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Journal articles on the topic 'Three-dimensional learning'

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

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Roth, Dan, Ming-Hsuan Yang, and Narendra Ahuja. "Learning to Recognize Three-Dimensional Objects." Neural Computation 14, no. 5 (May 1, 2002): 1071–103. http://dx.doi.org/10.1162/089976602753633394.

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A learning account for the problem of object recognition is developed within the probably approximately correct (PAC) model of learnability. The key assumption underlying this work is that objects can be recognized (or discriminated) using simple representations in terms of syntactically simple relations over the raw image. Although the potential number of these simple relations could be huge, only a few of them are actually present in each observed image, and a fairly small number of those observed are relevant to discriminating an object. We show that these properties can be exploited to yield an efficient learning approach in terms of sample and computational complexity within the PAC model. No assumptions are needed on the distribution of the observed objects, and the learning performance is quantified relative to its experience. Most important, the success of learning an object representation is naturally tied to the ability to represent it as a function of some intermediate representations extracted from the image. We evaluate this approach in a large-scale experimental study in which the SNoW learning architecture is used to learn representations for the 100 objects in the Columbia Object Image Library. Experimental results exhibit good generalization and robustness properties of the SNoW-based method relative to other approaches. SNoW's recognition rate degrades more gracefully when the training data contains fewer views, and it shows similar behavior in some preliminary experiments with partially occluded objects.

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2

Flores-Abreu, I. Nuri, T. Andrew Hurly, and Susan D. Healy. "Three-dimensional spatial learning in hummingbirds." Animal Behaviour 85, no. 3 (March 2013): 579–84. http://dx.doi.org/10.1016/j.anbehav.2012.12.019.

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3

Zelger, P., K. Kaser, B. Rossboth, L. Velas, G. J. Schütz, and A. Jesacher. "Three-dimensional localization microscopy using deep learning." Optics Express 26, no. 25 (December 5, 2018): 33166. http://dx.doi.org/10.1364/oe.26.033166.

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4

Grobéty, Marie-Claude, and Françoise Schenk. "Spatial learning in a three-dimensional maze." Animal Behaviour 43, no. 6 (June 1992): 1011–20. http://dx.doi.org/10.1016/s0003-3472(06)80014-x.

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5

Bryant, Rita. "Three-Dimensional Learning at Camp Mind's Eye." Gifted Education International 5, no. 1 (September 1987): 29–32. http://dx.doi.org/10.1177/026142948700500106.

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The article outlines the philosophy which guides the planning of creative, residential summer camps in Texas, U.S.A. The aim of the camps is to provide a protected environment in which young people and their teachers are able to take risks and reach high levels of innovative productivity beyond the attainment of a single individual.

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6

Gothwal, Pushpa, and Sandesh Singh Shekhawa. "Three Dimensional Cube." International Journal of Engineering & Technology 7, no. 3.30 (August 24, 2018): 90. http://dx.doi.org/10.14419/ijet.v7i3.30.18207.

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Three dimensional cube is the 3D display device implemented using RGB LED. The objective of this paper to design a 3D CUBE using shift registers controlled by the microcontroller. It consists of using a 8-bit microcontrollers (Arduino Mega 2560, Arduino Uno and AT89s52) with on chip ADC, PWM and UART, RGB LEDs, pulse sensor (SEN-11574), shift registers (74HC595N) as core element of the system. It has six keys used for selecting the defined patterns like square, cube, heart traingle etc.. The application of this project is used for entertainment, display the measured physiological parameter (Heart beat), learning and teaching purpose. It can also help to medical professionals for teaching purpose, defence personals in RADAR and architects.

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7

Garrett, Michael, and Mark McMahon. "Computer-Generated Three-Dimensional Training Environments." International Journal of Gaming and Computer-Mediated Simulations 2, no. 3 (July 2010): 43–60. http://dx.doi.org/10.4018/jgcms.2010070103.

