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Journal articles on the topic 'Surface learning'

To see the other types of publications on this topic, follow the link: Surface learning.

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Uma SV. "STUDENTS’ PERCEPTIONS ON CADAVERIC PAINTING AS A METHOD FOR LEARNING SURFACE ANATOMY." International Journal of Anatomy and Research 8, no. 2.3 (June 5, 2020): 7572–77. http://dx.doi.org/10.16965/ijar.2020.165.

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Chen, Ke-Wei, Laura Bear, and Che-Wei Lin. "Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks." Sensors 22, no. 6 (March 17, 2022): 2331. http://dx.doi.org/10.3390/s22062331.

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Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart’s surface using the potentials recorded at the body’s surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution methods using machine learning and deep learning frameworks. Electrocardiograms were simultaneously recorded from pigs’ ventricles and their body surfaces. The Fully Connected Neural network (FCN), Long Short-term Memory (LSTM), Convolutional Neural Network (CNN) methods were used for constructing the model. A method is developed to align the data across different pigs. We evaluated the method using leave-one-out cross-validation. For the best result, the overall median of the correlation coefficient of the predicted ECG wave was 0.74. This study demonstrated that a neural network can be used to solve the inverse problem of ECGi with relatively small datasets, with an accuracy compatible with current standard methods.

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Zhang, Wenhe. "Surface Roughness Prediction with Machine Learning." Journal of Physics: Conference Series 1856, no. 1 (April 1, 2021): 012040. http://dx.doi.org/10.1088/1742-6596/1856/1/012040.

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Wu, Zhaohui, Lu Jiang, Qinghua Zheng, and Jun Liu. "Learning to Surface Deep Web Content." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1967–68. http://dx.doi.org/10.1609/aaai.v24i1.7779.

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We propose a novel deep web crawling framework based on reinforcement learning. The crawler is regarded as an agent and deep web database as the environment. The agent perceives its current state and submits a selected action (query) to the environment according to Q-value. Based on the framework we develop an adaptive crawling method. Experimental results show that it outperforms the state of art methods in crawling capability and breaks through the assumption of full-text search implied by existing methods.

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Isikdogan, Furkan, Alan C. Bovik, and Paola Passalacqua. "Surface Water Mapping by Deep Learning." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 11 (November 2017): 4909–18. http://dx.doi.org/10.1109/jstars.2017.2735443.

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Cheng, Jieyu, Adrian V. Dalca, Bruce Fischl, and Lilla Zöllei. "Cortical surface registration using unsupervised learning." NeuroImage 221 (November 2020): 117161. http://dx.doi.org/10.1016/j.neuroimage.2020.117161.

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7

Winje, Øystein, and Knut Løndal. "Bringing deep learning to the surface." Nordic Journal of Comparative and International Education (NJCIE) 4, no. 2 (July 1, 2020): 25–41. http://dx.doi.org/10.7577/njcie.3798.

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Deep learning is a key term in current educational discourses worldwide and used by researchers, policymakers, stakeholders, politicians, organisations and the media with different definitions and, consequently, much confusion about its meaning and usage. This systematic mapping review attempts to reduce this ambiguity by investigating the definitions of deep learning in 71 research publications on primary and secondary education from 1970 to 2018. The results show two conceptualisations of the term deep learning—1) meaningful learning and 2) transfer of learning—both based on cognitive learning perspectives. The term deep learning is used by researchers worldwide and is mainly investigated in the school subjects of science, languages and mathematics with samples of students between 13 and 16 years of age. Deep learning is also a prevalent term in current international education policy and national curriculum reform, thus deeply affecting the practice of teaching and learning in general education. Our review identifies a lack of studies investigating deep learning through perspectives other than cognitive learning theories and suggests that future research should emphasise applying embodied, affective, and social perspectives on learning in the wide array of school subjects, in lower primary education and in a variety of sociocultural contexts, to support the adaptation of deep learning to a general educational practice.

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Xiong, Shiyao, Juyong Zhang, Jianmin Zheng, Jianfei Cai, and Ligang Liu. "Robust surface reconstruction via dictionary learning." ACM Transactions on Graphics 33, no. 6 (November 19, 2014): 1–12. http://dx.doi.org/10.1145/2661229.2661263.

