Study of double hypernuclei with a general scan Kyoto University Department of Physics Toshinao Tsunemi Masashi Hayata(Kyoto Univ.) Ken-ichi Imai (Kyoto.

Slides:



Advertisements
Similar presentations
Spike Based Visual Encoding Activity level (a m ) Visual encoder implemented in the NEF as network of 1024 laterally inhibiting neural columns Network.
Advertisements

COGNITIVE MEMORY HUMAN AND MACHINE
Slides from: Doug Gray, David Poole
Face Recognition: A Convolutional Neural Network Approach
1 Image Classification MSc Image Processing Assignment March 2003.
Chapter 2.
Neural NetworksNN 11 Neural Networks Teacher: Elena Marchiori R4.47 Assistant: Kees Jong S2.22
S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks / Fall 2004 Shreekanth Mandayam ECE Department Rowan University.
Handwritten Character Recognition Using Artificial Neural Networks Shimie Atkins & Daniel Marco Supervisor: Johanan Erez Technion - Israel Institute of.
Connectionist models. Connectionist Models Motivated by Brain rather than Mind –A large number of very simple processing elements –A large number of weighted.
Pattern Recognition using Hebbian Learning and Floating-Gates Certain pattern recognition problems have been shown to be easily solved by Artificial neural.
Autoencoders Mostafa Heidarpour
Study of alpha decays obtained by overall scanning method in nuclear emulsion Physics department K.Nakazawa, J.Yoshida, K.T.Tint, M.K.Soe, S.Kinbara, A.
September 28, 2010Neural Networks Lecture 7: Perceptron Modifications 1 Adaline Schematic Adjust weights i1i1i1i1 i2i2i2i2 inininin …  w 0 + w 1 i 1 +
Hub Queue Size Analyzer Implementing Neural Networks in practice.
Image Compression Using Neural Networks Vishal Agrawal (Y6541) Nandan Dubey (Y6279)
Face Recognition Using Neural Networks Presented By: Hadis Mohseni Leila Taghavi Atefeh Mirsafian.
Traffic Sign Recognition Using Artificial Neural Network Radi Bekker
Convolutional Neural Networks for Image Processing with Applications in Mobile Robotics By, Sruthi Moola.
Review – Backpropagation
1 Introduction to Artificial Neural Networks Andrew L. Nelson Visiting Research Faculty University of South Florida.
Soft Computing Colloquium 2 Selection of neural network, Hybrid neural networks.
Pulsed Neural Networks Neil E. Cotter ECE Department University of Utah.
 The most intelligent device - “Human Brain”.  The machine that revolutionized the whole world – “computer”.  Inefficiencies of the computer has lead.
Explorations in Neural Networks Tianhui Cai Period 3.
Waqas Haider Khan Bangyal. Multi-Layer Perceptron (MLP)
Artificial Intelligence Lecture No. 29 Dr. Asad Ali Safi ​ Assistant Professor, Department of Computer Science, COMSATS Institute of Information Technology.
Building high-level features using large-scale unsupervised learning Anh Nguyen, Bay-yuan Hsu CS290D – Data Mining (Spring 2014) University of California,
Status of automatic scanning and vertex recognition at Toho Univ. September 27, Gyeongsang National University H. Shibuya, S. Ogawa, S. Fukunaga,
Use of Multivariate Analysis (MVA) Technique in Data Analysis Rakshya Khatiwada 08/08/2007.
EAS Reconstruction with Cerenkov Photons Shower Simulation Reconstruction Algorithm Toy MC Study Two Detector Configuration Summary M.Z. Wang and C.C.
Nov Beam Catcher in KOPIO (H. Mikata Kaon mini worksyop1 Beam Catcher in the KOPIO experiment Hideki Morii (Kyoto Univ.) for the KOPIO.
Introduction to Neural Networks Introduction to Neural Networks Applied to OCR and Speech Recognition An actual neuron A crude model of a neuron Computational.
Face Image-Based Gender Recognition Using Complex-Valued Neural Network Instructor :Dr. Dong-Chul Kim Indrani Gorripati.
Quiz Week 8 Topical. Topical Quiz (Section 2) What is the difference between Computer Vision and Computer Graphics What is the difference between Computer.
C - IT Acumens. COMIT Acumens. COM. To demonstrate the use of Neural Networks in the field of Character and Pattern Recognition by simulating a neural.
Scanning systems for double strangeness nuclei in nuclear emulsion Junya Yoshida Hiroki Ito, Shinji Kinbara, Hidetaka Kobayashi, Daisuke Nakashima, Kazuma.
Mu3e Data Acquisition Ideas Dirk Wiedner July /5/20121Dirk Wiedner Mu3e meeting Zurich.
Machine Learning Artificial Neural Networks MPλ ∀ Stergiou Theodoros 1.
Leaves Recognition By Zakir Mohammed Indiana State University Computer Science.
Artificial Neural Networks By: Steve Kidos. Outline Artificial Neural Networks: An Introduction Frank Rosenblatt’s Perceptron Multi-layer Perceptron Dot.
Chapter 13 Artificial Intelligence. Artificial Intelligence – Figure 13.1 The Turing Test.
Speech Recognition through Neural Networks By Mohammad Usman Afzal Mohammad Waseem.
Light Microscope.
CS 6501: 3D Reconstruction and Understanding Convolutional Neural Networks Connelly Barnes.
Deep Learning Amin Sobhani.
Data Mining, Neural Network and Genetic Programming
A Personal Tour of Machine Learning and Its Applications
Data Mining, Neural Network and Genetic Programming
CSE 473 Introduction to Artificial Intelligence Neural Networks
A brief introduction to neural network
Image #1 Image Analysis: What do you think is going on in this picture? Which person, thing, or event does this image relate to (which Word Wall term)?
Prof. Carolina Ruiz Department of Computer Science
شبکه عصبی تنظیم: بهروز نصرالهی-فریده امدادی استاد محترم: سرکار خانم کریمی دانشگاه آزاد اسلامی واحد شهرری.
Image #1 Image Analysis: What do you think is going on in this picture? Which person, thing, or event does this image relate to (which Word Wall term)?
Face Recognition with Neural Networks
network of simple neuron-like computing elements
Neural Networks Chapter 5
Image #1 Image Analysis: What do you think is going on in this picture? Which person, thing, or event does this image relate to (which Word Wall term)?
Field Photos
Zip Codes and Neural Networks: Machine Learning for
Image #1 Image Analysis: What do you think is going on in this picture? Which person, thing, or event does this image relate to (which Word Wall term)?
Face Recognition: A Convolutional Neural Network Approach
CSE (c) S. Tanimoto, 2001 Image Understanding
Image #1 Image Analysis: What do you think is going on in this picture? Which person, thing, or event does this image relate to (which Word Wall term)?
Image #1 Image Analysis: What do you think is going on in this picture? Which person, thing, or event does this image relate to (which Word Wall term)?
Artificial Neural Networks / Spring 2002
Prof. Carolina Ruiz Department of Computer Science
Outline Announcement Neural networks Perceptrons - continued
Presentation transcript:

