Friday, 9 August 2019

IEEE Transactions on Neural Networks and Learning Systems, Volume 30, Issue 8, August 2019.

1. Dynamic Feature Acquisition Using Denoising Autoencoders
Author(s): Mohammad Kachuee; Sajad Darabi; Babak Moatamed; Majid Sarrafzadeh
Pages: 2252 - 2262

2. A Novel and Safe Two-Stage Screening Method for Support Vector Machine
Author(s): Xianli Pan; Yitian Xu
Pages: 2263 - 2274

3. Self-Tuned Discrimination-Aware Method for Unsupervised Feature Selection
Author(s): Xuelong Li; Mulin Chen; Qi Wang
Pages: 2275 - 2284

4. Fast-Time Stability of Temporal Boolean Networks
Author(s): Bowen Li; Jianquan Lu; Jie Zhong; Yang Liu
Pages: 2285 - 2294

5. A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification
Author(s): Yanan Sun; Bing Xue; Mengjie Zhang; Gary G. Yen
Pages: 2295 - 2309

6. Reconstructing Perceived Images From Human Brain Activities With Bayesian Deep Multiview Learning
Author(s): Changde Du; Changying Du; Lijie Huang; Huiguang He
Pages: 2310 - 2323

7. Integrated Sliding Mode Control and Neural Networks Based Packet Disordering Prediction for Nonlinear Networked Control Systems
Author(s): Bosen Lian; Qingling Zhang; Jinna Li
Pages: 2324 - 2335

8. Asynchronous Multiplex Communication Channels in 2-D Neural Network With Fluctuating Characteristics
Author(s): Shinichi Tamura; Yoshi Nishitani; Chie Hosokawa; Yuko Mizuno-Matsumoto
Pages: 2336 - 2345

9. Zeroing Neural Network for Solving Time-Varying Linear Equation and Inequality Systems
Author(s): Feng Xu; Zexin Li; Zhuoyun Nie; Hui Shao; Dongsheng Guo
Pages: 2346 - 2357

10. Global Synchronization of Coupled Fractional-Order Recurrent Neural Networks
Author(s): Peng Liu; Zhigang Zeng; Jun Wang
Pages: 2358 - 2368

11. Stochastic Graphlet Embedding
Author(s): Anjan Dutta; Hichem Sahbi
Pages: 2369 - 2382

12. Identification of the Structure of a Probabilistic Boolean Network From Samples Including Frequencies of Outcomes
Author(s): Tatsuya Akutsu; Avraham A. Melkman
Pages: 2383 - 2396

13. Two Birds With One Stone: A Coupled Poisson Deconvolution for Detecting and Describing Topics From Multimodal Web Data
Author(s): Junbiao Pang; Fei Tao; Qingming Huang; Qi Tian; Baocai Yin
Pages: 2397 - 2409

14. Modified Gram–Schmidt Method-Based Variable Projection Algorithm for Separable Nonlinear Models
Author(s): Guang-Yong Chen; Min Gan; Feng Ding; C. L. Philip Chen
Pages: 2410 - 2418

15. Power-Type Varying-Parameter RNN for Solving TVQP Problems: Design, Analysis, and Applications
Author(s): Zhijun Zhang; Ling-Dong Kong; Lunan Zheng
Pages: 2419 - 2433

16. Passivity and Synchronization of Coupled Uncertain Reaction–Diffusion Neural Networks With Multiple Time Delays
Author(s): Jin-Liang Wang; Zhen Qin; Huai-Ning Wu; Tingwen Huang
Pages: 2434 - 2448

17. Finding Principal Paths in Data Space
Author(s): Marco Jacopo Ferrarotti; Walter Rocchia; Sergio Decherchi
Pages: 2449 - 2462

18. t-Exponential Memory Networks for Question-Answering Machines
Author(s): Kyriakos Tolias; Sotirios P. Chatzis
Pages: 2463 - 2477

19. Near-Nash Equilibrium Control Strategy for Discrete-Time Nonlinear Systems With Round-Robin Protocol
Author(s): Peng Zhang; Yuan Yuan; Hongjiu Yang; Huaping Liu
Pages: 2478 - 2492

