Neural information systems
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Neural Information Systems. FACEFLOW: Face Recognition System ANSER :Rainfall Estimating System THONN:Date Simulation System Dr. Ming Zhang, Associate Professor Department of Physics, Computer Science & Engineering.

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Neural information systems

Neural Information Systems

FACEFLOW: Face Recognition System

ANSER :Rainfall Estimating System

THONN:Date Simulation System

Dr. Ming Zhang, Associate Professor

Department of Physics, Computer Science & Engineering

Dr. Ming Zhang


Neural information systems

ANSER System Interface


Neural information systems

PT-HONN Data Simulator


Neural information systems

FACEFLOW (1992 - 2002)A computer vision system for recognition of 3-dimensional moving faces using GAT model (neural network Group-based Adaptive tolerance Tree)

  • A$850,000 supported by SITA (Society Internationale de Telecommunications Aeronautiques)

  • A$40,500 supported by Australia Research Council

  • A$78,000 supported by Australia Department of Education.

  • US$160,000 supported by USA National Research Council.


Neural information systems

Why Develop FACEFLOW ?

  • To use new generation computer technique, artificial neural network, for developing information systems.

  • No real world face recognition system is running in the world.

  • Big security market

    • Biometric system

    • ID card identification system

    • Car and house security system


What approved

What Approved

Artificial Neural Network Techniques can :

  • Can recognition one face in the laboratory using less than 1 second

  • Currently can recognition about 1000 faces


Next step

Next Step

  • Rebuild interface for face recognition system

  • Face Detection

    • Lighting

    • Background

    • Make up

  • New neural network models

  • More complicated pattern recognition

  • Build a rear world face recognition System


Microsoft visual c net enterprise version

Microsoft Visual C++. NetEnterprise Version!


Pixelsmart image capture card source codes compiled linked

PixelSmart Image Capture CardSource Codes- Compiled & Linked!


Victor image processing library running in visual c net

Victor Image Processing LibraryRunning in Visual C++.NET !


Neural information systems

Faceflow: Face Model Simulator

Test Different Models!


Brainmaker neural network software the fastest training package

BrainMaker Neural Network Software the Fastest Training Package!


Explorenet neural network software the best interface package

ExploreNet Neural Network SoftwareThe Best Interface Package!


Feret facial image database standard face database

FERET Facial Image DatabaseStandard Face Database!


Research lab in modern building

Research LabIn Modern Building !

We have a pattern recognition lab in the ARC building

We have our own room to do research.

Dr. Ming Zhang


Research topics

Research Topics

  • Neuron Network Group Models

  • GAT Tree Model

    - real time and real world face recognition

  • Neuron-Adaptive Neural Network Models

    - best match real world data

  • Center Of Motion Model - motion center

  • Second Order Vision Model - motion direction

  • NAAT Tree Model - a possible more powerful model for face recognition


Dr ming zhang

Dr. Ming Zhang

  • 11/1999 – 07/2000:

    Senior USA NRC Research Associate

    NOAA,Funding $70,000.

  • 03/1995 – 11/1999: Ph.D. Supervisor

    University of Western Sydney

    Funding: A$203,724 Cash from Fujitsu, ARC, & UWSM

  • 07/1994-03/1995: Ph.D. Supervisor and Lecturer

    Monash University, A$50,000 Grant from Fujitsu)

  • 11/1992-07/1994: Project Manager & P.H.D. Supervisor

    University of Wollongong, (A$850,000 from SITA)

  • 07/1991-10/1992: USA NRC Postdoctoral Fellow

    NOAA, Funding: US$100,000)

  • 07/1989-06/1991: Associate Professor and Postdoctoral Fellow The Chinese Academy of the Sciences. Funding: RMB$2,000,000

Dr. Ming Zhang


Dr ming zhang s publications face recognition

Dr. Ming Zhang’ s Publications(Face Recognition)

1 Journal Papers

1)      Ming Zhang, Rex Gantenbein, Sung Y. Shin, and Chih-Cheng Hung, The application of

artificial neural networks in knowledge-based information systems, International Journal

of Computer and Information Science, Vol 2, No.2, 2001, pp.49 - 58.