Study of double hypernuclei with a general scan Kyoto University Department of Physics Toshinao Tsunemi Masashi Hayata(Kyoto Univ.) Ken-ichi Imai (Kyoto Univ) Kazuma Nakazawa(Gifu Univ.) Takaomi Watanabe(Gifu Univ.)

outline KEK-E373 emulsion image scan by human brain Neural network on a computer Simplify the recognized lines Summary

KEK-E373

There are more events NAGARA event 1) Online trigger efficiency 2) Other decay channel (neutral particle) A couple of dozen double hypernuclear events in the emulsions of KEK-E373 If we can establish a general scan, we can find a couple of dozen double hypernuclear events.

New microscope CCD 1 M pixels Shutter 100 Hz LED light Depth of field 5  m

Recognize a line There is a vertex in a double hyper or single hypernuclear event Recognize a line Reconstruct a vertex We are working on recognizing a line now.

Photo from CCD of the microscope How our brain works? 440 pixel 512 pixel

Human brain Picture is cited from

Human brain Picture is cited from ■is related to the first stage of visual sense

line recognition with a neural network on a computer 512 pixel 440 pixel 32 pixel Neural network 14 line types Scan a picture by 32pixel * 32pixel region

input Neural network ( perceptron ) Input layer 32*32 pixel=1024 Hidden layer 16 Output layer 14 Learning with a teacher ( Back Propagation method ) 1213 Learning stage

output Neural network ( perceptron ) Input layer 32*32 pixel=1024 Hidden layer 16 Output layer 14 Learning with a teacher ( Back Propagation method ) image 32px output stage

x160 y360 out: e-005 out: e-007 out: out: out: out: out: e-005 out: e-006 out: e-005 out: out: out: out: out: x240 y120 out: e-005 out: e-008 out: out: out: out: out: out: e-005 out: e-005 out: out: out: e-006 out: out: x376 y248 out: out: out: out: e-006 out: e-005 out: e-006 out: e-006 out: out: out: e-005 out: out: out: out: input Pattern 5 Pattern 9 Pattern

Result of pattern match

simplify Successive lines with same decline are to be a line.

Future plan Reconstruction of lines in 3 dimensional space

summary A couple of dozen hypernuclear events are in the emulsion of the KEK-E373. Method of a general scan is developing.