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Problem-based learning is an instructional strategy that emphasises the accumulation and development of knowledge via an active and experiential based approach to solving problems. This pedagogical framework can be instantiated using gaming technology to provide learners with the ability to control their learning experience within a dynamic, responsive, and visually rich three-dimensional virtual environment. In this regard, a conceptual framework referred to as the Simulation, User, and Problem-based Learning (SUPL) approach has been developed in order to inform the design of 3D simulation environments based on gaming technology within a problem-based learning pedagogy. The SUPL approach identifies a series of design factors relative to the user, the problem-solving task, and the 3D simulation environment that guide the learning process and facilitate the transfer of knowledge. This paper will present a simulation environment design according to this conceptual framework for a problem-solving task within the context of an underground mine emergency evacuation. The problem-solving task will be designed to satisfy learning objectives that relate to the development of knowledge and skills for emergency evacuation of the Dominion Mining’s Challenger mining operation located in South Australia.

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8

Yuan, Xin Lei. "Technical Analysis on Three-Dimensional Virtual Learning Community." Advanced Materials Research 557-559 (July 2012): 2029–32. http://dx.doi.org/10.4028/www.scientific.net/amr.557-559.2029.

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Cultivating a sense of community in education has become increasingly popular. The purpose of this study was to analysis the three-dimensional virtual learning community. States of students in virtual community discourse were discussed from the technical view, than the paper sought to explore how media affected relationship between students.

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9

Umetani, Nobuyuki, and Bernd Bickel. "Learning three-dimensional flow for interactive aerodynamic design." ACM Transactions on Graphics 37, no. 4 (August 10, 2018): 1–10. http://dx.doi.org/10.1145/3197517.3201325.

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10

Sinha, Pawan, and Tomaso Poggio. "Role of learning in three-dimensional form perception." Nature 384, no. 6608 (December 1996): 460–63. http://dx.doi.org/10.1038/384460a0.

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11

Jiang, Qiuping, Feng Shao, Gangyi Jiang, Mei Yu, and Zongju Peng. "Three-dimensional visual comfort assessment via preference learning." Journal of Electronic Imaging 24, no. 4 (July 20, 2015): 043002. http://dx.doi.org/10.1117/1.jei.24.4.043002.

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12

Molnár, György, and András Benedek. "Three Dimensional Applications in Teaching and Learning Processes." Procedia - Social and Behavioral Sciences 191 (June 2015): 2667–73. http://dx.doi.org/10.1016/j.sbspro.2015.04.600.

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13

Underwood, Sonia M., Lynmarie A. Posey, Deborah G. Herrington, Justin H. Carmel, and Melanie M. Cooper. "Adapting Assessment Tasks To Support Three-Dimensional Learning." Journal of Chemical Education 95, no. 2 (December 12, 2017): 207–17. http://dx.doi.org/10.1021/acs.jchemed.7b00645.

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14

Tripathi, Bipin K., and Prem K. Kalra. "On the learning machine for three dimensional mapping." Neural Computing and Applications 20, no. 1 (March 5, 2010): 105–11. http://dx.doi.org/10.1007/s00521-010-0350-3.

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15

Langley, Ann, Jean-Louis Denis, and Lise Lamothe. "Process research in healthcare: towards three-dimensional learning." Policy & Politics 31, no. 2 (April 1, 2003): 195–206. http://dx.doi.org/10.1332/030557303765371681.

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16

Bae, Jang Pyo, Siyeop Yoon, Malinda Vania, and Deukhee Lee. "Three Dimensional Microrobot Tracking Using Learning-based System." International Journal of Control, Automation and Systems 18, no. 1 (August 19, 2019): 21–28. http://dx.doi.org/10.1007/s12555-019-0241-z.

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17

Mendez, Jesus, and Mercedes Vila-Alonso. "Three-dimensional sustainability of Kaizen." TQM Journal 30, no. 4 (June 11, 2018): 391–408. http://dx.doi.org/10.1108/tqm-12-2017-0179.