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Ologunagba, Damilola, and Shyam Kattel. "Machine Learning Prediction of Surface Segregation Energies on Low Index Bimetallic Surfaces." Energies 13, no. 9 (May 1, 2020): 2182. http://dx.doi.org/10.3390/en13092182.

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Surface chemical composition of bimetallic catalysts can differ from the bulk composition because of the segregation of the alloy components. Thus, it is very useful to know how the different components are arranged on the surface of catalysts to gain a fundamental understanding of the catalysis occurring on bimetallic surfaces. First-principles density functional theory (DFT) calculations can provide deeper insight into the surface segregation behavior and help understand the surface composition on bimetallic surfaces. However, the DFT calculations are computationally demanding and require large computing platforms. In this regard, statistical/machine learning methods provide a quick and alternative approach to study materials properties. Here, we trained previously reported surface segregation energies on low index surfaces of bimetallic catalysts using various linear and non-linear statistical methods to find a correlation between surface segregation energies and elemental properties. The results revealed that the surface segregation energies on low index bimetallic surfaces can be predicted using fundamental elemental properties.

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10

T.V., Bijeesh. "Evaluation of Machine Learning Algorithms for Surface Water Delineation Using Landsat 8 Images." Journal of Advanced Research in Dynamical and Control Systems 12, no. 3 (March 20, 2020): 207–16. http://dx.doi.org/10.5373/jardcs/v12i3/20201184.

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11

Ryu, Semin, and Seung-Chan Kim. "Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics." Sensors 20, no. 2 (January 9, 2020): 369. http://dx.doi.org/10.3390/s20020369.

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Inspired by spiders that can generate and sense vibrations to obtain information regarding a substrate, we propose an intelligent system that can recognize the type of surface being touched by knocking the surface and listening to the vibrations. Hence, we developed a system that is equipped with an electromagnetic hammer for hitting the ground and an accelerometer for measuring the mechanical responses induced by the impact. We investigate the feasibility of sensing 10 different daily surfaces through various machine-learning techniques including recent deep-learning approaches. Although some test surfaces are similar, experimental results show that our system can recognize 10 different surfaces remarkably well (test accuracy of 98.66%). In addition, our results without directly hitting the surface (internal impact) exhibited considerably high test accuracy (97.51%). Finally, we conclude this paper with the limitations and future directions of the study.

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12

Zheng, Wendong, Huaping Liu, Bowen Wang, and Fuchun Sun. "Online weakly paired similarity learning for surface material retrieval." Industrial Robot: the international journal of robotics research and application 46, no. 3 (May 20, 2019): 396–403. http://dx.doi.org/10.1108/ir-09-2018-0179.

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Purpose For robots to more actively interact with the surrounding environment in object manipulation tasks or walking, they must understand the physical attributes of objects and surface materials they encounter. Dynamic tactile sensing can effectively capture rich information about material properties. Hence, methods that convey and interpret this tactile information to the user can improve the quality of human–machine interaction. This paper aims to propose a visual-tactile cross-modal retrieval framework to convey tactile information of surface material for perceptual estimation. Design/methodology/approach The tactile information of a new unknown surface material can be used to retrieve perceptually similar surface from an available surface visual sample set by associating tactile information to visual information of material surfaces. For the proposed framework, the authors propose an online low-rank similarity learning method, which can effectively and efficiently capture the cross-modal relative similarity between visual and tactile modalities. Findings Experimental results conducted on the Technischen Universität München Haptic Texture Database demonstrate the effectiveness of the proposed framework and the method. Originality/value This paper provides a visual-tactile cross-modal perception method for recognizing material surface. By the method, a robot can communicate and interpret the conveyed information about the surface material properties to the user; it will further improve the quality of robot interaction.

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13

Ho, Chao-Ching, Li-Lun Tai, and Eugene Su. "Deep-Learning-Based Surface Texture Feature Simulation for Surface Defect Inspection." Symmetry 14, no. 7 (July 18, 2022): 1465. http://dx.doi.org/10.3390/sym14071465.

