Open Access selected Papers
IEEE Open Access
IEEE Transactions on Emerging Topics in Computational Intelligence now offers publication of its highlighted papers in Open Access for the duration of 3 months in order to assist authors gain maximum exposure for their groundbreaking research and application-oriented papers to all reader communities.
The eighth highlighted paper offered to make Open Access in TETCI is now available for the duration of 3 months starting from 1 October 2020.
Pedestrian Flow Optimization to Reduce the Risk of Crowd Disasters Through Human-Robot Interaction
Authors: Chao Jiang, Zhen Ni, Yi Guo and Haibo He
Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
Issue: Volume 4, Issue 3 – June 2020
Pages: 298-311
Abstract: Pedestrian flow in densely populated or congested areas usually presents irregular or turbulent motion state due to competitive behaviors of individual pedestrians, which reduces flow efficiency and raises the risk of crowd accidents. Effective pedestrian flow regulation strategies are highly valuable for flow optimization. Existing studies seek for optimal design of indoor architectural features and spatial placement of pedestrian facilities for the purpose of flow optimization. However, once placed, the stationary facilities are not adaptive to real-time flow changes. In this paper, we investigate the problem of regulating two merging pedestrian flows in a bottleneck area using a mobile robot moving among the pedestrian flows. The pedestrian flows are regulated through dynamic human-robot interaction (HRI) during their collective motion. We adopt an adaptive dynamic programming (ADP) method to learn the optimal motion parameters of the robot in real time, and the resulting outflow through the bottleneck is maximized with the crowd pressure reduced to avoid potential crowd disasters. The proposed algorithm is a data-driven approach that only uses camera observation of pedestrian flows without explicit models of pedestrian dynamics and HRI. Extensive simulation studies are performed in both MATLAB and a robotic simulator to verify the proposed approach and evaluate the performances.
Index Terms: Pedestrian flow regulation, Human-robot interaction, Learning-based optimal control and Pedestrian crowd pressure.
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8805280
Available in Open Access from 1 October 2020 to 31 December 2020 in IEEE Xplore Digital Library.
Previous Open Access selected Papers
Light Gated Recurrent Units for Speech Recognition
Authors: Mirco Ravanelli, Philemon Brakel, Maurizio Omologo and Yoshua Bengio
Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
Issue: Volume 2, Issue 2 – April 2018
Pages: 92-102
Index Terms: Speech recognition, Deep learning, Recurrent neural networks, LSTM, GRU
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8323308
End-to-End Learning for Physics-Based Acoustic Modeling
Authors: Leonardo Gabrielli, Stefano Tomassetti, Carlo Zinato and Francesco Piazza
Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
Issue: Volume 2, Issue 2 – April 2018
Pages: 160-170
Index Terms: Physics-based acoustic modeling, End-to-end learning, Convolutional neural networks
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8323323
An All-Memristor Deep Spiking Neural Computing System: A Step Toward Realizing the Low-Power Stochastic Brain
Authors: Parami Wijesinghe, Aayush Ankit, Abhronil Sengupta and Kaushik Roy
Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
Issue: Volume 2, Issue 5 – October 2018
Pages: 345-358
Index Terms: Memristor, Stochasticity, Deep stochastic spiking neural networks
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8471280
New Shades of the Vehicle Routing Problem: Emerging Problem Formulations and Computational Intelligence Solution Methods
Authors: Jacek Mańdziuk
Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
Issue: Volume 3, Issue 3 – June 2019
Pages: 230-244
Index Terms: Computational intelligence, Vehicle routing, Combinatorial optimization, Metaheuristics
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8591961
Data-Driven Decision-Making (D3M): Framework, Methodology, and Directions
Authors: Jie Lu, Zheng Yan, Jialin Han and Guangquan Zhang
Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
Issue: Volume 3, Issue 4 – August 2019
Pages: 286-296
Index Terms: Data-driven decision-making, Decision support systems, Computational intelligence
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8732997
Well-M3N: A Maximum-Margin Approach to Unsupervised Structured Prediction
Authors: Shaukat Abidi, Massimo Piccardi, Ivor W. Tsang and Mary-Anne Williams
Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
Issue: Volume 3, Issue 6 – December 2019
Pages: 427-439
Index Terms: Structured prediction, Unsupervised training, Convex relaxation, Maximum-margin Markov networks, Well-SVM
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8515243
Tensor Deep Learning Model for Heterogeneous Data Fusion in Internet of Things
Authors: Wei Wang and Min Zhang
Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
Issue: Volume 4, Issue 1 – February 2020
Pages: 32-41
Index Terms: Big data, Heterogeneous data fusion, Tensor feature extraction, Tensor deep learning model.
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8522024