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Purpose The purpose of this paper is to know, from a three-dimensional perspective (operational, emotional and behavioral), the process of “putting down roots” related with the implementation of Kaizen until it becomes sustainable. The research aims to know how this “putting down roots” process is carried out, what transformations occur, what elements are involved and what role they represent in achieving sustainability. Design/methodology/approach For this purpose, a methodology based on the case study has been used, an interpretive approach to reality has been adopted as a paradigm and the Grounded Theory has been applied as an analytical technique. Findings The results suggest the existence of a transformation process that leads to creating new habits, beliefs and feelings, a phenomenon that the authors identify as a three-dimensional learning process (operational, emotional and behavioral). Practical implications This type of learning is perceived as a transition toward an organizational culture that ensures the roots of the Kaizen principles, which is essential for its sustainability and which favors the creation of talent and the well-being of employees, two challenges that the Kaizen of the twenty-first century must face. Originality/value The document includes innovative contributions to the Kaizen sustainability phenomenon, as it is dealt with from a three-dimensional perspective that underlies the inhibitors and enablers known in the current literature.

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18

Kobayashi, Masahiro, Tatsuo Nakajima, Ayako Mori, Daigo Tanaka, Toyomi Fujino, and Hiroaki Chiyokura. "Three-Dimensional Computer Graphics for Surgical Procedure Learning: Web Three-Dimensional Application for Cleft Lip Repair." Cleft Palate-Craniofacial Journal 43, no. 3 (May 2006): 266–71. http://dx.doi.org/10.1597/04-009.1.

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Objective In surgical procedures for cleft lip, surgeons attempt to use various skin incisions and small flaps to achieve a better and more natural shape postoperatively. They must understand the three-dimensional (3D) structure of the lips. However, they may have difficulty learning the surgical procedures precisely from normal textbooks with two-dimensional illustrations. Recent developments in 3D computed tomography (3D-CT) and laser stereolithography have enabled surgeons to visualize the structures of cleft lips from desired viewpoints. However, this method cannot reflect the advantages offered by specific surgical procedures. To solve this problem, we used the benefits offered by 3D computer graphics (3D-CG) and 3D animation. Design and Results By using scanning 3D-CT image data of patients with cleft lips, 3D-CG models of the cleft lips were created. Several animations for surgical procedures such as incision designs, rotation of small skin flaps, and sutures were made. This system can recognize the details of an operation procedure clearly from any viewpoint, which cannot be acquired from the usual textbook illustrations. This animation system can be used for developing new skin-flap design, understanding the operational procedure, and using tools in case presentations. The 3D animations can also be uploaded to the World Wide Web for use in teleconferencing.

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19

Skublewska-Paszkowska, Maria, Pawel Powroznik, and Edyta Lukasik. "Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks." Sensors 20, no. 21 (October 27, 2020): 6094. http://dx.doi.org/10.3390/s20216094.

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Human movement analysis is very often applied to sport, which has seen great achievements in assessing an athlete’s progress, giving further training tips and in movement recognition. In tennis, there are two basic shots: forehand and backhand, which are performed during all matches and training sessions. Recognition of these movements is important in the quantitative analysis of a tennis game. In this paper, the authors propose using Spatial-Temporal Graph Neural Networks (ST-GCN) to challenge the above task. Recognition of the shots is performed on the basis of images obtained from 3D tennis movements (forehands and backhands) recorded by the Vicon motion capture system (Oxford Metrics Ltd, Oxford, UK), where both the player and the racket were recorded. Two methods of putting data into the ST-GCN network were compared: with and without fuzzying of data. The obtained results confirm that the use of fuzzy input graphs for ST-GCNs is a better tool for recognition of forehand and backhand tennis shots relative to graphs without fuzzy input.

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20

Zhong, Chuqian, Zhan Gao, Xu Wang, Shuangyun Shao, and Chenjia Gao. "Structured Light Three-Dimensional Measurement Based on Machine Learning." Sensors 19, no. 14 (July 23, 2019): 3229. http://dx.doi.org/10.3390/s19143229.

