In order to satisfy the intelligent requirements of industrial systems and assist in automatic recognition of cutter wear, this paper proposes an image-based automatic detectionmethod for cutter ring edge wear of shie...
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ISBN:
(纸本)9789811945465;9789811945458
In order to satisfy the intelligent requirements of industrial systems and assist in automatic recognition of cutter wear, this paper proposes an image-based automatic detectionmethod for cutter ring edge wear of shieldmachine. The paper mainly studies: (1) Preprocess the original cutter images, the pixel image is generated by graying and thresholding methods, using the gray characteristics to suppress background, it has only two gray values;(2) Based on DBSCAN clustering algorithm, the optimization of cutter ring edge clusters is realized, and the edge pixel clusters are retained;(3) An ring edge extraction method based on structural constraints is proposed, the internal pixels are removed by orthogonal bidirectional projection, we obtained the preliminary image edge extraction results;(4) The circular edge of cutter-image is obtained by remaining pixels polynomial fitting based on polar coordinates. Finally, through the reference the actual size of cutter, the actual radius error is less than 3%. The experimental results show that this method can automatically and accurately detect the actual cutter wear of shield machine, and it provides an effective solution for the intelligent detection of cutter wear.
In recent years, the combination of deep reinforcement learning and unmanned aerial vehicle (UAV) to achieve autonomous flight has been a hot research field. In this paper, an obstacle avoidance navigation algorithm (...
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Model-based methods have significantly contributed to distinguishing task-irrelevant distractors for visual control. However, prior research has primarily focused on heterogeneous distractors like noisy background vid...
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Model-based methods have significantly contributed to distinguishing task-irrelevant distractors for visual control. However, prior research has primarily focused on heterogeneous distractors like noisy background videos, leaving homogeneous distractors that closely resemble controllable agents largely unexplored, which poses significant challenges to existing methods. To tackle this problem, we propose Implicit Action Generator (IAG) to learn the implicit actions of visual distractors, and present a new algorithm named implicit Action-informed Diverse visual Distractors Distinguisher (AD3), that leverages the action inferred by IAG to train separated world models. Implicit actions effectively capture the behavior of background distractors, aiding in distinguishing the task-irrelevant components, and the agent can optimize the policy within the task-relevant state space. Our method achieves superior performance on various visual control tasks featuring both heterogeneous and homogeneous distractors. The indispensable role of implicit actions learned by IAG is also empirically validated. Copyright 2024 by the author(s)
Scheduling task graphs with communication delay is a widely studied NP-hard problem. Many heuristics have been proposed, but there is no constant approximation algorithm for this classic model. In this paper, we focus...
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ISBN:
(纸本)9798350337662
Scheduling task graphs with communication delay is a widely studied NP-hard problem. Many heuristics have been proposed, but there is no constant approximation algorithm for this classic model. In this paper, we focus on the scheduling of the important class of fork-join task graphs (describing many types of common computations) on homogeneous processors. For this sub-case, we propose a guaranteed algorithm with a (1+ m m-1)approximation factor, where m is the number of processors. The algorithm is not only the first constant approximation for an important sub-domain of the classic scheduling problem, it is also a practical algorithm that can obtain shorter makespans than known heuristics. To demonstrate this, we propose adaptations of known scheduling heuristic for the specific fork-join structure. In an extensive evaluation, we then implemented these algorithms and scheduled many fork-join graphs with up to thousands of tasks and various computation time distributions on up to hundreds of processors. Comparing the obtained results demonstrates the competitive nature of the proposed approximation algorithm.
In the realm of medical image analysis, self-supervised learning (SSL) techniques have emerged to alleviate labeling demands, while still facing the challenge of training data scarcity owing to escalating resource req...
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With the rapid advancement of artificial intelligence technology, the development of intelligent manufacturing has become an inevitable trend. Utilizing AI technology to ensure the safe operation of factories is a cru...
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The issue of inaccurate user portrait recommendations for short-Term conversations is solved. The classical recommendation is based on an assumption: The user's historical behavior can represent the user's sta...
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Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study prop...
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Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.
Bundle recommendation offers users more holistic insights by recommending multiple compatible items at ***,the intricate correlations between items,varied user preferences,and the pronounced data sparsity in combinati...
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Bundle recommendation offers users more holistic insights by recommending multiple compatible items at ***,the intricate correlations between items,varied user preferences,and the pronounced data sparsity in combinations present significant challenges for bundle recommendation ***,current bundle recommendation methods fail to identify mismatched items within a given set,a process termed as‘‘outlier item detection’’.These outlier items are those with the weakest correlations within a *** them can aid users in refining their item *** the correlation among items can predict the detection of such outliers,the adaptability of combinations might not be adequately responsive to shifts in individual items during the learning *** limitation can hinder the algorithm’s *** tackle these challenges,we introduce an encoder–decoder architecture tailored for outlier item *** encoder learns potential item correlations through a self-attention ***,the decoder garners efficient inference frameworks by directly assessing item *** have validated the efficacy and efficiency of our proposed algorithm using real-world datasets.
Given the importance of ancient Chinese in capturing the essence of rich historical and cultural heritage, the rapid advancements in Large Language Models (LLMs) necessitate benchmarks that can effectively evaluate th...
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