In-water Algorithm (Neural Network) Motoaki Kishino ( RIKEN ) 8 - PowerPoint PPT Presentation

Sep 21, 2022 •163 likes •314 views

In-water Algorithm (Neural Network) Motoaki Kishino ( RIKEN ) 8 November 2000 Kanazawa PI and Co-i Roland Doerffer Motoaki Kishino Helmut Schiller Tomohiko Oishi Heinz van de Piepen Hiroh Satoh Carsten Brockmann

In-water Algorithm (Neural Network) Motoaki Kishino ( RIKEN ) 8 November 2000 Kanazawa
PI and Co-i � Roland Doerffer � Motoaki Kishino � Helmut Schiller � Tomohiko Oishi � Heinz van de Piepen � Hiroh Satoh � Carsten Brockmann � Kouji Suzuki � J. H. M. Hakvoort � Tooru Hirawake � Herbert Siegel
NN-Training
Radiative Transfer Model = = = = × × × × L E R n w s rs R = = = = × × × × R 0 . 533 rs Q − − − − k a = = = = t R Joseph (1950) + + + + k a t = = = = + + + + k a ( a 2 b ) t t bt
Model of Optical Properties Absorption Coefficient = = = = + + + + + + + + a a a a t w c y a : Pope and Fry (1997) w = = × × = = × × * a a C c c * a : average values of the sea truth c (off Sanriku, Japan, Kishino) − − − − λ λ λ λ − − − − = = = = × × × × 0 . 014 ( 440 ) a CDOM e Roesler et al. (1989) y
Model of Optical Properties Backscattering Coefficient = = = = + + + + + + + + b b b b b b b b t w c s − − − −  λ λ λ λ 4 . 32        = = = =         b 0 . 00144 * Morel (1974) b         w 500         550         = = = = + + + + − − − − 0 . 62 b 0 . 30 * C * ( 0 . 002 0 . 02 * ( 0 . 5 0 . 25 * log C )) *         λ λ 10 λ λ bc Gordon and Morel (1983) Morel (1988) − − − −  λ λ λ λ 0 . 812        = = = =         b bs S 0 . 001848 * *         550 Kronfeld (1988)
Parameters and their range for the Neural Network training Parameter Unit Range mg m -3 Chlorophyll a 0.1 - 50 Inorganic Suspension g m -3 0.1 - 50 CDOM m -1 0.01 - 10 Data Set for neural network training 100,000 Data set for validation 50,000
Neural Network example for OCTS data : unit : link nLw(412) nLw(443) chlorophyll a nLw(490) suspended matter nLw(520) yellow substance nLw(565) input layer hidden layer output layer
R M S All Bands 11 Bands Chl a IOS CDOM 0%: 0.00178 0.00189 0.000168 10%: 0.0302 0.0569 0.00312 20%: 0.0520 0.141 0.00601 Except Saturation Bands 9 Bands Chl a IOS CDOM 0%: 0.00473 0.00221 0.000212 10%: 0.0218 0.135 0.00828 20%: 0.0577 0.201 0.0207

Recommend

IMPLEMENTATION OF A PARALLEL BATCH TRAINING ALGORITHM FOR DEEP NEURAL NETWORK YUPING LIN IFLYTEK

IMPLEMENTATION OF A PARALLEL BATCH TRAINING ALGORITHM FOR DEEP NEURAL NETWORK YUPING LIN IFLYTEK LABORATORY FOR NEURAL COMPUTING FOR MACHINE LEARNING DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE YORK UNIVERSITY, TORONTO NOVEMBER

212 views • 20 slides

Longstaff Schwartz algorithm and Neural Network regression J er ome Lelong (joint work with

Introduction The Longstaff Schwartz algorithm Numerical experiments Longstaff Schwartz algorithm and Neural Network regression J er ome Lelong (joint work with B. Lapeyre) Univ. Grenoble Alpes Advances in Financial Mathematics 2020 J.

367 views • 23 slides

A Text Alignment Algorithm Based on Prediction of Obfuscation Types Using SVM Neural Network

Persian Plagdet 2016 A Text Alignment Algorithm Based on Prediction of Obfuscation Types Using SVM Neural Network Fatemeh Mashhadirajab, Mehrnoush Shamsfard NLP Research Lab, Faculty of Computer Science and Engineering, Shahid Beheshti

570 views • 31 slides

Perceptron Algorithm An aside: a hyperplane is a perceptron. (single layer neural network, do you

Perceptron Algorithm An aside: a hyperplane is a perceptron. (single layer neural network, do you see? Linear programming!) Labelled points with x 1 ,..., x n . Alg: Given x 1 ,..., x n . Hyperplane separator. + Let w 1 = x 1 .

322 views • 3 slides

Feed Forward Neural Network with Genetic Algorithm Problem definition and training data

Feed Forward Neural Network with Genetic Algorithm Problem definition and training data selection Z = f(x,y) = x*y x and y varied from 0 to 1 in intervals of 0.01 0<=x<=1 z = f(x,y) will have 100x100 grid

186 views • 8 slides

Neural Information Retrieval Wassila Lalouani 1 Plan Neural network architectures Neural

Neural Information Retrieval Wassila Lalouani 1 Plan Neural network architectures Neural IR tasks Neural IR architecture Feature Representations Neural IR query auto completion Neural IR query suggestion Neural IR document

1.48k views • 18 slides

Artificial Neural Networks By: Kodi Neumiller Overview What is an artificial neural network

Artificial Neural Networks By: Kodi Neumiller Overview What is an artificial neural network Perceptron Feed forward Backpropagation Learning rate/momentum What is a neural network? Artificial Human Neurons Each