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The three-dimensional measurement of structured light is commonly used and has widespread applications in many industries. In this study, machine learning is used for structured light 3D measurement to recover the phase distribution of the measured object by employing two machine learning models. Without phase shift, the measurement operational complexity and computation time decline renders real-time measurement possible. Finally, a grating-based structured light measurement system is constructed, and machine learning is used to recover the phase. The calculated phase of distribution is wrapped in only one dimension and not in two dimensions, as in other methods. The measurement error is observed to be under 1%.

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21

Jasti, Chandana, Hillary Lauren, Robert C. Wallon, and Barbara Hug. "The Bio Bay Game: Three-Dimensional Learning of Biomagnification." American Biology Teacher 78, no. 9 (November 1, 2016): 748–54. http://dx.doi.org/10.1525/abt.2016.78.9.748.

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Pressing concerns about sustainability and the state of the environment amplify the need to teach students about the connections between ecosystem health, toxicology, and human health. Additionally, the Next Generation Science Standards call for three-dimensional science learning, which integrates disciplinary core ideas, scientific practices, and crosscutting concepts. The Bio Bay Game is a way to teach students about the biomagnification of toxicants across trophic levels while engaging them in three-dimensional learning. In the game, the class models the biomagnification of mercury in a simple aquatic food chain as they play the roles of anchovies, tuna, and humans. While playing, the class generates data, which they analyze after the game to graphically visualize the buildup of toxicants. Students also read and discuss two articles that draw connections to a real-world case. The activity ends with students applying their understanding to evaluate the game as a model of biomagnification. Throughout the activity, students practice modeling and data analysis and engage with the crosscutting concepts of patterns and cause and effect to develop an understanding of core ideas about the connections between humans and the environment.

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22

Jin, Ying, Wanqing Zhang, Yang Song, Xiangju Qu, Zhenhua Li, Yunjing Ji, and Anzhi He. "Three-dimensional rapid flame chemiluminescence tomography via deep learning." Optics Express 27, no. 19 (September 13, 2019): 27308. http://dx.doi.org/10.1364/oe.27.027308.

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23

Ghosh, Nirmalya, and Bir Bhanu. "Incremental Unsupervised Three-Dimensional Vehicle Model Learning From Video." IEEE Transactions on Intelligent Transportation Systems 11, no. 2 (June 2010): 423–40. http://dx.doi.org/10.1109/tits.2010.2047500.

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24

Brock, Heike, Felix Law, Kazuhiro Nakadai, and Yuji Nagashima. "Learning Three-dimensional Skeleton Data from Sign Language Video." ACM Transactions on Intelligent Systems and Technology 11, no. 3 (May 13, 2020): 1–24. http://dx.doi.org/10.1145/3377552.

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25

Azumendi, G., C. Comas, I. Alonso, J. R. Herrero, M. Romero, J. Anderica, I. Narbona, A. Calvo, F. Rius, and R. Hidalgo. "OP09.07: Three- and four-dimensional ultrasound: the learning curve." Ultrasound in Obstetrics and Gynecology 28, no. 4 (August 31, 2006): 473. http://dx.doi.org/10.1002/uog.3250.

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26

Wyner, Yael, and Jennifer H. Doherty. "Developing a learning progression for three-dimensional learning of the patterns of evolution." Science Education 101, no. 5 (June 21, 2017): 787–817. http://dx.doi.org/10.1002/sce.21289.

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27

Posamentier, Alfred S., and L. Raphael Patton. "Enhancing Plane Euclidean Geometry with Three-Dimensional Analogs." Mathematics Teacher 102, no. 5 (December 2008): 394–98. http://dx.doi.org/10.5951/mt.102.5.0394.

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Human beings are born with an ability to sense, perceive, and then remember, and as we grow and develop this ability matures through exercise and practice. Most of us can recall learning to see how circles and lines are tangent to one another, how midpoints and perpendiculars partition triangles, and how one might prove conjectures about these. Learning how to see literally, how to make images of the world, and how to perceive the world more completely and accurately is part of that educational process.

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Posamentier, Alfred S., and L. Raphael Patton. "Enhancing Plane Euclidean Geometry with Three-Dimensional Analogs." Mathematics Teacher 102, no. 5 (December 2008): 394–98. http://dx.doi.org/10.5951/mt.102.5.0394.

