Results 131 to 140 of about 9,454 (183)

A deterministic neuromorphic architecture with scalable time synchronization. [PDF]

Nat Commun
Li C, Imam N, Manohar R.
europepmc   +1 more source

Modeling and Characterization of a Self‐Sensing Soft Hydraulic Muscle

Advanced Intelligent Systems, EarlyView.
This article presents the self‐sensing soft hydraulic muscle (SSHM), a novel soft actuator capable of simultaneously sensing force and length without external sensors. A comprehensive model accurately predicts SSHM behavior, validated experimentally with minimal errors. Using propylene glycol enhances durability and reduces hysteresis.
Nhu An Phan, Phuong Linh Dao, Duc Tu Vu, The Doan Le, Sy Trung Ngo, Minh Tri Phan, T. Thang Vo‐Doan, Ke Wu, Mai Thanh Thai   +8 more
wiley   +1 more source

Multi-objective route optimization for electric vehicle hazardous materials transportation in uncertain environments. [PDF]

Sci Rep
Zhang Q, Zhang Z, Ma C.
europepmc   +1 more source

Deep Reinforcement Learning Approaches for Sensor Data Collection by a Swarm of UAVs

Advanced Intelligent Systems, EarlyView.
This article presents four decentralized reinforcement learning algorithms for autonomous data harvesting and investigates how collaboration improves collection efficiency. It also presents strategies to minimize training times by improving model flexibility, enabling algorithms to operate with varying number of agents and sensors.
Thiago de Souza Lamenza, Bruno José Olivieri de Souza, Markus Endler   +2 more
wiley   +1 more source

Interpretable Machine Learning on Simulation-Derived Biomechanical Features for Hamstrings-Quadriceps Imbalance Detection in Running. [PDF]

Sports (Basel)
Mănescu AM, Tudor AC, Dinciu CC, Hangu SȘ, Mărgărit IR, Tudor V, Mănescu CO, Ciomag RV, Rădulescu ML, Hangu C, Smîdu N, Dulceață V, Barac IC, Niță SC, Grigoroiu C, Mănescu DC.   +15 more
europepmc   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source

Human‐Machine Mutual Trust Based Shared Control Framework for Intelligent Vehicles

Advanced Intelligent Systems, EarlyView.
This work introduces a bidirectional‐trust‐driven shared control framework for human‐machine co‐driving. The method models human‐to‐machine trust from intention discrepancies and Bayesian skill assessment, and machine‐to‐human trust from integrated ability evaluation.
Zhishuai Yin, Qian Li, Zhiwei Li, Xukai Zhai, Jinhui Gao   +4 more
wiley   +1 more source

Clinical Trial Simulation: Planning With the OCTAVE Framework, Implementation and Validation Principles. [PDF]

Stat Med
Lee KM, Choodari-Oskooei B, Grayling MJ, Jacko P, Kimani PK, Mukherjee A, Pallmann P, Parke T, Robertson DS, Wang Z, Yap C, Jaki T.   +11 more
europepmc   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source