Results 71 to 80 of about 4,564 (243)

Multi‐Objective Bayesian Co‐Optimization of Parameterized Moving Horizon Estimation and Model Predictive Control

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT This paper proposes a Machine Learning (ML)‐enabled estimator‐controller design framework, in which a parameterized Model Predictive Controller (MPC) and a parameterized Moving Horizon Estimator (MHE) are jointly refined using Bayesian Optimization (BO).
Hossein Nejatbakhsh Esfahani, Javad Mohammadpour Velni   +1 more
wiley   +1 more source

Some Probabilistic Interpretations Related to the Next-Generation Matrix Theory: A Review with Examples

Mathematics
The fact that the famous basic reproduction number R0, i.e., the largest eigenvalue of the next generation matrix FV−1, sometimes has a probabilistic interpretation is not as well known as it deserves to be.
Florin Avram, Rim Adenane, Lasko Basnarkov   +2 more
doaj   +1 more source

Integrating Image Segmentation and Deep Learning to Improve Radio Frequency Propagation Models

International Journal of Satellite Communications and Networking, EarlyView.
ABSTRACT This paper proposes a multi‐sensor approach to improve radio frequency (RF) propagation models, which play a key role in the rapidly expanding field of connected vehicle technology. Focusing on the 1‐ to 20‐GHz frequency range, which is critical for both satellite‐to‐vehicle and base station‐to‐vehicle communications, our study introduces a ...
Jonathan Israel, Xavier Boulanger, Vegard Arneson   +2 more
wiley   +1 more source

Carrier-Free Ultra-Wideband Sensor Target Recognition in the Jungle Environment

Remote Sensing
Carrier-free ultra-wideband sensors have high penetrability anti-jamming solid ability, which is not easily affected by the external environment, such as weather. Also, it has good performance in the complex jungle environment.
Jianchao Li, Shuning Zhang, Lingzhi Zhu, Si Chen, Linsheng Hou, Xiaoxiong Li, Kuiyu Chen   +6 more
doaj   +1 more source

On non-parametric estimation of the Lévy kernel of Markov processes

Stochastic Processes and their Applications, 2013
We consider a recurrent Markov process which is an Itô semi-martingale. The Lévy kernel describes the law of its jumps. Based on observations X(0),X(Δ),...,X(nΔ), we construct an estimator for the Lévy kernel's density. We prove its consistency (as nΔ->\infty and Δ->0) and a central limit theorem.
openaire   +2 more sources

Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi‐scenario applications, and translational prospects

Smart Molecules, EarlyView.
AI is transforming TPD by improving the design, prediction, and optimization of degraders such as PROTACs, molecular glues, and LYTACs. This review summarizes key AI‐driven advances, highlights applications across drug discovery stages, and discusses remaining challenges and future directions for accelerating the development of therapies against ...
Shuanglin Qin, Rui Peng, Guangshuai Zhang, Si Yan, Jin Xiao, Zishu Liu, Ziyu Tan, Qingqing Xie, Shangjie Li, Wei Luo, Xiaohe Xiao   +10 more
wiley   +1 more source

Clustering-Based Construction of Hidden Markov Models for Generative Kernels [PDF]

, 2009
Generative kernels represent theoretically grounded tools able to increase the capabilities of generative classification through a discriminative setting. Fisher Kernel is the first and mostly-used representative, which lies on a widely investigated mathematical background.
Pekalska, Elzbieta, Bicego, Manuele, Cristani, Marco, Murino, Vittorio, Pȩkalska, Elzbieta, Duin, Robert P W   +5 more
openaire   +1 more source

Denoise Stepwise Signals by Diffusion Model‐Based Approach

Small Methods, EarlyView.
This work presents SSDM, a diffusion‐model‐based framework for denoising stepwise signals in single‐molecule measurements. By learning the statistical structure of state transitions and noise, SSDM reconstructs signal levels and identifies transition time points more accurately than conventional approaches, enabling robust analysis of signals across ...
Xingdi Tong, Chenyu Wen
wiley   +1 more source

DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection

IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley   +1 more source

Physics-constrained adaptive kernel interpolation for region-to-region acoustic transfer function: a Bayesian approach

EURASIP Journal on Audio, Speech, and Music Processing
A kernel interpolation method for the acoustic transfer function (ATF) between regions constrained by the physics of sound while being adaptive to the data is proposed.
Juliano G. C. Ribeiro, Shoichi Koyama, Hiroshi Saruwatari   +2 more
doaj   +1 more source