889 views • 12 slides

Neural Network Optimization 1 CS 519: Deep Learning, Winter 2018 Fuxin Li With materials from

Neural Network Optimization 1 CS 519: Deep Learning, Winter 2018 Fuxin Li With materials from Zsolt Kira Backpropagation learning of a network The algorithm 1. Compute a forward pass on the compute graph (DAG) from the input to all the

407 views • 16 slides

A Memetic Algorithm for Water Distribution Network Design R. Baos* , C. Gil, J. I. Agulleiro,

A Memetic Algorithm for Water Distribution Network Design R. Baos* , C. Gil*, J. I. Agulleiro*, J. Reca * Dpt. Computer Architecture and Electronics, University of Almera (Spain) Dpt. Rural Engineering, University of Almera (Spain)

207 views • 17 slides

Same, Same But Different Recovering Neural Network Quantization Error Through Weight

Same, Same But Different Recovering Neural Network Quantization Error Through Weight Factorization Eldad Meller ICML 2019 Neural Network Quantization Quantization of Neural Networks is needed for efficient inference Quantization adds

482 views • 9 slides

Deep Learning Primer Nishith Khandwala Neural Networks Overview Neural Network Basics

Deep Learning Primer Nishith Khandwala Neural Networks Overview Neural Network Basics Activation Functions Stochastic Gradient Descent (SGD) Regularization (Dropout) Training Tips and Tricks Neural Network (NN)

828 views • 45 slides

Graph Neural Network Fang Yuanqiang, 2019/05/18 Graph Neural Network Why GNN? Preliminary

Graph Neural Network Fang Yuanqiang, 2019/05/18 Graph Neural Network Why GNN? Preliminary Fixed graph Vanilla Spectral Graph ConvNets ChebyNet GCN CayleyNet Multiple graphs Variable graph

817 views • 46 slides

Neural network applications ALVINN (Pomerleau, mid 1990s) Autonomous Land Vehicle in Neural

Neural network applications ALVINN (Pomerleau, mid 1990s) Autonomous Land Vehicle in Neural Network To date: Sharp Straight Sharp Left Ahead Right Neural networks: what are they 30 Output Units Backpropagation: efficient

346 views • 15 slides

Neural Networks for Machine Learning Lecture 2a An overview of the main types of neural network

Neural Networks for Machine Learning Lecture 2a An overview of the main types of neural network architecture Geoffrey Hinton with Nitish Srivastava Kevin Swersky Feed-forward neural networks These are the commonest type of neural

534 views • 32 slides

1 A Link-State Routing Algorithm Dijsktras Algorithm Dijkstras algorithm Notation: 5 1

Interplay between routing and Network Layer forwarding Goals: Overview: understand principles last time routing algorithm behind network layer network layer services services: virtual circuit and datagram local forwarding

450 views • 8 slides

1 A Link-State Routing Algorithm Dijsktras Algorithm Dijkstras algorithm Notation: 5 1

762 views • 7 slides

E9 205: Machine Learning for Signal Processing Introduction to 16-10-2019 Neural Network Models

E9 205: Machine Learning for Signal Processing Introduction to 16-10-2019 Neural Network Models Perceptron Algorithm Perceptron Model [McCulloch, 1943, Rosenblatt, 1957] Targets are binary classes [-1,1] What if the data is not linearly

420 views • 9 slides

Similarity of Neural Network Representations Revisited Simon Kornblith, Mohammad Norouzi,

Similarity of Neural Network Representations Revisited Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geofgrey Hinton Motivation We need tools to understand trained neural networks Neural network training involves interactions between an

588 views • 16 slides

Lecture 2: Deeper Neural Network 1 Objective In the second lecture, you will see How to

Lecture 2: Deeper Neural Network 1 Objective In the second lecture, you will see How to deepen your neural network to learn more complex functions Several techniques to fasten your learning process The most popular artificial

423 views • 40 slides

Neural network architectures for image captioning By Emily Kern Given a set of images and

Neural network architectures for image captioning By Emily Kern Given a set of images and accompanying human-generated captions, can we train a neural network to predict captions for new images? What is a neural network? A computer system

523 views • 16 slides

Unsupervised clustering with growing self-organizing neural network A comparison with non-neural

Unsupervised clustering with growing self-organizing neural network A comparison with non-neural approach Martin Hynar, Michal Burda, Jana Sarmanov a Department of Computer Science, V SB Technical University of Ostrava, 17.

473 views • 24 slides

DeepVision Fall Quarter Design Review Purpose We aim to build a Deep Neural Network Image

DeepVision Fall Quarter Design Review Purpose We aim to build a Deep Neural Network Image Recognition and Detection algorithm on a embedded GPU device Attending 2018 DAC Contest Sponsored and collaborated with Prof. Xie and SEAL LAB Finally

422 views • 20 slides

Local Representation Alignment: A Biologically Motivated Algorithm for Training Neural Systems

Local Representation Alignment: A Biologically Motivated Algorithm for Training Neural Systems Alexander G. Ororbia II The Neural Adaptive Computing (NAC) Laboratory Rochester Institute of Technology 1 Collaborators The Pennsylvania State

849 views • 65 slides

Propagating Error Backward Hyperparameters for Neural Networks } Multi-layer (deep) neural

Learning in Neural Networks w 1,3 w 3,5 1 3 5 w w 1,4 3,6 w w 2,3 4,5 2 4 6 w w 2,4 4,6 Class #18: Back-Propagation; } A neural network can learn a classification function by Tuning Hyper-Parameters adjusting its weights to

259 views • 4 slides