20. Covariance Matrix Adaptation for Multiobjective Multiarmed Bandits
Author(s): Mădălina M. Drugan
Pages: 2493 - 2502

21. A Two-Timescale Duplex Neurodynamic Approach to Biconvex Optimization
Author(s): Hangjun Che; Jun Wang
Pages: 2503 - 2514

22. Plume Tracing via Model-Free Reinforcement Learning Method
Author(s): Hangkai Hu; Shiji Song; C. L. Phillip Chen
Pages: 2515 - 2527

23. Extended Dissipativity Analysis for Markovian Jump Neural Networks With Time-Varying Delay via Delay-Product-Type Functionals
Author(s): Wen-Juan Lin; Yong He; Chuan-Ke Zhang; Min Wu; Jianhua Shen
Pages: 2528 - 2537

24. Recurrent Neural Network Model: A New Strategy to Solve Fuzzy Matrix Games
Author(s): Amin Mansoori; Mohammad Eshaghnezhad; Sohrab Effati
Pages: 2538 - 2547

25. Function Perturbation Impact on Feedback Stabilization of Boolean Control Networks
Author(s): Xiaodong Li; Haitao Li; Guodong Zhao
Pages: 2548 - 2554

26. Set Stabilization of Probabilistic Boolean Networks Using Pinning Control
Author(s): Fangfei Li; Lihua Xie
Pages: 2555 - 2561

27. Stability Analysis for Delayed Neural Networks via Improved Auxiliary Polynomial-Based Functions
Author(s): Zhichen Li; Huaicheng Yan; Hao Zhang; Xisheng Zhan; Congzhi Huang
Pages: 2562 - 2568

Saturday, 20 July 2019

IEEE TFS CALL FOR PAPERS – SPECIAL ISSUE ON TYPE-2 FUZZY-MODEL-BASED CONTROL AND ITS APPLICATIONS

I. AIM AND SCOPE
Nonlinear systems are difficult to analyze and control due to their intrinsic complexity. During the past decades, Fuzzy-Model-Based (FMB) control strategy has been recognized as one of the most effective control approaches for nonlinear systems. Takagi–Sugeno (T–S) fuzzy model plays an important role in FMB control systems and it has demonstrated a wide range of successful industrial applications. Thanks to its rigorous mathematical foundation, the stability analysis and control synthesis of T-S FMB control systems can be conducted in a systematic way. Prof. Kazuo Tanaka has made pioneering significant contribution to investigate the stability issues of T-S FMB control systems and relaxed the stability conditions by proposing the well-known parallel distributed compensation (PDC) method, which is the most popular method adopted to deal with (type-1) T-S FMB control systems.

Considering the ability of dealing with uncertainty directly, the importance and development of type-2 fuzzy sets and theory have been highly noticed and promoted by Prof. Jerry M. Mendel. Many researchers have devoted to contributing to the field of type-2 fuzzy set and its control applications. Just name a few, Prof. Jerry M. Mendel and Prof. Robert I. Bob John made significant contribution to advertize the necessity of (interval) type-2 fuzzy set; Prof. Dongrui Wu and Prof. Jerry M. Mendel developed the enhanced Karnik-Mendel algorithms for type-reduction; Prof. Woei Wan Tan utilized type-2 fuzzy logic to design the practical controllers; Prof. Tufan Kumbasar and Prof. Hani Hagras successfully applied the type-2 fuzzy set in control of mobile robots subject to uncertainty.

Introducing type-2 fuzzy sets into control strategies is a promising way to push the FMB control techniques to a new frontier. Beginning with the first attempt on the stability analysis and control synthesis of (interval) type-2 FMB control system by Dr. H.K. Lam in 2008 (Lam, H.K. and Seneviratne, L.D., 2008. Stability analysis of interval type-2 fuzzy-model-based control systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(3), pp.617-628), recently, the research on interval type-2 FMB control systems has drawn the attention of researchers. It is also worth mentioning that Prof. William Melek and Prof. Hao Ying have contributed excellent research works to the type-2 T-S/TSK FMB control field. Nonlinearity and uncertainty are generally considered as challenging components to be addressed during the system analysis and control design. The merit of type-2 FMB control techniques is to deal with nonlinearity/uncertainty in the system through type-2 fuzzy sets. Recent research outcomes of type-2 FMB have verified that type-2 FMB control strategy can be successfully applied to real-world nonlinear systems subject to uncertainty. Although there were already some seminal works on type-2 FMB control systems that can be found in the literature, there are still many interesting related topics await.