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Human beings are born with an ability to sense, perceive, and then remember, and as we grow and develop this ability matures through exercise and practice. Most of us can recall learning to see how circles and lines are tangent to one another, how midpoints and perpendiculars partition triangles, and how one might prove conjectures about these. Learning how to see literally, how to make images of the world, and how to perceive the world more completely and accurately is part of that educational process.

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29

Englund, Claire. "Exploring approaches to teaching in three-dimensional virtual worlds." International Journal of Information and Learning Technology 34, no. 2 (March 6, 2017): 140–51. http://dx.doi.org/10.1108/ijilt-12-2016-0058.

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Purpose The purpose of this paper is to explore how teachers’ approaches to teaching and conceptions of teaching and learning with educational technology influence the implementation of three-dimensional virtual worlds (3DVWs) in health care education. Design/methodology/approach Data were collected through thematic interviews with eight teachers to elicit their approaches to teaching in a 3DVW and their conceptions of teaching and learning with technology in online health care education. Findings Results indicate that teaching in 3DVWs necessitates the adoption of a student-centred approach to teaching. The teachers’ underlying approaches to teaching and learning became evident in their student-centred approach and use of problem-based activities. The immersive, social nature of the environment facilitated the creation of authentic, communicative learning activities created by the health care teachers and was in alignment with their disciplinary approaches to teaching and learning. Research limitations/implications The sample size of the study is relatively small which limits the degree of external validity and generalisability of the results. Practical implications If sustainability of 3DVWs is to be achieved, academic development activities for teachers and their communities of practice may be necessary to support conceptual change and facilitate a shift to student-centred teaching where necessary. Originality/value There is limited research concerning the relationship between teachers’ approaches to teaching and the use of educational technologies, in particular the implementation of 3DVWs.

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Zhang, Tao, Jin Xing Niu, Shuo Liu, Tao Tao Pan, and Brij B Gupta. "Three-dimensional Measurement Using Structured Light Based on Deep Learning." Computer Systems Science and Engineering 36, no. 1 (2021): 271–80. http://dx.doi.org/10.32604/csse.2021.014181.

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31

Park, Sohyun, Yumin Kim, Sohyeon Park, and Jung-A. Shin. "The impacts of three-dimensional anatomical atlas on learning anatomy." Anatomy & Cell Biology 52, no. 1 (2019): 76. http://dx.doi.org/10.5115/acb.2019.52.1.76.

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32

Conev, Anja, Eleni E. Litsa, Marissa R. Perez, Mani Diba, Antonios G. Mikos, and Lydia E. Kavraki. "Machine Learning-Guided Three-Dimensional Printing of Tissue Engineering Scaffolds." Tissue Engineering Part A 26, no. 23-24 (December 1, 2020): 1359–68. http://dx.doi.org/10.1089/ten.tea.2020.0191.

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33

Kenig, Tal, Zvi Kam, and Arie Feuer. "Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 12 (December 2010): 2191–204. http://dx.doi.org/10.1109/tpami.2010.45.

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34

Ren, Haoran, Wei Shao, Yi Li, Flora Salim, and Min Gu. "Three-dimensional vectorial holography based on machine learning inverse design." Science Advances 6, no. 16 (April 2020): eaaz4261. http://dx.doi.org/10.1126/sciadv.aaz4261.

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The three-dimensional (3D) vectorial nature of electromagnetic waves of light has not only played a fundamental role in science but also driven disruptive applications in optical display, microscopy, and manipulation. However, conventional optical holography can address only the amplitude and phase information of an optical beam, leaving the 3D vectorial feature of light completely inaccessible. We demonstrate 3D vectorial holography where an arbitrary 3D vectorial field distribution on a wavefront can be precisely reconstructed using the machine learning inverse design based on multilayer perceptron artificial neural networks. This 3D vectorial holography allows the lensless reconstruction of a 3D vectorial holographic image with an ultrawide viewing angle of 94° and a high diffraction efficiency of 78%, necessary for floating displays. The results provide an artificial intelligence–enabled holographic paradigm for harnessing the vectorial nature of light, enabling new machine learning strategies for holographic 3D vectorial fields multiplexing in display and encryption.