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In this research, a simulation system based on a physical model and its lighting feature is developed to perform three-dimensional model creation, and graphics software is used to randomly generate a simulated surface with defects, which also cooperates with the virtual environment to reproduce the original environment. Furthermore, the use of a generative adversarial network to optimize the virtual dataset created symmetrically by the system is studied in order to reduce the effect of the difference between the real and virtual images. This system compensates for the condition of data imbalance occurring between qualified products and defective products in the production line, and a large amount of random data with and without defects can be created. In addition, the process of the database creation is classified and marked, such that complicated and time-consuming preliminary steps can be reduced; therefore, the data collection cost can be significantly reduced and the uncertainly associated with manual operation is also reduced. When a simulated textured surface generated from this system is used to perform training, the inspection background accuracy reaches 98%, and the accuracy also reaches 78% in real defect inspection process; therefore, the location of the defect can be determined completely.

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Oishi, Atsuya, and Genki Yagawa. "A surface-to-surface contact search method enhanced by deep learning." Computational Mechanics 65, no. 4 (January 9, 2020): 1125–47. http://dx.doi.org/10.1007/s00466-019-01811-2.

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Yao, Gang, Fujia Wei, Yang Yang, and Yujia Sun. "Deep-Learning-Based Bughole Detection for Concrete Surface Image." Advances in Civil Engineering 2019 (June 16, 2019): 1–12. http://dx.doi.org/10.1155/2019/8582963.

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Bugholes are surface imperfections that appear as small pits and craters on concrete surface after the casting process. The traditional measurement methods are carried out by in situ manual inspection, and the detection process is time-consuming and difficult. This paper proposed a deep-learning-based method to detect bugholes on concrete surface images. A deep convolutional neural network for detecting bugholes on concrete surfaces was developed, by adding the inception modules into the traditional convolution network structure to solve the problem of the relatively small size of input image (28 × 28 pixels) and the limited number of labeled examples in training set (less than 10 K). The effects of noise such as illumination, shadows, and combinations of several different surface imperfections in real-world environments were considered. From the results of image test, the proposed DCNN had an excellent bughole detection performance and the recognition accuracy reached 96.43%. By the comparative study with the Laplacian of Gaussian (LoG) algorithm and the Otsu method, the proposed DCNN had good robustness which can avoid the interference of cracks, color-differences, and nonuniform illumination on the concrete surface.

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16

Bay, J. S., and H. Hemami. "Dynamics of a learning controller for surface tracking robots on unknown surfaces." IEEE Transactions on Automatic Control 35, no. 9 (September 1990): 1051–54. http://dx.doi.org/10.1109/9.58535.

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17

Luo, Yiming, Zhenxing Mi, and Wenbing Tao. "DeepDT: Learning Geometry From Delaunay Triangulation for Surface Reconstruction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2277–85. http://dx.doi.org/10.1609/aaai.v35i3.16327.

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In this paper, a novel learning-based network, named DeepDT, is proposed to reconstruct the surface from Delaunay triangulation of point cloud. DeepDT learns to predict inside/outside labels of Delaunay tetrahedrons directly from a point cloud and corresponding Delaunay triangulation. The local geometry features are first extracted from the input point cloud and aggregated into a graph deriving from the Delaunay triangulation. Then a graph filtering is applied on the aggregated features in order to add structural regularization to the label prediction of tetrahedrons. Due to the complicated spatial relations between tetrahedrons and the triangles, it is impossible to directly generate ground truth labels of tetrahedrons from ground truth surface. Therefore, we propose a multi-label supervision strategy which votes for the label of a tetrahedron with labels of sampling locations inside it. The proposed DeepDT can maintain abundant geometry details without generating overly complex surfaces, especially for inner surfaces of open scenes. Meanwhile, the generalization ability and time consumption of the proposed method is acceptable and competitive compared with the state-of-the-art methods. Experiments demonstrate the superior performance of the proposed DeepDT.

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18

Zhang, Yun, and Xiaojie Xu. "Machine learning modeling of metal surface energy." Materials Chemistry and Physics 267 (July 2021): 124622. http://dx.doi.org/10.1016/j.matchemphys.2021.124622.

