Results 51 to 60 of about 3,700 (193)

A Novel Deep Temporal Feature Enhanced Just‐in‐Time Learning Framework for Predicting Rare Earth Component Content

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT Real‐time online detection of rare earth element component contents is a crucial link in ensuring the stable production of the rare earth extraction and separation industry and improving the quality of rare earth products. The traditional methods for predicting the content of rare earth element components based on just‐in‐time learning fail to
Zhaohui Huang, Liangfang Liao, Chunfa Liao, Hui Zhang, Tao Qi, Rongxiu Lu, Wenhao Dai   +6 more
wiley   +1 more source

AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising [PDF]

IEEE Transactions on Geoscience and Remote Sensing
Spectral unmixing has been extensively studied with a variety of methods and used in many applications. Recently, data-driven techniques with deep learning methods have obtained great attention to spectral unmixing for its superior learning ability to automatically learn the structure information.
Min Zhao, Jie Chen, Nicolas Dobigeon
openalex   +4 more sources

Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance

Naval Research Logistics (NRL), EarlyView.
ABSTRACT The rapid expansion of cross‐border e‐commerce (CBEC) has created significant opportunities for small‐ and medium‐sized sellers, yet financing remains a critical challenge due to their limited credit histories. Third‐party logistics (3PL)‐led supply chain finance (SCF) has emerged as a promising solution, leveraging in‐transit inventory as ...
Qingkai Zhang, L. Jeff Hong, Houmin Yan
wiley   +1 more source

Adversarial Example Detection and Restoration Defensive Framework for Signal Intelligent Recognition Networks

Applied Sciences, 2023
Deep learning-based automatic modulation recognition networks are susceptible to adversarial attacks, posing significant performance vulnerabilities. In response, we introduce a defense framework enriched by tailored autoencoder (AE) techniques.
Chao Han, Ruoxi Qin, Linyuan Wang, Weijia Cui, Dongyang Li, Bin Yan   +5 more
doaj   +1 more source

Reduced‐Order Modeling of Energetic Materials Using Physics‐Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)

Propellants, Explosives, Pyrotechnics, EarlyView.
Physics‐Aware Recurrent Convolutional Neural Networks (PARC) can reliably learn the thermomechanics of energetic materials as a function of morphology. This work introduces LatentPARC, which accelerates PARC by modeling the dynamics in a low‐dimensional latent space.
Zoë J. Gray, Joseph B. Choi, Youngsoo Choi, H. Keo Springer, H. S. Udaykumar, Stephen S. Baek   +5 more
wiley   +1 more source

Transformer-based autoencoder with ID constraint for unsupervised anomalous sound detection

EURASIP Journal on Audio, Speech, and Music Processing, 2023
Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods.
Jian Guan, Youde Liu, Qiuqiang Kong, Feiyang Xiao, Qiaoxi Zhu, Jiantong Tian, Wenwu Wang   +6 more
doaj   +1 more source

Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou, Juan Zou, Gan Ruan, Yuan Liu, Yizhang Xia   +4 more
wiley   +1 more source

CCLCap-AE-AVSS: Cycle consistency loss based capsule autoencoders for audio–visual speech synthesis [PDF]

Journal of Intelligent Systems
AbstractAudio–visual speech synthesis (AVSS) is a rapidly growing field in the paradigm of audio–visual learning, involving the conversion of one person’s speech into the audio–visual stream of another while preserving the speech content. AVSS comprises two primary components: voice conversion (VC), which alters the vocal characteristics from the ...
Subhayu Ghosh, Nanda Dulal Jana, Tapas Si, Saurav Mallik, Mohd Asif Shah   +4 more
openalex   +3 more sources

Temporal Dependency‐Aware Trajectory‐Level Behavioural Metric for Exploration in Reinforcement Learning

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Intrinsic motivation serves as the predominant paradigm of exploration in reinforcement learning. In pursuit of an informative and robust state representation, the behavioural metric groups behaviourally equivalent states together, which share the same single‐step reward and transition distribution.
Anjie Zhu, Yongjun Yang, Guangyi Zhao, Jie Shao   +3 more
wiley   +1 more source

AE-ViT: Token Enhancement for Vision Transformers via CNN-Based Autoencoder Ensembles

International Journal of Artificial Intelligence & Applications
While Vision Transformers (ViTs) have revolutionized computer vision with their exceptional results, they struggle to balance processing speed with visual detail preservation. This tension becomes particularly evident when implementing larger patch sizes.
Heriniaina Andry Raboanary, R. Raboanary, Nirina Maurice HASINA TAHIRIDIMBISOA   +2 more
openalex   +2 more sources