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35

Laverty, James T., Sonia M. Underwood, Rebecca L. Matz, Lynmarie A. Posey, Justin H. Carmel, Marcos D. Caballero, Cori L. Fata-Hartley, Diane Ebert-May, Sarah E. Jardeleza, and Melanie M. Cooper. "Characterizing College Science Assessments: The Three-Dimensional Learning Assessment Protocol." PLOS ONE 11, no. 9 (September 8, 2016): e0162333. http://dx.doi.org/10.1371/journal.pone.0162333.

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36

Wu, Qiao, Li Gao, Wei Sun, and Jianzhong Yang. "Detection Method of Three-Dimensional Echocardiography Based on Deep Learning." Wireless Communications and Mobile Computing 2020 (November 25, 2020): 1–6. http://dx.doi.org/10.1155/2020/8886835.

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In order to improve the detection and recognition ability of 3D echocardiography, a method of 3D echocardiography detection based on depth learning is proposed. The information conduction model of three-dimensional echocardiography is constructed. The edge pixel feature matching method is used to extract the key information of echocardiography, and the information compensation method is used to repair the missing area of three-dimensional echocardiography information. The feature decomposition and information fusion of 3D ultrasonic imaging are carried out by using five stage wavelet decomposition method, and the feature reconstruction and adaptive template matching of 3D echocardiography are processed by depth learning algorithm, modeling and detecting the rationality of three-dimensional echocardiography. The simulation results show that this method has better detection performance; the accuracy of detection and recognition is high, which is more reasonable in the application of 3D echocardiography repair and detection recognition.

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37

Du, Yi, Jie Chen, and Ting Zhang. "Reconstruction of Three-Dimensional Porous Media Using Deep Transfer Learning." Geofluids 2020 (December 29, 2020): 1–22. http://dx.doi.org/10.1155/2020/6641642.

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The reconstruction of porous media is widely used in the study of fluid flows and engineering sciences. Some traditional reconstruction methods for porous media use the features extracted from real natural porous media and copy them to realize reconstructions. Currently, as one of the important branches of machine learning methods, the deep transfer learning (DTL) method has shown good performance in extracting features and transferring them to the predicted objects, which can be used for the reconstruction of porous media. Hence, a method for reconstructing porous media is presented by applying DTL to extract features from a training image (TI) of porous media to replace the process of scanning a TI for different patterns as in multiple-point statistical methods. The deep neural network is practically used to extract the complex features from the TI of porous media, and then, a reconstructed result can be obtained by transfer learning through copying these features. The proposed method was evaluated on shale and sandstone samples by comparing multiple-point connectivity functions, variogram curves, permeability, porosity, etc. The experimental results show that the proposed method is of high efficiency while preserving similar features with the target image, shortening reconstruction time, and reducing the burdens on CPU.

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Lim, Joowon, Ahmed B. Ayoub, and Demetri Psaltis. "Three-dimensional tomography of red blood cells using deep learning." Advanced Photonics 2, no. 02 (March 24, 2020): 1. http://dx.doi.org/10.1117/1.ap.2.2.026001.

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Shin, Dong-Hee, Frank Biocca, and Hyunseung Choo. "Exploring the user experience of three-dimensional virtual learning environments." Behaviour & Information Technology 32, no. 2 (February 2013): 203–14. http://dx.doi.org/10.1080/0144929x.2011.606334.

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Lei, Jing, and Qibin Liu. "Three‐dimensional temperature distribution reconstruction using the extreme learning machine." IET Signal Processing 11, no. 4 (June 2017): 406–14. http://dx.doi.org/10.1049/iet-spr.2016.0338.

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41

Bain, Kinsey, Lydia Bender, Paul Bergeron, Marcos D. Caballero, Justin H. Carmel, Erin M. Duffy, Diane Ebert-May, et al. "Characterizing college science instruction: The Three-Dimensional Learning Observation Protocol." PLOS ONE 15, no. 6 (June 16, 2020): e0234640. http://dx.doi.org/10.1371/journal.pone.0234640.