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19

Xiao, Dong, Siyou Lin, Zuoqiang Shi, and Bin Wang. "Learning modified indicator functions for surface reconstruction." Computers & Graphics 102 (February 2022): 309–19. http://dx.doi.org/10.1016/j.cag.2021.10.017.

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20

Huth, Benjamin, Andreas Salzburger, and Tilo Wettig. "Machine learning for surface prediction in ACTS." EPJ Web of Conferences 251 (2021): 03053. http://dx.doi.org/10.1051/epjconf/202125103053.

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We present an ongoing R&D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction. We investigate different approaches of training neural networks for surface prediction and compare their results. This work is carried out in the context of the ACTS tracking toolkit.

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21

Yan, Ke, Yuting Dai, Meiling Xu, and Yuchang Mo. "Tunnel Surface Settlement Forecasting with Ensemble Learning." Sustainability 12, no. 1 (December 26, 2019): 232. http://dx.doi.org/10.3390/su12010232.

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Ground surface settlement forecasting in the process of tunnel construction is one of the most important techniques towards sustainable city development and preventing serious damages, such as landscape collapse. It is evident that modern artificial intelligence (AI) models, such as artificial neural network, extreme learning machine, and support vector regression, are capable of providing reliable forecasting results for tunnel surface settlement. However, two limitations exist for the current forecasting techniques. First, the data provided by the construction company are usually univariate (i.e., containing only the settlement data). Second, the demand of tunnel surface settlement is immediate after the construction process begins. The number of training data samples is limited. Targeting at the above two limitations, in this study, a novel ensemble machine learning model is proposed to forecast tunnel surface settlement using univariate short period of real-world tunnel settlement data. The proposed Adaboost.RT framework fully utilizes existing data points with three base machine learning models and iteratively updates hyperparameters using current surface point locations. Experimental results show that compared with existing machine learning techniques and algorithms, the proposed ensemble learning method provides a higher prediction accuracy with acceptable computational efficiency.

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Lv, Xiaoming, Fajie Duan, Jia-Jia Jiang, Xiao Fu, and Lin Gan. "Deep Active Learning for Surface Defect Detection." Sensors 20, no. 6 (March 16, 2020): 1650. http://dx.doi.org/10.3390/s20061650.

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Most of the current object detection approaches deliver competitive results with an assumption that a large number of labeled data are generally available and can be fed into a deep network at once. However, due to expensive labeling efforts, it is difficult to deploy the object detection systems into more complex and challenging real-world environments, especially for defect detection in real industries. In order to reduce the labeling efforts, this study proposes an active learning framework for defect detection. First, an Uncertainty Sampling is proposed to produce the candidate list for annotation. Uncertain images can provide more informative knowledge for the learning process. Then, an Average Margin method is designed to set the sampling scale for each defect category. In addition, an iterative pattern of training and selection is adopted to train an effective detection model. Extensive experiments demonstrate that the proposed method can render the required performance with fewer labeled data.

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23

Fadli, Vira Fitriza, and Iwa Ovyawan Herlistiono. "Steel Surface Defect Detection using Deep Learning." International Journal of Innovative Science and Research Technology 5, no. 7 (July 23, 2020): 244–50. http://dx.doi.org/10.38124/ijisrt20jul240.

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Steel defects are a frequent problem in steel companies. Proper quality control can reduce quality problems arising from steel defects. Nowadays, steel defects can detect by automation methods that utilize certain algorithms. Deep learning can help the steel defect detection algorithm become more sophisticated. In this study, we use deep learning CNN with Xception architecture to detect steel defects from images taken from high-frequency and high-resolution cameras. There are two techniques used, and both produce respectively 0.94% and 0.85% accuracy. The Xception architecture used in this case shows optimal and stable performance in the process and its results.

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Pan, Yongping, and Haoyong Yu. "Composite Learning From Adaptive Dynamic Surface Control." IEEE Transactions on Automatic Control 61, no. 9 (September 2016): 2603–9. http://dx.doi.org/10.1109/tac.2015.2495232.

