Defect detection in steel materials is a critical task to ensure product quality and reliability in various industrial applications. Traditional methods for detecting defects in steel surfaces are often labor-intensiv...
Defect detection in steel materials is a critical task to ensure product quality and reliability in various industrial applications. Traditional methods for detecting defects in steel surfaces are often labor-intensive, time-consuming, and prone to subjectivity and errors. However, the advent of deep learning-based models and techniques has provided a promising avenue for automating this process, significantly reducing error rates. In this research, the authors propose a Steel Defect Detection system with the help of a custom designed deep neural network inspired by the ResNet architecture. Notably, the model incorporates innovative attention layers not previously integrated into similar architectures, enhancing its predictive capabilities. The authors make use of data augmentation techniques to improve the model performance to generalize, and detect defects in imperceptible data. The novel multiclass semantic segmentation model has achieved over 91% accuracy and can be used to automate the Steel defect detection process, which can remarkably decrease the costs and time associated with manually inspecting the Steel sheets.
Despite the recent success of Graph Neural Networks (GNNs), it remains challenging to train GNNs on large-scale graphs due to neighbor explosions. As a remedy, distributed computing becomes a promising solution by lev...
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It has become really important for patients to know the precise results that can happen post the treatment of lung cancer or that if they have any underlying health problem which can be the cause of death post lung ca...
It has become really important for patients to know the precise results that can happen post the treatment of lung cancer or that if they have any underlying health problem which can be the cause of death post lung cancer surgery. Doctors also need to know the full detailed report of patients’ health with the risk factors that can affect their survival rate for better treatment of their patients. As such results are crucial, we need to implement such techniques which can generate the most accurate results. For such accurate analysis, we use machine learning techniques to generate better results. Several machine learning and deep learning approaches are being used to estimate the survival rate of lung cancer patients post the surgery. Examination of various health factors is used for prediction, as health factors could be significant predictors. Seven machine learning techniques that are linear regression, XGBooster, random forests, Decision Tree, KNN, SVM, voting classifier and two deep learning techniques that are ANN and BILSTM are used for analyzing performance. Analysis of accuracy has been done using F1 score, precision, and recall as measuring factors. For the analysis of outcome prediction, we have taken accuracy.
Qualitative coding, or content analysis, extracts meaning from text to discern quantitative patterns across a corpus of texts. Recently, advances in the interpretive abilities of large language models (LLMs) offer pot...
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The primary objective of this study was to test the hypothesis that the binary information on the presence or absence of gene expression can sufficiently capture the inherent heterogeneity within single-cell RNA se qu...
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Task and motion planning (TAMP) can enhance intelligent multi-robot coordination. TAMP becomes signifi-cantly more complicated in obstacle-cluttered environments and in the presence of robot dynamic uncertainties. We ...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Task and motion planning (TAMP) can enhance intelligent multi-robot coordination. TAMP becomes signifi-cantly more complicated in obstacle-cluttered environments and in the presence of robot dynamic uncertainties. We propose a control framework that solves the motion-planning problem for multi-robot teams with uncertain dynamics, addressing a key component of the TAMP pipeline. The principal part of the proposed algorithm constitutes a decentralized feedback control policy for tracking of reference paths taken by the robots while avoiding collision and adapting in real time to the underlying dynamic uncertainties. The proposed framework further leverages sampling-based motion planners to free the robots from local-minimum configurations. Extensive experimental results in complex, realistic environments illustrate the superior efficiency of the proposed approach, in terms of planning time and number of encountered local minima, with respect to state-of-the-art baseline methods.
Background and Objective: Diffusion weighted imaging (DWI) is a noninvasive imaging technology that can observe the diffusion of water molecules. However, obtaining high-resolution DWI often needs updating hardware eq...
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Recently, the supervised learning paradigm's surprisingly remarkable performance has garnered considerable attention from Sanskrit Computational Linguists. As a result, the Sanskrit community has put laudable effo...
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This paper presents an Active Search policy that balances between moving the camera and removing occluding objects to search for and retrieve a target object in clutter. While both types of action can reveal unobserve...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
This paper presents an Active Search policy that balances between moving the camera and removing occluding objects to search for and retrieve a target object in clutter. While both types of action can reveal unobserved parts of a scene, they typically vary in execution complexity and time. Our proposed method explicitly reasons about the occluded spaces in the scene where the target object may be hidden, and uses reinforcement learning to compute the value of each action with the ultimate goal of finding and retrieving the target object in minimal time. Results in simulation and real-world experiments demonstrate a significant improvement in both task execution speed and success rate compared to baseline grasping strategies.
Traffic Weaver is a Python package developed to generate a semi-synthetic signal (time series) with finer granularity, based on averaged time series, in a manner that, upon averaging, closely matches the original sign...
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