The potential research topics for type-2 FMB control systems can be the relaxation of stability/stabilization conditions, control methodologies, design of robust type-2 fuzzy controller, performance realization of type-2 fuzzy controller, applications of type-2 fuzzy control theory, etc. Inspired by the great research potential of type-2 FMB control systems, we believe that further efforts are worth to be devoted into these areas, which can promote the development of type-2 FMB control research and take it into next level.

This special issue is to serve as a vehicle to promote some focused frontier topics in the field of type-2 FMB control systems. A collection of high-quality type-2 fuzzy control related papers in a special issue will lead to the long-term impact that the accepted papers will serve as an indicator of the most influential topics, highlight the unique advantages of type-2 fuzzy control techniques, and provide reference and driving force in support of the research development.

II. TOPICS COVERED

The topics cover a broad range of research on the control issues of type-2 fuzzy logic systems. The solicited contributions involve the following applications (but not limited to):
  • Membership-function-dependent (MFD) analysis;
  • Type-2 fuzzy modeling;
  • Design of robust type-2 controller subject to the external disturbance;
  • Combination of type-2 fuzzy logic to polynomial FMB control systems;
  • Model reduction of type-2 FMB control systems;
  • New type-reduction methods of type-2 membership functions in FMB control systems;
  • Adaptive control of type-2 FMB systems;
  • Optimal control of type-2 FMB systems;
  • Stability/performance/robustness analysis of type-2 FMB control systems
  • Type-2 fuzzy neural-network control systems;
  • Networked type-2 FMB control systems;
  • Industrial applications of type-2 fuzzy systems.

Advanced type-2 methods involve the following technologies (but not limited to):
  • Type-2 FMB control with reinforcement learning;
  • Type-2 FMB control with medical robotics;
  • Type-2 FMB control with mobile robots;
  • Type-2 FMB control of bio-systems;
  • Type-2 FMB control of continuum robotic manipulator;
  • Type-2 FMB control with bio-inspired robotics;
  • Type-2 FMB control with evolutionary algorithms;
  • Type-2 FMB control with machine learning;
  • Type-2 FMB control with visual servo.

III. SUBMISSION GUIDELINES

All authors should read ‘Information for Authors’ before submitting a manuscript at https://cis.ieee.org/publications/t-fuzzy-systems/tfs-information-for-authors

Submissions should be through the IEEE TFS journal website http://mc.manuscriptcentral.com/tfs-ieee.

Submissions should also be in the correct format https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/

It is essential that your manuscript is identified as a Special Issue contribution:
 Ensure you choose ‘Special Issue’ when submitting.
 A cover letter must be included which includes the title ‘Special Issue on Type-2 Fuzzy-Model-Based Control and its Applications’

IV. IMPORTANT DATES


1 February 2020 – submission deadline
April 2020 – notification of the first-round review (for guidance)
May 2020 – revised submission due
July 2020 – final notification of acceptance/rejection

V. GUEST EDITORS

Dr. Bo Xiao
Imperial College London, London, UK
Email: b.xiao@imperial.ac.uk

Dr. H.K. Lam
King’s College London, London, UK
Email: hak-keung.lam@kcl.ac.uk

Prof. Kazuo Tanaka
University of Electro-Communications, Tokyo, Japan
Email: ktanaka@mce.uec.ac.jp

Prof. Jerry M. Mendel
University of Southern California, Los Angeles, USA
Email: mendel@sipi.usc.edu

Recently approved conference with technical co-sponsorship

4th International Conference on Computational Intelligence and Networks (CINE 2020)
February 27-29, 2020
Place: Kolkata, India
General Chair: Sanghamitra Bandyopadhyay
Website: https://www.isical.ac.in/~scc/CINE/index.html

Recently approved conferences with technical sponsorship

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021)
July 11-14, 2021
Place: Luxembourg, Luxembourg
General Co-Chairs: Christian Wagner and Holger Voos
Website: TBA


