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
In this paper, we present a deep reinforcement learning(DRL) based strategy for optimizing the scheduling of satellite on-orbit services. The orbital maneuvers necessitate the servicing satellite to consecutively rendezvous with multiple targets to execute its on-orbit missions. The principal aim of our optimization approach is to ascertain the most advantageous sequence for servicing satellites, thereby minimizing the overall cost, contingent upon the expenditure of propulsion maneuvers. To surmount this formidable challenge, we introduce an attention-based encoder-decoder neural network and train its parameters utilizing the REINFORCE algorithm with a greedy rollout baseline. Ultimately, experimental results across diverse scenarios validate the efficacy and supremacy of our proposed algorithm. The chief contribution of this work lies in its conceptualization of the satellite on-orbit service scheduling optimization quandary as an extended traveling salesman problem, culminating in the introduction of an innovative DRL-based methodology.
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