Results 61 to 70 of about 95,488 (287)

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

Optimal Pole Assignment of Linear Systems by the Sylvester Matrix Equations

Abstract and Applied Analysis, 2014
The problem of state feedback optimal pole assignment is to design a feedback gain such that the closed-loop system has desired eigenvalues and such that certain quadratic performance index is minimized.
Hua-Feng He, Guang-Bin Cai, Xiao-Jun Han
doaj   +1 more source

On the quadratic assignment problem [PDF]

Omega, 1984
Peer Reviewed ; http://deepblue.lib.umich.edu/bitstream/2027.42/25006/1/0000433 ...
openaire   +2 more sources

A grid-based ant colony algorithm for automatic 3D hose routing [PDF]

Ant Colony Algorithms applied to difficult combinatorial optimization problems such as the traveling salesman problem (TSP) and the quadratic assignment problem.
Fernando, WAC, Kalganova, T, Thantulage, G   +2 more
core   +2 more sources

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

Clinically Informed Intelligent Classification of Ovarian Cancer Cells by Label‐Free Holographic Imaging Flow Cytometry

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone, Beatrice Cavina, Daniele Gaetano Sirico, Martina Mugnano, Vittorio Bianco, Lisa Miccio, Anna Myriam Perrone, Anna Maria Porcelli, Giuseppe Gasparre, Ivana Kurelac, Pasquale Memmolo, Pietro Ferraro   +11 more
wiley   +1 more source

VAE+DDPG: An Attention‐Enhanced Variational Autoencoder for Deep Reinforcement Learning‐Based Autonomous Navigation in Low‐Light Environments

Advanced Intelligent Systems, EarlyView.
Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee, Chanhong Hwang, Hyunseok Kang, Yechan Park, Hangwoo Jin, Tuan Anh Nguyen, Eunmi Choi, Dugki Min   +7 more
wiley   +1 more source

Yb2‐Tb Upconversion in a Hetero‐Trimetallic Molecular Lanthanide Complex

Angewandte Chemie, EarlyView.
Lanthanides are at the forefront of photon upconversion in molecular systems; however, the chemical nature of the lanthanides makes site‐specific coordination chemistry difficult to achieve. Here, we employ kinetically stabile building blocks to achieve hetero‐trimetallic 4f complexes with complete site‐specific chemical control.
Nicolaj Kofod, Matthew E. Thornton, Abigail Richardson, Charles Smith, Sabina Gurung, Patrick Parkinson, Stephen Faulkner, Sam Hay, Louise S. Natrajan   +8 more
wiley   +2 more sources

Real‐Time Sampling‐Based Model Predictive Control Based on Reverse Kullback–Leibler Divergence and Its Adaptive Acceleration

Advanced Intelligent Systems, EarlyView.
This study presents a new sampling‐based model predictive control minimizing reverse Kullback‐Leibler divergence to quickly find a local optimum. In addition, a modified Nesterov's acceleration method is introduced for faster convergence. The method is effective for real‐time simulations and real‐world operability improvement on a force‐driven mobile ...
Taisuke Kobayashi, Kota Fukumoto
wiley   +1 more source

A Simulated Annealing Algorithm for the Generalized Quadratic Assignment Problem

Algorithms
The generalized quadratic assignment problem (GQAP) involves assigning a set of facilities to a set of locations such that the sum of the assignment and transportation costs is minimized.
Alan McKendall, Yugesh Dhungel
doaj   +1 more source