2022 IEEE World Congress on Computational Intelligence (IEEE WCCI 2022)
July 11-16, 2022
Place: Padua, Italy
General Co-Chairs: Marco Gori and Alessandro Sperduti
Website: TBA

Call for papers: AIDA-AT 2020

The 1st International Conference on Artificial Intelligence and Data Analytics for Air Transportation
AIDA-AT 2020
3-4 FEB 2020, Nanyang Executive Centre, Nanyang Technological University

Contact:
http://event.ntu.edu.sg/aidaat2020
aidaat@ntu.edu.sg

CALL FOR PAPERS

Welcome to the 1st International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT 2020), which will be held in Nanyang Executive Centre, Nanyang Technological University (NTU), Singapore, during February 3-4, 2020. AIDA-AT 2020 is organized by Air Traffic Management Research Institute (ATMRI), School of Mechanical and Aerospace Engineering, NTU and technically co-sponsored by IEEE Computational Intelligence Society (CIS).
The aim of the conference is to bring together world leading experts in Artificial Intelligence and Air Transportation on one platform to promote a fruitful research on well-described Air Transportation problems, using and developing techniques from Artificial Intelligence and Data Science, such as Machine Learning, Evolutionary Computation, Constraint Programming, Graph-based methods, Data-driven methods, Data Analytics, etc. Participation is invited on all topics relevant to these research themes, including:
  • Data Mining and Knowledge Discovery
  • Machine Learning
  • Neural Networks and Deep Learning
  • Constraint Programming
  • Graph and Tree-search Methods
  • Agent Oriented Techniques
  • Meta-Heuristics Techniques
  • Swarm Intelligence
  • Evolutionary Computation 
  • Data-Driven Air Transportation 
  • Intelligent Automation
  • Operational and Policy issues in Automation
  • Case studies of AI in Air Transportation
  • Trusted AI Systems
All accepted and presented papers will be published in the conference proceedings and indexed by IEEE Xplore Digital Library and Scopus.

IMPORTANT DATES

Full Paper Submission Deadline: 30 October 2019
Acceptance Notification: 1 December 2019
Camera-Ready Submission: 15 December 2019
AIDA-AT 2020 Conference: 3-4 February 2020

AIDA-AT 2020 Program Committee
  • Hussein Abbass | UNSW, Australia
  • Sameer Alam | ATMRI-NTU, Singapore
  • Richard Alligier | ENAC, France
  • Jean-Marc Alliot | IRIT, France
  • Michael Ball | UMD, United States
  • Henk Blom | TU-Delft, Netherlands
  • Marc Bui | EPHE, France
  • Nicolas Couellan | ENAC, France
  • Daniel Delahaye | ENAC, France
  • Imen Dhief | ATMRI-NTU, Singapore
  • Vu Duong | ATMRI-NTU, Singapore
  • Nicolas Durand | ENAC, France
  • Laurent El Ghaoui | UC Berkeley, United States
  • Luis Farinas del Cerro | CNRS, France
  • Eric Feron | KAUST, Saudi Arabia
  • Kozo Fujii | TUS, Japan
  • David Gianazza | ENAC, France
  • Sim Kuan Goh | ATMRI-NTU, Singapore
  • Bob Graham | EUROCONTROL, France
  • Eri Itoh | ENRI, Japan
  • Frank Klawonn | OU, Germany
  • Lishuai Li | CUHK, Hong Kong
  • Duc-Thinh Pham | ATMRI-NTU, Singapore
  • Michael Schultz | TU-Dresden, Germany
  • John Shortle | GMU, United States
  • Banavar Sridhar | NASA Ames, United States
  • Phu Tran | ATMRI-NTU, Singapore
  • Vikrant Vaze | Dartmouth, United States
  • Yanjun Wang | NUAA, China

AIDA-AT 2020 Organizing Committee

General Chair
Vu Duong | vu.duong@ntu.edu.sg

Conference Co-Chairs:
Sameer Alam | sameeralam@ntu.edu.sg
Nicolas Durand | durand@recherche.enac.fr

Program Co-Chairs:
Michael Schultz | michael.schultz@tu-dresden.de
Duc-Thinh Pham | dtpham@ntu.edu.sg

