The estimator is finally tested on a participant walking with an active exoskeleton, demonstrating the robustness of D67 in interaction with an exoskeleton without being trained on any data from the test subject with or without an exoskeleton.
Finally, the proposed ISSA is utilized to solve the objective function.
no code implementations • • 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.
—The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT).
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.
Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a thirdorder low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation.
This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation.
Therefore, this letter proposes a shallow model for HSIC, which is called depthwise over-parameterized convolutional neural network (DOCNN).
It is challenging to solve this kind of stochastic nonlinear optimization problem.
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 • 29 May 2021 • Karen Montano-Martinez, Sushrut Thakar, Shanshan Ma, Zahra Soltani, Student Member, Vijay Vittal, Life Fellow, Mojdeh Khorsand, Raja Ayyanar, Senior Member, Cynthia Rojas, Member, IEEE
Reliable and accurate distribution system modeling, including the secondary network, is essential in examining distribution system performance with high penetration of distributed energy resources (DERs).
Abstract— Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among different locations in a city.
Despite recent advances of deep neural networks in hand vein identification, the existing solutions assume the availability of a large and rich set of training image samples.
Case studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that the proposed method is capable of accurately accounting the influence of wind curtailment dispatch in CCO.
Systems and Control Systems and Control
Extensive experiments on the public Dataset for Object deTection in Aerial images data set indicate that our CRPN can help our detector deal the larger image faster with the limited GPU memory; meanwhile, the SFNet is beneficial to achieve more accurate detection of geospatial objects with wide-scale range.
This work proposes a novel method to solve the problem of multi-label image annotation by unifying two different types of Laplacian regularization terms in deep convolutional neural network (CNN) for robust annotation performance.
Another contribution is that we propose an additional predictor to utilize the internal frames in the model training to improve the localization accuracy.
To solve this issue, transfer learning is proposed by leveraging knowl- edge learned from source domain to target domain.
Network slicing (NS) has been identiﬁed as one of the most promising architectural technologies for future mobile network systems to meet the extremely diversiﬁed service requirements of users.
In this letter, we propose space-time spreading (STS) of local sensor decisions before reporting them over a wireless multiple access channel (MAC), in order to achieve flexible balance between diversity and multiplexing gain as well as eliminate any chance of intrinsic interference inherent in MAC scenarios.
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.
In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL).
In this algorithm, a reward function is defined according to the features of tracking control in order to speed up the learning process, and then an RL tracking controller with a kernel-based transition dynamic model is proposed.
As an effective and efficient discriminative learning method, Broad Learning System (BLS) has received increasing attention due to its outstanding performance in various regression and classification problems.
Second, we propose an energy-efficient swarm-intelligence-based clustering (SIC) algorithm based on PSO, in which the particle fitness function is exploited for inter-cluster distance, intra-cluster distance, residual energy, and geographic location.
We show that a neural network whose output is obtained as the difference of the outputs of two feedforward networks with exponential activation function in the hidden layer and logarithmic activation function in the output node (LSE networks) is a smooth universal approximator of continuous functions over convex, compact sets.
Abstract—In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal impulsive control problems for infinite horizon discrete-time nonlinear systems.
To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs.
Due to limited resources with vehicles, vehicular edge computing and networks (VECONs) i. e., the integration of mobile edge computing and vehicular networks, can provide powerful computing and massive storage resources.
For the former, we propose a training scheme to estimate the overall channel, and for the latter the CRB and the optimal number of relays are derived when the distance between the source and the destination is fixed.
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.
The problem of formation control of a team of mobile robots based on the virtual and behavioral structures is considered in this paper.
Balancing convergence and diversity plays a key role in evolutionary multiobjective optimization (EMO).
no code implementations • 29 Jul 2011 • Christopher G. Scully, Student Member, Jinseok Lee, Joseph Meyer, Alexander M. Gorbach, Domhnull Granquist-Fraser, Yitzhak Mendelson, Member, and Ki H. Chon, Senior Member, IEEE
We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor.
Ranked #1 on SpO2 estimation on Video recordings of fingertip under mobile phone fla (using extra training data)
Abstract—We analyze the performance of a two hop channel state information (CSI)-assisted amplify-and-forward system, with co-channel interference at the relay.
Abstract—The graph structure is a very important means to model schemaless data with complicated structures, such as protein- protein interaction networks, chemical compounds, knowledge query inferring systems, and road networks.
Abstract—The synthesis-based sparse representation model for signals has drawn considerable interest in the past decade.