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25

Cheng, Lushan, Xu Zhang, and Jie Shen. "Road surface condition classification using deep learning." Journal of Visual Communication and Image Representation 64 (October 2019): 102638. http://dx.doi.org/10.1016/j.jvcir.2019.102638.

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Zhou, Zheng Hua, Jian Wei Zhao, and Fei Long Cao. "Surface reconstruction based on extreme learning machine." Neural Computing and Applications 23, no. 2 (February 28, 2012): 283–92. http://dx.doi.org/10.1007/s00521-012-0891-8.

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27

Dysart, Paul S. "Bathymetric surface modeling: A machine learning approach." Journal of Geophysical Research: Solid Earth 101, B4 (April 10, 1996): 8093–105. http://dx.doi.org/10.1029/95jb03737.

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Liu, Bin, Weifeng Chen, Bo Li, and Xiuping Liu. "Neural Subspace Learning for Surface Defect Detection." Mathematics 10, no. 22 (November 19, 2022): 4351. http://dx.doi.org/10.3390/math10224351.

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Surface defect inspection is a key technique in industrial product assessments. Compared with other visual applications, industrial defect inspection suffers from a small sample problem and a lack of labeled data. Therefore, conventional deep-learning methods depending on huge supervised samples cannot be directly generalized to this task. To deal with the lack of labeled data, unsupervised subspace learning provides more clues for the task of defect inspection. However, conventional subspace learning methods focus on studying the linear subspace structure. In order to explore the nonlinear manifold structure, a novel neural subspace learning algorithm is proposed by substituting linear operators with nonlinear neural networks. The low-rank property of the latent space is approximated by limiting the dimensions of the encoded feature, and the sparse coding property is simulated by quantized autoencoding. To overcome the small sample problem, a novel data augmentation strategy called thin-plate-spline deformation is proposed. Compared with the rigid transformation methods used in previous literature, our strategy could generate more reliable training samples. Experiments on real-world datasets demonstrate that our method achieves state-of-the-art performance compared with unsupervised methods. More importantly, the proposed method is competitive and has a better generalization capability compared with supervised methods based on deep learning techniques.

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Sharp, Nicholas, Souhaib Attaiki, Keenan Crane, and Maks Ovsjanikov. "DiffusionNet: Discretization Agnostic Learning on Surfaces." ACM Transactions on Graphics 41, no. 3 (June 30, 2022): 1–16. http://dx.doi.org/10.1145/3507905.

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We introduce a new general-purpose approach to deep learning on three-dimensional surfaces based on the insight that a simple diffusion layer is highly effective for spatial communication. The resulting networks are automatically robust to changes in resolution and sampling of a surface—a basic property that is crucial for practical applications. Our networks can be discretized on various geometric representations, such as triangle meshes or point clouds, and can even be trained on one representation and then applied to another. We optimize the spatial support of diffusion as a continuous network parameter ranging from purely local to totally global, removing the burden of manually choosing neighborhood sizes. The only other ingredients in the method are a multi-layer perceptron applied independently at each point and spatial gradient features to support directional filters. The resulting networks are simple, robust, and efficient. Here, we focus primarily on triangle mesh surfaces and demonstrate state-of-the-art results for a variety of tasks, including surface classification, segmentation, and non-rigid correspondence.

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Liu, Ming-min, L. Z. Li, and Jun Zhang. "Comparison of manifold learning algorithms used in FSI data interpolation of curved surfaces." Multidiscipline Modeling in Materials and Structures 13, no. 2 (August 14, 2017): 217–61. http://dx.doi.org/10.1108/mmms-07-2016-0032.

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Purpose The purpose of this paper is to discuss a data interpolation method of curved surfaces from the point of dimension reduction and manifold learning. Design/methodology/approach Instead of transmitting data of curved surfaces in 3D space directly, the method transmits data by unfolding 3D curved surfaces into 2D planes by manifold learning algorithms. The similarity between surface unfolding and manifold learning is discussed. Projection ability of several manifold learning algorithms is investigated to unfold curved surface. The algorithms’ efficiency and their influences on the accuracy of data transmission are investigated by three examples. Findings It is found that the data interpolations using manifold learning algorithms LLE, HLLE and LTSA are efficient and accurate. Originality/value The method can improve the accuracies of coupling data interpolation and fluid-structure interaction simulation involving curved surfaces.

