no code implementations • IEEE 2022 • Mikel Ferrero-Jaurrieta, Zdenko Taka ́cˇ, Javier Ferna ́ndez, Member, IEEE, Lˇubom ́ıra Horanska ́, Grac ̧aliz Pereira Dimuro, Susana Montes, Irene D ́ıaz and Humberto Bustince, Fellow, IEEE.
Choquet integral is a widely used aggregation oper- ator on one-dimensional and interval-valued information, since it is able to take into account the possible interaction among data.
no code implementations • IEEE Transactions on Smart Grid 2022 • Di Cao, Member, Junbo Zhao, Weihao Hu, Senior Member, Qishu Liao, Qi Huang, Zhe Chen, Fellow, IEEE
Abstract—This paper addresses the distribution system state estimation (DSSE) with unknown topology change.
no code implementations • 6 Mar 2022 • Jin Zhao, Member, Fangxing Li, Fellow, Srijib Mukherjee, Senior Member, Christopher Sticht
The proposed deep RL method provides real-time computing to support on-line dynamic MMGF scheme, and the scheme handles a long-term resilience enhancement problem using adaptive on-line MMGF to defend changeable conditions.
1 code implementation • IEEE Transactions on Evolutionary Computation 2021 • Yinglan Feng, Liang Feng, Senior Member, Sam Kwong, and Kay Chen Tan, Fellow, IEEE
In this way, the number of subpopulations is adaptively adjusted and better performing subpopulations obtain more individuals.
no code implementations • IEEE Transactions on Intelligent Transportation Systems 2021 • Liangtian Wan, Yuchen Sun, Lu Sun, Member, Zhaolong Ning, Senior Member, and Joel J. P. C. Rodrigues, Fellow, IEEE
Abstract— In this paper, a novel system architecture including a massive multi-input multi-output (MIMO) or a reconfigurable intelligent surface (RIS) and multiple autonomous vehicles is considered in vehicle location systems.
no code implementations • 17 Jun 2021 • Shiyu Jiao, Ximing Xie, Zhiguo Ding, Fellow, IEEE
This paper investigates the application of deep deterministic policy gradient (DDPG) to intelligent reflecting surface (IRS) based unmanned aerial vehicles (UAV) assisted non-orthogonal multiple access (NOMA) downlink networks.
no code implementations • 21 Jul 2020 • Xueping Wang, Sujoy Paul, Dripta S. Raychaudhuri, Min Liu, Yaonan Wang, Amit K. Roy-Chowdhury, Fellow, IEEE
In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision.
Multiple Instance Learning Video-Based Person Re-Identification
no code implementations • 2020 2020 • Guanglong Du, Zhiyao Wang, Chunquan Li, Peter X. Liu, Fellow
To effectively predict driving fatigue, this paper proposes a new deep learning framework called TSK-type Convolution Recurrent Fuzzy Network (TCRFN) based on the spatial and temporal characteristics of EEG signals.
no code implementations • 27 Feb 2020 • Kuljeet Kaur∗, Sahil Garg∗, Georges Kaddoum∗, Member, Mohsen Guizani†, Fellow, IEEE, and Dushantha Nalin K. Jayakody‡, Senior Member, IEEE.
With the advent of the Internet-of-Things (IoT), vehicular networks and cyber-physical systems, the need for realtime data processing and analysis has emerged as an essential pre-requite for customers’ satisfaction.
1 code implementation • 5 May 2019 • Qi. Wang, Senior Member, Zhenghang Yuan, Qian Du, Xuelong. Li, Fellow, IEEE
In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD).
no code implementations • IEEE Transactions on Image Processing 2019 • Yan-Tsung Peng, Keming Cao, and Pamela C. Cosman, Fellow, IEEE
Abstract— Images degraded by light scattering and absorption, such as hazy, sandstorm, and underwater images, often suffer color distortion and low contrast because of light traveling through turbid media.
no code implementations • 1 Mar 2019 • Liqun Lin, Shiqi Yu, Tiesong Zhao, Member, Zhou Wang, Fellow, IEEE
To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs.
no code implementations • 7 Jan 2019 • Guangliang Gao, Zhifeng Bao, Jie Cao, A. K. Qin, Timos Sellis, Fellow, IEEE, Zhiang Wu
Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently.
no code implementations • Conference 2018 • Zening Liu, Xiumei Yang, Yang Yang, Kunlun Wang, and Guoqiang Mao, Fellow, IEEE
Abstract—Fog computing has risen as a promising architecture for future Internet of Things (IoT), 5G and embedded artificial intelligence (AI) applications with stringent service delay requirements along the cloud to things continuum.
1 code implementation • IEEE Transactions on Biomedical Engineering 2018 • Dong Nie, Roger Trullo, Jun Lian, Li Wang, Caroline Petitjean, Su Ruan, Qian Wang, and Dinggang Shen, Fellow, IEEE
To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN.
no code implementations • IEEE 2018 • Tao Lei, Xiaohong Jia, Yanning Zhang, Lifeng He, Hongy-ing Meng, Senior Member, and Asoke K. Nandi, Fellow, IEEE
However, the introduction oflocal spatial information often leads to a high computationalcomplexity, arising out of an iterative calculation of the distancebetween pixels within local spatial neighbors and clusteringcenters.
1 code implementation • journals 2016 • Ding Liu, Zhaowen Wang, Bihan Wen, Student Member, Jianchao Yang, Member, Wei Han, and Thomas S. Huang, Fellow, IEEE
We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data.
no code implementations • IEEE 2007 • Ron Rubinstein, Member, Tomer Peleg, Student Member, and Michael Elad, Fellow, IEEE
Abstract—The synthesis-based sparse representation model for signals has drawn considerable interest in the past decade.