2)      Ming Zhang, Jing Chung Zhang, John Fulcher, "Neural network group models for data approximation", International Journal of Neural Systems, Vol. 10, No. 2, April, 2000, pp. 123-142.

3)      Ming Zhang, and John Fulcher, “ Face recognition using artificial neural network group-based adaptive tolerance (GAT) trees”, IEEE Transactions on Neural Networkis, vol. 7, no. 3, pp. 555-567, 1996.

2 Patents

 1)   Ming Zhang, et al, “Translation invariant face recognition using network adaptive tolerance tree”, Australian Patent PM 1828, Oct. 14, 1993.

2) Ming Zhang, Ruli Wang, and Yiming Gong, “Standard nonlinear signal wave generator based on the neural network”, Chinese Patents, No. 90 1 02857.6, May 17, 1990.

Dr. Ming Zhang


Dr ming zhang s publications face recognition1

Dr. Ming Zhang’ s Publications (Face Recognition)

3 Full Refereed Conference Papers

1)      Shuxiang Xu, and Ming Zhang, A Novel Adaptive Activation Function, Accepted by IJCNN’2001 (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp.2779 – 2782.

2)      Ming Zhang, Jing Chung Zhang, John Fulcher, "Neural network group models for data approximation", International Journal of Neural Systems, Vol. 10, No. 2, April, 2000, pp. 123-142.

3)      Ming Zhang, Shuxiang Xu, and Bo Lu, “Neuron-adaptive higher order neural network group models”, in Proceedings of IJCNN’99, Washington, D.C., USA, July 10-16, 1999.

4)      Ming Zhang, Shuxiang Xu, Nigel Bond, and Kate Stevens, “Neuron-adaptive feedforward neural network group models”, in Proceedings of IASTED International Conference on Artificial Intelligence and Soft Computing, Honolulu, Hawaii, USA, August 9-12, 1999, pp.281-284.

5)      John Fulcher, Ming Zhang, “Translation-invariant face recognition using the parellel NAT-tree neural network model”, in Proceedings of Parallel ComputingWorkshop 1997, Canberra, Australia, 25-26 September, 1997, pp. P1-U-1 – P1-U-1-4.

6)      Ming Zhang, John Fulcher, “Face recognition system using NAT tree”, in Proceedings of IASTED International Conference on Artificial Intelligence and Soft Computing, Banff, Canada, July 27 - August 1, 1997, pp. 244-247.

7)      Ming Zhang, and John Fulcher, “Face perspective understanding using artificial neural network group-based tree”, in Proceedings of International Conference on Image Processing, Lausanne, Switzerland, vol III, September 16-19, 1996, pp.475-478.

8) Ming Zhang, and John Fulcher, “Translation invariant face recognition using a network adaptive tolerance tree”, in Proceedings of World Congress On Neural Networks, San Diego, California, USA, September 15 -18, 1996, pp

Dr. Ming Zhang


Dr ming zhang s publications year 2001

Dr. Ming Zhang’ s Publications Year 2001

(1)  Hui Qi, Ming Zhang, and Roderick Scofield, Rainfall Estimation Using M-PHONN Model, Accepted by IJCNN’2001 (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp. 1620 - 1624.

(2)  Ming Zhang, and Roderick Scofield, Rainfall Estimation Using A-PHONN Model, Accepted by IJCNN’2001 (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp. 1583 - 1587.

(3)  Ming Zhang, and BO Lu, Financial Data Simulation Using M-PHONN Model, Accepted by IJCNN’2001 (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp. 1828 - 1832.

(4)  Ming Zhang, Financial Data Simulation Using A-PHONN Model, Accepted by IJCNN’2001 (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp.1823 - 1827.

(5)   Shuxiang Xu, and Ming Zhang, A Novel Adaptive Activation Function, Accepted by IJCNN’2001 (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp.2779 – 2782

(6) Ming Zhang, Rex Gantenbein, Sung Y. Shin, and Chih-Cheng Hung, The application of

artificial neural networks in knowledge-based information systems, International Journal

of Computer and Information Science, Vol 2, No.2, 2001, pp.49 - 58.