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31

KNOPF, GEORGE K., and ARCHANA P. SANGOLE. "FREEFORM SURFACE RECONSTRUCTION FROM SCATTERED POINTS USING A DEFORMABLE SPHERICAL MAP." International Journal of Image and Graphics 06, no. 03 (July 2006): 341–56. http://dx.doi.org/10.1142/s0219467806002343.

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Reconstruction of freeform surfaces from scattered coordinate data is a difficult problem encountered in many surface fitting and geometric modeling applications. Conventional tessellation and parametric surface fitting techniques are limited because they require prior knowledge about the connectivity between the sampled points. The method of surface reconstruction described in this paper exploits the learning capability of a self-organizing feature map (SOFM) to adaptively fit a deformable sphere to the unorganized 3D coordinate data. The learning algorithm automatically establishes the connectivity between the measured points by iteratively changing the topological relationships within the map. By incorporating additional constraints during the learning process it is possible to have the deformable map follow the shape of objects with surface holes and cavities. Several examples of freeform surfaces with varying levels of complexity are discussed in order to demonstrate the performance of the algorithm.

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32

Carelli, Francesco. "One hundred years ago New and original way for teaching, learning, going under the surface." Archives of Medical Case Reports and Case Study 4, no. 4 (September 20, 2021): 01–02. http://dx.doi.org/10.31579/2692-9392/080.

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It is the explanation and the unusual and special way through new and original ways for teaching and learning, also in very young ages, going under the surface, involving and how to create the interest and how to maintain it through the time. It involve active participation, creativity, imagination, looking from different points of view so to maintain this attitude lifelong and to maintain interest as well, sustaining interest for further discoveries. In teaching our medical students, we push and encourage and motivate them, for instance, asking how they feel about themselves in situations they have to face going under the appearances.

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Gradinšćak, Dafina, Nataša Branković, and Gordana Kozoderović. "Gardening-based learning." Norma 26, no. 1 (2021): 53–66. http://dx.doi.org/10.5937/norma2101053g.

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The paper provides a theoretical overview of studies conducted within the framework of gardening-based learning. In nature, children can acquire a variety of skills and develop their environmental awareness in a space that represents a world of living examples. People are distancing from the nature, despite the fact that it can be a stimulating learning environment. It is necessary to return to the nature and design activities and programs where students will directly learn in the real world. The paper reviews the studies related to gardening-based learning within five domains: ecological, psychosocial, perceptual, the domain of school achievement and nutrition-health. Many researchers have created and implemented school garden programs and projects through which various positive effects have been achieved on cognitive, psychosocial, moral and physical development of children. Gardening-based learning programs result in increased awareness of proper nutrition and environment, higher learning outcomes and increase students' life skills. Experience gained in the garden encourages environmental literacy and management skills, improves awareness of the connection between plants and our clothes, food, lifestyle and sense of well-being.

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Ju, Yakun, Yuxin Peng, Muwei Jian, Feng Gao, and Junyu Dong. "Learning conditional photometric stereo with high-resolution features." Computational Visual Media 8, no. 1 (October 27, 2021): 105–18. http://dx.doi.org/10.1007/s41095-021-0223-y.

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AbstractPhotometric stereo aims to reconstruct 3D geometry by recovering the dense surface orientation of a 3D object from multiple images under differing illumination. Traditional methods normally adopt simplified reflectance models to make the surface orientation computable. However, the real reflectances of surfaces greatly limit applicability of such methods to real-world objects. While deep neural networks have been employed to handle non-Lambertian surfaces, these methods are subject to blurring and errors, especially in high-frequency regions (such as crinkles and edges), caused by spectral bias: neural networks favor low-frequency representations so exhibit a bias towards smooth functions. In this paper, therefore, we propose a self-learning conditional network with multi-scale features for photometric stereo, avoiding blurred reconstruction in such regions. Our explorations include: (i) a multi-scale feature fusion architecture, which keeps high-resolution representations and deep feature extraction, simultaneously, and (ii) an improved gradient-motivated conditionally parameterized convolution (GM-CondConv) in our photometric stereo network, with different combinations of convolution kernels for varying surfaces. Extensive experiments on public benchmark datasets show that our calibrated photometric stereo method outperforms the state-of-the-art.