Publication Chair:
Phu Tran | phutran@ntu.edu.sg

Communication Chair:
Yanjun Wang | ywang@nuaa.edu.cn

Local Organizing Co-Chairs:
Sim Kuan Goh | skgoh@ntu.edu.sg
Imen Dhief | imen.dhief@ntu.edu.sg

Conference Secretariat:
Christabell Chua Lay Yen | lychua@ntu.edu.sg
Noorhayati Supari | noorhayati@ntu.edu.sg

IEEE Transactions on Neural Networks and Learning Systems, Volume 30, Issue 7, July 2019

1. The Boundedness Conditions for Model-Free HDP(λ)
Author(s): Seaar Al-Dabooni; Donald Wunsch
Pages: 1928 - 1942

2. Training Passive Photonic Reservoirs With Integrated Optical Readout
Author(s): Matthias Freiberger; Andrew Katumba; Peter Bienstman; Joni Dambre
Pages: 1943 - 1953

3. Seeing All From a Few: ℓ1-Norm-Induced Discriminative Prototype Selection
Author(s): Xingxing Zhang; Zhenfeng Zhu; Yao Zhao; Dongxia Chang; Ji Liu
Pages: 1954 - 1966

4. Inverting the Generator of a Generative Adversarial Network
Author(s): Antonia Creswell; Anil Anthony Bharath
Pages: 1967 - 1974

5. Neuroadaptive Fault-Tolerant Control of Quadrotor UAVs: A More Affordable Solution
Author(s): Yongduan Song; Liu He; Dong Zhang; Jiye Qian; Jin Fu
Pages: 1975 - 1983

6. Efficient Multispike Learning for Spiking Neural Networks Using Probability-Modulated Timing Method
Author(s): Ruihan Hu; Sheng Chang; Hao Wang; Jin He; Qijun Huang
Pages: 1984 - 1997

7. A Cross-Domain Recommender System With Kernel-Induced Knowledge Transfer for Overlapping Entities
Author(s): Qian Zhang; Jie Lu; Dianshuang Wu; Guangquan Zhang
Pages: 1998 - 2012

8. Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction With Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces
Author(s): Weiping Ding; Chin-Teng Lin; Zehong Cao
Pages: 2013 - 2027

9. Two-Dimensional Quaternion PCA and Sparse PCA
Author(s): Xiaolin Xiao; Yicong Zhou
Pages: 2028 - 2042

10. L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks
Author(s): Shuang Wu; Guoqi Li; Lei Deng; Liu Liu; Dong Wu; Yuan Xie; Luping Shi
Pages: 2043 - 2051

11. Multistability of Switched Neural Networks With Piecewise Linear Activation Functions Under State-Dependent Switching
Author(s): Zhenyuan Guo; Linlin Liu; Jun Wang
Pages: 2052 - 2066

12. Robust Subspace Clustering by Cauchy Loss Function
Author(s): Xuelong Li; Quanmao Lu; Yongsheng Dong; Dacheng Tao
Pages: 2067 - 2078

13. Tracking Sparse Linear Classifiers
Author(s): Tingting Zhai; Frédéric Koriche; Hao Wang; Yang Gao
Pages: 2079 - 2092

14. Extreme Learning Machine With Affine Transformation Inputs in an Activation Function
Author(s): Jiuwen Cao; Kai Zhang; Hongwei Yong; Xiaoping Lai; Badong Chen; Zhiping Lin
Pages: 2093 - 2107

15. Low-Voltage Low-Power Integrable CMOS Circuit Implementation of Integer- and Fractional–Order FitzHugh–Nagumo Neuron Model
Author(s): Farooq Ahmad Khanday; Nasir Ali Kant; Mohammad Rafiq Dar; Tun Zainal Azni Zulkifli; Costas Psychalinos
Pages: 2108 - 2122

16. Global Nonfragile Synchronization in Finite Time for Fractional-Order Discontinuous Neural Networks With Nonlinear Growth Activations
Author(s): Xiao Peng; Huaiqin Wu; Jinde Cao
Pages: 2123 - 2137

17. Topic-Based Algorithm for Multilabel Learning With Missing Labels
Author(s): Jianghong Ma; Tommy W. S. Chow
Pages: 2138 - 2152

