Call for Papers: IEEE Transactions on Smart Grid

CALL FOR PAPERS: IEEE TRANSACTIONS ON SMART GRID
Special Issue on Computational Intelligence Applications in Smart Grids

Computational Intelligence (CI) evolves computational models and tools of intelligence capable of handling large raw numerical sensory data directly, processing them by exploiting the representational parallelism and pipelining the problem, generating reliable and just-in-time responses, with high fault tolerance. Smart grid is basically the embedding of intelligence to enable bi-directional power flows between sources of electric power generation (traditional and renewable sources), and smart devices (traditional loads, energy storage, etc), within some specified constraints and performance requirements. CI deployment is essential for smart grids to be viable. The objective of this special issue is to address and disseminate state-of-the-art research and development in the applications of computational intelligence in smart grids. Authors are invited to submit their original and unpublished contributions to this special issue with emphasis on CI paradigms and their applicat! ! ions in smart grids, including, but not limited to:

- Adaptive dynamic programming
- Artificial immune systems
- Evolutionary computation
- Fuzzy Systems
- Neural Networks
- Reinforcement Learning
- Swarm Intelligence
- CI Algorithms for modeling, control and optimization
- Communication and control
- Cyber security
- Demand response and Demand side management
- Distributed energy resources
- Emission reductions
- Forecasting (loads and sources)
- Markets and economics
- Methods and algorithms for real-time analysis
- Optimization, Placements and Scheduling
- Optimal Power Flow
- Planning, operation and control
- Plug-in vehicles (G2V and V2G)
- Renewable energy (wind and solar)
- Smart micro-grids
- Smart sensing, sense-making and Situational Awareness
- Synchrophasors and state estimation
- Wide area monitoring, control and protection
- Visualizations for control centers

Submission Guidelines
Two page extended abstracts are solicited for the first round of reviews. Authors of selected abstracts will be invited to submit the full papers in the second round. Authors must refer to the IEEE Transactions on Smart Grid author guidelines at http://www.ieee-pes.org/publications/information-for-authors for information on content and formatting of submissions. The direct link to the Manuscript Central for the submission of papers is http://mc.manuscriptcentral.com/tsg-pes. In the Manuscript type drop-down menu box, the author must choose Special Issue on CIASG. For information purposes, please submit a PDF version of the abstracts including a cover letter with authors and contact information via e-mail to gkumar@ieee.org with the subject line "Special Issue on CIASG" by the submission date.

Important Dates
August 31, 2011: Deadline for the submission of extended abstract
Oct. 15, 2011: Completion for first-round of reviews
Dec. 15, 2011: Deadline for full paper submission
June 15, 2012: Final notification of authors

Guest Editorial Board
Guest Editor-in-Chief:
Ganesh Kumar Venayagamoorthy, Missouri University of Science and Technology, USA

Editors:
Jung-Wook Park, Yonsei University, Korea
Haibo He, University of Rhode Island, USA
Komla Folly, University of Cape Town, South Africa

Editor-in-Chief of IEEE Transactions on Smart Grid
Mohammad Shahidehpour, Illinois Institute of Technology, USA

IEEE Transactions on Neural Networks; Volume 22, Issue 6, June 2011

The following articles appear in the latest issue of IEEE Transactions on
Neural Networks; Volume 22, Issue 6, June 2011.

The articles can be retrieved on IEEE Xplore:
http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=5779942
or directly by clicking the individual paper URL below.

Volume 22, Issue 6, June 2011

1. Title: Causality Analysis of Neural Connectivity: Critical Examination of Existing Methods and Advances of New Methods
Authors: Sanqing Hu; Guojun Dai; Gregory A. Worrell; Qionghai Dai; Hualou Liang
Page(s): 829-844
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5751700

2. Title: Discriminant Independent Component Analysis
Authors: Chandra Shekhar Dhir; Soo-Young Lee
Page(s): 845-857
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5756242

3. Title: Implementation Study of an Analog Spiking Neural Network for Assisting Cardiac Delay Prediction in a Cardiac Resynchronization Therapy Device
Authors: Qing Sun; Francois Schwartz; Jacques Michel; Yannick Herve; Renzo Dalmolin
Page(s): 858-869
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5756693

4. Title: Kernel Map Compression for Speeding the Execution of Kernel-Based Methods
Authors: Omar Arif; Patricio A. Vela
Page(s): 870-879
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5762616

5. Title: A New Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation
Authors: Yuli Chen; Sung-Kee Park; Yide Ma; Rajeshkanna Ala
Page(s): 880-892
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5762617

6. Title: Adaptive Learning Control for Finite Interval Tracking Based on Constructive Function Approximation and Wavelet
Authors: Jian-Xin Xu; Rui Yan
Page(s): 893-905
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5764839