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Sasikala, D., and K. Venkatesh Sharma. "Future Intelligent Agriculture with Bootstrapped Meta-Learning andє-greedy Q-learning." September 2022 4, no. 3 (August 12, 2022): 149–59. http://dx.doi.org/10.36548/jaicn.2022.3.001.

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Agriculture is a noteworthy and vibrant domain in the fiscal evolution of the globe. With populationin progress, climatic situation and assets, and agriculture turn out dazed to be a crucial task to realize the necessities of the future population. Intelligent precision agriculture/intelligent smart farming has transpired as an innovative tool to tackle hovers of the future ahead in automated agricultural sustainability by leading Artificial Intelligence (AI) in agriculture automation.AI unravels critical farm labor challenges by improving or reducing work and lessening the necessity of numerous workers. Agricultural AI aids in reaping harvests quicker than human employees at a greater quantity, further precise in categorizing and eradicating unwanted plants, also dropping cost and menace. This process motivates the cutting-edge technologies capitulating the machine capability to learn by sourcing Bootstrapped Meta-learning also reinforcing with rewards as maximum crop yields and minimum resource utilizations as well as within time limits. AI empowered farm machinery is the key constituent of the future agriculture revolution ahead. In this exploratory work, an efficient automation of AI application in the field of agriculture sustenance is ensured for receipt of the most obtainable aids as outcomes and inhibiting the applied assets. Fixing the precise real-time issues trailed by unravelling it for agricultural augmentation or amplification thereby leads to the global best future agriculture.

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Singari, Ranganath, Karun Singla, and Gangesh Chawla. "Deep Learning Framework for Steel Surface Defects Classification." INTERNATIONAL JOURNAL OF ADVANCED PRODUCTION AND INDUSTRIAL ENGINEERING 4, no. 1 (January 5, 2019): 25–32. http://dx.doi.org/10.35121/ijapie201901135.

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Deep learning has offered new avenues in the field of industrial management. Traditional methods of quality inspection such as Acceptance Sampling relies on a probabilistic measure derived from inspecting a sample of finished products. Evaluating a fixed number of products to derive the quality level for the complete batch is not a robust approach. Visual inspection solutions based on deep learning can be employed in the large manufacturing units to improve the quality inspection units for steel surface defect detection. This leads to optimization of the human capital due to reduction in manual intervention and turnaround time in the overall supply chain of the industry. Consequently, the sample size in the Acceptance sampling can be increased with minimal effort vis-à-vis an increase in the overall accuracy of the inspection. The learning curve of this work is supported by Convolutional Neural Network which has been used to extract feature representations from grayscale images to classify theinputs into six types of surface defects. The neural network architecture is compiled in Keras framework using Tensorflow backend with state of the art Adam RMS Prop with Nesterov Momentum (NADAM) optimizer. The proposed classification algorithm holds the potential to identify the dominant flaws in the manufacturing system responsible for leaking costs.

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Han, Jun, and Chaoli Wang. "SurfNet: Learning Surface Representations via Graph Convolutional Network." Computer Graphics Forum 41, no. 3 (June 2022): 109–20. http://dx.doi.org/10.1111/cgf.14526.

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Cerezci, Feyza, Serap Kazan, Muhammed Ali Oz, Cemil Oz, Tugrul Tasci, Selman Hizal, and Caglayan Altay. "Online metallic surface defect detection using deep learning." Emerging Materials Research 9, no. 4 (December 1, 2020): 1266–73. http://dx.doi.org/10.1680/jemmr.20.00197.

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Dou, Jinchao, Daodang Wang, Qiuye Yu, Ming Kong, Lu Liu, Xinke Xu, and Rongguang Liang. "Deep-learning-based deflectometry for freeform surface measurement." Optics Letters 47, no. 1 (December 20, 2021): 78. http://dx.doi.org/10.1364/ol.447006.