18. Neural Network Filtering Control Design for Nontriangular Structure Switched Nonlinear Systems in Finite Time
Author(s): Shuai Sui; C. L. Philip Chen; Shaocheng Tong
Pages: 2153 - 2162

19. Biased Random Forest For Dealing With the Class Imbalance Problem
Author(s): Mohammed Bader-El-Den; Eleman Teitei; Todd Perry
Pages: 2163 - 2172

20. Fast and Accurate Sparse Coding of Visual Stimuli With a Simple, Ultralow-Energy Spiking Architecture
Author(s): Walt Woods; Christof Teuscher
Pages: 2173 - 2187

21. Adaptive Leader-Following Consensus for Multiple Euler–Lagrange Systems With an Uncertain Leader System
Author(s): Shimin Wang; Jie Huang
Pages: 2188 - 2196

22. Effects of State-Dependent Impulses on Robust Exponential Stability of Quaternion-Valued Neural Networks Under Parametric Uncertainty
Author(s): Xujun Yang; Chuandong Li; Qiankun Song; Hongfei Li; Junjian Huang
Pages: 2197 - 2211

23. Data Subset Selection With Imperfect Multiple Labels
Author(s): Meng Fang; Tianyi Zhou; Jie Yin; Yang Wang; Dacheng Tao
Pages: 2212 - 2221

24. Prescribed Performance Model-Free Adaptive Integral Sliding Mode Control for Discrete-Time Nonlinear Systems
Author(s): Dong Liu; Guang-Hong Yang
Pages: 2222 - 2230

25. Visualization Methods for Image Transformation Convolutional Neural Networks
Author(s): Églen Protas; José Douglas Bratti; Joel F. O. Gaya; Paulo Drews; Silvia S. C. Botelho
Pages: 2231 - 2243

26. Deep FisherNet for Image Classification
Author(s): Peng Tang; Xinggang Wang; Baoguang Shi; Xiang Bai; Wenyu Liu; Zhuowen Tu
Pages: 2244 - 2250

Thursday, 20 June 2019

Call for Papers: RiTA 2019

The 7th International Conference on Robot Intelligence Technology and Applications (RiTA 2019)
November 1 - 3, 2019
KAIST, Daejeon, KOREA
2019.icrita.org

The 7th International Conference on Robot Intelligence Technology and Applications (RiTA 2019) will take place on 1 – 3 November 2019 in conjunction with the 2nd AI World Cup 2019 at KAIST, Daejeon, KOREA. The theme of this year’s conference is: “Intelligent robots in daily life:  helping, entertaining and competing”. As robot intelligence improves we will have more robots to help us at home and at work. They will also entertain and comfort us and even help to educate our children at school. This conference will present the latest research results and applications in the field of robot intelligence. Researchers from around the world will share their knowledge and experience in artificial intelligence and robotics. The conference is an excellent opportunity for in-depth discussions and social networking with other researchers.

CONFERENCE SCOPE
  • Cognitive Intelligence
  • Social Intelligence
  • Ambient Intelligence
  • Collective Intelligence
  • Genetic Intelligence
  • Artificial Intelligence 
  • Applications

IMPORTANT DATES
  • July 15, 2019: Deadline of Paper Submission
  • August 31, 2019: Notification of Acceptance
  • September 30, 2019: Final Paper Submission Due

PUBLICATIONS
Accepted and presented papers will be published in the Conference Proceedings and submitted to IEEE Xplore® online digital library.

JOINT EVENT – AIWC 2019
Artificial Intelligence World Cup (AIWC) 2019 will be co-located with the RiTA 2019. It is the first world-wide competition of on-line soccer teams simulated by AI, held every year. AI World Cup provides an ambitious and technologically versatile platform to prove AI excellence of participants, and is open to a number of international teams. AI World Cup aims to inspire the future of AI technology through innovative solutions and technological competence. For more information, visit the website, aiworldcup.org.

TECHNICAL CO-SPONSOR: IEEE Computational Intelligence Society (CIS)

Organized by:
  • Institute of Control, Robotics and Systems (ICROS)
  • Korea Advanced Institute of Science and Technology (KAIST)
  • Machine Intelligence and Robotics Multi-Sponsored Research and Education Platform (MIR-MSREP)

RiTA 2019 Secretariat
rita@icrita.org
2019.icrita.org