7. Title: Transformation Invariant On-Line Target Recognition
Authors: Khan M. Iftekharuddin
Page(s): 906-918
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5766756

8. Title: Analyzing the Scaling of Connectivity in Neuromorphic Hardware and in Models of Neural Networks
Authors: Johannes Partzsch; Rene Schuffny
Page(s): 919-935
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5765695

9. Title: Practical Training Framework for Fitting a Function and Its Derivatives
Authors: Arjpolson Pukrittayakamee; Martin Hagan; Lionel Raff; Satish T. S. Bukkapatnam; Ranga Komanduri
Page(s): 936-947
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5768082

10. Title: Observability of Boolean Control Networks With State Time Delays
Authors: Fangfei Li; Jitao Sun; Qi-Di Wu
Page(s): 948-954
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5754601

11. Title: Feature Selection Using Probabilistic Prediction of Support Vector Regression
Authors: Jian-Bo Yang; Chong-Jin Ong
Page(s): 954-962
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5762619

12. Title: Improvements on Twin Support Vector Machines
Authors: Yuan-Hai Shao; Chun-Hua Zhang; Xiao-Bo Wang; Nai-Yang Deng
Page(s): 962-968
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5762620

13. Title: Hyperellipsoidal Statistical Classifications in a Reproducing Kernel Hilbert Space
Authors: Xun Liang; Zhihao Ni
Page(s): 968-975
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5762618

14. Title: Stability and Dissipativity Analysis of Distributed Delay Cellular Neural Networks
Authors: Zhiguang Feng; James Lam
Page(s): 976-981
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5764837

15. Title: Efficient Algorithm for Training Interpolation RBF Networks With Equally Spaced Nodes
Authors: Hoang Xuan Huan; Dang Thi Thu Hien; Huynh Huu Tue
Page(s): 982-988
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5764838

16. Title: Embedded Feature Ranking for Ensemble MLP Classifiers
Authors: Terry Windeatt; Rakkrit Duangsoithong; Raymond Smith
Page(s): 988-994
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5771118

Call for Participations: Tutorial on Conformal Predictions

Call for Participation: A Tutorial on "Conformal Predictions for Reliable Machine Learning: Theory and Applications"

Sunday, Jul 31, 2011 (IJCNN 2011)
San Jose, CA, 1:30 - 3:30 pm

Monday, Aug 8, 2011 (AAAI 2011)
San Francisco, CA, 2:00 - 6:00 pm

http://www.public.asu.edu/~vnallure/conformalpredictions/index.html

The Conformal Predictions framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This theory is based on the relationship derived between transductive inference and the randomness deficiency of an i.i.d. (identically independently distributed) sequence of data instances. One of the desirable features of this framework is the calibration of the obtained confidence values in an online setting. While probability/confidence values generated by existing approaches can often be unreliable and difficult to interpret, the theory behind the CP framework guarantees that the confidence values obtained using this transductive inference framework manifest as the actual error frequencies in the online setting i.e. they are well-calibrated. Further, this framework can be applied across all existing classification and regression methods (such as neural networks, Support Vector Machines, k-Nearest Neig! hbors, ridge regression, etc), thus making it a very generalizable approach.

Over the last few years, there has been a growing interest in applying this framework to real-world problems such as clinical decision support, medical diagnosis, sea surveillance, network traffic classification, and face recognition. The promising results have generated in further extensions of the framework to problem settings beyond just classification or regression. The framework has now been extended towards newer settings such as active learning, model selection, feature selection, change detection, outlier detection, and anomaly detection.

The key objectives of this tutorial are:
- to expose the audience to the basic theory of the Conformal Predictions framework
- to demonstrate examples of how the framework can be applied in real-world problems (including code simulations), and
- to provide sample adaptations of the framework to related machine learning problems such as active learning, transfer learning, anomaly detection and model selection, illustrating the potential of the framework in machine learning applications.

Presenters:
Vineeth N Balasubramanian, Arizona State University
Shen-Shyang Ho, University of Maryland

Co-organizers:
Sethuraman Panchanathan, Arizona State University
Vladimir Vovk, Royal Holloway University of London

Please see the website (http://www.public.asu.edu/~vnallure/conformalpredictions/index.html) for more details. We look forward to your participation in the tutorial at IJCNN or AAAI and sincerely hope that by the end of the tutorial you'll be able to utilize the framework in your future research.