(7)   Ming Zhang, Shuxiang Xu, and John Fulcher, Neuron-Adaptive Higher Order Neural Network Models

for Automated Financial Data Modeling”, Accepted by IEEEE transactions on Neural Networks, July,

2001.

Total 102 papers published

Dr. Ming Zhang


Why this project

Why This Project?

  • Visual Studio.NET

  • Image processing library

  • Image capture source codes

  • New generation computer models and techniques

  • Plenty of research topics

  • Good support of software and hardware

  • Strong support from our Department

  • Experienced supervisor

  • Paper to be published in the International Conference

  • Big market

Dr. Ming Zhang


Neural information systems

PT-HONN Data Simulator


Neural information systems

Artificial Neural network expert System for Estimation of Rainfall from the satellite data

ANSER System (1991-2000)

- 1991-1992:US$66,000 suported by USA National Research Council & NOAA

- 1995-1996:A$11,000 suppouted by Australia Research Council& NOAA

- 1999-2000:US$62,000 suported by USA National Research Council & NOAA


Neural information systems

Why Develop ANSER ?

- More than $3.5 billion in property is damaged and, more than 225 people are killed by heavy rain and flooding each year

- No rainfall estimating system in GIS system, No real time and working system of rainfall estimation in the world

- Can ANN be used in the weather forecasting area? If yes, how should we use ANN techniques in this area?


Neural information systems

Why Use Neural Network Techniques ?

- Two Directions of New generation computer

Quamtun Computer

Artificial Neural Network

- Much quicker speed ?

- Complicated pattern recognition?

- Unknown rule knowledge base?

- Self learning reasoning network?

- Super position for multip choice?


Neural information systems

ANSER Rainfall Estimation Result

9th May 2000

Time: 18Z

LAT LAN

Min 37.032 87.906

Max 38.765 88.480

ANSER

Min: 1.47 mm

Max: 6.37mm

NAVY

Min: 2.0mm

Max: 6.0mm


Conclusion what approved

Conclusion- What Approved

Artificial Neural Network Techniques can :

- Much quick speed: 5-10 time quick

- Unknown rule knowledge base: Rainfall

- Reasoning network: rainfall estimation


Conclusion next step

Conclusion- Next Step

- Rebuild interface & retraining neural networks

- New neural netowrk models:

more complicated pattern recognition

- Self expending knowledge base:

attract knowledge from real time cases

- Self learning reasoning network: automatic system to

- Study in advance in 15 years: Artificial Neural Network - one oftwo directions of new generation computer Research


Neural information systems

  • PHONN Simulator (1994 - 1996)

    - Polynomial Higher Order Neural Network financial data simulator

    - A$ 105,000 Supported by Fujitsu, Japan

  • THONN Simulator (1996 - 1998)

    - Trigonometric polynomial Higher Order Neural Network financial data simulator

    - A$ 10,000 Supported by Australia Research Council

  • PT-HONN Simulator (1999 - 2000)

    - Polynomial and Trigonometric polynomial Higher Order Neural Network financial data simulator

    - US$ 46,000 Supported by USA National Research Council


Neural information systems

PT-HONN Data Simulator


Neural information systems

Why Develop HONN ?

  • No system can automatically simulate discontinue, unsmooth data very well

  • No system can automatically find the perfect models for the discontinue, unsmooth data


Cloud merge using ann circle operator

Cloud Merge Using ANN Circle Operator


Conclusion what approved1

CONCLUSION- What Approved

  • The results of the comparative experiments show that THONG system is able to simulate higher frequency and higher order non-linear data, as well as being able to simulate discontinuous data.

  • The THONG model can not only be used for financial simulation, but also for financial prediction.

  • Complicated pattern recognition: cloud merger


Conclusion next step1

Conclusion- Next Step

- Rebuild interface & retraining neural networks

- New neural network models:

more complicated pattern recognition

  • Financial data simulation experiments

  • Rainfall data simulation experiments


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