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Li, Zhan, Lu Han, Xiaoping Ouyang, Pan Zhang, Yajing Guo, Dean Liu, and Jianqiang Zhu. "Three-dimensional laser damage positioning by a deep-learning method." Optics Express 28, no. 7 (March 24, 2020): 10165. http://dx.doi.org/10.1364/oe.387987.

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Setiyo, E., Harlin, and Kistiono. "Development of mechanics techniques learning media-based three-dimensional flipbook." Journal of Physics: Conference Series 1446 (January 2020): 012046. http://dx.doi.org/10.1088/1742-6596/1446/1/012046.

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44

Gérard, Philippe, and André Gagalowicz. "Three dimensional model-based tracking using texture learning and matching." Pattern Recognition Letters 21, no. 13-14 (December 2000): 1095–103. http://dx.doi.org/10.1016/s0167-8655(00)00067-2.

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45

Davis, Victoria A., Robert I. Holbrook, and Theresa Burt de Perera. "The influence of locomotory style on three-dimensional spatial learning." Animal Behaviour 142 (August 2018): 39–47. http://dx.doi.org/10.1016/j.anbehav.2018.06.002.

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46

Liu, Jiaolong, Xinmin Dong, Jianping Xue, Zutong Wang, and Zongcheng Liu. "Initial states iterative learning for three-dimensional ballistic endpoint control." Memetic Computing 9, no. 1 (June 15, 2016): 31–41. http://dx.doi.org/10.1007/s12293-016-0197-y.

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47

Peng, Lindsey, Anaya Srivastava, and Christopher M. Yip. "Towards Real-Time Holographic Three-Dimensional Imaging with Machine Learning." Biophysical Journal 114, no. 3 (February 2018): 681a. http://dx.doi.org/10.1016/j.bpj.2017.11.3674.

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Behl, Nicolas G. R., Christine Gnahm, Peter Bachert, Mark E. Ladd, and Armin M. Nagel. "Three-dimensional dictionary-learning reconstruction of 23 Na MRI data." Magnetic Resonance in Medicine 75, no. 4 (May 19, 2015): 1605–16. http://dx.doi.org/10.1002/mrm.25759.

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Chen, Juanjuan, Minhong Wang, Tina A. Grotzer, and Chris Dede. "Using a three-dimensional thinking graph to support inquiry learning." Journal of Research in Science Teaching 55, no. 9 (March 8, 2018): 1239–63. http://dx.doi.org/10.1002/tea.21450.

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Chen, Panqi, Lei Cheng, Ting Zhang, Hangfang Zhao, and Jianlong Li. "Tensor dictionary learning for representing three-dimensional sound speed fields." Journal of the Acoustical Society of America 152, no. 5 (November 2022): 2601–16. http://dx.doi.org/10.1121/10.0015056.

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Abstract:

Ocean sound speed field (SSF) representation is often plagued with low resolution (i.e., the capability of explaining fine-scale fluctuations). This drawback, however, is inherent in a number of classical SSF basis functions, e.g., empirical orthogonal functions, Fourier basis functions, and more recent tensor-based basis functions learned via the higher-order orthogonal iterative algorithm. For two-dimensional depth-time SSF representation, recent attempts relying on dictionary learning have shown that fine-scale sound speed information can be well preserved by a large number of basis functions. They are learned from the historical data without imposing rigid constraints on their shapes, e.g., the orthogonal constraints. However, generalizing the dictionary learning idea to represent three-dimensional (3D) spatial ocean SSF is non-trivial, in terms of both problem formulation and algorithm development. It calls for integrating the dictionary learning framework and the tensor-based basis function learning framework, a recently proposed one that captures the 3D sound speed correlations well. To achieve this goal, we develop a 3D SSF-tailored tensor dictionary learning algorithm that learns a large number of tensor-based basis functions with flexible shapes in a data-driven fashion. Numerical results based on the South China Sea 3D SSF data have showcased the superiority of the proposed approach over the prior method.

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