Call for Participations: Tutorial on Conformal Predictions

Call for Participation: A Tutorial on "Conformal Predictions for Reliable Machine Learning: Theory and Applications"

Sunday, Jul 31, 2011 (IJCNN 2011)
San Jose, CA, 1:30 - 3:30 pm

Monday, Aug 8, 2011 (AAAI 2011)
San Francisco, CA, 2:00 - 6:00 pm

http://www.public.asu.edu/~vnallure/conformalpredictions/index.html

The Conformal Predictions framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This theory is based on the relationship derived between transductive inference and the randomness deficiency of an i.i.d. (identically independently distributed) sequence of data instances. One of the desirable features of this framework is the calibration of the obtained confidence values in an online setting. While probability/confidence values generated by existing approaches can often be unreliable and difficult to interpret, the theory behind the CP framework guarantees that the confidence values obtained using this transductive inference framework manifest as the actual error frequencies in the online setting i.e. they are well-calibrated. Further, this framework can be applied across all existing classification and regression methods (such as neural networks, Support Vector Machines, k-Nearest Neig! hbors, ridge regression, etc), thus making it a very generalizable approach.

Over the last few years, there has been a growing interest in applying this framework to real-world problems such as clinical decision support, medical diagnosis, sea surveillance, network traffic classification, and face recognition. The promising results have generated in further extensions of the framework to problem settings beyond just classification or regression. The framework has now been extended towards newer settings such as active learning, model selection, feature selection, change detection, outlier detection, and anomaly detection.

The key objectives of this tutorial are:
- to expose the audience to the basic theory of the Conformal Predictions framework
- to demonstrate examples of how the framework can be applied in real-world problems (including code simulations), and
- to provide sample adaptations of the framework to related machine learning problems such as active learning, transfer learning, anomaly detection and model selection, illustrating the potential of the framework in machine learning applications.

Presenters:
Vineeth N Balasubramanian, Arizona State University
Shen-Shyang Ho, University of Maryland

Co-organizers:
Sethuraman Panchanathan, Arizona State University
Vladimir Vovk, Royal Holloway University of London

Please see the website (http://www.public.asu.edu/~vnallure/conformalpredictions/index.html) for more details. We look forward to your participation in the tutorial at IJCNN or AAAI and sincerely hope that by the end of the tutorial you'll be able to utilize the framework in your future research.

Call for Participations: International Joint Conference on Neural Networks (IJCNN 2011)

INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN 2011)
San Jose, California, July 31 - August 5, 2011

http://www.ijcnn2011.org

The 2011 International Joint Conference on Neural Networks will be held at the Doubletree Hotel
in San Jose, California, July 31-August 5, 2011. It is sponsored jointly by the International Neural
Network Society and the IEEE Computational Intelligence Society.

FINAL PROGRAM AVAILABLE
The final program for the 2011 International Joint Conference on Neural Networks is now available at
http://www.ijcnn2011.org

REGISTRATION:
To register, please go to:
http://www.ijcnn2011.org/registration.php
This link also provides information on how to request letters of invitation for visa applications.

HOTEL RESERVATIONS:
Hotel reservations at the special conference rate can be made though the following link:
http://www.ijcnn2011.org/accommodation.php
Please make your reservations as soon as possible to get the special rate.

The conference will feature oral and poster presentations, panels, tutorials and workshops on many
topic, including the following:
* Neural network theory & models.
* Collective intelligence.
* Computational neuroscience.
* Pattern recognition.
* Cognitive models.
* Machine vision.
* Brain-machine interfaces.
* Hybrid systems.
* Embodied robotics.
* Self-aware systems.
* Evolutionary neural systems.
* Data mining.
* Neurodynamics.
* Sensor networks.
* Neuroinformatics.
* Agent-based systems.
* Neuroengineering.
* Computational biology.
* Neural hardware.
* Bioinformatics
* Neural network applications.
* Artificial life.

The program includes several special sessions on topics of current prominence, with special tracks
on autonomous learning systems, neuromorphic hardware & memristors, and smart grid applications.

In addition to these, IJCNN 2011 will feature the following special events:
Plenary talks by:
Michael Arbib University of Southern California
Leon Glass McGill University
Dharmendra Modha IBM Almaden Research Center
Andrew Ng Stanford University
Stefan Schaal University of Southern California
Juergen Schmidhuber IDSIA, Switzerland

David Rumelhart Memorial Session
On Wednesday, August 3, IJCNN 2011 will feature a special plenary session honoring the life and work of the
late David Rumelhart.
Speaker: Michael I. Jordan (University of California, Berkeley)

Featured Plenary Session: The Emergence of Mind
On Thursday, August 4, IJCNN 2011 will feature a special plenary session focusing on how higher mental functions
emerge from the neural substrate of the brain.
Speakers: Bernard J. Baars (The Neurosciences Institute)
Walter J. Freeman (University of California, Berkeley)
Stephen Grossberg (Boston University)
Details at: http://www.ijcnn2011.org/plenaries.php