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Michel, J., and J. Inglada. "LEARNING HARMONISED PLEIADES AND SENTINEL-2 SURFACE REFLECTANCES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 265–72. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-265-2021.

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Abstract:

Abstract. In this paper, we investigate the use of machine-learning techniques in order to produce harmonised surface reflectances between Sentinel-2 and Pleiades images, and reduce the impact of the differences in sensors, view conditions, and atmospheric correction differences between them. We demonstrate that if a simple linear regression considering Sentinel-2 surface reflectances as the target domain can overcome this problem when both images are calibrated to Top of Canopy reflectances, the non-linearity brought by a simple Multi-Layer-Perceptron is already useful when Pleiades is corrected to Top of Atmosphere level and contributions of the atmosphere need to be learned. We also demonstrate that learning a Convolution Neural Network instead of a plain MLP can learn undesired spatial effects such as mis-registration or differences in spatial frequency content, that will affect the image quality of the corrected Pleiades product. We overcome this issue by proposing an adhoc input convolutional layer that will capture those effects and can later be unplugged during inference. Last, we also propose an architecture and loss function that is robust to unmasked clouds and produces a confidence prediction during inference.

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Dawson, M. S., A. K. Fung, and M. T. Manry. "Surface parameter retrieval using fast learning neural networks." Remote Sensing Reviews 7, no. 1 (February 1993): 1–18. http://dx.doi.org/10.1080/02757259309532163.

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Liu Yuqing, 刘雨青, 冯俊凯 Feng Junkai, 邢博闻 Xing Bowen, and 曹守启 Cao Shouqi. "Water Surface Object Detection Based on Deep Learning." Laser & Optoelectronics Progress 57, no. 18 (2020): 181502. http://dx.doi.org/10.3788/lop57.181502.

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Wang, Hua-Chen, Lyndsey Nickels, and Anne Castles. "Orthographic learning in developmental surface and phonological dyslexia." Cognitive Neuropsychology 32, no. 2 (February 2, 2015): 58–79. http://dx.doi.org/10.1080/02643294.2014.1003536.

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Groves, J. T. "Learning the Chemical Language of Cell-Surface Interactions." Science Signaling 2005, no. 301 (September 6, 2005): pe45. http://dx.doi.org/10.1126/stke.3012005pe45.

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Chou, Calvin L., Kimberly S. Topp, and Marieke Kruidering-Hall. "Integrating surface anatomy learning with clinical skills training." Medical Education 44, no. 11 (October 15, 2010): 1127–28. http://dx.doi.org/10.1111/j.1365-2923.2010.03839.x.

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Gönen, Mehmet, and Ethem Alpaydın. "Regularizing multiple kernel learning using response surface methodology." Pattern Recognition 44, no. 1 (January 2011): 159–71. http://dx.doi.org/10.1016/j.patcog.2010.07.008.

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Hu, Wei, Sheng Ye, Yujin Zhang, Tianduo Li, Guozhen Zhang, Yi Luo, Shaul Mukamel, and Jun Jiang. "Machine Learning Protocol for Surface-Enhanced Raman Spectroscopy." Journal of Physical Chemistry Letters 10, no. 20 (September 20, 2019): 6026–31. http://dx.doi.org/10.1021/acs.jpclett.9b02517.

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Paulo, Joel, and José Bento Coelho. "Statistical learning approach applied to road surface classification." Journal of the Acoustical Society of America 124, no. 4 (October 2008): 2590. http://dx.doi.org/10.1121/1.4783218.

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Tuccillo, D., M. Huertas-Company, E. Decencière, S. Velasco-Forero, H. Domínguez Sánchez, and P. Dimauro. "Deep learning for galaxy surface brightness profile fitting." Monthly Notices of the Royal Astronomical Society 475, no. 1 (December 11, 2017): 894–909. http://dx.doi.org/10.1093/mnras/stx3186.

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Azer, Samy A. "Can “YouTube” help students in learning surface anatomy?" Surgical and Radiologic Anatomy 34, no. 5 (January 26, 2012): 465–68. http://dx.doi.org/10.1007/s00276-012-0935-x.

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