Results 31 to 40 of about 176,660 (282)

Economic MPC of Nonlinear Processes via Recurrent Neural Networks Using Structural Process Knowledge [PDF]

, 2020
This work discusses three methods that incorporate a priori process knowledge into recurrent neural network (RNN) modeling of nonlinear processes to get increased prediction accuracy and provide information on how the neural network models are structured.
Park, Michael Jihyuck
core  

Neutrosophy-Driven Deep Learning for Predicting Student Performance [PDF]

Neutrosophic Sets and Systems
This paper proposes a hybrid architecture using several deep learning models in the neutrosophy environment for predicting student learning outcomes. The proposed framework proceeds on deep neural network models with the neutrosophy encoder/decoder.
N.T.K Son, N.T. Thong, N.H. Quynh
doaj   +1 more source

Low Precision RNNs: Quantizing RNNs Without Losing Accuracy

, 2017
Similar to convolution neural networks, recurrent neural networks (RNNs) typically suffer from over-parameterization. Quantizing bit-widths of weights and activations results in runtime efficiency on hardware, yet it often comes at the cost of reduced accuracy.
Kapur, Supriya, Mishra, Asit, Marr, Debbie   +2 more
openaire   +2 more sources

ChicGrasp: Imitation‐Learning‐Based Customized Dual‐Jaw Gripper Control for Manipulation of Delicate, Irregular Bio‐Products

Advanced Robotics Research, EarlyView.
Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar, Zhengtong Xu, Siavash Mahmoudi, Pouya Sohrabipour, Chaitanya Pallerla, Yu She, Wan Shou, Philip Glen Crandall, Dongyi Wang   +8 more
wiley   +1 more source

Recurrent Spatial Transformer Networks [PDF]

, 2015
We integrate the recently proposed spatial transformer network (SPN) [Jaderberg et. al 2015] into a recurrent neural network (RNN) to form an RNN-SPN model. We use the RNN-SPN to classify digits in cluttered MNIST sequences. The proposed model achieves a
Maaløe, Lars, Sønderby, Casper Kaae, Sønderby, Søren Kaae, Winther, Ole   +3 more
core   +1 more source

Gated Recurrent Neural Tensor Network

, 2017
Recurrent Neural Networks (RNNs), which are a powerful scheme for modeling temporal and sequential data need to capture long-term dependencies on datasets and represent them in hidden layers with a powerful model to capture more information from inputs ...
Adriani, Mirna, Manurung, Ruli, Nakamura, Satoshi, Sakti, Sakriani, Tjandra, Andros   +4 more
core   +1 more source

Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling

Advanced Robotics Research, EarlyView.
A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang, Ganesh Arivoli, Huzaifa Unjhawala, Luning Bakke, Radu Serban, Dan Negrut   +5 more
wiley   +1 more source

Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain

Advanced Science, EarlyView.
A new approach uses simple neural networks to create digital twins of brain activity, capturing how different patterns unfold over time. The method generates and recovers key dynamics even from noisy data. When applied to fMRI, it predicts brain signals and reveals distinctive activity patterns across regions and individuals, opening possibilities for ...
Gabriele Di Antonio, Tommaso Gili, Andrea Gabrielli, Maurizio Mattia   +3 more
wiley   +1 more source

Combining Deep Learning Models for Enhancing the Detection of Botnet Attacks in Multiple Sensors Internet of Things Networks

JOIV: International Journal on Informatics Visualization, 2021
Distributed-Denial-of-Service impacts are undeniably significant, and because of the development of IoT devices, they are expected to continue to rise in the future.
Abdulkareem A. Hezam, Salama A. Mostafa, Zirawani Baharum, Alde Alanda, Mohd Zaki Salikon   +4 more
doaj   +1 more source

Decoding Naturalistic Episodic Memory with Artificial Intelligence and Brain‐Machine Interface

Advanced Science, EarlyView.
Episodic memory weaves together what, where, and when of experience into a personal narrative. Cutting‐edge AI models may decode this intricate process in real‐life settings, revealing how neural activity encodes naturalistic memories. By merging AI with brain–machine interfaces, researchers are edging closer to mapping and even engineering memory ...
Dong Song
wiley   +1 more source