NSF-Sponsored Special Symposium: From Brains to Machines
On Tuesday, August 2, IJCNN 2011 will feature a special day-long symposium sponsored by the National Science
Foundation, with speakers on topics from cognitive neuroscience, brain-machine interfaces, cognitive computing
and neuromorphic hardware. Speakers include:
Michael Arbib (University of Southern California)
Ted Berger (University of Southern California)
Jose Carmena (Uiversity of California, Berkeley)
Adam Gazzaley (Uiversity of California, San Francisco),
Dileep George (Vicarious Systems)
Cheryl Grady (Rotman Research Institute)
Michel Maharbiz (University of California Berkeley)
Vinod Menon (Stanford University)
Dharmendra Modha (IBM Almaden)
Jennie Si (Arizona State University)
Details at: http://www.ijcnn2011.org/nsfsymposium.php

For information on special sessions, tutorials, workshops, panels and competitions, please use the appropriate
link at http://www.ijcnn2011.org for further information.

We look forward to seeing you in San Jose for an exciting conference!

General Chair: Ali A. Minai (University of Cincinnati)
Program Chair: Hava Siegelmann (University of Massachusetts Amherst)
Technical Co-Chairs: Michael Georgiopoulos (University of Central Florida)
Cesare Alippi (Politecnico di Milano)

CIS Transactions got very positively commented by the review committee

Four CIS Transactions, including IEEE Transactions on Neural Networks, on
Fuzzy Systems, on Evolutionary Computation, and on Autonomous Mental
Development, went through the IEEE five year review process recently (two
years for TAMD). All four Transactions were very positively commented by
the review committee on their high quality. I would like to take this
opportunity to thank all of you as readers, authors, reviewers and editors
of the Transactions for your continued support. No good journals can
survive without you. Please keep submitting your best papers to the best
journals in the fields -- our Transactions, and enjoy reading the papers
too. For more information about CIS's publications, visit the Publications
page at http://www.ieee-cis.org/

Xin Yao
Vice President for Publications
IEEE Computational Intelligence Society

CIS 2011 Awards

2011 CIS Fuzzy Systems Pioneer Award:
Piero Bonissone
GE Global Research
Computing and Decision Sciences
"For contributions to the representation and management of uncertainty in intelligent systems"

2011 CIS Fuzzy Systems Pioneer Award:
Abraham Kandel
Department of Computer Science and Engineering
The University of South Florida
"For theoretical and practical contributions to fuzzy switching systems and automata"

2011 CIS Evolutionary Computation Pioneer Award:
Russell C. Eberhart and James Kennedy
Department of Electrical and Computer Engineering
Indiana University Purdue University Indianapolis
and
Bureau of Labor Statistics
Washington, DC
"For the invention of particle swarm optimization"

2011 CIS Evolutionary Computation Pioneer Award:
J. David Schaffer
Philips Research
Briarcliff Manor
"For contributions to multi-objective optimization and to the fundamental understanding of genetic algorithms"

2011 CIS Meritorious Service Award:
Bernadette Bouchon-Meunier
University of Pierre and Marie Curie
Computer Science Laboratory of the University Paris 6 (LiP6)
"For long-time service and dedication to the Society"

2011 Outstanding Chapter Award:
The Singapore Chapter

2011 Outstanding Organization Award:
The European Centre for Soft Computing

2011 Outstanding Dissertation Award:
Dr. Dongrui Wu for
"Intelligent Systems for Decision Support", March 2009, The University of Southern California

2011 Outstanding Early Career Award:
Dr. Kay Chen Tan
Department of Electrical and Computer Engineering
National University of Singapore
"For contributions to evolutionary computation in multi-objective optimization"

2009 IEEE Transactions on Evolutionary Computation Outstanding Paper Award (bestowed in 2011):
A. K. Qin, V. L. Huang and P. N. Suganthan, "Differential Evolution Algorithm with Strategy Adaptation for Global Numerical Optimization", IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, April 2009, pp. 398-417.

2009 IEEE Transactions on Evolutionary Computation Outstanding Paper Award (bestowed in 2011):
AQ. C. Nguyen, Y. S. Ong and M. H. Lim , "Probabilistic Memetic Framework" IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3, pp. 604-623, June 2009

2009 IEEE Transactions on Neural Networks Outstanding Paper Award (bestowed in 2011):
H. Chen, P. Tino, X. Yao, "Probabilistic Classification Vector Machines", IEEE Transactions on Neural Networks, vol. 20, no. 6, pp. 901-914, June 2009.